The Energy and Resources Institute...BSY Biju Setu Yojana CAPEX Capital Expenditure CBP Community...

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The Energy and Resources Institute

TERI-NFA Project Report

2014

Analysing Rural Energy

Transitions and Inequities

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Acknowledgements

This paper was written as part of a project “Rural Energy Transitions” under the Program of

Activities, Framework Agreement between the Norwegian Ministry of Foreign Affairs (MFA) and

The Energy and Resources Institute (TERI), briefly referred to as the Norwegian Framework

Agreement (NFA).

We would like to thank Dr. R K Pachauri, Director-General, The Energy and Resources Institute

(TERI), for his continuous support and encouragement.

We would also like to extend our sincere thanks to Dr. Prodipto Ghosh, Dr. Ligia Noronha, Dr.

Ritu Mathur, Mr Ibrahim Rehman, Mr Debajit Palit, Dr Atul Kumar, Mr. Anandajit Goswami

and Dr. P C Maithani for their consistent support and guidance which has made this study possible.

We would like to thank the TERI Regional Offices at Goa (Dr. Fraddry D’Souza), Mumbai (Dr.

Anjali Parasnis) and Bengaluru (Dr. Pronab Dasgupta), NSSO and MNRE for providing their

support to execute the project effectively.

Email: [email protected]

Suggested format for citation

T E R I. 2014

Analysing Rural Energy Transitions and Inequities

New Delhi: The Energy and Resources Institute. 196 pp.

[Project Report No. 2010EM05]

Contacts

The Energy and Resources Institute

Darbari Seth Block

India Habitat Centre

Lodhi Road

New Delhi 110 003

Tel: + 91 - 11- 24682100 / 41504900

iv

Project Team Project Coordinators

Jaya Bhanot

Aditya Ramji

Anmol Soni

Anjali Ramakrishnan

Team Members

Ritika Sehjpal

Saptarshi Das

Ritu Singh

PR Krithika

G Mini

Siddharth Singh

Madhura Joshi

Arijit Das

Aditi Phansalkar

Kavita Vithal Patil

Lasya Gopal

Saahil M Parekh

Swati Dsouza

Prasun Kumar Gangopadhyay

Asha L Giriyan

Chinmay Kinjavdekar

Gad Santosh Rama

Swati Tomar

Ipsita Kumar

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Table of Contents

Acknowledgements ................................................................................................................................iii

Project Team .......................................................................................................................................... iv

Table of Contents .................................................................................................................................... 1

List of Tables .......................................................................................................................................... 3

List of Figures ......................................................................................................................................... 5

List of Acronyms .................................................................................................................................... 8

Executive summary ............................................................................................................................... 11

1. Introduction ................................................................................................................................... 18

1.1 Objective of the study ............................................................................................................. 19

2. Review of literature ...................................................................................................................... 21

2.1 Energy poverty ........................................................................................................................ 21

2.2 Energy consumption patterns and their determinants ............................................................. 23

2.3 Energy transitions ................................................................................................................... 31

2.4 Hypothesis............................................................................................................................... 34

3. Methodology and Sampling .......................................................................................................... 36

3.1 Data Collection ....................................................................................................................... 37

4. Overview of the States .................................................................................................................. 39

4. 1 Maharashtra............................................................................................................................ 39

4.2 Rajasthan ................................................................................................................................. 43

4.3 Goa .......................................................................................................................................... 48

4.4 Karnataka ................................................................................................................................ 53

4.5 Himachal Pradesh ................................................................................................................... 57

4.6 Odisha ..................................................................................................................................... 61

5. State Level Analysis – NSS and TERI data .................................................................................. 65

5.1 NSS data analysis .................................................................................................................... 65

5.2 Comparison of NSS and TERI surveys .................................................................................. 72

5.3 Energy Transitions .................................................................................................................. 74

5.4 Energy Inequalities ................................................................................................................. 96

6. Results from the Pilot Survey ..................................................................................................... 112

6.1 Background and Profile of Survey Sites ............................................................................... 112

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6.2 Analysis................................................................................................................................. 113

6.3 Energy Inequality .................................................................................................................. 120

6.4 Conclusion ............................................................................................................................ 122

7. Regression Analysis: State-wise and Overall ............................................................................. 124

7.1 Maharashtra........................................................................................................................... 128

7.2 Himachal Pradesh ................................................................................................................. 132

7.3 Goa ........................................................................................................................................ 136

7.4 Karnataka .............................................................................................................................. 139

7.5 Rajasthan ............................................................................................................................... 142

7.6 Odisha ................................................................................................................................... 145

7.7 All States ............................................................................................................................... 148

8. Gender Roles in Energy Transitions ........................................................................................... 152

9. Willingness to Pay ...................................................................................................................... 154

9.1 Maharashtra........................................................................................................................... 156

9.2 Himachal Pradesh ................................................................................................................. 157

9.3 Karnataka .............................................................................................................................. 158

9.4 Goa ........................................................................................................................................ 159

9.5 Rajasthan ............................................................................................................................... 160

9.6 Odisha ................................................................................................................................... 161

10. Lighting Index ........................................................................................................................... 163

11. Case Studies .............................................................................................................................. 167

11.1 Rajasthan: Gender and Energy Transitions......................................................................... 167

11.2 Himachal Pradesh: Innovation in cooking practices ........................................................... 167

11.3 Madhya Pradesh: Redefining Energy Access ..................................................................... 168

11.4 Maharashtra: Case of Reverse Transitions ......................................................................... 168

11.5 Odisha: Role of Local Government .................................................................................... 169

12. Setting the Policy Context ........................................................................................................ 170

Annexures ....................................................................................................................................... 174

Annexure I ...................................................................................................................................... 174

Annexure II ..................................................................................................................................... 175

Annexure III .................................................................................................................................... 176

Annexure IV ................................................................................................................................... 177

Bibliography ................................................................................................................................... 180

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List of Tables Table 1 Factors Impacting Energy Choices .......................................................................................... 25

Table 2: Population of Maharashtra ...................................................................................................... 40

Table 3: Population growth rate of Maharshtra4 ................................................................................... 40

Table 4: Urban – Rural Distribution over the decades ......................................................................... 44

Table 5: Population Growth Rates ........................................................................................................ 44

Table 6: Population of Goa ................................................................................................................... 50

Table 7: Population and Its Growth Rate: Karnataka (1961-2011) ...................................................... 54

Table 8: Literacy Rate in Karnataka (in percent).................................................................................. 55

Table 9: Decennial Growth Rate in Himachal Pradesh ........................................................................ 59

Table 10: Population and its Growth Rate (1961-2011) ....................................................................... 62

Table 11: Fuel consumption patterns over time in Rural India ............................................................ 69

Table 12: Average Fuel Consumption (as per NSSO 66th Round, 2009-10 and TERI Survey, 2013) 72

Table 13: Estimated Coefficients of the logit model(1) ...................................................................... 117

Table 14: Estimated Coefficients of logit model(2) ........................................................................... 118

Table 15: Generalized Ordered Logit Model Results ......................................................................... 128

Table 16: Generalized Ordered Logit Model findings ........................................................................ 129

Table 17: Appropriate targeting of population for interventions in cooking ...................................... 130

Table 18: Key parameters for intervention planning for cooking transitions ..................................... 131





Table 23: Regression Model Results .................................................................................................. 136

Table 24: Model findings .................................................................................................................... 136



Table 27: Tobit Model Results ........................................................................................................... 139

Table 28: Tobit Model findings .......................................................................................................... 139



Table 31: Regression Model Results .................................................................................................. 142

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Table 32: Regression Model findings ................................................................................................. 142



Table 35: Tobit Model Results ........................................................................................................... 145

Table 36: Tobit Model findings .......................................................................................................... 145







Table 43: Deprivation cut-off for Electricity access ........................................................................... 164

Table 44: Index parameters and weights ............................................................................................ 165

Table 45: Electricity Access Index results .......................................................................................... 166

Table 46: Policy linkages .................................................................................................................... 171

Table 47: Variable significance and its impact on the probability of transition ................................. 174

Table 48: Categorical variables as defined for the regression analysis in the Pilot Survey ............... 175

Table 49: Summarization of Literature on Energy Poverty, Accessibility and Transition. ................ 176

Table 50: Basic Household Characteristics of LPG and Biomass users ............................................. 177

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List of Figures Figure 1: Classical Energy Ladder ....................................................................................................... 32

Figure 2: Fuel stacking ......................................................................................................................... 33

Figure 3: Hypothesis for the Study ...................................................................................................... 35

Figure 4: Study Methodology .............................................................................................................. 36

Figure 5: Graph of Penetration rates of cooking fuel for 66th round .................................................. 65

Figure 6: Graph of penetration rates of lighting fuel for 66th round (2009-10) .................................. 66

Figure 7: Penetration rates of cooking fuels over time and across income classes ............................. 67

Figure 8: Penetration rates of lighting fuels over time and across income classes .............................. 68

Figure 9: Consumption of Firewood, LPG and Electricity over time across income classes .............. 70

Figure 10: Consumption of Kerosene over time and across income classes ....................................... 70

Figure 11: Average share of cooking fuels in Maharashtra ................................................................. 75

Figure 12: Cooking Energy Transitions in rural Maharashtra over time ............................................. 75

Figure 13: Cooking Energy Transitions in rural Maharashtra over time (based on TERI Survey) ..... 76

Figure 14: Lighting Energy Transitions in rural Maharashtra over time ............................................. 77

Figure 15: Lighting Energy Transitions in rural Maharashtra over time (based on TERI Survey) ..... 78

Figure 16: Average share of cooking fuels in Himachal Pradesh ........................................................ 79

Figure 17: Cooking Energy Transitions in rural Himachal Pradesh over time (NSS Data) ................ 80

Figure 18: Cooking Energy Transitions in rural Himachal Pradesh over time (based on TERI Survey)

............................................................................................................................................................... 80

Figure 19: Lighting Energy Transitions in rural Himachal Pradesh over time .................................... 81

Figure 20: Lighting Energy Transitions in rural Himachal Pradesh over time (based on TERI Survey)

............................................................................................................................................................... 82

Figure 21: Average share of cooking fuels in Goa .............................................................................. 82

Figure 22: Cooking Energy Transitions in rural Goa over time (NSS Data) ...................................... 83

Figure 23: Cooking Energy Transitions in rural Goa over time (based on TERI Survey) .................. 84

Figure 24: Lighting Energy Transitions in rural Goa over time .......................................................... 85

