National Energy Map for India

National Energy Map for India:

Technology Vision 2030

Office of the Principal Scientific Adviser,Government of India

The Energy and Resources Institute

ISBN 81-7993-099-8

Published by

TERI PressThe Energy and Resources InstituteDarbari Seth BlockIHC Complex, Lodhi RoadNew Delhi – 110 003, India

Tel. 2468 2100 or 2468 2111Fax 2468 2144 or 2468 2145

India +91 • Delhi (0)11E-mail [email protected]

Web www.teriin.org

Office of the Principal Scientific Adviser,Government of India

318, Vigyan Bhavan AnnexeMaulana Azad RoadNew Delhi – 110 011, India

Tel. 2302 2112Fax 2302 2113

India +91 • Delhi (0)11Web www.psa.gov.in

Contents

Preface v

Acknowledgements vii

Project team ix

Tables xi

Figures xix

Acronyms and abbreviations xxiii

11111 Introduction 1

22222 Methodology 9

33333 Sectoral demand projections, technological characterization,

and resources availability 29

44444 Energy scenarios 139

55555 Model results and analyses 149

66666 Key observations and recommendations 193

appendicesappendicesappendicesappendicesappendices

A1A1A1A1A1 Description of energy sector models 207

A2A2A2A2A2 Sectoral reference energy system (RES) 215

A3A3A3A3A3 Socio-economic drivers of energy demand 221

A4A4A4A4A4 Region-wise hydrocarbon reserves at the end of 2005 235

A5A5A5A5A5 Sankey diagrams 241

A6A6A6A6A6 Balance sheets 249

Bibliography 253

iv Contents

Preface

India has recorded impressive rates of eco-nomic growth in recent years, which providethe basis for more ambitious achievementsin the future. However, a healthy rate of eco-nomic growth equalling or exceeding thecurrent rate of 8% per annum would requiremajor provision of infrastructure and en-hanced supply of inputs such as energy. Higheconomic growth would create much largerdemand for energy and this would presentthe country with a variety of choices in termsof supply possibilities. Technology would bean important element of future energy strat-egy for the country, because related to arange of future demand and supply scenariowould be issues of technological choicesboth on the supply and demand sides, whichneed to be understood at this stage, if theyare to become an important part of India’senergy solution in the future.

The Indian government aims to achievean economic growth rate of over 8% in thenext two decades in order to be able to meetits development objectives. However, rapideconomic growth would also imply the needfor structural changes in the economy as wellas for induced shifts in the patterns of end-use demands. To meet the needs of the In-dian populace in the most effective manner,

it is important to map out the energy de-mand and supply dynamics in the country.This study estimates alternative trajectoriesof energy requirements and examines thelikely fuel mix for the country under variousresource and technological constraints overa 30-year time frame.

This study has been commissioned andsupported by the office of the PSA (Princi-pal Scientific Advisor) to the Government ofIndia. The two-year study has drawn inputsfrom several organizations and sectoralexperts across the country to gauge the like-lihood of technological progress and avail-ability of energy resources in the future.

The MARKAL model used in this studyis a widely used integrated energy system op-timization framework that enables policy-makers and researchers to examine the besttechnological options for each stage of en-ergy processing, conversion, and use. Thismodelling framework was used to representa detailed technological database for the In-dian energy sector with regard to energy re-sources (indigenous extraction, imports, andconversion) as well as energy use across thefive major end-use sectors (agricultural,commercial, residential, transport, and in-dustrial).

The report discusses the data, assump-tions, and methodological framework usedto estimate useful energy requirements ofthe country based on demographic and eco-nomic drivers. Technological assessments ofresources and energy conversion processeshave been described in the report. Economicand technological scenarios have been devel-oped within the integrated modelling frame-work to assess the best energy mix during themodelling time frame. Based on the scenarioassessment, the report provides directions tovarious stakeholders associated with the In-dian energy sector including policy-makers,technologists, and investors.

The report clearly points towards thecountry’s increasing import dependence ofall fossil fuels. It also indicates that coalwould continue to play a key role in meeting

the country’s energy requirements. How-ever, the indigenous availability of coal isexpected to plateau in the next couple ofdecades with the current exploitation plansand technology. The need for energy effi-ciency in the end-use sectors and radicalpolicy changes in the transport sector is alsohighlighted. The study points towards focus-sing efforts simultaneously on the demandand supply sides for the economy to attainthe most efficient utilization of available re-sources.

(R K Pachauri)Director-General, TERI

vi Preface

Acknowledgements

TER I acknowledges the high-level techni-cal inputs and guidance provided by variousnational experts in the development of themodel. TER I specially thanks the followingexperts: R Chidambaram, Kirit Parikh, A KKolar, Kamal Kapoor, Brahma Deo, V KSharma, R B Grover, Srinivas Shetty, H SKamath, V K Agarwal, L M Das, P K Sen,Adish Jain, Arun Kumar, Surya P Sethi,Arvinder S Sachdeva, Prodipto Ghosh, DilipChenoy, Sudhinder Thakur, P K Modi, AlokSaxena, and S Nand.

TERI also acknowledges the inputprovided by the following organizations:Department of Atomic Energy, Nuclear

Power Corporation of India Ltd, BharatHeavy Electricals Ltd, National ThermalPower Corporation Ltd, National HydroPower Corporation, North Indian TextilesManufacturers Association, Indian Rail-ways, Oil and Natural Gas Corporation Ltd,Engineers India Ltd, Indian AluminumManufacturers Association, Steel Authorityof India Ltd, Fertilizer Association of India,Cement Manufacturers Association, Con-federation of Indian Industry, and IndianPaper Manufacturers Association.

Thanks are due to Mr Rakesh KumarArora for his invaluable secretarial assis-tance.

Principal investigator Leena Srivastava

Core team Ritu Mathur, Pradeep K Dadhich, Atul Kumar,Sakshi Marwah, Pooja Goel

Sector experts in TERI Amit Kumar, Shirish S Garud, Mahesh Vipradas,V V N Kishore, Pradeep Kumar, Alok Adholeya,Girish Sethi, N Vasudevan, Shashank Jain, Abhishek Nath,Upasna Gaur, Ananya Sengupta, Parimita Mohanty,K Rajeshwari, Ranjan K Bose, Sudip Mitra, R C Pal

Advisors R K Pachauri, R K Batra, Y P Abbi, S K Chand,K Ramanathan, Preety M Bhandari

Project review monitoring S P Sukhatme, S K Sikka, E A S Sarma, Y S R Prasad,committee R P Gupta, Chandan Roy, R K Saigal

Editorial and production Ambika Shankar, Archana Singh, Gopalakrishnan,team Jaya Kapur, K P Eashwar, Richa Sharma, R K Joshi,

R Ajith Kumar, Subrat K Sahu, T Radhakrishnan

Project team

Table 1.1 Production of primary energy sources of conventional energy in India 2

Table 1.2 Estimated energy demand 6

Table 2.1 Demographic trends in India 15

Table 2.2 Assumptions for population projections 17

Table 2.3 Population projections (in million) 18

Table 2.4 Rural–urban distribution (%) as per the UNPD 18

Table 2.5 Rural–urban distribution (%) as per the Census of India 18

Table 2.6 Projected population and number of households in rural andurban areas (million) 19

Table 2.7 Number of rural households (in million) in various expenditurecategories for 6.7% GDP growth 20

Table 2.8 Number of urban households (in million) in various expenditurecategories for 6.7% GDP growth 20

Table 2.9 Number of rural households (in million) in various expenditurecategories for 8% GDP growth rate 21

Table 2.10 Number of urban households (in million) in various expenditurecategories for 8% GDP growth rate 21

Table 2.11 Number of rural households (in million) in various expenditurecategories for 10% GDP growth rate 22

Table 2.12 Number of urban households (in million) in various expenditurecategories for 10% GDP growth rate 22

Table 2.13 Projections of GDP at factor cost at 1993/94 prices (in crore rupees)under various GDP growth rate scenarios 23

Table 2.14 Sectoral composition of GDP (%) 25

Table 2.15 Sectoral GDP at factor cost (in crore rupees) under 6.7% GDPgrowth rate scenario 26

Tables

Table 2.16 Sectoral GDP at factor cost (in crore rupees) under 8%GDP growth rate scenario 26

Table 2.17 Sectoral GDP at factor cost (in crore rupees) under 10%GDP growth rate scenario 26

Table 3.1 Projected cropping intensity and gross cropped area 32

Table 3.2 Demand for land preparation at various GDP (gross domesticproduct) growth rates (in million hectares) 33

Table 3.3 GIA GCA under irrigation under various growth scenarios 34

Table 3.4 GIA under groundwater irrigation at various GDP growth rate scenarios 35

Table 3.5 Crop-wise GCA and water consumption 36

Table 3.6 Technology characterization of pump sets 38

Table 3.7 Comparison of transport sector demand estimates by variousagencies for the year 1999 41

Table 3.8 Comparison of transport sector demand estimates by variousagencies for the year 2000 41

Table 3.9 Assumptions on occupancy rate and utilization rate for cars 43

Table 3.10 Assumptions on occupancy rate and utilization rate for two-wheelers 46

Table 3.11 Assumptions on occupancy rate and utilization rate for buses 47

Table 3.12 Mode-wise road passenger travel demand (in billion passengerkilometres) under 6.7% GDP (gross domestic product) growth scenario 47

Table 3.13 Mode-wise road passenger travel demand (in billion passengerkilometres) under 8% GDP (gross domestic product) growth scenario 48

Table 3.14 Mode-wise road passenger travel demand (in billion passenger kilometres)under 10% GDP (gross domestic product) growth scenario 48

Table 3.15 Mode-wise freight travel demand (in billion tonne kilometres); 6.7% GDP (gross domestic product) growth scenario 49

Table 3.16 Mode-wise freight travel demand (in billion tonne kilometres);8% GDP (gross domestic product) growth scenario 50

Table 3.17 Mode-wise freight travel demand (in billion tonne kilometres);10% GDP (gross domestic product) growth scenario 50

Table 3.18 Rail passenger transport demand (in billion passenger kilometres)under alternative GDP (gross domestic product) growth scenarios 51

Table 3.19 Rail freight transport demand (in billion tonne kilometres)under alternative GDP (gross domestic product) growth rates 51

Table 3.20 Technological characterization of two-stroke two-wheelers 53

xii Tables

Table 3.21 Technological characterization of four-stroke two-wheelers 53

Table 3.22 Technological characterization of three-wheelers 54

Table 3.23 Percentage of cars sold by various manufacturers 55

Table 3.24 Technological characterization of cars 55

Table 3.25 Technological characterization of buses 56

Table 3.26 Technological characterization of goods vehicles 56

Table 3.27 Technological characterization of locomotives (freight) 56

Table 3.28 Technological characterization of locomotives (passenger) 57

Table 3.29 Estimates of bio-diesel production 57

Table 3.30 Assumptions in various transport scenarios 58

Table 3.31 Demand projection of caustic soda in India 60

Table 3.32 Projected demand of soda ash in India 60

Table 3.33 Demand projections of aluminium 61

Table 3.34 Demand projections for finished steel in India 62

Table 3.35 Cement demand projections 63

Table 3.36 Cotton cloth demand projection 64

Table 3.37 Demand projection for fertilizer 65

Table 3.38 Projected demand for paper and paper board in India 65

Table 3.39 Energy demand projection for other industries 66

Table 3.40 Production of caustic soda through different processes:1998/99 to 2003/04 67

Table 3.41 Technological characterization of caustic soda industry 67

Table 3.42 Details of Indian soda ash plants 68

Table 3.43 Technological characterization of soda ash industry 68

Table 3.44 Technological characterization of the aluminium industry 70

Table 3.45 Production and technological details of Indian steel industryduring the year 2001/02 71

Table 3.46 Efficiency improvement measures for integrated steel plants 72

Table 3.47 Efficiency improvement measures for EAF-based steel plants 73

Table 3.48 Level of share of BF–BOF and Scrap-EAF steel plants 74

Table 3.49 Percentage distribution of cement production in the year 2002/03 74

Table 3.50 Percentage distribution of input material requirement for differentvarieties of cement production in India 75

Table 3.51 Technological details of process-wise cement production in India 76

Tables xiii

Table 3.52 Variety-wise percentage distribution of cement production in2001 and 2036 76

Table 3.53 Technological characterization of cotton textile industry 79

Table 3.54 Installed capacity according to sources of feedstock (percentage)used for nitrogenous fertilizer production 80

Table 3.55 Specific energy consumption for urea production in India 80

Table 3.56 Technological characteristics of paper mills 82

Table 3.57 Energy conservation options for Indian paper mills 83

Table 3.58 Income categories based on MPCE in rural and urban areas 88

Table 3.59 Number of lighting points per household in various incomeclasses in rural and urban areas 88

Table 3.60 Demand for lighting (trillion lux hours) 89

Table 3.61 Useful energy demand for cooking (petajoules) 90

Table 3.62 Usage norms for electrical appliances 91

Table 3.63 Useful energy demand for various end uses at 6.7% GDP growth scenario 92

Table 3.64 Useful energy demand for various end uses (petajoules) at 6.7%GDP growth rate 92

Table 3.65 Useful energy demand for various end uses (petajoules) at 10%GDP growth rate 92

Table 3.66 Percentage distribution of households in various income groupsusing sources other than geyser for heating water 93

Table 3.67 Useful energy demand for heating water (petajoules) at the threeGDP growth rates 94

Table 3.68 Techno-economic parameters for various lighting devices 95

Table 3.69 Techno-economic parameters for kerosene-based lighting devices 95

Table 3.70 Techno-economic parameters of various cooking devices 96

Table 3.71 Characterization of refrigerators 97

Table 3.72 Technological characterization of fans 97

Table 3.73 Technological characterization of air conditioners 98

Table 3.74 Characterization of washing machines, televisions, VCRs/ VCPs,and music systems 98

Table 3.75 Technological options for cooking in the commercial sector 100

Table 3.76 Energy demand for cooking in commercial sector (in Mtoe) 101

Table 3.77 Technologies for lighting in the commercial sector 102

xiv Tables

Table 3.78 Electricity demand for lighting in the commercial sector (in GWh) 102

Table 3.79 Technologies for space conditioning in the commercial sector 102

Table 3.80 Electricity demand for space conditioning in the commercialsector (in GWh) 103

Table 3.81 Electricity demand for refrigeration in the commercial sector (in GWh) 103

Table 3.82 Electricity demand projections for other services (in GWh) 104

Table 3.83 Maximum values of domestic coal availability 105

Table 3.84 Coal-bed methane production potential in India 108

Table 3.85 Company-wise crude oil production (MT) 110

Table 3.86 Company-wise production of natural gas (MCM) 110

Table 3.87 Progress during NELP rounds 112

Table 3.88 Oil refinery capacity in India (2005) 114

Table 3.89 Refining capacity, actual crude throughput, and capacityutilization during the past five years 119

Table 3.90 New refineries planned in the Eleventh Five Year Plan 119

Table 3.91 Natural gas availability 120

Table 3.92 Prices of different types of coal in three different scenarios 121

Table 3.93 Price of crude and other petroleum products 122

Table 3.94 Prices of natural gas 122

Table 3.95 Power generation steam cycles with different unit ratings 123

Table 3.96 Contemporary gas turbines using natural gas as fuel—performance at ISO conditions 124

Table 3.97 Advance class gas turbines—performance at ISO conditions 126

Table 3.98 Integrated gasification combined cycle experience in the world 128

Table 3.99 Cost comparison of different IGCC technologies (1989 pricing) 129

Table 3.100 Upper bound on installed capacity of large hydro-based powergeneration (in GW) 131

Table 3.101 Installed capacity of nuclear energy based power generation 133

Table 3.102 Renewable energy source potential 134

Table 3.103 Techno-economic parameters of power generating technologies 137

Table 4.1 Installed capacity of nuclear-energy-based power generation 144

Table 4.2 Installed capacity of small hydro-based power generation 144

Tables xv

Table 4.3 Installed capacity of wind-based power generation 144

Table 4.4 Installed capacity of SPV- and biomass-based power generationin aggressive renewable energy scenario 145

Table 4.5 Availability of bio-diesel for transportation 146

Table 4.6 Description of energy-efficient scenarios for the transport sector 147

Table 5.1 Commercial energy requirements in the BAU (Mtoe) 150

Table 5.2 Annual production, import, and import dependency of coal 152

Table 5.3 Production, import, and import dependency of non-coking coal in thebusiness-as-usual scenario 153

Table 5.4 Production, import, and import dependency of coking coal in thebusiness-as-usual scenario 153

Table 5.5 Sector-wise commercial energy consumption in the business-as-usualscenario (in million tonnes of oil equivalent) 155

Table 5.6 Trends in sectoral shares in commercial energy consumption(in percentage) 155

Table 5.7 Supply and consumption of coal (million tonnes) in thebusiness-as-usual scenario 157

Table 5.8 Supply and consumption of petroleum products (million tonnes)in the business-as-usual scenario 157

Table 5.9 Supply and consumption of natural gas (billion cubic metres) in thebusiness-as-usual scenario 159

Table 5.10 Trend in the sectoral electricity consumption in the business-as-usualscenario (in terrawatt hours) 160

Table 5.11 Variations in commercial energy consumption across various scenarios(in Mtoe) 161

Table 5.12 Comparison of technology deployment for centralized anddecentralized power generation in the BAU and EFF scenarios for2021 and 2031 (in GW) 166

Table 5.13 Production, import, and import dependency of petroleum productsacross various scenarios in 2011 171

Table 5.14 Production, import, and import dependency of petroleumproducts across various scenarios in 2031 172

Table 5.15 Comparison of commercial energy consumption across variousscenarios (in Mtoe) 176

xvi Tables

Table 5.16 Technology deployment (including decentralized) during 2021 and2031 in the business-as-usual and high growth and their respectivehybrid scenarios (in GW) 178

Table 5.17 Coal consumption in various end-use sectors in 2011 (in Mtoe) 179



Table 5.20 Domestic production, net import, and import dependency ofpetroleum products in 2021 181

Table 5.21 Domestic production, net import, and import dependency ofpetroleum products in 2031 181

Table 5.22 Energy intensity (kgoe/Rs of GDP) for various scenarios 185

Table 5.23 Total commercial energy consumption in transport sector(in Mtoe) across various scenarios 187

Table 5.24 Projected fuel mix in transport sector (in Mtoe) across scenariosfor 2011 187

Table 5.25 Projected fuel mix in transport sector (in Mtoe) for variousscenarios for 2021 188

Table 5.26 Projected fuel mix in transport sector (in Mtoe) for variousscenarios for 2031 188

Table 5.27 Cumulative carbon dioxide emissions for different scenarios(from 2001 to 2036) 193

Table 6.1 Suggested technology deployment programme 201

Table 6.2 Suggested technology deployment pathway for power generation 202

Table 6.3 Suggested technology deployment pathway for end-use sectors 203

Tables xvii

Figures

Figure 2.1 Schematic representation of methodological framework 10

Figure 2.2 Energy sector models 11

Figure 2.3 MARKAL building blocks 13

Figure 2.4 Share of sectoral GDP in aggregate GDP (%) 24

Figure 3.1 Area under cultivation in India (million hectares) 30

Figure 3.2 Food grain production in India (million tonnes) 30

Figure 3.3 Trends in composition of fleet of registered passenger vehicles 39

Figure 3.4 Trends in passengers and freight carried by railways 40

Figure 3.5 Category-wise sales of two-wheelers 52

Figure 3.6 Primary aluminium production 69

Figure 3.7 Time trend of specific energy consumption of SAIL steel plants 71

Figure 3.8 Time trend of process profile of cement industry 74

Figure 3.9 Time trend of percentage distribution of different variety ofcement in India 75

Figure 3.10 Time trend of fuel and electricity consumption in the residential sector 85

Figure 3.11 Percentage distribution of households by source of cooking in rural India 86

Figure 3.12 Percentage distribution of households by source of cooking in urban India 86

Figure 3.13 Number of households per 1000 in highest income class possessingspecified durable goods (rural) 90

Figure 3.14 Number of households per 1000 in highest income class possessingspecified durable goods (urban) 91

Figure 3.15 Trend of electricity consumption in the commercial sector (1980–2003) 101

Figure 3.16 Trend of electricity consumption in other electricity consumingsectors (1980–2003) 104

Figure 3.17 Production and import of crude oil over the years 111

Figure 3.18 Improvement in heat rates with steam parameters 125

Figure 3.19 Economic impact of Integrated gasification combined cycledesign study improvements 129

Figure 5.1 Commercial energy use in the business-as-usual 150

Figure 5.2 Percentage share of fuel mix (business-as-usual scenario) 151

Figure 5.3 Variation in percentage share of traditional fuels in total primaryenergy supply 151

Figure 5.4 Production, import, and import dependency of non-coking coal in thebusiness-as-usual scenario 152

Figure 5.5 Production, import, and import dependency of coking coal in thebusiness-as-usual scenario 153

Figure 5.6 Production, import, and import dependency of natural gas

in the business-as-usual scenario 154

Figure 5.7 Production, import, and import dependency of petroleum in thebusiness-as-usual scenario 154

Figure 5.8 Sector-wise commercial energy consumption in the business-as-usualscenario 155

Figure 5.9 Trends in sectoral shares in commercial energy consumption 156

Figure 5.10 Sectoral consumption of petroleum products in the business-as-usualscenario 158

Figure 5.11 Trend in the sectoral electricity consumption in the business-as-usualscenario 159

Figure 5.12 Trends in percentage distribution of electricity consumption in thebusiness-as-usual scenario 160

Figure 5.13 Total commercial energy consumption across scenarios 162

Figure 5.14 Average annual fuel cost across various scenarios 162

Figure 5.15 Comparison of electricity consumption across various scenarios 163

Figure 5.16 Comparison of power generation capacity mix (includingdecentralized) across various scenarios 164

Figure 5.17 Average annualized investment cost in the centralized powergeneration across various scenarios 165

Figure 5.18 Comparison of fuel-wise technology deployment in thebusiness-as-usual and high-efficiency scenarios in the power sector 167

Figure 5.19 Sector-wise coal consumption across different scenarios for 2011,2021, and 2031 168

xx Figures

Figure 5.20 Comparison of import dependency of coking coal across variousscenarios 169

Figure 5.21 Import dependency of non-coking coal across various scenarios 169

Figure 5.22 Comparison of average annual cost of coal across various scenarios 170

Figure 5.23 Production, import, and import dependency of petroleum products acrossvarious scenarios in 2011 170

Figure 5.24 Production, import, and import dependency of petroleum products acrossvarious scenarios in 2031 171

Figure 5.25 Average annual cost of oil and oil products across various scenarios 172

Figure 5.26 Comparison of petroleum product consumption across variousscenarios in the end-use sectors 173

Figure 5.27 Refinery capacity across various scenarios 174

Figure 5.28 Refinery investment cost across various scenarios 174

Figure 5.29 Import of natural gas across various scenarios 175

Figure 5.30 Average annual cost of natural gas across various scenarios 175

Figure 5.31 Commercial energy supply in 2011, 2021, and 2031 177

Figure 5.32 Generation capacity mix for 2011, 2021, and 2031 (centralized anddecentralized) 177

Figure 5.33 Comparison of fuel-wise technology deployment for powergeneration across various scenarios for 2021 178

Figure 5.34 Comparison of fuel-wise technology deployment for powergeneration across various scenarios for 2031 179

Figure 5.35 Sectoral electricity consumption for 2011, 2021, and 2031 180

Figure 5.36 Import dependency of coking coal across various scenarios for2011 and 2031 181

Figure 5.37 Import dependency of non-coking coal across various scenarios in2011 and 2031 182

Figure 5.38 Domestic production, net import, and import dependency ofpetroleum products for 2021 182

Figure 5.39 Domestic production, net import, and import dependency ofpetroleum products for 2031 183

Figure 5.40 Import of natural gas across various scenarios for 2011, 2021, and 2031 183

Figure 5.41 Sectoral consumption of petroleum products in 2011, 2021, and 2031 184

Figure 5.42 Trends in energy intensity across various scenarios from 2001 to 2031 185

Figure 5.43 Comparison of energy consumption in transport sector across variousscenarios 186

Figures xxi

Figure 5.44 Comparison of fuel mix in transport sector across scenarios for2011, 2021, and 2031 189

Figure 5.45 Comparison of net import and import dependency of petroleumproducts across various scenarios for 2011, 2021, and 2031 191

Figure 5.46 Expenditure incurred on import of petroleum products 192

Figure 5.47 Cumulative carbon dioxide emissions across various scenarios (2001–36) 193

xxii Figures

Acronyms and abbreviations

AC Air conditionerADB Asian Development BankAHP Analytic Hierarchy ProcessAIM Asia–Pacific Integrated ModelAMAI Alkali Manufacturers’ Association of IndiaARIMA Auto regressive integrated moving averageATF Aviation turbine fuel

BALCO Bharat Aluminium Company LtdBAU Business-as-usualBbl BarrelBCM Billion cubic metresBCPP Biomass consumption based power projectBEE Bureau of Energy EfficiencyBF–BOF Blast furnace–basic oxygen furnaceBHEL Bharat Heavy Electricals LtdBHH Bayer–Hall–HeroultBIODSL Bio-dieselbkWh Billion kilowatt-hoursBOF Basic oxygen furnaceBPCL Bharat Petroleum Corporation Ltdbpkm Billion passenger kilometresBPL Below poverty lineBRPL Bongaigaon Refinery and Petrochemicals LtdBRUS Brundtland Scenario ModelBT Billion tonnesbtkm Billion tonne kilometresBtu British thermal unitCAGR Compounded annual growth rateCBM Coal bed methaneCCGT Combined cycle gas turbineCCPP Combined cycle power plant

CDH Crude distillation unitCDU Crude Distillation UnitCEA Central Electricity AuthorityCFL Compact fluorescent lampCFRI Central Fuel Research InstituteCGE Computable general equilibriumCHP Combined heat and powerCI Cropping intensitycif Cost, insurance and freightCII Confederation of Indian IndustryCIMS Canadian Integrated Modelling SystemCMA Cement Manufacturers’ AssociationCMIE Centre for Monitoring Indian EconomyCMPDIL Central Mine Planning and Design Institute LtdCNG Compressed natural gasCPCL Chennai Petroleum Corporation LtdCPPRI Central Pulp and Paper Research InstituteCSE Centre for Science and EnvironmentDC Direct currentDGH Directorate-General of HydrocarbonsDMT Di-methyl terephthalateDRI–EAF Direct reduction iron–electric arc furnaceDTC Delhi Transport CorporationEAF Electric arc furnaceEFF High efficiencyEFOM-ENV Energy Flow Optimization Model-ENVironmentEIA Energy Information AdministrationENPEP Energy and Power Evaluation ProgramFAI Fertilizer Association of IndiaFAO Food and Agricultural OrganizationFBR Fast breeder reactorFGD Flue gas desulphurizationFICCI Federation of Indian Chambers of Commerce and Industryfob free on boardFYP Five Year PlanGCA Gross cropped areaGDP Gross domestic productGDPA Gross domestic product from agricultureGEF Global Environment FacilityGHGs Greenhouse gasesGIA Gross irrigated areaGJ Gigajoules

xxiv Acronyms and abbreviations

GJ/t Gigajoules/tonneGLS Generalized lighting systemGoI Government of IndiaGSPCL Gujarat State Petroleum Corporation LtdGW GigawattGWh Gigawatt hourHCV Heavy commercial vehicleHEV Hybrid electric vehiclesHG High growthHHYB High growth hybridHINDALCO Hindustan Aluminium Company LtdHP HorsepowerHPCL Hindustan Petroleum Corporation LtdHSD High-speed dieselHYB HybridIEA International Energy AgencyIGCC Integrated gasification combined cycleIIASA International Institute for Applied Systems AnalysisIMF International Monetary FundINDAL Indian Aluminium Company LtdIOCL Indian Oil Corporation LtdIPCC Intergovernmental Panel on Climate ChangeIREP Integrated Rural Energy ProgrammeISLE Indian Society of Lighting EngineersISO Indian Statistical OrganizationJV Joint venturekcal Kilocalorieskg Kilogramkgoe Kilogram of oil equivalentkm KilometresKRL Kochi Refineries LtdkWh Kilowatt hourLBNL Lawrence Berkeley National LaboratoryLCV Light commercial vehicleLEAP Long-range energy alternative planningLG Low growthLNG Liquefied natural gasLP Linear programmingLPG Liquefied petroleum gasl Litresm Metresm3 Cubic metres

Acronyms and abbreviations xxv

MALCO Madras Aluminium Company LtdMARKAL MARKel ALlocation modelMESSAGE Model for Energy Supply Systems Analysis and General EquilibriumMha Million hectaresMmBtu Million metric British thermal unitsMMSCMD Million metric standard cubic metres per dayMMTPA Million metric tonnes per annumMNES Ministry of Non-conventional Energy SourcesMoA Ministry of AgricultureMoC Ministry of CommerceMoEF Ministry of Environment and ForestsMoF Ministry of FinanceMoP Ministry of PowerMoPNG Ministry of Petroleum and Natural gasMoSPI Ministry of Statistics and Programme ImplementationMoWR Ministry of Water ResourcesMPC Marginal propensity to consumeMPCE Monthly per capita expenditureMRPL Mangalore Refinery and Petrochemicals LtdMSEB Maharashtra State Electricity BoardMT Million tonnesMtoe Million tonnes of oil equivalentMTPA Million tonnes per annumMW MegawattNALCO National Aluminium Company LtdNCA Net cropped areaNCAER National Council for Applied Economic ResearchNELP New Exploration Licensing PolicyNIOC National Iranian Oil CompanyNPCIL Nuclear Power Corporation of India LtdNUC High nuclear capacityODC Oxygen depolarized cathodesOECD Organization for Economic Co-operation and DevelopmentOIDB Oil Industry Development BoardOIL Oil India LtdONGC Oil and Natural Gas Corporation LtdOPC Ordinary Portland CementPCRA Petroleum Conservation Research AssociationPFBG Pressurized fluidized bed gasificationPFI Population Foundation of IndiaPHWR Pressurized heavy water reactorPJ Petajoules

xxvi Acronyms and abbreviations

PLF Plant load factorPOLES Prospective Outlook on Long-term Energy SystemsPPC Portland Pozzolana CementPSA Principal Scientific AdviserPSC Portland Slag CementPSF Polyester staple fibrePSU Public Sector UndertakingRBPL Rural below poverty lineREN Aggressive renewable energyRES Reference energy systemRET Renewable energy technologyRH Rural highRL Rural lowRLM Rural lower middleRM Rural middleRPL Reliance Petroleum LtdRUM Rural upper middleSAIL Steel Authority of India LtdSCR Selective catalytic reductionSHP Small hydro powerSIAM Society of Indian Automobile ManufacturersSPV Solar photovoltaicSRTU State road transport undertakingSSP Single super phosphateTEDDY The Energy Data Directory YearbookTERI The Energy and Resources InstituteTPD Tonnes per dayTPES Total primary energy supplyUBPL Urban below poverty lineUH Urban highUL Urban lowULM Urban lower middleULSD Ultra-low sulphur dieselUM Urban middleUNPD United Nations Population DivisionUSC Ultra-supercriticalUSDoE US Department of EnergyUUM Urban upper middleVCP Video cassette playerVCR Video cassette recorderWBCSD World Business Council for Sustainable DevelopmentWEC World Energy Council

Acronyms and abbreviations xxvii

Introduction

11111

1.1 Background

The growth of a developing economy ishighly dependent on the growth on its en-ergy consumption. Because of the possibilityof inter-fuel substitution in end-use applica-tions, the optimal long-term energy supplyrequirements of a country necessitates ex-amination of all energy resources availableboth indigenously and globally.

The Government of India plans toachieve a GDP (gross domestic product)growth rate of 10% in the Eleventh Five YearPlan and maintain an average growth ofabout 8% in the next 15 years (PlanningCommission 2002).

Given the plans for rapid economicgrowth, it is evident that the country’s re-quirements for energy and supporting infra-structure would increase rapidly as well. Inview of the rising energy prices and othergeo-political considerations regarding en-ergy imports, it is important to identify andadopt policies and measures that enhanceenergy security and help reduce the final en-ergy requirements of the economy. An inte-grated assessment of all the technologicaloptions available to the economy is thereforecrucial to examine possible energy pathwaysand their impacts in terms of costs, infra-structure requirements, and fuel-mix pat-terns over time.

This chapter provides an overview ofIndia’s energy sector and the challenges itfaces.

1.2 Overview of the energy sector

Energy has been universally recognized asone of the most important inputs for eco-nomic growth and human development.There is a strong two-way relationship be-tween economic development and energyconsumption. On one hand, the growth of aneconomy, with its global competitiveness,hinges on the availability of cost-effectiveand environmentally benign energy sources,and on the other hand, the level of economicdevelopment has been observed to be relianton energy demand.

The energy–GDP elasticity, defined asthe ratio of the growth rate of energy to thegrowth rate of GDP, captures both the struc-ture as well as efficiency of the economy. Theenergy–GDP elasticity during 1953–2001has been above unity. However, the elasticityfor primary commercial energy consump-tion for 1991–2000 was less than unity(Planning Commission 2002). This could beattributed to several factors, some of thembeing demographic shifts from rural tourban areas, structural economic changestowards lesser energy industry, impressive

2 National Energy Map for India: Technology Vision 2030

growth of services, improvement in efficiencyof energy use, and inter-fuel substitution.

The energy sector in India has been re-ceiving high priority in the planning process.The total outlay on energy in the TenthFive Year Plan was about 4.03 trillion rupeesat 2001/02 prices, which comprised 26.7%of the total outlay. An increase of 84.2% isprojected over the Ninth Five Year Plan interms of the total plan outlay for the energysector. The Government of India in themid-term review of the Tenth Five Year Planrecognized the fact that under-performanceof the energy sector can be a major con-straint in delivering a growth rate of 8%GDP during the plan period. It has, there-fore, called for acceleration of the reformsprocess and adoption of an integratedenergy policy.

In the recent years, the government hasrightly recognized the energy security con-cerns of the nation and more importance isbeing placed on energy independence. Onthe eve of the 59th Independence Day (on14 August 2005), the President of India em-phasized that energy independence has to bethe nation’s first and highest priority, andIndia must be determined to achieve thiswithin the next 25 years.

1.3 Energy demand and supply

scenario

In the recent years, India’s energy consump-tion has been increasing at one of the fastestrates in the world due to population growthand economic development. Primary com-mercial energy demand grew at the rate of6% between 1981 and 2001 (Planning Com-mission 2002). India ranks fifth in the worldin terms of primary energy consumption, ac-counting for about 3.5% of the world com-mercial energy demand, as per 2003 data.Despite the overall increase in energy de-mand, per capita energy consumption in thecountry – 323 kilograms of oil equivalent in2003 – is still very low compared to other de-veloping countries (MoPNG 2004a).

India is relatively well endowed with bothexhaustible and renewable energy resources.Coal, oil, and natural gas are the three pri-mary commercial energy sources. India’s en-ergy policy, till the end of the 1980s, wasmainly based on the availability of indig-enous resources. Coal was by far the largestsource of energy. However, India’s primaryenergy mix has been changing over a periodof time. Table 1.1 gives the break-up of pri-

Table 1.1 Production of primary energy sources of conventional energy in India

Source Unit 1970/71 1980/81 1990/91 2001/02 2002/03 2003/04

Coal and lignite MT 76.34 119.02 228.13 352.60 367.29 389.11

Crude oil MT 6.82 10.51 33.02 32.03 33.04 33.38

Natural gas BCM 1.45 2.36 18.00 29.71 31.40 31.95

Nuclear power bkWh 2.42 3.00 6.14 19.48 19.39 17.78

Hydro power bkWh 25.25 46.54 71.66 73.70 64.10 75.33

Wind power bkWh – – 0.03 1.97 2.10 3.40

MT – million tonnes; BCM – billion cubic metres; bkWh – billion kilowatt-hours

Source MoC (2004); CEA (2005)

Introduction 3

mary energy production for various fuelsfrom 1970 onwards.

Despite the increasing dependency oncommercial fuels, a sizeable quantum ofenergy requirements (40% of total energyrequirement), especially in the rural house-hold sector, is met by non-commercialenergy sources, which include fuelwood,crop residue, and animal waste, includinghuman and draught animal power. However,other forms of commercial energy of a muchhigher quality and efficiency are steadily re-placing the traditional energy resources be-ing consumed in the rural sector.

Resource augmentation and growth inenergy supply have not kept pace with in-creasing demand and, therefore, India con-tinues to face serious energy shortages. Thishas led to increased reliance on imports.

1.4 Coal

India now ranks third amongst the coal pro-ducing countries in the world. Being themost abundant fossil fuel in India till date, itcontinues to be one of the most importantsources for meeting domestic energy needs.It accounts for 55% of the country’s totalenergy supplies. Power sector alone con-sumes 75% of the coal produced in thecountry (MoC 2005).

Through sustained increase in invest-ment, the production of coal has increasedfrom about 70 MT (million tonnes) in theearly 1970s to 382 MT in 2004/05 (MoC2005). Despite this increase in production,the existing demand exceeds the supply. In-dia currently faces coal shortages of 23.96MT. This shortage is likely to be met throughimports, mainly by the steel, power, and ce-

ment sectors (MoC 2005). The developmentof such core infrastructure sectors is depen-dent on coal.

1.5 Power

Access to affordable and reliable electricityis critical to a country’s growth and prosper-ity. India has made significant progress to-wards the augmentation of its powerinfrastructure. In absolute terms, the in-stalled power capacity has increased fromonly 1713 MW (megawatts) as on 31 De-cember 1950 to 118 419 MW as on March2005 (CEA 2005). The all-India gross elec-tricity generation, excluding that from thecaptive generating plants, was 5107 GWh(gigawatt-hours) in 1950 and increased to565 102 GWh in 2003/04 (CEA 2005).

Energy requirement increased from390 bkWh (billion kilowatt-hours) during1995/96 to 591 bkWh by 2004/05, and peakdemand increased from 61 GW (gigawatts)to 88 GW over the same time period. Thecountry experienced an energy shortage of7.3% and peak shortage of 11.7% during2003/04. The growth in electricity consump-tion over the past decade has, however, beenslower than the GDP growth. This could bedue to the high growth of the service sectorand efficient use of electricity.

Per capita electricity consumption rosefrom merely 15.6 kWh (kilowatt-hours) in1950 to 592 kWh in 2003/04 (CEA 2005).However, it is a matter of concern that percapita consumption of electricity is amongthe lowest in the world. Moreover, the poorquality of power supply and frequent powercuts and shortages impose a heavy burdenon India’s fast-growing trade and industry.


1.6 Oil and natural gas

1.6.1 Oil sector

India is becoming a major player in the inter-national oil and gas industry and is willing totake on the political and financial risks in-herent in overseas investments.

The country currently imports 70% of itsoil and this share is expected to exceed by90% by 2030. It began importing gas in2004 and is projected to reach an import de-pendency of almost 40% in 2030. It hasadopted a four-pronged approach to energysecurity, comprising import source diversifi-cation and acquisition of equity oil, strategicoil stocks, increased domestic explorationand production, and fuel diversification.

Indian oil and gas companies are encour-aged to invest overseas and to build strongrelations with strategically important coun-tries. India aims to produce 20 MT of equityoil by 2010 and 60 MT by 2025 so that do-mestic consumption could reach 250 MT.

The latest estimates indicate that Indiahas about 0.4% of the world’s provenreserves of crude oil. The production ofcrude oil in the country has increased from6.82 MT in 1970/71 to 33.38 MT in2003/04 (MoPNG 2004b). The quantity ofcrude oil imported increased from 11.66MT during 1970/71 to 81 MT by 2003/04.Besides, imports of other petroleum prod-ucts increased from 1 MT to 7.3 MT duringthe same period. The exports of petroleumproducts went up from about 0.5 MT during1970/71 to 14 MT by 2003/04. The refiningcapacity, as on 1 April 2004, was 125.97MTPA (million tonnes per annum). Theproduction of petroleum products increasedfrom 5.7 MT during 1970/71 to 110 MT in2003/04.

1.6.2 Natural gas sector

India has recently entered a new era in itsgas industry with large discoveries of indig-enous gas and the arrival of the first LNG(liquefied natural gas) tanker in 2004. Theimportance of gas in India’s energy mix isexpected to increase sharply from 7% ofTPES (total primary energy supply) in 2000to 13% by 2030. In the same year, importdependency on gas will reach almost 40%.In order to meet the projected consumption,investment needs of about 44 billion dollarsbetween 2001 and 2030 are projected.

India will continue to depend on import-ing LNG in the short- to medium-term tobridge the demand gap. The capacity ofIndia’s only operating LNG terminal is ex-pected to double by 2005 and two moreLNG terminals are expected to become op-erational in the next two years.

The major challenges that India faces to-wards becoming a sophisticated gaseconomy include lack of sufficient transmis-sion infrastructure and lack of a coherent le-gal and regulatory framework.

India’s consumption of natural gas hasrisen faster than any other fuel in the recentyears. Natural gas demand has been growingat a growth rate of about 6.5% for the last 10years. Industries such as power, fertilizer,and petrochemical are shifting towards natu-ral gas. Although India’s natural gas demandhas traditionally been met entirely throughdomestic production for the past few years,the core sectors of the economy have startedfacing a gas shortage. To bridge this gap,apart from encouraging domestic produc-tion, the import of LNG is being consideredas one of the possible solutions. SeveralLNG terminals have been planned in thecountry and two have already been commis-

Introduction 5

sioned: (1) a petronet LNG terminal of5 MTPA at Dahej, and (2) an LNG importterminal at Hazira. In addition, an in-prin-ciple agreement has been reached with Iranfor import of 5 MTPA of LNG.

1.7 Renewable energy sources

Renewable energy sources are clean and in-digenously available, and can play an impor-tant role in addressing the energy securityconcerns of a country. Today, India has oneof the highest potentials for effectively usingrenewable energy sources. The country is theworld’s fifth largest producer of wind powerafter Germany, USA, Spain, and Denmark.There is a significant potential in India forthe generation of power from renewable en-ergy sources—wind, small hydro, biomass,and solar energy. The country has an esti-mated SHP (small hydro power) potential ofabout 15 000 MW. The installed combinedelectricity generation capacity of hydro andwind has increased from 19 194 MW in1991/92 to 31 995 MW in 2003/04, with acompound growth rate of 4.35% during thisperiod (MoF 2005). The penetration ofother renewable energy technologies, in-cluding solar photovoltaic, solar thermal,small hydro, and biomass power is also in-creasing. Greater reliance on renewableenergy sources offers enormous economic,social, and environmental benefits.

The potential for power productionfrom captive power plants and field-basedbiomass resources, using technologies fordistributed power generation, is currentlyassessed at 19 500 MW, including 3500 MWof exportable surplus power from bagasse-based cogeneration in sugar mills (MNES2005).

1.8 Future scenario

Increasing pressure of population and in-creasing use of energy in different sectors ofthe economy are concern areas for India.With a targeted GDP growth rate of 8% dur-ing the Tenth Five Year Plan, energy demandis expected to grow at the rate of 5.2%.Driven by the rising population, expandingeconomy, and a quest for improved qualityof life, the total primary energy consumptionis expected to be about 412 Mtoe (milliontonnes of oil equivalent) and 554 Mtoe inthe terminal years of the Tenth and EleventhFive Year Plans, respectively (Planning Com-mission 1999) (Table 1.2).

The International energy outlook 2005(EIA 2005) projects India’s gas consump-tion to grow at an average annual rate of5.1%, thereby reaching 2.8 trillion cubic feetby 2025 with electric power accounting for ashare of 71%. Coal consumption is expectedto increase to 315 MT over the forecast pe-riod. In India, slightly less than 60% of theprojected growth in coal consumption is at-tributed to the increased demand of coal inthe electricity sector while the industrial sec-tor accounts for most of the remainingincrease. Coal-fired generation capacity isexpected to increase by 59 000 MW between2002 and 2025 such that use of coal forelectricity generation would be 2.2% perannum. Oil demand in India is expected toincrease by 3.5% per annum during thesame period.

It is quite apparent that coal will continueto be the predominant form of energy infuture. However, imports of petroleum andgas would continue to increase substantiallyin absolute terms, involving a large energyimport bill. There is, therefore, an urgentneed to reduce energy requirements by


demand-side management and by adoptingmore efficient technologies in all sectors.

1.9 The road ahead

The energy needs of the country are ex-pected to increase at a rapid rate in the com-ing decades. Therefore, it is imperative totake steps to increase the indigenously avail-able energy resources so as to avoid excessivereliance on external sources. The optionsavailable in terms of nuclear and hydel en-ergy, as well as non-conventional sources ofenergy, also need to be seriously looked into.Non-conventional sources of energy maycome to play an increasing role in meetingenergy needs, particularly of the rural popu-lation, which depends mostly on non-com-mercial sources of energy today. Themid-term appraisal of the Tenth Five YearPlan (Planning Commission 2005) also em-

Table 1.2 Estimated energy demand

Demand (in original units) Demand (Mtoe)

Primary fuel Unit 2006/07 2011/12 2006/07 2011/12

Coal MT 460.50 620.00 190.00 254.93

Lignite MT 57.79 81.54 15.51 22.02

Oil MT 134.50 172.47 144.58 185.40

Natural gas BCM 47.45 64.00 42.70 57.60

Hydro power bkWh 148.08 215.66 12.73 18.54

Nuclear power bkWh 23.15 54.74 6.04 14.16

Wind power bkWh 4.00 11.62 0.35 1.00

Total commercial energy 411.91 553.68

Non-commercial energy 151.30 170.25

Total energy demand 563.21 723.93

MT – million tonnes; BCM – billion cubic metres; bkWh – billion kilowatt-hours; Mtoe – million tonnes of oil equivalent

Source Planning Commission (2002)

phasizes on regulatory reform to improvethe energy efficiency of the country (Box 1).

However, availability of capital and envi-ronmental considerations is appearing as se-rious constraints to the efforts of generatingmore capacity to meet the growing demand.Prudent management of the indigenous en-ergy resources; judicious approach to energyimports; and progressive shift in favour ofenvironmentally benign sources of energy,including non-renewable sources and de-mand-side management, are some of the so-lutions to the problems and can, therefore,be the guiding principles for long-term en-ergy policy of the country.

1.10 Investments in energy

infrastructure

India faces the challenging task of mobiliz-ing financial resources to invest in energy

Introduction 7

infrastructure. The problem is further aggra-vated by the different financial risks thathave been introduced by the transition tocompetitive markets. Far-reaching reformsare urgently needed to facilitate higher capitalflows in the energy sector.

1.11 Role of new and energy-

efficient technologies

New and energy-efficient technologies havea key role to play in the optimal utilization ofresources. At this juncture, there is an urgentneed to address barriers to the adoption ofclean coal technologies and other newenergy processing technologies. R&D (re-search and development) and demonstrationof these technologies under government-supported programmes are very crucial.

1.12 Energy security

The focus currently is on energy security.The energy security concerns now encom-pass access to coal, natural gas, electricity,and oil, and insulation from abnormal pricefluctuations. India’s oil import dependencyis expected to increase from 70% at presentto 90% by 2030. Apart from this, imports ofcoal as well as gas are expected to increasesignificantly in the next couple of decades.Furthermore, access to energy supply needsto be compatible with policy objectives likemitigation of environmental consequences.The Government of India has a policy tomitigate risks by diversifying geological re-sources of fuel supply and maximizing indig-enous use of renewable energy sources andnon-renewable energy sources.

Box 1 Highlights of the proposals made under the mid-term appraisal of the Tenth Five Year

Plan across the energy sector

Improve regulation by

� creating a regulatory academy;

� institutionalizing the selection of regulators under the regulatory academy;

� mandating training for all regulators;

� granting financial autonomy to regulatory institutions;

� limiting the quasi-judicial role of regulators to tariff setting and dispute resolution, providing a sys-

tem to make regulators accountable to the Parliament; and

� developing a debt pool that would provide up to 20-year loan funding for energy projects, estab-

lishing and enforcing energy efficiency standards (the Bureau of Energy Efficiency and PCRA Petro-

leum Conservation Research Association must develop standards for energy-intensive industries

and appliances, and develop modalities for a system of incentives/penalties for compliance/non-

compliance).


1.13 Objectives of the study

In order to have an integrated energy ap-proach and to meet the policy goals ofeconomic efficiency, energy security, energyaccess, and environment protection, thePrincipal Scientific Adviser’s Office to theGovernment of India awarded the study, Anational energy map for India—technology vi-sion 2030, to TERI in March 2004. Thetime frame of the study is from 2001 to2031. The objectives of the study are1 to develop a framework for optimal ex-

ploitation of energy resources through ap-propriate technology deployment;

2 to determine the energy technology poli-cies and strategies that would lead to opti-mal use of energy resources;

3 to suggest a technology deployment strat-egy at the national level; and

4 to identify energy demand and supply, andenergy-technology related data gaps thatwill strengthen such analyses in the future.

The above-mentioned objectives are to beaddressed by building a national-level, bot-tom-up, technology-driven, optimization-modelling framework. The model is to berun under various scenarios to capture un-certainties and bring out its implications onthe national energy scene.

Methodology

22222

2.1 Approach

As described in Chapter 1, the key focus ofthis study was to examine the role that vari-ous technological options could play underalternative scenarios of economic growthand development; resource availability; andtechnological progress. For this purpose, itwas important to choose an integrated mod-elling framework that would facilitate thecreation and analysis of various scenarios ofenergy demand and supply at the nationallevel, as well as provide a detailed represen-tation and analysis at the technological levelfor each category of resource as well assectoral end-use demand.

Energy demand is driven by the GDP(gross domestic product) and populationgrowth. Different GDP growth rates wereused to develop various scenarios of eco-nomic growth while the population projec-tions of the PFI (Population Foundation ofIndia) were considered to reflect trends inpopulation growth. These population andGDP figures were used to estimate end-usedemand in the five sectors of the economy(agriculture, commercial, residential, indus-trial, and transport) over the modelling period.

The MARKAL (MARket ALLocation)Program model was selected to examine thepathways for optimal energy supply to meetthe end-use services in the five economicsectors under each scenario. The model indi-

cates the minimized total system cost of theenergy sector under various scenarios. Also,the main outputs provided by the model in-clude information regarding the level of up-take of total energy resources, theirdistribution across the consuming sectors,choice of the technological options at the re-source supply end, conversion and end-uselevels, investment levels during each five-year time period, an indication of capacityaddition, retirement of equipment and ap-pliances, emission levels associated with re-sources, end-use technological optionsadopted, and so on. The modelling timeframe is from 2001 to 2031, and the data in-put to the model is from 2001 to 2036. Theoverall methodology is schematically de-picted in Figure 2.1.

Given wide scope and vast nature of theexercise, it was important to draw on theknowledge base of a large and varied team ofexperts so as to provide inputs of adequatequality to the model. Several rounds of inter-actions with policy-makers and experts ineach sector were crucial to the developmentof the modelling framework and the overallanalysis. The choice of the possible techno-logical options (existing and futuristic) to beincluded in the model, the development ofthe RES (reference energy system), and thetechnology characterization for each optionon the demand side as well as the supply sideevolved on the basis of an extensive litera-


ture review. In addition, several rounds ofdeliberations and focused interactions withsectoral experts, researchers, industryassociations, research and development in-stitutions, government agencies, and policy-makers in each of the individual sectors wereheld to finalize the input data to the model.

2.2 Choice of modelling framework

Figure 2.2 illustrates various types of energysector models widely used across severaldeveloped and developing countries for car-rying out their economic and energy sectorplanning. These models also consider differ-

ent overall approaches towards the analysisof energy and environmental systems. Theevaluation of the model to be finally used forthe analysis was governed primarily by theability of the models to undertake a detailedrepresentation as well as evaluation of thetechnological options for both existing andemerging technologies related to energy ex-traction/supply, conversion, transportation,and economy. Some of these models, includ-ing the ENPEP (Energy and Power Evalua-tion Program), the MARKAL, the LEAP(Long-range Energy Alternatives Planning)System, the AIM (Asia-Pacific IntegratedModel), the MESSAGE (Model for EnergySupply Systems Analysis and General Envi-

Figure 2.1 Schematic representationof methodological framework

Methodology 11

tion, conversion, transmission, and utili-zation. Supply technologies are describedin terms of their initial, construction, andoperating costs; fuel requirements; dis-count rates; useful lives; and efficiencies.Such assumptions may vary with time.Definition and description of technolo-gies are limited only by the modeller’spreferences. A single energy system maycontain definitions of several hundredtechnologies that contribute to supply,demand, efficiency, production, or any re-lationship defined by the modeller.

4 The model is dynamic in the sense that itoptimizes over the entire time period andconsiders retirement of equipment andappliances over their lifetime and invest-ment in new capacities while consideringthe entire modelling time period.MARKAL is accordingly an appropriateframework for conducting a medium- tolong-term analysis over a 30-year model-ling time frame.

5 The model has the ability to include user-defined constraints to reflect physical re-strictions on the availability or processingof resources, maximum penetration ratiosfor a set of technologies, and so on.

6 MARKAL model provides a generalizedstructure that can be adapted well for theIndian energy sector analysis. It allows forformulating and analysing policy andtechnology scenarios easily, so that theimplications pertaining to economy, en-ergy, and environment of a particular setof policies can be evaluated.

7 The MARKAL being an optimizationframework was preferred to a simulationframework as it helps in providing a rela-tive ranking of various technological op-tions from the viewpoint of cost minimi-zation as well.

ronment), and the POLES (ProspectiveOutlook on Long-term Energy Systems),were extensively reviewed to evaluate thebest option to be used for this study. Variousenergy sector models listed in Figure 2.2 aredescribed in detail in Appendix 1.

Following extensive survey of the existingmodels and frameworks, the MARKALmodel was selected as the preferred frame-work on account of several reasons, as dis-cussed below.1 MARKAL provides an integrated frame-

work for examining the flow of resourcesfrom the point of extraction up to thepoint of end-use, while accounting forconversion efficiencies, losses, costs oftransport and transmission, and so on.

2 Using a bottom-up framework providesan adequate scope for representing eachtechnological option in detail in terms ofits availability, costs, and so on.

3 As a technology mix optimization model,MARKAL database typically contains afairly comprehensive account of tech-nologies for supply and end-use produc-

Figure 2.2 Energy sector models


8 The MARKAL model was developed bythe IEA (International Energy Agency) toevaluate optimal mix of energy supplyand demand technologies under differentscenarios and objectives. The MARKALframework is, therefore, geared towardsexpressing energy technology interac-tions. As a mathematical tool, it is usedfor optimizing technology mixes to meetspecified objectives such as least energysystem costs. The MARKAL family ofmodels provides a flexible, easy-to-under-stand, proven, and verifiable methodol-ogy that can provide insights to assist withinformed decision-making. Policy ana-lysts in several developed and developingcountries have used this model to frameenergy policy and evaluate options basedon their projected financial and environ-mental effects.

2.3 Modelling framework of the

MARKAL model

2.3.1 Model structure

The MARKAL model is a dynamic LP (lin-ear programming) model of a generalizedenergy system. It uses LP methods to solvefor the technology mix that best meets thespecified objectives. It is demand-driven forwhich feasible solutions are obtained only ifall specified end-use energy demands aresatisfied for every time period. The end-usedemands for each sector and for each timeperiod are exogenously forecasted.

Being an LP model, the main function ofthe MARKAL model is to optimize a linearobjective function under a set of linear con-straints. The problem is to determine the

optimum activity levels of processes that sat-isfy the constraints at a minimum cost. Ex-amples of constraints in the model includeavailability of primary energy resources, pro-duction/use balances, electricity/heat peak-ing, availability of certain technologies, andupper bounds on pollution emissions.

The elements of the MARKAL simulatethe flow of energy in various forms (energycarriers), from the sources of supply (im-port, export, mining, and stockpiling)through transformation systems (resource,process, conversion, and demand technolo-gies) to the devices that satisfy the end-usedemands. The basic structure of theMARKAL model is shown in Figure 2.2.

The elements of an energy system inMARKAL can be grouped as follows.� Energy carrier The component that

encompasses all the energy forms in theenergy system.

� End-use demands The component thatcomprises the demands for end-useenergy services in the economy.

� Demand technologies All devices thatconsume energy carriers to meet energydemands.

� Conversion technologies All load-depen-dent plants that generate electricity ordistrict heat or both.

� Process technologies All load-indepen-dent processes that convert one energycarrier to another.

� Resource technologies The means bywhich energy enters or leaves the energysystem, other than end-use consumption.

� Emissions The component that encom-passes the environmental impacts of theenergy system.

Figure 2.3 depicts the MARKAL build-ing blocks, also called the RES. The RES is a

Methodology 13

Figure 2.3 MARKAL building blocks

season and time of day and energy priceprovided by renewable technologies

� Emission levels for each technologyand the total energy system in each timeperiod

2.3.3 Description of the model

framework used in analysis

The MARKAL database has been createdover a 35-year period, from 2001 to 2036, atfive-year intervals, coinciding with the Gov-ernment of India’s Five Year Plans. The year2001/02 is chosen as the base year as it coin-cides with the first year of the Governmentof India’s Tenth Five Year Plan (2001/02–2006/07). In the model, the Indian energysector is disaggregated into five major en-ergy-consuming sectors, namely, agricul-ture, commercial, industry, residential, andtransport. Each of these sectors is furtherdisaggregated to reflect the sectoral end-usedemands. The model would be driven by thedemands on the end-use side.

convenient tool to map the flow of each en-ergy resource over its entire fuel cycle. Itprovides a blueprint for each sector in termsof the resources that it uses/could use andthe end-use demands that are associatedwith it. It provides a flow chart of the basicbuilding blocks of the overall model that canthen be easily mapped onto the actual modelwithout missing out on important compo-nents or links.

2.3.2 Solutions of the models

The MARKAL creates solutions by mini-mizing the present value of the total energysystem costs throughout the planning hori-zon, subject to specified constraints. Assuch, it uses perfect foresight whereby alldecisions are made with full knowledge ofthe future events. The MARKAL solutionsinclude the following.� An optimal resource/fuel/technology mix� A complete breakdown of the costs asso-

ciated with each technology

� Seasonal activity and capacitylevel for each conversiontechnology in each time pe-riod

� Annual activity and capacitylevel for each process and de-mand technology in each timeperiod

� The level of additional capac-ity for each conversion pro-cess and demand technologydeveloped in each time period

� Activity level for each re-source technology in eachtime period

� A full range of energy prices,such as electricity price by


On the supply side, the model considersvarious energy resources that are availableboth domestically and from abroad formeeting various end-use demands. These in-clude both the conventional energy sourcessuch as coal, oil, natural gas, hydro power,nuclear power as well as renewable energysources such as wind, solar, biomass, and soon. The availability of each of these fuels isrepresented by constraints on the supplyside. The relative energy prices of variousforms and sources of fuels dictate the choiceof fuels, which play an integral role in cap-turing inter-fuel and inter-factor substitu-tion within the model. Furthermore, variousconversion and process technologies charac-terized by their respective investment costs,operating and maintenance costs, technicalefficiency, operational life, and so on, tomeet the sectoral end-use demands are alsoincorporated in the model. Although, as-sumptions related to resource availabilityand other input parameters of the model re-lated to technology characterization are pre-sented till 2036, the results from the modelare presented till 2031. The effects of incon-sistent behaviour of the MARKAL, an opti-mization model, are thus avoided withperfect foresight. A detailed description ofthe main resources and technology choicesincluded within each of the main sectors inthe modelling framework is provided inChapter 3. A detailed schematic representa-tion of the RES for these sectors, tracing theresource from the point of extraction to thepoint of use, is given in Appendix 2.

2.3.4 Integrating the workshop

inputs into the exercise

Integrating up-to-date data and informationwas the key to this integrated and well-tested

methodological framework. Towards thisend, several sectoral workshops were con-ducted, which involved discussions and fo-cused deliberations with researchers,stakeholders, and experts from each of theindividual consuming and supplying sectors.The experts from various fields provided notonly their knowledge and judgments regard-ing various technological options to be con-sidered in each of the sectors, but alsoinformation regarding key parameters asso-ciated with these technologies. Detailed de-scriptions of each of the technologicaloptions in terms of its techno-economic sta-tus and potential at the national and interna-tional levels including details regarding itscosts, efficiencies, availability and diffusiontimelines, and so on came to the fore in thedeliberations and discussions held in theworkshops. This helped in the technologycharacterization of each of the sectoral op-tions in the model.

2.4 Socio-economic drivers of

final energy demands

Population and GDP were considered as themain drivers for estimating and projectingsectoral end-use demands in terms of physi-cal industrial production or useful energyservices. Based on the optimal fuel–technol-ogy mix chosen by the MARKAL model tomeet these demands, final energy required isobtained from the model run.