Figure 25: Lighting Energy Transitions in rural Goa over time (based on TERI Survey) .................. 85

Figure 26: Average share of cooking fuels in Karnataka .................................................................... 86

Figure 27: Cooking Energy Transitions in rural Karnataka over time (NSS Data) ............................. 87

Figure 28: Cooking Energy Transitions in rural Karnataka over time (based on TERI Survey) ........ 87

Figure 29: Lighting Energy Transitions in rural Karnataka over time ................................................ 88

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Figure 30: Lighting Energy Transitions in rural Karnataka over time (based on TERI Survey) ........ 89

Figure 31: Average share of cooking fuels in Rajasthan ..................................................................... 89

Figure 32: Cooking Energy Transitions in rural Rajasthan over time (NSS Data) ............................. 90

Figure 33: Cooking Energy Transitions in rural Rajasthan over time (based on TERI Survey) ......... 90

Figure 34: Lighting Energy Transitions in rural Rajasthan over time ................................................. 92

Figure 35: Lighting Energy Transitions in rural Rajasthan over time (based on TERI Survey) ......... 92

Figure 36: Average share of cooking fuels in Odisha ......................................................................... 93

Figure 37: (above) Cooking Energy Transitions in rural Odisha over time (NSS Data)..................... 93

Figure 38: Cooking Energy Transitions in rural Odisha over time (based on TERI Survey) ............. 94

Figure 39: Lighting Energy Transitions in rural Odisha over time...................................................... 95

Figure 40: Lighting Energy Transitions in rural Odisha over time (based on TERI Survey).............. 96

Figure 41: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) ............... 96

Figure 42: Inequality in LPG consumption (GINI_Pz) and income (GINI_Inc) ................................. 97

Figure 43: Inequality in electricity consumption (GINI_Elec) and income (GINI_Inc) ..................... 98

Figure 44: Lorenz curve for Income Inequality ................................................................................... 98

Figure 45: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) in

Himachal Pradesh ................................................................................................................................. 99

Figure 46: Inequality in LPG consumption (GINI_Pz) and income (GINI_Inc) ............................... 100

Figure 47: Inequality in electricity consumption (GINI_ELEC) and income (GINI_Inc) ................ 100

Figure 48: Lorenze curve for Income Inequality ............................................................................... 101

Figure 49: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) in Goa . 102





Karnataka ............................................................................................................................................ 104





Rajasthan ............................................................................................................................................. 107



7


Figure 61: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) in Odisha

............................................................................................................................................................. 110




Figure 65: Lorenz curve for income inequality among pilot sites sample data ................................. 121

Figure 66: Biomass and Petroleum fuels inequality across income groups ...................................... 121

Figure 67: Energy and Development Linkages ................................................................................. 173

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List of Acronyms

BCC Behavior Change Communication

BGJY Biju Gram Jyoti Yojana

BSVY Biju Saharanchala Vidyutikaran Yojana

BSY Biju Setu Yojana

CAPEX Capital Expenditure

CBP Community biogas plants

CFL compact fluorescent lamps

CSD Commission for Sustainable Development

CWA Constituency-wise Allotment

DDG Decentralized Distributed Generation

DESI Development Programmes for Energy System Improvement

DFID Department for International Development

DRDA District Rural Development Agency

EAG Empowered Action Group

EC European Commission

GEDA Goa Energy Development Agency

Gen General

GGSY Goa Gram Samrudhi Yojana

GGUY Goa Grameen Urja Yojana

HDI Human Development Index

IAP Indoor Air Pollution

IAY Indira Awas Yojana

IBP Institutional biogas plants

IGA Income Generating Activities

IHHL Individual Household Latrines

IREP Integrated Rural Energy Programme

IWMP Integrated Watershed Management Program

JNNSM Jawaharlal Nehru National Solar Mission

Kgoe Kilogram of Oil equivalent

KREGS Karnataka Rural Employment Guarantee Scheme

KSRLM Karnataka State Rural Livelihood Mission

KSRLPS Karnataka State Rural Livelihood Promotion Society

LED Light Emitting Diode

LPG Liquefied Petroleum Gas

MDG Millennium Development Goals

MEPI Multidimensional Energy Poverty Index

MNRE Ministry of New and Renewable Energy

MPCE Monthly Per Capita Expenditure

MSRLM Maharashtra State Rural Livelihoods Mission

NBIC National Biomass cookstoves initiative

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NBMMP National Biogas and Manure Management Program

NGP Nirmal Gram Puraskar

NOAPS National Old Age Pension Scheme

NPIC National Program for Improved Cookstoves

NRDWP National Rural Drinking Water Programme

NREGS The National Rural Employment Guarantee Scheme

NRLM National Rural Livelihoods Mission

NSAP National Social Assistance Programme

NSSO National Sample Survey Organisation

OBC Other Backward Classes

PC Production Centres

PDS Public Distribution System

PMGSY Pradhan Mantri Gram Sadak Yojana

PRI Panchayati Raj Institutions

R&D Research and Development

REC Rural Electrification Corporation

REDA Rajasthan Energy Development Agency

REDB Rural Electricity Distribution Backbone

RERC Rajasthan Electricity Regulatory Commission

RGGLVY Rajiv Gandhi Grameen LPG Vittaran Yojana

RGGVY Rajiv Gandhi Grameen Vidyutikaran Yojana

RIDF Rural Infrastructure Development Fund

RPO Renewable Procurement Obligation

RRECL Rajasthan Renewable Energy Corporation limited

RVE Remote Village Electrification Program

SC Scheduled Castes

SGRY Sampoorn Gramin Rozgar Yojana

SGSY Swarnjayanti Gram Swarozgar Yojana

SHG Self Help Groups

SPV Solar Photovoltaic

ST Scheduled Tribes

SVO straight vegetable oil

TSC Total Sanitation Campaign

UNDP United Nations Development Programme

VEI Village Electrification Infrastructure

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Executive summary

Access to modern energy services plays a key role in fulfilling basic social needs, fuelling economic

growth and augmenting human development. Studies have found a significant correlation between the

provision of energy and the achievement of higher Human Development Index (HDI) levels. This is

because improved access to energy services facilitates better health and education, better access to

information, improved agricultural productivity and better water availability.

At the same time, energy production and consumption generates negative externalities which

adversely impacts the local, regional and global environments. This in turn has implications on

human development, economic growth and the general well-being of communities.

Modern energy sources such as electricity, natural gas and modern cooking fuels prove to be far more

efficient and cleaner alternatives to traditional biomass1 based energy sources, which is used by 86

percent of India‘s rural households as a primary cooking fuel, as revealed by the latest census (Census

of India, 2011). Further, studies show that in rural India, the energy consumption is skewed towards

firewood and other traditional biomass fuels such as chips, charcoal and dung cake (Husain, 2005).

The provision of and access to modern fuels thus forms a cornerstone in meeting the development

objectives of any society. In order to improve the provision of energy to the people, nation states thus

pursue the security of energy supplies as a first step towards improving energy access. However, the

term energy security encompasses more than just the security of energy supply. India defines energy

security as the ability of the nation to “...supply lifeline energy to all our citizens as well as meet their

effective demand for safe and convenient energy to satisfy various needs at affordable costs at all

times with a prescribed confidence level considering shocks and disruptions that can be reasonably

expected”(Planning Commission, 2006). Note that the affordability of energy finds a place in such a

definition. Further, the European Commission (EC, 2000) and United Nations Development

Programme (UNDP/ ESMAP, 2003) have also included the addressing of environmental concerns

and working towards sustainable development in their energy security strategies.

It follows that the simple availability of modern fuels is not enough. It is imperative that the people

are able to access the energy generated by them, which is enabled by the existence of a minimal

standard of physical infrastructure. Further, the pricing of such fuels must be such that the society at

large can accrue benefits from the regular use of modern fuels instead of traditional biomass based

fuels. The affordability of fuels is indeed one of the key determinants of adoption of particular fuels.

Affordability in turn has two aspects – the ability and the willingness to pay. The former is

constrained by income while the latter is influenced by the availability of the resource and the

opportunity cost of investing that particular fuel. Therefore the availability and affordability of fuels

are key determinants of energy access.

In addition to these tangible factors, there exists a factor that is not as tangible, in the form of attitudes

and perceptions of the people. This has proven to be a key determinant in the achievement of energy

access for all. Socio-cultural norms and traditions influence such perceptions and can often pose as

barriers to the adoption of modern fuels.

1 Here, biomass based sources includes firewood, agricultural residue, and animal residue.

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In sum, fuel choices are governed by a host of factors, including availability of the fuel, the ease of

substitution, ease of use, affordability and acceptability of the households or individuals. Policies to

encourage the transition of fuels from traditional biomass based options to cleaner, modern fuels must

therefore account for all these factors. There happen to be three significant barriers to such a

transition in India‘s case. First, there is a shortage of useful data and information to guide

policymaking. Secondly, there is a strong need to integrate energy access with other development

priorities from the perspective of policy planning. Further, there is a lack of responsive and

accountable institutional and market mechanisms to further this cause. Thirdly, financing of large

programmes to enhance energy access is still a key issue that needs to be addressed at the earliest.

Such constraints create the space for further studies into issues surrounding energy poverty, access

and the transition of households from being consumers of traditional fuels to becoming consumers of

modern fuels.

The broad objectives of the study include:

Identify reasons for differing energy consumption patterns

Analyse inequality measures in energy consumption patterns in rural India

Identify the drivers of transition to clean energy: relationships between socio-economic parameters,

household fuel choice and energy demand using a generalized ordered logit model

Barriers and effectiveness of interventions

Gender implications and its impact on uptake of modern fuels

While most rural households use multiple energy sources for cooking and lighting, NSS data shows

that many households use modest quantities of kerosene for cooking, augmenting it with use of

biomass fuels. In rural areas, biomass fuel use is prevalent across all income groups and remained

virtually unchanged between 1993–94 and 1999–2000, with more than 90 percent of rural households

using wood, dung, or both. Mirroring the findings in other countries, wood consumption rises with

increasing income among rural households, indicating a lag between a positive income change and

reduction in biomass consumption. Close to 60 percent of all rural households were using cash-free

wood in 1999–2000. In contrast, the use of kerosene as the primary cooking fuel was essentially

nonexistent among rural households in 1999–2000; this applies across all income groups with the

exception of the richest 10 percent. In short, supply conditions in rural areas favour the use of

biomass for cooking because of its low labour costs and the ready availability of free biomass.

Data from 1999-2000 to 2009-10 shows that there has been considerable changes in energy use

patterns across rural households. In the case of firewood, there is an increase in household

consumption between 1999-2000 and 2004-05 followed by a slight decline in 2009-10. It should be

noted however, that the consumption level of firewood in absolute terms during 2009-10 was higher

than that reported in 1999-2000. The overall consumption of firewood actually went up in the past

decade by about 7.5%. In the case of electricity, there was an increase in electricity consumption by

almost 25 – 30% overall in the last decade; while for LPG, though there was a marginal change in

consumption over time but it remained more or less constant (NSSO, 2001, 2006, 2011).

While analysing determinants of fuel choice, it has been found that rational consumers choose the

most preferred bundle of commodities from a set of alternatives based on socio-economic constraints.

Economic constraints include market price of fuel and household‘s income where as non-economic

13

constraints include a set of household demographic and social factors (Pundo & Fraser, 2006).

Similarly, energy sources are treated as commodities and each source has multiple purposes and

attributes. Purposes are linked to various domestic activities such as cooking, heating, and lighting,

while the attributes refer to energy content, convenience, safety, speed of cooking, taste of food, and

quality of light.

It is found that determinants can be broadly categorized into four types: Affordability, Availability

and Accessibility, Perception and social factors, and, other non-income determinants.

Factors that determine affordability include individual or household income, price of the fuel, wealth

(which refers to ownership of land and livestock), income generating infrastructure and urbanization.

Availability and accessibility are determined by the presence of infrastructure, regularity of supply,

state interventions to enhance uptake of modern fuels and technologies and broad frameworks that

facilitate access to modern energy systems. Finally, perceptions and social factors refer to socio-

cultural preferences, and social identities, in particular gender.

Therefore, in order to make useful policy interventions to foster the development of any society, it is

imperative to look at the role of energy poverty and access. In turn, this requires the study of a host of

factors that influence them, as literature on the subject shows. In order to facilitate the transition of

households from being consumers of traditional fuels to modern fuels, policy interventions will have

to be based on evidence which reveal which sets of policy actions will be most effective in which

demographic.

The study aimed to test four key hypotheses with regard to energy transitions across rural households

in India. The hypotheses focused around:

Testing for the impacts of increasing incomes on household energy baskets

The decision-making power of women in the household

The impact of interventions, such as livelihood and energy interventions, and

Role of energy in the priority of development goals for the household.

The analysis in this report is based on both primary and secondary data. The secondary data was

sourced from the household consumption surveys carried out by the National Sample Survey

Organisation (NSSO), Government of India. A large-scale household primary survey was also

conducted as part of this study in selected states, namely: Himachal Pradesh, Rajasthan, Maharashtra,

Goa, Karnataka and Odisha. In total, 6020 households were selected across these states and the

sample size for each state is based on a stratified random sampling methodology. A comprehensive

pilot covering 200 households was also conducted in Madhya Pradesh to test the methodology and

questionnaire and the sampling methodology.

There could be various reasons apart from income that may be impacting the expenditure patterns on

fuels for households. Through the field experiences during the pilot survey and secondary literature it

has been observed that levels and forms of fuel consumed by the household sector depend not only on

incomes but also on various other factors such as size of settlements, households, geographic

location, price of fuels, the availability and accessibility of modern commercial fuels, the efficiency

of the end-use equipment and the socio-cultural environment that people live in which to a large

extent drive household consumption patterns. Thus, given the vast size of the country and the myriad

cultures and social constructs that exist, it is critical that these factors are addressed at various levels

14

in the economy i.e. national, regional, district and household level, which may influence household

energy choices as desirable.

These have important policy implications, i.e. it indicates that variations exist in energy use and these

are not driven primarily by income, thus making it imperative to understand in detail the causes for

these differences so as to facilitate appropriate policy design and effective implementation.

The analysis approach followed in this report indicates that to ensure a sustained and effective

transition to cleaner energy forms while at the same time achieving the dual goals of livelihood and

energy security, it is important to understand the target population and prioritize the delivery

mechanism to ensure maximum coverage. The distinction made in this analysis between labour,

agriculture and salaried households allows a comparison to other datasets such as the National

Sample Survey or Census data.

One of the key insights from this analysis is that till a cost-effective and scalable alternative to LPG,

in terms of a cleaner fuel, is found, it is imperative that the right-type of policy innovations are made

so that the available options are made affordable to people. To this effect, a proposal of an additional

LPG connection subsidy of Rs. 1400 can go a long way in ensuring significant changes in household

energy baskets. The additional subsidy would bring down the household cost for a connection to

around Rs. 1500, which would lead to a greater uptake of LPG among rural households in India.

Improved supply streams to reduce the cost of entry to LPG, and a public education campaign, are

necessary if LPG is to have a role in displacing biomass dependence. Biogas from existing

agricultural, livestock, or sewage waste streams, has the potential to fill this niche (Gwavuya et al.,

2012; Lee, 2013).

The analysis indicates that interventions such as the eco-village program in Maharashtra, have

significant positive impacts on the current status of households in terms of cleaner cooking choices

but such programs need to be up-scaled to ensure sustained long-term impacts on household energy

transitions. Integration of energy services within the architecture of current development schemes will

be very critical to ensure both goals of universal energy access as well as ensuring productive use of

energy services towards enhanced livelihoods, which is also a core objective of the National Rural

Livelihoods Mission of the Government of India. Along with this, expanding the coverage of Self

Help Group‘s needs to be actively pursued as the ability of women to generate additional income has

a significant impact on household energy choices.

Solutions need to have a participatory approach. There is a need to involve grass root level

organizations as well as the intended beneficiaries in the planning process. Communities also differ in

their essential fabric. There are areas where community based solutions will be successful and others

where these may not be the best solution. Electricity access (including decentralized energy options),

as defined in this report, will have significant impacts as the model results indicate an exponential

increase in the probability of switching to modern fuels with improved availability of electricity

allowing for extra time during day-light for monetary activities, thus resulting in greater purchasing

power of the household.

The bandwagon effect of interventions is not seen yet as a strong factor. Greater emphasis on

awareness programs highlighting the importance of clean energy use are needed to push energy as a

development priority for households.

Setting regional or national policies targeting controllable factors, specifically, education, income,

and public infrastructure, can achieve the objective of facilitating a switch to modern and cleaner

15

cooking fuels, considering the positive effects these variables generally have on fuel switching

(Leach, 1992; Jingchao and Kotani, 2012; Lee, 2013; Sehjpal et al., 2014).

Finally, in the Indian context, two critical findings from this study have been identified that indicate

an overarching impact on energy choices and access among rural households. Firstly, social status,

which in this study has been defined as the caste identities of the household, has been found to be

significant in impacting access to energy options, not only in the quantitative analysis but also from

field experiences. For example, in certain areas, it was found that given low coverage of distributors

of LPG, preferences were given to households who belonged to the same caste identity as the

distributor. Such instances reduce the access of households to modern fuels including those who show

a willingness and ability for uptake. Secondly, coordination between line departments within the state

as well as between the Centre and states prove to be determinants of supply infrastructure, both in the

case of lighting and cooking.

The table below summarizes the impact of different variables considered in the study to analyse the

impact on transition.

Y=Transition Maharashtra Himachal

Pradesh Goa

Karnataka

(Tobit) Rajasthan

Odisha

(Tobit)

L A S L A S L A S L A S L A S L A S

Social status - - + + + + +

MPCE class + -

Timelive - - - - - - -

Price of kerosene

- - + +

Price of LPG - - - - - - - - - - - - - - - - - -

Price of firewood

+ + + - +

Education level of males

+ - + +

Education level of females

+

Land Size + + + + +

Electricity Access

Location of kitchen

-

Kitchen window + +

Distance to collect firewood

- + - - - -

Female decision-making

+ + + + +

District + + + - + +

Intervention + - + + +

16

Key for table above

Identifying Labour Households

Identifying Agricultural Households

Identifying Salaried Households

+ sign The variable has a positive impact on the household transitioning to LPG

- sign The variable has a negative impact on the household transitioning to LPG

The table below provides a policy context to possibly effecting transitions to clean energy based on

the analysis of the data collected from the survey

S.No. Recommendation Reasoning

1. Interventions indicate significant impacts on current

status of households but need to be up-scaled to see any

significant impacts on household energy transitions

• Change in livelihood choices needs to be more

pronounced

• Integration of energy services within the architecture

will be very critical to ensure both goals of universal

energy access as well as ensuring productive use of

energy services towards enhanced livelihoods (a core

objective of NRLM)

• SHG/Grassroot institutions need wider coverage– by

way of banking linkages as well as skill development

programmes

• Housing scheme grants should be conditional to

inclusion of a window in cooking area

Skill development programmes

should be carried out based on

available local employment

opportunities. These needs to be

actively pursued as the ability of

women to generate additional

income has a significant impact on

household energy choices

Just as inclusion of toilets in

household structure are

mandatory under central housing

grants, inclusion of a window in

cooking area will help reduce IAP

impacts for households that are

biomass dependent by

compulsion.

2. LPG availability and accessibility must be improved to

ensure sufficient uptake

• Reallocation of unutilized subsidy resources from

cylinder-based subsides to subsidizing new LPG

connection cost to increase uptake.

• Subsidy reform through DBT program as well as

providing alternate (for example: CFL and Solar)

lighting sources to reduce dependency on kerosene

for lighting

• Widen LPG distributer coverage and ease the process

of procuring an LPG connection

With the initial cost for procuring

an LPG connection subsidized,

immediate fiscal burden on

household budget will be reduced

Unwanted divergence of kerosene

will fall overtime, facilitating a

shift to alternate and more efficient

fuels for cooking and lighting

Increased distributor (delivery)

coverage will reduce

transportation costs for households

and simplification of procuring

connection will encourage uptake

17


3. Incentivizing higher enrollment ratios at the school

Specific schemes to promote Girl Child enrollment

in schools. (For example, cash transfer scheme,

Ladli scheme)

Inclusion of basic knowledge on energy efficiency in

school curriculum to spread awareness

An increase in male education

level with increase awareness both

in terms of benefits of modern fuel

as well as improve social cohesion.