Population growth and the dynamics ofthe demographic shifts across various in-come classes have a direct influence on thefuture energy demands. Accordingly, estima-tion of growth and distribution of popula-tion are vital for any government toefficiently allocate its resources and plan to-wards achieving its economic and social

Methodology 15

goals. Similarly, a high rate of economicgrowth, as measured by the GDP and its al-location across the agriculture, industry, andservices sectors, has an impact on energyconsumption. This is mainly because rise inthe GDP, which manifests itself in the formof increased agricultural production, accel-erated industrial production, and rising de-mand for commercial services, wouldinevitably lead to a rise in the energy re-quirement of the economy at large.

2.4.1 Population projection and

distribution across various income

groups in India

2.4.1.1 Population and

demographic trends in India

India’s population stood at nearly 350 mil-lion at the time of independence. It in-creased at an unprecedented annual growthrate of 2.11% during 1951–2001 to reachthe one billion mark at the dawn of new mil-lennium.

Although the family planning programmewas initiated in the country in 1951, the1971 census indicated that due to highgrowth rate, the population had increased by24.8% during 1961–71 as compared to21.5% during 1951–61. In spite of the vastnetwork of personnel involved in theprogramme and sizable expenditure fromthe centre, this continuing increase in popu-lation growth rate disturbed the policy-mak-ers and programme administrators, whichled to the adoption of draconian measuresduring the emergency period of 1975/76. Arevised population policy was adopted in1977 and the ‘family planning programme’was renamed as ‘family welfare programme’.This programme chose to achieve demo-graphic change through education and moti-vation. As indicated in Table 2.1, populationgrowth decelerated marginally during the1980s, evident from the decadal populationgrowth rate that reduced by 23.9%. Con-certed efforts by the government and the in-centives offered during 1986–91 to youngcouples having one or two children led to aslow but steady acceptance of family plan-ning programme.

Table 2.1 Demographic trends in India

Rate/measure for the 10-year period before the census

Enumerated Percentage Crude Crude Total Expectation of

population change in birth death fertility life at birth

Census year (in million) population rate rate rate Male Female

1951 361.1 13.3 40–44 28–32 5.3–6.0 32–34 32–34

1961 439.2 21.6 46–48 26–28 6.3–6.6 37–39 37–39

1971 548.2 24.8 43–44 21–22 6.4–6.6 43–45 42–44

1981 683.3 24.6 37 15 5.1 50 49

1991 846.6 23.9 35 13 4.3 54 53

2001 1027.0 21.3 29 10 3.7 59 60

Source Bhat (2001)


2.4.1.2 Population projection for

India

A number of national and internationalagencies have attempted to project India’spopulation over various time periods. Inter-nationally, the Population Division of theDepartment of Economic and Social Affairsof the United Nations Secretariat (UNPD orUnited Nations Population Division) isentrusted with the responsibility of demo-graphic estimation and projections for allcountries and areas of the world, includingurban and rural areas and major cities, andthese projections serve as the standard andprovide consistent set of population figuresfor use throughout the United Nations sys-tem. Within the country, prominent researchorganizations like the PFI and renowned de-mographers like P N Mari Bhat have alsomade population projections for India. Theirprojections are based on the Componentmethod,1 although different assumptions forvarious influencing factors, such as fertilityrate, mortality rate, and migration, are used.Table 2.2 provides a comparative assessmentof the assumptions used by the above-men-tioned agencies.

Table 2.3 provides the population projec-tions for India as estimated by varioussources. A close look at the estimates revealsthat the UNPD (medium variant) estimatesare not very different from the PFI esti-mates. For the period 2001–36, the UNPDprojects population to increase at an annualgrowth rate of 1% while the PFI estimatesthe growth rate to be 1.14%. Both the agen-cies estimate that the annual population

growth rate would decline over the decadesduring the forecast period. As per theUNPD figures in the medium-variant sce-nario, annual growth rate of population de-clines from 1.41% to 1.08% to 0.73%during 2001–11, 2011–21, and 2021–31, re-spectively. As per the PFI’s projections, thegrowth rate is 1.37%, 1.34%, and 0.92%during the same time periods.

For this study, the PFI estimates are pre-ferred to the UNPD estimates, since the PFIrelies more on the country-specific details.The UNPD estimates are based on the as-sumptions that are derived on the basis ofexperience of all the countries in the world,which might not reflect the specific charac-teristics inherent in Indian demography. ThePFI estimates, on the other hand, have beenderived on the assumptions specific to vari-ous Indian states. The PFI population pro-jections are used by the Office of RegistrarGeneral of India that conducts the popula-tion census in the country every 10 years.Moreover, the Planning Commission alsoadopts the set of population projections pro-vided by the PFI for formulation of variousnational plans and policies.

2.4.1.2.1 Rural–urban population

As energy-use patterns, choice of fuels, andso on vary considerably among rural and ur-ban areas, examining the trend of urbaniza-tion in the future assumes high significancefor a country like India.

India’s population has grown 2.84 timesfrom 1951 to 2001, that is, from 361 millionin 1951 to 1027 million in 2001. Its rural–

11111 The component method for population projections separately studies the drivers of the future size of the population,

such as fertility rate, mortality rate, and migration.

Methodology 17

Table 2.2 Assumptions for population projections

Factors

Total fertility

rate

Mortality rate

Migration

UNPD

Assumed to decline based on

the past trend of fertility rate

during 1950–2000 (a lower limit

of 1.85 children per woman)

Medium path of mortality de-

cline is used to project future

mortality levels

The future path of international

migration is set on the basis of

past international migration es-

timates and on assessment of

the policy stance of countries

with regard to future interna-

tional migration flows

PFI

Extrapolation by fitting lin-

ear trend (1971–96) for

each of the larger states in

India. The figure 1.6 is

taken as the floor value

Three values of life ex-

pectancy for the periods

1991–95, 2011–16, and

2021–26 were extrapo-

lated, using linear fit, at

five-year intervals from

2001 to 2051, for each of

the 15 larger states. The

figures for India are ob-

tained as weighted aver-

age of the figures of the

major states

No large-scale inter-state

migration in the country

Mari Bhat

Assumed to fall to

2.8 in 2010 and

reach very close to

the replacement

level only by 2025

The life expectancy

at birth has been as-

sumed to reach 67

for males and 71 for

females by 2025

Net migration to In-

dia assumed to be

zero

urban distribution has also undergone struc-tural changes over the same period. Itspopulation in rural areas has more thandoubled (~2.5 times) from 298 million dur-ing 1951 to 740 million in 2001, whereaspopulation in urban areas has increasedmore than four times (~4.6 times) from 62million to 287 million during the same time

period. The UNPD estimates the urbanpopulation to increase by about 33% by2016 and 42% by 2031, while correspondingfigures given by the Census of India are 34%and 40% for the same time period.

As indicated in Tables 2.4 and 2.5, thepercentage shares of the population residingin urban areas projected by both the UNPD

UNDP – United Nations Population Division; PFI – Population Foundation of India


Table 2.3 Population projections (in million)

Source Scenario 2001 2006 2011 2016 2021 2026 2031 2036

UNPD Low variant 1031 1099 1156 1203 1242 1269 1282 1283

Medium variant 1033 1112 1188 1259 1323 1378 1424 1461

High variant 1034 1125 1220 1315 1405 1490 1573 1653

Mari Bhat Optimistic 1026 1109 1191 1271 1345

Realistic 1025 1103 1173 1244 1320

PFI 1027 1092 1177 1264 1344 1413 1473 1526

PFI – Population Foundation of India; UNPD – United Nations Population Division

Note UNPD projections were available from 2000 to 2050 on a five-year interval. Figures presented in this table are

interpolated for the years mentioned.

Table 2.4 Rural–urban distribution (%) as per the UNPD

Region 2001 2006 2011 2016 2021 2026 2031 2036

Urban 28 29 31 33 35 37 42 45

Rural 72 71 69 67 65 62 58 56

UNDP – United Nations Population Division; PFI – Population Foundation of India

Source UNPD (2003)

Note Projection distribution was available from 2000 to 2016 on a five-year interval. Figures presented in this table

are interpolated for 2001, 2006, 2011, etc. and, based on the past trend of 2001–16, these have been extrapolated

for the period 2016–36.

Table 2.5 Rural–urban distribution (%) as per the Census of India

Region 2001 2006 2011 2016 2021 2026 2031 2036

Urban 28 30 32 34 36 38 40 42

Rural 72 70 68 66 64 62 60 58

Source Census of India (1991)

Note Figures were available till 2016 and, based on the past trend of 2001–16, these have been extrapolated for

the period 2016–36.

and the PFI are more or less similar. Thedata given by the PFI is considered in thisanalysis to ensure consistency with the set ofpopulation projection provided by the PFIand adopted by the Office of Registrar Gen-eral of India, Government of India.

2.4.1.2.1.1 Number of households

The household size for rural and urban areashas been estimated based on the rate ofdecrease in average number of householdsin rural and urban areas during the period

Methodology 19

cate the number of urban andrural households under six ex-penditure classes, namely, BPL(below poverty line), L (low),LM (lower middle), M(middle), UM (upper middle),and H (high) for the three sce-narios based on three differentprojected GDP growth rates,namely, 6.7%, 8%, and 10%,considered in this study. It maybe noted that in the tables, thesesix expenditure classes in ruralareas are denoted by RBPL, RL,RLM, RM, RUM, and RHwhereas the correspondingclasses in urban areas are de-noted by UBPL, UL, ULM,UM, UUM, and UH, respec-tively, with the prefixes R and U

Table 2.6 Projected population and number of

households in rural and urban areas (million)

Population Number of households

Year Rural Urban Rural Urban

2001 739.44 287.56 137.34 56.26

2006 764.40 327.60 144.53 65.36

2011 800.36 376.64 154.06 76.62

2016 834.24 429.76 163.48 89.14

2021 860.16 483.84 171.60 102.34

2026 876.06 536.94 177.92 115.80

2031 883.80 589.20 182.73 129.57

2036 885.08 640.92 186.30 143.71

Source Census of India (1991)

Note The figures were available till 2016 and, based on the past

trends, have been extrapolated for the period 2016–36.

1981–2001. Following the rate of decreaseduring this period, household size has beenprojected to decline by 4.75% and 4.46% forrural and urban, respectively, by 2036. Table2.6 presents the projected population andnumber of households in rural and urban areas.

2.4.1.2.1.1.1 Income-wise house-

hold distribution

To estimate the distribution of population invarious income groups, a lognormal curve isfitted on the data sets for the MPCE(monthly per capita expenditure) for ruraland urban areas, and frequency of popula-tion in various income groups has been cal-culated using statistical software packageSYSTAT11. A detailed methodology for es-timating and projecting the number ofhouseholds in various expenditure classes isgiven in Appendix 3. Tables 2.7–2.12 indi-

denoting rural and urban households, re-spectively.

It is clear from the projections presentedin Tables 2.7–2.12 that as the economyachieves high GDP growth rate by 2031, thenumber of urban and rural households in theBPL and L expenditure classes diminishes.Subsequently, there is a rise in the numberof households in the higher expenditureclasses in the rural and urban areas by 2031.

2.5 Gross domestic product as a

measure of economic growth

The significance of economic growth for apolity cannot be undermined in the contextof overall development. Economic growthresults from an increased production ofgoods and services leading to high incomegeneration. This ultimately translates intoimprovement in the quality of life of the


people in terms of various economic andsocial indicators such as enhanced purchas-ing power and improved access to qualityeducation and health care services. GDP isconsidered as the most commonly used mea-sure of economic growth. It tracks thedomestic economic activity in terms ofthe value added and income generated(in monetary terms) during a specified timeperiod.

2.5.1 Literature review on gross

domestic product growth projections

There are several agencies engaged in mak-ing short-term forecasts of the growth rate ofreal GDP for the Indian economy based onthe regular monitoring of key parametersthat influence it. These agencies includemany bilateral and multilateral organiza-

Table 2.8 Number of urban households (in million) in various expenditure categories for 6.7%

GDP growth

Urban 1999 2001 2006 2011 2016 2021 2026 2031

UBPL (<665) 15.6 18.1 13.3 8.0 3.2 0.6 0.0 0.0

UL (665–1120) 14.5 19.2 22.5 23.1 18.1 8.9 2.1 0.1

ULM (1120–1500) 6.4 8.9 12.7 17.1 19.6 16.2 7.4 1.4

UM (1500–1925) 3.5 5.0 8.0 12.6 18.1 20.8 15.3 5.2

UUM (1925–4000) 3.4 4.8 8.3 14.9 27.8 49.2 71.3 70.7

UH (>4000) 0.2 0.3 0.5 1.0 2.3 6.7 19.7 52.1

UBPL – urban below poverty line; UL – urban low; ULM – urban lower middle; UM – urban middle; UUM – urban upper

middle; UH – urban high; GDP – gross domestic product

Note Figures in brackets represent monthly per capita consumption expenditure in rupees.

Table 2.7 Number of rural households (in million) in various expenditure categories for

6.7% GDP growth

Rural 1999 2001 2006 2011 2016 2021 2026 2031

RBPL (<615) 88.5 84.5 68.2 49.0 27.3 10.0 2.1 0.2

RL (615–775) 20.0 21.8 26.7 29.1 25.3 15.4 5.3 0.9

RLM (775–950) 12.4 14.0 19.9 25.7 28.1 22.5 11.0 2.7

RM (950–1200) 8.2 9.9 15.8 23.7 32.0 34.0 23.8 9.3

RUM (1200–2800) 5.8 7.0 13.7 26.0 49.2 84.3 117.3 118.0

RH (>2800) 0.1 0.1 0.1 0.5 1.5 5.5 18.3 51.5

RBPL – rural below poverty line; RL – rural low; RLM – rural lower middle; RM – rural middle; RUM – rural upper middle;

RH – rural high; GDP – gross domestic product


Methodology 21

Table 2.9 Number of rural households (in million) in various expenditure categories for 8%

GDP growth rate

Rural 1999 2001 2006 2011 2016 2021 2026 2031

RBPL (<615) 88.50 84.46 60.56 32.66 11.77 2.40 0.18 0.00

RL (615–775) 20.00 21.84 27.17 25.57 15.86 5.49 0.89 0.00

RLM (775–950) 12.43 14.01 21.39 26.34 22.07 10.98 2.85 0.18

RM (950–1200) 8.24 9.89 17.92 28.19 31.88 22.82 8.54 1.64

RUM (1200–2800) 5.81 7.00 17.20 40.06 76.67 110.68 110.67 68.52

RH (>2800) 0.14 0.14 0.29 1.23 5.23 19.22 54.80 112.38




Table 2.10 Number of urban households (in million) in various expenditure categories for 8%

GDP growth rate

Urban 1999 2001 2006 2011 2016 2021 2026 2031

UBPL (<665) 15.64 18.06 11.44 4.90 1.25 0.10 0.00 0.00

UL (665–1120) 14.46 19.19 21.31 18.01 9.81 2.87 0.35 0.00

ULM (1120–1500) 6.36 8.89 13.07 16.32 14.62 7.68 1.85 0.13

UM (1500–1925) 3.53 5.01 8.76 13.87 17.03 13.41 5.44 0.91

UUM (1925–4000) 3.35 4.84 10.07 21.30 39.67 58.23 57.90 32.78

UH (>4000) 0.22 0.28 0.72 2.22 6.78 20.06 50.26 95.75




tions like the World Bank and the IMF(International Monetary Fund); leadingfinancial consulting firms like McKinsey,Goldman Sachs, Morgan Stanley, and so on;and prominent Indian business associationslike the CII (Confederation of Indian Indus-

try) and the FICCI (Federation of IndianChambers of Commerce and Industry).These organizations forecast GDP growthrate using econometric techniques such asARIMA (Autoregressive Integrated MovingAverage) and Exponential Smoothing.2

22222 The ARIMA model is a univariate method in which the values of the variable under consideration are forecasted based

on the lagged/past values of the variable itself. Exponential Smoothing technique simplifies the time-series data of the

variable under consideration by reducing or cancelling the effect due to random variations in the data. This technique

can be used for forecasting by assigning weights to the past observations of the variable under consideration.


Table 2.11 Number of rural households (in million) in various expenditure categories for 10%

GDP growth rate

Rural 1999 2001 2006 2011 2016 2021 2026 2031

RBPL (<615) 88.50 84.46 54.92 21.26 4.09 0.34 0.00 0.00

RL (615–775) 20.00 21.84 27.03 20.95 7.68 1.03 0.00 0.00

RLM (775–950) 12.43 14.01 22.40 24.50 13.73 3.09 0.18 0.00

RM (950–1200) 8.24 9.89 19.51 30.04 24.85 8.92 1.07 0.00

RUM (1200–2800) 5.81 7.00 20.38 54.69 98.74 104.85 55.87 11.88

RH (>2800) 0.14 0.14 0.29 2.62 14.39 53.37 120.81 170.85




Table 2.12 Number of urban households (in million) in various expenditure categories for

10% GDP growth rate

Urban 1999 2001 2006 2011 2016 2021 2026 2031

UBPL (<665) 15.64 18.06 10.00 2.83 0.36 0.00 0.00 0.00

UL (665–1120) 14.46 19.19 20.39 13.56 4.37 0.51 0.00 0.00

ULM (1120–1500) 6.36 8.89 13.33 14.86 8.91 2.15 0.12 0.00

UM (1500–1925) 3.53 5.01 9.35 14.40 13.28 5.42 0.69 0.00

UUM (1925–4000) 3.35 4.84 11.37 27.12 47.34 49.73 24.32 4.15

UH (>4000) 0.22 0.28 0.92 3.83 14.89 44.52 90.67 125.42




However, these forecasts provide a short-term view of the Indian economy, rangingfrom one quarter to one year.

The Institute of Economic Growth, NewDelhi, has projected the GDP growth ratefor the Tenth Five Year Plan period (2002–07) using a macro-econometric model thatgenerates forecasts of the GDP growth tra-jectory at the aggregate as well as thesectoral levels. The Institute has developed

three alternative long-term growth scenariosbased on different assumptions regardingthe investment rate. In the BAU (business-as-usual) scenario, which assumes normalrainfall and an investment rate of 27.6% ofthe GDP, the GDP is projected to grow at arate of 6.1% over the modelling time frame.In the optimistic scenario, assuming normalrainfall and an investment rate of 31% of theGDP, the growth rate forecast for the period

Methodology 23

2002–07 is 6.8%. Similarly, with an invest-ment rate of 32.9%, the growth rate forecastis 7.4%. The pessimistic scenario, which as-sumes low rainfall and an investment rate of28% of the GDP, forecasts a GDP growth ofabout 5.4% for the economy.

The NCAER (National Council for Ap-plied Economic Research) has also projectedthe GDP growth rate based on a macro-econometric model consisting of simulta-neous system of equations at an aggregate aswell as at a sectoral level. The NCAER hasforecast three alternative growth scenariosusing this model. In the realistic scenario, re-ferred to as the ‘most likely scenario’,the GDP growth rate is projected to increasefrom 6.54% in 2004/05 to 7.82% in2008/09. In the pessimistic and optimisticscenarios, the GDP growth rate is forecast at7.18% and 8%, respectively, for 2008/09.

2.5.2 Gross domestic product

growth projections

TERI worked out the most likely GDPgrowth scenario for the long term. It was es-timated that the GDP would be no morethan 6.7% over the 30-year modellingperiod. The methodology for the same is

provided in detail in Appendix 3. This par-ticular study, however, was conducted with aprojected GDP growth rate of 8% consid-ered in the BAU scenario that reflected gov-ernment plans. The rationale for choosing aGDP growth rate of 8% throughout the pe-riod 2004–36 is also explained in detail inAppendix 3. Given that the Indian economyis already in the last year of the Tenth FiveYear Plan, the Planning Commission is inthe process of readying the Eleventh FiveYear Plan (2007–12). The Government ofIndia is expected to target a growth rate of10% for the Eleventh Five Year Plan period.Hence, additionally, a 10% GDP scenariohas been considered to reflect an even highergrowth rate of the economy, as suggested bythe Office of the PSA (Principal ScientificAdvisor), to examine the impact of a two-digit (a higher rate of GDP growth relativeto the BAU) GDP growth on the future tra-jectories of energy consumption. Accord-ingly, three GDP growth rates have beenconsidered in this study: 8% (reflecting theBAU scenario), 6.7% (representing a low-growth scenario), and 10% (representing ahigh-growth scenario).

Table 2.13 presents the figures foraggregate GDP under the three growthscenarios.

Table 2.13 Projections of GDP at factor cost at 1993/94 prices (in crore rupees) under vari-

ous GDP growth rate scenarios

GDP growth (%) 2001 2006 2011 2016 2021 2026 2031

6.7 1 267 945 1 676 029 2 240 639 3 061 793 4 258 687 6 004 800 8 551 719

8 1 267 945 1 802 078 2 647 845 3 890 552 5 716 498 8 399 411 12 341 490

10 1 267 945 1 904 059 3 066 507 4 938 640 7 953 729 12 809 559 20 629 924

GDP – gross domestic product


Source MoF (2005)

2.5.3 Trends in sectoral composi-

tion of gross domestic product

The sectoral composition of the GDP hasundergone significant transformation, start-ing from the First Five Year Plan (1950–55).

Figure 2.4 depicts the historical trend ofthe contribution of sectoral GDP in aggre-gate GDP through the period 1980–2003.The trend clearly suggests that the share ofagriculture sector in the aggregate GDP de-clined from 60% in 1950/51 to about 40% in1979/80 and to 24% in 2003/04. The shareof industry in the GDP has increased from13% in 1950/51 to about 22% in 1979/80and 24.5% in 2003/04, whereas the share ofservices in GDP has exhibited a rise from27% in 1950/51 to about 37% in 1979/80and 51% in 2003/04.

2.5.4 Projections of sectoral com-

position of gross domestic product

The India Vision 2020 document (PlanningCommission 2002) highlights that knowl-edge resources (technology, organization,information, education, and skills) have re-placed capital as the most important deter-minants of development. This is the primereason for a rapidly increasing share of ser-vices sector in GDP as the sector is essen-tially knowledge-based. The document laysdown the reference levels for sectoral com-position in GDP (%) that India should striveto attain by 2020. The reference levels for2020 as presented in the document and byTERI estimates for 2020 for sectoralcomposition of GDP (%) are presented inTable 2.14.

The reference 2020 levels mentioned inTable 2.14 are highly optimistic. As per theselevels, share of agriculture in aggregate GDPis projected to decline to 6% with a corre-

Figure 2.4 Share of sectoral GDPin aggregate GDP (%)

sponding rise in theshare of industryand services to 34%and 60%, respec-tively, in 2020. Suchlevels in 2020 couldbe possible only withthe economy achiev-ing and sustaining ahigh aggregate GDPgrowth rate of 10%per annum, begin-ning from 2004/05till 2036/37.

The historicaltime trend of theGDP of the agricul-ture sector suggests

Methodology 25

that although the contribution of agriculturein the GDP has declined, the proportion ofpopulation dependent on agriculture has notdeclined in a similar fashion. According tothe Census of India (2001), 65% of the totalpopulation is still dependent on agriculturefor its livelihood. In this context, it is essen-tial to highlight that 6% share of agriculture,34% of industry, and 60% of services sectorin total GDP implies that income generatedby the agriculture sector would be quite lowand hence would necessitate shifting oflarge chunks of population engaged in theagriculture activities to industry and servicessectors that employ skilled labour. Further-more, given the thrust on accelerating therate of agricultural growth in the Tenth FiveYear Plan, various policies focusing on agri-culture growth are being formulated andimplemented. Moreover, achieving food se-curity3 has been a major goal of developmentin India after independence. Despite the factthat food production in the country has in-creased from 51 MT (million tonnes) in

1950/51 to 210.8 MT in 2003/04, completefood security at the household level has stillnot been achieved, with 21% of the popula-tion still suffering from under-nourishment(FAO 2004). Thus, the decline in the shareof agriculture sector in the scenarios using6.7% and 8% projected GDP growth rate isnot expected to be as rapid as mentioned inthe report of the Planning Commission(Vision 2020).

For the 6.7% and 8% GDP scenarios, weassume that the services sector continues togrow at the current rate (0.51% during2003/04) till 2036/37, achieving a share of60% in the GDP by 2036/37. The share ofindustrial sector in the aggregate GDP hasincreased at an average annual growth rateof 0.31%. It is estimated that it will achieve ashare of 30% in the GDP by 2036/37. Therest of the share (10%) is accounted for bythe agriculture sector in our analysis.

The sectoral GDPs at factor cost undereach of the three GDP scenarios are pre-sented in Tables 2.15–2.17.

2.6 Approach for sectoral end-

use demand estimation

Econometric techniques, such as regressiontechniques, process models, and end-usemethods, are deployed to estimate andproject the end-use sectoral demand. Thepopulation and GDP projections were usedas the main driving force for estimatingthe end-use demands in each of the energyconsuming sectors.

Table 2.14 Sectoral composition of GDP (%)

Sectoral composition of GDP (%)

Reference T E R I estimates

Sector 2020 2020

Agriculture 6 17

Industry 34 28

Services 60 55


Source Reference 2020 data is from the World Bank

(2001)

33333 According to the World Food Summit (1996), ‘Food security exists when all people, at all times, have physical and

economic access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for an

active and healthy life’ (FAO 1996).


Table 2.16 Sectoral GDP at factor cost (in crore rupees) under 8% GDP growth rate scenario

Sector 2001 2006 2011 2016 2021 2026 2031

Agriculture 333 274 395 320 533 024 711 190 936 593 1 213 019 1 536 733

(26%) (22%) (20%) (18%) (16%) (14%) (13%)

Industry 309 557 491 106 732 865 1 093 635 1 632 004 2 435 397 3 634 279

(24%) (27%) (28%) (28%) (28%) (29%) (29%)

Services 625 114 915 652 1 381 955 2 085 727 3 147 901 4 750 995 7 170 477

(49%) (51%) (51%) (54%) (55%) (57%) (58%)

Total 1 267 945 1 802 078 2 647 845 3 890 552 5 716 498 8 399 411 12 341 490


Table 2.17 Sectoral GDP at factor cost (in crore rupees) under 10% GDP growth rate scenario

Sector 2001 2006 2011 2016 2021 2026 2031

Agriculture 333 274 358 212 383 575 410 734 477 224 768 574 1 237 795

(26%) (19%) (13%) (8%) (6%) (6%) (6%)

Industry 309 557 539 630 987 235 1 670 286 2 704 268 4 355 250 7 014 174

(24%) (26%) (29%) (31%) (34%) (34%) (34%)

Services 625 114 1 006 218 1 695 697 2 857 620 4 772 237 7 685 736 12 377 954

(49%) (53%) (58%) (58%) (60%) (60%) (60%)

Total 1 267 945 1 802 078 2 647 845 3 890 552 5 716 498 8 399 411 12 341 490


Table 2.15 Sectoral GDP at factor cost (in crore rupees) under 6.7% GDP growth rate scenario

Sector 2001 2006 2011 2016 2021 2026 2031

Agriculture 333 274 367 050 450 368 560 308 698 425 864 691 1 068 965

(26%) (22%) (20%) (18%) (16%) (14%) (13%)

Industry 309 557 457 556 620 657 860 364 1 213 726 1 741 392 2 514 205

(24%) (27%) (28%) (28%) (28%) (29%) (29%)

Services 625 114 851 423 1 169 614 1 641 121 2 346 537 3 398 717 4 968 549

(49%) (51%) (51%) (54%) (55%) (57%) (58%)

Total 1 267 945 1 676 029 2 240 639 3 061 793 4 258 688 6 004 800 8 551 719


Methodology 27

The industrial sector is disaggregatedinto eight energy-consuming industries,namely, chlor-alkali, aluminium, iron andsteel, cement, textile, fertilizer, and pulp andpaper, along with other manufacturing unitsgrouped as other industries. The physicaloutputs from the above-mentioned indus-tries are considered as the demands of in-dustrial outputs. The future demand ofindustrial output for each of the aforemen-tioned industrial sub-sectors is based on in-come generated by various sectors of theeconomy. This is measured by the GDP andthe value added by the industrial sector(GDP of industry), per capita income, andso on. Similarly, the transportation demand(disaggregated further into mode-wisepassenger demand and freight transportdemand) is projected using various socio-

economic indicators such as per-capita in-come (indicator of purchasing power), per-centage share of population residing inurban areas, population, and so on. In theagriculture sector, demand is estimated forland preparation and irrigation pumping. Inthe residential sector, the demand is pro-jected for lighting, space conditioning, cook-ing, and refrigeration separately for urbanand rural households to account for the dif-ferences in lifestyles and choice of fuel andtechnology options. In the commercial sec-tor, the demand is projected for cooking,lighting, and space conditioning, using thevalue added by the services sector as anexplanatory variable. The detailed method-ology and estimates of energy demandsfor each of the end-uses are presented inChapter 3.

Sectoral demand projections,

technological characterization,

and resource availability33333

This chapter presents the input parametersto the MARKAL (MARket ALLocation)model. As discussed in Chapter 2, demandsfor five end-use sectors (agriculture, indus-try, transport, residential, and commercial)have been considered in this analysis. Theestimates of these demands across differentGDP (gross domestic product) growth ratesare presented in this chapter. Further, thetechnology characteristics of various optionsin each supply- and demand-side sector aredescribed. The characterization includes de-tails of the efficiency, cost, life, availability,and penetration over the modelling timeframe (2001–31).

3.1 Demand sectors

3.1.1 Agriculture sector

Traditionally, India has been an agriculturaleconomy. Since Independence, the share ofagriculture in the country’s GDP has beendeclining in comparison to the growth of theindustrial and services sectors. The percent-age share of GDP from agriculture at factorcost at current prices has come down from28.4 in 1993/94 to 20.3 in 2002/03 (MoA2004). However, agriculture is still a majorsource of income for about 53.2% of thepopulation (MoA 2004). It provides raw

material to several major industries, such assugar, textiles, jute, paper, food processing,and milk and milk processing. Agriculture iscrucial for maintaining the food security ofthe country. This sector has forward andbackward linkages with other economic sec-tors. Therefore, changes in the agriculturalsector have a multiplier effect on the entireeconomy. High growth rate of agricultureensures good performance of agro-based in-dustries, supports creation and improve-ment of the rural infrastructure, andfacilitates reduction in poverty.

Agriculture accounts for 43% of the totalgeographical area. In terms of cultivatedarea, the leading crop is rice – the staple foodof a large section of the Indian population(Figure 3.1) – followed by wheat.

There has been a continuous fragmenta-tion of land holdings, partly because of thegrowing population pressure and partly be-cause of the peculiar slow shift of the labourforce from agriculture to non-agriculturaleconomic activities. Per capita availability ofcultivable land (excluding forests) has de-creased from 0.48 ha (hectares) in 1951 to0.15 ha in 2000.

Development of improved productiontechnologies, efficient input use and im-proved delivery system, rural infrastructuredevelopment, pricing policies, and market-ing arrangements have led to a remarkable


increase in food grain production from just51 MT (million tonnes) in 1950/51 to174.19 MT in 2002/03 and further to212.02 MT in 2003/04 (Figure 3.2).

to 1072 kg/ha and 623 kg/ha, respectively,during the same period.

Horticultural production was 156.1 MTin 2003/04. This sector contributed 30% ofthe share of agriculture to the GDP. Indiawas the largest producer of vegetables andthe second largest producer of fruits in

Source FAI (2004)

Figure 3.1 Area under cultivationin India (million hectares)

Source FAI (2004)

Figure 3.2 Food grain productionin India (million tonnes)

There has been a spectacu-lar increase in agriculturalproductivity since 1950/51,whereby yield of food grainswent up from 522 kg/ha (kilo-grams per hectare) during1950/51 to 1707 kg/ha in2003/04. Yield of rice andwheat increased from 668kg/ha and 663 kg/ha to 2051kg/ha and 2707 kg/ha, respec-tively, during the same pe-riod. Yield of coarse cerealswent up from 408 kg/ha in1950/51 to 1228 kg/ha in2003/04. Yield of nine oil-seeds and pulses increasedfrom 481 kg/ha and 441 kg/ha

the world with 90 MT and47.5 MT of production,respectively, and accountedfor about 10% of the globalproduction of fruits. India isranked first in the productionof mango, banana, sapota, acidlime, and cauliflower; secondin onion; and third in cabbage(MoF 2005).

India is the largest producerand consumer of tea in theworld, accounting for 27% ofthe world production, with850.5 thousand tonnes ofproduction in 2003/04. Indiais also among the leading

Sectoral demand projections, technological characterization, and resource availability 31

producers of sugar cane, cotton, and jute inthe world, with production of 236.2 MT,13.8 MT, and 11.2 MT, respectively, in2003/04. Cashews, coffee, and spices arealso important cash crops (MoF 2005).

3.1.1.1 End-use demand estima-

tion for the agriculture sector

The country had a stagnant agriculture atthe time of Independence. The traditionaltools and implements used in agriculture re-lied mostly on human and animal power.The sector used a negligible amount of com-mercial energy. However, during the pastfive-and-a-half decades, Indian agriculturehas witnessed numerous changes. The‘Green Revolution’ is one of the most strik-ing success stories of the post-Independenceera. The impact of the Green Revolutionwas, however, so dramatic that India becamea role model for many developing countries.This innovation – coupled with investmentsin irrigation infrastructure and expansion ofcredit, marketing, and processing facilities –led to a significant increase in the use ofmodern inputs. As a consequence, the re-quirement of commercial energy of the farmsector increased by several times.

The availability of farm power per unitarea (kW/ha [kilowatt per hectare]) has beenconsidered as one of the parameters for ex-pressing the level of mechanization. Poweravailability for carrying out various agricul-tural operations has increased from 0.3 kW/hain 1971/72 to 1.4 kW/ha in 2003/04 (MoF2005).

The contribution of different powersources to the total power has also changedover time. The share of mechanical and elec-trical power in agriculture increased from

40% in 1971/72 to 84% in 2003/04 (MoF2005). However, the extent of use of me-chanical power in agriculture is much belowthe ideal value. In 1996, the net cultivatedarea in the country stood at 142 Mha (mil-lion hectares), of which 53.5 Mha was irri-gated. Even if it is assumed that tractors areused only in irrigated areas, there were 38tractors per 1000 ha. This translates into1.14 hp (horsepower) per hectare of mecha-nized power as compared to 2–5 hp per hect-are in developed countries (Venugopal 2004).

Various agricultural operations likethreshing, harvesting, land preparation, andirrigation, account for energy demand in theagricultural sector. But, energy demand inthe agricultural sector in India is mainly attrib-uted to two major agricultural operations.1 Land preparation2 Irrigation

3.1.1.1.1 Demand for land

preparation

3.1.1.1.1.1 Gross cropped area

Energy demand for land preparation de-pends on the extent of area under cultiva-tion. The total land area being constant,NCA (net cropped area) has also remainedconstant at about 141 Mha since 1970s. Thisimplies that increase in GCA (gross croppedarea) has been made possible by increase inCI (cropping intensity) over the years. NCAhas been assumed to remain constant in thenext 30 years also.

Facilitated by improvement in the irriga-tion sector, CI is initially expected to in-crease. However, with further developmentof the sector, CI will move towards its satu-ration level because production time of


crops cannot be reduced beyond a certainlevel. Therefore, CI has been assumed to fol-low a logistic growth path—increasing at anincreasing rate in the initial years of develop-ment, followed by an increase at diminishingrate, and finally attaining the asymptoticlimit.

Logistic curve equation

( )( )btaexp1

btaexpYY 0 ++

+= (3.1)

where,Y is the CI;Y

0 is the asymptotic limit of CI;

a and b are the parameters to be esti-mated from the time series data of CI;andt denotes the time period.The parameters are estimated by a linear

regression of the log–log form of Equation3.2.

z = α + βt (3.2)

where,

z = ln ( )0

0

Y/Y1

)Y/Y(

−

a and b in Equation 3.1 are estimated val-ues of α and β. The asymptotic limit of CI istaken to be 3 in a year, and Z = −0.4478 +0.0084t

GCA = NCA × CI (3.3)

where,GCA is gross cropped area; andNCA is net cropped area.To validate the predictive accuracy of

the logistic equation, Theil’s Inequality

Coefficient is calculated. Theil’s InequalityCoefficient always lies between zero andone, where the smaller the Theil’s value, thebetter the forecasting technique, relative tothe naïve method. Zero value indicates a per-fect fit. In this study, the calculated Theil’sInequality Coefficient is 0.0026, which isnear to zero. This indicates that the logisticequation for estimating CI is a perfect fit forestimating the GCA.

During 1971–99, GCA increased at anannual growth rate of 0.496% and is ex-pected to increase at the rate of 0.430% dur-ing 2001–36 (Table 3.1).

3.1.1.1.1.2 Number of tractors

At the time of Independence, and even inthe 1950s, the use of tractors for agriculturalpurpose was very limited. Tractor manufac-turing in India started in 1961 with acapacity to manufacture 11 000 tractorsper year. The level of mechanization hasbeen increasing steadily over the years as aresult of joint efforts made by the govern-

Table 3.1 Projected cropping intensity

and gross cropped area

Cropping Gross cropped area

Year intensity (million hectares)

2001 1.360 192.054

2006 1.391 196.472

2011 1.423 200.904

2016 1.454 205.345

2021 1.485 209.792

2026 1.517 214.240

2031 1.548 218.687

2036 1.580 223.127


ment and the private sector. The number oftractors manufactured from all units during1997 was over 255 000 (Venugopal andPingali 2004). Annual average rate of growthfor tractors manufactured was 9.73% during1971–2001.

In the following equation, the number oftractors has been determined by the GDP inthe agriculture sector and GIA (gross irri-gated area).

Tractors =−1628872 + 6.388 (GDPA) +

(−11.863) (2.7644)

20617.42 (GIA)(2.009) (3.4)

(R2 = 0.97)

where, GDPA represents the gross domestic

product from agriculture.The above regression equation for the

sample period 1971–98 shows a high R2

(0.97), indicating that the regressor used ex-plains 99.7% of the variation in the numberof tractors. The t-statistics denotes that coef-ficients are significant.

The negative intercept indicates that thenumber of tractors starts increasing only af-ter a certain level of GDP is attained. Inother words, it implies that mechanization ofagriculture picked up only after a certainlevel of growth was achieved by the agricul-ture sector.

3.1.1.1.1.3 Area under tractors

The average command area per tractor was15–16 ha in 1995 (Singh and Singh 1995). Itwas 18 ha per tractor in 2001, and has beenassumed to remain constant since. This isbecause, over time, the efficiency of tractorin terms of command area may increase butthe fragmentation of landholdings might

reduce the size of average landholdings, andthe average command area per tractor is ex-pected to remain more or less the same. Thearea under tractors is derived by multiplyingthe number of tractors with the averagecommand area per tractor.

At a GDP growth rate of 6.7%, the num-ber of tractors increases and, therefore, areaunder tractors increases at the annual aver-age growth rate of 4.1% during 2001–36. Atthe end of forecast period, that is, 2036, 71%of the total GCA is expected to be undertractors. At 8% and 10% GDP growth ratescenarios, the GCA under tractors increasesat the rate of 5.2%, and by 2036, the entireGCA would be under tractors. The GCA un-der tractors is lower in the 10% GDP growthrate scenario as compared to that in 8%growth scenario during 2006–31, because at10% GDP growth rate, agricultural contri-bution to GDP is relatively lower, implying arelatively low GDP from agriculture. It maybe noted here that in 2036, maximum limitof GCA under tractors is equal to the totalGCA in the country. The projected demandfor land preparation is presented in Table 3.2.

Table 3.2 Demand for land preparation

at various GDP (gross domestic product)

growth rates (in million hectares)

Year 6.7% GDP 8% GDP 10% GDP

2001 38.28 38.28 38.28

2006 43.88 47.06 44.87

2011 55.14 64.57 50.98

2016 69.34 86.75 57.41

2021 86.93 114.40 68.48

2026 108.17 147.93 105.50

2031 132.67 186.93 163.08

2036 158.48 223.13 223.12


3.1.1.1.1.4 Demand for irrigation

Besides China, the irrigation system in noother country is as extensive as in India. It isthe irrigation system that has fuelled India’sgrowth in agricultural production. Irrigationplays a vital role in Indian agriculture fortwo important reasons. First, India has amonsoon-dependent farming system, withlarge areas receiving inadequate rainfall.Moreover, much of this rainfall is restrictedtemporally to a few months while the rest ofthe year is predominantly dry. In such a cir-cumstance, it is only with irrigation that cul-tivation on an annual basis is possible.Second, irrigation has acquired an addi-tional importance since the Green Revolu-tion in India. The Green Revolution hasbeen characterized by the use of high-yield-ing crop varieties, fertilizers, and other in-puts. These inputs into agriculture arecombined with a regular water supply pro-vided by irrigation. In such a situation, irri-gation has assumed considerablesignificance at the state, regional, and na-tional levels.

3.1.1.1.1.5 Gross irrigated area

Increase in cropping intensity is difficult inthe absence of proper irrigation facilities.Therefore, it has been assumed that the in-crease in GCA is due to an increase in thearea irrigated, and thus, GIA is calculated as.

∆GIAt, t-1

= GCAt– GCA

t-1(3.5)

The GIA has increased from 38.4 Mha in1971/72 to 76.3 Mha in 1998/99.

GIA increases at the rate of 0.97% duringthe forecast period (2001–36) (determinedby the equation above) and increases fromabout 78 Mha in 2001 to 110 Mha by 2036in the low- and medium-growth scenarios.

In the high-growth scenario, it has beenassumed that the government allocatesfunds to make more canals and builds moreinfrastructure for irrigation. Therefore, thepercentage area under irrigation follows therate of increase during 1971–2001, therebyincreasing from 41% in the base year to 65%by 2036 (Table 3.3).

One of the biggest developments that hastaken place in Indian irrigation after Inde-pendence is in the field of groundwater irri-gation, and one of the major engineeringinputs adopted has been irrigation pumps.Farmers use electric-motor- and diesel-

Table 3.3 GIA and GCA under irrigation

under various growth scenarios

6% and 8% GDP 10% GDPGCA GCA

GIA under GIA under(million irrigation (million irrigation

Year hectares) (%) hectares) (%)

2001 78.90 41.22 78.90 41.22

2006 83.34 42.55 88.93 45.26

2011 87.85 43.85 97.54 48.55

2016 92.42 45.11 106.46 51.84

2021 97.07 46.33 115.66 55.13

2026 101.79 47.51 125.16 58.42

2031 106.58 48.66 134.95 61.71

2036 110.46 49.34 145.03 65.00

GIA – gross irrigated area; GCA – gross cropped area;



engine-operated irrigation pumps with apreference for the former. Groundwater nowis an important source of irrigation and ful-fils about 43.6% of the total irrigation de-mand in the country (CMIE 2004). Thecontribution of groundwater irrigation inachieving self-sufficiency in food grain pro-duction in the past three decades has beenphenomenal. In the coming years, ground-water utilization is likely to increase for theexpansion of irrigated agriculture. Giventhat tube wells (especially, individual-ownedtube wells) are a perennial source of irriga-tion, as they encourage crop activity in rain-deficient seasons with minimum risk, thepercentage area under groundwater irriga-tion is expected to increase with time.

Pump sets costing about 10 000–15 000rupees are encouraged in the wake of subsi-dized power tariffs, soft loans, and subsidies.

The government can make efforts to bringabout more area under irrigation by increas-ing the production of pump sets.

In the low-growth scenario, it is assumedthat the government does not provide incen-tives to bring more area under groundwaterirrigation. Therefore, the percentage GIAunder groundwater remains constant at43.6%, that is, the 2001 level (CMIE 2004).The projections of GIA for groundwater irri-gation are shown in Table 3.4.

In the medium- and high-growthscenarios, it is assumed that the governmenthas resources to allocate for boosting thenumber of pump sets. Therefore, the per-centage area under groundwater irrigationincreases at an average annual growth rate of1.11%—the rate of increase during 1971–2001 (CMIE 2004). Accordingly, the per-centage area under groundwater irrigation

Table 3.4 GIA under groundwater irrigation at various GDP growth rate scenarios

6% GDP 8% GDP 10% GDPGIA under GIA under GIA undergroundwater GIA under groundwater GIA under groundwater GIA underirrigation groundwater irrigation groundwater irrigation groundwater(million irrigation (million irrigation (million irrigation

Year hectares) (%) hectares) (%) hectares) (%)

2001 34.40 43.60 34.40 43.60 34.40 43.60

2006 36.33 43.60 38.40 46.08 40.84 46.08

2011 38.30 43.60 42.79 48.71 47.33 48.71

2016 40.29 43.60 47.59 51.49 54.57 51.49

2021 42.3 43.60 52.83 54.43 62.64 54.43

2026 44.38 43.60 58.56 57.73 71.61 57.73

2031 46.46 43.60 64.82 60.81 81.58 60.81

2036 48.16 43.60 71.01 64.28 92.62 64.28

GIA – gross irrigated area; GDP – gross domestic product


increases from 43.6% in the base year to64% by 2036.

3.1.1.1.1.6 Groundwater

requirement

Table 3.5 gives the crop-wise GCA and wa-ter consumption. The weighted average ofwater consumption for GIA under variouscrops was calculated to get the water con-sumption per hectare of GIA. The weightedwater requirement per hectare for agricul-

ture has been assumed to remain constantover the years.

Total groundwater demand = water demandper hectare × GIA under groundwater irri-gation

Although groundwater is an annuallyreplenishable resource, its availability isnon-uniform in space and time. A complex-ity of factors – hydrological and climatologi-cal – controls the groundwater occurrenceand movement. Energy requirement for

Table 3.5 Crop-wise GCA and water consumption

Irrigation water Water Percentage Water

requirement consumption GCA of GCA consumption

Crop (mm) (m3) (Mha) irrigated (MCM)

Rice 300–950 6250 45.16 53.9 152 133

Jowar 350–650 5000 10.25 7.7 3946

Maize 400–750 5750 6.42 22.9 8453

Wheat 300–450 3750 27.49 87.2 89 892

Pulses 5000 21.12 16.1 17 002

Soyabean 500–860 6800 6.22 1.6 677

Sugar cane 1000–1500 12 500 4.22 92.0 48 530

Cotton 550–950 7500 8.71 35.2 22 994

Tobacco 600 6000 0.43 46.0 1187

Groundnut 506 5060 24.28 25.2 30 960

Bajra 5000 8.9 8.3 3693

Gram 5000 6.15 29.1 8948

Sunflower 350–500 4350 1.29 23.3 1307

Total 170.64 389 723

GCA – gross cropped area; Mha – million hectares; mm – millimetres; m3 – cubic metres; MCM – million cubic

metres

Sources <http://www.iasri.res.in/agridata/db2002tb3_27.htm>; <http://www.ikisan.com/links/ap_irrigation.shtml>;

<www.Indiastat.com>; MoA 2004

Note Average figure is considered for water consumption.

For pulses, bajra, and gram, water consumption corresponding to Jowar is considered


pumping out water depends on the watertable.

3.1.1.1.1.7 Water head

It is a very difficult exercise to determine thelevel of water table at the national level andto forecast it. However, an attempt has beenmade wherein maximum number of villagesin India at a particular water head is taken asa representative figure of water head for In-dia based on the Third Census of Minor Irriga-tion Schemes 2000/01 (MoWR 2005). TheCensus reveals that shallow tube wells con-stitute 94.03% of the total tube wells in thecountry. Andhra Pradesh, Bihar, Haryana,Madhya Pradesh, Punjab, Uttar Pradesh,and West Bengal constitute 85.7% of the to-tal shallow tube wells in India. In thesestates, pumps of 6–8 hp are dominant, con-stituting 26.4% of the total pumps, whereas,pumps of 4–6 hp constitute 24% of the totalpumps. Moreover, in these states, maximumnumber of villages, that is, 36% of the totalvillages, is at a water head of less than 10 m(metres). States like Haryana, Punjab, andUttar Pradesh having more than 89%, 90%,and 62% of the NCA irrigated have maxi-mum number of villages at 10–15 m waterhead. Consequently, these states have themaximum number of pump sets of 6–8 hp.This supports the fact that as the area underirrigation increases, groundwater extractionincreases and so does the water head.

In 2001, we take the average water head at10 m and, based on the discharge/groundwa-ter exploitation, calculate it to go down inthe forecasted period by applying the follow-ing formula.

dh/dt = (Rc– Q)/0.8S

y(3.6)

where, dh/dt = change in water head tochange in time

Rc = recharge (recharge is 325 m3 for 140

Mha)Q = discharge of water for irrigation0.8 is the constantSy = specific yield of the aquifer

Specific yield of the aquifer (which is aproperty of the aquifer determining the vol-ume of water that can be taken out from theaquifer per unit area per unit fall of watertable) is taken as 0.1. When extractionreaches the limit of utilizable groundwaterpotential, which means when static waterhead is reached and thereafter there are frac-tured zones, then the specific yield is takenas 0.25, and the water head decreasessharply after this limit.

3.1.1.2 Technologies in the

agriculture sector

In the past two decades, there has been aproliferation of groundwater irrigation in In-dia and, therefore, large penetration ofpump sets. Estimates put the figure of dieselpump sets in India at 6.5 million. To this, an-other 11 million pumps with electric motorcan be added (Bom and Steenbergen 1997).

The Minor Irrigation Census 2001 revealsthat 94% of the total tube wells in India areshallow tube wells. About 85% of the shal-low tube wells are accounted for by statessuch as Andhra Pradesh, Haryana, Punjab,Madhya Pradesh, Uttar Pradesh, West Ben-gal, and Bihar.

The configuration of pump sets in areaswith shallow water tables ranges between2.5- and 10-hp engines. The typical irriga-tion tube well configuration differs within


this broad range in different areas dependingon the depth of water table, prevailing landownership, soil conditions, and local tradi-tion. However, the tube well configuration isnot optimal in terms of fuel consumption orwater saving. In fact, substantial improve-ment in well technology, pump set design,and conveyance systems is possible at amodest cost (Bom and Steenbergen 1997).

Modifications such as flow restriction/drum cooling, reduced speed, and removedfoot value can help increase the fuel effi-ciency of a diesel pump set by 45%–60%(Bom, Van, Majumdar, et al. 2001). Electric-ity consumption for electric pump sets canbe reduced by 30%–50% employing simplemeasures, such as pipes with larger diameter(Sant and Dixit 1996).

Therefore, diesel and electric pump setsare mainly divided into two categories: stan-dard and efficient (Table 3.6). An attempthas been made to study the energy-savingpotential. Fuel consumption of the efficientdiesel pump is 45% lower than that of a stan-dard pump set. For efficient electric pumpsets, it has been assumed that 30% efficiencyimprovements would be realized by 2036.Other than improvement in the efficiency ofpump sets, the efficiency scenario also con-siders augmentation of irrigation efficiency.

In India, the existent irrigation practiceresults in considerable amount of waterwastage. For example, the evapotranspira-tion requirement for growing paddy is about800–1000 mm (millimetres), whereas in ca-nal/tank command areas, farmers use asmuch as 2000–2500 mm, which is wastefuland also affects the yield due to drainageproblems. Scientists have found that there isno need to flood the paddy field to a depth of15–20 cm (centimetres), as practised byfarmers, and it is enough to irrigate the fieldto a depth of 3–5 cm as soon as the standingwater disappears. This can reduce the wateruse by 30% while increasing the productivitysubstantially. For row crops, such as cotton,sugar cane, and vegetables, the furrowmethod is suitable. In addition, the skip fur-row, pair row, or alternate furrow methodcan reduce the need for water by 25%–30%,without affecting the yield (Sivanappan1995).

Therefore, the efficiency scenario alsoconsiders the improvement in irrigation effi-ciency, whereby, the concern of water wast-age is addressed and 30% reduction in thewater requirement of 2036 is realized.

Technical specifications are consideredfor standard and efficient tractors. A 35-hpstandard tractor is priced at 260 000 rupees

Table 3.6 Technology characterization of pump sets

Diesel pump sets Electric pump sets

Standard Efficient Standard Efficient

Price (rupees) 10 000 14 600 8000 10 600

Water discharge (litres per second) 4 4.5 5.5 5.5

Diesel/electricity consumption 1.1 litres 0.6 litres 4.8 4.8–3.4

per hour per hour kWh kWh

kWh – kilowatt hour


and it ploughs 0.31 ha in one hour by con-suming 4.5 litres of fuel. It is assumed thatan efficient tractor ploughs 0.40 ha of landin 1 hour by consuming 3 litres of fuel perhour and is priced at 310 000 rupees.

3.1.2 Transport sector

The transport sector plays a crucial role inshaping the nation’s economic development.The GDP from the transport sector is theaggregate of GDP from various means suchas railways, road, water, and air transport.The GDP accruing from the services inci-dental to transport is also included in theGDP generated by the transport sector. TheGDP (measured at 1993/94 prices) accruing

In the analysis, the focus is mainly onroad- and rail-based freight and passengertraffic, although air- and coastal-basedmovements are also included in the frame-work.

Figure 3.3 depicts the composition offleet of registered passenger vehicles consist-ing of cars, jeeps, taxis, and buses for theperiod 1980–2003. The fleet of cars, jeeps,taxis, and two-wheelers (depicted on pri-mary y-axis in Figure 3.3) taken together ex-hibits an average annual growth rate of 13%for the 1980–2003 period. In contrast, thefleet of buses has registered a low growth of7.4% for the same period. Two-wheelers ac-count for more than four-fifth, that is, 84%,of the total passenger vehicle fleet. The re-maining 16% is accounted for by cars, jeeps,

from the transport sectoractivities (comprising rail-ways, road, air, and coastaltransportation) has in-creased at an average an-nual rate of 6.42% for thetime period 1990–2003,doubling from 35 356crore rupees in 1990/91 to79 374 crore rupees in2003/04 (MoSPI 2005).

Historically, road andrail transport have domi-nated the passenger aswell as freight movementwithin the country. Theroad and rail transportmodes carried about 95%of the total passenger andfreight traffic in the coun-try in 2001 (GoI 2001).Air and inland water trans-port assume importancefor long-distance travel. Source MoRTH (2005)

Figure 3.3 Trends in the composition offleet of registered passenger vehicles


taxis, and buses. Of all the road passengervehicles, the number of cars, jeeps, and taxishas increased at an average annual growthrate of 10%, whereas the two-wheelers haveexhibited the highest average annual growthrate of 14% during the period 1980–2003.However, the bus fleet has grown at an aver-age annual growth rate of 7%.

Railways have been the principal mode oflong-distance freight and passenger trans-port within the country. The growth of rail-ways is closely interlinked with the overalleconomic, agricultural, and industrial devel-opment of the country. Fuelled by thecountry’s economic growth and an expand-ing population base, Indian railways havegrown to a national network moving, on anaverage, 1.5 MT of freight and 14 million

to 541.2 billion passenger kilometres in2003. The freight traffic (both the revenue-earning and non-revenue-earning traffic)handled by railways has more than doubledfrom 158.5 billion tonne kilometres in 1980to 384.1 billion tonne kilometres in 2003.

3.1.2.1 Transport sector end-use

demands

3.1.2.1.1 Data problems in

road-based movement

The road passenger and freight transportdemand estimation and projection exerciseis beset with data gaps. Furthermore, noreliable data at a point in time or over timepassengers per day (2003/

04) data.The long-term trends of

passenger traffic (in terms ofbillion passengerkilometres1) and freight traf-fic (in terms of billion tonnekilometres2) are shown inFigure 3.4.

The passenger and freighttraffic handled by railwayshas exhibited an upwardtrend, as shown in the figure,during the period 1980–2003, with the passengertraffic recording an averageannual growth rate of 4.2%and the freight traffic. Thepassenger traffic more thandoubled from 208.6 billionpassenger kilometres in 1980 Source MoR (2005)

Figure 3.4 Trends in passengersand freight carried by railways

11111 Passenger kilometres is the product of the number of passengers carried and average distance travelled.22222 Tonne kilometres is the product of the tonnes of freight moved and average distance travelled.


is available of the actual road passengerand freight traffic. There exist wide varia-tions in the estimates of various agenciesfor the year 1999 and 2000 as shown inTables 3.7 and 3.8.

3.1.2.1.2 Methodology for

projecting mode-wise road

transport demand

A bottom-up approach has been deployed toestimate and project the road passenger, andfreight transport demand. For estimatingand projecting the mode-wise transportation

Table 3.7 Comparison of the transport sector demand estimates by various agencies for the

year 1999

Estimated road traffic movement in 2000

Study Passenger traffic (billion Freight traffic (billion

passenger kilometres) tonne kilometres)

RITES study (1998) 1880 1136

Lucknow Plan (1984) 2152 1004

MOST: Study on estimation of total road

transport in 2000 3000–4000 600–1000

Vehicle Fleet Modernization Study (1988) 2300–3800 800–1030

Steering Committee on Respective

Planning for Transport 2400–4000 540–900

India Infrastructure Report 3000 800

Source Kapoor (2002)

Table 3.8 Comparison of the transport sector demand estimates by various agencies for the

year 2000

Estimates of Indian

Demand estimates Planning Commission Roads Congress

Billion passenger kilometres 2450 2087

Billion tonne kilometres 870 1102

Source Kapoor (2002)

33333 Taxis have been considered separately as they are used for carrying passengers on a commercial basis. The utiliza-

tion rate for taxis is higher when compared to the utilization rates for cars and jeeps. This is due to the increased num-

ber of trips per day because of the commercial use of taxis.


demands, the motorized transport vehicleshave been classified separately into transportvehicles for passenger and freight movementas follows.

� Vehicles for passenger movement� Cars and jeeps� Taxis3

� Two-wheelers� Buses� Three-wheelers

� Vehicles for freight movement� LCVs (Light goods/commercial

vehicles)� HCVs (Heavy goods/commercial

vehicles)

The objective is to estimate the travel de-mand separately for each of the vehicle types(on-road/in use4) mentioned above. The fol-lowing equation is used to estimate the totalpassenger or freight travel demand in theyear ‘t’ by the vehicle type ‘j’.

PKmtj or TKmtj = Vtj × Otj × (Utj × 365)(3.7)

where, PKmtj is the passenger travel demandby the vehicle type j in the year t (measuredin passenger kilometres). TKmtj is the freighttravel demand by the vehicle type j in theyear t (measured in tonne kilometres). V

tj is

the number of vehicles (on-road/in use) ofthe type j in the year t. Otj is the occupancyrate (measured in number of persons per ve-hicle per trip) for the year t of the vehicletype j. Utj is the utilization factor (kilometrestravelled by a vehicle per day) for the vehicleof type j for the year t. Multiplying Utj by 365gives the annual utilization rate for the ve-hicle type j for the year t.

3.1.2.1.2.1 Cars

The historical annual time-series data on thenumber of registered passenger cars for theperiod 1980 until 20035 is used for estimat-ing and projecting the travel demand by carsuntil 2036. The relationship between theregistered car fleet (representing the stock ofcars) and car sales is

Carst+1 = Carst + Sales (during periodt and t+1) (3.8)

As mentioned in Equation 3.8, the differ-ence between cumulative number of regis-tered vehicles at time t and time t+1 givesthe car sales between these two time periods.

Econometric technique (regressionmodel) is used for estimating the car salesfor the period 1980–2003. The variables thatare most likely to influence the passenger carsales in India are the consumer’s purchasing

44444 On-road vehicles refer to the vehicles actually plying on road.55555 The Motor Transport Statistics, official document of the Ministry of Shipping, Road Transport and Highways, Govern-

ment of India, does not give the number of cars, jeeps, and taxis separately. The CMIE Infrastructure (2002 issue) gives

the number of registered cars, jeeps, and taxis separately. However, their total does not match with the total number

of registered cars, jeeps, and taxis reported in the Motor Transport Statistics. Furthermore, on analysing the historical

data of CMIE Infrastructure, cars and jeeps account for 91% of the total in all the years while the rest 9% are taxis.

Applying this percentage to the total numbers reported in the Motor Transport Statistics, the number of cars, jeeps,

and taxis is obtained separately. The two series so obtained are used for analysis.


power measured by the per capita income aswell as the percentage of population in urbanareas (urbanization index/urban size).

In order to measure the extent of respon-siveness of demand for passenger cars in In-dia to changes in per capita income, and toaccount for the impact of increasing urbaniza-tion on car sales, log–linear (double log) speci-fication of regression model was foundappropriate. The estimated regression equa-tion is

Log (car sales) = 1.03 Log (PCY) + 0.16 × (UI) (10.7) (3.36)

(3.9)(adjusted R2 = 0.72)

where, PCY= per capita income UI = urbanization index

Both the independent variables (percapita income and urbanization index) arefound to be statistically significant in ex-plaining the passenger sales as indicated byvalues of the t-statistic (given in brackets)associated with the coefficients of the modelestimated above. The adjusted R2 is a statis-

tical measure of goodness of fit of the modelto the historical data. In this case, it is ashigh as 0.72, implying that 72% of the varia-tion in passenger sales can be explained byvariations in the per capita income ofeconomy and the urbanization index.

Using Equation 3.9, car sales are pro-jected till 2036. The number of registeredpassenger cars for each year within the fore-cast period 2004–36 is obtained by addingthe forecasted annual sales figures to thenumber of registered vehicles. The numberof cars in use/on-road is less than the totalnumber of registered cars. Therefore, thenumber of passenger cars, in use is obtainedby deducting the number of cars consideringa lifetime of eight years.

The travel demand by cars (measured inpassenger kilometres) is estimated usingEquation 3.7. There exist variations in theaverage annual utilization rate as reported indifferent sources (Table 3.9).

Based on discussion with experts andwith reference to the above sources, the oc-cupancy rate for cars is assumed to declinefrom three persons per car in 1980 to 1.5persons in 2036 (that is, decline by half).

Table 3.9 Assumptions on occupancy rate and utilization rate for cars

Assumptions on occupancy Assumptions on utilization

Source rate per car rate per car

IEA (2004) 1.89 persons per car 8000 km/year equivalent to

in 2000 declining to 21.4 km/day (assumed constant

1.64 persons in 2035 throughout the projection period)

Kapoor (2002) 1.5 persons 7000 km/year (equivalent to

19.76 km/day) in 1995 increasing

by 100 km/year (0.27 km/day)

Bose and Chary (2003) 1.9–2.9 persons per car/jeep 26 km/day in 2000/01


The rationale behind this assumption is thatwith increasing passenger car sales, vehicleownership (number of vehicles owned percapita) would rise. As a result, the number ofpersons travelling per car is assumed to declineduring the entire period, from 1980 to 2036.Similarly, with reference to the sources men-tioned in the table, it has been assumed thatthe utilization rate of passenger cars (effec-tive average distance travelled by a passengercar) would increase by 100 km every year(that is, 0.27 km daily), starting from21.4 km/day in 1980.

3.1.2.1.2.2 Taxis

The historical annual time-series data on thenumber of registered commercial passengertaxis for the period 1980 until 2003 is usedfor estimating and projecting the travel de-mand by taxis until 2036.

Econometric technique (regressionmodel) is used for estimating the number ofcommercial passenger taxis for the period1980–2003. The percentage of population inurban areas (urbanization index/urban size)and the growth of the economy in generalmeasured by GDP are considered as vari-ables significant in explaining growth in thenumber of taxis plying on Indian roads.

In order to measure the extent of respon-siveness of demand for taxi services in Indiato changes in economic growth, and to ac-count for the impact of increasing urbaniza-tion, log–linear (double log) specification ofregression model was considered appropri-ate. The estimated regression equation is

Log (Taxis) = 0.11 × (UI) +0.70 × Log (GDP) (4.7) (14.8)

(3.10)

(adjusted R2 = 0.91)

where, UI = urbanization index GDP = gross domestic product

Both the independent variables (GDPand UI) are found to be statistically signifi-cant in explaining the equation as indicatedby values of the t-statistic (given in brackets)associated with the coefficients of the modelestimated above. The adjusted R2 is as highas 0.91, implying that 91% of the variationin passenger sales can be explained by varia-tions in the economic growth and the urban-ization index.

The number of registered passenger taxisfor each year within the forecast period2004–36 is obtained by inserting the pro-jected values of GDP and UI. The number oftaxis in use/on-road is less than the totalnumber of registered taxis. Therefore, thenumber of passenger cars in use is obtainedby deducting the number of taxis, consider-ing a lifetime of eight years (same as that ofpassenger cars).

The travel demand by taxis (measured inpassenger kilometres) is estimated usingEquation 3.7. The occupancy rate for taxis isassumed to remain constant at three personsper taxi throughout the projected period.The effective distance travelled daily by ataxi is assumed to increase from 60 km/dayin 2001 to 80 km/day in 2036. The rationalebehind assuming varying utilization rate liesin the fact that with huge investmentspumped into the construction of roads andhighways, commercial passenger taxi ser-vices are being used for long-distance inter-city travel as well.


3.1.2.1.2.3 Two-wheelers

The historical annual time-series data on thenumber of registered two-wheelers for theperiod 1980 until 2003 is used for estimat-ing and projecting the travel demand by two-wheelers until 2036.

TWt+1

= TWt + Sales (during time t and t+1)

(3.11)

where, TW = two-wheelerAs mentioned above, the difference be-

tween cumulative number of registered ve-hicles at time t and t+1 gives thetwo-wheeler sales between the two time peri-ods, t and t+1.

The variables most likely to influence thetwo-wheeler sales in India are the propor-tion of the middle-income group residing inurban areas (UMIG [urban middle-incomegroups]) as well as the consumer’s purchas-ing power measured by per capita income. Inorder to measure the extent of responsive-ness of demand for two-wheelers in India tochanges in per capita income, and to ac-count for the impact of rising proportion ofthe UMIG on car sales, log–linear (doublelog) specification of regression model wasfound appropriate. The estimated regressionequation is

Log (two-wheeler sales) = 0.57 × (UMIG) +(3.37)

0.62 × Log (PCY)(11.2) (3.12)


Both the independent variables (percapita income and percentage of the middle-income group) are found to be statistically

significant in explaining the sales of two-wheelers, as indicated by the values of the t-statistic (mentioned in brackets) associatedwith the coefficients of the model estimatedabove. Furthermore, 81% of the variation inthe two-wheeler sales can be explained byvariations in the per capita income andUMIG, given that the adjusted R2 is as highas 0.81. Using Equation 3.12, the two-wheeler sales are projected till 2036.

The number of registered two-wheelersfor each year within the forecast period2004–36 is obtained by adding the forecastannual sales figures to the number of regis-tered two-wheelers. The number of two-wheelers in use/on-road is less than the totalnumber of registered two-wheelers. There-fore, the number of two-wheelers in use isobtained by deducting the number of two-wheelers, considering lifetime of eight years.

The travel demand by two-wheelers(measured in passenger kilometres) is esti-mated using Equation 3.7. The assumptionson occupancy rate and utilization rate fortwo-wheelers as reported in differentsources are as follows.

For the purpose of our analysis, the occu-pancy rate for a two-wheeler is assumed tobe constant at 1.2 persons per two-wheelerthroughout the projection period (2004–36).The average annual utilization rate is as-sumed to be constant at 27.4 km per two-wheeler per annum (Table 3.10).

3.1.2.1.2.4 Buses

The historical annual time-series data on thenumber of registered buses for the period1980 until 2003 is used for estimatingand projecting the travel demand by busesuntil 2036.


The estimated regression equation is

Log (Buses) = −11.06 + 3.51 × Log (POP) (-41.4) (88.9)

(3.13)


where, POP = populationThe only independent variable, that is,

population, is found to be statistically sig-nificant in explaining the number of buses asindicated by values of the t-statistic (given inbrackets) associated with the coefficients ofthe model. In order to measure the extent ofresponsiveness to the population base of theIndian economy, log–linear (double log)specification of regression model is foundappropriate. In this case, the adjusted R2 isas high as 0.99, implying that 99% of thevariation can be explained by variations inthe population growth rate.

The number of registered buses for theperiod 2004–36 is obtained using Equation

3.13. The number of buses plying on road isobtained by taking into account the utiliza-tion rate of the fleet of buses (in %), as indi-cated by the data on fleet utilization of busesoperated by the SRTUs (state road transportundertakings). The fleet-utilization rate isestimated and projected (yt) for the period2004–36 using a logistic curve representedby the following equation.

Yt= 100[exp (1.78 + 0.474 × t)/1 + exp

(1.78 + 0.474 × t)]t = 1,2-49 (3.14)

where, 100 is the asymptotic limit for thefleet utilization6; 1.78 and 0.474 are the val-ues of the coefficients to be estimated fromhistorical time-series data; and t denotes thetime period

The travel demand by buses (measured inpassenger kilometres) is estimated usingEquation 3.7 as the product of number ofbuses on road, the occupancy rate, andthe average annual utilization rate. Theoccupancy rate for buses is assumed to be

66666 The historical data on fleet utilization of buses operated by state road transport undertakings clearly shows that the fleet

utilization (%) lies in the range 90%–95%. Thus, the maximum asymptotic limit is taken to be 100 for fleet utilization.

Table 3.10 Assumptions on occupancy rate and utilization rate for two-wheelers

Assumptions on effective

Assumptions on occupancy distance travelled per day

Source rate per two-wheeler by a two-wheeler

IEA (2004) 1.7 (assumed constant) 10 000 km/year equivalent to

27.4 km/day (assumed constant

throughout the projection period)

Kapoor (2002) 1.2 (assumed constant) 3500 km/year (equivalent to

9.6 km/day) assumed constant

Bose and Chary (1993) 1.2–1.7 (assumed constant) 25 km/day in 2000/01 (assumed

constant throughout the

projection period)


constant at 50 persons per bus throughoutthe projection period (2004–36). The aver-age annual utilization is assumed to increaseby 400 km/year over the modelling timeframe starting from 40 000 km/year in 1995(Table 3.11).

3.1.2.1.2.5 Three-wheelers

The historical annual time-series data on thenumber of registered passenger three-wheel-ers for the period 1980 until 2001 is used forestimating and projecting the travel demandby three-wheelers until 2036.

Log–linear (double log) specification ofregression model is found appropriate to ac-count for the responsiveness of three-wheeler fleet to the increasing population.The estimated regression equation is

Log (3-W) = −29 + 6.3 × Log (POP)(−34) (50)

(3.15)


where, POP = population3-W = three-wheeler

The independent variable (population) isfound to be statistically significant in ex-plaining the increasing number of three-wheelers, as indicated by the values of thet-statistic (given in brackets) associated withthe coefficients of the model estimatedabove. In this case, the adjusted R2 is as highas 0.99, implying that 99% of the variationin three-wheelers can be explained byvariations in the population of Indianeconomy.

The number of registered three-wheelersfor each year within the forecast period2002–36 is obtained using the regressionequation estimated above by inserting thevalues of forecast population into the regres-sion equation.

The travel demand by three-wheelers(measured in passenger kilometres) is esti-mated using Equation 3.8. The occupancyrate for two-wheelers is assumed to be con-stant at two persons per three-wheelerthroughout the projection period (2002–36).

The average annual utilization rate is as-sumed to increase by 80 km/year from29 200 km/year in 1980 until 2036.

The figures for mode-wise road passengerdemand expressed in billion passengerkilometres are presented in Tables 3.12–3.14

Table 3.11 Assumptions on occupancy rate and utilization rate for buses

Assumptions on occupancy Assumptions on utilization

Source rate per bus rate per bus

IEA (2004) 28 persons per bus 40 000 km/year in 2000 (assumed

to be constant throughout the

projection period)

Kapoor (2002) 40 persons per bus (assumed 40 000 km/year in 1995, increasing

constant throughout the by 400 km/year

projection period)

Bose and Chary (1993) 30–47 persons per bus 46 355 km/year


Table 3.13 Mode-wise road passenger travel demand (in billion passenger kilometres)

under 8% GDP (gross domestic product) growth scenario

Mode 2001 2006 2011 2016 2021 2026 2031

Cars and taxis 102 142 216 412 733 1550 3 117

Two-wheelers 255 344 354 466 616 823 1 107

Buses 1177 1594 2141 2790 3493 4234 4 969

Three-wheelers 116 200 306 447 618 808 1 003

Total 1650 2280 3018 4114 5461 7416 10 196

Table 3.14 Mode-wise road passenger travel demand (in billion passenger kilometres)

under 10% GDP (gross domestic product) growth scenario

Mode 2001 2006 2011 2016 2021 2026 2031

Cars and taxis 102 144 236 487 956 2167 4 760

Two-wheelers 255 351 394 558 799 1230 1 908

Buses 1177 1594 2141 2790 3493 4234 4 969

Three-wheelers 116 200 306 447 618 808 1 003

Total 1650 2289 3077 4281 5866 8440 12 641

Table 3.12 Mode-wise road passenger travel demand (in billion passenger kilometres) un-

der 6.7% GDP (gross domestic product) growth scenario

Mode 2001 2006 2011 2016 2021 2026 2031

Cars and taxis 102 139 195 346 574 1187 2307

Two-wheelers 255 341 332 413 524 678 891

Buses 1177 1594 2141 2790 3493 4234 4969

Three-wheelers 116 200 306 447 618 808 1003

Total 1650 2274 2974 3996 5210 6908 9170

for 6.7%, 8%, and 10% projected GDPgrowth rates.

3.1.2.2 Freight transport

The historical annual time-series data on thenumber of registered HCVs and LCVs for

the period 1980 till 2002 is used for estimat-ing and projecting the travel demand by two-wheelers till 2036.

The variables most likely to influence thegrowth in the number of HCVs and LCVsplying on Indian roads are the values of theoutput from the agriculture and industrialsectors (measured by the GDP of agricul-


ture and industry). The number of HCVsand LCVs is estimated separately by usingthe linear specification of the regressionmodel as follows.

HCVs = (−398100)+3.96 × (GDPI + GDPA)(−4.36) (19.61)

(3.16)


LCVs = (−792686) + 2.38 × (GDPI + GDPA)(−6.42) (9.14)

(3.17)


where, GDPA = gross domestic product ofthe agriculture sector

GDPI = gross domestic product of theindustrial sector

Both the independent variables (GDPAand GDPI) are found to be statistically sig-nificant, as indicated by values of the t-sta-tistic (given in brackets) associated with thecoefficients of the model estimated above. Inthis case, the adjusted R2 is as high as 0.94and 0.81 for the regression equations (Equa-tions 3.16 and 3.17), estimated separatelyfor the HCVs and LCVs, respectively. Thisimplies that 94% and 81% of the variation innumber of HCVs and LCVs (respectively) can

be explained by variations in the GDPA andGDPI.

The number of registered HCVs andLCVs for each year within the forecast pe-riod 2003–36 is obtained from the estimatedregression equations by inserting the pro-jected values of GDPA and GDPI.

The travel demand (measured in tonnekilometres) for HCV is estimated usingEquation 3.7. The payload for HCV is as-sumed to increase by 0.1 tonne until 2036,from 5.5 in 1995. Similarly, it is assumedthat the average annual utilization for HCVwill increase by 400 km every year until2036, from 40 000 km in 1995 (PlanningCommission 2001).

Similarly, the travel demand (measured intonne kilometres) for LCV is estimated us-ing Equation 3.7. The payload for LCV is as-sumed to be constant at 1.7 tonnesthroughout the projection period. Similarly,it is assumed that the average annual utiliza-tion for LCV will increase by 200 km everyyear, from 23 000 km in 1995 until 2036.

The above demand estimation and pro-jection exercise has been undertaken for6.7%, 8%, and 10% growth rates of GDP.The projected figures for mode-wise freighttransport movement by road under alterna-tive growth scenarios, expressed in billiontonne kilometres, are presented in Tables3.15–3.17.

Table 3.15 Mode-wise freight travel demand (in billion tonne kilometres); 6.7%

GDP (gross domestic product) growth scenario

Mode 2001 2006 2011 2016 2021 2026 2031

HCV 531 842 1268 1926 2933 4478 6838

LCV 37 50 78 120 181 269 398

Total 568 892 1347 2046 3114 4747 7236

HCV – heavy commercial vehicle; LCV – light commercial vehicle


Table 3.17 Mode-wise freight travel demand (in billion tonne kilometres); 10% GDP (gross

domestic product) growth scenario

Mode 2001 2006 2011 2016 2021 2026 2031

HCV 531 1044 1858 3219 5615 10 085 17 948

LCV 37 66 124 214 365 632 1 078

Total 568 1111 1982 3433 5980 10 717 19 026


3.1.2.3 Rail transport

3.1.2.3.1 Passenger movement

The historical annual time-series data on thepassenger traffic (in billion passengerkilometres) for the period 1980 until 2003 isused for estimating and projecting the traveldemand by rail till 2036.

Log (rail passenger) = 0.78 × Log (GDP) +(3.83)

30.95 × Log (POP)(2.88) (3.18)


The independent variables (POP andGDP) are found to be statistically significantas indicated by values of the t-statistic (givenin brackets) associated with the coefficientsof the model estimated above in Equation

3.18. In this case, the adjusted R2 is as highas 0.98, implying that 98% of the passengermovement by railways can by explained by thevariations in the socio-economic indicators,namely, GDP and population.

The projections for rail passenger trans-port demand for the period 2004–36 are ob-tained by inserting the projected values ofGDP and POP in Equation 3.18.

The demand estimation and projectionexercise has been undertaken for 6.7%, 8%,and 10% growth rates projected for GDP.

The figures for projected rail passengertravel demand expressed in billion passengerkilometres are presented for the three alter-native growth scenarios in Table 3.18.

3.1.2.3.2 Freight movement

The historical annual time-series data on thefreight traffic (in billion tonne kilometres)

Table 3.16 Mode-wise freight travel demand (in billion tonne kilometres); 8%

GDP (gross domestic product) growth scenario

Mode 2001 2006 2011 2016 2021 2026 2031

HCV 531 914 1523 2487 3996 6341 9 955

LCV 37 57 99 162 256 393 593

Total 568 970 1622 2649 4252 6734 10 548



for the period 1980 till 2003 is used for esti-mating and projecting the travel demand byrail till 2036.

The variables that are most likely to influ-ence the freight transport by rail are the val-ues of outputs from the agriculture andindustrial sectors (measured by the GDP ofagriculture and industry). The linear specifi-cation of the regression model is found ap-propriate to estimate and project the freighttransport demand by rail.

Freight movement= (52.84) + 0.00045 ×(6 .72 )

(GDPI + GDPA)(27.52) (3.19)


where,GDPA = gross domestic product of the

agriculture sectorGDPI = gross domestic product of the

industrial sector

Both the independent variables (GDPAand GDPI) are found to be statistically sig-nificant, as indicated by values of the t-sta-tistic (given in brackets) associated with thecoefficients of the model estimated above. Inthis case, the adjusted R2 is as high as 0.97,implying that 97% of the freight movementby railways can be explained by the varia-tions in the GDPA and GDPI.

The projections for freight transport de-mand by rail for the period 2004–36 are ob-tained by inserting the projected values ofGDP and POP in Equation 3.19.

The demand estimation and projectionexercise has been undertaken for 6.7%, 8%,and 10% growth rates of GDP.

The figures for projected rail freighttransport demand expressed in billion tonnekilometres are presented for the three alter-native GDP growth rates in Table 3.19.

Table 3.18 Rail passenger transport demand (in billion passenger kilometres) under

alternative GDP (gross domestic product) growth scenarios

GDP growth rate (%) 2001 2006 2011 2016 2021 2026 2031

6.7 491 608 770 1000 1329 1791 2424

8 491 637 864 1184 1634 2264 3125

10 491 673 986 1458 2174 3254 4853

Table 3.19 Rail freight transport demand (in billion tonne kilometres) under alternative GDP

(gross domestic product) growth rates

GDP growth rate (%) 2001 2006 2011 2016 2021 2026 2031

6.7 336 423 534 691 912 1223 1662

8 336 451 621 863 1206 1691 2375

10 336 456 668 987 1481 2354 3758


Source SIAM (2005)

Figure 3.5 Category-wise saleof two-wheelers

3.1.2.4 Description of technology

options in the transport sector

3.1.2.4.1 Two-wheelers

There are three different types of two-wheel-ers that have been considered in the model:scooters, motorcycles, and mopeds. Figure3.5 is the graphical representation of the cat-egory-wise sales of two-wheelers.

The figure indicates that the motorcyclesegment exhibits the highest growth rate(37%) amongst the three categories of two-wheelers. Motorcycles now dominate thetwo-wheeler market that was dominated byscooters and mopeds until the late 1990s.

At present, two-wheelers use petrol asfuel and employ the spark-ignition system.They can be classified further into those em-ploying the two- and four-stroke technology.The population of two-stroke engines is verylarge. Two-stroke engines are widely used formotorcycles, scooters, and mopeds, prima-rily because of their high specific power out-put, simple and compact design, lower

engine fraction, lesspumping losses at partload, better coldstartability, and lowproduction and main-tenance cost (MoPNG2005). The disadvan-tages are high fuel con-sumption and highunburnt hydrocarbonemission. Hence, pen-etration of the four-stroke technology intodifferent segments oftwo-wheelers has beenincreasing rapidly overthe last few years. Thiscan be attributed

partly to the enforcement stringent emissionregulations and partly to the fast-changingconsumer preferences. However, penetra-tion of the four-stroke technology is limitedin the scooter and moped category as com-pared to motorcycles. As such, many buyersstill prefer two-stroke to four-stroke. Thetechno-economic parameters of two-wheel-ers are presented in Tables 3.20 and 3.21.

3.1.2.4.2 Three-wheelers

A wide variation exists in the Indian three-wheeler market in terms of the current tech-nological status as well as its progressionover the modelling time frame. Three-wheel-ers powered by petrol two-stroke engines oc-cupy a major share in the Indianthree-wheeler market. The penetration ofthree-wheelers powered by petrol four-stroke engine is lower as compared to itstwo-stroke counterpart due to the resistanceoffered by owners/operators ofautorickshaws. This resistance is derivedfrom the notions that the maintenance


Table 3.20 Technological characterization of two-stroke two-wheelers

Efficiency Investment Fixed operating

Two-wheeler Start (km/litre)/ cost* and maintenance

category Technology year (MJ/km) (rupees) cost (rupees/km)

Motorcycle � Improved engine with 2001 53.83 36 000 0.18

improved oxicat

using petrol as fuel

� Hydrogen IC engine 2031 0.56 42 000

Scooters � Improved engine with 2001 66.11 32 000 0.14

improved oxicat



Mopeds � Improved engine with 2001 78.51 22 000 0.18

improved oxicat



MJ – megajoules; IC – internal combustion

* Investment cost here is the vehicle price.

Source TER I (2004)

Table 3.21 Technological characterization of four-stroke two-wheelers

Investment Fixed operating

Two-wheeler Start Efficiency cost* and maintenance

category Technology year (km/litre) (rupees) cost (rupees/km)

Motorcycles � Improved engine with 2001 85.64 43 500 0.11

improved oxicat



Scooters � Improved engine with 2001 71.10 39 000 0.13

improved oxi-cat



Mopeds � Improved engine with 2001 94.21 34 000 0.12

improved oxi-cat



MJ – megajoules; IC – internal combustion

* Efficiency of hydrogen IC engine technologies is given in MJ/km.

Source TER I (2004)


expense of a three-wheeler employing afour-stroke engine is much higher than thatof a two-stroke engine. The CNG (com-pressed natural gas) three-wheelers areprevalent mainly in the four-stroke version.The penetration of CNG-basedautorickshaws is limited mainly to majormetropolitan cities like Delhi and Mumbai.The limited penetration of CNG three-wheelers can be attributed primarily to theinadequate CNG supply infrastructure andthe high investment cost entailed in develop-ing this infrastructure. Diesel three-wheelerspowered by four-stroke technology are alsoavailable in the country. The introduction ofhybrid electric vehicles7 (powered by CNGand petrol) as well as the electric/battery-op-erated vehicles is likely only by 2020 and2025, respectively. The introduction of

hybrids before the battery-operated vehiclesis due to concerns regarding the range ofbattery-operated vehicles. Three-wheelersand electric vehicles are also commerciallyavailable in the country. Scooter India Ltd,Mahindra Eco Mobiles, Bajaj, Eicher, and soon are entering the electric three-wheelermarket. The three-wheelers are used forcommercial purposes and thus, have a highdaily utilization. The techno-economic pa-rameters for three-wheelers are given inTable 3.22.

3.1.2.4.3 Cars

As per the classification norms adopted bySIAM (Society of Indian AutomobileManufacturers) in 2002, passenger cars are

Table 3.22 Technological characterization of three-wheelers

Fixed operating and

Efficiency Investment cost maintenance cost

Technology Start year (km/litre) (rupees) (rupees/km)

Petrol two-stroke 2001 36.00 75 000 0.27

Petrol four-stroke 2001 41.00 100 000 0.22

CNG four-stroke 2001 1.00* 95 000 0.22

Diesel four-stroke 2001 27.00 125 000 0.21

Battery operated 2026 0.36* 115 000 0.22

Petrol hybrid 2021 120.00 125 000 0.30

CNG hybrid 2021 120.00 125 000 0.30

Hydrogen four-stroke 2031 51.00 114 000 8.45

CNG – compressed natural gas; MJ – megajoules

* Efficiency expressed in MJ/km.

Source TER I (2004)

77777 HEVs (hybrid electric vehicles) use the combination of engine of a conventional vehicle and electric motor pow-

ered by traction batteries and/or fuel cells. This combination helps in achieving both the energy and environment

goals. In HEV propulsion, energy is available from more than one source. The three configurations of HEVs are series

hybrid system, parallel hybrid system, and split hybrid system.


classified, according to lengths, under thefollowing five categories.� Mini (upto 3400 mm)� Compact (3401–4000 mm)� Mid-size (4001–4500 mm)� Executive (4701–5000 mm)� Luxury (5001 mm and above)

The classification is further extended totake into account the fuel- and technology-wise break-up. Petrol-based cars (based oninternal combustion engine) constitute themajority of the passenger car segment. In In-dia, indirect injection diesel engine is used inpassenger cars. However, diesel car saleshave not kept pace with the correspondingrise in the variant (Table 3.23). The benefitoffered by diesel cars in terms of higher fuelefficiency relative to the gasoline cars is off-set by the higher maintenance/servicing cost.However, due to the pricing policies of fuels,the running cost of diesel cars is lower ascompared to petrol cars. This makes dieselengines more popular for taxis.

New technologies, such as battery-oper-ated cars, are also available commercially inthe country. At present, the Bangalore-basedelectric car company REVA is the solemanufacturer of electric cars in India. Fur-thermore, cars running on alternative fuelssuch as CNG have also penetrated the In-dian market. The technology characteriza-tion of cars is given in Table 3.24.

Table 3.23 Percentage of cars sold by

various manufacturers

Petrol Diesel

Model car (%) car (%)

Fiat Siena 70 30

Fiat Uno 45 55

Mitsubishi Lancer 90 10

Ford Ikon 79 21

Mercedes Benz 45 55

GM Astra 85 15

Source The Economic Times (2002)

Table 3.24 Technological characterization of cars

Fixed operating and

Efficiency Investment maintenance cost

Technology Start year (km/litre) cost (rupees) (rupees/km)

Small car diesel 2001 13.39 388 000 0.80

Small car gasoline 2031 12.25 387 000 1.43

Small car gasoline hybrid 2021 14.70 670 140 1.43

Small car diesel hybrid 2021 16.06 671 140 0.80

Battery-operated car 2001 14.70* 249 500 0.64

CNG car 2001 13.37** 354 000 1.64

Large car based on diesel 2001 10.85 646 000 0.80

Large car based on gasoline 2001 9.55 625 667 1.43

MJ – megajoules; CNG – compressed natural gas

*Efficiency expressed in MJ/km.

** Efficiency expressed in km/kg.

Source Figures of fuel economy compiled from Overdrive and Autocar (October 2005); TER I (2004)


3.1.2.4.4 Buses

The fuels used by buses plying on Indianroads are mostly diesel and CNG. Until1991, buses powered by compression igni-tion engines consuming diesel were plyingon Indian roads. DTC (Delhi TransportCorporation) became the first transport cor-poration in the country to have inductedCNG buses in its city fleet in 2001. The en-tire fleet of DTC buses has been replaced byCNG buses. Table 3.25 presents the techno-logical characteristics of buses indicated byefficiency, investment cost, and so on.

Table 3.25 Technological characterization of buses

Start year of Life Efficiency Investment cost

Types of buses technology (years) (km/litre) (million rupees/bus)

Diesel bus 2001 15 4.63 2.48

CNG bus 2001 15 3.84* 3.66

Hybrid electric bus 2021 15 6.71 8.38

powered by diesel

* Efficiency expressed in km/kg

Table 3.26 Technological characterization

of goods vehicles

Types of Start

good year of Life Efficiency

vehicles technology (years) (km/litre)

HCV: diesel 2001 15 5.0

HCV: ULSD 2031 15 5.0

LCV: diesel 2001 15 8.5

LCV: ULSD 2031 15 8.5

HCV – heavy commercial vehicle; LCV – light com-

mercial vehicle; ULSD – ultra-low sulphur diesel

Table 3.27 Technological characteriza-

tion of locomotives (freight)

Fuel Investment

efficiency cost (million

Type (Mtoe/btkm) rupees/btkm)

Diesel locomotive 0.0041 344

Electric locomotive 0.0021 450

Mtoe/btkm – million tonnes of oil equivalent per bil-

lion tonne kilometres

3.1.2.4.5 Goods vehicles

Both the heavy and light goods vehicles usediesel and ULSD (ultra low-sulphur diesel)as fuels. The parameters related to cost, effi-ciency, and so on associated with each of thetechnologies are shown in Table 3.26.

3.1.2.4.6 Locomotives

Diesel and electric locomotives are used forboth passenger- and freight-based rail move-ment. The technological details of these op-tions are provided in Tables 3.27 and 3.28.


3.1.2.5 Alternative fuels for transport

3.1.2.5.1 Biofuels

Biofuels are receiving a great deal of atten-tion as a substitute to petroleum since theycan be produced from several agriculturalsources and also because of their low-emis-sion characteristics. The two biofuels con-sidered as the potential fuels for surfacetransportation are bio-diesel and ethanol.The term ‘bio-diesel’ refers to the neat ethylesters of vegetable oils. Presently, pure 100%or neat methyl esters of rapeseed, soyabean,sunflower, talon, and other fats and oils areused as diesel fuel without any substantialmodification to the existing design of theengine. According to a survey of 26 coun-tries by the IEA (International EnergyAgency), biofuels are being produced for thepast six years in 21 countries, mainly in theEuropean Union, East Europe, Malaysia,and the US, with an overall capacity of about1.3 MT. In most of the developed countries,bio-diesel is produced from saffola, sun-flower, peanut, and so on that are essentiallyedible in the Indian context. On the otherhand, there are a host of forest and non-ed-ible plant resources from which oil can be

generated. Biofuels have the following ad-vantageous properties: high oil-bearing ca-pacity, low cost, easy to develop and use,environmentally safer and compatible, bio-degradable, non-toxic, and free of sulphurand aromatic compounds.

In this analysis, the maximum productionof bio-diesel is assessed based on the poten-tial area for jatropha plantation, which is es-timated at about 40.03 Mha. Based on theseed yield of 2 tonnes/hectare, oil yield of27%, and percentage area brought under theplantation of jatropha over the modellingtime frame, Table 3.29 provides the esti-mates of bio-diesel production as used inthis study. Based on the discussion with ex-perts, it has been assumed that 5% of thepotential area is likely to be brought underjatropha plantation by 2011, 25% by 2021,and 100% by 2036.

Various scenarios have been developedfor the transport sector, which represent dif-ferent types of policy interventions, techni-cal measures, and so on. A detaileddescription of the transport sector scenariosis given in Chapter 4. Assumptions for eachof the scenarios are detailed in Table 3.30.

Table 3.29 Estimates of bio-diesel

production

Area under Bio-diesel

Year plantation (%) (million tonnes)

2006 0 0

2011 5 2.0

2016 10 3.9

2021 25 9.8

2026 70 27.5

2031 90 31.9

2036 100 35.4

Table 3.28 Technological characterization

of locomotives (passenger)

Fuel Investment

efficiency cost (million

Type (Mtoe/bpkm) rupees/bpkm)

Diesel locomotive 0.0041 156

Electric locomotive 0.0021 132

Mtoe/bpkm – million tonnes of oil equivalent per bil-

lion passenger kilometres


Table 3.30 Assumptions in various transport scenarios

Scenario

Business-as-usual

High efficiency

Bio-diesel

Hybrid

Parameter

� Share of rail vis-à-vis road in pas-

senger movement

� Share of rail vis-à-vis road in freight

movement

� Share of public transport modes

vis-à-vis personalized transport

modes in road transport

� Bio-diesel penetration in transport

� Autonomous efficiency improve-

ments in transport

� Share of rail vis-à-vis road in pas-

senger movement

� Share of rail vis-à-vis road in freight

movement

� Share of public transport modes

vis-à-vis personalized transport

modes in road transport

� Autonomous efficiency improve-

ments in transport

Bio-diesel penetration in transport

Year 2001

23%

37%

80%

23%

37%

80%

Year 2036

23%

17%

51%

35%

50%

60%

3.1.3 Industry sector

The Indian industrial sector is a major en-ergy user, accounting for 48% of the com-mercial energy consumption. The increasedenergy intensity in Indian industry is partlydue to investments in basic and energy-

intensive industries due to the emphasis laidin the past development plans on achievingself-reliance. Industrial fuel use (includingnon-energy uses) grew from 45.7 Mtoe in1984/85 to 76 Mtoe in 2001/02. The indus-trial sector8 contributed about 25% ofIndia’s GDP in 2002/03 (CMIE 2004).

No bio-diesel penetration in transport

Fuel economy of existing motorized transport

modes constant throughout the period 2001–36

Fuel economy of existing motorized transport

modes increasing by 50% throughout the period

2001–36

Maximum level of bio-diesel penetration is

35.4 million tonnes by 2036

Combination of high efficiency

and bio-diesel scenario

88888 According to the Central Statistical Organization, Ministry of Statistics and Programme Implementation, Govern-

ment of India, the industrial sector is subdivided into manufacturing, mining, and electricity.


Production (as a proxy of demand) ineach of the industrial sub-sectors is esti-mated using econometric techniques. Linearregression analysis is carried out for each ofthe major industry sub-sectors, taking pro-duction as the dependent variable and usingvarious macro-economic indicators, such asGDP (aggregate), GDP of industrial sector,services, and agriculture, as the independentvariables. As described in the earlier chapter,in the present study – three GDP growthrates – 6.7%, 8%, and 10% have been con-sidered for demand projections.

3.1.3.1 Industrial sectoral demands

The demand for industrial goods in sevenenergy-intensive industry sectors is esti-mated and projected. Time-series produc-tion data (1980/81 to 2003/04) wasconsidered. Production (as a proxy of de-mand) in each of the industrial sub-sectorsis estimated using econometric techniques.The sections below present the demand pro-jections for different industrial sub-sectorsconsidered in the analysis.

3.1.3.1.1 Demand for chlor-alkali

Chlor-alkalis are used as feedstock in manyindustries. The chlor-alkali industry is char-acterized by the production of three inor-ganic chemicals: caustic soda, soda ash, andchlorine. Although they have different enduses, caustic soda and chlorine are producedsimultaneously in the same plant; the pro-cess for soda ash production is different.Contrary to the US and European countriesthe demand for chlor-alkalis in India isdriven by caustic soda while chlorine is con-sidered as a by-product. Therefore, only the

demands for caustic soda and soda ash havebeen considered in this study.

3.1.3.1.1.1 Demand for caustic

soda

The caustic soda industry in India is ap-proximately 65 years old. There are about 40major caustic soda plants in India. The aver-age plant size is about 150 TPD (tonnes perday), which is relatively small compared tothe average size of 500 TPD in developedcountries. The production of caustic sodawas about 1.73 MT in 2001/02.

Regression analysis was used to projectthe future caustic soda demand in the coun-try. Since caustic soda is used in many indus-tries, its production has been correlated withthe GDP contributed by the industrial sec-tor in the country, using data from 1980/81to 2003/04. The following linear relationshipis established.

DCS,t = 286 + 0.0042 (GDPI,t) (3.20)(16.26)

(R2 = 0.92)

where, DCS,t and GDPI,t represent the de-mand of caustic soda (in thousand tonnes)and GDP contributed by the industrial sec-tor (at 1993/94 prices in crore rupees) in theyear t, respectively. The figure in parenthesisis the value of t-statistics. Table 3.31 pre-sents the projected demand of caustic sodain the country.

3.1.3.1.1.2 Demand for soda ash

Soda ash (sodium bicarbonate) is one of thebasic ingredients for manufacturing soaps,


detergents, and glass. About 40% of the sodaash produced in India is consumed by thedetergent industry, 20% by the glass indus-try, and 16% by the sodium silicate industry.The remaining is consumed by the chemicalindustry. In India, soda ash is not obtainedas a naturally occurring product. It is pro-duced through a synthetic manufacturingprocess. Currently, there are six soda ashmanufacturing plants in India. In view of thelocal availability of inputs such as salt, lime-stone, coke, water, chemical compounds,and power, five out of six soda ash plants arelocated in the state of Gujarat. The domesticdemand was 1.82 MT in 2003/04, while thetotal production was 2.23 MT.

Soda ash is used as a raw material forhousehold consumer goods such as glass,soaps, and detergents whose demands are afunction of per capita income of the con-sumers. Thus, in order to capture the re-sponsiveness of this demand to changes in

per capita income, the following equation isestimated

Log (DSA,t) = 0.79 Log (PGDPt) + 0.84 [AR(1)](80.3) (3.21)

(R2 = 0.97)

where, DSA,t

represents the demand of sodaash (in thousand tonnes) in the year t andPGDPt represents the per capita GDP in theyear t (at 1993/94 prices in rupees percapita). The coefficient 0.79 associated withLog (PGDPt) indicates the income elasticityof demand for soda ash. The [AR (1)] term isused to correct for autocorrelation in therandom error terms. The projected demandof soda ash is given in Table 3.32.

3.1.3.1.2 Demand for aluminium

Aluminium is an essential raw material formodern manufacturing. It is a light-weight,

Table 3.32 Projected demand of soda

ash in India

Demand (thousand tonnes)

6.7% 8% 10%

GDP GDP GDP

Year growth growth growth

2001 1 560 1560 1 560

2006 2 260 2040 2 160

2011 3 140 2450 2 790

2016 4 380 3000 3 620

2021 6 150 3750 4 720

2026 8 690 4760 6 200

2031 12 350 6150 8 200

2036 17 660 8010 10 900


Table 3.31 Demand projection of caustic

soda in India


6.7% 8% 10%

GDP GDP GDP

growth growth growth

Year rate rate rate

2001/02 1 732 1 732 1 732

2006/07 2 209 2 346 2 462

2011/12 2 896 3 360 3 846

2016/17 3 909 4 873 6 108

2021/22 5 404 7 131 9 810

2026/27 7 615 10 500 15 863

2031/32 10 886 15 529 25 765

2036/37 15 722 23 032 41 961



high-strength, corrosion-resistant metalwith high electrical and thermal conductiv-ity. Aluminium is extensively used in thepower (for transmission and distribution),transport, construction, and domestic sec-tors. Aluminium is easy to recycle. Its pro-duction is highly electrical-energy-intensive,and it requires uninterrupted power supply(commencing with an installed capacity of4055 tonnes in 1950/51, the aluminium in-dustry has grown to 880 000 tonnes by2003/04, with an average annual growth rateof 5.7%. While 73% of aluminium was im-ported in 1950/51, since 2000/01, India hasbecome an exporter of aluminium).

In India, five industries account for theentire production of aluminium (TERI2005b). Of these, four units – HINDALCO(Hindustan Aluminium Company Ltd),MALCO (Madras Aluminium CompanyLtd), INDAL (Indian Aluminium CompanyLtd), and BALCO (Bharat AluminiumCompany Ltd) – belong to private sectorand NALCO (National Aluminium Com-pany) is a public sector unit. HINDALCOand NALCO together account for 76% of thetotal installed capacity in India.

Since aluminium is used for infrastruc-ture development in the power, transport,and construction sectors, a rapid growth inits demand is expected along with the ex-pected high economic growth rate. In thepresent study, aluminium demand has beencorrelated with GDP, using the linear regres-sion technique. Using GDP and aluminiumconsumption data for the period 1980/81 to2002/03, the following linear relationshiphas been obtained.

DAL,t = 27 + 0.00051 (GDPt)(14.35) (3.22)

(R2 = 0.90)

where, DAL,t and GDPt, respectively, repre-sent the demand of aluminium (in thousandtonnes) and GDP of entire economy (at1993/94 prices in crore rupees) in the year t.The projected demand for aluminium isgiven in Table 3.33 for 6.7%, 8%, and 10%GDP growth rates.

3.1.3.1.3 Demand for steel

The iron and steel sector is one of the largestenergy-consuming sectors in the Indianmanufacturing industry. Crude steel pro-duction, which was 1.4 MT in 1950/51, hasincreased to 30 MT in 2000/01 (SAIL2002). During the first two decades of theFive Year Plan, that is, 1950–60 and 1960–70, the economy saw growth in steel produc-tion touching 8%. The growth rate of steelproduction declined during the next two de-cades to pick up again in the 1990s to about

Table 3.33 Demand projections of

aluminium


6.7% 8% 10%

GDP GDP GDP


Year rate rate rate

2001 636 636 636

2006 888 950 1 002

2011 1179 1383 1 597

2016 1601 2019 2 556

2021 2216 2954 4 100

2026 3113 4328 6 586

2031 4422 6347 10 591

2036 6328 9312 17 040



6.65%. However, the industry grew in ahighly protected and controlled environ-ment, with massive import tariffs, and ad-ministrative control over process,distribution, and imports. The centralizedplanning process allocated resources for theindustry. The major change in policy deci-sions started in 1999, with de-licensing ofthe steel industry. Decontrol of the produc-tion, distribution, pricing, and import/ex-port of steel products has made a significantimpact on the industry.

Steel being a vital input for economic de-velopment, a linear relationship is obtainedbetween demand for steel and GDP.

DS,t = −9381 + 0.032 (GDPt)(25.76) (3.23)

(R2 = 0.97)

where, DS,t represents the demand for fin-ished steel (in thousand tonnes) in the year t.Table 3.34 presents the estimated demandfor finished steel in India.

3.1.3.1.4 Demand for cement

Cement is a key component of infrastructuredevelopment. It is used in the constructionof buildings, bridges, roads, airports, and soon. India is the second-largest producer ofcement in the world. Cement production ca-pacity in India has grown from 3.2 MT in1950 to 136 MT in 2002/03 (CMA 2004).

A tremendous growth in cement produc-tion has been registered, especially duringthe past two decades. During this period(1981–2001), the production has increasedfrom about 21 MT to 107 MT, with an an-nual average growth rate of 8.4%. However,the per capita cement consumption of 110

kg (kilograms) in India is much below theworld average per capita of 273 kg. It evenfalls much behind almost all Asian major ce-ment producers like Japan (540 kg), SouthKorea (1090 kg), Taiwan (754 kg), Thailand(300 kg), and Indonesia (150 kg). In view ofthe expected high infrastructure growth inIndia, the growth of cement is also expectedto be high.

A linear regression has been establishedfor cement demand projection in India.

DC,t = −19 + 0.0001 (GDPt)(62.25) (3.24)

(R2 = 0.99)

where, DC,t represents the demand for ce-ment (in MT) in the year t. Table 3.35 pre-sents the projected demand for cement inIndia under 6.7%, 8%, and 10% GDPgrowth scenarios.

Table 3.34 Demand projections for

finished steel in India


6.7% 8% 10%

GDP GDP GDP


Year rate rate rate

2001 31 372 31 372 31 372

2006 44 768 48 630 51 913

2011 63 010 75 856 89 334

2016 89 540 115 861 149 601

2021 128 210 174 641 246 661

2026 184 624 261 008 402 977

2031 247 884 387 909 654 726

2036 386 756 574 369 1 060 171



3.1.3.1.5 Demand for cotton

The Indian textile industry contributesabout 3% to GDP and 14% of the total in-dustrial production. This sector also con-tributes to 27% of the national exportearnings. Moreover, the textile industryplays a major role in employment genera-tion, accounting for about 27% of the totalwork-force of the country (second after theagriculture sector).

The textile industry can be classified intotwo categories: (i) organized sector and (ii)unorganized or rural sector. The organizedsegment of the textile industry produces 4%of the total fabrics produced in the country,with most of it being manufactured in powerlooms. The total yarn required by both theorganized and the decentralized sectors isproduced entirely within the organized seg-ment. The cotton textile/man-made fibre in-dustry is the single-largest organizedindustry in the country. The decentralized

segment comprises mainly small powerlooms and the handloom units.

Cotton is the predominant fabric used inthe Indian textile industry—nearly 60% ofthe overall consumption in textiles and morethan 75% in spinning mills is cotton. India isamong the world’s largest producers of cot-ton, with over 9 Mha of land under cultiva-tion, and an annual crop output of about1.7 MT (2001/02) (MoA 2004).

Clothes are essential commodities in thebasket of consumption goods for every con-sumer. Cotton cloth being a high-value com-modity, its consumption is influenced by theconsumer’s purchasing power measured bythe per capita income. Thus, in order tomeasure the income elasticity of cotton-cloth demand, the following linear regres-sion relationship has been established.

Log(DCC,t) = 0.81 [Log(PGDP

t)] + 0.57AR(1)

(249.3) (3.25)

(R2 = 0.94)

where, DCC,t represents the demand of cot-ton cloth (in thousand tonnes) in the year tand PGDPt represents the per capita GDP inthe year t (at 1993/94 prices in rupees percapita). Table 3.36 presents the projecteddemand for cotton cloth in India under6.7%, 8%, and 10% GDP growth scenarios.

3.1.3.1.6 Demand for fertilizer

The inorganic, organic, natural, or syntheticchemical elements that provide nutrient forthe growth of plants are generally consideredas fertilizers. These play an exceedingly im-portant role in the country’s performance inthe agriculture sector. They are usually clas-sified according to the plant nutrients. Three

Table 3.35 Cement demand projections


6.7% 8% 10%

GDP GDP GDP


Year rate rate rate

2001 107 107 107

2006 148 167 184

2011 204 254 309

2016 286 382 509

2021 405 570 831

2026 579 846 1350

2031 833 1252 2186

2036 1203 1782 3532



types of primary fertilizers are used in India:N (nitrogen), P2O5 (phosphorous), and K(potassium). Secondary and micronutrientsalso play an important role in plant growth.However, the primary nutrients are relevantin the present context of the energy con-sumption as most of the energy input is inthe form of primary nutrient fertilizers, par-ticularly nitrogenous fertilizers. Fertilizerscontaining only one primary nutrient arecalled ‘straight fertilizers’ whereas thosewith more than one are called ‘complex fer-tilizers’. India produces nitrogenous andphosphatic fertilizers only. Due to the un-availability of raw materials, the entire re-quirement of potassic fertilizers is metthrough import. During 2001/02, produc-tion figures of nitrogenous and phosphaticfertilizers were 10.7 and 3.9 MT, respec-tively (in terms of N and P2O5 nutrients).

Fertilizer production is estimated usingthe production of high-yielding varieties ofcrops. The following linear regression equa-tions are established for demand projectionof nitrogenous and phosphatic fertilizers inIndia.

DN,t = −5480 + 197.4 (PHYV,t)(20.55) (3.26)

(R2 = 0.95)

DP,t = −2102 + 68.5 (PHYV,t)(18.60) (3.27)

(R2 = 0.94)

where, DN,t

and DP,t

represent the demand ofnitrogenous and phosphatic fertilizer, re-spectively, in thousand tonnes (in terms ofnutrient N and P

2O

5) in the year t and P

HYV,t

production of high-yielding varieties crops(in thousand tonnes).

Production of high-yielding variety cropshas been estimated using the GIA and GDP(discussed in detail in the section on de-mand for agriculture sector). Table 3.37 pre-sents the projected demand for fertilizers inIndia.

3.1.3.1.7 Demand for paper

The pulp and paper industry provides em-ployment to about 3.5 million people di-rectly and indirectly. The Indian pulp andpaper industry recorded a constant averageannual growth rate of 5.47% over the pastthree years. Broadly, there are two types ofpaper products: paper and paperboard, andnewsprint. Paper and paperboard can fur-

Table 3.36 Cotton cloth demand

projection


6.7% 8% 10%

GDP GDP GDP


Year rate rate rate

2001 2 210 2 210 2 210

2006 2 520 2 680 2 800

2011 3 030 3 470 3 910

2016 3 730 4 530 5 500

2021 4 680 5 950 7 790

2026 5 990 7 870 11 100

2031 7 780 10 490 15 940

2036 10 220 14 050 23 010



Table 3.38 Projected demand for paper

and paper board in India


6.7% 8% 10%

GDP GDP GDP


Year rate rate rate

2001 4 950 4 950 4 950

2006 6 929 7 615 8 198

2011 9 345 11 479 13 719

2016 12 823 16 949 22 238

2021 17 839 24 765 35 508

2026 25 096 36 034 56 363

2031 35 613 52 385 89 370

2036 50 836 76 185 141 820


Table 3.37 Demand projection for fertilizer


6.7% and 8% GDP 10% GDP

growth rate growth rate

Year N P2O5 N P2O5

2001 10 690 3873 10 690 3 870

2006 12 351 4090 14 010 4 670

2011 13 692 4555 16 570 5 550

2016 15 051 5027 19 220 6 470

2021 16 432 5506 21 950 7 420

2026 17 833 5993 24 770 8 400

2031 19 256 6487 27 680 9 410

2036 20 409 6887 30 680 10 450

GDP – gross domestic product; N – nitrogen;

P2O5 – phosphorus pentaoxide

ther be sub-divided into industrial grade(wrapping and packaging, specialty, kraft,and so on) and cultural (writing and print-ing) paper. The output of the Indian paperindustry is about 5.4 MT, with a turnover ofabout 120 billion rupees.

Paper consumption in India was about5.5 kg per capita in 2003 as against the worldaverage of 50 kg (TERI 2005b). Moreover,the demand for paper and paper products inIndia has continuously been increasing overtime. Since the demand for paper is directlyrelated to economic development, India willhave higher growth in future as compared tothe average worldwide growth rate. Demandfor paper and paperboard has been esti-mated using per capita GDP to account forboth demographic and economic growth im-pacts on paper demand. The following linearrelationship has been established for de-mand projection.

DP,t = 2658 + 0.638 (PGDPt)(40.48) (3.28)

(R2 = 0.98)

where, DP,t and PGDPt represent demand forpaper and paperboard (in thousand tonnes)and per capita GDP (at 1993/94 prices inrupees) in the year t, respectively. Table 3.38presents the projected demand for paper andpaperboard in India.

3.1.3.1.8 Demand of other

industries

The other energy-consuming industries in-clude small-scale industries such as foodprocessing, glass and ceramics, sugar mills,


brick making, foundry, and leather/tanning.These industries are quite fragmented. Inthe model, all these industries are groupedunder a single sub-sector, since data on pro-duction of each of these industries is notavailable. In this study, the residual energyconsumption of the industrial sector (energynot accounted for in the seven industries de-scribed above) is assigned to other indus-tries. For demand projection in this study, itis assumed that other industries will grow atthe same growth rate as that of GDP growth.Table 3.39 presents projected useful energydemand for other industry. Efficiency of en-ergy utilization in other industry is consid-ered at 40% for 2001. Further, it is assumedthat efficiency of utilization of energy ofother industry will increase to 44% and 57%in the BAU (business-as-usual) and highefficiency scenario, respectively.


options in the industry sectors

This section describes the status of each ofthe industrial sector technological optionsand its penetration level, as assumed acrossdifferent scenarios.

3.1.3.2.1 Caustic soda industry

Caustic soda is produced by the electrolysisof brine (a solution of common salt and wa-ter). In this process, chlorine and causticsoda are produced simultaneously. Addi-tionally, hydrogen is also produced. The pro-duction of caustic soda is a veryelectric-energy-intensive process. In view ofhigh electric tariff in India, it is reported thatthe cost of power accounts for about 50%–65% of the total production cost (Pramanik2002).

Worldwide, there are three processes usedfor manufacturing caustic soda: (i) dia-phragm cell process, (ii) mercury cell pro-cess, and (iii) membrane cell process. Thediaphragm cell process is the oldest amongall three. However, in India, presently, noneof the plants are using this technology(AMAI 2004). In 1996, majority of theplants in India were using mercury cell pro-cess (56% of the total installed capacity).During 2004, this share reduced to 29%.The share of membrane cell process in-creased from 56% in 1996/97 to 71% in2003/04. Table 3.40 presents the time trendof percentage share of different processesused for caustic soda production in India(AMAI 2004).

Membrane process is the most energy-ef-ficient process followed by the mercury cellprocess. Therefore, the shift towards more

Table 3.39 Energy demand projection for

other industries


6.7% 8% 10%

GDP GDP GDP


Year rate rate rate

2001 726 726 726

2006 1004 1066 1 169

2011 1389 1566 1 883

2016 1920 2302 3 033

2021 2656 3382 4 884

2026 3673 4969 7 866

2031 5080 7301 12 668

2036 7026 10 728 20 402



penetration of the membrane cell processhas resulted in the reduction of average spe-cific energy consumed during caustic sodaproduction. As per a study by the LBNL(Lawrence Berkeley National Laboratory),US, the specific energy consumption duringcaustic soda production in India is about36% lower than that in the US (LBNL2005). Similarly, the specific energy con-sumption is also lower than the reported val-ues for the European Union (LBNL 2005).

In India, due to environmental concerns(heavy metal pollution), no new plants basedon mercury cell process are allowed. There-fore, the specific energy consumption is ex-

pected to decrease in the near future. How-ever, the current process (the membrane cellprocess) is a mature technology that has verylittle scope for further efficiency improve-ment. Moreover, due to the lack of domesticproduction of membrane cell in the country,India is entirely dependent on importedtechnology of membrane cell. Therefore, allnew plants are coming with state-of-the-arttechnology. A new technology called ODC(oxygen depolarized cathodes) is currentlydeveloped. In Europe, a new plant using theODC technology has been built in Germanyat Brunsbuttel (LBNL 2005). It is reportedthat the ODC technology has a substantialpotential for saving electricity (440–530kWh/t) (LBNL 2005). In the present analy-sis, it is assumed that in India, ODC tech-nology will be commercially available from2016. Since this technology is still in the de-velopment phase and a reliable cost figure isnot available, the capital cost of the ODCplant is assumed to be 10% higher than thecost of the membrane-cell-based plant.Since no new plants based on the mercurycell technology are being built and cost datais also not available, for modelling purpose,the capital cost is taken to be the same asthat for the membrane-cell-based plant.Table 3.41 presents the technological char-

Table 3.41 Technological characterization of caustic soda industry

Average specific Repair and

electricity Capital cost maintenance cost

consumption (million rupees/ as a percentage

Process (kWh/t) MTPA) of capital cost Life (year)

Mercury cell 3300 41 000 2.5 10

Membrane cell 2848 41 000 2.5 10

ODC 2363 45 100 2.5 10

ODC – oxygen depolarized cathodes; MTPA – million tonnes per annum; kWh/t – kilowatt-hour per tonne

Table 3.40 Production of caustic soda

through different processes: 1998/99 to

2003/04

Percentage share

Process 1996/97 1998/99 2001/02 2003/04

Membrane 56 65 69 71

cell

Mercury 37 34 31 29

cell

Diaphragm 7 <1 0 0

cell


Table 3.43 Technological characterization of soda ash industry

Repair and

Average specific maintenance

consumption Capital cost cost as a

Fuel Electricity (million rupees/ percentage

Process (GJ/t) (kWh/t) MTPA) of capital cost Life (year)

Solvay 15.93 282 19 800 2.5 10

Modified Solvay 14.48 257 19 800 2.5 10

Akzo dry lime 9.31 607 24 800 2.5 10

GJ/t – gigajoules per tonne; kWh/t – kilowatt-hour/tonne; MTPA – million tonnes per annum

acterization of caustic soda industry in themodel (TERI 2004; CMIE 1996).

3.1.3.2.2 Soda ash industry

The manufacture of soda ash includes pul-verizing of the salt, brine purification,absorption of ammonia in brine, and car-bonation. The precipitate of sodium bicar-bonate is filtered and calcined to obtain sodaash. The technologies mostly used by the in-dustries are Solvay process, modified Solvayprocess (or dual process), and Akzo dry-limeprocess. The Akzo dry-lime process is con-

Table 3.42 Details of Indian soda ash plants

Capacity Total

Year of (thousand tonnes capacity

Company commissioning Process per year) (%)

Tata Chemicals 1948 Standard Solvay 875 33

Saurashtra Chemicals Ltd 1960 Standard Solvay 650 25

GHCL 1988 Akzo dry lime 525 20

Nirma Ltd 1998 Akzo dry lime 365 14

Tuticorin Alkalis 1982 Modified Solvay 115 4

DCW Ltd 1939 Standard Solvay 96 4

GHCL – Gujarat Heavy Chemicals Ltd; DCW – Dhrangadhie Chemical Works

sidered as a state-of-the-art technology. InIndia, soda ash is produced in six plants.Table 3.42 presents the production capacityand the technology being used in these sixplants (LBNL 2005). It may be noted thatthree of these plants are based on the stan-dard Solvay process (62% of the productioncapacity), one unit on the modified Solvayprocess (4% of the production capacity),and the remaining two units use the Akzodry-lime process (34% of the production ca-pacity) (LBNL 2005). Technology charac-terization of soda ash industry considered inthe model is given in Table 3.43.


Figure 3.6 Primary aluminium production

calcination of aluminium hydrate. The ex-traction of aluminium involves the electroly-sis of alumina at 950–970 oC in electrolyticcells (smelter). While the cathode in the elec-trolytic cells is made of carbon, two types ofanodes are used (i) Soderberg (or self-bak-ing) and (ii) pre-baked. In India, about 76%of the installed capacity is based on pre-baked system while only 24% is based on theSoderberg technology (TERI 2004).

Electricity cost forms about 40% of thetotal production costs and hence, energy ef-ficiency continues to be a major area of focusfor the aluminium industry. In India, the in-dustry average for the electrical consump-tion in smelters has reduced from18 000–20 000 kWh/tonne of aluminiumproduced in 1960s to 14 000–17 000 kWh/tonne of metal produced in 2000s (TERI2005b).

3.1.3.2.3.1 Energy efficiency options

The aluminium manufacturing process ishighly electrical-energy-intensive. The break-up of energy indicates that more than 80% ofthe energy is electrical energy, and is con-sumed in the smelting of alumina. The majorenergy-saving opportunities in the Indian alu-minum industry lie in the switch over to gas-suspension calciners (as against rotary kilns)and waste heat utilization, and converting thesmelters from Soderberg systems to pre-bakedsystems. The other operational improvementsinclude current efficiency improvements andreduction in operating voltage (TERI 2005b).

In the MARKAL model, the aluminiumindustry is modelled in two steps: (1) Bayerprocess, and (2) smelting process. Table 3.44presents the technological characterizationof the aluminium industry in India. Eco-nomic life of the aluminium plant is taken as30 years, and the annual repair and mainte-

3.1.3.2.3 Aluminium industry

Bauxite is the primary raw material used inproduction of primary aluminium. India hasabout 3037 MT of bauxite reserves andranks sixth in the world. The BHH (Bayer–Hall–Heroult) process being used for morethan 100 years is practically the only viablecommercial manufacturing process used forthe production of aluminium. Though con-siderable research efforts have been madefor alternate processes for aluminium pro-duction, these are not yet commercialized.Production of aluminium has two distinctprocesses: production of alumina frombauxite ore (Bayer process) and conversionof alumina to aluminium in smelters (smelt-ing process). A process flow diagram for pri-mary aluminium production is shown inFigure 3.6.

There are five different steps involved inthe manufacturing of alumina: (i) bauxitecrushing/grinding/slurrying, (ii) digestion(iii) precipitation, (iv) evaporation, and (v)


nance cost is assumed at 2.5% of the totalcapital cost.

Further, it is assumed that the improvedBayer process and the improved pre-bakedprocess will be available only in the (EFF)high-efficiency scenario by 2011.

3.1.3.2.4 Iron and steel industry

The four different steel manufacturing tech-nologies existing in the country are: (a) BF–BOF (blast furnace–basic oxygen furnace),(b) scrap–EAF (scrap–electric arc furnace),(c) DRI–EAF (direct reduction iron–EAF),and (d) COREX. In scrap–EAF, the processscrap steel is used in place of iron ore. Thereare eight integrated steel plants in India pro-ducing steel using the BF–BOF process.During 2001/02, of the total 31.37 MT ofsteel produced in India, 12.98 MT was pro-duced in those eight integrated steel plants.Table 3.45 presents the process-wise break-up of steel production in India as well as thetechnological characterization of the tech-

nology used in India. Repair and mainte-nance cost is considered at 4% of the capitalcost of the plant (Hidalgo et al. 2005).

3.1.3.2.4.1 Energy efficiency

options

Though the specific energy consumption ofthe Indian integrated steel plants decreasedsignificantly (by about 22%) from 1990/91to 2003/04 (Figure 3.7), it is still high whencompared to the US and Japan. This indi-cates scope for further improvement in en-ergy efficiency. Table 3.46 presents theenergy-efficiency measures applicable to theintegrated steel plants in India (LBNL1999). Similarly the efficiency improvementmeasures for EAF-based plants are given inTable 3.47 (LBNL 1999). The data on costestimates for different efficiency improve-ment options is only available for 1995 indollars (LBNL 1999). The same value isconverted to Indian rupees for 2001/02prices by using the exchange rate for 1995/96

Table 3.44 Technological characterization of the aluminium industry

Average specific energy

consumption (per

tonne of aluminium) Capital cost

Technology Fuel (GJ) Electricity (kWh) (dollars/tonne)

Bayer process 32.00 583 1200

Improved Bayer process 28.80 525 1500

Soderberg process 1.81 17 449 2900

Pre-baked process 3.03 15 613 3000

Improved pre-baked process 2.50 13 200 3300

GJ – gigajoules; kWh – kilowatt-hour

Sources Vasudevan (1999); TERI (2004); <http://www.aluminum.org/Content/ContentGroups/News_Releases1/

October_2005/AlumPriceTrends.pdf>


Table 3.45 Production and technological details of Indian steel industry during 2001/02

Production Capital cost Specific fuel consumption

Process (million tonnes) (dollars/tonne) Fuel (GJ/t) Electricity (kWh/t)

BF–BOF 12.98 240 29.01 401

Scrap–EAF 7.87 173 2.23 622

DRI–EAF (coal-based) 5.66 214 26.63 453

DRI–EAF (gas-based) 3.46 214 22.63 453

COREX* 1.40 583 28.81 —

Total 31.37

GJ/t – gigajoules per tonne; kWh/t – kilowatt-hour per tonne; BF–BOF – blast furnace–basic oxygen furnace; EAF –

electric arc furnace; DRI – direct reduction iron

* In COREX plant, electricity requirement is met through internally generated electricity using COREX gas in

cogeneration plant.