Female education indicates

positive impacts on uptake of

modern fuels

4. Re-defining Electricity Access

Improving Access to Electricity during post sunset

hours

Measurement of access to electricity to include not

just availability of physical infrastructure but also

reliability and quality of supply

Improvement of supply infrastructure

Upgrading grid infrastructure to allow for greater

number of users

Increase coverage of decentralized energy options

such as smart/micro grids and rooftop SPV by

way of innovation financing mechanisms

Decentralized energy options have

significant potential as analysis

indicates exponential increase in

probability of switching to modern

fuels with improved availability of

electricity between 6 – 9 PM

allowing for extra time during day-

light in monetary activities2

5. Designing an appropriate Intervention

Replication of successful delivery models after

ensuring that the design and implementation are

made context-specific to the region in which it is

being targeted

A successful model in particular

location bound to have spill-over

effects in terms of increased

awareness in neighboring regions

as well.

To convert this new knowledge

increased usage, location specific

factors need to be accounted for.

2 For detailed information on electricity access refer to Chapter 6.

18

1. Introduction

Access to modern energy services plays a key role in fulfilling basic social needs, fuelling economic

growth and augmenting human development. Studies have found a significant correlation between the

provision of energy and the achievement of higher Human Development Index (HDI) levels. This is

because improved access to energy services facilitates better health and education, better access to

information, improved agricultural productivity and better water availability.

At the same time, energy production and consumption generates negative externalities which

adversely impacts the local, regional and global environments. This in turn has implications on

human development, economic growth and the general well-being of communities.

Modern energy sources such as electricity, natural gas and modern cooking fuels prove to be far more

efficient and cleaner alternatives to traditional biomass3 based energy sources, used by 86 percent of

India‘s rural households as a primary cooking fuel, as revealed by the latest census (Census, 2011a).

Further, studies show that in rural India, the energy consumption is skewed towards firewood and

other traditional biomass fuels such as chips, charcoal and dung cake (Husain, 2005).

This dependence on biomass-based sources for cooking energy has impacts on the environment,

health and even, the time available for other productive activities. For instance, black carbon emitted

from the traditional cookstoves and the incomplete burning of fuel also lead to emissions that causes

Indoor Air Pollution (IAP) and compound climate change. Further, IAP is a major cause of premature

deaths all across the world and one of its major causes is burning of firewood and other biomass

based fuels in cookstoves across rural households.4 Additionally, the collection of fuel wood,

agricultural residue and preparation of cow dung cakes is done by the women and young girls in the

household, leading to loss of time from employment, schooling and other related activities (UNDP,

2010).

On the other hand, modern energy sources present several advantages over such traditional fuels.

Pachauri, et al (2011) mention five key benefits of the adoption of modern energy systems. First,

there are private benefits that arise from the usage of household lighting, access to improved

communication and entertainment and thermal comfort. Second, income generation options are

enhanced owing to the use of mechanical power and better communication. Third, community

services see an improvement with the provision of public lighting, and improved healthcare and

education. Fourth, there are livelihoods or economic impacts from the adoption of modern energy

sources, such as the freeing up of time that would have otherwise been spent looking for biomass

based fuel and improved transport systems. Finally, there are environmental benefits of modern fuels,

as solid fuel dependence can lead to deforestation and land degradation.

3Here, biomass based sources includes firewood, agricultural residue, and animal residue. 4For more details see (Lim, et al., 2013), Inhal et al (2007), and (Smith & Ezzati, 2005), all cited in (Thurber, Phadke, Nagavarapu,

Shrimali, & Zerriffi, 2014)

19

The provision of modern fuels and ensuring universal access to them forms a cornerstone in meeting

the development objectives of any society. In order to improve the provision of energy to the people,

nation states thus pursue the security of energy supplies as a first step towards improving energy

access. However, the term energy security encompasses more than just the security of energy supply.

India defines energy security as the ability of the nation to “...supply lifeline energy to all our citizens

as well as meet their effective demand for safe and convenient energy to satisfy various needs at

affordable costs at all times with a prescribed confidence level considering shocks and disruptions

that can be reasonably expected”(Planning Commission, 2006). Note that the affordability of energy

finds a place in such a definition. Further, the European Commission (EC, 2000) and United Nations

Development Programme (UNDP/ESMAP, 2004) have also included the addressing of environmental

concerns and working towards sustainable development in their energy security strategies.

It follows that the simple availability of modern fuels is not enough. It is imperative that the people

are able to access the energy generated by them, which is enabled by the existence of a minimal

standard of physical infrastructure. Further, the pricing of such fuels must be such that the society at

large can accrue benefits from the regular use of modern fuels instead of traditional biomass based

fuels. The affordability of fuels is indeed one of the key determinants of adoption of particular fuels.

Affordability in turn has two aspects – the ability and the willingness to pay. The former is

constrained by income while the latter is influenced by the availability of the resource and the

opportunity cost of investing that particular fuel. Therefore the availability and affordability of fuels

are key determinants of energy access.

In addition to these tangible factors, there exists an important factor that is not as tangible, in the form

of attitudes and perceptions of the people that is not as tangible. This has proven to be a key

determinant in the achievement of energy access for all. Socio-cultural norms and traditions influence

such perceptions and can often pose as barriers to the adoption of modern fuels.

In sum, fuel choices are governed by a host of factors, including availability of the fuel, the ease of

substitution, ease of use, affordability and acceptability of the households or individuals. Policies to

encourage the transition of fuels from traditional biomass based options to cleaner, modern fuels must

therefore account for all these factors. There happen to be three significant barriers to such a

transition in India‘s case. First, there is a shortage of useful data and information to guide

policymaking. Secondly, there is a strong need to integrate energy access with other development

priorities from the perspective of policy planning. Further, there is a lack of responsive and

accountable institutional and market mechanisms to further this cause. Thirdly, financing of large

programmes to enhance energy access is still a key issue that needs to be addressed at the earliest.

Such constraints create the space for further studies into issues surrounding energy poverty, access

and the transition of households from being consumers of traditional fuels to becoming consumers of

modern fuels.

1.1 Objective of the study

The primary objective of the ‗Rural Energy Transitions‘ project is to analyse the prevailing

consumption patterns and inequities of rural energy and to determine how transitions to clean energy

20

can be enabled in rural India. The study involves an in-depth understanding of the barriers and drivers

to transitioning to cleaner energy forms among rural households.

A pan-India primary household level survey was conducted as part of the study to assess the choice of

fuel at the household level in rural areas, and to discern the factors that affect fuel choices, the

relationship between socio-economic parameters and choice of energy source, as well as the

difference in impact of interventions, if any, on men and women.

The study also includes an analysis of variations in energy consumption patterns for different end

uses at both the ‗intra‘ and ‗inter‘ agro-climatic zone level which would also facilitate a better

understanding of changing lifestyles and patterns of convergence. The selection of agro-climatic

zones was made in order to capture regional variation in terms of climate patterns, crop patterns and

other social factors.

This study aims at developing a comprehensive understanding of the barriers and drivers of

transitions to clean energy forms in rural India.

The broad objectives of the study include:

Identify reasons for differing energy consumption patterns

Analyze inequality measures in energy consumption patterns in rural India

Identify the drivers of transition to clean energy: relationships between socio-economic

parameters, household fuel choice and energy demand using a generalized ordered logit model

Barriers and effectiveness of interventions

Gender implications and its impact on uptake of modern fuels

The report has been structured as follows: Section 2 provides a review of literature establishing the

foundation for the study based on existing research; Section 3 traces the methodology that has been

adopted to carry out the study, from selection of the states, districts and villages for the study to the

sampling and surveying technique. This is followed by Section 4 that gives a detailed overview of

each state that has been surveyed for the study. Section 5 begins with the NSS findings on state wise

household cooking and lighting demand patterns and further examines the same across the states

surveyed as part of the TERI study. It also provides a comparison of the NSS and TERI survey

outcomes and a preliminary analysis of the survey data. Section 6 expands on the results from the

Pilot Study in Madhya Pradesh followed by Section 7 which undertakes the regression modeling

adding statistical value to the study outcomes. The significance of gender roles in enabling transitions

is covered in Section 8 while the willingness to pay for alternate clean energy options across states is

covered in Section 9. The indices for lighting energy consumption are discussed in Section 10. Case

studies based on the experiences of the team on the field have been enumerated in Section 11. Finally,

Section 12 sets the policy context for the study providing further recommendations based on the study

results.

21

2. Review of literature

The benefits of clean energy both for enhanced economic growth and for improving quality of life

were recognized as necessary conditions for achieving the Millennium Development Goals (MDGs).

It is recognized that energy issues must be dealt with in order to alleviate poverty. As part of the

Millennium Development Goals, the UN Commission for Sustainable Development 9th Session [

(United Nations Economic and Social Council, 2001) also explicitly acknowledged that access to

sustainable energy services is an essential element of sustainable development, stating that:

“To implement the goal accepted by the international community to halve the proportion of people

living on less than US$1 per day by 2015, access to affordable energy services is a prerequisite.”

Thus, the importance of energy in development policy cannot be undermined and it is critical to

understand the factors that drive household energy consumption patterns so as to facilitate appropriate

policy design and implementation.

2.1 Energy poverty

Poverty is not only a problem of low income, even though it is predominantly defined that way. The

World Bank (1994) states that poverty is ―a multi-dimensional problem that includes low access to

opportunities for developing human capital and to education..." (Tilak, 2005).Although the

importance of income-poverty cannot be ignored, it happens to be only one aspect of deprivation.

Various participatory appraisals have confirmed several dimensions and criteria of disadvantage, ill-

being and well-being as people experience them (Chambers, 1995).

One such dimension of deprivation is energy poverty, which is the lack of access to modern energy

services. The relationship between energy and poverty has featured in many recent policy documents

of various international agencies including the World Bank, United Nations Development

Programme, World Energy Council and the UK‘s Department for International Development (DFID).

All of these documents affirm that energy must be made a crucial part of all development and poverty

alleviation projects and programmes (WEC 1999, WB 2000, UNDP 2000, DFID 2002).

It is thus imperative to understand the characteristics of energy access in order to make policy

interventions. It would thus be useful to explore the concept of energy poverty further. While energy

poverty has been examined widely, in the absence of a single widely accepted definition, authors

usually use a combination of approaches to study the concept of energy poverty.