Sources SAIL (2002); OECD (2001); CCME (2002); TERI estimates

Source SAIL (2006)

Figure 3.7 Time trend of specific energyconsumption of SAIL steel plants

MARKAL model, the above-mentioned options aregrouped into different catego-ries. For example, integratedsteel plants are divided intoexisting efficient plants. It isassumed that only the retiringcapacity could be retrofitted.Similarly, two categories areconsidered for scrap-EAFand DRI–EAF: (a) existingand (b) efficient. The specificenergy consumption of newcategories of plants has beenestimated in terms of energysavings from the existingplants.

During 2001/02, the shareof steel production throughBF–BOF and scrap–EAFplants was 41% and 24%, re-

spectively. The scrap–EAF technology usesscrap steel in place of iron ore. In India,scrap steel is obtained from domestic oldsteel, ship breaking, and import of scrap

and inflation rate during that period (5.76%per annum).

In view of the large number of mitigationoptions, for modelling purpose in the


Table 3.46 Efficiency improvement measures for integrated steel plants

Fuel Electricity Retrofit cost

savings saving (million

Option (GJ/t) (GJ/t) rupees/MTPA)

Adopt continuous casting 0.24 0.08 554.30

Preventative maintenance 0.43 0.02 0.50

Pulverized coal injection to 130 kg/thm 0.69 0.00 529.70

Hot blast stove automation 0.33 0.00 254.60

Use of waste fuels in sinter plant 0.04 0.00 53.80

Improved blast furnace control systems 0.36 0.00 275.00

Energy monitoring and management system 0.11 0.01 7.00

Programmed heating-coke plant 0.05 0.00 14.40

Controlling oxygen levels and VSDs on combustion air fans 0.29 0.00 20.40

Automated monitoring and targeting system 0.00 0.12 29.20

Process control in hot strip mill 0.26 0.00 28.30

Efficient ladle pre-heating 0.02 0.00 2.30

Improved process control 0.01 0.00 13.90

Recuperative burners 0.61 0.00 101.10

Recovery of blast furnace gas 0.06 0.00 45.50

Sinter plant heat recovery 0.12 0.00 30.60

Energy-efficient drives (rolling mill) 0.00 0.01 7.90

Heat recovery on the annealing line 0.17 0.01 71.90

Cogeneration 0.03 0.35 673.50

Reduced steam use (pickling line) 0.11 0.00 74.70

Hot charging 0.52 0.00 607.10

Recuperator hot blast stove 0.07 0.00 55.20

Variable speed drive: flue gas control, pumps, fans 0.00 0.02 60.30

BOF gas and sensible heat recovery 0.92 0.00 1020.40

Waste heat recovery (cooling water) 0.03 0.00 32.50

Coke dry quenching 0.37 0.00 104.40

Top pressure recovery turbines (wet type) 0.00 0.10 199.00

Insulation of furnaces 0.14 0.00 404.90

Coal moisture control 0.09 0.00 25.50

Total 6.07 0.72 5308.40

GJ/t – gigajoules per tonne; MTPA – million tonnes per annum; kg/thm – kilogram/tonnes of hot metal;

BOF – basic oxygen furnace; VSD – variable speed drive

Sources LBNL (1999); TERI estimates


Table 3.47 Efficiency improvement measures for EAF-based steel plants

Fuel Electricity Retrofit cost

savings saving (million rupees/

Option (GJ/t) (GJ/t) MTPA)

Oxy-fuel burners 0.00 0.14 223

Scrap pre-heating, post combustion: −0.70 0.43 278

Shaft furnace (FUCHS)

Bottom stirring/stirring gas v 0.00 0.07 28

Improved process control (neural network) 0.00 0.11 44

Scrap preheating: tunnel furnace (CONSTEEL) 0.00 0.22 232

Controlling oxygen levels and VSDs on combustion air fans 0.29 0.00 20

Process control in hot strip mill 0.26 0.00 28

Efficient ladle pre-heating 0.02 0.00 2

Energy monitoring and management system 0.02 0.01 7

Recuperative burners 0.61 0.00 101

Twin-shell DC w/scrap pre-heating 0.00 0.07 278

Flue gas monitoring and control 0.00 0.05 93

Transformer efficiency: UHP 0.00 0.06 128

EBT on existing furnace 0.00 0.05 148

Foamy slag practice 0.00 0.07 464

Waste heat recovery from cooling water 0.03 0.00 32

Insulation of furnaces 0.14 0.00 405

Total 0.67 1.28 2512

GJ/t – gigajoules per tonne; MTPA – million tonnes per annum; VSD – variable speed drive; DC – direct current;

UHI – ultra high power; EBT – eccentric bottom tapping; EAF – electric arc furnace

Sources LBNL (1999); TERI estimates

from other countries. In view of the existinglow per capita steel consumption, domesticavailability of steel scrap is low in the country.

In view of the low per capita steel con-sumption in India, and due to the environ-mental concerns associated with shipbreaking, production of steel through scrap–EAF technology is expected to reduce in thefuture. Accordingly, the share in 2036 is ex-pected to reduce to 10%. Because of the

high decommissioning cost of BF–BOFplant, all existing plants are expected to pro-duce steel in the future also. Furthermore,due to economy of scale of BF–BOF plants,a single plant caters to significant domesticdemand. Table 3.48 provides our assump-tions regarding the maximum/minimumlevels of BF–BOF and scrap–EAF plantsin 2001 and 2036 under the BAU and EFFscenarios.


Table 3.49 Percentage distribution of

cement production in the year 2002/03

Process Total production (%)

Dry process 94.1

Wet process 1.3

Semi-dry process 0.2

Others 4.4

Source CMA (2003) (others are added in the dry

process in the model)

Table 3.48 Level of share of BF–BOF and Scrap-EAF steel plants

Share (%)

Scenario Parameter Level 2001 2036

BAU Share of BF–BOF Minimum 41 20

BAU Share of scrap–EAF Maximum 24 10

High efficiency Share of BF–BOF Minimum 41 80

High efficiency Share of scrap–EAF Maximum 24 10

BAU – business-as-usual; BF – blast furnace; BOF – basic oxygen furnace; EAF – electric arc furnace

Source TERI (2004)

Figure 3.8 Time trend of processprofile of cement industry

3.1.3.2.5 Cement industry

Three different cement manu-facturing processes in the coun-try are: (a) wet process, (b)semi-dry process, and (c) dryprocess. The contribution of ce-ment production from the wetand semi-dry processes hasbeen decreasing over the pastfour decades. Until 1960, themajor share of cement capacitywas from the wet process(94.4%); the semi-dry processcontributed 4.5%; and the dryprocess only 1.1%. During2003, the share of wet processwas only 3.7% whereas the dryprocess accounted for 94.7% of the total in-stalled capacity. Figure 3.8 presents the timetrend of process-wise cement production ca-pacity in India (TERI 2005). Table 3.49 pre-sents the process-wise production shareduring 2002/03 (CMA 2003).

There are more than 13 different varietiesof cement produced in India. Amongst themthe three main varieties are: OPC (OrdinaryPortland Cement), PPC (Portland Poz-zolana Cement), and PSC (Portland SlagCement) (Figure 3.9). These three varieties


which OPC is used, PPC can-not be used for pre-stressedand high-strength concrete,as in bridges and airports.

On the basis of the tech-nology level, the dry processplants are further classifiedinto three main categories:(a) 4-stage pre-heater pre-calcinator, (b) 5-stage pre-heater pre-calcinator, and (c)6-stage pre-heater, twin-stream, pre-calcinator, pyro-step cooler. Table 3.51presents the technologicaldetails of process-wise ce-ment production (TERI2005b and NCCBM 2003).

Source Cement Statistics (2003)

Figure 3.9 Time trend of percentage distributionof different variety of cement in India

accounted for more than 99% of the totalproduction in India during 2001/02 (CMA2003). The variation in cement products isdue to the type of additives blended with theclinker at the stage of grinding and theirshare in per tonne of cement. Table 3.50 pre-sents the typical share of additive used inOPC, PPC, and PSC cement (Das 1997).While PSC can be used for all purposes for


input material for different varieties of

cement production in India

Percentage distribution

Input material OPC PPC PSC

Clinker 95 80 65

Gypsum 5 5 5

Fly ash — 15 —

Slag — — 30

OPC – Ordinary Portland Cement; PPC – Portland

Pozzolana Cement; PSC – Portland Slag Cement

For estimating variety-wise specific heatconsumption for plants using differenttechnology levels, their respective clinker-to-cement ratio (Table 3.52) and specific heatconsumption for clinker production are used.However, specific electricity consumption isassumed to be the same for all varieties of ce-ments, depending on the technology used.

It may be noted that wet and semi-dryprocess technologies were commercializedduring the 1950s and 1970s (TERI 2005b).These few available plants are almost at theend of their economic life. Therefore, it is as-sumed that all wet and semi-dry processplants will die out by 2011 (TERI 2005b).Similarly the 4- and 5-stage dry-processplants were commercialized in the countryduring the 1980s and 1990s. Therefore, it isassumed that all 4- and 5-stage plants will beretrofitted to 6-stage plants within the next 20and 30 years, respectively, in a phased manner.

Coal is the primary fuel for the cementindustry. It is used for both thermal applica-tion as well as electricity generation in the


Table 3.51 Technological details of process-wise cement production in India

Specific Specific Capital

heat power cost (million

consumption consumption rupees/

(kcal/kg (kWh/tonne MTPA of Life

Process of clinker) of cement) cement) (years)

Wet 1300 115 3300 10

Semi-dry 900 110 3300 10

Dry process

4-stage pre-heater pre-calcinator 800 105 3300 20

5-stage pre-heater pre-calcinator 750 88 3500 30

6-stage pre-heater, twin-stream,

pre-calcinator, pyro-step cooler 665 68 3800 50

kcal/kg – kilocalories per kilogram; kWh/tonne – kilowatt-hour per tonne; MTPA – million tonnes per annum

Sources TERI (2004); NCCBM (2003); TERI estimates

Table 3.52 Variety-wise percentage distribution of cement production in 2001 and 2036

Production (%)

Scenario Parameter Level 2001 2036

BAU Share of OPC Minimum 56 28

Share of PSC Maximum 12 12

Share of PPC Maximum 32 60

High efficiency Share of OPC Minimum 56 5

Share of PSC Maximum 12 30

Share of PPC Maximum 32 65

BAU – business-as-usual; OPC – Ordinary Portland Cement; PSC – Portland Slag Cement; PPC – Portland Pozzolana

Cement

captive plants. Besides the linked quota, thecement industry takes coal from the openmarket and through import. The presentcost of gas is uneconomic for cement plants(as compared to imported coal) as no plantis using natural gas for process heating.

However, in some plants, natural gas is usedfor captive generation. Moreover, in themodel, 6-stage natural-gas-based plants (forall three varieties of cement) are also mod-elled to allow the model to choose naturalgas for its future economic viability.


3.1.3.2.6 Textile industry

The textile production process consists offour main activities: spinning, weaving, knit-ting, and wet processing. The productionfrom fibres to spun yarn takes place throughthe spinning process and constitutes the firststage. Then the yarn is weaved to make fab-rics in looms. These fabrics then undergoseveral different processes including bleach-ing, printing, dyeing, and finishing—theseare grouped under the category of wet pro-cessing. The industry uses cotton, jute, wool,silk, and synthetic fibres as raw material. InIndia, cotton accounts for about 60% of theraw material being used in the industry. In-dia is also the largest producer of cotton.

The textile industry can be classified into:(i) textile mills comprising composite andspinning mills in the organized segment and(ii) small power loom and handloom units inthe unorganized sector. The organized sectorproduces only 4% of the total fabrics pro-duced in the country. Yarn is produced bythe mills in the organized segment but isconsumed by power loom and handloom inthe unorganized sector as well.

There is a wide variation in the processesand technologies employed in different fac-tories across India. Composite mills covercomplete sets of processes, from raw mate-rial to final products. However, most manu-facturing units tend to deal only with a partof the process. The primary energy inputs inthe textile industry are steam and power.The requirement of steam and power varieswith the yarn count, yarn productivity, typeof fabric (product mix), fabric productivity,and extent of wet processing (dyeing andprinting). In view of the increased mechani-


options

The energy efficiency options for the cementindustry are conversion of 4- and 5-stage ce-ment plants to modern 6-stage plants (withpre-heater, twin-stream, pre-calcinator, andpyro-step cooler) and higher share ofblended cement in the total cement produc-tion.

Clinker production is the most energy-in-tensive process in the manufacture of ce-ment. Due to lower clinker requirements inthe blended cement, its specific energy con-sumption is lower than the OPC cement.The energy cost accounts for about 30%–50% of the production cost of cement.Therefore, the share of blended cement pro-duction in India is increasing, it has in-creased from 28% in 1993/94 to 44% in2000/01. It is also opined that the share ofblended cement will continue to increase inthe future also.

Since the share of PSC remained almostconstant from 1993/94 to 2000/01, in theBAU scenario, the share of PSC is assumedto be the same (12%) during the entire mod-elling period. The share of PPC that uses flyash (a waste from power plant) will contrib-ute upto 60% of the total cement produc-tion. While in the EFF scenario, the share ofPSC and PPC cement is assumed to increaseto 30% and 65%, respectively. Table 3.52presents the maximum/minimum share as-sumed for different varieties of cement pro-duction in the year 2001 and 2036 in theBAU and EFF scenarios.


zation, the energy consumption in the textileindustry has also increased. Specific energyconsumption of modern textile mills ishigher due to replacement of manual labourby electric power. Moreover, there is a trendof shift towards more mechanization.


options

Some of the major energy saving options intextile mills as reported in a study conductedby TERI are as follows (TERI 1995 andTERI 2005a).

Adoption of new spinning processes: it is pos-sible to save 15%–20% of the energy re-quirement in spinning by adoption of newspinning technologies like friction spinningand air-jet spinning. Modifications in ringframe spinning machines that account formajority of the power consumption in acomposite textile mill can reduce energyconsumption by 5%–10%.

Use of advanced drying processes: processeslike high-speed drying machine and stentersfor reducing energy consumption (20%–30%) can be adopted. Use of radio fre-quency dryers can eliminate use of thermalenergy in drying applications.

Use of solar energy for water heating: in thetextile industry, 80% of the energy used isutilized in wet processing, with temperaturesranging from 40 oC to 140 oC. There is sub-stantial scope to reduce fossil fuels used inboilers by utilizing solar thermal energy.

The energy costs in textile production ac-count for up to 17% of the total manufactur-ing costs (ADB 1998). Therefore, energyconservation has become quite important.Most of the textile units in India have madelot of efforts towards energy conservation on

a short- and medium-term basis. As a resultof these efforts, the extent of energy savingsreported by many mills varies from 5% to15%. Some of the progressive mills have in-vested a huge amount of money for imple-mentation of long-term measures such asboiler replacement, cogeneration system,and changing of process machines. Thesemeasures have resulted in energy savings tothe extent of 20%–25%. Moreover,electric energy consumption is expected tocontinue rising over time due to increasingautomation and higher running speeds formachines.

The textile sector is very diverse and thus,data collection is a challenging task. There-fore, due to unavailability of adequate disag-gregated data on technologies, thissub-sector is not modelled in detail and iscaptured as two technologies (existing andefficient), representing the cotton textile in-dustry. The specific energy consumption ofan efficient mill is taken to be 10% lowerthan the existing one. Further it is assumedthat efficient mill will be available by 2011.Table 3.53 presents technological character-ization of the textile sector.

3.1.3.2.7 Fertilizer industry

The fertilizer industry is one of the largestconsumers of energy. Earlier in an fertilizerindustry, various feedstock like firewood,coke, lignite, and coke oven gas were utilizedfor ammonia production. This wide spec-trum of feedstock changed gradually withthe advent of new process techniques andavailability of petroleum-based feedstock(naphtha, fuel oil, natural gas, and so on).Over the past decade, there has been a no-ticeable decline in the use of coal and naph-


tha as feedstock, and natural gas has increas-ingly been used instead (Table 3.54) (TER I2005b).

In view of the largest share of urea andSSP (single super phosphate) in the produc-tion of nitrogenous and phosphatic fertiliz-ers, respectively, only the production of ureaand SSP is considered in this study. Theprinciple raw materials used for making urea

are ammonia and carbon dioxide. Produc-tion of ammonia is the highest energy-inten-sive process in fertilizer manufacturing. Itaccounts for almost 80% of the energy con-sumption in the manufacturing processes ofa variety of final fertilizer products. There-fore, ammonia is considered as a key inter-mediate for determining the overall energyefficiency of fertilizer production. Besides

Table 3.53 Technological characterization of a cotton textile industry

Capital cost

Energy consumption (million rupees/ Life time

Technology Thermal (GJ/t) Electricity (kWh/t) MTPA) (year)

Existing 32.69 3500 280 000 30

Efficient 29.42 3150 280 000 30

GJ/t – gigajoules per tonne; kWh/t – kilowatt-hour per tonne; MTPA – million tonnes per annum

Sources ARRPEEC (2003); CMIE (1996); Swaminathan and Rudramoorthy (2004); TERI (2004)

Table 3.54 Installed capacity according to sources of feedstock (percentage) used for ni-

trogenous fertilizer production

Natural Electric Coke Fuel Ammonia

Period Naphtha gas power oven gas Lignite Coal oil (external supply)

1965 43.5 — 14.0 30.3 12.2 — — —

1970 65.3 10.2 6.0 13.3 5.2 — — —

1975 73.2 13.7 3.1 7.3 2.7 — — —

1980 51.7 13.0 1.7 1.4 — 9.9 19.6 2.7

1985 42.6 24.0 1.4 1.1 — 7.7 19.8 3.4

1990 30.4 41.9 1.0 0.9 — 5.6 14.5 5.7

1995 27.4 47.6 — 1.4 — 5.0 13.5 5.1

1997 24.5 53.9 — 1.5 — 3.1 11.9 5.1

1998 28.5 50.0 — 1.4 — 2.9 11.2 6.0

1999 30.8 47.2 — 1.3 — 2.7 10.7 7.3

2000 29.9 45.4 — 1.3 — 2.6 10.3 10.5

Source FAI (various years)


Table 3.55 Specific energy consumption for urea production in India

Fuel* (Gcal/tonne of urea) Electricity (kWh/tonne of urea)

Feedstock All India average Best plant in India All India average Best plant in India

Natural gas 5.61 4.94 261 235

Naphtha 6.04 5.46 261 235

Fuel oil 8.00 7.42 261 235

* Fuel used for both feedstock and heat production, specific ammonia requirement for urea production is taken

at 0.58 tonne (TERI 2004)

Gcal – gigacalories; kWh – kilowatt-hour

Source TERI (2004)

air as the source of nitrogen, the ammonia-manufacturing process requires various rawmaterials such as water, natural gas, naph-tha, fuel oil, coal, and coke oven gas. Naturalgas is the best feedstock for ammonia pro-duction. Worldwide, about 83% of ammoniaproduction capacity is based on natural gas(GoI 2003). However, in India, the choice offeedstock was determined by the Govern-ment of India, depending on the availabilityof resources (TERI 2005b). Feedstockchoice was not necessarily governed by theenergy efficiency consideration. In India,during 2003/04, about 64% of ammoniaproduction was based on natural gas, 12%on fuel oil and LSHS, and 24% on naphtha(TERI 2005b).

Better feedstock and process technolo-gies, together with improved operation andmaintenance practices, retrofitting, and soon, have resulted in significant amount ofenergy savings during ammonia production.The average specific energy consumption forammonia production in India has declinedsignificantly from 13.7 Gcal (gigacalories)/tonne in 1985/86 to 9.14 Gcal/tonne in2003/04 (a remarkable reduction of 33%)(TERI 2005b; Das 1997). The reported low-est specific energy consumption of an am-

monia plant in India is about 7.3 Gcal/tonneduring 2003/04, while the reported figure ofthe lowest specific energy consumption allaround the world was 7 Gcal/tonne of am-monia production (TERI 2005b) during thesame period. Moreover, the slightly higherenergy consumption can be attributed to thehot and humid condition in India, and con-sequently higher cooling water temperatureas compared to the western countries. Fur-thermore, the average energy consumptionof 25% of the most-efficient Indian ammo-nia plants is 8.14 Gcal/tonne. This figure islower than 8.49% for the most-efficient 25%ammonia plants in the world. The FAI (Fer-tilizer Association of India) is targeting toreduce specific energy consumption to thelevel of 6.5 Gcal/tonne within the next 15years (TERI 2005b). Table 3.55 presents thefeedstock-wise specific energy consumptionfor urea production in India (TERI 2005b).

SSP is the only straight phosphatic fertil-izer produced in India. The raw materialsused in the production of SSP are rock phos-phate and sulphuric acid. In SSP plants, themajor energy consumption is in the form ofelectricity for rock-grinding and material-handling equipment. Most of the SSP plantshave their own sulphuric acid production


facility. A major portion of the power re-quired for production is used in thesulphuric acid plant. A small amount ofsteam is also required for granulated SSP.Since most of the SSP plants are locatedalong with the sulphuric acid plants, surplussteam is always available. The average spe-cific electricity requirement for SSP produc-tion is 34.9 kWh/tonne of SSP (Das 1997).


options

In this study, natural-gas-based urea plantsare classified into three categories: (a) exist-ing plant having specific energy consump-tion equal to all India average, (b) efficient-1plant having efficiency equal to today’s bestplant in India, (c) efficient-2 plant with spe-cific energy consumption equal to FAI target(6.5 Gcal/tonne of ammonia). For a naph-tha-based plant, only existing and efficient(specific energy consumption equal to today’sbest plant) are considered. In case of a fuel-oil-based urea plant and SSP plant, only one cat-egory (existing plant) is considered. Sincethe average specific energy consumptiontakes into consideration all the existingplants (inefficient and efficient), the introduc-tion year for efficient-1 plants is considered2006. For efficient-2 plants, the introductionyear is 2016 (target year set by FAI).

As mentioned earlier, natural gas is thebest feedstock for urea production. It is re-ported that the worldwide share of naturalgas for ammonia production is more than80% (LBNL 2005). However, the use ofnatural gas in India for urea production isconstrained due to its unavailability. In thisstudy, the share of natural gas is assumedconstant at 64% over the entire modelling

time period in a BAU scenario. In the EFFscenario it is assumed that the share of natu-ral gas for urea production will increase to100% by the year 2036.

3.1.3.2.8 Pulp and paper industry

Paper making essentially consists of fourmajor stages: preparation of pulp; stockpreparation; sheet formation; and water re-moval and sheet finishing. Different types ofprocesses are used in the paper industry de-pending upon the type of raw material usedand the end product desired. Although thekraft sulphate process, semi-mechanical pro-cess, and sulphite process are the mostpopular ones, the kraft technology accountsfor about 80% of the pulping in the Indianindustry. Therefore, in this study, only thekraft process is considered.

Based on the installed capacity, the In-dian mills are categorized into two types:large mills (with an installed capacity ofmore than 100 TPD) and small mills (capac-ity less than 100 TPD). There are 525 pulpand paper mills with an installed capacity of6.5 MT and production capacity of 5.5 MT.Paper production can also be classified onthe basis of raw material: wood- and bam-boo-based (38%), agricultural-residue-based (32%), and waste-paper-based (30%).Out of 525 units, 67% are waste-paper-based, 28% are agro-based, and the remain-ing 5% are forest-based. All 27 large millsuse hardwood and bamboo, while thesmaller ones utilize agri-residue and wastepaper. These small mills account for morethan 50% production capacity with poor en-ergy and environment performance levels.

Agricultural residues are emerging as asignificant alternative raw material source


for the pulp and paper industry in India. Theuse of agricultural residues has grown sincethe early 1970s, partly due to the reducingresources of wood and bamboo, and partlydue to the government’s industrial policyencouraging investment in agri-residue-based paper production. The agri-residue-based paper mills mainly use bagasse andstraw as raw material. Even if the theoreticalavailability of bagasse and straw is high,there are limitations in their use due to sea-sonal availability, transportation cost, andenvironmental problems. The third raw ma-terial is waste paper. It comes from both do-mestic and imported sources. The installedcapacities of Indian mills vary over a widerange, from 5 TPD to 600 TPD.

Pulp and paper production is highly en-ergy-intensive. Most of the energy (80%–85%) is used as process heat and 15%–20%as electrical power. The technologicalcharacteristics of kraft mills are shown inTable 3.56 (TERI 2005b).

Coal and electricity are the main sourcesof energy for the industry. In addition tocoal, internally available waste biomass isalso used to supplement heat requirement.In Indian mills, internally available biomasscontributes to about 35% of the total ther-mal energy requirement of the mill (CSE

2004). It is also reported that in 2001/02, ofthe total electricity consumed in large-scalewood-based mills, about 81% was self- gen-erated primarily through cogeneration (CSE2004).

The capital cost of wood-based mill isworked out using the values of average costsof four mills installed during 1998, and byusing inflation rate. For agri-residue- andwaste-paper-based mills, the cost of cogen-eration facility is subtracted from the cost ofwood-based mill. The annual repair andmaintenance cost is assumed at 1.5% of thecapital cost for all types of mills. The eco-nomic life of all paper mills is considered at30 years.


options

Many of the paper mills that exist today havebeen installed over a span of more than 100years, and the technologies range from veryold ones to the most modern ones. Indianplants are well below the standards of energyperformance when compared to their coun-terparts in the developed countries. Beingprotected from international competitionfor about four decades, Indian paper mills,

Table 3.56 Technological characteristics of paper mills

Specific energy consumption Capital cost

Thermal energy Power consumption (million rupees/ Life

Input material (GJ/t of paper) (kWh/t of paper) MTPA) (year)

Agri residue 27.3 1250 90 700 30

Wood 27.3 1450 93 700 30

Waste paper 11.3 725 45 400 30

GJ/t – gigajoules per tonne; kWh/t – kilowatt-hour per tonne; MTPA – million tonnes per annum


in general, did not keep up with the techno-logical advancement in the other parts of theworld. A few large paper mills have imple-mented new technologies because of highproduct quality, international competition,mounting pressure from environmentalregulatory, rise in energy prices, and so on.Most of the paper mills operating in India,particularly the small ones, are very old, us-ing outdated technology. In fact, most of theIndian mills have imported old, used ma-chinery from Europe. However, several pa-per mills are taking steps to restructure,upscale, and replace old and outdated ma-chinery with new ones. A remarkable gap

between specific energy consumption in In-dia and developed countries indicates thescope for efficiency improvement. Table 3.57presents the suitable options for Indian pa-per mills (TERI 2005b).

For modelling purpose, agri-residue andwaste-paper-based mills are classified intotwo categories (1) existing mills and (2) effi-cient mills, that include efficiency improve-ment options listed in Table 3.57. Retrofitoption is also considered for retiring capac-ity. For wood-based paper mills, the threecategories considered are: (1) existing mill,(2) efficient-1 mill, with efficiency improve-ment options listed in Table 3.57, and (3)

Table 3.57 Energy conservation options for Indian paper mills

Savings Retrofit cost

Thermal Power (million

(GJ/t of (kWh/t of rupees/

Energy saving options paper) paper) MTPA) Remark

Cogeneration 3667 Applicable to all

Blow heat recovery 3.32 — 150 Applicable to all

Fibre recovery system 0.40 15 117 Applicable to all

Oxygen de-lignification 0.54 183 Applicable to all

Replacement of turbine with — 32 233 Applicable to all

DC drive

Press section re-building/long 0.66 — 300 Applicable to all

nip (shoe) press

Hot dispersion system — 120 500 Applicable to all

Drum chipper — 11 100 Only in wood-based plant

Long-tube falling-film evaporators 0.83 — 3288 Only in wood-based plant

High solid concentration of

black liquor 8.97 — 60 Only in wood-based plant

Continuous digester 5.81 75 685 Only in new wood-

based plant

GJ/t – gigajoules per tonne; kWh/t – kilowatt-hour per tonne; MTPA – million tonnes per annum; DC – direct current

Source TERI (2004)


efficient-2 mills, that incorporate all effi-ciency improvement measures. Retrofit op-tion in wood-based mills is consider onlyfrom existing to efficient-1 mill.

In view of the high cost of financing andsmall-scale nature of waste paper and agri-residue-based paper mills in India, it is as-sumed that in a BAU scenario, all existingwaste-paper and agri-residue-based millswill remain operational beyond their eco-nomic life, without any improvement in theirenergy efficiency. Similarly, in case of wood-based paper mills, efficient-2 mills are notallowed in the BAU scenario. In the EFFscenario, all retrofit options are allowed, andwood-based efficient-2 mills are also allowedby 2011.

As mentioned earlier, the Government ofIndia is encouraging the use of agri-residuefor paper production. However, its use is re-stricted by localized availability. The maxi-mum potential of pulp production fromagri-residue is estimated at 14 MT (CPPRI2003) for 2001—that translates into 9.8 MTof paper production. During the same year,production of agri-residue-based paper wasabout 1.58 MT, which is only 16% of themaximum potential. In the last decade, theaggregated growth of wheat, paddy, andsugar cane crops was about 2%. Residues ofthese crops are used for paper production.Therefore, in this study, it is assumed thatthe potential will also increase by an averageannual growth rate of 2% during the model-ling period. It is assumed that 35% of themaximum potential of agri-residue-basedpaper could be achieved by 2036 as com-pared to 16% in the year 2001. The percent-ages share of maximum potential translatesinto 9% of the total paper production in2036. It may be noted that the growth in pa-per demand is higher than the growth rate of

potential of agri-residue-based paper.Therefore, despite the increased penetrationlevel, to the maximum potential, of agri-resi-due-based paper, its overall percentage con-tribution to total paper production willdecrease significantly from 32% in the 2001to 9% in 2036.

During 2001, the share of waste-paper-based paper production was 30% of the totalproduction. Waste paper is obtained fromdomestic collection and through import.Due to exclusive reuse of paper in India, thecollection rate is relatively low (22%) ascompared to other countries—China (33%),Thailand (42%), and Germany (71%). Thecollection rate is expected to be constant inthe future. This essentially means that do-mestic waste paper can only contribute upto15% of the total paper production, and theremaining 15% is produced by using im-ported waste paper. In view of environmen-tal concerns, there is a possibility of a ban onimport of waste material to India as some-times these materials also contain hazardouswaste. Therefore, in this study, it is assumedthat the import of waste paper will be com-pletely stopped by 2036 in a phased manner.

3.1.4 Residential sector

The population of India was about 1.027billion in 2001 as per Census 2001 of theGovernment of India (GoI 2001).

The average number of members perhousehold is 5.15 in rural areas and 4.47 inurban areas. Out of 10 households, seven inIndia are in the rural areas, and 0.09% of thehouseholds do not have a dwelling unit. Ofthe every 100 households in the rural areas,36 are pucca houses, 43 are semi-puccahouses, and the rest are kuchcha houses. On


Source CEA (2004); MoPNG (2004)

Figure 3.10 Time trend of fuel and electricityconsumption in the residential sector

households in the lowest MPCE (monthlyper capita consumption expenditure) classof less than 225 rupees in rural areas, occupy31 m2 of floor area and those in urban slums,29 m2. About 30% of the dwelling units inrural and 4% in urban areas do not have ba-sic facilities like drinking water, electricityfor lighting, and a toilet. About 97% of therural and 99% of the urban households getdrinking water within half a kilometre oftheir premises (MoSPI 2004).

3.1.4.1 End-use demands in the

residential sector

Energy services make up a sizeable part ofthe total household expenditure. The resi-dential sector in India is responsible for13.3% of the total commercial energy use(TERI 2004). The energy sources utilized bythe residential sector in India mainly includeelectricity, kerosene, LPG (liquefied petro-

leum gas) (propane), firewood, crop residue,dung, and other renewable sources such assolar energy.

Figure 3.10 indicates that commercial en-ergy use has been growing quite rapidly inthe residential sector. During the period1990–2003, of the three commercial fuels,consumption of LPG has grown at the an-nual rate of 11.26%. The average annualgrowth rate of electricity consumption hasbeen 8.25%. However, kerosene consump-tion has grown at the rate of 0.85% only.Since 2000, kerosene consumption in theresidential sector has declined in absoluteterms. Kerosene use in the residential sectorcame down by 13.9% during 2000–03. Thishigh rate of consumption of LPG and elec-tricity vis-à-vis kerosene explains the substi-tution of kerosene, a primary source ofenergy, amongst the lower- and middle-in-come groups.

Despite its impressive growth in the resi-dential sector, the fuel consumption is still

the other hand, out of ev-ery 100 households in ur-ban areas, 77 are puccastructures, 20 semi-pucca,and only 3 are kuchchastructures. Plinth level ofthe house, that is, theheight of ground floor ofthe house from the landon which the building isconstructed, is zero for36% of the rural and 32%of the urban households.On an average, a ruralhousehold occupies 38 m2

(square metre) of floorarea and an urban house-hold occupies 37 m2. Thepoorest segment, that is,


major source of cooking for61.1% of the total householdsin India. Among the differentsources, firewood and chipsare used by almost three-fourths of the rural house-holds. Only 3% of thehouseholds have switchedaway from it since 1993/94(Figure 3.11).

As can be seen in Figure3.12, in urban areas of thecountry, the households usemainly three primary sourcesof energy for cooking: fire-wood and chips, kerosene, andLPG. Of these, LPG is pre-dominant, with 45% of thehouseholds using it. About22% of the urban householdsuse firewood and chips. There

Source MoSPI (1997, 2001)

Figure 3.11 Percentage distribution ofhouseholds by source of cooking in rural India

very low in India as compared to that inother countries. Commercial energy con-sumption in the residential sector in the USfor 2002 was 2466.91 Mtoe (US DoE 2003)whereas for India, the figure is only about 22Mtoe (TERI 2004). The per capita energyuse in the residential sector of the US isabout 8.56 ToE/year while this is as low as0.22 ToE/year for India.

Households use energy for many pur-poses: cooking; cooling and heating theirhomes; heating water; and for operatingmany appliances such as refrigerators,stoves, and televisions.

The energy mix for cooking in the domes-tic sector in India shows that traditional fu-els are predominantly used in the householdsector. In the rural areas of the country, thehouseholds use mainly three primarysources of energy for cooking: firewood andchips, dung cake, and LPG. Fuelwood is a

has been an increase of about 15% in the num-ber of households using LPG and a decrease ofabout 8% in the number of households usingfirewood and chips since 1993/94.

Although electricity, kerosene, gas,candles, and other oils are used for lighting,at the national level, kerosene and electricityconstitute the primary fuel for lighting in99% of the households. There has been anincrease in the percentage of households us-ing electricity as the primary source of light-ing over the years. During the period 1993/94 to 1999/2000, the number of householdsusing electricity as the primary source oflighting grew at the rate of 11% for rural and6% for urban India. However, an estimated84 million households still do not have ac-cess to electricity. The majority of thesehouseholds are using fuel-based lighting sys-tems, mainly in the form of kerosene. Thesesystems are less energy-efficient than electri-


cal lighting systems, and have a wide rangeof adverse social and environmental impacts.

The Rajiv Gandhi Grameen VidyutikaranYojana of the Government of India (MoP2005) plans to provide electricity to allhouseholds in the next five years. It shouldbe noted here that providing access to elec-tricity to all households does not necessarilyimply that every household will have a me-tered connection. Therefore, we expect thatall households may have access to electricityby 2010/11 but actually every household willhave a metered connection only by 2020/21.

The amount of energy that the house-holds consume and the types of fuel they usealso depend on a variety of other factors. Themicro-perspective of each consumer is thedriving force behind the sector’s use of en-ergy, and opportunities for change in the de-

and modern fuels would provide constantimpetus to the growth of energy demand inthe residential sector.

For the present study, on the basis of enduse, a household’s energy consumption hasbeen divided into six categories.� Lighting� Cooking� Space conditioning� Refrigeration� Water heating� Others

The energy demand for fans, air condition-ers, and air coolers has been categorized asenergy demand for space conditioning. Thecategory ‘others’ comprises energy demand forappliances such as televisions, washing ma-chines, VCRs/VCPs, and music systems.

mand and supply patterns.This is because, a household’stotal energy consumption anduse of mix of fuels are the re-sult of the family’s attempt toprovide for its various needsby employing its labour orcash and specific technologiesthat use a certain type of en-ergy. Other factors includeissues of supply such as avail-ability of fuels, energy prices,and technologies, which have avery large range of end-use ef-ficiencies and hence, a largepotential for energy saving.The rising rate of growth ofGDP, growth in disposable in-come, improved lifestyles, andthe rising purchasing power ofpeople with higher propensityto consume with preferencefor sophisticated appliances

Source MoSPI (1997, 2001)

Figure 3.12 Percentage distribution ofhouseholds by source of cooking in urban India


Table 3.59 Number of lighting points per household in various income classes in rural and

urban areas

Lamp Lamp Light Lamp Lamp Light

Rural type wattage points Urban type wattage points*

RL GLS 60 1 UL GLS 60 2

GLS 60

RM GLS 60 3 UM TL 55 4

GLS 60 TL 55

GLS 60 GLS 60

GLS 60

RH TL 55 4 UH TL 55 6

TL 55 TL 55

GLS 60 TL 55

GLS 60 GLS 60

GLS 60

CFL 11

RL – rural low; RM – rural middle; RH – rural high; UL – urban low; UM – urban middle; UH – urban high;

GLS – generalized lighting system; TL – tube light; CFL – compact fluorescent lamp

* These are total light points actually used at a time by a household.

Table 3.58 Income categories based on

MPCE in rural and urban areas

Rural Urban

Class (rupees) (rupees)

Low < 615 < 665

Middle 615–950 665–1925

High > 950 > 1925

MPCE – monthly per capita consumption expenditure

3.1.4.1.1 Demand for lighting

Electricity and kerosene being the primaryfuels used for lighting, the energy demandfor lighting has been estimated for house-holds that have electrified source of lightingand for those that depend on kerosene forlighting.

3.1.4.1.2 Electricity demand for

lighting

Data on income-wise number of lighting de-vices and usage is not available. Therefore,for estimating the electricity demand forlighting, households have been divided into

three categories in rural and urban areasbased on MPCE (Table 3.58).

The number of light points per householdhas been assumed as shown in Table 3.59.

It has further been assumed that for ruralhouseholds, a light point is used for 4 hours

Sectoral demand projections, technological characterization, and resources availability 89

per day whereas for urban households, theusage is 5 hour per light point per day. Thelighting requirement has been taken as 100lux per light point.

DLi= HH

i× Lp

i× H × 100 × 365 (3.29)

where,DL

i = annual demand for lighting by elec-

trified households in ith income groupHHi = number of electrified households

in the ith income groupLpi = light points per household in the ith

income groupH = hours of usage

3.1.4.1.3 Kerosene demand for

lighting

It has been assumed that the unelectrifiedhouseholds used one lamp for lighting.

A hurricane lamp gives an illuminance of70 lux per hour while a wick lamp provides

7 lux per hour (Stanford University 2003).

DLi= HH

i× H × 365 (3.30)

where,DL

i = annual demand for lighting by

unelectrified households in the ith incomegroup

HHi = number of unelectrified house-

holds in the ith income groupH = hours of usage

The demand for kerosene-based lightingis expected to decrease and become zero by2021 because of the assumption that as perthe Rajiv Gandhi Grameen VidyutikaranYojana of the Ministry of Power (MoP2005), all households may have access toelectricity by 2010/11. However, everyhousehold is assumed to have a meteredconnection only by 2020/21.

Demand for lighting for various GDPgrowth scenarios is presented in Table 3.60.

Table 3.60 Demand for lighting (trillion lux hours)

8% GDP 6.7% GDP 10% GDP

Electricity-based Kerosene-based Electricity-based Kerosene-based Electricity-based Kerosene-based

Year Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban

2001 36.0 33.8 2.8 0.7 36.0 33.8 2.8 0.7 36.0 33.8 2.8 0.7

2006 54.9 44.3 2.1 0.5 51.1 43.2 2.1 0.5 56.6 45.1 2.1 0.5

2011 77.1 57.6 1.4 0.3 69.9 55.1 1.4 0.3 80.4 59.6 1.4 0.3

2016 100.3 72.6 0.7 0.2 91.8 69.0 0.7 0.2 104.7 75.4 0.7 0.2

2021 120.9 89.0 0.0 0.0 112.9 84.7 0.0 0.0 124.3 91.9 0.0 0.0

2026 129.1 104.3 0.0 0.0 125.7 101.1 0.0 0.0 129.9 105.5 0.0 0.0

2031 133.4 118.0 0.0 0.0 132.6 117.0 0.0 0.0 133.4 118.2 0.0 0.0

2036 136.0 131.1 0.0 0.0 135.9 131.0 0.0 0.0 136.0 131.1 0.0 0.0



Source MoSPI (2001)

Figure 3.13 Number of households (per 1000) in highestincome class possessing specified durable goods (rural)

Table 3.61 Useful energy demand for cooking (petajoules)

2001 2006 2011 2016 2021 2026 2031 2036

Rural 700.60 724.25 758.32 790.42 814.98 830.04 837.38 838.59

Urban 228.51 260.33 299.30 341.51 384.49 426.68 468.21 509.31

3.1.4.1.4 Demand for cooking

Cooking requires energy in the form of heat.Cooking energy consumption has been esti-mated separately for rural and urban re-gions.

The per capita per day useful energyrequirement for cooking is taken to be 620kcal in rural areas and 520 kcal in urban ar-eas (ABE 1985)

Since the per capita cooking energy re-quirement remains constant, the total en-ergy demand for cooking increases at therate of population growth,that is, at the rate of 2.32%in urban areas and at 0.51%in rural areas during the timeperiod 2001–36 (Table3.61).

3.1.4.1.5 Demand for

electrical appliances

The energy demand for ap-pliances in the country de-pends on the number ofappliances being used/ex-pected to be used, hours ofusage, and wattage of the ap-pliance.

Data on penetration ofappliances per 1000 house-holds is available from the

NSS (National Sample Survey) for 1999/2000 (Figures 3.13 and 3.14).

As the households shift from one incomeclass to another over time, and as their pur-chasing power increases, the ‘demonstrationeffect’ sets in. In other words, person alwaysaspires to reach the consumption level ofrelatively higher income groups. Therefore,as a household moves up the income ladder,it tries to adopt the consumption pattern ofthe higher income group. It also aspires toacquire the basket of goods/appliances pos-sessed by the relatively higher income group.


Source MoSPI (2001)

Figure 3.14 Number of households (per 1000) in highestincome class possessing specified durable goods (urban)

For the highest income groups in ruraland urban, the increase in penetration of ap-pliances over the forecast period has beencalculated based on the growth rate of pen-etration of appliances for the same incomegroup during 1993/94 to 1999/2000. Thepenetration rate per 1000 households hasbeen capped in the case of growth rate beingunreasonably high.

Based on the appliance penetration rate(Figures 3.13 and 3.14), the income shiftsover time, and the usage norms (Table 3.62),the energy demand has been calculated.

The demand for fans, coolers, and airconditioners has been categorized under‘space conditioning’.

In the BAU scenario, useful energy de-mand for space conditioning is expected toincrease at the rate of 14.16% in rural areasand at 12.87% in urban areas during 2001–36 due to the air conditioners becomingmore popular and affordable in the future.

Useful energy demand for re-frigeration is expected to in-crease at the rate of 13.03%and 7.05%, respectively, dur-ing the same time period. TVs,VCRs/VCPs, washing ma-chines, and music systemscomprise the category ‘oth-ers’. The useful energy de-mand for this category isexpected to increase at therate of 11.1% in rural areasand 7.7% in urban areas dur-ing 2001–36. The energy de-mand is likely to increase at arelatively faster rate in ruralareas as a result of greaterreach of these appliances inthe rural market.

Useful energy demand for

Table 3.62 Usage norms for electrical

appliances

Working Working

hours/ hours/

Device day Watt year

Fan 10 60 225

Geyser 1 1500 150

Refrigerator 24 2400 365

AC 4 2100 100

Cooler 8 250 90

Washing machine 0.5 1000 200

TV 3.1 120 365

VCR/VCP 3 20 25

Music system 1 60 200

space conditioning, refrigeration and ‘others’under various GDP growth rate scenarios ispresented in Tables 3.63, 3.64, and 3.65.


Table 3.65 Useful energy demand for various end uses (petajoules) at 10% GDP growth rate

2001 2006 2011 2016 2021 2026 2031 2036

Space conditioning Rural 10.38 25.07 65.81 155.01 302.30 470.23 711.32 1069.70

Urban 23.51 38.88 78.19 172.60 349.81 599.98 989.36 1628.31

Refrigeration Rural 8.08 30.06 95.55 212.40 347.54 432.62 510.49 587.51

Urban 41.77 71.11 120.42 186.29 258.65 322.24 385.08 453.21

Others Rural 2.76 7.35 19.30 40.17 64.75 80.58 95.29 109.96

Urban 7.66 12.08 20.49 34.23 51.58 67.80 84.25 102.86


Table 3.64 Useful energy demand for various end uses (petajoules) at 8% GDP growth rate

2001 2006 2011 2016 2021 2026 2031 2036


Urban 23.51 37.50 69.22 145.42 310.48 576.15 984.40 1628.31


Urban 41.77 67.73 109.03 166.96 240.18 314.05 383.76 453.21

Others Rural 2.76 6.85 16.50 34.62 60.17 79.16 95.21 109.96

Urban 7.66 11.62 18.48 29.76 46.48 65.31 83.82 102.86


Table 3.63 Useful energy demand for various end uses (petajoules) at 6.7% GDP growth rate

2001 2006 2011 2016 2021 2026 2031 2036


Urban 23.51 35.70 59.40 114.56 248.89 510.40 952.27 1623.24


Urban 41.77 63.30 95.46 143.17 210.85 292.20 376.11 452.40

Others Rural 2.76 6.21 13.32 27.26 50.75 73.53 93.57 109.77

Urban 7.66 11.02 16.23 24.78 38.97 58.92 81.41 102.59



3.1.4.1.6 Demand for water heating

Data on penetration of geysers is not avail-able with the NSSO (National Sample Sur-vey Organization) for 1999/2000. Therefore,it has been assumed that the penetration rateof geysers is equal to that of air conditionersand coolers. Moreover, apart from electric-ity, LPG, kerosene, and firewood are alsoused for meeting the energy needs for heat-ing water in the country. It has been as-sumed that 80% of the total households inthe country do not require hot water becauseof hot/moderate climatic conditions or be-cause of their preferences. Specifically, it canbe observed in rural areas that people do notheat water for bathing. Instead, they rely onfresh water at early morning hours. There-fore, households requiring hot water but nothaving geysers have been assumed to dependon fuels other than electricity. For estimat-ing energy demand for lighting, householdshave been divided into three categories inrural and urban areas (Table 3.66).

It has been assumed that a household de-pending on firewood, on an average, requires1 kg of wood for heating water per day. Onthe other hand, for households using LPG, ithas been assumed that an LPG cylinder of14.2 kg lasts roughly 30 days, that is, 0.5 kgper day, and that 30% of the LPG is con-sumed for heating water, that is, 0.15 kg/day/household. An electric rod has been as-sumed to be consuming 2 kW/h electricity.

Dw = HH × N (3.32)

where,Dw = energy demand for water heatingHH = number of households using differ-

ent fuels for lightingN = the usage and fuel consumption

norms

The useful energy demand for heatingwater is likely to increase at the rate of13.65% and 8.27% in rural and urban areas,respectively, during the time period 2001–36for 8% GDP. The useful energy demand forheating water under various GDP growthrate scenarios is presented in Table 3.67.


options in the residential sector

3.1.4.2.1 Lighting

Although the light bulb was invented in1854, the first usable electric lamp was de-veloped in 1879. The early lamp had a deli-cate carbon filament with a very short life.The first commercial lamp with tungstenfilament was made in 1905. Since then, thelamps have undergone a process of continu-ous improvement. New ways of generatinglight have been invented, and new technolo-


households in various income groups

using sources other than geyser for

heating water

Income Electric

class Firewood rod LPG

RL 100 0 0

RM 70 10 20

RH 60 20 20

UL 60 30 10

UM 20 20 60

UH 0 30 70

RL – rural low; RM – rural middle; RH – rural high;

UL – urban low; UM – urban middle; UH – urban high;

LPG – liquefied petroleum gas


gies have been developed with the objectiveof achieving economy, efficient use of light-ing, comfort, and aesthetic applications.

Broadly there are GLS (generalized light-ing system); incandescent lamps; tungstenhalogen lamps; CFLs (compact fluorescenttubes); gaseous discharge lamps such asmercury vapour, sodium vapour, metal ha-lide; light-emitting diodes; and so on.

3.1.4.2.1.1 Incandescent gener-

alized lighting system bulbs

It is the earliest and simplest of lamps, andconsists of a gas-filled glass tube with tung-sten wire filament, which glows when elec-tric current is passed through it. The GLSbulb is often described as poor man’s lightbulb but with the escalating energy costs.Only the rich are able to afford these grosslyinefficient lamps.

Through a gradual and evolutionary pro-cess, the lighting industry in India is today aself-sufficient producer of lighting systems.

As per the ISLE (Indian Society of Light-ing Engineers) (1999), the demand for GLSis increasing by approximately 5% everyyear. The production of GLS has reached afigure of 750 million pieces per annum, outof which 500 million pieces are produced bythe organized sector and 250 million piecesby the unorganized sector, each having over20% higher installed capacity. However, themarket is still at a low level of sophistication.Incandescent lamps of 25 W (watt), 40 W,60 W, and 100 W in clear bulb form the larg-est segment. Fluorescent lamps used to beavailable only in cool daylight colour tem-peratures, and were restricted to the linear20-W and 40-W execution. The energy-sav-ing 18-W and 36-W linear fluorescent lampsare now available in the market. The demandgrowth of fluorescent lamps declined from7% to 5.5%, and in 1998/99, 145 million

3.1.4.2.1.2 Compact

fluorescent lamps

These are essentially fluores-cent tubes packaged in thecompact form and are, there-fore, easy to use. CFLs giveout excellent light with sig-nificant energy savings andgreat look. They also have avery long life.

The development of light-ing in India started 70 yearsago. During the pre-Indepen-dence era, the lighting indus-try in India was in its infancy,importing finished productsand assembling components.

Table 3.67 Useful energy demand for heating water

(petajoules) under the three GDP growth rates

8% GDP 6.7% GDP 10% GDP

Year Rural Urban Rural Urban Rural Urban

2001 1.4 5.8 1.4 5.8 1.4 5.8

2006 4.7 8.8 4.1 8.4 5.1 9.2

2011 14.0 14.5 10.6 12.6 17.0 16.3

2016 33.6 24.6 25.3 20.0 40.0 28.9

2021 62.4 40.3 51.4 33.0 67.7 45.4

2026 84.6 58.4 78.0 51.9 86.3 60.9

2031 103.5 75.8 101.5 73.3 103.6 76.2

2036 120.7 93.1 120.5 92.9 120.7 93.1

GDP (gross domestic product)


pieces were sold, 10% of which were manu-factured by the organized sector. The de-mand for CFLs is growing at a steep rate of35% per annum, with sales in 1998/99 cross-ing 6.5 million pieces.

The GLS lamps account for nearly 80%of the lighting source market, and the rest isclaimed mainly by the tubes market. CFLshave managed to gain a share of 1%–2%.This low share is due to high per unit price.Table 3.68 gives the characteristics of elec-tricity-based lighting devices.

Despite the developments, the domesticmarket for lighting equipment remains slug-gish and localized. Major share of the marketis limited to urban areas and even there,power shortages, voltage fluctuations, and soon, limit the usage of electricity-based light-ing equipment. Moreover, India is yet toachieve 100% electrification. Therefore, theuse of kerosene-based lighting devices be-comes imperative in the country, particu-larly in the rural areas. Moreover, the cost

per lumen is more important to a user thanefficiency in lumen per watt. Kerosenelamps are a cheaper source of lighting, andthis makes up for their inefficiency but notfor their poor quality of light (Table 3.69).

3.1.4.2.2 Cooking

Households in India use various fuels formeeting the energy requirements for cook-ing. Among the traditional fuels, firewood isused for cooking. Even the high-incomegroups, especially those in rural areas, arereluctant to switch away from using freelyavailable firewood.

Biogas is produced by the anaerobic di-gestion of animal dung and other biomass.The gas can be burnt in a specially designedstove that produces little CO (carbon mon-oxide) and no smoke. Furthermore, the di-gester slurry provides more fixed nitrogen tothe soil than dung.

Charcoal emits relatively less smoke butgenerates considerable CO. Convertingwood to charcoal has long been a way to im-prove fuel quality.

Table 3.68 Techno-economic parameters

for various lighting devices

Device

GLS FTL CFL

100 W 40 W TL + (13 W

15 W CFL + 3 W

Characteristics Choke Choke) × 2

Lumen output 1360 2500 2 200

Lux available 100 100 100

Total assembly

cost (rupees) 20 240 1 360

Life (hours) 1000 5000 10 000

FTL – fluorescent tube light; CFL – compact fluores-

cent lamp; W – watt

Note The figures have been normalized to 100 Lux.

Table 3.69 Techno-economic parameters

for kerosene-based lighting devices

Wick Hurricane

Consumption of kerosene 0.008 0.05

(litre per hour)

Lumen output 10.000 100.00

Lux available 7.000 70.00

Total assembly cost 45.000 135.00

(rupees)

Life (years) 3.000 3.00

Source Rubab and Khandpal (1997)


Crop residues and other biomass wastesare alternative cooking fuels. When they haveno fertilizer value, their use reduces theproblem of waste disposal and the demandfor fuelwood. Plant stalks and straw can gen-erally be burnt in traditional fuelwoodstoves.

A variety of stoves made up of mud areused in the country, which use firewood,crop residue, or dung. Many attempts havebeen made to improve the energy efficiencyof these stoves.

Kerosene is an important cooking fuelamong the urban poor. However, it is asmelly fuel that blackens pots and ranks lowin convenience of use as compared to themore modern fuels like LPG and electricity.

Electricity can be termed as the cleanestfuel. However, because of its expensive na-ture and unreliable supply, its use in cookingis still minimal.

LPG is recommended both for its higherefficiency and lower environmental impactthan the alternatives. Techno-economic pa-

rameters of various cooking devices areshown in Table 3.70.

3.1.4.2.3 Electrical appliances

3.1.4.2.3.1 Refrigerators

Among the consumer durables, refrigeratorsrank next to televisions in the Indian middle-class homes.

The refrigerator market in India has twosegments: the conventional direct-cool sys-tem having a share of about 83% and thefrost-free type, which accounts for the re-maining 17%. The frost-free type enjoys aprice supremacy between Rs 6000 and 8500.Households account for 85% of the refrig-erator market, and the remaining 15% is in-stitutional. Rural areas have a share of just22% in the total refrigerator sales as com-pared to the urban areas (78%). The 165-li-tre refrigerators, which were the most

Table 3.70 Techno-economic parameters of various cooking devices

Device Capital cost (rupees) Efficiency (%) Life (years)

Liquefied petroleum gas stove 1 200 60 20

Kerosene stove: wick 150 40 5

Kerosene stove: pressure 250 45 4

Dung chulha 10 8 1

Firewood based chulha 10 10 1

Biogas burner 800 55 4

Electric oven 5 000 100

Electric hot plate 400 71 15

Solar cooker 1 460 100 5

Crop residue chulha 10 8 1

Microwave oven 10 000 100 15


preferred over, have now given way to 185–225 litre ones, and recently 200–300 litre re-frigerators are witnessing an emerging trend.Table 3.71 gives the technological character-ization of refrigerators.

3.1.4.2.3.2 Fans

Electric fan is a necessity for more than sixmonths in most parts in a tropical countrylike India.

In India, 65% of the fans are ceiling fansand 33% are wall/table ones, whereas, ped-estal fans make up for a small share of 2%.About 45% of the market for fans is orga-nized and 55% is informal. Urban areas ac-count for a bigger market share (58%)compared to rural areas (42%). Table 3.72gives the technological characterization offans.

3.1.4.2.3.3 Air conditioners

Among the consumer durables, the marketfor ACs (air conditioners) is growing at a fastpace.

Reduction in excise and import duties oncomponents has brought down the price ofthe products manufactured by the organized

Table 3.71 Characterization of refrigerators

Standard Efficient

Cost (rupees) 8000 15 000

Capacity (watt) 1570 1 115

Working hours/day 24 24

Working days/year 365 365

Life (years) 25 25


tion of fans

Standard Efficient

Cost (rupees) 1000 1300

Capacity (watt) 60 55



Life (years) 20 20

and unorganized market, and has expandedthe market. However, still the unorganizedmarket of ACs has a 25% market share. Thedomestic sector accounts for 20% of the to-tal ACs. Window ACs are the most populartype with a share of 48% of the total market.Packaged/ducked and mini split make up for40% and 12% of the AC market, respec-tively. Table 3.73 gives the characterizationof ACs.

3.1.4.2.3.4 Washing machines

Increasing incomes and changing lifestyleshave resulted in a spectacular increase in the


tion of air conditioners

Standard Efficient

Cost (rupees) 20 000 45 000

Capacity (watt) 2000 1300



Life (years) 15 15

Investment cost

(million rupees/PJ) 6510 2893

PJ – petajoules


penetration of washing machines, especiallyin urban households. About 82% of thewashing machines are sold in urban areaswhile the rural market accounts for only18%. A price differential of about 10 000–15 000 rupees between a semi-automaticand a fully automatic washing machinemakes the semi-automatic one more popularwith a share of 85% in the market, and fullyautomatic accounts for 15% of the market.

3.1.4.2.3.5 Television

About 75 million households in India pos-sess a television. The market for CTVs(colour televisions) is expanding very fast.Between 1996 and 1999, the market regis-tered a growth rate of 28%. About 60% ofthe market is organized whereas 40% is un-organized. About 98% of the products areconventional, and flat-screen televisionshave negligible share of 2%. Urban areashave a share of 60% in the CTVs market andrural areas account for the remaining 40%.However, in case of black and white televi-sion, the rural areas account for 75% of thetotal market and the share of urban area is25%.

3.1.4.2.3.6 Audio–video systems

The audio industry can be divided into vari-ous segments: radios, cassette recorders/players, CD players, and their combination.CD-based systems are a recent developmentbut are replacing the cassette players veryrapidly. The market is divided into organizedand unorganized segments. Rural and urbanareas have a 50:50 share in the market.

VCRs and VCPs were a craze till late 1990s.With the advent of VCDs, there has been asharp decline in the VCR/ VCP market. Videosystems are more popular with urban areas.They have a share of 90% and the rural areashave a share of 10%. About 60% of the marketis organized and 40% is unorganized. Table3.74 gives the characterization of washingmachines, televisions, VCRs/ VCPs, and mu-sic systems.

3.1.5 Commercial sector

The commercial sector comprises variousinstitutional and industrial establishmentssuch as banks, hotels, restaurants, shoppingcomplexes, offices, and public departmentssupplying basic utilities. In other words, the

Table 3.74 Characterization of washing machines, televisions, VCRs/ VCPs, and music systems

Washing machine Television VCR/VCP Music system

Cost (rupees) 7500 8000 2500 1500

Capacity (watt) 1000 120 20 60

Working hours/day 0.5 3.1 3 1

Working days/year 200 365 25 200

Life (years) 15 20 20 20


commercial sector is a subset of the servicessector as defined by the Central StatisticalOrganization, Government of India.9 Giventhe structural changes in the economy, espe-cially during the post-liberalization period,the services sector now accounts for a highshare (about 50% share of the GDP of ser-vices sector in aggregate GDP) in the totalnational income. Economic growth haspaved the way for increasing demand for ser-vices fuelled by rising personal disposableincomes/enhanced purchasing power. More-over, the structural reforms in the bankingsector has led to a fall in interest rates andresulted in real estate boom, encompassingconstruction of large-scale commercialbuildings, shopping malls, and so on, espe-cially in urban centres. Coupled with this,increased spending by the government onproviding public services such as publiclighting, water works, and sewer pumps hasgiven a fillip to the commercial sector. En-ergy consumption in the commercial sectorhas, thus, increased as a consequence of theaccelerated growth of the commercial sector.

Most commercial energy is used in build-ings or structures for the purpose of spaceheating, water heating, lighting, cooking,and cooling. Energy consumed for servicesnot associated with buildings such as fortraffic lights, city water, and sewer services isalso categorized under commercial sectorenergy use.

3.1.5.1 End-use demands in the

commercial sector

In India, the commercial energy demand es-timation and projection are beset with nu-merous data gaps, particularly with respectto the reporting of the number of commer-cial establishments/consumers, their energyconsumption patterns, degree of usage ofenergy for different end-use energy consum-ing activities, and penetration of appliancesand other end-use devices in the sector.

Therefore, the entire demand estimationexercise is driven by assumptions on the dis-tribution of fuels consumed for cooking,lighting, space conditioning, refrigeration,and miscellaneous services.

For the purpose of energy demand esti-mation and projections in the commercialsector, a top-down approach is used inwhich the total fuel consumption is first esti-mated and projected using an appropriateeconometric model. The projected fuel con-sumption is then divided amongst variousend-use activities involving that particularfuel.

Fuels such as LPG and kerosene, and tra-ditional fuels such as firewood/charcoal areused for cooking in the commercial sector.The historical data on LPG consumed in thecommercial sector for the period 1980–2002(MIE 2005) is used for estimating and pro-jecting the total LPG consumption. LPG isused as fuel for cooking in hotels and restau-rants, that is, under the purview of the ser-vices sector. Thus, a high rate of growth ofthe services sector measured by the GDP

99999 The services/tertiary sector, as defined by the Central Statistical Organization, consists of trade, hotels and restau-

rants, financing, insurance, real estate and business services, public administration, defence, and other services.


generated by the sector results in high LPGconsumption and vice-versa. The appropri-ate regression equation is as follows.

Log(LPGC,t) = 0.58 × Log(GDPSt) + 0.94(6.55) (24.7)

× AR (1) (3.32)

(Adjusted R2 = 0.98)

where, LPGC,t is the LPG consumption inthe commercial sector (in thousand tonnes)in the year t and GDPSt is the GDP contrib-uted by the services sector (in crore rupeesat 1993/94 prices) in the year t. The values inthe brackets give the t-statistic associatedwith the coefficients. The log–log specifica-tion of the regression model is found appro-priate as the coefficient associated with theLPG consumption measures the incomeelasticity of LPG consumption. The coeffi-cient 0.58 being less than 1 implies that LPGconsumption is income-inelastic. Thismeans that LPG is a necessary fuel for cook-ing in the commercial sector. The AR (1)term corrects for the auto correlated distur-bances present in the data. The adjusted R2

is a measure of the goodness of fit of the re-gression equation. It is as high as 0.98 andthis implies that 98% of the variation in LPGconsumption can be explained by GDP gen-erated by the services sector. The t-statisticassociated with the coefficients presented inbrackets above clearly shows that the vari-ables are statistically significant in explain-ing LPG consumption.

However, due to other exogenous factorssuch as constraints on the accessibility to thesmall vendors, eateries in the rural and re-mote areas use kerosene as a fuel for cook-ing. Historical data on kerosene consumedin all sectors is available but the quantities

consumed in the commercial sector are notknown. Hence, it has been assumed that1.42 MT of kerosene is consumed in thecommercial sector in 2001 (14% of totalkerosene consumed). The underlying ratio-nale is that kerosene consumption would de-cline in absolute terms in the future asbottlenecks to the accessibility of LPG areexpected to ease in the future. However, theextent of decline in kerosene consumed inthe commercial sector is not reported.Hence, it has been assumed that the con-sumption of kerosene in the commercial sec-tor would remain constant at the 2001consumption level of 1.42 MT over themodelling time frame of 2001 till 2036.Moreover, in the commercial sector in India,firewood-based stove is used commonly forgrilled food items. It has been assumed that10% of the total useful energy demand in thecommercial sector is met by firewood.

Therefore, the end-use devices in thecommercial sector comprise the LPGburner, wick-type kerosene stove, and fire-wood-based stove. The efficiency of thesedevices is listed in Table 3.75.

The energy demand for cooking in thecommercial sector under different GDPgrowth scenarios (expressed in Mtoe) is pre-sented in Table 3.76.

Table 3.75 Technological options for

cooking in the commercial sector

Technology Efficiency (%)

LPG burner 60

Wick-type kerosene stove 48

Firewood-based stove 10

LPG – liquefied petroleum gas


3.1.5.2 Electricity demand in the

commercial sector

Electricity consumption in the commercialsector is estimated using the historical dataon electricity sale to the commercial sector.Electricity consumption in the commercialsector has been growing at an average annualrate (Figure 3.15) of 8.1% per annum. Thegrowing electricity demand can be explainedby the increasing demand for services mea-

sured by value of output from the servicessector, that is, GDP of the services sector.

The appropriate regression model for es-timating electricity demand in the commer-cial sector is as follows.

Log(ELCC,t

) = (−)2.87 + 0.97 ×(-2.58) (11.36)

Log(GDPSt) + 0.70 × AR (1)(4.89) (3.33)


The coefficient associated withGDPS is 0.97. This implies that1% rise in value added by the ser-vices sector would increase elec-tricity demand by 0.97%, implyingthat electricity demand is income-inelastic. This further implies thatelectricity is necessary for thecommercial sector in carrying outits operations.

However, the bifurcation ofelectricity consumption amongstvarious electricity consuming ac-tivities such as lighting, space con-ditioning, and refrigeration isbased on electricity usage norms.Based on the President’s addressat the CPWD (Central PublicWorks Department) in 2004, it has

Source CEA (2004)

Figure 3.15 Trend of electricity consumptionin the commercial sector (1980–2003)

Table 3.76 Energy demand for cooking in commercial sector (in Mtoe)

GDP growth rate (%) 2001 2006 2011 2016 2021 2026 2031

6.7 65 81 100 125 157 198 250

8 65 83 107 139 180 234 302

10 65 84 114 155 212 290 397

Mtoe – million tonnes of oil equivalent


Table 3.77 Technologies for lighting in the

commercial sector

Technologies Efficiency

GLS (generalized lighting Normalized to 1

system)

Tube light 1.818

CFL (compact fluorescent 3.125

lamp)

Table 3.78 Electricity demand for lighting in the commercial sector (in GWh)

GDP growth rate (%) 2001 2006 2011 2016 2021 2026 2031 2036

6.7 14 484 19 813 26 971 37 504 53 059 76 066 110 113 160 445

8 14 484 21 260 31 726 47 342 70 646 105 420 157 312 234 746

10 14 484 22 429 36 594 59 702 97 404 158 913 259 264 422 986

GDP – gross domestic product; GWh – gigawatt hour

Table 3.79 Technologies for space conditioning in the commercial sector

Technologies Efficiency

Fan (standard) Normalized to 1

Fan (efficient) 10% efficient compared to standard (1.1)

Air conditioner (standard) Normalized to 1

Air conditioner (efficient) 50% more efficient compared to standard

been assumed that 60% of the total electric-ity is consumed for lighting, 32% for spaceconditioning, and 8% for refrigeration in thecommercial sector. These shares are as-sumed to remain constant over the model-ling time frame. The efficiency oftechnologies for lighting in the commercialsector is shown in Table 3.77.

Upper and lower bounds represent therealistic levels of penetration of each of theabove technologies. It is assumed that 50%

of the total lighting demand is met by GLS,49% by tube lights, and 1% by CFLs. Theseshares are fixed for the modelling time frame2001–36. Thus, the electricity demand forlighting (in GWh) under different scenariosis presented in the Table 3.78.

The technologies for space conditioningtogether with their efficiency in the commer-cial sector are listed in Table 3.79.

The total electricity demand for spaceconditioning in the commercial sector is metby fans and air conditioners. The share offans in the total electricity is assumed fixedat 70% and the remaining 30% is met by airconditioners. Each of these electrical appli-ances has an efficient counterpart. Underthe pessimistic scenario, it is assumed thatthe penetration of efficient appliances is onlyto the extent of 45% within both the fan andair conditioner segments. These sharesare assumed based on the shares of theorganized market dealing with electricalappliances.


Thus, the electricity demand for spaceconditioning (in GWh) under different sce-narios is presented in Table 3.80.

The demand for refrigeration is met by astandard refrigerator alone, with its efficiencynormalized to one. Thus, the electricity de-mand for refrigeration (in GWh) under differ-ent scenarios is presented in Table 3.81.

3.1.6 Electricity demand in other

sectors

The other sectors consuming electricity con-sist of public lighting, public water works, andsewage pumping. The electricity consumptionin these sectors is assumed to be a function ofthe expenditure incurred by the governmenton providing services to these sectors.

The historical time-series data clearly de-picts that electricity consumption has grownat an average annual growth rate of 6% from1980–2003 (Figure 3.16).

The appropriate regression model for es-timating and projecting electricity demandin the commercial sector is as follows.

Log(ELCo,t) = 0.74 × Log(GDPSt) + 0.91(32.58) (12.5)

× AR(1) (3.34)


The coefficient associated with GDPS is0.74. This implies that 1% rise in valueadded by the services sector would increasethe electricity demand by 0.74% implyingthat electricity demand is income-inelastic.This further implies that electricity is a ne-cessity for the other sector in carrying out itsoperations.

Electricity demand projections (in GWh)for other services under different growthscenarios are presented in Table 3.82.

Table 3.81 Electricity demand for refrigeration in the commercial sector (in GWh)

GDP growth rate (%) 2001 2006 2011 2016 2021 2026 2031 2036

6.7 1931 2642 3596 5001 7 074 10 142 14 682 21 393

8 1931 2835 4230 6312 9 419 14 056 20 975 31 299

10 1931 2991 4879 7960 12 987 21 188 34 569 56 398


Table 3.80 Electricity demand for space conditioning in the commercial sector (in GWh)

GDP growth rate (%) 2001 2006 2011 2016 2021 2026 2031 2036

6.7 7725 10 567 14 384 20 002 28 298 40 569 58 727 85 571

8 7725 11 339 16 920 25 249 37 678 56 224 83 900 125 198

10 7725 11 962 19 517 31 841 51 949 84 753 138 274 225 593



3.2 Description of resource supply

and conversion technologies

3.2.1 Coal and lignite

Coal is increasingly catering to our growingenergy needs. It meets about 60% of ourcommercial energy needs, and about 70% ofthe electricity produced in India comes fromcoal. With proper technologies and initia-tives for better management, it is possible to

Table 3.82 Electricity demand projections for other services (in GWh)

GDP growth rate (%) 2001 2006 2011 2016 2021 2026 2031 2036

6.7 21 551 23 868 30 858 40 229 52 876 69 986 93 137 124 412

8 21 551 25 188 34 931 48 059 65 793 89 789 122 294 166 359

10 21 551 26 239 38 953 57 371 84 078 122 830 179 089 260 791


Figure 3.16 Trend of electricity consumption inother electricity consuming sectors (1980–2003)

Source CEA (2004)

reduce the hazards other-wise associated with coal.Through scientific miningpractices followed by landreclamation, beneficiationto reduce ash at source, andbetter ways of utilization ofcoal like liquefaction ofcoal, coal gasification, insitu coal gasification, andcoal-bed methane recovery,coal can be used judiciouslyas a major source of energy.

The geological coal re-sources of the country areestimated at 220.98 billiontonnes as on January 2001.Of this, proven reserves are84.41 billion tonnes, while98.55 billion tonnes are in-

dicated reserves and 38.02 billion tonnes areinferred reserves. Coal continues to remainthe principal source of commercial energy,accounting for nearly 50% of the total sup-plies.

The current estimates of geological lig-nite reserves in India are 34.76 billiontonnes spread over Tamil Nadu andPondicherry (87.5%), Rajasthan (6.9%),Gujarat (4.9%), Kerala (0.31%), and Jammuand Kashmir (0.37%). The lignite depositsin the southern and western regions have


emerged as an important source of fuel sup-ply for states like Tamil Nadu, Rajasthan,and Gujarat. Over the years, considerableemphasis has been placed on the develop-ment of lignite for power generation.

The indigenous production of coking coalin the country was 30 MT during 2001/02and is expected to increase to 50 MT by2036/37. The production of non-coking coalwas about 299 MT in 2001/02 and the maxi-mum production is expected to be no morethan 550 MT in 2036/37. The values of in-digenous production of different types ofcoal are shown in Table 3.83.

3.2.1.1 Status of coal to oil

technologies in India

In India, studies on coal hydrogenation arerestricted to laboratory-scale R&D activi-ties, principally at the CFRI (Central FuelResearch Institute), Jharkhand. A 0.5 TPDhigh-pressure plant was set up at the CFRIto study the hydrogenation of coal. Thesingle-stage process followed by the plantyielded 25% oil amidst many operationalproblems.

Studies on the continuous reactor else-where have shown a very poor conversion ofdistillate product. The reactor also faces tre-

mendous difficulties in terms of operation,valve erosion, choking, and so on. Basicstudies on reaction kinetics and mechanism,action of catalyst, solvent quality, character-ization of products, and comparative amena-bility of Indian coal towards hydrogenationhave been carried out in detail. This coal hy-drogenation technology is quite differentfrom the commercial operation undertakenduring the modelling time frame and henceis not considered in our analysis.

3.2.1.2 Status of coal gasification

in India

Though coal reserves in India are abundant,the quality of coal in general is inferior, withmineral content as high as 50%. Since re-serves of oil and natural gas in the countryare meagre, they need to be substituted withcoal to the extent feasible. At the same time,all three fuels, especially coal, need to beconserved for the future generation. The en-ergy sector requires efficient, clean, and de-pendable energy supplies. Hence, coal has tobe utilized with a multi-pronged strategythat aims at higher efficiency, ensures its en-vironmental acceptance and judicious use,thus prolonging its availability, considers itas a replacement for oil, and so on. This ispossible only through sustainable develop-ment, and gasification is the best option toachieve it. The major advantage of gasifica-tion is that coal is converted into a gaseousfuel that is a clean form of energy and easy tohandle. Thus, in gaseous form, coal is able tosubstitute for petroleum products and natu-ral gas.

Synthesis gas has a wide range of applica-tion. It can be used in a combined cycle sys-tem that ensures an efficient and clean

Table 3.83 Maximum values of domestic

coal availability

Fuel (MT) 2001/02 2036/37

Coking coal 27 50

Non-coking coal 299 550

Lignite 25 50

MT – million tonnes


generation of electric power. It is suitable forthe manufacturing of hydrogen and chemi-cals such as ammonia, methanol, and aceticacid; as substitute natural gas; as a reducinggas for metallurgical purposes; and so on. Itcan be used in multipurpose plants for thesimultaneous production of electric power,chemicals/fertilizers, and fuels that also im-prove the economics of coal gasification.India already has some experience in coalgasification and has even made advances indeveloping an indigenous technology.

The scenario of coal gasification, which isintimately connected to the particular char-acteristics of Indian coal, is not as bright asthat of oil gasification. Indian coal has theadvantage of relatively low sulphur content.The problem lies in the extremely high ashcontent, which can often be as high as 40%,and nature of the ash, which contains veryhigh amounts of silica and alumina. (Typicalfigures from the Talcher coalfield show that60% and 30% of the ash contains silica andalumina, respectively. The ash deformationtemperature is 1170–1240 oC and the fusiontemperature is above 1400 oC.) This high ashcontent combined with a high melting pointpresents great difficulties to all slagging pro-cesses. Any gasifier operating in slaggingmode consumes more oxygen because of theheat required to keep the ash molten at theslag tap. In most coal types, this disadvan-tage is outweighed by the advantages ofhigh-temperature operation, which ensureselimination of all volatiles in the gas, and re-duced methane slip. Thus, modern processdevelopments have taken the high-tempera-ture route. The high-ash content of theIndian coals, however, makes modern high-temperature processes extremely expensivedue to their high oxygen demand. Besides,there are problems of handling large vol-umes of silica in an entrained flow process.

Thus, when looking at gasifying Indiancoal, the tendency has been to take into ac-count non-slagging processes. The IndianInstitute of Chemical Technology Unit atHyderabad is a small test unit involved pri-marily in research and coal testing. TheBHEL (Bharat Heavy Electricals Ltd) Unitat Trichy is an indigenous development, alsoaimed at finding a way to improve coal use inIndia. It is, however, necessary to take cogni-zance of the fact that since most Indian coalshave low sulphur content, relatively simplegas cleaning technologies can be introducedin conventional combustion plants to meetthe environmental requirements.

Indian scientists and engineers havegained experience in the gasification of coalthrough moving bed process on pilot/dem-onstration scale. This process (Lurgi dry ashprocess) is a commercially proven process inGermany and South Africa, and uses high-ash coal for power generation and produc-tion of synthesis gas, chemicals, and liquidfuels. Therefore, it may be a low risk ormostly no risk strategy to pursue with mov-ing bed gasification process.

Fluidized bed gasification process is su-perior to moving bed process for utilizationof high-ash Indian coals through gasificationroute. The country has very limited experi-ence in fluidized bed process. Internation-ally also, the experience gained so far islimited only to low-ash coals. The first107-MW IGCC (integrated gasificationcombined cycle) demonstration plant basedon air blown, pressurized, and fluidized bedprocess (that is, KRW [Kellogg–Rust–Westinghouse] process) has been establishedat Reno, USA (Pinion pine IGCC powerproject). The plant is already in operation.Results and performance of this plant wouldhelp India in adopting the fluidized bedprocess.


India can go in for a hybrid concept, thatis, a combination of moving bed and fluid-ized bed gasification processes. Based on theresults obtained from this concept, a fluid-ized bed coal gasification system can beadded to the moving bed plant at the samelocation. As India has a vast reserve of coal,it will be advantageous for it to adopt twocoal gasification processes. The hybrid con-cept results in the economy of coal, as coal ofvarying sizes supplied to a power plant canbe utilized, that is, 6–50 mm size coal can beused for moving bed and coal below 6 mmfor fluidized bed. Moreover, moving bedcannot tolerate more than 10% of coal finesdue to operational problems.

The Trichy BHEL pilot plant establishedin 1988 has gained rich experience in de-signing, engineering, fabrication, erection,and operation of the integrated gasificationcombined cycle technology. In fact, theBHEL’s operational experience of over 5000hours of the PFBG (pressurized fluidizedbed gasification)-based IGCC is significantin the context of just about 100 hours loggedon by other plants using similar coal gasifi-cation technology in USA and other coun-tries. The combined cycle technology usesgas turbine–steam turbine combination forpower generation, and instead of natural gas,uses coal gas along with steam for powergeneration in the turbines to achieve higherefficiency. Gasification is the cleanestmethod of utilization of coal, while com-bined cycle generation gives the highest effi-ciency. Hence, the integration of the twotechnologies for power generation in IGCCplants offers the benefit of very low emis-sions, higher efficiency, and the potential forlower cost of electricity generation. TheBHEL Trichy set up a 6.2-MW IGCC powerplant at a cost of 15 crore rupees in 1989,which is the first coal-based IGCC in Asia

and the second in the whole world. In 1996,a PFBG demonstration plant of 150 TPDcapacity was designed and retrofitted in the6.2-MW plant to supply coal gas to the exist-ing unit.

3.2.1.2.1 Status of coal-bed

methane in India

There are very good prospects for the devel-opment of coal-bed methane in India. Thecoal-bearing formations of India occur intwo distinct geological horizons in the LowerGondwana (Permian) belts of India and theTertiary sediments (Eocene–Oliocene) ofnorth-eastern India, Rajasthan, Gujarat, andJammu and Kashmir. Methane gas is en-trapped within these formations at a widerange of sub-surface depths. Indian coal hasgas content values ranging from 1 to 23 m3

(cubic metres)/tonne.The coal-bed methane occurrence is pre-

dicted in Damodar Valley basin, a potentialsource presently under consideration. Also,Amlabad, Jharkhand, is expected to givehigher specific gas yield compared to Raniganjfield, West Bengal. Amlabad is well known asa potential source of coal-bed methane.

Giving highest priority to the efficient useof energy resources and long-termsustainability of energy supplies, the Gov-ernment of India requested international as-sistance in coal-bed methane recovery andits commercial utilization. The country isone of the chief producers of coal from un-derground mines in the world. One of themajor fields in Jharia is on fire as can be seenfrom satellites in space. Two of the mines inthis coalfield are particularly ‘gassy’, andhave been selected as demonstration sites fora GEF (Global Environment Facility)project.


In India, the Reliance Gas has carried outcomprehensive geologic assessment ofcoal/lignite basins, based on which about20 000 km2 (square kilometres) of area hasbeen identified as a prospective site for coal-bed methane, with an estimated in-place re-source of about 2000 BCM (billion cubicmetres). The recoverable reserve of about

800 BCM and gas production potential ofabout 105 million cubic metres per day over aperiod of 20 years have been estimated. Coal-bed methane potential is thus about 1.5 timesthe present natural gas production in India,which is capable of generating about 19 000MW of electricity. The potential of gas produc-tion in India is given in Table 3.84.

Table 3.84 Coal-bed methane production potential in India

CBM production Energy equivalent

potential (million Power

Basin/area cubic metres/day) generation (MW) LNG (MTPA)

Cambay Basin

North Gujarat 30.0 5500 7.50

Barmer Basin

South Rajasthan 19.0 3500 4.75

Damodar Basin

Raniganj 12.0 2200 3.00

Jharia 3.5 650 1.00

East Bokaro 2.5 450 0.60

North Karanpura 6.0 1100 1.50

Rajmahal Basin

Rajmahal 4.5 800 1.20

Birbhum 6.0 1100 1.50

Others

Singrauli 1.0 180 0.25

Sohagpur 4.0 720 1.00

Satpura 1.5 270 0.40

Ib River 5.0 900 1.25

Talcher 2.5 450 0.60

Wardha Valley 1.5 270 0.40

Godavari Valley 4.0 720 1.00

Cauvery Basin 2.5 450 0.60

All India 105.5 19 260 26.55

CBM – coal-bed methane; MW – megawatts; LNG – liquefied natural gas; MTPA – million tonnes per annum


3.2.2 Hydrocarbons

The year 2004/05 was a mixed one for theIndian oil and gas sector. The import depen-dency was over 75%, as the country im-ported 95.86 MT of crude oil for refinerythroughput of 127.368 MT. The year wit-nessed extreme volatility in the internationalcrude oil market, with crude oil touching anall time high of over 70 dollars per barrel.Increasing input costs but stagnant retailprices of the transport fuels resulted in lossesfor oil companies in the country. There weresome positive developments too. Under thefifth round of the NELP-V (New ExplorationLicensing Policy-V), 20 blocks were offeredand an overwhelming response was receivedfrom both Indian and international compa-nies. Sixty-nine bids were received for theseblocks and finally 18 of these blocks wereawarded to different companies and consor-tium. India’s second LNG (liquefied naturalgas) terminal was commissioned at Hazira,Gujarat, this year. The terminal is the invest-ment of Shell Ventures and includes an LNGreceiving and storage terminal within a func-tional port. Following section highlights thekey developments in the E&P (exploration andproduction) of oil and natural gas. Subsequentsections deal in detail with crude oil, petro-leum products, and natural gas.

3.2.2.1 Exploration and

development

3.2.2.1.1 Overview

In continuation with the previous year,2004/05 saw significant activity in explora-tion and development of oil and gas. In

2004/05, ONGC (Oil and Natural GasCorporation Ltd) had four new oil and gasfinds at Vashista (eastern offshore), D-33(western offshore), Wamaj (Gujarat), andTiphuk (Assam shelf). The total reserve ac-cretion to ONGC for the year was about49.40 MT.

ONGC also reported a new offshore oildiscovery in the Cambay Basin. Oil Indiahad three discoveries in Assam, namelySamdang (Eocene), West Zaloni, and NorthTanali. The GSPCL (Gujarat State Petro-leum Corporation Ltd)-led consortium withGeo Global Resources and Jubiliant made agas discovery at KG-8 well, which it won inthe second round of the NELP. TheGSPCL’s claim of 20 TCF (trillion cubicfeet) of gas remains to be certified by an in-dependent agency. Apart from these domes-tic finds, 2004/05 saw significantdevelopments in India’s efforts towards ac-quiring oil and gas equity abroad. ONGCsigned important deals in Egypt, Qatar, andVenezuela.

3.2.2.1.2 Crude oil/natural gas

production

The crude oil production for 2004/05 was33.98 MT, of which 11.59 MT was onshoreand 22.39 MT offshore. The natural gasproduction was 31.77 BCM, of which8.97 BCM was onshore and 22.88 BCMoffshore (MoPNG 2005). Company-wisedetails of crude oil and natural gas are givenin Tables 3.85 and 3.86.

The two national oil companies – ONGCand OIL (Oil India Ltd) – accounted for87.34% and 79.66% of the total crude oiland natural gas production in the country,respectively, with ONGC accounting for the


Table 3.85 Company-wise crude oil production (MT)

Onshore Offshore

Year OIL ONGC Private/JV Total ONGC Private/JV Total Grand total

1994/95 2883 9130 4 12 017 20 226 251 20 477 32 494

1995/96 2882 8971 26 11 879 22 665 624 23 289 35 168

1996/97 2870 8504 38 11 412 20 181 1307 21 488 32 900

1997/98 3094 8387 42 11 523 19 863 2472 22 335 33 858

1998/99 3295 8100 77 11 472 18 286 2965 21 251 32 723

1999/2000 3283 7921 94 11 298 16 727 3924 20 651 31 949

2000/01 3286 8428 293 12 007 16 629 3788 20 417 32 424

2001/02 3183 8635 71 11 889 16 073 4070 20 143 32 032

2002/03 2950 8445 75 11 470 17 559 4013 21 572 33 042

2003/04 3002 8384 74 11 460 17 681 4240 21 921 33 381

2004/05 3196 8321 74 11 591 18 164 4226 22 390 33 981

OIL – Oil India Ltd; ONGC – Oil and Natural Gas Corporation Ltd; JV – joint venture; MT – million tonnes

Source MoPNG (2005)

Table 3.86 Company-wise production of natural gas (MCM)

Year OIL ONGC Private/JV Total

1995/96 1433 20 875 331 22 639

1996/97 1496 21 281 479 23 256

1997/98 1670 23 050 1681 26 401

1998/99 1713 22 841 2874 27 428

1999/2000 1729 23 252 3465 28 446

2000/01 1861 24 020 3596 29 477

2001/02 1619 24 041 4054 29 714

2002/03 1744 24 244 5407 31 395

2003/04 1880 23 584 6491 31 955

2004/05 2007 22 985 6782 31 774

OIL – Oil India Ltd; ONGC – Oil and Natural Gas Corporation Ltd;

JV – joint venture; MCM – million cubic metres

Source MoPNG (2005)


major share, as per 2005 data. Private play-ers continued to make their presence felt incrude oil production, with a share of 18.87%in 2004/05 in offshore production, up fromvirtually nil some years back. However, theirshare in onshore production is still less than1%. Their share in natural gas productionhas also gone up from 2% in 1996/97 to21.34% in 2004/05, which is slightly higherthan the previous year and promises to go upeven further with several NELP fields yield-ing natural gas.

However, an important cause of concernis that the domestic crude oil production inthe country has not kept pace with risingdemand. The R/P (reserves/production) ra-tio for crude oil has stagnated over the pastfew years at 22 years. For gas, the R/P ratio ismarginally better at about 29 years. In 2004/05, India imported 95.86 MT of crude oilfor a total refinery throughput of 127.368

MT. This translates into a crude oil importdependency of almost 75%. Figure 3.17summarizes the trend in India’s import de-pendency. The IEA (International EnergyAgency) (2002) has projected that if thistrend continues, then India’s import depen-dency may increase to 94% by 2030. How-ever, the crude import dependency gives apartial picture. In fact, with an increase inrefining capacity and India becoming a netexporter of petroleum products, country’snet import dependency has decreased from80% in 1999/2000 to nearly 70% in 2004/05(MoPNG 2005).

The Government of India has initiatedmany steps to ensure oil security for thecountry. One such step was to intensify do-mestic exploration and development effortsto explore new fields and increase the re-serve base of the country. Hydrocarbon Vision2025 laid down a phased programme for re-

appraising all the sedimentarybasins of the country by 2025(Planning Commission 1999).This includes intensive explo-ration in the producing basinsto upgrade ‘yet-to-find’ hydro-carbon resource and promoteexploration in ‘non-produc-ing’, ‘poorly explored’, andnew frontier basins like the Hi-malayan foothold. To meetthese objectives, the DGH (Di-rectorate General of Hydrocar-bons) has conducted a numberof studies to upgrade informa-tion on the unexplored or lessexplored regions in the coun-try. About 1.96 million km2 ofthe regions – of which 86% areoffshore and the rest on-land –have already been coveredSource MoPNG (2005)

Figure 3.17 Production and importof crude oil over the years


under these efforts. These surveys have giveninformation about structure, tectonics, andsedimentary thickness of these areas.

Overseas acquisition of equity oil is an-other major strategy adopted to enhance oilsecurity of the country. The Government ofIndia aims to produce 20 MTPA (milliontonnes per annum) of equity oil and gasabroad by 2010. Under the Tenth Five YearPlan, the target for oil and gas equity abroadwas 5.2 MT and 4.88 BCM, respectively.The likely achievement under the plan pe-riod is expected to be about 16.45 MT foroil and 4.41 BCM for natural gas. The po-tential in-place reserves of oil for the blockhave been estimated to be more than 600million barrels.

3.2.2.1.3 New Exploration

Licensing Policy

The DGH has divided India’s topographyinto 26 sedimentary basins comprising1.35 million km2 of onshore area and0.39 million km2 of offshore area (up to200 metre isobaths). Despite several devel-opments in the country’s hydrocarbon sec-tor, plenty of areas, which may havehydrocarbon reserves, remain to be ex-

plored. Though the bidding for explorationblocks started as early as 1979, earlierrounds were not successful. In all, ninerounds were conducted from 1979 to 1995,which resulted in a total investment of 2 bil-lion dollars. In the ninth round, the conceptof JV (joint venture) fields was mooted,which met with moderate success. In 1997/98, the Government of India announced theNELP with the twin objectives of enhancingthe indigenous production by attracting pri-vate capital and foreign technology for In-dian upstream sector and for mapping thesedimentary basins of the country as exten-sively as possible. Under this framework, to-tal freedom has been given to market crudein the domestic market and a company canbid directly without the participation of theONGC or the OIL, which was mandatoryearlier.

Till date, five rounds of the NELP havebeen conducted, and a total of 109 onshoreand offshore blocks have been awarded com-pared to 21 blocks in 1992–97. This has ledto a number of new operating companies inthe private and joint sectors entering the up-stream petroleum sector. The details of fiveNELP rounds are given in Table 3.87.

There was a mixed response in the first tworounds of the NELP. One of the criticisms

Table 3.87 Progress during NELP rounds

Blocks NELP I NELP II NELP III NELP IV NELP V

Offered 48 25 27 24 20

Awarded 25 23 23 20 18

Awardees

PSU and PSU-led consortiums 9 18 14 14 8

Private players and consortiums 16 5 9 6 10

NELP – New Exploration Licensing Policy; PSU – public sector undertaking


often made about these rounds was that theblocks offered under the successive roundswere largely recycled from the previousrounds and were the ones that national oilcompanies did not consider as prospective.The NELP-III, launched in April 2002, hadan attractive feature to counter these criti-cisms. All the 27 blocks offered under theNELP-III were divided into three catego-ries: Category 1 comprised those blocks thathad never been offered before; Category 2comprised those blocks that were offeredbefore but extensive data is now available;Category 3 comprised those blocks that wereoffered before with limited data provided bythe ONGC and the OIL, but were now beingoffered with re-interpreted, repackaged, andreprocessed data. The 20 blocks under theNELP-IV attracted several international aswell as private players, including Cairn En-ergy, Hardy Exploration and ProductionInc., Canada GeoGlobal Resources Ltd, Re-liance Industries Ltd, Jubilant Enpro, EnproFinance, and GSPCL.

3.2.2.1.4 Strategic reserves

In a major move aimed at enhancing energysecurity of the country, on 7 January 2004,the union cabinet approved the setting up ofstrategic storage facilities for 5 MT of crudeoil, sufficient to meet 15-day consumption atthree locations on the east and west coasts.The construction of underground rock cav-erns has been proposed at Mangalore(1.5 MT), Visakhapatnam (1 MT), and at asuitable location south of Mangalore(2.5 MT). This strategic storage will be inaddition to the existing storage facilities forcrude and petroleum products and will pro-vide an emergency response mechanism in

case of short-term disruptions. Currently,the total crude oil storage capacity of domes-tic refineries is 19 days (5.7 MT). Besides,the country at present has tankages to pro-vide for 45-day cover to petroleum products.The IEA requires oil-importing membercountries to hold stocks equivalent to90 days of net imports. Though India is not amember of the IEA, after the setting of pro-posed strategic storage, it will also have grossstorage capacity in line with the IEA guide-lines. According to some reports, India willseek Saudi Arabia’s help in building strategicoil reserves. It was proposed that IOC (In-dian Oil Company), a major public sector oilcompany, float a SPV (special purpose ve-hicle) for the purpose of construction andoperation of the storage system. However,the project has not taken off yet, mainly be-cause of the issue of funding and the highcrude oil prices in the international market.

With increased availability of natural gasin the country, the Government of India isalso considering building of undergroundnatural gas storage facilities for strategic use.The government has recognized that withthe growing importance of natural gas asfuel/feedstock for several key sectors likepower, fertilizer, steel, transport, and do-mestic, creation of strategic gas storage sys-tems would be imperative for assuringuninterrupted supplies. An expert team hasrecommended building of reserves for 15days of gas consumption at the current rateof consumption of about 1000 million stan-dard cubic metres or 1 BCM of gas. Initialinvestment estimates for creating an under-ground gas storage facility have been peggedat 100 million dollars. It has been proposedthat a detailed feasibility survey be carriedout for development of underground gasstorage facilities in the country, which may


be funded by the OIDB (Oil Industry Devel-opment Board) through an initial grant.

3.2.2.2 Global hydrocarbon

reserves

The global hydrocarbon reserves as per theBP (2006) indicate that oil availability at thecurrent R/P ratio is expected to be about40.7 years. The global natural gas availabilityat the current R/P ratio is 65 years. Appendix4 gives information on the region-wise hy-drocarbon reserves till the end of 2005, andthe daily production and R/P ratios over thepast 26 years, from 1980 to 2005 (BP 2006).

3.2.2.3 Refineries in India

As of July 2005, there are a total of 18 refin-eries in the country—17 in the public sectorand one in the private sector. Company-wiselocation and capacity of the refineries (as on1 July 2005) are given in Table 3.88.

3.2.2.3.1 Brief description of

existing refineries

3.2.2.3.1.1 Guwahati Refinery,

Indian Oil Corporation Ltd (Assam)

Guwahati Refinery, the first in public sector,was set up in collaboration with Rumania ata cost of 17.29 crore rupees and commis-sioned on 1 January 1962 with a design ca-pacity of 0.75 MTPA. The present capacity(2004/05) of this refinery is 1 MTPA. AHydrotreater Unit for improving the qualityof diesel has been installed, which was

Table 3.88 Oil refinery capacity in India

(2005)

Name of the Location of Capacity

company the refinery (MTPA)

IOCL Guwahati 1.00

IOCL Barauni 6.00

IOCL Koyali 13.70

IOCL Haldia 6.00

IOCL Mathura 8.00

IOCL Digboi 0.65

IOCL Panipat 6.00

HPCL Mumbai 5.50

HPCL Visakhapatnam 7.50

BPCL Mumbai 6.90

CPCL Manali 9.50

CPCL Nagapattinam 1.00

KRL Kochi 7.50

BRPL Bongaigaon 2.35

NRL Numaligarh 3.00

MRPL Mangalore 9.69

Tatipaka Andhra Pradesh 0.08

refinery

(ONGC)

RPL Jamnagar 33.00

Total 127.37

IOCL – Indian Oil Corporation Ltd;

HPCL – Hindustan Petroleum Corporation Ltd;

BPCL – Bharat Petroleum Corporation Ltd;

CPCL – Chennai Petroleum Corporation Ltd;

KRL – Kochi Refineries Ltd; BRPL – Bongaigaon

Refinery and Petrochemicals Ltd; NRL – Numaligarh

Refinery Ltd; MRPL – Mangalore Refinery and

Petrochemicals Ltd; ONGC – Oil and Natural Gas

Corporation Ltd; RPL – Reliance Petroleum Ltd;

MTPA – million tonnes per annum


commissioned in 2002. The refinery also in-stalled in 2003 an Indmax Unit, a noveltechnology developed by its R&D Centre forupgrading heavy-end LPG, motor spirit,and diesel.

3.2.2.3.1.2 Barauni Refinery,

Indian Oil Corporation Ltd (Bihar)

Barauni Refinery in the eastern India wasbuilt in collaboration with the Soviet Unionat a cost of 49.4 crore rupees and went onstream in July 1964. The initial capacity of2 MTPA (in November 1967) was increasedto 3 MTPA by 1969. The current capacity(2004/05) of this refinery is 6 MTPA.A CRU (Catalytic Reformer Unit) was alsoadded to the refinery in 1997 for the produc-tion of unleaded motor spirit. Projects arealso being planned for meeting improvingfuel quality.

3.2.2.3.1.3 Koyali Refinery, Indian

Oil Corporation Ltd (Gujarat)

Gujarat Refinery was built with the Sovietassistance at a cost of 26 crore rupees andwent on stream in October 1965. The refin-ery had an initial installed capacity of2 MTPA and was designed to process crudefrom Ankleshwar, Kalol, and Nawagamoilfields of ONGC in Gujarat. In September1967, the capacity of the refinery was in-creased to 3 MTPA, which was further in-creased to 4.3 MTPA (2004/05) throughdebottlenecking measures and to 7.3 MTPAin October 1978 with the setting up of anexpansion project worth 56.07 crore rupees.With the addition of additional processing

facilities, the refinery could achieve a capac-ity of 9.5 MTPA in 1989. The refining ca-pacity was further increased to 12.5 MTPAwith the commissioning of a 3 MTPA CDU(Crude Distillation Unit) in September1999. The current refining capacity (as of2004/05) of this refinery is 13.70 MTPA. Inorder to improve fuel quality, motor spiritquality improvement facilities are beingplanned to be installed.

3.2.2.3.1.4 Haldia Refinery, Indian

Oil Corporation Ltd (West Bengal)

Haldia Refinery for processing 2.5 MTPA ofMiddle East crude was commissioned inJanuary 1975 and comprised two sectors:one for producing fuel products and theother for producing lube base stocks. Thefuel sector was built with French collabora-tion while the lube sector resulted from Ro-manian collaboration. The capacity of therefinery was increased to 2.75 MTPA in1989 through debottlenecking measures.With the commissioning of a new CDU of1 MTPA in March 1997, the capacity wasfurther increased to 3.75 MTPA. Thepresent refining capacity (as of 2004/05) ofthis refinery is 6 MTPA.

3.2.2.3.1.5 Mathura Refinery,

Indian Oil Corporation Ltd

(Uttar Pradesh)

Mathura Refinery with a capacity of6 MTPA was set up at a cost of 253.92 crorerupees. The refinery was commissioned inJanuary 1982, excluding FCCU (FluidizedCatalytic Cracking Unit) and Sulphur


Recovery Units, which were commissionedin January 1983. The refining capacity ofthis refinery was increased to 7.5 MTPA in1989 by debottlenecking and revamping. ADHDS (Diesel Hydrodesulohurization)Unit was commissioned in 1989 for the pro-duction of diesel with low sulphur content of0.25% wt (max.). The present refining capac-ity (as of 2004/05) of this refinery is 8 MTPA.

3.2.2.3.1.6 Digboi Refinery (Assam)

The refinery was set up at Digboi in 1901 byAssam Oil Company Ltd. The Indian OilCorporation Ltd took over the refinery andmarketing management of Assam Oil Com-pany Ltd with effect from 14 October 1981and created a separate division. This divisionhas both refinery and marketing operations.The refinery at Digboi had an installed ca-pacity of 0.5 MTPA. The refining capacity ofthe refinery was increased to 0.65 MTPA inJuly 1996 by modernizing the refinery. Anew delayed Coking Unit of 170 000 TPA(tonnes per annum) capacity was commis-sioned in 1999. A new Solvent DewaxingUnit for maximizing production of micro-crystalline wax was installed and commis-sioned in 2003. The refinery has alsoinstalled a Hydrotreater to improve the qual-ity of diesel.

3.2.2.3.1.7 Panipat Refinery,

Indian Oil Corporation Ltd

(Haryana)

The refinery was set up in 1998 at Baholi vil-lage in Panipat district, Haryana, at a cost of3868 crore rupees. The refining capacity of

this refinery was 6 MTPA in 2004/05. It wasincreased to 12 MTPA in 2005/06.

3.2.2.3.1.8 Mumbai Refinery,

Hindustan Petroleum Corporation

Ltd (Maharashtra)

The refinery at Mumbai came into stream in1954 under the ownership of ESSO. TheGovernment of India acquired it in March1974. The HPCL (Hindustan PetroleumCorporation Ltd) came into existence on15 July 1974, after the merger of these com-panies. The installed capacity of the Mumbairefinery of HPCL was 3.5 MTPA, which wasincreased to 5.5 MTPA in 1986 following anexpansion programme.

3.2.2.3.1.9 Visakh Refinery,

Hindustan Petroleum Corporation

Ltd (Andhra Pradesh)

Visakh Refinery went on stream under theownership of M/s Caltex India Ltd in 1957.In May 1978, M/s Caltex Oil Refinery (India)was amalgamated with HPCL. The installedcapacity of 1.5 MTPA was increased to4.5 MTPA in 1985 and 7.5 MTPA in 1999through an expansion programme.

3.2.2.3.1.10 Bharat Petroleum

Corporation Ltd (Maharashtra)

The refinery at Mumbai came on stream inJanuary 1955 under the ownership ofBurmah-Shell Refineries Ltd. Following thegovernment’s acquisition of the Burmah-


Shell, name of the refinery was changed toBharat Refineries Ltd on 11 February 1976.In August 1977, the company was given itspermanent name Bharat Petroleum Corpora-tion Ltd. The installed capacity of 5.25 MTPAwas increased to 6 MTPA in 1985. The cur-rent (as on 2004/05) refining capacity of therefinery is 6.9 MTPA.

3.2.2.3.1.11 Manali Refinery,

Chennai Petroleum Corporation

Ltd (Tamil Nadu)

CPCL (Chennai Petroleum CorporationLtd), formerly known as MRL (Madras Re-fineries Ltd), was formed as a JV in 1965 be-tween the Government of India, AMOCO,and NIOC (National Iranian Oil Company),having a share holding in the ratio74%:13%:13%, respectively. From thegrass-roots stage, the CPCL refinery was setup with an installed capacity of 2.5 MTPA ina record time of 27 months at a cost of43 crore rupees, without any time or costover run. The Manali refinery has a capacityof 9.5 MTPA and is one of the most complexrefineries in India with fuel, lube, wax, andpetrochemical feedstock production facilities.

3.2.2.3.1.12 Cauvery Basin

Refinery, Chennai Petroleum

Corporation Ltd (Tamil Nadu)

CPCL’s second refinery is located atCauvery Basin at Nagapattinam. The initialunit was set up with a capacity of 0.5 MTPAin 1993, and later on, the capacity was en-hanced to 1 MTPA.

3.2.2.3.1.13 Kochi Refineries Ltd

(Kerala)

KRL (Kochi Refineries Ltd) is a public sec-tor undertaking, set up in pursuance of a for-mation agreement dated 27 April 1963between the Government of India, PhillipsPetroleum Co. of USA, and Duncan Broth-ers of Calcutta, with an authorized capital of15 crore rupees. The installed capacity of2.5 MTPA was increased to 3.3 MTPA inSeptember 1973 and to 4.5 MTPA inNovember 1994. The capacity of the refinerywas further enhanced to 7.5 MTPA inDecember 1995.

3.2.2.3.1.14 Bongaigaon Refinery

and Petrochemicals Ltd (Assam)

On 20 January 1974, M/s BRPL(Bongaigaon Refinery and PetrochemicalsLtd) was incorporated in Assam under theCompanies Act, 1956, with an authorizedcapital of 50 crore rupees. The refinery wasinstalled with a crude processing capacity of1 MTPA and comprised a PetrochemicalsComplex consisting of Xylene, DMT (Di-Methyl Terephthalate), and PSF (PolyesterStaple Fibre) Units. The complex was builtand commissioned in phases. From April1987, the capacity of the CDU was in-creased to 1.35 MTPA by debottlenecking.Now the authorized capital (equity) of thecompany is 200 crore rupees. The paid-upcapital as on date is 199.82 crore rupees. Asa part of the restructuring steps taken up bythe Government of India, IOCL (IndianOil Corporation Ltd) acquired thegovernment’s equity in 2000/01. In view ofthis, BRPL became subsidiary of IOCL in


2001. The capacity of the refinery has beenincreased to 2.35 MTPA in June 1995 byinstalling an additional unit.

3.2.2.3.1.15 Numaligarh Refinery

Ltd (Assam)

NRL (Numaligarh Refinery Ltd), popularlyknown as ‘Assam Accord Refinery’, has beenset up as a grass-root refinery at Numaligarhin the district of Golaghat (Assam) at an ap-proved cost of 2724 crore rupees. Thisproject has been set up in fulfilment of thecommitment made by the Government ofIndia in the historic ‘Assam Accord’, signedon 15 August 1985. NRL was incorporatedon 22 April 1993. The refining capacity ofthis refinery is 3 MTPA (as on 2004/05).

3.2.2.3.1.16 Mangalore Refinery

and Petrochemicals Ltd (Karnataka)

The government approved on 11 April 1991the setting up of a 3 MTPA Oil Refineryat Mangalore at an estimated cost of 1160crore rupees, including foreign exchangecomponent of 300 crore rupees. The projecthas been implemented by a JV company withHPCL, Mumbai, and Indian Rayon and In-dustrial Ltd, Gujarat, as co-promoters.The refinery was commissioned in March1996. MRPL (Mangalore Refinery and Pet-rochemicals Ltd), which was a joint sectorcompany, became a public sector undertak-ing subsequently on acquisition of majorityof shares by ONGC. The capacity of the re-finery was assessed at 3.69 MTPA and wasbeen further enhanced to 9.69 MTPA inSeptember 1999.

3.2.2.3.1.17 Tatipaka Refinery, Oil

and Natural Gas Corporation Ltd

(Andhra Pradesh)

A mini refinery of ONGC with a capacity ofabout 0.1 MTPA and an approved cost of29.9 crore rupees was commissioned in Sep-tember 2001 at Tatipaka in East Godavaridistrict of Andhra Pradesh.

3.2.2.3.1.18 Reliance Petroleum

Ltd (Jamnagar, Gujarat)

The private sector refinery RPL (ReliancePetroleum Ltd) was commissioned on14 July 1999 with an installed capacity of27 MTPA at Jamnagar. The capacity of thisrefinery as on 2004/05 is 33 MTPA.

3.2.2.3.2 Refining capacity and

capacity utilization

To meet the growing demand of petroleumproducts, the refining capacity in the coun-try has been gradually increased over theyears by setting up new refineries as well asby enhancing the refining capacity of the ex-isting refineries. The total refining capacityin the country as on 1 July 2005 stands at127.37 MTPA.

The refining capacity, actual crudethroughput, and capacity utilizationduring the past five years are indicated inTable 3.89.


3.2.2.3.3 Expansion of existing

refineries

Expansion plans of the refining capacities ofthe existing refineries of the HPCL are de-tailed below.� HPCL is enhancing the refining capacity

of Mumbai Refinery from 5.5 MTPA to7.9 MTPA at an estimated cost of 1152crore rupees. The project is expected tobe completed by December 2006.

� Expansion of Visakh Refinery of HPCLfrom 7.5 MTPA to 8.33 MTPA is takingplace at an estimated cost of 1635 crorerupees. The project is expected to becompleted by December 2006.

Table 3.90 provides details of the newrefineries planned in the Eleventh Five YearPlan.

3.2.2.3.4 Hydrocarbon resources

(input to the model)

The latest estimates indicate that India hasabout 0.4% of the world’s proven reserves ofcrude oil. The domestic crude consumptionis estimated at 2.8% of the world’s consump-tion. The balance of recoverable reserves asestimated in the beginning of 2001 is 733.70MT of crude and 749.65 BCM of naturalgas. The share of hydrocarbons in the pri-mary commercial energy consumption of thecountry has been increasing over the yearsand is presently estimated at 44.9% (36%for oil and 8.9% for natural gas). The de-mand for oil is likely to increase further dur-ing the next two decades. The transportsector will be the main driver for the pro-jected increase in oil demand. Consequently,the import dependency for oil, which is

Table 3.90 New refineries planned in the Eleventh Five Year Plan

Capacity Expenditure Actual/anticipated

Name of refineries (MTPA) (in crore rupees) completion date

IOCL, Paradip 9 8312 March 2010

BPCL, Bina 6 6354 September 2009

HPCL, Bhatinda 9 9806 December 2006

IOCL – Indian Oil Corporation Ltd; BPCL – Bharat Petroleum Corporation Ltd; HPCL – Hindustan Petroleum

Corporation Ltd; MTPA – million tonnes per annum

Table 3.89 Refining capacity, actual crude throughput, and capacity utilization during the

past five years

2000/01 2001/02 2002/03 2003/04 2004/05

Refining capacity (as on 1 April) 114.59 114.66 116.96 127.37 127.37

Actual crude throughput (MTPA) 103.10 106.50 10.60 118.70 124.30

Capacity utilization (%) 91.00 93.00 95.00 99.00 —

MTPA – million tonnes per annum


presently about 70%, is likely to increasefurther during the Tenth and Eleventh Plans.

India has about 0.4% of the world’s natu-ral gas reserves. Initially, the gas reserves hadbeen developed largely for use as petro-chemical feedstock and for the production offertilizers, but gas is now increasingly beingused for power generation, in industrial ap-plications and, more recently, in the trans-port sector. Presently, the share of powergeneration capacity based on gas is about10% of the total installed capacity. The IndiaHydrocarbon Vision 2025 of the governmentidentifies natural gas as the preferred fuel forthe future and several options are being ex-plored to increase its supply including build-ing facilities to handle imports of LNG andbringing gas from major gas-producingcountries by setting up pipelines. India isalso reported to have significant deposits ofgas hydrates. However, the true extent of thisresource and its potential for commercialexploitation are still being evaluated. In ad-

dition, deep-sea gas reserves are unknownand need to be explored.

Crude oil production in 2001/02 was32 MT while crude imports were about80 MT. The production levels have remainedmore or less stagnant over the past few yearswhile the imports of crude and productshave been increasing. The refining crudethroughput in 2001/02 was 107 MT with aproduction of 100 MT.

The production of natural gas in 2001/02was 29.71 BCM. Natural gas may be im-ported in the form of LNG by trans-nationalpipelines. At present, India is importingnatural gas in the form of LNG by two ter-minals, and three more terminals are beingplanned. The daily availability of natural gasin India through domestic extraction andimport through LNG terminals and pipe-lines, as considered in our model, is shownin Table 3.91. Besides these levels, we haveassumed LNG imports from the outerharbour at an additional cost.

Table 3.91 Natural gas availability

Natural gas availability (MSCMD)

2006/07 2011/12 2016/17 2021/22 2026/27

Total domestic 84 123 125 125 125

Total LNG import 25 65 95 125 135

Trans-national pipelines

Iran–Pakistan–India 0 30 90 90 90

Myanmar–India 0 0 30 30 30

Total pipelines 0 30 120 120 120

Total imports 25 95 215 245 255

Total 109 218 340 370 380

MSCMD – million standard cubic metre daily; LNG – liquefied natural gas


Table 3.92 Prices of different types of coal

in three different scenarios

Current

price

Current deflated

price to 2001

(dollar/ (dollar/

Fuel tonne) tonne)

Non-coking Import 60 50

coal Export 41 34

Domestic 35 29

Coking coal Import 85 71

Export 59 49

Domestic 59 49

Lignite Domestic 25 21

1 dollar is 47.7 rupees for 2001 and 43.53 rupees for

2005.

3.2.2.3.5 Energy prices

The economic costs of energy resources havebeen considered in the model. Accordingly,taxes and subsidies are not considered to re-flect the price differentiation across variousconsuming segments/uses. As such, c.i.f.(cost insurance freight) prices are consid-ered for imported fuels while f.o.b. (freighton-board) prices are taken into account fordomestic extraction and exports. Owing tolarge variation in the fuel prices during thepast three to four years, we have consideredcurrent fuel prices for this analysis. For coal,correction factors are used with f.o.b. price,taking into account different calorific valuesof domestic coal, and imported and ex-ported coal. For non-coking coal, an importprice of 60 dollars per tonne is used. Table3.92 presents the prices considered for dif-ferent types of coal.

The current f.o.b price for petroleumproducts is estimated by using average valueof the ratios of their prices with respect tothe crude oil price (average value during theperiod 2001–04). The c.i.f. prices are esti-mated by adding load port charges, freight,insurance, and ocean losses to the f.o.b.prices. Table 3.93 presents the prices consid-ered for crude oil and other key petroleumproducts.

For LNG, the c.i.f. cost of the latest Ira-nian deal (3.515 dollars/MMBTU), with anadditional re-gasification cost of 0.58 dollar/MMBTU, has been used. For the import ofnatural gas by pipelines, re-gasification costis not included. For domestic natural gas,f.o.b. price of 3.21 dollars/MMBTU hasbeen considered. These prices have been de-flated for 2001. Table 3.94 presents prices ofnatural gas considered in this study.

3.2.3 Power sector

Total installed capacity of power utilities in-creased from 5106 GW (gigawatts) in 1950to 264 231 GW in 1991, registering an an-nual growth rate of 10.4% over the period.Until 1980s, the growth rate in hydro powerand thermal power was comparable, butduring the 1980s, hydro power generationincreased at a rate of 4.4% compared to thegrowth rate of 11.6% in the thermal power.Owing to the decline in hydro power devel-opment and prevailing peak power deficits,coal-fired thermal power units are oftenused for meeting peak loads. Ten nuclearpower plants account for 2.5%–2.7% of totalutility generation.

The poor performance of India’s existinggenerating units has been a principal causeof power shortages and unreliable power


supply. The primary culprits are coal-firedthermal power stations that account for over65% of the total installed capacity. The aver-age PLF (plant load factor) of thermalpower stations in India is less than 60%, butvaries considerably across regions. In 1989/90, the southern region had the highest PLFof 65.6%, while the eastern and the north-eastern regions recorded very low PLFs of38.5% and 26.8%, respectively. In contrast,hydro power stations have far better trackrecord due to the fact that their performancerelies largely on water flow.

However, not all thermal power generat-ing stations have such dismal records. Forinstance, the performance of 500-MW and200-MW units has been satisfactory, andtheir PLFs have been higher than the

Table 3.94 Prices of natural gas

Current Current price

price deflated to

(dollars/ 2001 (dollars/

MMBTU) MMBTU)

Domestic natural gas 3.210 2.68

Import of natural gas 3.515 2.93

by pipelines

LNG import by terminal 4.100 3.42

LNG – liquefied natural gas; MMBTU – million British

thermal unit

Table 3.93 Price of crude and other petroleum products

f.o.b./ Current Current price

Fuel c.i.f. Unit price deflated to 2001

Crude oil f.o.b. dollars/bbl 60 50

c.i.f. dollars/bbl 62 51

HSD f.o.b. dollars/tonne 531 443

c.i.f. dollars/tonne 544 453

Gasoline f.o.b. dollars/tonne 627 523


Kerosene f.o.b. dollars/tonne 567 472


ATF f.o.b. dollars/tonne 567 472


Naphtha f.o.b. dollars/tonne 544 453


LPG f.o.b. dollars/tonne 554 462


HSD – high speed diesel; ATF – aviation turbine fuel; LPG – liquefied petroleum gas;

f.o.b. – freight on-board; c.i.f. – cost insurance freight; bbl – barrel


national average. It is, in fact, the thermalunits of 120 MW, 140 MW, and less that arethe cause for concern. Most of these unitshave already logged more than 100 000 run-ning hours, and their performance can onlybe improved through a long-term rehabilita-tion or re-powering programme.

3.2.3.1 Thermal power generation

Till 1969, the thermal power generationplants in India were in the capacity range of30–60 MW, having moving grate stoker orpulverized coal firing and conventionalsteam cycle with steam parameters of 90 ata(atmospheres absolute) and 540 oC, and noreheating (Table 3.95). This gave heat ratesabove 2200 kcal/kWh for the turbine–gen-erator system. With pulverized coal firing,there has been a gradual rise in unit ratingsto 210, 250, and 500 MW over the years. Theheat rate has thus been improved to a level of1950 kcal/kWh for 250- and 500-MW units.The earlier heat rate of 1970 for a 210-MWunit has also been improved recently by

39 kcal/kWh (BHEL 2002) through T4blading, used in place of the earlier T2 typeblading, and implemented for KhaperkhedaTPS extension units 3 and 4 of MaharashtraState Electricity Board. Introduction of T4blade profiles for the future 250- and 500-MW units will also improve upon the exist-ing heat rates. However, as far as sub-criticalsteam cycle is concerned, the plant effi-ciency has reached virtually its peak. Furtherimprovement will be possible only by adopt-ing super-critical steam parameters andother advanced cycles based on PFBC andgasification.

While using premium fuels like naturalgas and naphtha, contemporary design ofgas turbines (Table 3.96) has been adoptedin the country, and the combined cyclepower generation efficiency to the level of53% has been achieved at ISO conditions,that is, 15 °C ambient temperature, 60%relative humidity, and barometric pressurecorresponding to mean sea level. The effi-ciency levels in Indian conditions are, thus,50%–51%. Now, we have to look further toachieve higher efficiencies.

Table 3.95 Power generation steam cycles with different unit ratings

Turbine heat rate *Gross plant heat

Unit rating (MW) Cycle parameters (kcal/kWh) rate (kcal/kWh)

70 90 ata, 537 oC, non-reheat 2200 2588

120/130 130 ata, 537 oC/537 oC, reheat 1980 2330

210 150 ata, 537 oC/537 oC, reheat

(with motor-driven BFP) 1970 2318

250 150 ata, 537 oC/537 oC, reheat

(with motor-driven BFP) 1970 2314

500 170 ata, 537 oC/537 oC, reheat

(with steam-driven BFP) 1945 2288

*Considering boiler efficiency as 85%. For net heat rate, auxiliary power consumption also to be considered.

MW – megawatts; ata – atmospheres absolute; BFP – back focal plane; kcal – kilocalories; kWh – kilowatt-hours


From atmospheric pollution controlpoint of view, there has been a significantprogress with respect to control of particu-late matter emissions to the desired levels of150 mg/Nm3 (milligrams per Newton percubic metre) for most of the 200- and 210-MW units, and 100 mg/Nm3 for 500-MWunits. Emissions from the older units, inwhich retrofit of modified ESP (electrostaticprecipitator) has not been done, are high.However, there are no mandatory controlsdesired for SOx (oxides of sulphur) and NOx

(oxides of nitrogen). We in India are luckythat coal contains generally less than 0.5%sulphur, and SOx emissions are within limits.

3.2.3.1.1 Advanced technologies

3.2.3.1.1.1 Flue gas desulphurization

and deNOx system

Even though SOx emissions from individualstacks, while using low-sulphur coal, arewithin limits, those from super thermalpower stations within a small space may lead

to overall high concentration of SOx, leadingto acid rain. In such cases, removing SO

x by

scrubbing off flue gases with lime, known asFGD (flue gas desulphurization), may be-come necessary. This will lead to an increasein capital and operating cost. Literature sur-vey reveals that the increase in capital costwill be of the order of 15%–20%, and cost ofgeneration may increase by 10%–15%.

The FGD technology is fully establishedin advanced countries for the past two de-cades, and can be obtained for applicationsin India whenever required.

Presence of NOx in the flue gases of pul-

verized coal-fired boilers can be controlledat the combustion stage (through low-NOx

burners/overfire air) or through SCR (selec-tive catalytic reduction). In this process,NOx and NH3 (ammonia) react to form ni-trogen and water vapour. The capital cost ofSCR system is in the range 90–100 dollarsper kW of the installed capacity. The systemscan be designed both for high dust applica-tions (before subjecting dust to APH [airpre-heater] and low dust applications (aftersubjecting dust to ESP). However, Indialacks experience with respect to application.

Table 3.96 Contemporary gas turbines using natural gas as fuel—performance at ISO

conditions

Exhaust GT inlet CCPP

ISO rating Heat rate Efficiency flow temperature efficiency

Model (MW) (kcal/kWh) (%) (kg/s) (oC) (%)

V94.2 163.3 2496 34.5 526.0 1060 52.5

PG9171(E) 126.1 2545 33.8 418.0 1124 52.7

GT13E2 172.2 2363 36.3 532.0 1150 53.1

M701 144.1 2472 34.8 440.8 1120 51.4

MW – megawatts; kcal – kilocalories; kWh – kilowatt-hours; kg – kilogram; s – seconds; GT – gas turbine;

CCPP – combined cycle power plant


3.2.3.1.1.2 Supercritical steam

cycle

The steam cycle operating at steam pressureabove 225.36 ata is called supercriticalsteam cycle. At this pressure, the density ofwater and steam is same. Thus, there is noneed for a boiler drum that separates steamfrom water. The boiler used for this applica-tion is called once-through unit. The rest ofthe power plant remains the same, except thenumber of HP/LP (high pressure/low pres-sure) heaters chosen to optimize the cycle.The improvement in heat rates while adopt-ing supercritical parameters for Indian am-bient conditions is shown in Figure 3.18.