Approaches of measuring energy poverty have ranged from development of a fuel poverty line in

both monetary terms as well as relating energy poverty to other measures of development, apart from

defining it on the basis of actual energy requirement in households. For instance, (Pachauri, Muller,

Kemmler, & Spreng, 2004) have prepared a two-dimensional indicator to measure energy poverty as

well as distribution. They define an energy poverty line based on the energy consumption of those

above the income poverty line. This method has also been used by (Foster, Tre, & Wodon, 2000)who

define the fuel poverty line as the average energy consumption of those who lie between +/- 10% of

the official expenditure poverty line. Energy poverty has also been calculated at the aggregate

22

national level in relation to other measures of development such as Human Development Index or

Physical Quality of Life Index (Krugman & Goldemberg, 1983) and (Goldemberg & Johansson,

1995). The authors have constructed an access-consumption matrix by segregating households into

groups of energy carriers and the useful energy consumed. Energy poverty has also been defined in

terms of expenditure on energy as a proportion of household expenditure (Leach, 1987). DFID

defines an energy poor household as one that needs to spend more than ten per cent of its income on

fuel use and to heat its home to an adequate standard of warmth.

Khandker et al (2010) propose the measurement of energy poverty on the basis of income. They

define energy poverty as the threshold point at which energy consumption begins to rise with

increases in income. They add that for people below the energy poverty line, an increase in the

income does not lead to rise in the consumption of energy. On the other hand, as the income falls and

the consumption of energy does not, energy begins to comprise a larger share of total income. A

demand function for energy consumption is constructed with energy demand as the dependent

variable. They use a Tobit model for the estimation.

2.1.1 Determining the Energy Poverty Line

There is wide variation in the energy poverty lines estimated in different studies. Khandker et al

(2010) estimate energy poverty line for urban households at 8.6 kilograms of oil equivalent (Kgoe)

per capita per month for total energy and 2.4 Kgoe per capita per month for end-use energy whereas

for the rural households, the rural households the energy poverty line has been estimated at 17.9 Kgoe

and 3.4 Kgoe for total and end-use energy respectively. Owing to the large discrepancy in the two

estimates for rural energy, it has been suggested that the analysis be done only on the basis of end-use

energy. Based on this almost, 57% of the rural population and 28% of the urban population surveyed

in India is energy-poor. (Pachauri, Muller, Kemmler, & Spreng, 2004) use a nominal energy poverty

line of 500 Watts5 on the basis of actual level of energy that may be needed to meet basic energy

needs. However, this number will be specific to age-group, region, time period etc.

While calculating norm-based energy poverty lines, assumptions relating to all socio-culture factors

also need to be stated clearly. For instance, the Millennium Development Goals define energy

poverty as ―the minimum needs correspond to about 50 Kgoe of annual commercial energy per

capita; this estimate is based on the need for approximately 40 Kgoe per capita for cooking and 10

Kgoe used as fuel for electricity‖ (Modi, McDade, Lallement, & Saghir, 2005). For Brazil, Pereira et

al (Pereira, M.A.V., & Silva, 2010)analyse minimum energy requirements from specific field work.

They estimate a minimum rural energy requirement of 9.65 GJ per household per year in terms of

cooking and lighting needs. Foster et al (2000) in consultation with energy experts in Guatemala have

estimated the energy poverty line of 2,154 kilowatt-hours per year per household (5.9 kilowatt-hours

per day) for the country.

Bravo et al (1983) provide a norm based calculation of energy requirements for specific and derived

energy requirements including space conditioning and lighting, cooking and preservation of food,

5 Unit of power (energy per unit time) in watt (W = J/s), when referring to energy needs per person. This should not be

confused with the installed power of equipment or appliances.

23

personal cleanliness, recreation and social communication, and water pumping. The total demand for

energy in rural areas with hot climate is 312 Kgoe/capita/annum. This was further expanded in scope

and coverage by (Goldemberg J. , 1990)and was raised to 32.1 Kgoe per capita per month. However,

Modi et al (2005) analysed the energy required for cooking and lighting across the world and put the

cut-off much lower at 50 Kgoe per annum.

For India, the Advisory Board for Energy provides the estimates for this cut-off for meeting the

energy requirement for households in the country. As per the Board‘s report, ―about 629 kcal of

useful heat is needed per capita each day for meeting cooking energy requirements, about 30 kcal of

useful heat per capita per day for meeting space heating needs and 30 kcal of useful energy per capita

per day needs to be provided for meeting lighting needs‖ (Advisory Board on Energy, 1985). In 2006,

the Integrated Energy Policy defined lifeline energy at 30 units of electricity per household per month

for lighting and space cooling, and the equivalent of 6 kg of LPG per month which could be as LPG,

kerosene or bio-gas for cooking purposes (Planning Commission, 2006). This would equal

approximately 235 kcals per capita per day of useful cooking energy assuming a household size of

5.5 persons and a thermal efficiency of 60% for LPG stoves (Srivastava, Goswami, Diljun, &

Chaudhury, 2012).

2.1.2 Energy inequality

Siddiqi (1995) analyses the inequality of energy consumption in India and Pakistan on the basis of

income groups and fuel for different end uses. Fernandez et. al (2005) also study the level of

inequality by calculating the Gini coefficient for a village in Uttaranchal. Different patterns are

obtained based on the type of household (joint/nuclear) for different carriers (electricity, kerosene,

fuel wood and LPG). Here, while overall analysis of the pattern of energy consumption in the country

are available and have been done at both – the national as well as regional levels, quantification in

terms of calculating the actual level of inequality is still lacking. More detailed analysis of the factors

affecting the use of different type of fuels and quantifying these effects will aid in developing a clear

understanding the backward and forward linkages between determinants and measure of energy

poverty and inequality. A comprehensive analysis of energy inequality across income classes is

presented in Chapter 5 of this report.

2.2 Energy consumption patterns and their determinants

India is a geographically and culturally diverse country. Based on the differences in climate, soil and

food habits across the country, energy consumption patterns vary widely. However, a large part of the

country is still heavily dependent on traditional biomass based fuels. The consumption patterns at the

household level are often determined by a host of parameters, of which most predominant are socio-

economic factors. Information on determinants may be obtained through multi-topic socio-economic

household surveys such as the National Sample Surveys (NSS) conducted annually by the

Government of India. However, while these surveys provide general information on overall

household consumption patterns and basic socio-economic characteristics, to be able to study in detail

specific issues with respect to energy choices, independent surveys are often required to return

valuable information on key determinants.

24

2.2.1 Consumption patterns in India

While most rural households use multiple energy sources for cooking and lighting, NSS data shows

that many households use modest quantities of kerosene for cooking, augmenting it with use of

biomass fuels. In rural areas, biomass fuel use is prevalent across all income groups and remained

virtually unchanged between 1993–94 and 1999–2000, with more than 90 percent of rural households

using wood, dung, or both. Mirroring the findings in other countries, wood consumption rises with

increasing income among rural households, indicating a lag between a positive income change and

reduction in biomass consumption. Close to 60 percent of all rural households were using cash-free

wood in 1999–2000. In contrast, the use of kerosene as the primary cooking fuel was essentially non-

existent among rural households in 1999–2000; this applies across all income groups with the

exception of the richest 10 percent. In short, supply conditions in rural areas favour the use of

biomass for cooking because of its low labour costs and the ready availability of free biomass. This

suggests that the effectiveness of fiscal instruments, such as changing relative fuel prices or

increasing income relative to fuel prices, in promoting a switch from traditional biomass to petroleum

fuels in rural areas would have serious limitations (UNDP/ESMAP, 2003)

Data from 1999-2000 to 2009-10 shows that there has been considerable changes in energy use

patterns across rural households. In the case of firewood, there is an increase in household

consumption between 1999-2000 and 2004-05 followed by a slight decline in 2009-10. It should be

noted however, that the consumption level of firewood in absolute terms during 2009-10 was higher

than that reported in 1999-2000. The overall consumption of firewood actually went up in the past

decade by about 7.5%. In the case of electricity, there was an increase in electricity consumption by

almost 25 – 30% overall in the last decade; while for LPG, though there was a marginal change in

consumption over time but it remained more or less constant (NSSO, 2001, 2006, 2011).

Other studies, in India and elsewhere, support the observation that traditional and modern fuels

increasingly coexist in the household energy mix. The social benefits, such as health and time savings

for women and children, of partial fuel switching—whereby wood continues to be used and only

partially substituted by cleaner fuels—need to be better understood. Specifically, the health benefits

of the smoke-free indoor environment that is achieved by full fuel switching from traditional biomass

are likely to be compromised by partial fuel switching, but the exact effects of different combinations

of fuels and stove technologies are hardly known. The benefit in the terms of time savings, however,

is broadly in line with the amount of biomass used, and accrues to women even with partial fuel

switching. To the extent that partial fuel switching is the first step toward full fuel switching – and

may facilitate accelerated switching – efforts to promote the switch may be justifiable even should

their immediate social benefits be limited.

Hence, in order to gain a more holistic picture of rural households‘ fuel consumption basket and

factors behind fuel switching, there is a need to study the several determinants that affect the use of

energy in the household sector.

25

2.2.2 Determinants of fuel choices

While analysing determinants of fuel choice, it has been found that rational consumers choose the

most preferred bundle of commodities from a set of alternatives based on socio-economic constraints.

Economic constraints include market price of fuel and household‘s income where as non-economic

constraints include a set of household demographic and social factors (Pundo & Fraser, 2006).

Similarly, energy sources are treated as commodities and each source has multiple purposes and

attributes. Purposes are linked to various domestic activities such as cooking, heating, and lighting,

while the attributes refer to energy content, convenience, safety, speed of cooking, taste of food, and

quality of light.

Analysis of NSS data for period 1999-2000 done by Rao and Reddy (2007) reveals that household

expenditure, household size, education and gender play important role in determining the fuel

choices. The results also indicate a non-linear relationship with respect to monthly household

expenditure/household size and the fuel choices. The inference is that households with more members

or with an increase in household expenditure are less likely to use modern fuel compared to

traditional fuels. (Rao & Reddy, 2007)find that the demand for fuels has risen more rapidly than per

capita income. Also, if the rural population continues to grow and use traditional biomass as its main

cooking fuel, it will result in an increase in the absolute number of people affected by the adverse

effects of traditional fuel consumption. Prices of available sources of energy have also been found to

have an impact on the choice of fuel. Fuel wood and biomass that are available for very little or no

monetary cost continue to form a large share of the energy basket of households.

Age of the head of the household does not appear to be a significant factor. The type of food mostly

cooked also contributes towards fuel choice. The results also reflect that differences in accessibility of

fuel resources determine fuel choice. High proportion of biomass to total energy use reflects

inaccessibility or relatively less accessibility to modern fuel, low level of urbanization, and low

annual average temperature. Moreover literature also shows that households with increasing income

decrease their consumption of biomass.(Jiang & O'Neill, 2004).