It may be seen from this figure that com-pared to the base case of steam parameters(170 ata/537 oC/537 oC), the improvementin heat rate will be 2.1% when steam param-eters adopted are 246 ata/537 oC/565 oC and5% when USC (ultra-supercritical) param-

eters of 306 ata/598 oC/598 oC are adopted.For a pithead 3 × 660 MW supercriticalstation, the capital cost saving projectedin 1999 was about 2.5% as compared to4 × 500 MW units. In developed countries,where the technologies for supercriticalpower plants are mature, the capital cost perkW is virtually the same as that of sub-criti-cal plants. Thus, selection of a sub-critical orsupercritical unit often depends upon apower producer’s experience and the pres-sure to reduce fuel consumption (givingbenefits of reduction of cost of power gen-eration as well reduced emissions of particu-lates, SO

X, NO

X, and CO

2).

In terms of operational availability andreliability, the EPRI (Electric Power Re-search Institute) study of supercritical plantsoperating in USA has confirmed that outagerates are comparable to drum-type units, af-ter initial period of learning of technologyoperations.

With the commercial in-troduction of new steel al-loys with higher allowablestresses and longer life at el-evated temperatures, a num-ber of power plants withUSC parameters (above 280ata with double reheat or306 ata/598 oC/598 oC) havecome up in advanced coun-tries like Japan, EU, andUSA. Based on these suc-cesses, researchers continueto improve designs and ma-terials, and it appears thatthe USC plants with mainsteam parameters of 357 ata/625 oC/625 oC will becomefully commercial in the next5–10 years.

Figure 3.18 Improvement in heatrates with steam parameters


3.2.3.1.1.3 Advance class gas

turbines

With the increase in the cost of premium fu-els like natural gas, naphtha, and LNG, thereis an ever-increasing pressure on gas turbinedesigners and manufacturers of higher effi-ciency combined cycle systems to producepower at competitive rates compared tocoal-fired plants. The improved efficiencyobviously leads to reduction in emissions ofSOx, NOx, and CO2 also.

Introduction of advance class turbineswith inlet temperature in the range 1250–1350 oC has led to combined cycle powerplant efficiency of about 58% on LHV (lowheating value) basis and under ISO condi-tions (Table 3.97). Corresponding value inIndian conditions is in the range 55%–56.5%. A number of plants are in operationthroughout the world. However, there areonly a few in India (for example, 2 × 9 FA atDabhol and 3 × 6 FA at Kovilkallapal,Peringulam, and Dhuvaram). Advance classgas turbines with dry low NOx combustion

system using natural gas also generate lessthan 25 PPM (parts per million) NOx.

Further research to improve efficiency isin progress, and gas turbines employingsteam injection with gas inlet temperature of1430 oC and combined cycle efficiency of 60%are available commercially in the UK andUSA.

3.2.3.1.1.4 Coal-based

combined cycle systems

The approach towards further improvementin efficiency of, or reduction of pollutionfrom, coal-based power generation leads totwo thermodynamic cycles including gasturbine in topping cycle and a steam turbinein a bottoming cycle, and hence is calledcombined cycle. However, gas turbines needclean fuel gas or clean flue gas. Therefore,use of coal calls for its conversion to cleancombustion products or coal gas at highpressure. Two technologies have been devel-oped: (a) PFBC and (b) IGCC.

Table 3.97 Advance class gas turbines—performance at ISO conditions

Exhaust CCPP

ISO rating Heat rate Efficiency flow GT inlet/exhaust efficiency

Model (MW) (kcal/kWh) (%) (kg/s) temperature (oC) (%)

V94.3A 278.0 2239 38.4 670.0 1300/582 57.5

9FA 255.6 2331 36.9 641.0 1300/602 57.1

GT26 281.0 2245 38.3 631.7 1280/615 57.8

M701F 270.3 2250 38.2 650.8 1350/586 57.3

M701G 334.0 2180 39.4 736.8 1400/587 58.7

MW – megawatts; kcal – kilocalories; kWh – kilowatt-hour; kg – kilogram; s – seconds; GT – gas turbine;

CCPP – combined cycle power plant


3.2.3.1.1.4.1 Pressurized fluidized

bed combustion

In the PFBC concept, the conventional com-bustion chamber of the gas turbine is re-placed with PFB combustor (bubbling orcirculating) and hot gas clean-up system.The combustion products pass through gasturbine and the heat recovery steam genera-tor. The system is thus a combined cycle,which is capable of giving generation effi-ciency 5%–6% higher than sub-criticalsteam cycle plants. Therefore, the system is astrong competitor for USC steam cycle.

Six commercial PFBC demonstrationplants (each less than 100-MW capacity) areoperating around the world. The applicationis generally CHP (combined heat andpower). A 360-MW unit based on ABB tech-nology and a 250-MW unit based on Hitachitechnology were commissioned in 2003/04in Japan. The operating experience obtainedfrom these units will have a strong influence onthe future of commercial PFBC technology.

In India, only BHEL has done R&D workon pilot-scale PFBC, and tested combustioncharacteristics of few coal types. Recently,they have also tested ceramic-candle-basedhot gas clean-up system. The data generatedwill be useful in designing a demonstrationplant in India.

3.2.3.1.1.4.2 Integrated

gasificaiton combined cycle

Coal gas can be produced by reacting coalwith air/steam or oxygen/steam; the formerreaction produces low CV (calorific value)gas whereas the latter reaction produces me-dium CV gas. For combined cycle operation,

it is economical to adopt pressurized gasifi-cation. The hot raw gas from the gasifier iscooled by generating steam through HRSG(heat recovery steam generation). This steamis integrated in the combined cycle with thesteam produced from HRSG downstream ofthe gas turbine. Part of the steam producedis used in the gasifier. Thus, the cycle iscalled IGCC.

Typically, the IGCC efficiency is theproduct of the gasifier efficiency (achievable90%) and the combined cycle efficiency(55% with contemporary gas turbines, asexplained in Section 4.3), giving a value of41%–42% compared to 40% achievablethrough USC steam cycle. This will propor-tionately reduce CO2 emission. The SOx

emission can be brought down to 40–115mg/Nm3, as the sulphur is removed in thegasification process itself. The NOx emissionhas also been reported to reduce to levelsbelow 125 mg/Nm3. A number of commercialplants using coal or refinery residues as fuelhave come up all over the world (Table 3.98).

The main barriers to widespread adop-tion of IGCC technologies are: (a) high capi-tal cost compared to pulverized coal plantand (b) demonstration of high availability, atleast equal to existing PC plants. However,the costs are coming down. A recent jointstudy by Texaco, General Electric, andPraxair has shown that for a 550-MW powerblock, with the introduction of 9H gas tur-bine technology with firing temperature inthe range 1400–1450 0C, the efficiency,capital cost, and cost of generation have sig-nificantly improved (Figure 3.19) for the pe-riod 1994–2000.

In India, pioneering work has been doneon coal-based IGCC by BHEL on a 6.2-MWe pilot plant at Trichy, using both pres-surized moving bed gasifier and PFBG.


Table 3.98 Integrated gasification combined cycle experience in the world

Project Process Start-up Output Feed Power block

GSK (Japan) Texaco 2001 540 MW VB Tar 2xGE 9EC

Fife Power (Scotland) BGL 2000 400 MW Coal/RdF 2xGE 9FA

Shell Pernis (the Netherlands) Shell 1997 120 MW + H2 Heavy oil 2xGE 6B

Sierra Pacific (1) (Nevada) KRW 1998 100 MW Coal GE 106 F

Elcogas (Spain) Pernflow 1998 300 MW Coal/coke KWU V94.3

ISE (Italy) Texaco 2000 520 MW Asphalt 2xKWU V94.3

SARAS (Italy) Texaco 2000 550 MW VB Tar 3xGE 109E

Star (Delaware) Texaco 1999 240 MW Petcoke 2xGE 6FA

API (Italy) Texaco 2000 275 MW VB Tar ABB 13 E2

Cool Water (California) Texaco 1984 120 MW Coal GE 107E

Dow Plaquemine (USA) Destec 1986 220 MW Coal GE 107E

Demkolee (the Netherlands) Shell 1993 250 MW Coal KWU V94.2

Tampa Electric (Florida) Texaco 1996 260 MW Coal GE 107 FA

Texaco-Eldorado (Kansas) Texaco 1996 40 MW Petcoke GE 6B

PSI-Wabash (1) (Indiana) Destec 1996 262 MW Coal GE 7FA

Schwarze/Pumpe (Germany) Noell 1996 40 MW Coal/oil GE 6B

Fife Power (Scotland) BGL 1999 120 MW Coal/sldg GE 106FA

Total (France) Texaco 2004 365 MW Ref. residue ABB

EXXON (USA) Texaco 1999 40 MW Petcoke GE 6B

EXXON (Singapore) Texaco 2000 180 MW Ref. residue 2xGE 6FA

NPRC (Japan) Texaco 2003 340 MW Asphalt

Repsol (Spain) Texaco 2004 824 MW Ref. residue

CITAGO (USA) Texaco 2004 350 MW Petcoke

MW – megawatts; BGL – British gas Lurgi; KRW – Kellogg–Rust–Westinghouse

Based on this work, design of a 100-MWIGCC demonstration plant with PFBG hasbeen developed. It is learnt that BHEL andNational Thermal Power Corporation arejointly working for setting up a plant of thisrating. Also a techno-economic feasibilitystudy for a 500-MW IGCC plant is beingworked out. The Council of Scientific andIndustrial Research has also published in

1992 a feasibility assessment report ofIGCC for a 500–600 MW plant with theprimary objective of selecting gasificationtechnology for its application for high-ashIndian coal (base case of North Karanpuracoal with HHV [high heating value] of3332 kcal/kg). This study gave the costcomparison as presented in Table 3.99.


Figure 3.19 Economic impact of integrated gasificationcombined cycle design study improvements

natural gas for power genera-tion is picking up, the advan-tages being no particulatematter pollution and reducedCO2 emission per kWh ofpower generated. The presentenvironment policy definesprimarily for particulate mat-ter control, but gives no strictconformance standards forother gaseous pollutants likeSO

x, NO

x, and CO

2 (except a

gazette notification of theMinistry of Environment andForests stipulating NO

x

emissions for gas turbines).The higher chimney heightmay disperse SO

x and NO

x in

low concentrations overlarger area, but does not re-duce/eliminate their effects.Besides, the international

Table 3.99 Cost comparison of different IGCC technologies (1989 pricing)

IGCC plant PC plant

Entrained Fluidized Moving Without With

bed bed bed FGD FGD

Net power output (MW) 564.40 496.20 577.20 585.70 549.00

Capital cost ratio 2.17 1.33 1.36 1.00 1.22

Cost of generation ratio 1.94 1.18 1.32 1.00 1.17

MW – megawatts; IGCC – integrated gasification combined cycle; PC – pulverized coal; FGD – flue gas

desulphurization

protocols in future may require limitingemissions of CO

2 and NO

x, the greenhouse

gases that lead to global warming.The integrated policy for technology and

environment for thermal power generationshould encompass the following actionplans.

3.2.3.1.2 Technology and

environment policy

Coal is the primary fuel for thermal powergeneration in India. In the process, it givesrise to atmospheric pollution due to particu-late matter, SO

x, NO

x, and CO

2. The use of


� All future coal-based thermal powerplants of 250 MW and above should havesupercritical steam parameters. Immedi-ately, studies should also be initiated forultra-supercritical steam parameters andthe aim should be to establish these plantsin next six to seven years.

� CFBC (circulation fluidized bed combus-tion)-based plants of 250 MW, with highsulphur lignite and petcoke, and very highash coal/washery rejects should beencouraged.

� Environment (Protection) AmendmentRules, 1997, for using washed coal for theplants located beyond 1000 km should beenforced without giving further exten-sion. This will definitely reduce the prob-lems related to particulate emissions andfly ash disposal.

� All the generating stations should be di-rected to examine the techno-economicfeasibility of using blended coal in a mix-ture of high-ash and good quality coalfrom other mines in India or through im-port of coal. This can be easily establishedthrough generation efficiency (specificfuel consumption) tests on an operatingstation.

� Benchmark for the introduction of IGCCtechnology in India should be seven toeight years. A decision for a 250–300-MW commercial demonstration plantshould be taken up immediately.

� Regular energy audit of operating plantsfor generation efficiency should be mademandatory, and the recommendations forimprovements should be implemented.This is possible under the Energy Conser-vation Act, 2001.

� The new Electricity Tariff Policy (draftcirculated in March 2004) shouldsuitably reward improvements in energy

efficiency through sharing the benefitsbetween the power generator and the con-sumer.

� The coal pricing should be linked to thecalorific value of the delivered fuel so thatsupplier has an incentive to improve qual-ity and the power generator gets good andconsistent quality of fuel.

� The development of advanced technologyfor thermal power generation is veryclosely linked to the environment policywith respect to emissions of particulatematter, SOx, NOx, and CO2. It takes 10–15 years for introduction of any new tech-nology. Thus, we must have long-term en-vironment policy to guide the develop-ment and introduction of new technologies.

3.2.3.1.3 Technology forecast till

2030

It is felt that till 2007, no new technologywill be introduced. All new plants will bebased on sub-critical steam parameters. Inaddition, stress will be on renovation andmodification or performance improvementof old power plants to get higher output/PLFfrom them.

The technology for natural gas-/naphtha-fired combined cycle plants will also not un-dergo much change except that plants withTech. FA will be introduced at few sites.

The period 2007–12 will see the commis-sioning of the first thermal plant withsupercritical steam parameters, and also set-ting up of a 100-MW coal-based IGCCplant. This period may also see the introduc-tion of the first combined cycle plant basedon gas turbine with Tech. H. Based on theexperience gained from the introduction of


these new technologies, more plants willcome up in the subsequent plan period of2012–17. During this period, the firstcommercial IGCC technology will come up.This will also set up the trend for the refin-ery rejects—vistar- and petcoke-fired IGCCplants.

During 2017–22, the first coal-firedpower plant with ultra-supercritical steamparameters is also likely to come up. Thiswill be followed by the introduction of thistechnology fully. Then, till 2030, no newtechnology will be introduced. However,further improvement in steam temperaturemay be witnessed. This will mainly dependupon the development of high-temperaturemetallic alloys internationally.

During 2022–27, a demonstration plantfor the generation of power using natural-gas-based solid oxide fuel cell technologymay come up, and the first commercial plantbased on this technology will then come upduring 2027–32. During this decade, newcoal-based plants will be based on ultra-supercritical steam parameters.

3.2.3.2 Hydroelectric potential

India is endowed with economically viablehydro potential. The CEA (Central Electric-ity Authority) has assessed India’s hydropower potential to be about 148 700 MW ofinstalled capacity. The hydroelectric capac-

ity currently under operation is about 26000 MW and 16 083 MW capacity is undervarious stages of development. The CEA hasalso identified 56 sites for pumped storageschemes with an estimated aggregate in-stalled capacity of 94 000 MW. In addition, apotential of 15 000 MW in terms of installedcapacity is estimated from small, mini, andmicro hydel schemes.

It may be noted that due to lower cost ofper unit power generation by large hydro,this option is introduced into the model asan upper bound over the modelling timeframe as shown in Table 3.100. Hydro ca-pacity utilization is assumed to be 32%.

3.2.3.3 Nuclear energy resources

Nuclear energy has the potential to meet thefuture electricity demand of the country.The country has developed the capability tobuild and operate nuclear power plants ob-serving international standards of safety.The current installed capacity of nuclearpower plants is 2860 MW, accounting for2.8% of the total installed capacity of thecountry. The NPCIL (Nuclear Power Cor-poration of India Ltd) proposes to increasethe installed capacity to 9935 MW by 2011/12. The future strategies focus on a three-stage nuclear power programme for the opti-mal utilization of the available nuclearenergy resources. The first stage of 10 000

Table 3.100 Upper bound on installed capacity of large hydro-based power generation

(in GW)

2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32 2036/37

24.9 37.0 60.54 84.08 107.63 131.17 150.0 150.0


MW of nuclear power generation is based onPHWR (pressurized heavy water reactor)technology using indigenous natural ura-nium resources. The second stage is pro-posed to be based on FBR (fast breederreactor) technology using plutonium ex-tracted by reprocessing of the spent fuel ob-tained from the first stage. In the third stage,the country’s vast thorium resources will beutilized for power generation.

India has limited availability of uraniumresources (about 70 000 tonnes), but hasone of the largest resources of thorium in theworld, amounting to 360 000 tonnes. There-fore, India needs to adopt a fuel cycle thatmaximizes the energy yield of the nuclearenergy producing ores. The adoption of thethree-phase development of nuclearprogramme in India was envisaged by DrHomi Bhabha way back in 1944. India iscurrently in the second phase where theFBRs are to be commissioned.

India’s nuclear programme is describedbelow in three stages.1 Stage I construction of natural uranium-

based, and pressurized heavy-water-mod-erated and cooled reactors. Spent fuelfrom these reactors can be reprocessed toobtain plutonium.

2 Stage II construction of FBRs fuelledby the plutonium produced in Stage I.These reactors are also to breed U-233from thorium.

3 Stage III power reactors using U-233/thorium as fuel.

India’s uranium resource base can onlysupport 10 000 MW of power generationthrough the PHWR route, which is the StageI of India’s nuclear programme. Stage II,that is, the FBR route, will require the pluto-nium derived from the Stage I. This has

technological limitations with respect to theproduction of plutonium by using the fuel inthe oxide form. If the FBRs are fuelled byusing metallic fuel, the rate of plutoniumgeneration is twice as fast as the MOX (me-tallic oxide) route, which will generate therequired fuel for rapid growth of FBRs. In-dia currently has the experience and capabil-ity to use only MOX-derived fuels and itneeds to invest in the development of metal-lic fuel based reactors. Therefore, it cur-rently needs international cooperation tomeet its fuel requirements in the Stage II sothat the FBRs become self-sustaining.

In the model, nuclear-energy-based powergeneration has been included as per the gov-ernment plans. The installed capacity of thenuclear-energy-based power generation in2001/02 was 2820 MW and increased to 3310MW as on 31 January 2006. This capacity isexpected to increase to 6780 MW by 2010 and21 180 MW by 2020. Accordingly, as shown inTable 3.101, we expect 21.18 GW of nuclear-energy-based capacity to materialize by 2020under the baseline as well alternative sce-narios. Beyond 2021, in the baseline scenario,we assume that availability of nuclear fuelwould be constrained and that the generationcapacity would remain constant from 2021 till2035 in the baseline. However, in the alterna-tive scenario that considers an aggressivepursuit of nuclear-energy-based power gen-eration, we consider the nuclear generationcapacity to increase to 70 GW by 2031/32 bybeing able to import nuclear fuel (enricheduranium) (Table 3.101).

Beyond 2030, enough plutonium is ex-pected to be generated so that the thorium–plutonium fuel cycle (advanced fast breederreactors) can be commissioned. This couldenable a maximum potential generationcapacity of about 530 GW (after 2030).


3.2.3.4 Renewable energy sources

India is endowed with abundant natural andrenewable sources of energy like sun, wind,and biomass. The country has been ableto achieve significant capacity addition of1367 MW through wind farms and ranksfifth in the world after Germany, USA,Spain, and Denmark in the generation ofwind energy, as per 2004/05 data. The avail-able renewable resources need to be ex-ploited by giving a commercial orientationwherever possible. It may be necessary tocontinue with subsidies in the case of so-cially oriented programmes to meet the en-ergy requirements of rural areas,particularly, remote villages, which may bedifficult to service through the conventionalpower grid in the near future. Table 3.102gives the available potential and the actualpotential exploited till August 2001 for vari-ous renewable sources of energy as providedby the MNES (Ministry of Non-conven-tional Energy Sources).

Apart from these resources, the countryhas significant potential for ocean thermalpower, sea wave power, and tidal power,which at this point of time are not expectedto be realized due to high cost.

Renewable natural sources, such as biom-ass, wind, water, and solar energy, have beenincluded in the model. The RETs (renew-

able energy technologies) are environmen-tally sustainable and have a vast potentialthat can be exploited for energy generationin the future.

3.2.3.4.1 Wind energy

Wind-based generation capacity has beenrapidly growing in India. The installed windpower capacity increased from 40 MW at thebeginning of the Eighth Plan to 992 MW inDecember 1998 (MNES 2004). The poten-tial of wind farms is estimated at 28 910 MWor 1038 TWh (terrawatt-hours) (TERI 1995).

3.2.3.4.2 Solar energy

Apart from using solar energy for the gen-eration of grid-based power, decentralizedsolar devices are also included in the model.PV (photovoltaic) systems have emerged asuseful power sources for applications such aslighting, water pumping, telecommunica-tions, and power for meeting the require-ments of villages, hospitals, lodges, and soon. Based on the reports from the stateimplementing agencies, 15 206 home-light-ing systems, 20 484 solar lanterns, and 437street lighting systems were installed in1997/98.

Table 3.101 Installed capacity of nuclear energy based power generation

Expected installed capacity (GW)

Scenario 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32 2036/37

BAU 2.8 3.31 6.78 13.98 21.18 21.18 21.18 21.18

NUC 2.8 3.31 6.78 13.98 21.18 45.5 70 70

BAU – business-as-usual; GW – gigawatts; NUC – High nuclear capacity


3.2.3.4.3 Small hydel

The total potential for small hydro power upto 3 MW in India has been estimated atabout 10 000 MW (MNES 2004/05). Theinstalled capacity of units less than 3 MWwas 170 MW in 1997 while an additional191 MW capacity was under construction.Their installed capacity as on 31 March1998 was 155 MW.

The MNES has identified the potentialfor small hydel sites of up to 3 MW as2852 MW and for sites between 3 and15 MW as 5519 MW (MNES 1999).

3.2.3.4.4 Biomass gasifiers

Decentralized biomass-based power plantsare ideal in cases where it is either too costlyto extend the grid or the power demand isvery low.

Biomass is produced by numerous smallagro-processing industries such as cigarettefactories, cashew-processing units, andayurvedic medicine manufacturing units.The main problem is of collecting and trans-

porting the biomass to places where it maybe required.

The biomass yield is estimated at35 tonnes/hectare/year, and biomass con-sumption is 1.2 kg/kWh, assuming a PLF of60% for biogas plants.

Due to the poor quality and unreliabilityof the grid, industries in many states areforced to switch over to diesel-based captivepower generation. The low cost of procuringbiomass makes it desirable to couple thesegensets with gasifiers. Dual-fuel (gasifierand diesel) electric power generators, there-fore, offer a great potential for fuel savingand decentralized power generation.

Till 1998, more than 1000 wood gasifiershave been installed in the country with agenerating capacity of 14 MW. A 0.5-MWgrid-connected gasifier-based R&D projectwas also commissioned in 1997.

3.2.3.4.5 Biomass consumption

The MNES has already implemented threemajor BCPPs (biomass-consumption-basedpower projects). A 6-MW prosopis-based

Table 3.102 Renewable energy source potential

Potential/ Potential

Source/technology Unit availability exploited

Biogas plants Million 12 3.22

Biomass-based power MW 19 500 384.00

Efficient wood stoves Million 120 33.86

Solar energy MW/km2 20 1.74

Small hydro MW 15 000 1398.00

Wind energy MW 45 000 1367.00

Energy recovery from wastes MW 1700 16.20

MW – megawatts; sq km – square kilometres


project was set up in the state of AndhraPradesh in June 1999 and a 5-MW rice-husk-based project in Madhya Pradesh wascommissioned in August 1999. A 12-MWcane trash and bagasse-based private sectorproject, supported by IREDA (Indian Re-newable Energy Development Agency),came up in Tamil Nadu.

During 1994–97, 18 BCPPs with a totalcapacity of 69 MW were installed for supply-ing power to the grid. So far, over 100 mil-lion units of electricity have been fed to thegrid from these plants. Seventeen projectsaggregating 97 MW are under implementa-tion and once these are commissioned, morethan 800 million units will be fed to the gridsevery year, saving 0.5 MT of coal.

3.2.3.4.6 Cogeneration potential

from bagasse

The biomass waste generated from the sugarindustry has a large potential for generatingpower. Although the total installed capacityas on 31 March 1998 is only 82 MW, it hasbeen estimated that nearly 3500 MW ofpower can be generated from this industry ifthe existing sugar mills adopt modern tech-niques of cogeneration. High capital invest-ment costs and lack of proper mechanismsfor pricing and wheeling of power exportedby the cogenerating industries are the mainobstacles to the development of this technol-ogy at this stage.

3.2.3.5 Traditional fuels

Biofuels play an important role in the energyscenario of the developing countries. Interms of their use in physical energy, biofuelsare very much similar to coal. However, dueto their low calorific content as well as lowend-use efficiencies associated with theiruse, the useful energy demand met by thesesources is much smaller.

The Indian residential sector continues tobe dominated by biofuels, with about 95% ofrural households and 40% of urban house-holds still relying mainly on these traditionalenergy forms. All these fuels are generallycollected free of cost and do not find theirway to commercial markets. Moreover, thesupply RES (reference energy system) of thetraditional energy forms is simplistic andconsists of only the domestic availability ofthe resource, as there are no associated im-ports or exports for these fuels.

3.2.3.5.1 Fuelwood

According to the IREP (Integrated RuralEnergy Programme 1992), the supply offuelwood was estimated at 169 MT (3294 PJ[petajoules]). However, this level offuelwood use is considered to be unsustain-able in the long run. The sustainablefuelwood supply is, therefore, estimatedbased on future estimates of the area underforests and a sustainable yield of 55 tonnes /km2 of forestland. In 1997, the area underforests was 63 million hectares10 and this isprojected to increase to 93 million hectares

1010101010 100 hectares = 1 km2.


by 2020 (DISHA 2000). The supply offuelwood is assumed to decline from the cur-rent levels to 51.2 MT (998 PJ) by 2020, andremain at this level henceforth. The supplyof fuelwood is considered at zero cost in themodel.

3.2.3.5.2 Dung

Dung and crop residue are generally used byhouseholds that own cattle or farmlands.Therefore, the issue of unsustainable use ofthese fuels as in the case of fuelwood doesnot arise. However, estimates on the avail-ability and use of dung and crop residue varywidely.

The supply of dung depends on the cattlepopulation in the country, the proportion ofdung collected, and the share used for pro-ducing energy. Dung has a calorific value of3290 kcal/kg. Estimates on the availability ofdung range from 30 MT to 100 MT for2001. The model assumes a dung availabilityof about 100 MT at zero cost. The REDB(rural energy database) estimates an averageavailability of 106.9 MT of dung. The use of

dung is, therefore, really constrained by therestrictions on utilization levels of technolo-gies using the fuel and the share of popula-tion using this form of energy in the future.Dung can be used directly in the form ofdung cakes for cooking in the traditionalcook stoves or in the form of biogas that is acleaner form of using energy.

3.2.3.5.3 Crop residue

Biomass production is pegged at 127 MT/year, of which half goes to the sugar industry.With a calorific value of 3500 kcal/kg, itsproduction is kept constant at 912 PJ in themodel.

3.2.3.6 Power generation tech-

nologies: techno-economic input

parameters

Table 3.103 provides the characteristics ofall the power-generating technologies inputto the model.

Se

cto

ral d

em

an

d p

roje

ctio

ns, te

ch

no

log

ica

l ch

ara

cte

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n, a

nd

reso

urc

es a

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ilab

ility137

Table 3.103 Techno-economic parameters of power generating technologies

Annual

operation and

Capital maintenance

Availability cost (million cost (million Life Efficiency

Technology factor Plant characteristics rupees /GW) rupees/GW) (years) (%)

Coal-fired plant–old (before 1980) 0.58 Base load Centralized Sunk costs 988 10 22.7

Coal-fired plant–old (after 1980) 0.58 Base load Centralized Sunk costs 988 30 29.5

New coal plant (sub-critical) 0.85 Base load Centralized 39 547 988 30 32.3

Retrofit coal plant (first built before 1980) 0.85 Base load Centralized 15 000 988 30 30.0

Retrofit coal plant (1980–2000) 0.85 Base load Centralized 12 500 850 30 32.2

CFBC 0.85 Base load Centralized 45 653 1141 30 39.0

IGCC (refinery residue) 0.85 Base load Centralized 52 753 1141 30 46.0

IGCC (coal) 0.85 Base load Centralized 52 753 1141 30 44.0

Coal supercritical 0.85 Base load Centralized 42 600 1065 30 37.7

Coal pressurized bed combustion 0.85 Base load Centralized 45 653 1141 30 43.0

Coal ultra-supercritical 0.85 Base load Centralized 51 120 1331 30 44.0

Lignite power plant (existing subcritical tech) 0.58 Base load Centralized 40 000 988 30 29.5

Small generator set (2 kW) 0.20 Base load Decentralized 27 000 712.5 10 25.0

Existing open cycle gas based 0.90 Standard Centralized Sunk costs 520 20 28.0

Existing combined cycle gas based plant 0.90 Base load Centralized Sunk costs 399 25 44.1

New open cycle gas based plant 0.90 Standard Centralized 15 975 240 20 39.0

NGCC (New) 0.90 Base load Centralized 22 000 330 25 53.8

NGCC (New high efficiency) 0.90 Base load Centralized 27000 405 25 60.0

Hydro reservoir – new Fixed capacity Standard Centralized 40 000 600 50 32.3

Small hydro – grid connected Fixed capacity Standard Centralized 90 000 1350 40 32.3

Heavy water reactor 1 (using natural uranium) 0.90 Base load Centralized 60 000 1500 25 21.4

Light water reactor 1 (using enriched uranium) 0.90 Base load Centralized 78 750 1969 25 17.0

Decentralized electricity from fuelwood 0.20 Standard Decentralized 27 000 713 15 21.7

Solar photovoltaic with battery bank 0.29 Standard Decentralized 300 000 4500 25

Solar photovoltaic without battery bank 0.29 Standard Decentralized 200 000 1000 25

Grid interactive solar photovoltaic power Fixed capacity Standard Centralized 250 000 1250 25

Wind turbines Fixed capacity Standard Centralized 38 000 570 20

CFBC – circulating fluidized bed combustion; IGCC – integrated gasification combined cycle; NGCC – natural gas combined cycle; Rs/GW – rupees/gigawatts;

kW – kilowatt

Energy scenarios

44444

4.1 Introduction

Scenarios are images of alternative futures.Energy scenarios provide a framework forexploring future energy perspectives, includ-ing various combinations of technology op-tions and their implications. Many scenariosin the literature illustrate how energy systemdevelopments will affect national and inter-national issues. Scenarios are neither predic-tions nor forecasts. Each scenario can be in-terpreted as one particular image of how thefuture could unfold. Scenarios are usefultools for investigating alternative future de-velopments and their implications, for learn-ing about the behaviour of complex systems,and for policy-making. Some scenarios de-scribe energy futures that are compatiblewith sustainable development goals, such asimproved energy efficiencies and adoptionof advanced energy supply technologies.Sustainable development scenarios are alsocharacterized by low environmental impacts(local, regional, and global) and equitableallocation of resources and wealth.

Sustainable development has become asynonym for desirable transitions into thenew millennium. This is often reflected inenergy scenarios that consider conditions forachieving sustainable development. Becauseenergy systems change slowly, energyscenarios have long time horizons—often

extending over 100 years into the future.These long time periods are needed to allowtransition to sustainable development paths.

4.2 Brief review of the literature

on energy scenarios

The development of scenarios to investigatealternative future developments under a setof assumed conditions dates far back in his-tory. Scenarios were, and continue to be, oneof the main tools for dealing with the com-plexity and uncertainty of future challenges.

Perhaps most famous in the literature isthe use of scenarios by the Shell Group inthe wake of the so-called oil crisis to plan itscorporate response strategies (Schwartz1991). Today, scenarios are quite widespreadand are found in enterprises of all kindsaround the world. Many are quantitative, asis often the case with enterprises in the en-ergy sector. Some of them also include con-cepts of sustainability. Recently, theWBCSD (World Business Council for Sus-tainable Development) presented a set ofscenarios that were developed in collabora-tion with 35 major corporations (WBCSD1998).

A number of global studies have usedscenarios as a tool to assess future paths ofenergy system development over the past


30 years. One of the first global studies toemploy scenarios for this purpose was con-ducted by the IIASA (International Institutefor Applied Systems Analysis) during thelate 1970s (Hafele 1981). Another influen-tial series of scenarios that included the as-sessment of sustainable development wasdeveloped by the WEC (World EnergyCouncil) (WEC 1993). The IPCC (Inter-governmental Panel on Climate Change) hasused scenarios since its inception to assessgreenhouse gas emissions and climatechange. In 1992, it developed a set of verywidely-accepted scenarios that gave a de-tailed account of energy sector develop-ments. The set includes six scenarios calledIS92, three of which describe futures thatinclude characteristics of sustainable devel-opment (Pepper, Leggett, Swart, et al.1992).

A growing number of global studies con-sider futures with radical policy andbehavioural changes to achieve sustainabledevelopment (Goldemberg, Johansson,Reddy, et al. 1988). One of the first globalscenarios to focus on achieving sustainabledevelopment was put forward byGreenpeace International (Lazarus, Greber,Hall, et al. 1993). Another among the firstglobal energy scenarios, with characteristicsof sustainable development, describes atransition to renewable energy futures(Johansson, Kelly, Reddy, et al. 1993). In itssecond assessment report, the IPCC alsoconsidered a range of global energy sce-narios, based on some elements of the IS92set, with varying degrees of sustainability(Ishitani, Johansson, Al-Khouli, et al. 1996).

In more recent studies, sustainable devel-opment scenarios are usually includedamong other alternative futures. This class ofsustainable scenarios can be characterized

by low environmental impacts at all scalesand more equitable allocation of resourcesand wealth relative to current situations. Re-cently, the Global Scenario Group presenteda set of three scenarios that received consid-erable attention (Raskin, Gallopin, Gutman,et al. 1998). These scenarios were based onelaborate narratives describing alternativefutures, including some that are decisivelysustainable. The set of scenarios developedby the WBCSD also includes narratives anddescribes alternative development paths,some of which place strong emphasis on sus-tainable development (WBCSD 1998).

There is also substantial literature on glo-bal energy scenarios that serves as a refer-ence for showing that under business-as-usual conditions, many of the developmentscrucial for the achievement of sustainabilitywould not be realized. Many of these globalenergy scenarios are limited to develop-ments during the next 20–30 years.

The literature on sustainable energy sce-narios is vast, and this brief review cannotgive a comprehensive account. The IPCChas developed a database that includes anumber of global energy scenarios that canbe characterized as describing sustainabledevelopment (Morita and Lee 1998). Thisdatabase, which includes more than 400 glo-bal and regional scenarios, illustrates thatthe literature is quite rich. Not all the sce-narios can be described in this chapter.

The IPCC, in its recent Special report onemissions scenarios considers 40 scenariosthat include a large number of sustainablefutures (Nakicenovic, Alcamo, Davis, et al.2000). This set of scenarios is unique inmany respects—it was developed using sixdifferent models, covers a wide range ofalternative futures based on the scenariosin the literature, includes narrative

Energy scenarios 141

descriptions of alternative futures, and hasbeen reviewed extensively.

4.3 Energy scenarios for

sustainable development in India

Eight alternative development scenarios –namely, BAU (business-as-usual), LG (lowgrowth), HG (high growth), EEF (high effi-ciency), NUC (high nuclear), REN (aggres-sive renewable energy), HYB (hybrid), andHHYB (high-growth-cum-hybrid) – areanalysed in this exercise.

The aforementioned eight scenarios canbe broadly classified into two categories:(1) varying economic growth rate scenarios,and (2) technological progression scenarios.Economic growth scenarios are preparedbased on different projected GDP (gross do-mestic product) growth rates for theeconomy as a whole. However, technologicalprogression scenarios deal with varying levelsof technology penetration across different timehorizons in the modelling framework.

It may be noted that these eight scenariosprovide a holistic picture of the entire inte-grated energy system of the economy. In ad-dition to these economy-wide scenarios, al-ternative scenarios encompassing differentpolicy and technology options related to thetransport sector having a high share in theconsumption of petroleum products areanalysed in detail in view of high import de-pendency, especially in the case of petro-leum products.

The section below briefly explains theunderlying assumptions for each of theabove-mentioned scenarios.

4.3.1 Economy-wide scenarios

4.3.1.1 Business-as-usual scenario

This scenario is characterized as the mostlikely path of development in the absenceof any major intervention. This scenarioincorporates existing government plans andpolicies.

In the BAU, an 8% GDP growth rate(uniform growth rate over the entire model-ling time frame, 2001–31) reflects the Gov-ernment of India’s expectations as high-lighted in various government policy docu-ments.

The estimates regarding the domesticavailability of various fuels are also incorpo-rated in this scenario. Maximum availabilityof imported natural gas is considered as perthe Government of India’s plan fortransnational gas pipelines and the construc-tion of LNG terminals. However, there areno import constraints on coal and oil to sat-isfy energy demand.

With regard to technology penetration inthe power sector, limited deployment ofclean coal technologies is assumed. The pen-etration of various renewable energy tech-nologies is considered as per the existingtrend and expert opinion. The nuclear-en-ergy-based power generation capacity isconstrained to the extent of 21.18 GW (gi-gawatts) from 2021 onwards in view of thenon-availability of indigenous nuclear fueland import restrictions. The capacity real-izations of large hydroelectric plants to amaximum level of 150 GW are assumed asper the expectations of the Government of In-dia. Autonomous efficiency improvements arebuilt as per the current technological diffusionin both conversion and end-use sectors.


Thus, although a substantial improve-ment over the current situation, this scenariofalls short of achieving a transition towardssustainable development.

4.3.1.2 Low-growth scenario

This scenario assumes a low GDP growthrate of 6.7% relative to the 8% GDP growthrate assumed in the BAU scenario. Thus, theimpact of projected GDP growth rates onthe future trajectories of energy demand iscaptured by this scenario.

All other underlying assumptions with re-spect to resource availability, technologyprogression, and other parameters are simi-lar to those in the BAU scenario.

4.3.1.3 High-growth scenario

This scenario assumes a very high GDPgrowth rate of 10% (uniform over the mod-elling time frame, 2001–31) relative to theGDP growth rate of 8% assumed in the BAUscenario. This scenario paints an optimisticpicture of the Indian economy and envisagesthe ensuing influence a growth rate of suchmagnitude would have on overall energyconsumption in the country. It also reflectssignificant structural changes in the Indianeconomy by apportioning a greater percent-age (94%) of the GDP generated by the ser-vices and industry sectors relative to theGDP contributed by the agriculture sectorin the aggregate GDP. The macroeconomicshifts in the GDP amongst the agriculture,industry, and services sectors of theeconomy manifest themselves in the form ofchanging the demand of industrial outputand transport and commercial services,thereby exhibiting differences in the inter-

sectoral energy consumption patterns. Therationale for choice of different GDP growthrates was explained in Chapter 2.

As in the case of the LG scenario, allother underlying assumptions with respectto resource availability, technology progres-sion, and other parameters are similar tothose in the BAU scenario.

4.3.1.4 High-efficiency scenario

This scenario takes into account the energy-efficiency measures spanning across allsectors.

On the supply side, advanced gas-basedpower generation (for example, the H-framecombined-cycle gas turbine) with 60% effi-ciency is assumed to be commercially avail-able by 2016/17. Renovation and modern-ization of old coal plants are allowed only till2011 as per the government’s plan. In viewof the possibility of greater technology trans-fer across countries and a greater thrust onindigenous R&D (research and develop-ment) in the power sector in this scenario, allclean coal technologies are allowed to pen-etrate in an unconstrained manner to theirmaximum capacity from their year of intro-duction. The availability factor of windpower plants is assumed to increase from17.5% in 2001 to 26% in the year 2011 and35% in 2016 and onwards as compared tothe constant figure of 17.5% in the BAU sce-nario.

On the demand side, efficiency improve-ments, such as increased share of efficientelectrical appliances used to meet the de-mands for space-conditioning, lighting, andrefrigeration in residential and commercialsectors, are considered in various end-usesectors In addition, this scenario also incor-porates the faster rate of displacement of in-


ferior fuels like firewood and kerosene byclean fuels such as LPG (liquefied petro-leum gas) vis-à-vis the BAU scenario forcooking in the residential and commercialsectors. Furthermore, energy-efficient mea-sures in transport sectors in the form ofpolicy interventions by the government –such as increased share of rail vis-à-vis roadin passenger and freight movement, and pro-moting public transport – are also incorpo-rated in this scenario. The industry sectoralso boasts of measures that lead to signifi-cant energy savings. For instance, in the ironand steel industry, the penetration of effi-cient BF-BOF (blast furnace–basic oxygenfurnace) is allowed up to 80% of the manu-facturing capacity by the year 2036. Further,a higher share of blended cement in total ce-ment production (95% by the year 2031, upto 100% by the year 2036) is allowed, an in-creased share of natural gas (100% by theyear 2036) is used in the fertilizer and othersectors, etc. The details related to the level ofefficiency improvements in different sectorswere described in Chapter 3.

However, this scenario assumes a pro-jected GDP growth rate of 8% (uniform overthe modelling time frame, 2001–31) as inthe BAU scenario.

4.3.1.5 High nuclear capacity

scenario

In this study, nuclear-energy-based powergeneration has been included as per govern-ment plans. The installed capacity of nuclearpower plants was 2.82 GW in 2001/02 and

3.31 GW on 31 January 20061. The nuclear-energy-based power generation capacity isexpected to increase to 6.78 GW2 by 2010and further to 21.18 GW by 20203 as per thefirst stage Indian nuclear power programme.Beyond 2021, in the BAU scenario, thenuclear-energy-based power generation ca-pacity is constrained in view of the non-availability of indigenous nuclear fuel andthe import restrictions that have several geo-political dimensions associated with it. TheNUC scenario assumes importance in viewof the latest development in the nuclear sec-tor due to enhanced international civilnuclear cooperation and the Government ofIndia’s initiative in this direction. This sce-nario considers an aggressive pursuit ofnuclear-energy-based power generationwhereby the nuclear-energy-based genera-tion capacity is considered to increase to 40GW by 2021 and 70 GW by 2031/32, drivenby the assumption that the country is able toimport nuclear fuel (enriched uranium).

Table 4.1 presents the expected installedcapacity of nuclear-energy-based power gen-eration over the modelling time frame in theNUC scenario vis-à-vis the BAU scenario.

4.3.1.6 Aggressive renewable

energy scenario

In this scenario, high penetration of renew-able energy is considered. Anout 4233 po-tential sites are identified for small hydropower plants in the country. The corre-sponding capacity is worked out at about10 GW (MNES 2005a). It is assumed that

1 Ministry of Power, Government of India2 Nu Power, Vol. 18 (2–3), Department of Atomic Energy, 20043 Anil Kakodkar, Department of Atomic Energy


the maximum identified potential could betapped by 2016. Similarly, for wind powergeneration in India, gross potential is esti-mated at 49 GW (MNES 2005a). However,the technically feasible potential is reportedat 13 GW (MNES 2005a). In the RENscenario, it is assumed that 12 GW of windcapacity could be created by 2036. In thisscenario, in addition to the increase in thecapacity of wind-based power generation,the availability factor of wind power plants isalso assumed to increase from 17.5% in

2001 to 26% in the year 2011 and 35% 2016onwards.

Tables 4.2 and 4.3 present the level of theinstalled capacity of small hydro-based andwind-based power generation, respectively.

India is blessed with abundant sunshineas most parts of the country have 230–300sunny days in a year. Average daily solar ra-diation incident over the land area is in therange of 4–7 kWh/m2 (kilowatt hours persquare metre). The potential of SPV (solarphotovoltaic) power in India is estimated at

Table 4.1 Installed capacity of nuclear-energy-based power generation

Expected installed capacity (GW)

Scenario 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

BAU 2.82 3.31 6.78 13.98 21.18 21.18 21.18

NUC 2.82 3.31 6.78 13.98 40.0 55.0 70.0

GW – gigawatts; BAU – business-as-usual; NUC – high nuclear capacity

Note All other assumptions are similar to those in the BAU scenario.

Table 4.2 Installed capacity of small hydro-based power generation

Lower bound on installed capacity of small hydro in GW

Scenario 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

BAU 1.5 2.0 8.0 8.0 8.0 8.0 8.0

REN 1.5 2.0 8.0 10.0 10.0 10.0 10.0

GW – gigawatts; BAU – business-as-usual; REN – aggressive renewable energy

Table 4.3 Installed capacity of wind-based power generation

Lower bound on installed capacity of wind turbine in GW

Scenario 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

BAU 1.63 4.23 4.23 4.23 4.23 4.23 4.23

REN 1.63 5.00 7.00 8.00 9.00 10.00 11.00

GW – gigawatts; BAU – business-as-usual; REN – aggressive renewable energy


20 MW/km2 (megawatts per squarekilometre) (MNES 2005a). The current costof an SPV cell is 150 rupees/ W

p (watt peak)

(MNES 2005b). Because of the high cost ofsolar cells, the cost of electricity generationfrom SPV is also very high. For example, thecost of electricity generation from a grid-in-teractive SPV system without storage is esti-mated at 20 rupees/kWh (MNES 2005b).For stand-alone systems, the cost of genera-tion is higher due to the additional cost ofthe battery. However, the National new andrenewable energy policy statement 2005 of theMinistry of Non-conventional EnergySources reports that the cost of generation isexpected to reduce to the level of 4 rupees/kWh by 2021/22 (MNES 2005b). Becauseof high capital costs, the current installed ca-pacity of the SPV system is only 2.25 MW(GoI 2005). However, SPV production inthe country is increasing by an annual aver-age growth rate of 25%. It is assumed thatthe installed capacity of an SPV-based powerplant will increase up to 20 GW in 2036 inthe REN scenario.

Biomass can be used as a primary fuel bydirect combustion or as a secondary fuel(solid, liquid, and gaseous) by conversion abiological or thermochemical using process.The main aim of the conversion process is toincrease efficiency of utilization for various

end-uses. Biomass gasification is basicallythe conversion of solid biomass into a pro-ducer gas, which has carbon monoxide as acombustible gas. Several institutes includingTERI are engaged in the R&D of gasifiertechnology in India. The potential for biom-ass-based power plants has been estimatedto be 16 GW, of which 234 MW has been es-tablished so far, and a target of installationof 250 MW of biomass-based power is set forthe Tenth Five Year Plan (2002–07). Table4.4 presents the lower bound on the installedcapacity of SPV- and biomass-based powergeneration in the REN scenario.

In addition to the power generation tech-nologies, bio-diesel is also assumed to beavailable to the transport sector in this sce-nario. Based on the maximum potential areathat is available for plantation for bio-dieselproduction, the lower bound is imposed onthe availability of bio-diesel in the REN sce-nario. Table 4.5 below presents the availabil-ity of bio-diesel for transportation in India(detailed assumptions were given in theanalysis of the transport sector in Chapter 3)

However, this scenario assumes a pro-jected GDP growth rate of 8% (uniform overthe modelling time frame) as in the BAUscenario. All other assumptions are similarto those in the BAU scenario.

Table 4.4 Installed capacity of SPV- and biomass-based power generation in aggressive

renewable energy scenario

Lower bound on installed capacity

2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

SPV (GWp) 0.00 0.05 0.14 0.39 1.04 2.78 7.46

Biomass (GW) 0.00 0.25 0.50 1.00 2.00 4.00 8.00

GW – gigawatts; GWp – gigawatt peak; SPV – solar photovoltaic


4.3.1.7 Hybrid scenario

This scenario is a combination of the BAU,EEF, REN, and NUC scenarios. It describesthe energy future of the Indian economy byincorporating the entire range of energy-effi-cient measures in the end-use sectors, thecomplete deployment of clean coal tech-nologies, aggressive penetration of nuclear-energy-based power generation technolo-gies, and an aggressive push towards renew-able energy sources.

4.3.1.7 High-growth-cum-hybrid

scenario

This scenario combines a high GDP growthrate of 10% coupled with high efficiency lev-els, high nuclear capacity, and an aggressiveuse of renewable energy. This scenario isrepresentative of the most optimistic sce-nario in terms of both economic growth and

technological advancements geared towardssteering the economy on the most energy-ef-ficient path.

4.3.2 Intra-sector scenarios:

transport sector illustration

The transport sector is a major consumer ofpetroleum products. From the point of viewof energy security concerns for the Indianeconomy at large, five alternative scenariosin addition to the BAU have been developedusing the MARKet ALLocation model.These scenarios enable the analysis of theimpact of different policy and technology al-ternatives and their quantitative significanceon energy consumption in transport. Each ofthe five scenarios encompass different policyand technology options related to the trans-port sector. Table 4.6 lists these scenariosand provides a description.

Table 4.5 Availability of bio-diesel for transportation

Lower bound on the availability of bio-diesel in MT

Scenario 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

REN 0 0 2 3.9 9.8 27.5 31.9

MT – million tonnes; REN – aggressive renewable energy


Table 4.6 Description of energy-efficient scenarios for the transport sector

Scenario Description

Enhanced share of public transport Share of public transport modes to increase to 60%

in 2036.

Increased share of rail in passenger and Railway freight share to increase from 37% in 2001

freight movement vis-à-vis road to 50% in 2036.

Railway passenger share to increase from 23% in 2001

to 35% in 2036.

Share of electric traction to increase for rail

passenger and freight to 80%.

Fuel efficiency improvements Fuel efficiency of all existing motorized transport

modes to increase by 50% from 2001 to 2036.

Use of bio-diesel in transport Enhanced penetration of bio-diesel by 65 Mtoe

by 2036.

Transport sector hybrid Incorporates all the above-mentioned scenarios,

in addition to those in the BAU.


Model results and analyses

55555

5.1 Introduction

This chapter presents the analytical resultsof the scenarios mentioned in Chapter 4.The results were obtained after running theIndia MARKAL (MARKet ALlocation)model for eight alternative scenarios: (1)BAU (business-as-usual) at 8% GDP (grossdomestic product), (2) LG (low growth) at6.7% GDP, (3) HG (high growth) at 10%GDP, (4) EFF (high efficiency) at 8% GDP,(5) NUC (high nuclear capacity) at 8%GDP, (6) REN (aggressive renewable en-ergy) at 8% GDP, (7) HYB (hybrid) at 8%GDP, and (8) HHYB (high-growth hybrid)at 10% GDP. In addition to these eighteconomy-wide macro scenarios, the analyti-cal results of five transport sector scenariosare also presented in this chapter. The re-sults of all the above-mentioned scenariospertain to the following issues: total andfuel-wise energy requirement; trends insectoral energy mix; trends in energy supply(domestic and imported resources); technol-ogy shifts; and cost implications.

5.2 Results and analysis of the

business-as-usual scenario

The BAU scenario, as described in Chapter4, considers the Government of India’s

targets and existing policies and plans. Inaddition, the adoption of efficient and newtechnological options continues as per thelikely progression, without any major inter-ventions.

5.2.1 Total commercial energy

requirements in the business-as-

usual scenario

Total commercial energy consumption in-creases by 7.5 times (6.9% growth rate) overthe 30-year period (2001/02–2031/32) inthe BAU. Table 5.1 presents the fuel-wisecommercial energy requirements. This datais also represented pictorially in Figure 5.1.

Coal remains the dominant fuel as far asthe commercial energy consumption is con-cerned. Its consumption increases from150 Mtoe (million tonnes of oil equivalent)in 2001 to 1176 Mtoe in 2031, that is, byabout 7.9 times (compound average annualgrowth rate of 7.1%).

The variation of percentage share of com-mercial fuels over the modelling time frameis shown in Figure 5.2. The percentage shareof coal in the commercial energy mix is thehighest, ranging from 45% to 55% over theentire modelling period. The share of oil intotal commercial energy ranges between36% and 40% during 2001–31. The oil


requirement increases by about 7.5times during the same period.

Although the use of natural gas in-creases over the modelling time periodin terms of magnitude, its share in totalcommercial energy is observed to de-crease after 2021. Indigenous gas pro-duction reaches its maximum capacityby 2011/12 (~44 Mtoe). Imports of gasincrease till 2021 after which their in-crease is restricted due to infrastructuralconstraints in the model. Due to its highefficiency and better overall economics,gas is a preferred option among the fossilfuels for power generation and fertilizerproduction in the model, especially post2016/17.

The model indicates that hydropower is also a preferred option, whichreaches the maximum allowed potentialover the time period. From ~25 GW(gigawatts) in 2001/02, large hydropower generation capacity increases to61 GW by 2011/12, 108 GW by 2021/22,and 150 GW by 2031/32. The installed

Table 5.1 Commercial energy requirements in the BAU (Mtoe)

Fuel 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

Coal 150 193 242 344 466 757 1176

Natural gas 25 36 51 74 132 136 136

Oil 101 151 211 298 405 555 757

Hydro power

(large and small) 7 9 18 24 30 36 40

Nuclear energy 2 2 4 8 13 13 13

Renewable energy 0 1 1 1 1 1 1

Total 285 391 527 749 1046 1497 2123

BAU – business-as-usual; Mtoe – million tonnes of oil equivalent

Figure 5.1 Commercial energyuse in the business-as-usual

Model results and analyses 151

capacity for small hydro is low initially butincreases to 8 GW by 2011/12.

Although the percentage share of hydro

The share of renewable energy (so-lar, wind, and bio-diesel) in commer-cial energy supply remains lower than1% throughout the modelling timeframe. None of the options are pre-ferred in terms of their relative eco-nomics.

In the BAU, consumption of tradi-tional fuels, such as firewood, cropresidue, and dung in the residentialand commercial sectors, decreases tohalf the current level of consumptionduring the modelling time frame (from149 Mtoe in 2001 to 73 Mtoe in2031). The percentage share of the tra-ditional fuels in the total primaryenergy (commercial and non-commer-cial) supply decreases from 36% in2001 to 4% in 2031, as shown inFigure 5.3. This is mainly due toswitching over from non-commercialfuels to commercial ones for cookingpurposes in the residential sector.

Figure 5.2 Percentage share of fuelmix (business-as-usual scenario)

Figure 5.3 Variation in percentage share oftraditional fuels in total primary energy supply

power in the total power gen-eration capacity is 23% in2031, its contribution in thetotal commercial energy mixis as low as 2% due to lowPLF (plant load factor) ofabout 30%.

The country has a nuclearpower programme that is ex-pected to increase the currentcapacity (2004/05) of 2.7GW to 6.78 GW by 2011/12and 21.18 GW by 2021/22 inthe BAU scenario. This shareis, however, insignificant(0.6%–1.2%) in the totalcommercial energy mix.


Table 5.2 Annual production, import, and import dependency of coal

2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

Production (million tonnes) 343 396 440 485 530 574 619

Import (million tonnes) 10 45 92 223 384 811 1438

Total (million tonnes) 353 440 532 708 913 1385 2057

Import dependency (%) 3 10 17 31 42 59 70

Figure 5.4 Production, import, andimport dependency of non-cokingcoal in the business-as-usual scenario

5.2.2 Import dependency of

fuels in the business-as-usual

scenario

The model results indicate that the maxi-mum allowable indigenous production forall fuels is achieved by 2016. The results fur-ther point to the fact that the dependency onimports for coal, oil, gas, and nuclear fuelwould increase significantly in the future,which is described in the following section.

5.2.2.1 Import depen-

dency of coal

Although the production ofcoal nearly doubles over the30-year period, it reaches itsmaximum annual productioncapacity and the economyneeds to resort to increasingcoal imports, as shown in Table5.2. The total coal import de-pendency (percentage of im-ported fuel to total fuelconsumption) increases from3% to 70% over the modellingtime frame.

Import dependency of non-coking coal increases very rap-

idly in the BAU scenario, from almost 0% in2001 to 71% by 2031. Figure 5.4 and Table5.3 show variation in production, import,and import dependency of non-coking coalover the modelling time frame in the BAUscenario.

Figure 5.5 and Table 5.4 show the pro-duction, import, and import dependency ofcoking coal in the BAU scenario over themodelling time frame. Due to the increasedsteel demand and inadequate availability ofcoking coal in the country, import depen-dency increases from 25% in 2001 to 75%by 2031.


5.2.2.2 Import depen-

dency of natural gas

Gas is being targeted as the fu-ture fuel and it is likely that itsuse would be more wide-spread. Large-scale invest-ments would be required toenable gas import, handling,and transportation. As ob-served in Figure 5.6, the im-port dependency of gas in theBAU scenario increases fromalmost negligible levels in2001 to 66% by 2021. In2031, as per the model, it hov-ers at about 66%–67% due toconstraints imposed on infra-structure (LNG [liquefiednatural gas] terminals andpipelines), but is likely to behigher if adequate facilities forthe import and distribution ofgas are made available. Natu-ral gas is a preferred fuel forpower generation at currentprices as compared with coaland is also more economicalfor fertilizer production.

5.2.2.3 Import

dependency of

petroleum products

In the BAU scenario, importdependency (Figure 5.7) of oilincreases from 68% in 2001 to90% by 2031, mainly on ac-count of the rapid growth in thetransport sector for moving

Table 5.3 Production, import, and import dependency of

non-coking coal in the business-as-usual scenario

2001 2011 2021 2031

Production (million tonnes) 289 372 443 515

Import (million tonnes) 0 61 306 1265

Import dependency (%) 0 14 41 71

Figure 5.5 Production, import, andimport dependency of coking coal inthe business-as-usual scenario

Table 5.4 Production, import, and import dependency

of coking coal in the business-as-usual scenario

2001 2011 2021 2031

Production (million tonnes) 30 37 47 57

Import (million tonnes) 10 32 78 173

Import dependency (%) 25 46 62 75


Figure 5.6 Production, import, and import dependencyof natural gas in the business-as-usual scenario

both passengers and freight,followed by growth in the in-dustry sector.

5.2.3 Sectoral energy

consumption in the busi-

ness-as-usual scenario

Figure 5.8 and Table 5.5 showthe trends in commercial en-ergy consumption from 2001to 2031 in the BAU scenario.The total consumption fromthe end-use side grew by 8times (CAGR [compoundedannual growth rate] of about7%) over the modelling timeframe (2001–31). This is dueto the rapid increase in oilconsumption in the transportsector, which grew by 13.7times (CAGR of about 9%)during the same period. Thisrapid growth in the transportsector can be attributed to ashift towards more energy-in-tensive modes of transport forpassengers and freight.

The second highest con-tributor to this growth in com-mercial energy consumption isincreasing consumption in theindustrial sector, which in-creases by 7.9 times (CAGR ofabout 7%) in the BAU sce-nario during 2001–31. Thisrapid growth in energy con-sumption in the industrial sec-tor is largely on account of thegrowth in infrastructural de-mands of the country (steel

Figure 5.7 Production, import, andimport dependency of petroleum inthe business-as-usual scenario


Figure 5.8 Sector-wise commercial energyconsumption in the business-as-usual scenario

Table 5.5 Sector-wise commercial energy consumption in the business-as-usual scenario

(in million tonnes of oil equivalent)

Sector 2001 2006 2011 2016 2021 2026 2031

Agriculture 15 17 18 20 22 23 25

Commercial 7 9 12 17 23 32 45

Residential 25 32 46 63 85 106 129

Industry 107 145 202 286 407 584 848

Transport 34 67 106 161 231 328 461

and cement demands) aswell as small-scale indus-trial growth.

The overall final en-ergy consumption in theresidential sector in-creases by only 5.2 timesfrom 2001 to 2031. How-ever, during the first twodecades, the increase inenergy consumption is al-most twice that in the baseyear (2001).

Figure 5.9 and Table5.6 depict the trends insectoral shares in com-mercial energy consump-tion in the BAU over themodelling time frame(2001–31). The figure and

Table 5.6 Trends in sectoral shares in commercial energy consumption (in percentage)

Sector 2001 2006 2011 2016 2021 2026 2031

Agriculture 8 6 5 4 3 2 2

Commercial 4 3 3 3 3 3 3

Residential 13 12 12 12 11 10 9

Industry 57 54 53 52 53 54 56

Transport 18 25 28 29 30 31 31


ing the modelling time frame(2001–31). The share ofpower generation in total coalconsumption is the highestover the modelling timeframe. However, its share ex-hibits a decline from 70% in2001 to 58% in 2031. This de-cline is due to the preferenceof natural gas for power gen-eration due to its better eco-nomics. While the share of theprocess heating in coal con-sumption increases from 14%in 2001 to 24% in 2031, thepercentage share of iron orereduction and captive powergeneration in coal consump-tion remains almost constantduring the modelling timeframe.

Figure 5.9 Trends in sectoral shares incommercial energy consumption

the table show that the percentage share ofindustrial sector in commercial energy con-sumption is maximum throughout the mod-elling time frame, accounting for more than50% of the total commercial energy con-sumption. Furthermore, the share of thetransport sector in total commercial energyconsumption is observed to increase from18% in 2001 to 30% in 2031.