A detailed review of literature indicates that different researchers categorize the determinants of fuel

choices differently. For instance, (Kowsari & Zerriffi, 2011)look at two broad sets of categories:

endogenous and exogenous. The former looks at economic characteristics, behaviour and other

related factors, while the latter looks at the physical environment, policies, the nature of energy

supply and related factors. These are summarised in the table below.

Table 1 Factors Impacting Energy Choices

Categories Factors

Endogenous factors (household characteristics)

Economic characteristics Income, expenditure, landholding,

Non-economic

characteristics

Household size, gender, age, household composition, education, labour,

information

Behavioural and cultural

characteristics

Preferences (e.g. food taste), practices, lifestyle, social status, ethnicity

26

Categories Factors

Exogenous factors (external conditions)

Physical environment Geographic location, climatic condition,

Policies Energy policy, subsidies, market and trade policies

Energy supply factors Affordability, availability, accessibility, reliability of energy supplies

Energy device

characteristics

Conversion efficiency, cost and payment method, complexity of operation,

Source: (Kowsari & Zerriffi, 2011)

Based on our assessment of literature, we find that determinants can be broadly categorized into four

types: Affordability, Availability and accessibility, Perception and social factors, and, other non-

income determinants.

Factors that determine affordability include individual or household income, price of the fuel, wealth

(which refers to ownership of land and livestock), income generating infrastructure and urbanization.

Availability and accessibility are determined by the presence of infrastructure, regularity of supply,

state interventions to enhance uptake of modern fuels and technologies and broad frameworks that

facilitate access to modern energy systems. Finally, perceptions and social factors refer to socio-

cultural preferences, and social identities, in particular gender.

Affordability

Affordability essentially refers to the ability of a household to pay for a particular energy source.

Factors that determine and impact affordability include income, price of the fuel and wealth6 of the

household. The linkage between energy and income is examined by Khandker et al (2010) who find

that energy poverty is much worse than expenditure based poverty in India.

Non-conventional fuels have remained the mainstay for a large part of the population as they are

usually obtained without a monetary price and as a result are an attractive option for a large part of

the rural households.

Household income is perhaps the most important socio-economic indicator in determining the energy

use patterns as it determines affordability. In India inelastic household income to a large extent

especially in low income group is prevalent in rural India. Even with an increase in income, it is not

sufficient to buy conventional forms of fuel, thus resulting in only an increase in the amount of non-

conventional fuel types. Around 70% of rural households have an income of less than Rs. 3000 per

month (NSSO, 66th Round) and as a result cleaner fuel options are a luxury that many cannot afford.

On the other hand conventional sources of energy are obtained free of cost (not taking into account

opportunity cost) and are preferred by such households.

In a similar exercise conducted among rural households in China, it was found that for lower income

levels, an increase in income did not necessarily increase the budget share for fuel and the switch to

6 This is generally measured on the basis of ownership of landholding and livestock

27

cleaner fuels happens only beyond a certain threshold income. It was also found that the population

with 80% of the income consumed a similar quantity of biomass, thus indicating that biomass

consumption remained high even when incomes increased. Purchase of domestic appliances increased

with a rise in incomes, reflecting most in the highest income groups. For example the number of

refrigerators doubled from 16 to 32 between the 80th

and the 90th

percentile. Analysing energy use by

household expenditure, it was found that as expenditure increases, all types of energy consumption

increase accordingly and the proportion of energy from biomass use declines very little. Households

with high expenditure essentially consume a smaller share of coal, and a larger share of modern

energy sources (electricity and LPG) as well as coal product. Consequently there is not a clear

transition from biomass to commercial energy. What we observe is an increase of energy

consumption of all fuel types with expenditure, while those with higher expenditure spend slightly

more on more convenient, efficient fuels (Jiang & O‘Neill, 2004).

Accessibility and availability

Availability and accessibility of energy can be measured in terms of the regularity of supply of the

energy source and presence of infrastructure to use the fuel once available. Accessibility to and

availability of energy is also affected by the location of the area, distance from the closest source and

presence of infrastructure.

Accessibility indicates whether a household can or cannot access the fuel irrespective if its

affordability. Access to modern fuels (i.e. LPG and Electricity) is considered an essential condition

for improved economic growth and for improving the quality of life of people (World Bank, 1996).

Access to clean and affordable energy to the poor is a major concern for sustainable development.

India houses a large section of people without access to clean energy.

Existing literature shows that indicators of availability have been found to have a significant impact

on energy choices. The heterogeneity in household‘s fuel choices and the relation between the choice

for traditional and modern fuels show the ease or difficulty to adjustments to both the fuel/device

purchased and its availability. Accessibility can be seen from three aspects – physical access to

energy supply, physical access to market and access to information. Browne 2010) shows that

government subsidy policy is actually benefitting higher income households more than the intended

beneficiaries. The paper also suggests that focus should be on subsidizing energy infrastructure

expansion rather than on fuel subsidies.

At the national level, the Indian government aims to improve the coverage of modern fuels i.e.

electricity and LPG. Embarking on a massive rural electrification programme to achieve universal

electrification by 2012, the government launched the Rajiv Gandhi Grameen Vidyutikaran Yojana

(RGGVY) scheme in 2006. It has helped in increased access for lighting in rural areas, but policies

directed towards rural electrification alone are unlikely to resolve the energy access problem as

electricity is used only for lighting purposes and accounts for only 10% of the energy demand by the

rural household (Bhattacharya, 2006).

For economic and financial viability of rural electrification projects, expansion of productive use of

electricity is essential. For instance, use of electricity should result in supply of adequate money flows

28

to the poor so that they have a willingness to spend some part of the monetary earning on purchase of

cleaner commercial energy. In addition, for commercial energy to successfully penetrate into the

energy baskets of the poor, the energy supplied should be regular and sufficient to meet the energy

needs of the household and should have a low cost of supply. Irregular supply of electricity,

especially at odd hours (i.e. during day time when people are mostly at work), proves to be of less

utility for rural households. The unreliable supply also forces households to use diesel gen-sets and

car (storage) batteries. Moreover, the Government‘s centralized rural electrification program i.e.

supply of power by conventional methods using exhaustible resources has proved un-economical and

more importantly un-manageable, particularly with regards to supply in remote places. Instead, the

decentralized approach based on supply of power produced with renewable energy resources like

solar photovoltaic (SPV) systems available locally is a viable alternative (Chakrabarti & Chakrabarty,

2002).

In order to increase LPG uptake, the government launched the ―Rajiv Gandhi Grameen LPG Vittaran

Yojana (RGGLVY)‖ on October 16, 2009. The Scheme aims at setting up small size LPG distribution

agencies in order to increase rural penetration and to cover remote as well as low potential areas

(locations having potential of 600 cylinders refill sales per month). However, the impacts of this

scheme have been variable across states in India. While subsidy for LPG exists, it is still a relatively

expensive fuel and its supply is made mostly in the urban area and is seriously constrained in the rural

areas.

The Government of India also provides kerosene at a subsidized price directly to the consumer

through the Public Distribution System (PDS) unlike in the case of LPG, wherein it is supplied by

distributors. Most of rural households in India that have access to kerosene, use it for lighting

purposes, while for cooking they have to fall back on other options.

Pandey (2002) finds that those populations in proximity to forest resources have higher per capita

consumption of fuel wood than those far away as adequate quantities of preferred fuel (wood) is

available and mixing or substitution with inferior fuel (crop residue, dung cake) is not required. The

total biomass consumption could still be lower since fuel wood is a better form of energy and as a

result they would be requiring less compared to those who substitute with other inferior fuel. The

total bio-mass consumption could be lower for this reason and is something that could be taken on for

further research.

Kanagawa and Nakata (2007) have argued that energy access improvement contributes to freeing up

the time spent by women and girls in gathering firewood and cooking with an inefficient stove. In

addition, it has indirect benefits for women‘s enterprises through utilization of improved energy

services. To prove their viewpoint, they have estimated the opportunity cost for consuming firewood

for cooking. Assuming that women in rural areas spend 8 hours a day in income generating activities,

(UNDP, 1995), the authors have used the following equation to estimate the opportunity cost for

women using firewood:

Women‘s opportunity cost with firewood (US$/GJ) = [{

,

29

Where,

Women‟s opportunity cost (US$/h) = {women‟s contribution to household‟s income

(US$/year)/Hours of labour (h/year)

And,

Women‟s contribution to household‟s income (US$/year) = 0.53*household‟s income (US$/year)

The paper goes on to suggest that for women to participate in income generating activities as reflected

by the opportunity cost, it requires the adoption of improved wood and gas devices. Increased income

might result in changes in patterns and amounts of energy consumption. It will also help in reducing

the average RSPM exposure to the level set by international organizations, thus resulting in the

improvement of the socio-economic status of rural households. Another argument is made by Palmer

and Mac Gregor (2009), who state that an increase in the wage rate of hired farm workers leads to

increase in the opportunity cost of collecting firewood. For the firewood self-sufficient households,

the increased productivity of agriculture labour (in terms of higher wage rate) leads to reduced

firewood collection (Palmer & MacGregor, 2009).

Perception and Social factors

The third broad category which influences energy use is the perception of the people and social

factors such as the role of women in decision making. Burning of biomass in conventional ways for

household energy requirement has considerable implications on the environment (emission of

greenhouse gases, brown clouds and black carbon) with increasing pressure on forests and associated

natural resources apart from health impacts. Emission of smoke from burning of biomass fuels leads

to respiratory problems (indoor air pollution). (Edwards, Smith, Jhang, & Ma, 2004)( Chengappa,

Edwards, Bajpai, & Shields , 2007). Health benefits of the smoke-free indoor environment and time

savings (in collection of firewood) for women and children are achieved by full fuel switching.

Indeed, the gender of the household head determines the priority of investment in clean fuels by the

household. Since in India women are the ones who spend the most time in the kitchen, indoor air

pollution affects them the most. As a result a woman headed household would recognize this fact

better and hence prioritize on clean fuel options.

Clancy et al. (2003) have looked at the impact of fuel consumption patterns on the vulnerability of

women from poorer households. Kelkar and Nathan (2002) suggest policy measures in order to

reduce the asymmetry in gender via better and more inclusive energy policy. There is a need to

accelerate the effort of partial fuel switching, so that full fuel switching is achieved soon.

Further, households incorrectly perceive modern fuels to be more expensive than traditional fuels,

especially with respect to cooking. Anozie et al. (2007) used energy costs based on energy prices of

different energy sources along with energy consumption patterns to measure the actual cost of

30

cooking. The energy efficiency of different energy sources was used to measure the energy intensity7.

Low-energy intensity corresponds to high-energy consumption efficiency and vice versa. Besides

higher rate of heating would also give higher energy intensities (lower efficiencies). Fuelwood has

highest energy intensity and least energy consumption efficiency in compare to fuels like gas and

electricity. Hence the energy consumption pattern along with energy price used to calculate energy

costs for cooking with the different energy sources show fuelwood is the most expensive, which

people do not perceive.

The literature on improved cookstoves focuses on the triple benefit that it provides – improved health

and time for households, preservation of forests and associated ecosystem services and reducing

emissions that contribute to global climate change (Jeuland & Pattanayak, 2012). Surprisingly,

households using improved cookstove used more firewood than the households with mud stoves. The

empirical study in Nepal showed that if new technology improved stove efficiency by 20% and fuel

wood consumption dropped by 15%, then the 5% differential between the fuel efficiency and the

decrease in fuel wood demand might be due to an increase in consumption of fuel wood as a result of

efficient stoves. The author indicated that such a rebound effect got larger if a household‘s budget

share on fuel wood was large, the income elasticity of fuel wood demand was high and the supply

elasticity was low (Zein-Elabdin, 1997) (Nepal, Nepal, & Grimsrud, 2010).

Other household factors like education and main profession of the family along with religion and

caste have a significant role to play in household energy choices. Education level of the head of the

household plays a significant role as more educated people would better understand the problems of

indoor pollution brought about by most non-conventional sources of energy. If affordability is

overcome, education would play a role in placing clean fuel option higher up the priority in the

consumption basket such that families are willing to forego some other luxuries for a better and

cleaner energy solution.

Family professions too would dictate consumption patterns. Those working in the agriculture sector

like agricultural labourers and farmers with livestock have easy access to biomass. As a result the

opportunity cost of having to collect biomass goes down significantly for these people. Hence they

have a preference for non-conventional sources of energy.

Further, in several places in rural India, religion and caste still hold an important place in the social

order. In some cases, there are instances of partiality against lower caste people with regards to access

to PDS services or other community services. In such instances, it can be difficult for people to attain

access to any kind of modern or conventional fuel source and lead to further inequity.

Other non-income determinants

In a study done by Jiang and O‘Neill (2004), they find that in rural China variables such as household

size, age, sex, education and occupation of household, geographic condition, location, and per capita

income were significant factors in determining fuel choices at the household level. Specifically,

7 The energy intensity is defined as the energy consumed per unit of food material cooked.

31

households in the northeast, south, or southwest were more likely to use biomass than households in

the north, southeast or northwest; households in the plains used biomass less frequently than those in

mountainous and hilly areas. The results reflected differences in accessibility of natural resources.

With increase in income and expenditure, the likelihood of using biomass decreases. Moreover,

households headed by a professional are significantly less likely to use biomass; as the educational

level of the head increases, the likelihood of the household using biomass decreases. Smaller

households, and those headed by females, are less likely to use biomass.

The study concluded that, for per capita total energy use, household expenditure is the most powerful

predictor, followed by south/north location and yearly temperatures. Households in the north and in

regions with low temperature consumed more. Coal production, electricity production and price are

also important if the region has good access to coal and electricity, households use less of total energy

due to less use of biomass. Household size is negatively related to total energy use; urbanization level

increases energy use, and mountainous areas consume more since they have relatively better access to

biomass resources and poorer access to commercial energy.

For per capita biomass use, climate was important (represented by south/north location); household

size and expenditure also played a role. Accessibility to a commercial energy source showed different

impacts. On the one hand, the impacts of production and prices of electricity, coal, and gas indicated

that accessibility of these energy sources decreases the amount of biomass use. On the other hand,

rural households in areas with high petroleum production consume more biomass, which implied that

petroleum is not widely used by rural households and has little impact on substitution for biomass.

That urbanization negatively related to biomass use indicated that urban growth may save biomass

used as an energy source. Moreover, on the other hand it was found that forest coverage was

negatively related to biomass use and contradicted the assumption that biomass accessibility

contributes to the use of biomass. The study mentioned that to explain this phenomenon, one might

need to change the idea about high forest coverage from a cause of more biomass use to a

consequence of less biomass use.

2.3 Energy transitions

The term energy transition or fuel transition is simply understood as the progression to ―modern‖

fuels from ―primitive‖ ones. Kowsari & Zerriffi (2011) and Massera et al. (2000) described this as the

upward movement on the ―energy ladder‖8, as shown in Figure 1. The energy ladder concept

assumes linear movement and has been widely critiqued as it assumes that households will move to

more sophisticated energy carriers as their income increases. Fuel switching is a key theme in the

energy transition process, referring to the complete displacement or substitution of one fuel by

another. This theory has been widely questioned as it does not portray the transition to modern energy

access, because households use a combination of fuels and technologies at all income levels and keep

moving back and forth on the energy ladder. Complete substitution of one fuel by the other is rare.

This use of multiple fuels is a result of their differing end-use efficiency, affordability and of social

8 The energy ladder model envisages a three stage fuel switching process. The first stage is marked by reliance on biomass. In the

second stage, households move to transition fuels such as kerosene, coal, and charcoal in response to higher income and urbanization.

The third phase is marked by use of electricity and gas, once households have sufficient income

32

preferences, such as the preference of a particular fuel for cooking. Some households use multiple

fuels for security of supply. For instance, households have been seen to use multiple fuels for

cooking – traditional biomass cookstoves, improved cookstoves as well as LPG. This characteristic is

known as ‗fuel stacking‘9 (Figure 2).

The empirical results of India, Mexico and Africa show that households tend to use multiple fuels

rather than have a single fuel in their energy baskets (Masera et al., 2000) (Johnson & Bryden, 2012).

From energy surveys carried out in rural areas in Nigeria, it was found that firewood is the least

expensive cooking energy source and because of its ready availability, it has remained the dominant

household energy source for cooking (Anozie et al., 2007). Similarly, households in India continue to

use traditional biomass based cookstoves called ―chulhas‖ for baking traditional breads (rotis).

Figure 1: Classical Energy Ladder (Kowsari & Zerriffi, 2011)

9 Fuel Stacking is a strategy by which new cooking technologies and fuels are added, but even the most traditional systems are rarely

abandoned.

33

Figure 2: Fuel stacking (Kowsari & Zerriffi, 2011)

Therefore, in order to make useful policy interventions to foster the development of any society, it is

imperative to look at the role of energy poverty and access. In turn, this requires the study of a host of

factors that influence them, as literature on the subject shows. In order to facilitate the transition of

households from being consumers of traditional fuels to modern fuels, policy interventions will have

to be based on evidence which reveal which sets of policy actions will be most effective in which

demographic. Thus, some of the key questions that any study in the domain of energy access must

consider are summarized below.

A. How is energy poverty and energy inequality defined?

Although interlinked, energy poverty and energy inequality are two dimensions of the energy access

linkage and can be looked at individually. Inequality can be in terms of fuel-use, quantity of energy

used, useful energy, prices of fuel and also energy access. We have already seen in the earlier

sections, that the relationship of income and energy choices is not so direct after all, but there are

many patterns that are not completely explained by income, thus it brings forth the need to look into

further dimensions of society and culture which impact people‘s lifestyles. Based on the literature

available the existing concepts and measures of energy inequality (such as the DFID definition of

energy poverty), need to be examined to determine if these suffice or whether any modifications are

needed to align them especially in the context of rural India. Once the appropriate measures are

identified, the quantification of energy poverty and inequality can be done to actually gauge the level

energy poverty in rural India.

34

B. What is the relation between energy poverty and inequality and other social and

macroeconomic variables such as level of income, education levels, and so on?

There exist strong linkages between access to energy/energy poverty and the other macroeconomic

variables particularly income, occupation, household size, education and religion. These relations

need to be kept in mind while analysing the evolution in the pattern of energy consumption across

various regions. The results presented subsequently in the report bring out the relation between

energy inequality and other socio-economic and macro variables.

C. What are the linkages between gender issues and energy consumption choices?

The choice of fuel has implications on the health and wellbeing of the women in rural households.

More often than not, it is the women who are expected to collect biomass fuels for the household as

cooking is a domestic chore that is expected to be carried out by them. Choice of cleaner fuels for

cooking such as LPG reduces the time spent in collecting fuel and also has positive implications on

the health of women and children in the household. Thus, it is very critical to consider the role of

gender when it comes to household energy transitions. For this purpose, there is a need to go beyond

the existing NSS data to capture key variables relating to gender and society.

D. What will be the transition path in the consumption of rural energy?

Several approaches can be used to understand the pathway of transition to cleaner and more efficient

forms of fuel. Further, while analysing the transition in energy consumption, it is useful to map the

change in energy sources to the change in other social and macroeconomic variables in order to

understand not only the change in fuel-mix but also the factors that determine that change over time.

2.4 Hypothesis

Based on a detailed literature review and consultations with other stakeholders, the study aimed to

test four key hypotheses with regard to energy transitions across rural households in India.

1. Increasing incomes would lead to changing energy baskets of households with a shift towards

modern and cleaner fuels such as LPG for cooking.

2. As decision-making power of women increases within the household, either by way of social

changes or by way of increased contribution towards household income, the probability of

choosing cleaner cooking fuels would increase.

3. If households are beneficiaries of either a direct energy intervention or any other intervention

such as a livelihood intervention, there would be a positive impact on the household energy

basket, in terms of an increased share of modern fuels.

4. Any additional income for the household would translate into an expenditure on cleaner fuels

only if ‗energy‘ was a key development priority for households along with food, health and

education.

35

Figure 3: Hypothesis for the Study

Changes in Income flows

(Occupation and Opportunities)

Value of Labor

(Gender roles)

Intervention

(Energy or livelihoods)

Development Priorities

(Importance of energy)

Household Energy Basket

36

3. Methodology and Sampling

As mentioned, this project employs a combination of existing literature analysis and primary data

collection to examine the pattern of energy consumption and energy choices among rural households

across various regions throughout the country. A summary of the methodology employed in this

study is presented in Figure 4. A review of literature was conducted to identify the key findings in

existing and ongoing studies and has been summarized in Chapter 2. Simultaneously, the raw data

collected as part of latest rounds of the National Sample Survey (NSS) was also extracted and the

relevant information for energy consumption and choices was analyzed to assess the changes in basic

consumption patterns over time.