5.2.3.1 Supply and consumption

of coal in the business-as-usual

scenario

Table 5.7 presents the supply and consump-tion of coal in the BAU scenario from 2001to 2031. The coal consumption increasesfrom 353 to 2057 MT (million tonnes) dur-


of oil in the business-as-usual

scenario

Table 5.8 gives the supply and consumptionof petroleum and petroleum products in thefive end-use sectors in the BAU scenarioover the modelling time frame, from 2001 to2031. The supply of petroleum products in-creases from 104 to 767 MT during themodelling period. Similarly, their consump-tion increases from 91 to 686 MT during thesame period. The difference in the supplyand consumption is primarily due to fueland oil losses in the refinery processes. Figure5.10 gives a graphical representation of thesectoral consumption of petroleum products.

The total consumption of petroleumproducts increases at the rate of 7% during


Table 5.7 Supply and consumption of coal (million tonnes) in the business-as-usual scenario

2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

Supply (A) A = B + C 353 440 532 708 913 1385 2057

Production (B)

Coking coal 30 31 37 42 47 52 57

Non-coking coal 289 336 372 408 443 479 515

Lignite 25 28 32 36 39 43 46

Total production 343 396 440 485 530 574 619

Net import (C)

Coking coal 10 19 32 51 78 117 173

Non-coking coal 0 26 61 172 306 693 1265

Total net import 10 45 92 223 384 811 1438

Consumption

(D)

Industry

(process heating) 49 71 109 157 234 337 498

Captive power 27 32 37 52 82 114 160

Ore reduction 31 42 58 81 112 155 214

Power 246 296 328 418 485 779 1185

Total consumption 353 440 532 708 913 1385 2057

Table 5.8 Supply and consumption of petroleum products (million tonnes) in the business-

as-usual scenario

2001/02 2006/07 2011/12 2016/17 2021/22 2025/26 2031/32

Supply (A) A = B + C 104 152 211 300 409 563 767

Production (B) 33 40 55 79 79 79 79

Net import (C) 71 112 156 222 330 484 688

Consumption (D)

Agriculture 8 8 9 9 10 10 11

Commercial 3 3 4 5 7 8 11

Domestic 17 19 25 30 34 37 39

Industry 31 41 53 70 93 131 184

Transport 32 64 101 153 220 313 441

Total 91 136 192 268 364 500 686


estimates are based on the as-sumption that the natural gashas a calorific value of 10 000kcal (kilocalories)/standardcubic metre.

As already stated, due toeconomic reasons, natural gasis preferred in the fertilizersector, followed by the powersector. The consumption inthe fertilizer sector is re-stricted to a certain extent be-cause of the continued use ofother feedstocks like naphthaand fuel oil.

Figure 5.10 Sectoral consumption of petroleumproducts in the business-as-usual scenario

2001–31. It grows fastest in the transportsector at the rate of about 9.1% during thesame time period. The share of the transportsector in total petroleum product consump-tion increases from 36% in 2001 to 64% in2031. This points towards the fact that thetransport sector has limited options forswitching to efficient options that reduce theconsumption of petroleum products unlikethe industry and other oil-consuming sec-tors. This explains the continuously increas-ing percentage share of transport sector inthe consumption of petroleum products overthe modelling time frame.

Traditional fuels are used mainly in theresidential sector and to a very small extentin the commercial sector. Given that these

fuels get increasingly replaced with modernenergy options such as kerosene and LPG(liquefied petroleum gas), the final energyuse in the residential sector does not seem toincrease significantly due to the higher effi-ciency of the commercial energy forms.


of natural gas in the business-as-

usual scenario

Table 5.9 gives the net supply and consump-tion of natural gas over the modelling timeframe in the BAU scenario. The gas supplyincreases from 26 BCM (billion cubicmetres) in 2001 to 139 BCM in 2031. These


5.2.4 Sectoral electricity

consumption in the

business-as-usual scenario

total electricity consumption as comparedwith 63% in 2001. The consumption in thedomestic sector increases by 12.6 times dur-ing the modelling time frame.

Table 5.9 Supply and consumption of natural gas (billion cubic metres) in the business-as-

usual scenario

2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

Supply (A) A = B + C 26 36 52 75 135 139 139

Production (B) 26 31 45 46 46 46 46

Net import (C) 0 6 7 29 89 93 93

Consumption (D)

Industry (process) 5 5 5 6 6 6 7

Industry (captive) 3 4 7 9 6 8 13

Fertilizer 8 10 11 11 11 12 12

Power 10 17 28 49 112 113 107

Transport 0.16 0.15 0.15 0.15 0.15 0.15 0.15

Total 26 36 52 75 135 139 139

Figure 5.11 Trend in the sectoral electricityconsumption in the business-as-usual scenario

Figure 5.11 andTable 5.10 presentsector-wise electric-ity consumption inthe BAU scenarioover the modellingtime frame (2001–31). The total elec-tricity consumptionincreases by 8.9times over the mod-elling time frame.This increase ismostly in the indus-try and residentialsectors, and by2031, these two sec-tors account fornearly 80% of the


Figure 5.12 shows the trends in percent-age distribution of electricity consumptionin the BAU scenario over the modelling timeframe. The percentage share of the industrialsector in total electricity consumption in-creases from 42% in 2001 to 51% by 2031.During the same period, the percentageshare of domestic sector in the electricity

narios is presented in this section. It alsoprovides a deeper insight into the variationsin the final energy and end-use consumptionmix under alternative sets of assumptions. Adetailed examination of these trends and in-vestment requirements would be used toframe policies.

Table 5.10 Trend in the sectoral electricity consumption in the business-as-usual scenario

(in terawatt hours)

Sector 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

Agriculture 87 99 111 124 138 152 167

Commercial 48 63 91 132 191 276 399

Domestic 82 134 222 365 573 786 1034

Industry 163 237 369 571 874 1212 1748

Transport 9 17 27 40 56 79 112

Figure 5.12 Trends in percentage distribution ofelectricity consumption in the business-as-usual scenario

consumption increases from21% to 30%. However, therehas been a decline in the per-centage share of the agricul-ture sector in total electricityconsumption, from 22% to5% over the modelling timeframe. The share of the com-mercial and transport sectorsin electricity consumptionhas remained constant overthe 30-year modelling timeframe.

5.3 Inter-scenario

comparisons

A comparative analysis of thekey results across all the sce-


5.3.1 Total and fuel-wise energy

requirements across different

scenarios

Table 5.11 presents the variations in com-mercial energy consumption across various

scenarios. These are pictorially representedin Figure 5.13. In the BAU scenario, the to-tal commercial energy consumption in-creases by 6.9% during the period 2001–31.However, it increases by 5.9% and 8.6% inthe LG and HG scenarios, respectively. Ithas been observed that in the EFF scenario,

Table 5.11 Variations in commercial energy consumption across various scenarios (in Mtoe)

Scenario 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

LG 285 361 456 605 816 1134 1579

BAU 285 391 527 749 1046 1497 2123

REN 285 391 524 740 1033 1479 2097

NUC 285 391 527 749 1030 1455 2061

EFF 285 379 479 623 838 1131 1542

HG 285 435 638 962 1438 2186 3351

LG – low growth; BAU – business-as-usual; REN – aggressive renewable energy; NUC – high nuclear capacity;

EFF – high efficiency; HG – high growth; Mtoe – million tonnes of oil equivalent

Figure 5.13 Total commercial energy consumption across various scenarios


Figure 5.14 Average annual fuelcost across various scenarios

characterized by the most probable growthrate (8% GDP), the total commercial energyconsumption increases only by 5.8%.

The difference in energy consumptionbetween the EFF scenario and the BAU sce-nario in 2031 is 581 Mtoe (the saving in en-ergy consumption in the EFF scenario istwice the consumption in 2031). This differ-ence is mainly on account of the reduction inconsumption of coal by 337 Mtoe and thatof oil by 244 Mtoe for the period 2001–31.This reduction in consumption of coal andoil can be attributed to the adoption of en-ergy-efficient technologies by the power, in-dustrial, and transport sectors.

The diagrammatic representation of thedetailed energy balance across various sce-narios for 2001 and 2031 is providedthrough the Sankey diagrams in Appendix 5and the decadal energy balance are shown inAppendix 6.

Figure 5.14 provides a com-parison of the fuel costs acrossvarious scenarios. It can be ob-served that the cost reduces by30% in the EFF scenario as com-pared to that in the BAU in 2031.In the REN scenario, althoughconsumption of coal and oil re-duces by 31 and 28 Mtoe, respec-tively, in 2031 as compared to theBAU, the fuel cost increases mar-ginally by 5000 crore rupees dueto the higher cost of bio-diesel. Inthe NUC scenario, the fuel cost ismarginally higher when comparedto the BAU scenario.

5.3.2 Electricity requirement

across different scenarios

In the BAU (8% GDP growth), electricityconsumption increases at an average growthrate of 7.6% over the period 2001–31, whilethe rate of growth is 6.8%, 9.1%, and 6.9%,respectively, in the LG (6.7% GDP), HG(10% GDP), and EFF scenarios (Figure5.15).

In the agriculture sector, electricity con-sumption increases by about 2.2% over the30-year period in the BAU scenario. How-ever, the EFF scenario indicates that elec-tricity consumption increases only by 1.2%during the same period on account of effi-cient pump sets and judicious water utiliza-tion as well as the water table remainingnearly constant.


Figure 5.15 Comparison of electricityconsumption across various scenarios

In the industrial sector, the growth rate ofelectricity consumption comes down from8.2% in the BAU scenario to 7.6% in theEFF scenario. This reduction is primarilydue to the adoption of energy- efficient tech-nologies in the various industrial sub-sectors.

The electricity consumption increases by8.8% in the residential sector in the BAUscenario during the 30-year period. How-ever, in the EFF scenario, it reduces by 7.9%,with a reduction by 23% in absolute levels in2031. This is primarily due to the adoptionof efficient lighting systems, refrigerators, airconditioners, and other appliances.

Electricity consumption in the transportsector in the BAU scenario exhibits a growth

rate of 8.7% over the 30-year period, whilein the EFF scenario it exhibits a growth rateof 10.9% over the same time period. This ison account of the increase in the share ofrail-based movement for passengers andfreight as well as higher electrification of rail.

5.3.2.1 Projected generation

capacity across scenarios

Figure 5.16 presents a comparison ofelectricity generation capacity mix acrossvarious scenarios. In the BAU scenario, thetotal generating capacity increases from125 GW in 2001 to 795 GW in 2031


(6.3 times). The coal-based capacity de-creases from 466 GW in 2031 in the BAUscenario to 349 GW in the EFF scenario.Gas-based capacity increases from 137 GWin the BAU scenario in 2031 to 141 GW inthe EFF scenario.

The total power generating capacity re-mains almost constant in the BAU, NUC,and REN scenarios. However, there existsvariation in the technology deployment forpower generation across various scenarios.In the NUC scenario, the nuclear powergeneration replaces coal-based generation.

Same happens in the REN scenario in whichrenewable-energy-based generation replacesgas-based generation that is already at itsmaximum because of the non-availability ofinfrastructure to import additional gas.

Figure 5.17 presents a comparison of theaverage annualized investment costs acrossvarious scenarios for 2011, 2021, and 2031.During the past five-year period of model-ling time frame (2026–31), a reduction of18 000 crore rupees per annum is effectedby way of reduction in annualized capital costsof coal-based power plants (centralized).

Figure 5.16 Comparison of power generation capacitymix (including decentralized) across various scenarios


However, there is an increase in the annual-ized cost of gas-based power plants (central-ized) by 5000 crores vis-à-vis the BAUscenario. The increase in the cost of gas-based power plants is due to increased ca-pacity of gas-based generation as well aspenetration of H-frame combined cycle gasturbine that has higher capital cost.

In the NUC scenario, although the annu-alized costs for coal-based capacity decreaseby 29 000 crore rupees as compared to theBAU scenario, the increase in the annualizedcost of nuclear power capacity (43 crore ru-pees) is more than the cost reduction.

5.3.2.2 Technology deployment

in the power sector across the

business-as-usual and high-

efficiency scenarios

Table 5.12 presents the technology deploy-ment in the power sector in 2021 and2031 for the BAU and EFF scenarios. Picto-rial representation of the same is given inFigure 5.18.

The total installed capacity for powergeneration from both centralized and decen-tralized technologies decreases from

Figure 5.17 Average annualized investment cost in thecentralized power generation across various scenarios


441 GW in the BAU scenario to 392 GW inthe EFF scenario in 2021, which is about11% reduction to meet the required de-mand. Similarly, in 2031, the power generat-ing capacity reduces from 795 to 681 GW,which amounts to 14% reduction. This isprimarily due to the improvement in effi-ciency in the various end-use sectors.

The model results indicate that IGCC(integrated gasification combined cycle) ispreferred to super-critical- and ultra-supercritical-based power generating tech-nologies. In the BAU scenario, IGCCtechnologies were not introduced in themodel. These were introduced in the EFFscenario; due to better economics, the modelpreferred IGCC to other coal-based tech-nologies.

In the BAU scenario, gas-based powergeneration hits the upper limits based on theavailability of natural gas in 2021 and 2031,

replacing sub-critical coal-based power gen-eration. This is primarily due to the higherefficiency of the CCGT (combined cycle gasturbine) compared to rankine cycle powergeneration in coal-based power generation.

IGCC and H-frame CCGT are almostequally preferred options for power genera-tion in the EFF scenario. IGCC based onimported coal has better economics (lowercost of generation) and, hence, is a preferredoption as against the IGCC based on indig-enous coal. In the EFF scenario, the in-stalled capacity based on H-frame CCGTtechnology hits the upper bound based onthe limits of gas availability.

In the BAU scenario, for 2021, the sub-critical coal-based generation capacity is175 GW, whereas the power generation ca-pacity of natural-gas-based CCGT is 118GW. However, in the EFF scenario, the sub-critical coal-based generation capacity is

Table 5.12 Comparison of technology deployment for centralized and decentralized

power generation in the BAU and EFF scenarios for 2021 and 2031 (in GW)

Year 2021 Year 2031

Technology BAU EFF BAU EFF

Coal sub-critical 170 92 456 135

Coal-efficient 5 0 10 1

Coal IGCC 0 47 0 213

Gas-based 118 95 137 89

CCGT (H-frame GT) 0 10 0 53

Diesel 7 7 8 8

Hydro power (large and small) 116 116 158 158

Nuclear energy 21 21 21 21

Renewable energy 4 4 4 4

Total 441 392 795 681

BAU – business-as-usual; EFF – high efficiency; IGCC – integrated gasification combined cycle;

CCGT – combined cycle gas turbine; GW – gigawatts


reduced to 92 GW, and IGCC is preferred tothe extent of 47 GW and efficient CCGT(H-frame) to the extent of 10 GW. In 2031,IGCC is the preferred option in the EFFscenario among the coal-based technologies,with the generation capacity of 213 GW thatis much more than the sub-critical coal-based generation capacity of 135 GW. In2031, efficient CCGT generation capacityincreases to 53 GW compared to negligiblegeneration capacity in the BAU scenario.The nuclear-energy-based generation capac-ity remains constant at 21 GW throughoutthe decade (2021–31). The hydro capacityremains at 116 GW (in 2021) and 158 GW(in 2031) in both the scenarios.

The coal consumption in 2031 in theEFF scenario is only 839 Mtoe, which islower than the BAU scenario by 337 Mtoe.

The power sector has the maximum sharein coal consumption across all scenarios, fol-lowed by the industrial sector for processheating and captive power generation. Thecoal consumption for process heating andcaptive power generation is the least in theEFF scenario due to the adoption of effi-cient technologies by the end-use sectors. Itis about 34% lower than that in the BAUscenario in 2031. Coking coal is used for orereduction in the blast furnace for makingiron. The coking coal consumption is thehighest in the EFF scenario because of

Figure 5.18 Comparison of fuel-wise technologydeployment in the business-as-usual andhigh-efficiency scenarios in the power sector

5.3.3 Coal require-

ment across various

scenarios

Figure 5.19 shows the sec-tor-wise coal consump-tion across variousscenarios. The rate of coalconsumption grew at7.1% in the BAU scenarioduring the modelling timeframe. In contrast, it grewat a rate of only 6% in theEFF scenario over themodelling time frame. Inthe NUC and the RENscenarios, the consump-tion growth was only mar-ginally lower at 6.8% and7.0%, respectively. TheHG scenario (based on aGDP growth rate of 10%)exhibits the highestgrowth rate of coal con-sumption at 9%.


Figure 5.19 Sector-wise coal consumption acrossdifferent scenarios for 2011, 2021, and 2031

increased iron making through the blastfurnace route, which is environmentallypreferred to the direct reduction route.The coking coal consumption in 2031is about 1.6 times higher in the EFFscenario than the BAU scenario.

5.3.3.1 Import dependency

of coal across different

scenarios

Figure 5.20 shows the import depen-dency of coking coal across various sce-narios for 2011 and 2031. Because ofthe non-availability of high-grade cok-ing coal, India is highly dependent oncoking coal for iron making. This de-pendency is the highest in the EFF sce-nario because of the adoption of blastfurnace route for iron making in inte-grated steel plants.

India is highly dependent on coal forits energy requirement. However, dueto growing energy demands and con-straints on the coal-mining capacity,India will have to resort to imports.Other additional factors responsible forincreased dependency on imports are:(a) location of mines predominantly inthe eastern part of the country and (b)location of load centres of coal prima-rily in the south and west. The Indiangovernment already has a policy to lo-cate thermal power plants based on im-ported coal in coastal locations.

The import dependency of non-cok-ing coal is the highest at 82% in the HGscenario as compared to 71% in theBAU scenario in 2031 (Figure 5.21). In2011, it is 3% in the EFF scenario and


Figure 5.20 Comparison of import dependencyof coking coal across various scenarios

zero in the LG scenario,and it is the highest for theHG scenario at 27% in thesame year.

5.3.3.2 Average

annual cost of coal

across various

scenarios

The average annual cost ofcoal in each of the decadalyears (2011, 2021, and2031) in all the sectors ofthe economy is shown inFigure 5.22.

In the BAU scenario, theannual cost nearly doublesfrom 2011 to 2021, and in-creases further by 2.6 timesfrom 2021 to 2031. Thecoal cost is minimal in theLG scenario (GDP 6.7%),334 000 crore rupees in2031. However, in the EFFscenario, the coal cost is367 000 crore rupees in2031, even when the energydemand is much higherthan the LG scenario. Inthe EFF scenario, the coalcost increases by only 1.7times from 2011 to 2021and by 2.5 times for the pe-riod 2021–31. In 2031, thecoal cost is 25% lower inthe EFF scenario than theBAU scenario. Corre-spondingly, the cost of coalis 7.6% lower in the NUC

Figure 5.21 Import dependency of non-cokingcoal across various scenarios


Figure 5.22 Comparison of average annualcost of coal across various scenarios

scenario than the BAU sce-nario as nuclear power re-places coal-based powergeneration in 2031. Thecoal cost is only marginallylower (in 2031) in the RENscenario when comparedwith the BAU scenario.

5.3.4 Petroleum

product requirement

across various

scenarios

Total petroleum consump-tion increases by 7.6 timesduring the 30-year period inthe BAU scenario. In theEFF scenario, the corre-sponding increase is only5.1 times—the decrease be-ing accounted for mainly bythe transport sector.

The oil imports remainhigh in all the scenarios dueto constant production ofdomestic crude. In the BAUscenario, the import depen-dency is 74% and 90%, re-spectively, in 2011 and2031. In the EFF scenario,it is 71% and 85%, respec-tively, and 78% and 90% inthe NUC scenario for 2011and 2031, respectively. Fig-ures 5.23 and 5.24 and theircorresponding tables(Tables 5.13 and 5.14)present domestic produc-tion, net import, and import

Figure 5.23 Production, import, and import dependencyof petroleum products across various scenarios in 2011


dependency of petroleumproducts across differentscenarios for 2011 and2031.

This has implicationswith respect to energy secu-rity—the EFF scenario isthe best for the economy interms of energy securityand monetary savings dueto reduced petroleum im-ports.

Figure 5.25 presents theaverage annual cost of oiland oil products acrossvarious scenarios for 2011,2021, and 2031. It is ob-served to be doubling everydecade in the BAU sce-nario. Compared to this,the increase is only by about1.6 times in the EFF sce-nario over the two decades.

5.3.4.1 Sectoral

petroleum consump-

tion trends across

scenarios

The petroleum consump-tion in the transport sectorincreases by about 13.6times in the BAU scenarioover the 30-year time pe-riod as compared to an in-crease by 7.8 times in theEFF scenario. This indi-cates that the magnitude ofincrease in the petroleumconsumption in the trans-

Figure 5.24 Production, import, and import dependency ofpetroleum products across various scenarios in 2031


petroleum products across various scenarios in 2011

Production Net import Import dependency

Scenario (Mtoe) (Mtoe) (%)

BAU 55 156 74

EFF 55 137 71

REN 55 190 77

NUC 55 194 78

LG 55 133 71

HG 55 204 79

BAU – business-as-usual; EFF – high efficiency; REN – aggressive

renewable energy; NUC – high nuclear capacity; LG – low growth;

HG – high growth; Mtoe – million tonnes of oil equivalent


Figure 5.25 Average annual cost of oil and oilproducts across various scenarios


petroleum products across various scenarios in 2031

Production Net import Import dependency


BAU 79 688 90

EFF 79 443 85

REN 79 687 90

NUC 79 742 90

LG 79 506 87

HG 79 1079 93

BAU – business-as-usual; EFF – high efficiency; REN – aggressive

renewable energy; NUC – high nuclear capacity; LG – low growth;

HG – high growth; Mtoe – million tonnes of oil equivalent

sumption of petroleumproducts is attributed tothe efficient modes forroad-based passenger andfreight vehicles, alongwith a shift in the share ofrail-based movement andhigher utilization of pub-lic transport. In the caseof renewable energy, thisdecrease is due to theavailability of bio-diesel.

In the industry sector,total petroleum consump-tion increases by six timesin the BAU scenario andby 4.7 times in the EFFscenario. Naphtha con-sumption decreases byabout 26% in the EFFscenario as compared tothe BAU scenario in2031. This is due to theshift towards natural-gas-based fertilizer produc-tion.

In the commercial sec-tor, the EFF scenario in-dicates a slight increase inthe consumption of petro-leum products as com-pared to the BAUscenario, as there is a shiftfrom traditional fuels tokerosene and LPG.

In the residential sec-tor, the consumption of

port sector declined by almost 50% in theEFF scenario vis-à-vis the BAU scenarioover the 30-year time frame (2001–31). Inthe EFF scenario, the decline in the con-

petroleum products increases in the EFFscenario compared to the BAU scenario dueto the displacement of traditional fuels at arelatively faster rate.


Figure 5.26 Comparison of petroleumproduct consumption across variousscenarios in the end-use sectors

Figure 5.26 presents a comparison ofthe sectoral consumption of petroleumproducts across various scenarios for2011, 2021, and 2031.

5.3.4.2 Capacity and

investments in the oil refinery

As observed in Figure 5.27, only theEFF scenario has the potential to re-duce refinery capacity, which decreasesby 19% and 26%, respectively, by 2021and 2031 when compared with the BAUscenario. Annualized refinery costs forthe same years decrease by 29% and40%, respectively (Figure 5.28).

5.3.5 Natural gas requirement

across various scenarios

Natural gas production in the country isestimated to increase from 26 BCM in2001 to 45 BCM in 2011 and 46 BCMby 2021 and 2031.

In 2011, imports of natural gas de-crease marginally in the EFF scenario ascompared to the BAU scenario (as wellas other scenarios). However, in 2021, adecrease in gas imports is observed onlyin the NUC scenario. This is due to thereplacement of gas-based power gener-ating capacity by nuclear capacity. Gasis a preferred option, especially forpower generation as well as in the fertil-izer sector. However, its offtake throughimports in the model is similar across allthe scenarios and reaches its maximuminfrastructural constraint by 2011 (Fig-ure 5.29). Similar trends are indicatedwith regard to cost of natural gas supplyas seen in Figure 5.30.


Figure 5.27 Refinery capacityacross various scenarios

Figure 5.28 Refinery investmentcost across various scenarios

5.4 Comparison of

hybrid scenarios

A comparative analysis ofthe impacts of two hybridscenarios (namely, HYBand HHYB, explained indetail in Chapter 4) on theenergy system is presentedin this section.

5.4.1 Commercial

energy consumption

Table 5.15 gives the com-mercial energy consump-tion over the modellingperiod across four sce-narios, namely, BAU, HYB,HG, and HHYB. The en-ergy consumption growsfrom 285 Mtoe in 2001 to1503 Mtoe in 2031 in theHYB scenario. In theHHYB scenario, the energyconsumption grows from285 Mtoe in 2001 to 2320Mtoe in 2031. The com-mercial energy consump-tion in 2031 is lower by29.2% in the HYB scenariowhen compared with BAUscenario. The consumptionin the HHYB scenario ishigher by about 9.3% com-pared to the BAU scenarioin 2031.

However, the consump-tion in the HHYB scenario


Figure 5.29 Import of natural gasacross various scenarios

Figure 5.30 Average annual cost ofnatural gas across various scenarios

5.4.2 Generation

capacity mix

Figure 5.32 shows thepower generation capacitymix in the BAU, HYB, HG,and HHYB scenarios for2011, 2021, and 2031. Itcan be seen that the coalpower generation is thehighest in the HG scenarioin all the years. In the HYBscenario, nuclear powergeneration displaces coal-and gas-based power gen-eration but hydro-basedpower generation capacityincreases marginally. Therenewable energy genera-tion capacity reaches itsmaximum potential of 26GW in the HYB andHHYB scenarios in 2031.Hydro- and nuclear-basedpower generation is ex-ploited to its maximum po-tential of 160 and 70 GW,respectively, in 2031. How-ever, coal-based generationcapacity accounts for over59% of the total power gen-eration capacity in theHHYB scenario and about42% of the total power gen-eration capacity in theHYB scenario. Technologydeployment for power gen-

is about 1.5 times higher than the consump-tion in the HYB scenario in 2031.

The commercial energy supply in 2011,2021, and 2031 is shown in Figure 5.31.

eration for 2021 and 2031 across the BAU,HYB, HG, and HHYB scenarios is pre-sented in Table 5.16 and Figures 5.33 and5.34.


5.4.3 Electricity consumption in

the end-use sectors

Figure 5.35 shows the electricity consump-tion in the BAU, HYB, HG, and HHYB sce-narios for 2011, 2021, and 2031. It is seenthat the industry and domestic sectors arethe largest consumers of electricity, account-ing for almost three-fourth of the total elec-tricity consumption across all scenariosduring the entire modelling time frame.

5.4.4 Coal consumption in the

end-use sectors

The coal consumption in the various end-use sectors in the HYB scenario in 2011,2021, and 2031 is shown in Tables 5.17–5.19. The coal consumption in 2031 is 1.7times higher in the HG scenario comparedto the BAU scenario. The coal consumptionreduces by 35% in the HYB scenario whencompared to the BAU scenario and by 32%in the HHYB scenario when compared toHG scenario in 2031. However, the con-sumption in ore reduction increases in theHHYB scenario when compared to the HGscenario due to higher production of ironand steel from the blast furnace route in2031.

Table 5.15 Comparison of commercial energy consumption across various scenarios (in Mtoe)

Scenario 2001/02 2006/07 2011/12 2016/17 2021/22 2026/27 2031/32

BAU 285 391 527 749 1046 1497 2123

HYB 285 379 478 619 823 1101 1503

HG 285 435 638 962 1438 2186 3351

HHYB 285 405 544 760 1087 1576 2320

BAU – business-as-usual; HYB – hybrid; HG – high growth; HHYB – high-growth hybrid; Mtoe – million tonnes of oil

equivalent

5.4.5 Import dependency of coal

Figures 5.36 and 5.37 show the import de-pendency of coking and non-coking coal re-spectively, in 2011 and 2031 across variousscenarios. The coking coal import depen-dency in the HYB and HHYB scenarios ishigher than that in the BAU scenario. Fornon-coking coal, the import dependency isthe lowest in the HYB scenario but similar inthe HHYB and the BAU scenarios.

5.4.6 Import, import

dependency, and production of

petroleum products

Figures 5.38 and 5.39 and Tables 5.20 and5.21 show the net import, import depen-dency, and production of the petroleumproducts in the BAU, HG, and their respec-tive HYB scenarios for 2021 and 2031. Theimport dependency is lowest in the HYBscenario and highest in the HG scenario forall the years. This is primarily due to the fueldemand in the transport sector, details ofwhich are explained under the transport sce-nario.


Figure 5.31 Commercial energy supply in2011, 2021, and 2031

Figure 5.32 Generation capacity mix for2011, 2021, and 2031 (centralized anddecentralized)


Table 5.16 Technology deployment (including decentralized) during 2021 and 2031 in the

business-as-usual and high growth and their respective hybrid scenarios (in GW)

Year 2021 Year 2031

Technology BAU HYB HG HHYB BAU HYB HG HHYB

Coal sub-critical 170 88 264 107 456 131 728 171

Coal-efficient 5 0 5 0 10 1 10 1

Coal IGCC 0 32 0 96 0 160 0 387

Gas 118 89 114 90 137 91 125 90

Gas-efficient 0 10 10 10 0 53 23 23

Diesel 7 7 7 7 8 8 8 8

Hydro power (large and small) 116 118 116 118 158 160 158 160

Nuclear energy 21 40 21 40 21 70 21 70

Renewable energy 4 12 4 12 4 26 4 26

Total 441 395 541 480 795 700 1076 935

BAU – business-as-usual; HYB – hybrid; HG – high growth; HHYB – high-growth hybrid; IGCC – integrated gasification

combined cycle; GW – gigawatts

5.4.7 Consumption of

natural gas

Figure 5.40 shows the im-port of natural gas in theBAU, HG, and their respec-tive hybrid scenarios for2011, 2021, and 2031. It canbe seen that the imports inthe BAU and the HG sce-narios for 2021 and 2031 arethe same. The consumptionof natural gas in 2031 in theBAU, HHYB, and HG sce-narios hits the upper bound.However, in the HYB sce-nario, the natural gas con-sumption for the sameperiod is below the upperbound. This is due to the

Figure 5.33 Comparison of fuel-wise technology deploymentfor power generation across various scenarios for 2021


Figure 5.34 Comparison of fuel-wise technology deploymentfor power generation across various scenarios for 2031

Table 5.17 Coal consumption in various end-use sectors in 2011 (in Mtoe)

Sector BAU HYB HG HHYB

Industry (process heating) 48 40 57 46

Industry (captive power generation) 16 16 19 18

Power 141 116 180 143

Ore reduction 37 42 44 49

Total 242 215 300 256

BAU – business-as-usual; HYB – hybrid; HG – high growth; HHYB – high-growth hybrid

Table 5.18 Coal consumption in various end-use sectors in 2021 (in Mtoe)


Industry (process heating) 118 76 167 108

Industry (captive power generation) 41 42 51 58

Power 235 115 405 217


Total 466 329 723 517

BAU – business-as-usual; HYB – hybrid; HG – high growth; HHYB – high-growth hybrid;



Table 5.19 Coal consumption in various

end-use sectors in 2031 (in Mtoe)


Industry

(process heating) 285 146 490 253

Industry (captive

power generation) 91 106 139 160

Power 663 296 1148 581


Total 1176 767 2008 1364

BAU – business-as-usual; HYB – hybrid;

HG – high growth; HHYB – high-growth hybrid;


Figure 5.35 Sectoral electricityconsumption for 2011, 2021, and 2031

preference of coal-based IGCC-based powergeneration to the power generation by im-ported natural gas.

5.4.8 Sectoral end-use

consumption of the petroleum

products

Figure 5.41 shows that the transport and in-dustrial sectors accounted for more than80% in the total petroleum product con-sumption in 2011. Their share increases tomore than 90% by 2031. The petroleumproduct consumption in the transport sectoraccounted for more than 50% in all the sce-narios in all the years.

5.5 Comparison of energy

intensity across different scenarios

Energy intensity indicates the extent towhich energy is efficiently utilized in gener-ating a unit of income/output (GDP) for theeconomy. Table 5.22 presents a comparison


Figure 5.36 Import dependency of coking coalacross various scenarios for 2011 and 2031

that the energy intensity exhibitsa declining trend from 0.022kgoe (kilogram of oil equivalent)per rupee of GDP in 2001 to0.017 kgoe per rupee of GDP in2031 (a decrease of 23%) in theBAU scenario. It can be inferredthat owing to the GDP growthrate of 8% and adoption of gov-ernment plans and policies, theeconomy is progressing along anenergy-efficient path in the BAUscenario. However, the scenariotakes a conservative view with re-spect to the technology deploy-ment by way of limitedpenetration of clean-coal tech-nologies, H-frame combinedcycle gas turbine, the timing ofpenetration of efficient powergeneration technologies, a lowdegree of penetration of nuclear

Table 5.20 Domestic production, net

import, and import dependency of

petroleum products in 2021

Net Import

Production import dependency


BAU 79 330 81

HYB 79 223 74

HG 79 566 88

HHYB 79 299 79

BAU – business-as-usual; HYB – hybrid; HG – high

growth; HHYB – high-growth hybrid; Mtoe – million

tonnes of oil equivalent

Table 5.21 Domestic production, net

import, and import dependency of

petroleum products in 2031

Net Import

Production import dependency


BAU 79 688 90

HYB 79 415 84

HG 79 1079 93

HHYB 79 641 89

BAU – business-as-usual; HYB – hybrid; HG – high

growth; HHYB – high-growth hybrid; Mtoe – million

tonnes of oil equivalent

of energy intensity of GDP across the eighteconomy-wide scenarios over the modellingtime frame.

Figure 5.42 presents the trends of energyintensity for various scenarios over the mod-elling time frame. The figure clearly depicts

energy and renewable energy, and so on.In the EFF scenario, there is a decline in

energy intensity from 0.022 kgoe per rupeeof GDP in 2001 to 0.012 kgoe per rupee ofGDP in 2031. Thus, there is a decline by27% in the energy intensity when compared


Figure 5.38 Domestic production, net import, andimport dependency of petroleum products for 2021

Figure 5.37 Import dependency ofnon-coking coal across variousscenarios in 2011 and 2031


Figure 5.39 Domestic production, net import, and importdependency of petroleum products for 2031

Figure 5.40 Import of natural gas across variousscenarios for 2011, 2021, and 2031


with the BAU scenario. In the HYBscenario (that includes all plausibleenergy-efficient measures consid-ered in the EFF scenario coupledwith enhanced nuclear capacity andaccelerated penetration of renewableenergy), the energy intensity steadilydeclines from 0.022 kgoe per rupeeof GDP in 2001 to 0.012 kgoe perrupee of GDP in 2031. The extent ofdecline is about 29% when com-pared with the BAU.

Thus, it can be inferred that thereexists a considerable scope for bring-ing about reduction in energy inten-sity, if policies are formulated topromote clean-coal technologies (inview of the economy’s continuousdependence on coal) and barriers tothe uptake of more energy-efficienttechnology options are removed.With time-bound targets and con-certed action plans towardsstrengthening indigenous researchand development facilities, there is apossibility of further reduction inenergy intensity, as highlighted inthe HYB scenario.

However, the HG and HHYBscenarios also exhibit a decliningtrend in energy intensity; reducingfrom 0.022 kgoe per rupee of GDPin 2001 to 0.016 kgoe per rupee ofGDP in 2031 in the HG scenarioand further to 0.011 kgoe per rupeeof GDP in the HHYB scenario in thesame year. This implies that evenwith a high growth rate of 10% GDPover the period 2001–31, leading togrowth in commercial energy con-sumption, the economy can stillmove along a declining energy-in-

Figure 5.41 Sectoral consumption of petroleumproducts in 2011, 2021, and 2031


Table 5.22 Energy intensity (kgoe/Rs of GDP) for various scenarios

Scenario 2001 2006 2011 2016 2021 2026 2031

LG 0.022 0.022 0.020 0.020 0.019 0.019 0.018

BAU 0.022 0.022 0.020 0.019 0.018 0.018 0.017

REN 0.022 0.022 0.020 0.019 0.018 0.018 0.017

NUC 0.022 0.022 0.020 0.019 0.018 0.017 0.017

EFF 0.022 0.021 0.018 0.016 0.015 0.013 0.012

HYB 0.022 0.021 0.018 0.016 0.014 0.013 0.012

HG 0.022 0.023 0.021 0.019 0.018 0.017 0.016

HHYB 0.022 0.021 0.018 0.015 0.014 0.012 0.011

LG – low growth; BAU – business-as-usual; REN – aggressive renewable energy; NUC – high nuclear capacity;

EFF – high efficiency; HYB – hybrid; HG – high growth; HHYB – high-growth hybrid; kgoe – kilogram of oil

equivalent; GDP – gross domestic product

Figure 5.42 Trends in energy intensity acrossvarious scenarios, from 2001 to 2031


Figure 5.43 Comparison of energy consumptionin transport sector across various scenarios

tensity path if energy-efficient measures arepursued aggressively.

5.6 Transport sector scenarios

A comparative analysis of the results is pre-sented in detail for the five alternative trans-port sector scenarios described in Chapter 4.The nomenclature used for the transportsector scenarios in the graphical representa-tion are: RAIL-ROAD (characterized by in-creased share of rail vis-à-vis road inpassenger and freight transport demand),PUB-PVT (characterized by enhanced shareof public transport vis-à-vis personalizedmode of transport), FUEL EFF (fueleconomy improvements), BIODSL (pen-etration of bio-diesel), and TPT-HYB (com-bination of RAIL-ROAD, PUB-PVT,FUELEFF, BIODSL, and BAU).

of these scenarios, the figures for projectedenergy consumption in the transport sectorare obtained. The optimal fuel technologymix in each of these scenarios is modifiedbased on the assumptions relating to variousparameters such as inter-modal mix in totaltransport demand, share of public–privatemodes in transport demand, fuel economy,and so on. This explains the difference in totalenergy consumption across these scenariosover the modelling time frame.

The total energy consumption in thetransport sector has increased by 14 times,from 34 Mtoe in 2001 to 461 Mtoe in 2031,registering an average annual growth rate of9.1%. However, as shown in Figure 5.43,there exists a possibility of achieving a re-duction in energy consumption to a maxi-mum level of about 35% in 2031 in theTPT-HYB scenario vis-à-vis the BAU

Table 5.23 presents the figuresfor the projected commercial en-ergy consumption in the transportsector

Figure 5.43 gives the compari-son of total commercial energyconsumption (including electric-ity) in the transport sector acrossvarious scenarios. The figureclearly indicates that the projectedenergy consumption (includingelectricity) in the transport sectorexhibits a consistent upward trendin all the five scenarios, includingthe BAU, over the 30-year timeframe (2001–31). In all the trans-port sector scenarios, the freightand passenger transport demandexhibits an upward trend. Consid-ering the optimal fuel technologymix in the transport sector in each


Table 5.23 Total commercial energy consumption in transport sector (in Mtoe) across

various scenarios

Scenario 2001 2006 2011 2016 2021 2026 2031

BAU 34 67 106 161 231 328 461

RAIL-ROAD 34 67 105 158 223 312 430

PUB-PVT 34 68 107 154 219 310 436

FUEL EFF 34 63 94 135 184 249 336

BIODSL 34 67 104 157 222 310 433

TPT-HYB 34 64 94 126 171 228 302

BAU – business-as-usual; Mtoe – million tonnes of oil equivalent

scenario. In absolute terms, the energy con-sumption in the TPT-HYB scenario declinesby 125 Mtoe for 2031 vis-à-vis the BAUscenario.

Tables 5.24–5.26 present the results forthe projected fuel mix in the transport sectoracross various scenarios for 2011, 2021, and2031.

The inter-scenario comparison of fuelmix in the transport sector is presented pic-torially in Figure 5.44.

Diesel consumption accounts for maxi-mum share (more than three-fourth) in thetotal energy consumption throughout theperiod 2001–31 in the BAU. However, its

predominance as a major transport fuelfades away due to substitution by electricity,as a result of the enhanced share of electrictraction, and substitution by bio-diesel.Other fuels like CNG (compressed naturalgas), electricity, and bio-diesel account for aminiscule share in the total energy consump-tion, although the extent of their penetrationacross various scenarios differs.

In absolute terms, the gasoline consump-tion has increased from about 7 Mtoe in2001 to 40, 74, and 107 Mtoe in 2011, 2021,and 2031, respectively, in the BAU, at an av-erage annual growth rate of 10% during theperiod 2001–31. This is because of the

Table 5.24 Projected fuel mix in transport sector (in Mtoe) across scenarios for 2011

Fuel BAU RAIL-ROAD PUB-PVT FUEL EFF BIODSL TPT-HYB

Gasoline 40.00 40.00 40.00 35.00 40.00 35.00

Diesel 60.00 58.00 60.00 53.00 58.00 50.00

Compressed natural gas 2.00 3.00 2.00 2.00 2.00 3.00

Electricity 0.15 0.15 0.15 0.13 0.15 0.15

Bio-diesel 0.00 0.00 0.00 0.00 2.00 2.00

Others 4.00 4.00 4.00 4.00 4.00 4.00

Mtoe – million tonnes of oil equivalent; BAU – business-as-usual


Table 5.25 Projected fuel mix in transport sector (in Mtoe) for various scenarios for 2021


Gasoline 74.00 74.00 74.00 57.00 74.00 57.00

Diesel 144.00 135.00 132.00 114.00 135.00 79.00


Electricity 0.15 0.15 0.15 0.11 0.15 9.42

Bio-diesel 0.00 0.00 0.00 0.00 9.00 9.00

Others 9.00 9.00 9.00 9.00 9.00 9.00


Table 5.26 Projected fuel mix in transport sector (in Mtoe) for various scenarios for 2031


Gasoline 107.00 107.00 107.00 75.00 107.00 75.00

Diesel 325.00 290.00 300.00 232.00 297.00 138.00


Electricity 0.15 0.15 0.15 0.10 0.15 24.19

Bio-diesel 0.00 0.00 0.00 0.00 28.00 28.00

Others 19.00 19.00 19.00 19.00 19.00 19.00


higher share of the road-based movement intotal passenger transport demand. Further-more, the share of personalized modes ofroad transport in the total passenger trans-port exhibits that majority of the road-basedpassenger transport vehicles are gasoline-based.

Similarly, the diesel consumption in thetransport sector has multiplied 14-fold dur-ing the period 2001–31, increasing at an av-erage annual growth rate of about 10%. Thisrise is mainly because the share of the road-based freight transport demand is projectedto increase to 73% till 2036 from 37% in2001. The massive growth displayed by theroad-based freight transport demand, as

highlighted by various policy documents, ison account of the movement of bulk and fin-ished products for both long and short dis-tances. Earlier, railways displayed strengthin the movement of bulk goods includingmovement for very short distances. Finishedgoods requiring higher flexibility handlingand better transit times have gradually beenmoving to the roads with continued increasein freight tariffs. Thus, the shift from rail toroad in freight transport demand has be-come clearly apparent in the BAU scenarioin the form of rising energy consumption.

Figure 5.44 clearly indicates that there isa decline in diesel consumption to the extentof 10, 65, and 187 Mtoe for 2011, 2021, and


2031 when the BAU scenario iscompared with the TPT-HYBscenario that combines all pos-sible energy-efficient measuresinduced by policy interventionsby the government.

If the railways are able to winback their market share to theextent of 50% in freight move-ment and 35% in passengermovement vis-à-vis road over theperiod 2001–36, as mentioned invarious policy documents of theGovernment of India, reductionin diesel consumption by 3%,6%, and 11% can be achieved for2011, 2021, and 2031. In abso-lute terms, the diesel consump-tion declines by 2, 9, and 35Mtoe for 2011, 2021, and 2031in RAIL-ROAD scenario whencompared to the BAU scenario.

Similarly, by enhancing theshare of public transport in totalpassenger transport demand to amaximum of 60% by 2036, theconsumption of diesel exhibits adecline by 12 and 25 Mtoe for2021 and 2031, respectively.This is mainly because of the factthat with the enhanced share ofpublic transport in transport de-mand, the spiralling passengertransport demand would be ca-tered to a greater extent by en-ergy-efficient public transportmodes vis-à-vis the personalizedmodes of transport that consumemore energy per passengerkilometre.

In the BIODSL scenario, die-sel is substituted with bio-diesel

Figure 5.44 Comparison of fuel mix in transportsector across scenarios for 2011, 2021, and 2031


to the extent of 2, 9 and, 28 Mtoe in 2011,2021, and 2031, respectively.

Finally, in the TPT-HYB scenario, maxi-mum possible savings in diesel consumptionto the extent of about 190 Mtoe can beachieved if all the possible policies aimed atoil conservation are promoted. Electricityconsumption in the transport sector in-creases from 2, 5, and 9 Mtoe in the BAUscenario to 3, 7, and 17 Mtoe in the TPT-HYB scenario in 2011, 2021, and 2031, re-spectively. This is mainly due to theaccelerated electrification of railway tracks(that is, a higher share of electric tractionvis-à-vis diesel traction) both in passengerand freight transport demand.

Moreover, in the TPT-HYB scenario, theCNG consumption is also higher vis-à-visother scenarios because the CNG hybrid ve-hicles in this particular scenario start pen-etrating from 2021. However, it may benoted that it is the inter-sectoral substitutionof natural gas in power generation and trans-port sectors that is dictating the quantum ofCNG availability to the transport sector.

Figure 5.45 presents the comparison ofthe net import and import dependency ofpetroleum products across various scenariosfor 2011, 2021, and 2031. The figure clearlyindicates that if thrust is provided on pro-moting energy efficiency – thus reducing en-ergy consumption – in the transport sector,the import dependency of petroleum prod-ucts declines from about 74%, 81%, and90% in the BAU scenario for 2011, 2021,and 2031, respectively, to 72%, 76%, and 85%in the TPT-HYB for the same time period.

Figure 5.46 presents a comparison of ex-penditure incurred by the economy on thenet import of products across various sce-narios. As depicted in the figure, the net ex-penditure on import of petroleum productshas increased to 15 226 billion rupees in theBAU scenario, 14 644 billion rupees in thePUB-PVT scenario, 14 423 billion rupees inthe RAIL-ROAD scenario, 14 568 billionrupees in the BIODSL scenario, 12 333 bil-lion rupees in the FUEL EFF scenario, andto 10 193 billion rupees in the TPT-HYBscenario in 2031. Thus, a decline to the ex-tent of 33% (that is, by more than one-third)in the import bill of petroleum products canbe achieved if all possible energy-efficientmeasures are undertaken.

5.7 Cumulative carbon dioxide

emissions

The cumulative CO2 emissions for the pe-riod 2001–36 in each of the scenarios aregiven in Table 5.27. These emissions are sig-nificantly lower in the EFF and HYB sce-narios: 25% and 29% lower than theemissions in the BAU scenario, respectively.In the HHYB scenario, the CO2 emissionsare higher by only 8% compared to those ofthe BAU scenario. The emissions are alsorepresented in Figure 5.47 to show the mag-nitude of variations across various scenarios.CO2 emissions are the least in the EFFscenario.


Figure 5.45 Comparison of net import and importdependency of petroleum products across variousscenarios for 2011, 2021, and 2031


Figure 5.47 Cumulative carbon dioxideemissions across various scenarios (2001–36)

Figure 5.46 Expenditure incurredon import of petroleum products

Table 5.27 Cumulative carbon

dioxide emissions for different

scenarios (from 2001 to 2036)

Cumulative CO2

emissions

Scenario (million tonnes)

LG 12 172

BAU 16 223

REN 15 805

NUC 15 678

EFF 12 113

HYB 11 501

HG 25 004

HHYB 17 533

LG – low growth; BAU – business-as-usual;

REN – aggressive renewable energy;

NUC – high nuclear capacity;

EFF – high efficiency; HYB – hybrid;

HG – high growth; HHYB – high-growth

hybrid; CO2 – carbon dioxide

Key observations and

recommendations 66666

6.1 Key observations from the

model runs

The analysis of results in Chapter 5 providesvarious key observations for India’s energysector over the next couple of decades.

In the BAU (business-as-usual) scenario,total commercial energy consumption in-creases by 7.5 times, from 285 Mtoe (milliontonnes of oil equivalent) in 2001/02 to 2123Mtoe in 2031/32. Furthermore, the BAUexhibits a decline in energy intensity to theextent of 23%, from 0.022 kgoe (kilogramsof oil equivalent) per rupee of GDP (grossdomestic product) in 2001/02 to 0.017 kgoeper rupee of GDP in 2031/32. The HYB(hybrid) scenario is representative of ahighly optimistic scenario that incorporatesall possible energy-efficient measures span-ning across the entire Indian energy sector.This scenario is also characterized by declin-ing energy intensity, from 0.022 kgoe perrupee of GDP in 2001/02 to 0.012 kgoe perrupee of GDP in 2031/32. The decline inenergy intensity in this scenario is 29% fromthe corresponding level in the BAU for2031/32.

Interestingly, in the HHYB (high-growthhybrid) scenario at 10% GDP growth, theenergy intensity is exactly halved from0.022 kgoe per rupee of GDP in 2001/02 to0.011 kgoe per rupee of GDP in 2031/32.

But due to the structural changes in theeconomy, the rate of decline is lower in theinitial period as compared to the HYB sce-nario at 8% GDP growth rate. It may benoted further that energy intensity in theHHYB scenario is 8% lower compared tothat in the HYB scenario. Thus, it is evidentthat even with the optimistic rate of GDPgrowth of 10%, causing total commercialenergy consumption to rise from 285 Mtoein 2001/02 to 2320 Mtoe in 2031/32, the In-dian economy can still progress along an en-ergy-efficient path through a host ofenergy-efficient technological options pen-etrating into the system, both on the demandas well as the supply side.

The reduction in the final energy require-ments in the NUC (high nuclear capacity)and REN (aggressive renewable energy) sce-narios is minimal. The final energy require-ment in the NUC scenario in 2031 is 2061Mtoe, and in the REN scenario is 2097Mtoe.

On the supply side, coal still remains thepredominant fuel, accounting for 50% of thetotal fuel mix, followed by oil contributing toabout 35%–40% of the commercial energysupply over the entire modelling period inthe BAU scenario. Natural gas is the pre-ferred choice of fuel for power generationand as a feedstock for fertilizer production.The model results indicate that the overalleconomics of using natural gas for fertilizer


production is better than using it for powergeneration. The current level of natural gasutilization in the overall energy mix suffersdue to infrastructural constraints on the im-port of natural gas.

The model run indicates that clean coaltechnologies for power generation are thepreferred options for the economy at large. Ifcommercially available clean coal technolo-gies such as the ultra-supercritical boilersand IGCC (integrated gasification com-bined cycle) are allowed to compete with thesub-critical boilers and CCGTs (combinedcycle gas turbines), IGCC plants are the pre-ferred choice and would compete with highefficiency H-frame CCGT plants. The im-pact of the NUC scenario is primarily on thepower sector as it displaces coal-based gen-eration. Similarly, the impact of REN is onthe power sector due to the introduction ofdecentralized power generating technologieslike photovoltaic, wind, biomass-basedpower, and small hydro. The impact of pen-etration of bio-diesel is felt only in the trans-port sector. Large hydro is a preferredoption across all the modelling scenarios,reaching its full potential of 150 GW (giga-watts) by 2031. However, its contribution tototal energy supply is very small due to thelow plant load factor of about 30%.

Total investments in supply technologiesand fuels are the least in the EEF (high effi-ciency) scenario. For instance, the refinerycapacity requirements in the EEF scenariodecrease by 19% and 26%, respectively, by2021 and 2031, when compared to the BAUscenario. The corresponding annualized re-finery costs also decrease by 29% and 40%,respectively, for the same period in the EEFscenario vis-à-vis the BAU.

Import dependency of all fossil fuels islikely to increase significantly by 2031. Forcoal, it is increasing from the negligibly low

level of 3% in 2001 to a staggering level of70%, and for coking coal, it was thrice thelevel in 2001 in the BAU in 2031. This rise inimport dependency of coking coal is prima-rily due to the increased demand of steel. In2001/02, more than 60% of the total oil sup-ply demand was met by imports. In the BAU,it is expected that by 2031/32, only 10% ofthe supply demand could be met by indig-enously available oil, the remaining 90%would be met by oil imports. From the view-point of energy security, the transport sectorhas the largest capacity for reducing the useof petroleum fuels. The final energy con-sumption of petroleum fuels in the transportsector increases from 34 Mtoe in 2001 to441 Mtoe (64% of total petroleum products)in 2031 in the BAU scenario. Totalenergy consumption is 461 Mtoe, whichincludes electricity, primarily for railtransportation.

In the HYB scenario of the transport sec-tor, the consumption of petroleum productsis 232 Mtoe in 2031 and total energy is 302Mtoe, which is about 34% lower than that inthe BAU scenario. Furthermore, bio-dieselcan play an important role in decreasing thepetroleum requirements in the transportsector. This is clearly evident from the modelresults which indicate that if the completepotential of bio-diesel is exploited based onthe availability of degraded land in the coun-try, as considered in the BIODSL (bio-die-sel) scenario of the transport sector, 28Mtoe of diesel can be displaced in the 2031when compared with the BAU scenario.

6.2 Policy recommendations

emerging from the model runs

It can be observed from the key resultsmentioned in the previous sections that even

Key observations and recommendations 195

under the HG scenario, the country’s energyrequirements would increase only by 15% ifit were to pursue a multi-pronged strategy ofadopting not only the policies of energy effi-ciency but also aggressive nuclear and ag-gressive renewable energy.

In order to address the earlier-mentionedpoints, the following policies/measures aresuggested.

6.2.1 Providing thrust to

exploration and production of coal

Clearly, coal continues to be the mainstay ofenergy production, accounting for about45%–55% of the commercial energy mixthroughout the modelling time frame.

By 2031, imports of coal in the BAU sce-nario are expected to be about 1176 Mtoe.Even at the current price of 60 dollars pertonne for imported coal, this would translateto a foreign exchange outflow of about 4000billion rupees.

With coal demand expected to increase inthe Asian market, the price of coal may alsoincrease rapidly, imposing an even higherpressure on the economy in the future.Therefore, it is extremely important to re-duce the import dependency of coal by gear-ing up the exploration and productionactivities in this sector with a view to in-creasing the extractable coal reserves. Forthis, a multi-pronged approach would beneeded that would� bring about technological upgrading of

mining technologies;� open up the coal sector to private inves-

tors (a policy similar to the new explora-tion policy of the Ministry of Petroleumand Natural Gas may be adopted aftermodifications to suit the coal sector re-quirements);

� enable the CMPDIL (Central Mine Plan-ning and Design Institute Ltd) to under-take more intensive R&D (research anddevelopment) and scale-up its efforts toimprove coal extraction technology andmethods, especially beyond the depth of300 metres;

� undertake joint ventures for the extrac-tion of coal from deep coal seams with aview to upgrade technology and improveproductivity; and

� adopt advanced exploration and produc-tion technologies to identify and producecoal from seams beyond 300 metres.

6.2.2 Involving private sector in

exploration and production of

hydrocarbons

Initiatives taken by the DGH (DirectorateGeneral of Hydrocarbons) are already pro-ducing results in the exploration and pro-duction area. The recent findings of theReliance Industries Ltd and other joint ven-ture operations indicate possibilities ofmuch greater findings in the oil and gas sec-tor. The NELP–V (New Exploration Licens-ing Policy–Phase V) is a step in the rightdirection, but it is important to continuepursuing exploratory efforts for tapping in-digenous oil and gas.

6.2.3 Steps towards energy

security in hydrocarbons

The efficiency improvement on the demandside has implications on the supply of petro-leum products. Since there is an upper limiton the domestic availability of crude oil at77.3 MT by 2031/32, it becomes imperative


for the Indian economy to meet the bur-geoning oil demand through imports tobridge the demand–supply gap. However,the anticipated rise in oil demand is makingthe Indian economy increasingly dependenton oil imports, creating an additional finan-cial burden on the Indian economy. Thismakes the economy more prone to oil supplyshocks emanating from external factors suchas wars and political instability due to geo-political situations. Another concern is thatof market risks arising from sudden in-creases in oil prices. The price rise adverselyhampers the process of economic growthdue to the inflationary impact caused bynorthward bound oil prices.

Furthermore, since the bulk of oil con-sumption is in the transport sector, this sec-tor presents the maximum potential foroil-use efficiency, conservation, and substi-tution with alternative forms of energy fromthe viewpoint of energy security.

The model results in the transport sce-narios clearly indicate that if thrust is pro-vided on promoting energy efficiency in thetransport sector, the import dependency ofpetroleum products would decline fromabout 74%, 81%, and 90% in the BAU to72%, 76%, and 85% in the HYB scenario for2011, 2021, and 2031, respectively. This de-cline in import dependency is mostly due tothe reduction in the quantities of petroleumproducts imported mainly because of reduc-tion in the energy consumption due to en-ergy efficiency.

In the following section, the steps takenby the Government of India towards meet-ing the energy security objectives are brieflyreviewed. The model results support thesepolicies.

6.2.3.1 Intensive exploration and

production

The Government of India has initiated manysteps to ensure oil security for the country.One such step was to intensify domestic ex-ploration and development efforts to explorenew fields and increase the reserve base ofthe country. Hydrocarbon vision 2025 laiddown a phased programme for reappraising100% of sedimentary basins of the countryby 2025 (Planning Commission 1999). It in-cludes intensive exploration in producingbasins to upgrade ‘yet-to-find’ hydrocarbonresource and promotion of exploration in‘non-producing’, ‘poorly explored’, and newfrontier basins like the Himalayan foothold.To meet these objectives, the DGH has con-ducted a number of studies to upgrade infor-mation on the unexplored or the lessexplored regions of the country. About 1.96million km2 (square kilometres) of the regionhas already been covered under these efforts,of which 86% is offshore and the rest on-land. These surveys have given informationabout the structure, tectonics, and sedimen-tary thickness of these areas.

Overseas acquisition of equity oil is an-other major strategy adopted to enhance theoil security of the country. The Governmentof India aims to produce 20 MTPA (milliontonnes per annum) of equity oil and gasabroad by 2010. Under the Tenth Five YearPlan, the target for oil and gas equity abroadwas 5.2 MT and 4.88 BCM (billion cubicmetres), respectively. The likely achievementunder the plan period is expected to beabout 16.45 MT for oil and 4.41 BCM inthe case of natural gas. The potential in-place reserves for the block have been esti-mated to be more than 600 million barrels.


6.2.3.2 Strategic reserves

In a major move aimed at enhancing energysecurity for the country, on 7 January 2004,the Union Cabinet approved the setting upof strategic storage facilities for 5 MT ofcrude oil, sufficient to meet 15 days of con-sumption at three locations on the east andwest coasts. The construction of an under-ground rock cavern has been proposed atMangalore (1.5 MT), Vizag (1 MT), and ata suitable location south of Mangalore(2.5 MT). This strategic storage will be inaddition to the existing storage facilities forcrude and petroleum products and will pro-vide an emergency response mechanism incase of short-term disruptions. Currently,the total crude oil storage capacity with do-mestic refineries is 19 days (5.7 MT).

With increased availability of natural gasin the country, the Government of India isalso considering the building of under-ground natural gas storage facilities in Indiafor strategic use. The government has recog-nized that with the growing importance ofnatural gas as fuel/feedstock for several keysectors like power, fertilizer, steel, and trans-port and domestic, creation of strategic gasstorage systems would be imperative for as-suring security for uninterrupted supplies.Initial investment estimates for creating anunderground gas storage facility have beenpegged at 100 million dollars. Detailed feasi-bility studies have been proposed to be car-ried out for development of undergroundgas storage facilities in the country, whichmay be funded by the OIDB (Oil IndustryDevelopment Board) through an initial grant.

6.2.4 Reduce coal requirements

It is observed that coal would continue toaccount for 50% of the energy mix, withabout 70% being used by the power sector.Therefore, it is important to accelerate thetransition to the more efficient coal-basedpower generation technologies—specificallythe IGCC and the ultra-supercriticaltechnologies.

For this purpose, demonstration plantsusing IGCC should be set up. Faster learn-ing can be achieved by outright purchase oftechnology. A relentless effort is required inthis direction to achieve continuous adop-tion of the emerging technologies. For ex-ample, in case of the indigenousdevelopment of the supercritical boilers,technology development was adopted in aphased manner.

Apart from the power sector, the possibil-ity for reducing coal exists in the steel re-heating furnaces, the ceramic industry, brickunits, through adoption of improved tech-nology in coal-based captive power genera-tion units, and through the increasedadoption of blended cements. Appropriatepricing of energy would play a crucial role inthis regard.

6.2.5 Reduce consumption of

petroleum products

Since the transport sector accounts for nearly70% of the total petroleum consumption, thefollowing measures are recommended to re-duce the consumption of petroleum productsand thereby their imports.� Enhancing the share of public transporta-

tion, promoting MRTS (Mass Rapid


Transit System), ensuring better connec-tivity of trains to urban areas of the cities,introducing high-capacity buses, etc.

� Electrification of railway tracks to themaximum extent possible.

� Increasing the share of rail in freightmovement and enhancing containermovement, while introducing door-to-door delivery systems.

� Introducing Bharat–III norms across thecountry for road-based personal vehicles.

� Introducing cleaner fuels such as low sul-phur diesel, ethanol blending, and bio-diesel.

In the industry sector, given the ineffi-ciency of diesel consumption by the DG(diesel generator) sets for captive power gen-eration, phasing out the use of diesel recom-mended. The provision of reliable powersupply is imperative to achieve this. This rec-ommendation applies to the sectors as wellas agriculture.

The use of naphtha for fertilizer produc-tion and power generation should be avoidedto make it available for the petrochemicalssector. Natural gas should therefore be madeavailable in adequate quantities for off-takeby the fertilizer industry and power plants.

6.2.6 Natural gas to be the

preferred fuel for the country

Natural gas availability needs to be facili-tated by removing infrastructural con-straints. It is the preferred option for powergeneration as well as for nitrogenous fertil-izer production. Besides its high end-use ef-ficiency, it is a cleaner fuel and relativelymuch easier to handle than coal. It is there-fore important to enhance natural gas explo-

ration and production from deep-sea. More-over, we should source gas from within theAsian region (including Turkmenistan,Bangladesh, Iran, and Myanmar).

In general, the resource needs to betapped to a greater extent—since the use ofnatural gas has implications in several sec-tors such as fertilizers, cement, sponge iron,power, and transport.

6.2.7 Make renewables

competitive and target their use in

remote areas and decentralized

power generation

Renewable-energy-based power generationis not a preferred option due to the highupfront costs and low capacity utilization ofthese technologies. Apart from continuingthe schemes to provide support, large-scaledeployment of these technologies wouldserve to bring down the costs further. Thegovernment’s policies in this regard are al-ready on the right track but need to beimplemented aggressively, especially if thetarget of providing electricity to all has to bemet by 2012.

Decentralized power generation, espe-cially in remote locations where the gridcannot be extended, should necessarily bebased on renewable energy forms to providethese regions with access to clean and reli-able energy.

6.2.8 Hydro power

Despite its low capacity utilization factor,hydro power is a cheap option as indicatedby the model. Accordingly, investments in


hydropower should be accelerated to tap thisperennial source of power.

6.2.9 Nuclear power

Since additional nuclear-based capacity dis-places coal, it is important to enhance thepenetration of this option to the extent pos-sible. Efforts should be directed to step upnuclear capacity to about 70 GW during themodelling time frame, from 2001 to 2031.However, if the modelling time frame is ex-tended to 2050 and beyond, the positive im-pacts of nuclear energy can be captured. Thebenefits become quite evident because of thethree-stage nuclear policy adopted by theGovernment of India, especially with the in-troduction of the fast breeder reactors withthorium as fuel. The estimated potential ofthese reactors is about 530 GW.

6.2.10 Recommendations for the

industry sector

Energy efficiency in the various industrysub-sectors can be achieved through the fol-lowing measures.� Ban import of second-hand machinery in,

for example, sponge iron plants and papermills.

� Use of cleaner fuels.� Facilitate shifts towards cogeneration,

tapping waste heat for process heat.� Provide support to large-, medium-, and

small-scale industry.� Adopt sub-sectoral technology options

that will result in large-scale energy sav-ings including• introducing blast furnace with top re-

covery turbine in integrated steel

plants, BOF (basic oxygen furnace) forsteel making, and continuous castingfor finished steel;

• adopting and improvising COREXprocess for integrated steel plants;

• setting up new cement plants to adoptsix-stage preheating and use of blend-ing materials like slag and fly ash;

• moving towards larger integrated papermills with continuous digesters, black li-quor boilers, and cogeneration; and

• adopting efficient pre-baked elec-trodes in the aluminium manufactur-ing process.

6.2.11 Recommendations for the

residential and commercial sectors

The majority of energy consumed in theresidential and commercial sectors isthrough lighting, space-conditioning, andcooking. The measures that can have a majorimpact on energy consumption in these sec-tors are enumerated below.� Lighting is the major electricity consum-

ing end-use in the residential sector. Thereplacement of light bulbs with tubelights and CFLs (compact fluorescentlamps) can bring about huge energy sav-ings. Towards this end, the cost of CFLsneeds to be reduced by promoting itslarge-scale manufacturing.

� Even with a conservative estimate of effi-ciency improvement possibilities, thereexists tremendous scope for savings inresidential and commercial space-condi-tioning. For this, it is necessary to makeavailable efficient motors as against localmakes, provide incentives to buy fromgovernment-certified outlets, and createawareness among consumers.


� Although traditional fuels such as dung,firewood, and crop residue are freelyavailable, their low efficiencies, highlypolluting nature, and other social and en-vironmental impacts associated with theiruse do not make them a sustainable op-tion in the long-term. Although govern-ment initiatives would ensure that themajority of the population would be pro-vided with access to modern fuels (citygas and LPG [liquefied petroleum gas]),some of the rural poor are expected tocontinue supplementing their energyneeds with freely available traditional fu-els. Hence, replacement of traditional fu-els with cleaner fossil fuels isrecommended. For the population thathas not shifted to cleaner options,programmes for improved cook stoves,etc. should be introduced.

6.2.12 Rationalize agricultural

power tariffs

Power tariffs for the agricultural sectorshould be at least at a level where the cost ofgeneration can be recovered.

6.3 Technology pathways

The model results indicate that maximumreduction in the energy consumption in In-dia can be achieved by carrying out interven-tions in the power sector on the supply sideand in the transport and residential sectorson the end-use side.

The model results also indicate that if allpower generation technologies were allowedto compete for new capacity additions, the

preferred choice of technologies in the orderof economic merit would be (1) large hydro;(2) refinery-residue-based IGCC; (3) im-ported-coal-based IGCC; (4) high-effi-ciency CCGT (H-frame has turbine);(5) indigenous coal-based IGCC; (6) nor-mal CCGT; (7) ultra-super critical boiler;(8) super-critical boiler.

Therefore, the Government of Indiashould pursue policies to accelerate the pen-etration of hydro-based power generation, asthis is a mature technology. However, thereare technical and non-technical barriers inthe adoption of other power generation tech-nologies mentioned above.

The analyses of the model results at theend-use side indicate that the maximum im-pact on final energy demand can be achievedby the adoption of energy-efficient technolo-gies in the end-use sectors like the transportand residential.

In addition, the results indicate thatIndia’s commercial energy demand will growby 7.5 times during the next 30 years. Fur-ther, India’s energy import dependency willincrease significantly over the next 30 years,with import dependency of coal expected toincrease from 3% to 70% and that of oilfrom 68% to 90% during the same period.Therefore, it becomes imperative to increasethe supply of indigenous energy resources.Hence, India should plan to enhance effortsin R&D in the exploration and production ofenergy resource—especially in the area ofdeep-sea natural gas exploration, technolo-gies to exploit coal from seams that are over300 metres deep, in-situ coal gasification,and gas hydrates.

A brief status of various technologiesand recommendations for their deploymentare indicated below. Also, Tables 6.1,6.2, and 6.3 show the pathways that are


Table 6.1 Suggested technology deployment programme

2006–11

Power generation technologies

Hydro power generation

Supercritical boilers/ultra-supercritical boilers

Advanced gas turbines (for example, H-frameturbine)

Refinery-residue-based IGCC

Demonstration of commercial scale IGCCplants using indigenous and imported coals

Fast breeder nuclear reactor

End-use technologies

Cogeneration

Use of waste recovery in industrial processes

Lighting technologies: CFL, LED

Energy-efficient white goods: refrigerators,alternating current

T&D loss reduction: HVDC, HVAC, and amor-phous core transformer

R&D in exploration and production of fuels

Natural gas from gas hydrates

In-situ coal gasification

Deep-sea natural gas

CBM

Mining of coal from seams greater than 300metres

2011–21

Commercialize IGCC

Ultra-supercritical boiler to becommercialized

State-of-the-art industrial pro-cesses to be adopted

In-situ coal gasification to becommercialized

Deep-sea natural gas com-mercially available

CBM production to be com-mercialized

Commercial mining of coalfrom seams greater than 300metres

2021–31

Demonstrationof commercial-scale thorium-based reactorsdemonstrated

State-of-the-artindustrial pro-cesses to beadopted

Natural gasfrom gas hy-drates to becommercial-ized

CBM – coal bed methane; CFL – compact fluorescent lamp; LED – light emitting diode;

HVDC – high voltage direct current; HVAC – high voltage alternating current;

IGCC – integrated gasification combined cycle; T&D – transmission and distribution;

R&D – research and development

Table 6.2 Suggested technology deployment pathway for power generation

Table 6.3 Suggested technology deployment pathway for end-use sectors


apparent to achieve these goals over thenext 30 years.

6.3.1 Power generation

technologies

6.3.1.1 Nuclear

Keeping the long-term time frame of 50years or more, nuclear-based power genera-tion would emerge as the clear winner interms of sustainable and energy-efficientpower generation. The three-phaseprogramme proposed by the Department ofAtomic Energy, using fast breeder reactorsin the second phase and subsequently tho-rium-based reactors for power generation inthe third phase, is well conceived.

6.3.1.2 Integrated gasification

combined cycle

Because of the technical barrier to the adop-tion of Indian high-ash coals, it is recom-mended that commercial-scale coal-basedIGCC demonstration projects be set up onindigenous and imported coal. This will fa-cilitate familiarization with technology andcost reduction of IGCC-based power plants.

With increased refining capacity, refineryresidue such as vacuum residue and petro-leum coke will be available on large scale. Itis recommended that refinery-residue-basedIGCC power generation plants also be setup. International experience in this technol-ogy is already available. Handling refiningresidue is comparatively easier than han-dling high-ash coal for gasification for pro-

duction of ‘syn’ (synthetic) gas and use ingas turbines for power generation. The gov-ernment should adopt this technology assoon as possible.

6.3.1.3 Advanced gas turbines

Adoption of aero derivative advanced gasturbines like H-frame for power generationshould be aggressively promoted. In the fu-ture, it is possible that natural gas reserveswill increase especially due to the efforts ofthe Government of India in deep-sea explo-ration and due to the viability of extractingnatural gas from gas hydrates. Therefore,aggressive adoption of advanced gas tur-bines will also help in enhancing the efficien-cies of IGCC plants. It will also be useful ifthe Government of India can adopt aresearch programme on advanced gasturbines in national research institutions orlaboratories like National Aeronautics Ltdand Hindustan Aeronautics Ltd.

6.3.1.4 Supercritical/ultra-

supercritical boiler

Supercritical steam properties require ‘oncethrough’ or ‘Benson’ boilers, which are dif-ferent from the drum-type boilers used forpower generation based on sub-critical con-ditions of steam. Since coal-based powergeneration will continue to play a criticalrole in the next 30–50 years, it becomes es-sential to adopt well-proven technologieslike super-critical and ultra-supercriticalboilers in the immediate future, that is, inthe Eleventh Five Year Plan, instead of using


sub-critical technology. The Benson boilerwas first designed in 1924,1 and ever sincethese boilers are being designed and oper-ated at higher steam properties (for example,pressures of 300 bar and temperaturesgreater than 600 oC). It is strongly recom-mended that India adopt this technologyimmediately. Experience worldwide hasshown that Benson boilers become cost ef-fective if the unit size is around 1000 MW(megawatts) or more.

6.3.2 Transmission and

distribution loss

It is also possible to reduce technical T&D(transmission and distribution) losses to8%–12% as against 16%–19% in the coun-try. The technologies for these would be toadopt very high voltage AC (alternating cur-rent) transmission and HVDC (high voltagedirect current) transmission. Distributionlosses can be reduced by adoption of anenergy-efficient transformer, which useshigh-grade steel in the transformer core.

6.3.3 End-use technologies

The adoption of energy-efficient technolo-gies in the end-use energy-consuming sec-tors can have a major impact on the finalenergy demand, primarily in transport andresidential sectors. In addition, there is apossibility of technical loss reduction in thetransmission and distribution of power.

6.3.3.1 Industrial sector

Although commercial energy consumptionis the highest in the industrial sector, majorenergy-intensive industries are already mov-ing towards energy-efficient technologies.The cement and the iron and steel sectorsare already adopting state-of-the-art tech-nologies, barring a few old plants. However,there are sectors where the energy-efficienttechnologies can be penetrated at a fasterrate, especially for technologies which arewell proven; for example, cogeneration inthe industrial sector and use of waste heat inthe industrial processes. These technologiesare well known and can be promoted by theBureau of Energy Efficiency by proper dis-semination of information.

6.3.3.2 Residential sector

Electricity consumption in the residentialsector will increase at the rate of 8.8%,which is primarily due to increased utiliza-tion and the policies of the Government ofIndia to provide electricity to all. Promotionof energy-efficient lighting like CFL and en-ergy-efficient white goods like refrigeratorsand air-conditioners can achieve a reductionof about 23% in 2030. Although thesetechnologies are well known, the Govern-ment of India needs a policy to promotethese technologies.

11111 Siemens Power Generation (1995)


6.3.3.3 Transport sector

The transport sector requires planning toincorporate integrated transport systems inurban areas so that public transport systemsare easily accessible to the public at large.Apart from the shift to a public transportsystem and the use of rail, energy-efficient

automobile technologies, which are continu-ously improving in the OECD (Organizationfor Economic Co-operation and Develop-ment) countries, should be adopted. Thetechnological features are wide ranging(from fuel injection improvements to effi-cient combustion and efficient control dueto electronic governance).

Description of energy sector models

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A1.1 Introduction

Energy models can be developed using thebottom-up approach or the top-down ap-proach. The bottom-up energy models aredeveloped from engineering data applied tospecific technologies whereas the top-downenergy models are based on statistical analy-sis of past data. Both can be useful in under-standing the effects of policy on energymarkets. However, the bottom-up modelsoften neglect certain costs that reduce re-turns on investment below what is predicted,resulting in unrealistic estimates of what willoccur if energy markets are shocked. On theother hand, the top-down models are basedon the technology and institutions existingat the time their data applies to, and hencemay underestimate the ability of markets toadapt.

Within these two approaches, energymodels can be categorized into four broadcategories: (i) optimization models, (ii)simulation models (bottom-up), (iii) energysector equilibrium models, and (iv) input–output models (top-down). The characteris-tic features of these models are summarizedbelow.

A1.2 Energy optimization models

These technology-oriented models mini-mize the total costs of the energy system, in-cluding all end-use sectors, over a 40–50year horizon. The costs include investmentand operation costs of all sectors based on adetailed representation of factor costs. Therecent versions of these models allow de-mand to respond to prices. A link has alsobeen established between aggregate macro-economic demand and energy demand. Pro-jections of future development are oftenimplemented with a model generator via theoptimization algorithms based on linear pro-gramming. Given below are some examplesof these models.

A1.2.1 Model for Energy Supply

Systems Analysis and General

Environment

MESSAGE (Model for Energy SupplySystems Analysis and General Environment)is generally used for the optimization of en-ergy supply systems. However, other systems

APPENDIX 1

208 Appendix 1

supplying specified demands of goods,which have to be processed before deliveryto the final consumer, could be optimized.MESSAGE is an instrument for medium- tolong-term dynamic planning of the opera-tion and expansion of energy systems. Theobjectives include resource extraction analy-sis, estimation of import/export of energy,energy conversion analysis, energy transportand distribution analysis, final energy utili-zation by consumer analysis, recommenda-tions for environmental protection policyand investment policy, and analysis of op-portunity costs (shadow prices and marginalcosts).

A1.2.2 Asia–Pacific Integrated

Model

AIM (Asia–Pacific Integrated Model) is atechnology selection framework for analysisof country-level policies related to GHG(greenhouse gas) emissions mitigation andlocal air pollution control. It can also assistin energy policy analysis. It simulates flowsof energy and materials in an economy, fromsupply of primary energy and materials,through conversion and supply of secondaryenergy materials, to satisfaction of end-useservices. AIM/ENDUSE models these flowsof energy and materials through detailedrepresentation of technologies. Selection oftechnologies takes place in a linear optimiza-tion framework where system cost is mini-mized under several constraints likesatisfaction of service demands, availabilityof energy and material supplies, and so on.Various scenarios including policy counter-measures can be analysed in AIM/ENDUSE.

A1.2.3 Energy Flow Optimization

Models

EFOM-ENV (Energy Flow OptimizationModels) are national dynamic optimizationmodels (employing linear programming),representing the energy producing and con-suming sectors in each state/province. Theyoptimize the development of these sectorsunder given fuel import prices and usefulenergy demand over a pre-defined time hori-zon. The development of national energysystems can be subject to energy and envi-ronment constraints like availability of fuel,penetration rates of certain technologies,emission standards, and emission ceilings.The model databases contain a wide range ofconversion and end-use technologies such asconventional technologies, renewable energytechnologies, efficient fossil fuel burningtechnologies, combined heat and powertechnologies, and energy conservation tech-nologies in the demand sectors. The mainobjective of EFOM-ENV is energy andenvironment policy analysis and planning,particularly cost-effectiveness analysis ofenergy policy options for reducing pollutantemissions.

A1.2.4 Modular Energy System

Analysis and Planning software

MESAP (Modular Energy System Analysisand Planning software) is a modularenergy planning package developed with thespecific needs of developing countries inmind. It is designed as a flexible planningpackage providing energy analysts and plan-ners with tools to perform complex energy

Appendix 1 209

scenarios. It is based on a hierarchical sys-tem of interconnected sub-models at the in-ternational, regional, and national levels.Simulation is carried out for the interna-tional energy markets, national energy bal-ances, and technical subsystems for finalenergy consumption, energy transformation,and production. Technological developmentand diffusion of new technologies as well asGHG emissions of the energy sector aretaken into account.

A1.3.2 Model of Power system

planning and Comprehensive

Assessment

This model is composed of two parts. Thefirst part compares and assesses comprehen-sively the different development options ofthe power system (decision-making analy-sis). It uses the AHP (Analytic HierarchyProcess) for comparing and ranking differ-ent development options. The second part ofMOPCA (Model Of Power system planningand Comprehensive Assessment) is a simu-lation model of the power producing optionsfor power system development planningwith constraints regarding financing, re-sources, and environment. In the first part,the most important aspects of the power sys-tem such as energy independence, economicaspects, system reliability, environmentaland ecological impacts as well as social im-pacts are taken into account. In the secondpart, macro-economic analysis, power sys-tem analysis, and environmental burdensanalysis are carried out for the purpose ofpower system development planning. Thetwo parts of MOPCA can be used separately.The model is designed to serve small coun-

analysis. It consists of basic techniques forenergy planning, a set of tested energy mod-ules, and data management and processingsoftware. At the heart of MESAP is a net-work-oriented database. Its objective is toassist in energy and environmental policyanalysis and planning.

A1.3 Simulation models

These models involve a detailed representa-tion of energy demand and supply technolo-gies, which include end-use, conversion, andproduction technologies. Demand and tech-nology developments are driven by exog-enous scenario assumptions often linked totechnology vintage models and econometricforecasts. The demand sectors are generallydisaggregated for industrial sub-sectors andprocesses, residential and service categories,transport modes, and so on. This allows de-velopment trends to be projected throughtechnology development scenarios. Qualityof the expert estimations is the decisive fac-tor to ensure the quality of the simulation.The main areas of application for simulationmodels are research questions concerningtechnologically oriented measures, where ahigh level of detailed knowledge is necessary,and where macro-economic interaction andprice are less important. Some of the simula-tion models include the following.

A1.3.1 Prospective Outlook on

Long-term Energy Systems

POLES (Prospective Outlook on Long-termEnergy Systems) is a simulation model pro-viding long-term energy supply and demand

210 Appendix 1

tries, especially developing countries, andregions of large countries.

A1.3.3 Brundtland Scenario model

BRUS (Brundtland Scenario) is a long-termsimulation model for the energy demandand supply system. Being a technical–eco-nomic model, it allows for the calculation ofdemand-driven scenarios for the total na-tional energy system. The main purpose ofthe model is to analyse cost-effective strate-gies for the reduction of CO2 (carbondioxide). Simultaneously, potential minimi-zations of exhaustible resources areanalysed. The model is total (in contrast topartial or marginal models), making it pos-sible to introduce significant changes, for ex-ample, on the demand side, and even thenget reliable results for the total energy sys-tem. It is subdivided into different sectors ofenergy demand and supply, which are inte-grated to provide useful and comprehensivetool.

A1.3.4 Long-range Energy

Alternatives Planning

LEAP (Long-range Energy AlternativesPlanning) is an energy planning model thatcovers energy demand, transformation, andsupply. It uses a simulation approach to rep-resent the current energy situation for agiven area and to develop forecasts for thefuture under certain assumptions. LEAP is acomputer-based accounting and simulationtool designed to assist policy-makers inevaluating energy policies and developingsound, sustainable energy plans. LEAP canbe used to project the energy supply and

demand situation in order to glimpse futurepatterns, identify potential problems, andassess the likely impacts of energy policies. Itcan assist in examining a wide variety ofprojects, programmes, technologies, andother energy initiatives, and in arriving atstrategies that best address environmentaland energy problems.

A1.3.5 Multinational Integrated

Demand And Supply

MIDAS (Multinational Integrated DemandAnd Supply) is a large-scale energy systemplanning and forecasting model. It performsdynamic simulation of the energy system,which is represented by combining engi-neering process analysis and econometricformulations. The model is used for scenarioanalysis and forecasting. MIDAS covers thewhole energy system and ensures, on an an-nual basis, the consistent and simultaneousprojection of energy demand, supply, pric-ing, and costing, so that the system is in bothquantity- and price-dependent balance. Themodel output is a time-series of detailedEUROSTAT energy balance sheets, lists ofcosts and prices by sector and fuel, and a setof capacity expansion plans including emis-sion data.

A1.4 Energy sector equilibrium

models

These models are conceptually similar to theeconomic equilibrium models that representdecision-making processes of producers andconsumers. They typically simulate marketsfor factors of production (such as labour,

Appendix 1 211

capital, and energy), products, and foreignexchange, with equations that specify supplyand demand behaviour. They offer a closedtheoretical approach of obtaining marketequilibrium that can increasingly be ad-justed for imperfect market conditions. Theonly difference is that the non-energy mar-kets are not represented here. These modelsrequire the energy demand as an exogenousinput, which is typically based on other eco-nomic and demographic forecasts. Thesemodels include the following.

A1.4.1 GEM-E3

The GEM-E3 model is an applied generalequilibrium model, simultaneously repre-senting world regions or European coun-tries, linked through endogenous bilateraltrade flows and environmental flows. GEM-E3 aims at covering the interactions betweenthe economy, the energy system, and the en-vironment. It is built in a modular wayaround its central CGE (computable generalequilibrium) core. It supports defining sev-eral alternative regimes and closure ruleswithout having to re-specify or re-calibratethe model. Although global, the model ex-hibits a sufficient degree of disaggregationconcerning sectors, structural features of en-ergy/environment, and policy-oriented in-struments (for example, taxation). Themodel formulates production technologiesin an endogenous manner allowing for price-driven derivation of all intermediate con-sumption, and the services from capital andlabour. In the electricity sector, the choice ofproduction factors can be based on the ex-plicit modelling of technologies. For the de-mand side, the model formulates consumerbehaviour, and distinguishes between

durable (equipment) and consumable goodsand services.

A1.4.2 PRIMES

The PRIMES model, used by the EU (Euro-pean Union) environmental agencies, is de-signed only for measuring sectoral effectsand not economy-wide effects. PRIMES, apartial equilibrium model, is primarily de-signed to show the effect of policy changeson energy markets. It can calculate the directcost implications of reduced energy use, butnot the economy-wide impact on GDP(gross domestic product), employment, andinvestment.

A1.4.3 Energy and Power

Evaluation Program

ENPEP (Energy and Power EvaluationProgram) is a set of microcomputer-based energy planning tools that aredesigned to provide an integrated analysiscapability. ENPEP begins with a macro-economic analysis, develops an energydemand forecast based on this analysis,carries out an integrated demand/supplyanalysis for the entire energy system, evalu-ates the electric system component ofthe energy system in detail, and determinesthe impacts of alternative configurations.Also, it explicitly considers the impacts thepower system have on the rest of the energysystem and on the economy as a whole.ENPEP is mainly employed for energypolicy analysis, energy tariff development,energy project investment analysis, electricsystem expansion planning, and environ-mental policy analysis.

212 Appendix 1

A1.4.4 Canadian Integrated

Modelling System

CIMS (Canadian Integrated Modelling Sys-tem) is a nearly full equilibrium system,which tracks the flow of energy in the entireeconomic system beginning with productionprocesses through to the eventual end-useby individual technologies. It may incorpo-rate demand-dependent energy supply costs,price-driven demand feedbacks, second or-der macro-economic effects, and energytrade. CIMS is ideal for modelling policiesintended to affect energy efficiency, GHGemissions, and air quality.

A1.4.5 National Energy Modelling

System

NEMS (National Energy Modelling System)is a computer-based, energy economy mod-elling system of the US energy markets forthe medium-term period through 2020. De-signed and implemented by the US Depart-ment of Energy, it represents domesticenergy markets by explicitly representing theeconomic decision-making involved in theproduction, conversion, and consumption ofenergy products. NEMS provides a consis-tent framework for representing the complexinteractions of the US energy system and itsresponse to a wide variety of alternative as-sumptions and policies or policy initiatives.As an annual model, it can also highlight theimpacts of transitions to new energyprogrammes and policies.

A1.4.6 Input–output models

Input–output models are based on the timeseries of the macro-economic interactionmatrices with their input–output tables, en-ergy balances, and labour market statistics.Activities are explained with respect tosectoral development, energy carrier con-sumption, and emission development. Thissegment includes the following.

A1.4.6.1 Energy Scenario

Generator

The main purpose of the ESG (Energy Sce-nario Generator) model is to generate con-sistent scenarios of economic development,which simultaneously determine energy de-mand and supply as well as the major envi-ronmental impacts. The feedback betweeneconomic development, energy demand, andenergy supply is fully integrated into themodel, that is, an energy technology model islinked with a macro-economic model. Themodel aims at coordination of macro-eco-nomic energy and environmental policies atthe national level. As inputs, this model re-quires data such as (i) base year energy bal-ances, (ii) base year economic data, (iii) baseyear input–output table, (iv) time series ofmajor economic data (consumption, trade,investment), (v) data on disaggregated capi-tal stocks, (vi) capital market data (interestrates, inflation), (vii) population data, and(viii) energy technology data like efficiency(actual and expected future), disaggregatedinvestments (actual and expected), emissiondata (plus reduction potential), labour in-put, and technology lifetime.

Appendix 1 213

A1.4.6.2 MEGEVE-E3ME

This is a general energy–environment–economy model, developed for Europe, ca-pable of addressing issues that linkdevelopments and policies in the areas ofenergy, environment, and economy. Themain purpose of the model is to provide aframework for evaluating different policies,particularly those aimed at achieving sus-tainable energy use over the long term.MEGEVE-E3ME uses a neo-Keynesianeconometric input–output model in a gen-eral equilibrium framework. It provides de-tailed results for the economy, the energysector, and environmental emissions. Basicinput data are input–output tables; nationalaccounts; investment data; energy balances,energy prices, and taxes; electricity stationdata; and emissions into air.

A1.4.6.3 MICRO-MELODIE

MELODIE is a French macro-economicmodel with a detailed technological descrip-tion of the energy sector, especially in theelectricity sector. The model also computespolluting emissions such as NO

x, SO

2, and

CO2. The economy, energy, and environ-ment are then described in a single frame-work, but for each topic, a specificmethodology has been developed.MELODIE is adapted to measure any en-ergy policy modifying the cost structure ofelectricity supply. Input/output tables at cur-rent and constant prices, economic accountsof the institutional sectors, and technologi-cal and economic data on the electricity sec-tor including fuel cycle, internationaleconomic data energy balances in physicaland monetary units, and environmental data(polluting emissions) are the main inputs re-quired for this model.

Sectoral reference energy system

(RES)

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APPENDIX 2

Figure A2.1 Reference energy system for the agriculture sector

216 Appendix 2

Figure A2.2 Reference energy system for the transport sector

Appendix 2 217

Figure A2.3 Reference energy system for the residential sector

218 Appendix 2

Figure A2.4 Reference energy system for the industry sector

Appendix 2 219

Figure A2.5 Reference energy system for the electricity sector

Socio-economic drivers of energy

demand

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A3.1 Methodology for estimating

income-wise household distribution

The overtime proportion of households ineach expenditure class depends on factorssuch as rate of growth of population, share ofurban population to rural population,household size, and rate of growth of GDP.

Given these parameters, distribution ofhouseholds in various expenditure classes isgenerated using a lognormal distribution forMPCE (monthly per capita consumptionexpenditure) data for rural and urban avail-able from NSSO (National Sample SurveyOrganization) for ‘consumer expenditurerounds; 1993/94 and 1999/2000’.

The lognormal distribution of MPCE hasprobability density function

22 2/)x(lne2x

1),;x(f σµ−−

πσ=σµ

where, x is the household consumption ex-penditure for x > 0, where µ and σ are themean and standard deviation of the MPCE’slogarithm. The expected value is

E(X) = eµ+σ2/2 (A-3.1)

and the variance isvar (X) = (eσ2 − 1)e2µ+σ2

The cumulative probability of populationbelow an expenditure level is given by

(ln (L) − µ)/σ (A-3.2)

where, L is the consumption expenditurelevel.

In order to forecast the probability ofpopulation in an expenditure class, the twounknowns – µ and σ – for the above twoequations need to be estimated over the fore-cast period.

σ has been assumed to follow the pasttrend of decline during 1993/94 to 1999/2000 for rural and urban areas. µ, mean ex-penditure, has been determined by incomeas per the Keynesian consumption theory.Therefore, increase in GDP implies an in-crease in expenditure thereby implying arightward shift in the lognormal curve. Pri-vate final consumption expenditure has beenused for consumption expenditure. There-fore, forecast of growth rate of private finalconsumption expenditure determines thegrowth rate of MPCE. The growth rate ofprivate final consumption expenditure hasbeen forecasted using the following equa-tion.

APPENDIX 3

222 Appendix 3

PFCE = 115 235 + 0.54 (Y) (A-3.3)(-11.79) (20.83)

(Adjusted R2= 0.953)

where,PFCE = private final consumption ex-

penditureand Y = GDP

Coefficient of GDP is the MPC (marginalpropensity to consume). In other words, anMPC of 0.54 implies that one rupee increasein income leads to an increase of 0.54 rupeein consumption.

MPCE = PFCE/P

where,H = population

In India, the per capita income increasedfrom 5823 rupees in 1981 to 12 281 rupeesin 2001. Correspondingly, the per capita ex-penditure increased from 5044 rupees to8441 rupees during the same time period.This increase in per capita expenditure wasat the annual rate of 2.48% during 1981–2001, when the per capita income growthrate was 3.64%. The same is expected to in-crease at the rate of 4.8%, 6.0%, and 7.8%with the per capita income growing at therate of 5.5%, 6.7%, and 8.5% at a GDP

growth rate of 6.7%, 8%, and 10% respec-tively during 2001–36.

The NSS (National Sample Survey) dataof per capita calorie intake by MPCE classeshas been used to find out the monetary cutoff corresponding to minimum calorie re-quirement norm. The national-level officialpoverty line corresponds to a basket of goodsand services, which satisfies the calorie normof per capita daily requirement of 2400 kcal(kilocalories) in rural areas. Accordingly,people below an MPCE of 525 rupees inrural areas and 575 rupees in urban areashave been considered to be below povertyline (Table A3.1).

For simplifying the analysis, these expen-diture classes have been categorized into sixexpenditure groups namely BPL (belowpoverty line), L (low), LM (lower middle),M (middle), UM (upper middle), and H(high) in rural and urban areas. MPCE lessthan or equal to 525–615 rupees is consid-ered to be under the BPL group. For the ur-ban low-income group, the figure is 575–665 rupees (Table A3.1).

Based on the probabilities computed forrural and urban population under variousGDP growth rate scenarios (Tables A3.2–A3.7), the number of households in ruraland urban areas is estimated for six expendi-ture classes.

Appendix 3 223

Table A3.1 Monthly per capita expenditure and calories intake

Rural Urban

Monthly per Calorie intake Monthly per Calorie intake

capita expenditure (in Rs) (kcal) capita expenditure (kcal)

0–225 1383 0–300 1398

225–255 1609 300–350 1654

255–300 1733 350–425 1729

300–340 1868 425–500 1912

340–380 1957 500–575 1968

F380–420 2054 575–665 2091

420–470 2173 665–775 2187

470–525 2289 775–915 2297

525–615 2403 915–1120 2467

615–775 2581 1120–1500 2536

775–950 2735 1500–1925 2736

Above 950 3178 Above 1925 2938

Source NSO (2000)

Table A3.2 Probability of households (rural) 6.7% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2121 2026 2031 2036

0–225 0.101 0.057 0.044 0.016 0.005 0.001 0.000 0.000 0.000 0.000

225–255 0.048 0.034 0.028 0.014 0.004 0.001 0.000 0.000 0.000 0.000

255–300 0.083 0.065 0.056 0.031 0.014 0.004 0.001 0.000 0.000 0.000

300–340 0.079 0.065 0.059 0.038 0.019 0.006 0.001 0.000 0.000 0.000

340–380 0.077 0.071 0.065 0.046 0.026 0.010 0.002 0.000 0.000 0.000

380–420 0.075 0.070 0.068 0.052 0.032 0.014 0.004 0.001 0.000 0.000

420–470 0.084 0.086 0.084 0.070 0.049 0.024 0.007 0.001 0.000 0.000

470–525 0.082 0.087 0.087 0.079 0.061 0.034 0.012 0.002 0.000 0.000

525–615 0.106 0.120 0.124 0.126 0.108 0.073 0.031 0.008 0.001 0.000

615–775 0.122 0.148 0.159 0.185 0.189 0.155 0.090 0.030 0.005 0.000

775–950 0.070 0.092 0.102 0.138 0.167 0.172 0.131 0.062 0.015 0.002

950–1200 0.044 0.061 0.072 0.109 0.154 0.196 0.198 0.134 0.051 0.009

1200–1500 0.019 0.028 0.033 0.057 0.094 0.148 0.196 0.186 0.105 0.029

1500–2000 0.008 0.013 0.015 0.030 0.057 0.109 0.191 0.260 0.230 0.111

2000–2800 0.001 0.002 0.003 0.008 0.018 0.044 0.104 0.213 0.311 0.271

> 2800 0.001 0.001 0.001 0.001 0.003 0.009 0.032 0.103 0.282 0.578

MPCE – monthly per capita expenditure; GDP – gross domestic product

224 Appendix 3

Table A3.4 Probability of households (rural) 8% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2121 2026 2031 2036

0–225 0.101 0.057 0.044 0.012 0.002 0.000 0.000 0.000 0.000 0.000

225–255 0.048 0.034 0.028 0.010 0.002 0.000 0.000 0.000 0.000 0.000

255–300 0.083 0.065 0.056 0.025 0.006 0.001 0.000 0.000 0.000 0.000

300–340 0.079 0.065 0.059 0.031 0.010 0.002 0.000 0.000 0.000 0.000

340–380 0.077 0.071 0.065 0.038 0.014 0.003 0.000 0.000 0.000 0.000

380–420 0.075 0.070 0.068 0.046 0.020 0.005 0.001 0.000 0.000 0.000

420–470 0.084 0.086 0.084 0.063 0.031 0.009 0.001 0.000 0.000 0.000

470–525 0.082 0.087 0.087 0.073 0.043 0.015 0.003 0.000 0.000 0.000

525–615 0.106 0.120 0.124 0.121 0.084 0.037 0.009 0.001 0.000 0.000

615–775 0.122 0.148 0.159 0.188 0.166 0.097 0.032 0.005 0.000 0.000

775–950 0.070 0.092 0.102 0.148 0.171 0.135 0.064 0.016 0.001 0.000

950–1200 0.044 0.061 0.072 0.124 0.183 0.195 0.133 0.048 0.009 0.001

1200–1500 0.019 0.028 0.033 0.069 0.130 0.188 0.181 0.100 0.026 0.002

1500–2000 0.008 0.013 0.015 0.039 0.094 0.181 0.252 0.218 0.099 0.020

2000–2800 0.001 0.002 0.003 0.011 0.036 0.100 0.212 0.304 0.250 0.101

> 2800 0.001 0.001 0.001 0.002 0.008 0.032 0.112 0.308 0.615 0.876


Table A3.3 Probability of households (urban) 6.7% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2021 2026 2031 2036

0–300 0.094 0.047 0.035 0.012 0.003 0.000 0.000 0.000 0.000 0.000

300–350 0.046 0.031 0.025 0.011 0.003 0.001 0.000 0.000 0.000 0.000

350–425 0.076 0.058 0.050 0.028 0.011 0.002 0.000 0.000 0.000 0.000

425–500 0.079 0.067 0.061 0.038 0.019 0.005 0.001 0.000 0.000 0.000

500–575 0.077 0.071 0.067 0.049 0.026 0.010 0.001 0.000 0.000 0.000

575–665 0.086 0.085 0.083 0.066 0.043 0.018 0.004 0.000 0.000 0.000

665–775 0.093 0.098 0.098 0.088 0.065 0.034 0.010 0.001 0.000 0.000

775–915 0.097 0.109 0.111 0.111 0.094 0.059 0.022 0.004 0.000 0.000

915–1120 0.106 0.125 0.132 0.145 0.142 0.110 0.055 0.013 0.001 0.000

1120–1500 0.117 0.146 0.158 0.195 0.223 0.220 0.158 0.064 0.011 0.000

1500–1925 0.064 0.081 0.089 0.122 0.164 0.203 0.203 0.132 0.040 0.004

1925–2400 0.033 0.043 0.047 0.069 0.100 0.148 0.193 0.181 0.093 0.018

2400–3200 0.021 0.026 0.030 0.045 0.071 0.120 0.199 0.268 0.228 0.090

3200–4000 0.007 0.008 0.009 0.013 0.023 0.044 0.089 0.167 0.225 0.160

> 4000 0.004 0.005 0.005 0.008 0.013 0.026 0.065 0.170 0.402 0.728


Appendix 3 225

Table A3.5 Probability of households (urban) 8% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2021 2026 2031 2036

0–300 0.094 0.047 0.035 0.009 0.001 0.000 0.000 0.000 0.000 0.000

300–350 0.046 0.031 0.025 0.009 0.002 0.000 0.000 0.000 0.000 0.000

350–425 0.076 0.058 0.050 0.023 0.006 0.001 0.000 0.000 0.000 0.000

425–500 0.079 0.067 0.061 0.033 0.010 0.002 0.000 0.000 0.000 0.000

500–575 0.077 0.071 0.067 0.042 0.017 0.003 0.000 0.000 0.000 0.000

575–665 0.086 0.085 0.083 0.059 0.028 0.008 0.001 0.000 0.000 0.000

665–775 0.093 0.098 0.098 0.080 0.045 0.015 0.003 0.000 0.000 0.000

775–915 0.097 0.109 0.111 0.104 0.071 0.030 0.006 0.001 0.000 0.000

915–1120 0.106 0.125 0.132 0.142 0.119 0.065 0.019 0.002 0.000 0.000

1120–1500 0.117 0.146 0.158 0.200 0.213 0.164 0.075 0.016 0.001 0.000

1500–1925 0.064 0.081 0.089 0.134 0.181 0.191 0.131 0.047 0.007 0.000

1925–2400 0.033 0.043 0.047 0.080 0.130 0.174 0.168 0.092 0.022 0.002

2400–3200 0.021 0.026 0.030 0.053 0.107 0.183 0.243 0.207 0.088 0.014

3200–4000 0.007 0.008 0.009 0.021 0.041 0.088 0.158 0.201 0.143 0.042

> 4000 0.004 0.005 0.005 0.011 0.029 0.076 0.196 0.434 0.739 0.942


Table A3.6 Probability of households (rural) 10% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2121 2026 2031 2036

0–225 0.101 0.057 0.044 0.009 0.001 0.000 0.000 0.000 0.000 0.000

225–255 0.048 0.034 0.028 0.008 0.001 0.000 0.000 0.000 0.000 0.000

255–300 0.083 0.065 0.056 0.020 0.002 0.000 0.000 0.000 0.000 0.000

300–340 0.079 0.065 0.059 0.027 0.005 0.000 0.000 0.000 0.000 0.000

340–380 0.077 0.071 0.065 0.034 0.008 0.001 0.000 0.000 0.000 0.000

380–420 0.075 0.070 0.068 0.040 0.012 0.002 0.000 0.000 0.000 0.000

420–470 0.084 0.086 0.084 0.057 0.019 0.002 0.000 0.000 0.000 0.000

470–525 0.082 0.087 0.087 0.069 0.029 0.005 0.001 0.000 0.000 0.000

525–615 0.106 0.120 0.124 0.116 0.061 0.015 0.001 0.000 0.000 0.000

615–775 0.122 0.148 0.159 0.187 0.136 0.047 0.006 0.000 0.000 0.000

775–950 0.070 0.092 0.102 0.155 0.159 0.084 0.018 0.001 0.000 0.000

950–1200 0.044 0.061 0.072 0.135 0.195 0.152 0.052 0.006 0.000 0.000

1200–1500 0.019 0.028 0.033 0.079 0.160 0.187 0.101 0.020 0.001 0.000

1500–2000 0.008 0.013 0.015 0.047 0.133 0.237 0.216 0.079 0.009 0.000

2000–2800 0.001 0.002 0.003 0.015 0.062 0.180 0.294 0.215 0.055 0.004

> 2800 0.001 0.001 0.001 0.002 0.017 0.088 0.311 0.679 0.935 0.996


226 Appendix 3

the performance of the Indian economy dur-ing the Eighth and the Ninth Plan periods.During these plan periods many of the com-monly held beliefs regarding the potentiali-ties and constraints that govern the opera-tion of the economic system have been ques-tioned and highlighted.

There are three major experiences fromthe previous plan periods, as highlighted inthe Tenth Five Year Plan that lay down theguidelines for setting the growth targets forthe future.

Firstly, the growth rate of the Indianeconomy is no longer constrained by theavailability of savings or investible resources.The clearest evidence for this is given by thepersistent difference between the externalcapital inflows and the CAD (current ac-count deficit) that has existed through muchof the 1990s. CAD represents the excess oftotal investment in the country over domes-

A3.2 Rationale for choice of 8%

gross domestic product growth rate

The Tenth Five Year Plan covering the period2002–07 prepared by the Planning Commis-sion, GoI (Government of India) aims atachieving an average growth rate of realGDP of 8% per annum over the period2002–07. The 8% average growth rate targetset for the Tenth Plan appears quite optimis-tic when compared with the short-termGDP growth rate forecasts of other organi-zations. However, the rationale behind tar-geting 8% GDP growth rate is doubling theper capita incomes over the next decade witha more equitable regional distribution. Thiswould bring about substantial improvementin the welfare of the entire population.

Furthermore, the Tenth Five Year Planhas been prepared against the backdrop of

Table A3.7 Probability of households (urban) 10% GDP

MPCE (in Rs) 1993 1999 2001 2006 2011 2016 2021 2026 2031 2036

0–300 0.094 0.047 0.035 0.007 0.000 0.000 0.000 0.000 0.000 0.000

300–350 0.046 0.031 0.025 0.008 0.001 0.000 0.000 0.000 0.000 0.000

350–425 0.076 0.058 0.050 0.019 0.003 0.000 0.000 0.000 0.000 0.000

425–500 0.079 0.067 0.061 0.028 0.006 0.001 0.000 0.000 0.000 0.000

500–575 0.077 0.071 0.067 0.037 0.010 0.001 0.000 0.000 0.000 0.000

575–665 0.086 0.085 0.083 0.054 0.017 0.002 0.000 0.000 0.000 0.000

665–775 0.093 0.098 0.098 0.074 0.031 0.005 0.000 0.000 0.000 0.000

775–915 0.097 0.109 0.111 0.099 0.052 0.012 0.001 0.000 0.000 0.000

915–1120 0.106 0.125 0.132 0.139 0.094 0.032 0.004 0.000 0.000 0.000

1120–1500 0.117 0.146 0.158 0.204 0.194 0.100 0.021 0.001 0.000 0.000

1500–1925 0.064 0.081 0.089 0.143 0.188 0.149 0.053 0.006 0.000 0.000

1925–2400 0.033 0.043 0.047 0.088 0.151 0.170 0.095 0.018 0.001 0.000

2400–3200 0.021 0.026 0.030 0.064 0.141 0.224 0.201 0.073 0.007 0.000

3200–4000 0.007 0.008 0.009 0.022 0.062 0.137 0.190 0.119 0.024 0.001

> 4000 0.004 0.005 0.005 0.014 0.050 0.167 0.435 0.783 0.968 0.999


Appendix 3 227

tic savings while external capital flows repre-sent the inflow of potential savings fromabroad. The excess of external capital in-flows over CAD is therefore an indication ofthe failure of investment demand to absorbforeign savings. Thus, it can be stated thatthe availability of investible resources wasnot the primary constraint to growth and in-vestment in India.

Secondly, the growth rate of an economyis not wholly determined by the level of in-vestment activity. The Tenth Five Year Planhighlights the fact that the rate of real invest-ment as a percentage of GDP was higherduring the Ninth Five Year Plan as comparedto the previous plan period. The Ninth FiveYear Plan recorded a real investment rate of26.3% of GDP as compared to 24.9% dur-ing the Eighth Five Year Plan. However, theeconomy registered an average annual GDPgrowth rate of 6.7% per annum as against5.3% during the Ninth Plan. This is ex-plained by the fact that the investment ratewhen measured in nominal terms has de-clined from 24.8% in the Eighth Plan to24.3% in the Ninth Plan period. Also, thenominal investment rate has been at or be-low the private savings rate. The Ninth Planperiod was characterized by a decline in thelevels of capacity utilization thereby explain-ing a decline in the investment rate in nomi-nal terms.

Thirdly, the growth of the agriculture sec-tor is a key determinant of the overall eco-nomic growth rate. Although the share ofagriculture in aggregate GDP has declinedto 26.9% of GDP reducing the sensitivity ofGDP growth rate to fluctuations in agricul-

tural performance, the agricultural incomesplay an important role in determining thedemand for non-agricultural commodities.Therefore, growth of the agriculture sectoris a determinant of future growth rate.

The imperatives for achieving an 8% realGDP growth rate given in the Tenth FiveYear Plan document of the Planning Com-mission are as follows.(a) The Planning Commission envisages

that the investment rate be acceleratedfrom 24.4% in 2001/02 to 32.6% in2006/07 for achieving a target GDPgrowth rate of 8%. This targeted in-vestment rate differs from the invest-ment rate projected by other organiza-tions such as IEG (Institute of Eco-nomic Growth).

In order to finance a gross capitalformation (investment) of this magni-tude, the Tenth Five Year Plan targets adomestic savings rate of 29.8% ofGDP and foreign savings rate of 2.8%.That is, the domestic savings1 ratewould have to rise by 6 percentagepoints from the current levels over theTenth Five Year Plan period. Of this6% increase in the domestic savingsrate, 2.11% is expected to be in the pri-vate sector and the rest in the publicsector. In this context, it is mentionedthat the savings rate in the domestichousehold sector is expected to declineduring the Tenth Plan period. This isbecause, on the one hand, rapid in-crease in personal disposal incomes (asa result of rise in GDP) would raise thesavings rate and on the other, fiscal

11111 Domestic savings comprise domestic public and domestic private savings. Domestic private savings are further sub-

divided into household savings and savings by the private corporate sector.

228 Appendix 3

policy would necessitate significantstepping up of tax/GDP ratio. Thisstepping up would tend to reduce thehousehold savings. Furthermore, thesavings rate of the private corporatesector is determined mainly by itsshare in GDP, its profit rate, and capi-tal intensity. It is envisaged that in theTenth Five Year Plan, the savings rateof this sector will rise sharply with im-proved capacity utilization thereby im-proving both its profitability and GDPshare. The Tenth Five Year Plan re-quires the government sector to reduceits dissavings by nearly 2 percentagepoints in order to meet the aggregatedomestic savings target.

(b) The Tenth Five Year Plan projects theexports to grow at the rate of 12.4%and invisibles to perform strongly. Thiswould further raise the real GDPgrowth rate by creating demand abroadfor domestic goods and services.

(c) The Tenth Five Year Plan further rec-ognizes four priority sectors that arecritical for generating high rate of eco-nomic growth. These sectors are agri-culture, construction, transport, andother services. Public investment inthe agriculture sector would be in-creased significantly to reduce the sen-sitivity of this sector to weather-relatedfluctuations. The construction sector isconsidered as a potential sector forgrowth given that the land-related sug-gestions mentioned in the plan areimplemented judiciously. A fastergrowth in other transport can beachieved if required policy changespermitting greater involvement of theprivate sector are implemented. This,coupled with high growth rates in the

information, communication, and en-tertainment sectors would lead to ac-celeration in growth of other services.

Thus, the 8% growth rate is consideredfeasible in the Tenth Plan period since thescope for realizing improvements in effi-ciency is very large both in the public andprivate sector assuming that the policy im-peratives discussed above materialize.

For these aforementioned reasons, TERIhas adopted the GDP growth rate of 8% forenergy demand projections for the TenthFive Year Plan period consistent with theplans of the GoI. Based on the assumptionthat the 8% growth rate can be sustained fora period extending beyond the Tenth FiveYear Plan period, TERI has projected GDPto grow at an average annual rate of 8% perannum through the entire modelling period(2001–36).

A3.3 Methodology for GDP projec-

tions under 6.7% GDP growth rate

In a separate exercise to project GDP growthfor India, TERI has modified the model de-veloped by Goldman Sachs (2003) for long-term GDP projections in Brazil, Russia, In-dia, and China popularly referred to asBRICs countries. For this purpose, we haveused the growth accounting framework usedby Goldman Sachs, which was first devel-oped by Solow in 1956. According to thisframework, growth in output can be brokendown into the following components.(a) Growth in output due to measured

growth in labour input(b) Growth in output due to measured

growth in capital input(c) Technological progress

Appendix 3 229

A3.3.1 Assumptions

A3.3.1.1 Production function

The main point of departure from theBRICs study16 is in our choice of a labouraugmenting Cobb Douglas production func-tion. The production function exhibits con-stant returns to scale, and the technologicalprogress is of the type that increases the effi-ciency of the abundant factor of production,which in the case of India is labour. Labour-augmenting technical change can be mani-fested in the adoption of technologies that leadto the production of more labour-intensivegoods or in technologies that increase the effi-ciency of the labour input (Acemoglu 2002).

We believe that this specification is morerelevant than the Goldman Sachs specifica-tion, to the direction that Indian growth ismost likely to take.

Our production function is specified asY= (AL)1-αKαwhere A= labour-augmenting technologyL = labour inputK = capitalα = share of capital in income

A3.3.1.2 Convergence

One of the factors driving growth in themodel is the rate of growth of TFP (totalfactor productivity). The difference betweenthe per capita income of the US and the percapita income of India determines the po-tential for technological ‘catch up’. The rateof convergence would depend on the initialincome of the developing country. Underthese conditions, technological progresscould be expected to be faster in developing

countries such as India than in the US. Ashigher TFPG (total factor productivitygrowth) rates and diminishing returns tocapital lead to higher output growth rates,the potential for catch up decreases and thedeveloping country converges towards thesteady-state growth rate of technologicalprogress in the US.

Unconditional convergence would implythat the steady-state balanced growth pathsfor the developing and developed countrycoincide (Islam 1998). This, however, neednot be the case when conditional conver-gence is assumed. In this case, any one ormore of the parameters defining steady statecan differ among countries. This model as-sumes the convergence of TFP growth ratesin steady state. The economies can, however,differ in terms of steady-state growth rates ofpopulation, savings rates, educational attain-ment, depreciation, and TFP levels (Jones1997). The growth rates that a country canachieve in the steady state would depend oncountry-specific factors such as techniquechoice, geography, and institutional struc-tures that affect saving and investment rates,physical and social infrastructure, educationlevels, and quality of governance. This wouldimply that given the same initial levels of percapita income, a country with an underde-veloped infrastructure or lower levels of edu-cational attainment would converge atslower rates than a country with morefavourable conditions.

We use the same specification for the evo-lution of TFPG as in the BRICs paper. Thegrowth rate of TFP in the developing coun-try is given by the following relation.

Log (At/At-1) = (long-run TFPG for theUS) − βLog [(per capita GDPDC)/(per capitaGDP

US)]

230 Appendix 3

where At = TFP level at time tAt-1 = TFP level at time t-1βLog [(per capita GDP

DC)/(per capita

GDPUS)] = conditional convergence rate forthe developing country

This specification assumes that the TFPGrates along the path to steady state in thedeveloping country are higher than thesteady state TFP growth rate in the US . Thehigher the rate of convergence (β), and thelarger the difference in per capita incomes,the higher the rate of TFP growth in the de-veloping country relative to the long-runTFPG rate in the US. Conventional esti-mates of the rates of convergence in percapita income for developed countries totheir own steady states are about 2%(Mankiew, Romer, and Weil 1992). Wewould expect conditional convergence ratesin developing countries to be lower as a con-sequence of retarding institutional factors.The BRICs paper has assigned a conver-gence rate of about 1.5% to developingcountries. It mentions that calculations forlong-term projections of GDP use lower ini-tial convergence rates for Brazil and India.These increase to 1.5% through the periodfor which the projections are made. We haveassumed the conditional β convergence ratesfor India to be equal to 1.3% throughout ouranalysis. We expect convergence rates forIndia to be lower than those used for Chinaand Russia in the BRICs paper, because ofhigher illiteracy levels, infrastructuralbottlenecks, and social constraints that af-fect the participation of women in theworkforce and the large proportion of thepopulation employed in subsistence or unor-ganized activities in both the agriculturaland urban sectors.

We have used long-term TFP growth ratesfor the US to be consistent with those used

by the CBO (Congressional Budget Office)of the Government of the US. The CBO(2002) uses a TFP growth rate of 1.3% fortheir long-term GDP projections. This wasrevised upwards from 1% average annualgrowth (CBO 1997). The CBO also usesmore conservative long-run US TFPG(1.1%) rates for alternative projections.

The results from our analysis are verysensitive to the assumptions made regardingthe convergence rates and long-run TFPGrates. Changing long-term US TFPG ratesfrom 1.3% to a more pessimistic 1.1% wouldchange our results appreciably.

A3.3.1.3 Capital stock

The growth of net capital stock in the modelfollows the equation given below.

Kt = Kt-1(1 − δ) + sYt-1

where Kt = net capital stock at time t

Kt-1 = net capital stock at time t-1s = investment rateY

t-1 = GDP at time t-1

From this specification we find that sYt-1

gives us the gross capital formation in timet-1. This forms a part of the net capital stockthat is available for use in the following year.To calculate the net capital stock for the ini-tial year in our analysis, we have used data onnet capital stock and gross capital formationfrom the National Accounts Statistics pub-lished by the CSO (Central Statistical Orga-nization), GoI. To calculate the subsequentcapital series we assume that the investmentrate in India will remain at 24% throughoutthe period for which projections are made.We also assume that the depreciation ratewill remain at 5% for the entire period. This

Appendix 3 231

assumption is again a very stringent one andthe rates of capital attrition (a combinationof deterioration and technological obsoles-cence) can be expected to change. In the In-dian case we could expect rates of capital at-trition to rise in the long term as capital in-tensity and ICORs (incremental capital–output ratio) decline due to relocation ofcapital from investment in infrastructure toinvestment in production. Capital attritiondue to technological obsolescence could alsobe expected to rise with changing marketstructures favouring the increase in competi-tion and a corresponding increase in theshare of R&D (research and development)expenditures in total investment.

The share of capital in income, as com-puted from data on operating surplus andnet national product from the National Ac-counts Statistics, is approximately 60%. Thisis on the higher side and we would expectthis share to decline in the long run with theadoption of more labour-augmenting tech-nologies, increased employment, and regula-tion in the unorganized sector. The long-runshare of capital in income could be taken as1/3, which is the share of capital in the in-come of the US and several other developedcountries. For the purpose of our analysis wekeep the share of capital in GDP as 3/5.

A3.3.1.4 Demographics

We have used population figures given by thePFI (Population Foundation of India). Wehave projected population till 2030 assum-ing growth rates to decrease from currentlevels of 2003 to about 0.9% in 2030. Wehave assumed a constant rate of increase of1.07% in the Indian work force. Assuming ashift in the age structure of the population infavour of people above the age of 65, and a

decline in population growth, a constant rateof increase allows for future increases in therate of participation of women in the workforce. This is an expected consequence of in-creased expenditures on social infrastruc-ture and increased life expectancies.

A3.3.1.5 Other assumptions

As in the BRICs paper, we assume that the GoIcontinues to pursue liberal economic and so-cial policy with emphasis on the gradual with-drawal of government intervention in industryand trade, and increasing government expen-ditures on health and education.

We have used the estimates of the US percapita GDP calculated in the BRICs paperto arrive at TFP growth rate figures for In-dia. We have deflated these values to 1993/94rupee values. We have taken 2003 as the ini-tial year in our analysis, and have used avail-able data on GDP and capital stock valued at1993/94 prices. Labour force, work force,and population figures are in millions andthe values for the initial year are obtainedfrom the PFI.

We have computed long-term GDPgrowth rates for India for the base modelwith investment rates at 24%, depreciationat 5%, convergence rate at 1.3%, and long-term US TFPG at 1.3%.

A3.3.2 Results

The results from the simulations in our basemodel indicate that the long-term averageannual GDP growth rate for India is 6.7%per annum for the modelling time frame2001–36. TERI considers it be the low-growth scenario relative to the 8% GDPgrowth rate adopted in the baseline scenario.

232 Appendix 3

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Region-wise hydrocarbon reserves

at the end of 2005

AAAAA

44444

APPENDIX 4

Figure A4.1 Distribution of proved reserves of

hydrocarbons in 1985, 1995, 2005

236 Appendix 4

Figure A4.2 Production of crude oil in

different regions (million barrels daily)

Appendix 4 237

Figure A4.3 Reserves-to-production ratio and

reserves (in percentage) for crude oil

Figure A4.4 Proved reserves of gas at the

end of 2005 (trillion cubic metres)

238 Appendix 4

Figure A4.5 Distribution of proved

reserves in 1985, 1995, and 2005

Figure A4.6 Production of gas in different regions (billion cubic metres)

Appendix 4 239

Figure A4.7 Reserves-to-production ratio

and reserves (in percentage) for gas

Sankey diagrams

AAAAA

55555

APPENDIX 5

Figure A5.1 Sankey diagram for the business-as-usual scenario (2001)

Figure A5.2 Sankey diagram for the business-as-usual scenario (2031)

Figure A5.3 Sankey diagram for low-growth scenario (2031)

Figure A5.4 Sankey diagram for the high-growth scenario (2031)

Figure A5.5 Sankey diagram for high energy efficiency scenario (2031)

Figure A5.6 Sankey diagram for high nuclear capacity scenario (2031)

Figure A5.7 Sankey diagram for renewable energy scenario (2031)

Balance sheets

AAAAA

66666

Table A6.1 Energy balance in the business-as-usual scenario in 2011 (all figures are in Mtoe)

Oil and

natural Petroleum Hydro (large Nuclear Renewable Total

Supply–demand Coal gas products and small) energy energy power Total

Supply 242 51 211 18 4 1 527

Conversions

Power generation 45 14 — 82

Conversion losses and

auxiliary consumption

Power generation 96 14 110

Oil refining 12 12

Transmission and distribution 21 21

Consumption

Agriculture — — 9 9 18

Industry 102 23 54 23 202

Transport — — 104 2 106

Residential — — 27 19 46

Commercial — — 4 8 12

End-use consumption 102 23 199 61 384

Notes— Nil or negligible.

Figures may not add up to the total due to rounding off.

Energy supply from hydro and nuclear options are considered equal to the amount of electricity generated.

Energy consumption in industry includes energy use for process heating, captive power generation, and feedstock.

APPENDIX 6

250 Appendix 6


Oil and



Supply 466 132 405 30 13 1 1046

Conversions





Oil refining 28 28


Consumption


Industry 231 23 96 58 407

Transport — — 226 5 231









Oil and



Supply 1176 136 757 40 13 1 2123

Conversions





Oil refining 50 50


Consumption


Industry 513 31 190 114 848

Transport — — 452 9 461








Appendix 6 251

Table A6.4 Energy balance in the hybrid scenario in 2011 (all figures are in Mtoe)

Oil and



Supply 215 49 189 18 4 3 478

Conversions


Conversions losses and



Oil refining 12 12


Consumption


Industry 98 24 48 21 191

Transport — — 89 2 3 93









Oil and



Supply 329 129 299 31 24 11 823

Conversions





Oil refining 22 22


Consumption

Agriculture 0 0 9 10 18

Industry 214 30 79 47 370

Transport 0 10 144 9 7 171

Residential 0 0 37 39 76

Commercial 0 0 8 12 20






252 Appendix 6


Oil and



Supply 767 136 484 41 42 33 1503

Conversions





Oil refining 41 41

T&D 51 51

Consumption


Industry 471 37 148 86 743

Transport — 25 231 28 17 302








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