On the basis of the findings from the literature analysis and the data extracted from NSS, a pilot

questionnaire was developed to help test the sampling strategy and form basic hypotheses for the

survey. The pilot was then conducted across 200 households in Madhya Pradesh. Based on the

findings of the pilot study, the questionnaire was revised and the hypotheses finalized for conducting

the main survey. The final survey was conducted in six states – Goa, Himachal Pradesh, Odisha,

Karnataka, Maharashtra, and Rajasthan. Together, these states form 10 out of the 15 agro-climatic

zones of the country.

Figure 4: Study Methodology

Pilot survey

Final questionnaire and survey design

Final survey

Analysis

Review of literature Extraction of NSSO data

Designing of pilot questionnaire

Selection of zones and areas

37

The primary data collected has been used to determine the patterns of energy choices across rural

households in India and also identify the factors that impact these fuel choices. Household level

regression models of energy choices that look at switching between fuel baskets have been prepared.

In the analysis, a distinction needs to be drawn between the energy used for cooking and that for

lighting at the household level. This is primarily because the choice of moving to electricity from any

other alternatives is determined by the provision of access through grid based or off-grid supply of

power. On the other hand, choice of cooking fuels (and technologies) is made at the household level

and is affected by certain micro and regional variables. Each of these stages from the methodology is

covered in detail in the following chapters.

3.1 Data Collection

3.1.1Data sources

The analysis in this report is based on both primary and secondary data. The secondary data was

sourced from the household consumption surveys carried out by the National Sample Survey

Organisation (NSSO), Government of India. A large-scale household primary survey was also

conducted as part of this study in selected states, namely: Himachal Pradesh, Rajasthan, Maharashtra,

Goa, Karnataka and Odisha. In total, 6020 households were selected across these states and the

sample size for each state is based on a detailed sampling methodology, which is explained in the

following section.

3.1.2 Sampling Techniques

For the purpose of this research project, various sampling techniques were considered. They included

simple random sampling, stratified random sampling and cluster sampling. Given the complex nature

of the issues that the study aims to address, Stratified Random Sampling was considered to be the

appropriate sampling technique after selection of the states.

Stratified random sampling is a technique which attempts to restrict the possible samples to those

which are ``less extreme'' by ensuring that all parts of the population are represented in the sample in

order to increase the efficiency (that is to decrease the error in the estimation). We divide the

population into L strata, where the variation within strata is small relative to the variation between

strata (unlike cluster sampling), in terms of some underlying response variable.

Sampling Methodology

The division of agro-climatic zones was chosen as most rural households depend on biomass for their

energy needs and these are to a large extent apart from socio-economic factors, governed by natural

resources which in turn are a function of the climate type and the other factor being that easy and

low-cost availability of biomass fuels is dependent on agricultural practices (type of crops grown)

which again is associated to the climate type of that region. The division of agro-climatic zones

serves as the stratum and the population within each agro-climatic zone is considered homogenous.

All the districts in India were divided into their respective agro-climatic zones. The division

according to agro-climatic zones is irrespective of administrative boundaries.

38

Each zone was attributed a sample weight which was calculated as the ratio of the zonal rural

population to the total rural population of India. This gave weightage to the fact that population size

plays a large role in determining resource use and dependence. The average Monthly Per Capita

Expenditure (MPCE) which is the proxy for income and useful energy consumption for each district

were calculated from the National Sample Survey 66th Round which collects data pertaining to

household expenditure on various aspects. Based on the standard deviation of useful energy

consumption for each zone and the respective weights for each zone, the sample size for each agro-

climatic zone was estimated using the ‗optimum allocation‘ method assuming equal costs.

One of the key challenges before estimating the sample sizes for each zone was to estimate the total

sample size given the budget constraint. Various grass-roots level institutions and individuals that

have been involved in conducting primary household surveys were consulted to understand the key

considerations so as to ensure that we can cover the maximum sample size given the project costs.

Based on discussions, we arrived at a national figure for average cost per household to be surveyed.

Equal costs have been assumed since agro-climatic zones are not bound by administrative boundaries

whereas costs are governed by these boundaries, thus making it difficult to arrive at an accurate cost

figure for each zone. Once the total sample size was estimated by dividing the survey budget by the

average cost per household to be surveyed, the total sample size was set as the bound such that the

zonal sample sizes would add up to the total sample size and thus, the zonal sample sizes were

estimated, optimizing the sample size for each zone given the costs.

The other important challenge after estimating total and zonal sample sizes was to estimate which

districts would be chosen for the sample survey and what would be the sample size in each district. It

was important to ensure that while natural resource availability and dependence were given

importance by way of agro-climatic zone divisions, there was a need to also ensure that each state had

sufficient representation in terms of sample sizes so that the dataset that would be generated would be

useful for both zone-wise as well as state-wise analysis.

Thus, in each zone, the districts from each state were listed separately and the sample size for the

respective states falling in each zone were calculated based on a weight (where, weight = Rural

population of state falling in the zone / total rural population of the zone) that was assigned as a

function of population. Then, an index of MPCE and Useful energy was calculated such that the

index values are in the range of 0 to 1 with 0 being the lowest and 1 the highest. The districts in each

zone were arranged in descending order of the index value. The 33rd and 66th percentile of the index

values of the districts in each zone were calculated, thus, dividing the set of districts in each zone into

three parts, namely, low, medium and high. Then, the districts in each sub part were selected based on

a detailed qualitative assessment of natural resource endowments such as forests, land, water

resources, socio-economic characteristics such as occupation, tribal or non-tribal populations, and

backward districts and so on. Each of the chosen districts was assigned a sample size as a proportion

of the total zonal sample size. After the selection of the districts, based on the sample size, blocks

were chosen within each district, after which, selection of villages was done based on similar

qualitative parameters used for district selection. Within each village, the choice of households was

based on random sampling.

39

4. Overview of the States

This chapter will look at the geography, demographics and the socio-economic profiles of the states

where the TERI household sample survey was conducted.

4. 1 Maharashtra

Maharashtra is the most industrialized, the second most urbanized and, judged by the per capita

income, the second richest state in India (IGIDR, 2009). It is spread over a total area of 3,07,713

sq.km, and is the third largest state after Madhya Pradesh and Rajasthan. Its capital Mumbai is

considered to be the financial and commercial hub of the country. Maharashtra is also the second

largest state in terms of population.

Maharashtra‘s 35 districts are divided into six revenue divisions: Konkan, Pune, Nashik, Aurangabad,

Amravati and Nagpur for administrative purposes (IL&FS Infrastructure, 2012). These 35 districts

are further divided into 109 sub-divisions of the districts and 357 Talukas. For local self-governance

in rural areas, there are 33 Zilla Parishads, 351 Panchayat Samitis and 27,935 Gram Panchayat. The

urban areas are governed through 22 Municipal Corporations, 222 Municipal Councils, 3 Nagar

Panchayat and 7 Cantonment Boards.

4.1.1 Geography

The state is located between 16º N and 22º N latitudes and 72º E and 80º E longitudes and falls in the

western part of India, along the Arabian Sea. A 720 km long coastline stretches from Daman in the

North to Goa in the South. Based on its physical features, the state is divided into three parts viz,

Maharashtra Plateau, the Sahyadri Range and the Konkan Coastal Strip.

4.1.2 Demographic profile

With a population of 11.2 crores, Maharashtra ranks second among all the states and UTs in the

country.10

The decadal growth of population in the state has seen a sharp decline from 22.6 percent

during 1991-2001 to 15.99 percent in 2001-2011(Table 2). On the whole, Maharashtra‘s population

growth rate has been higher than that of India since 1961 with the exception of the 2001-11 decade,

where it was lower than the national population growth rate (17.64 percent).

10Economic survey of Maharashtra 2011-12, Directorate of Economics and Statistics.

40

Table 2: Population of Maharashtra11

Year

Total population (in crores)

Maharashtra India

Rural Urban Total

1961 2.8 1.1 3.9 43.9

1971 3.5 1.6 5.1 54.8

1981 4.1 2.2 6.3 68.5

1991 4.8 3.1 7.9 84.6

2001 5.6 4.1 9.7 102.7

2011 6.2 5.1 11.23 121.0

Source: Government of Maharashtra 1990, 2000, 2010

Table 3: Population growth rate of Maharshtra4

Year Total Population (in crore)

Maharashtra India

1961-71 27.45 24.80

1971-81 24.54 25.00

1981-91 25.73 23.85

1991-01 22.57 21.35

2001-11 15.99 17.64

Source: Government of Maharashtra 1990, 2000, 2010

4.1.3 Socio-economic Profile

The population density of Maharashtra has increased from 314 persons per km² in 2001 to 363

persons per km² in 2011. Approximately 42 percent of the state population is concentrated in two

divisions, Konkan and Pune. This region also forms the most industrialized part of the state including

Mumbai. According to the 2011 Census, the population density within the state ranges from 74

persons per km² in Nandurbar district to 20925 persons per km² in Mumbai (Sub-urban) district.

Maharashtra is one of the most economically developed states in India. The per capita income at

current prices was Rs. 87,686 during 2010-2011 compared to the national per capita income of Rs.

53,331.

Currently, the share of agriculture, industrial and service sectors in the total state income is 13

percent, 28 percent, and 60 percent respectively. The corresponding shares in the year 1960-61, when

the state was created, were 40 percent, 34 percent, and 26 percent, respectively, which indicates a

remarkable decline in dependence of the state economy on the agricultural sector and an increase in

the share of industrial and services sector. During the period 1991-2011, there was a total investment

of Rs. 874053 crores in various industrial proposals. The main contributors being IT industry,

Financial services sector and Hotel and Tourism industry. The latest available data6 on factory

employment for 2011 indicates that Maharashtra continues to lead the country in average daily

factory employment and ratio of main workers to the total population was about 38 percent.

11 Economic surveys of Maharashtra: 1991, 2001, 2011, Directorate of Economics and statistics

41

According to the data6, 38 percent of the state population is below poverty line. The percentage of

scheduled castes and scheduled tribes population to the total population was 19.08 percent (2001).

4.1.4 Overview of state policies related to rural development

The Department of Rural Development, deals mostly with the implementation of all the central

government schemes for rural development. A range of welfare activities designed for the betterment

of rural masses help to overcome the challenges of poverty and vulnerability that have been a major

threat to the rural population.

Some of the major programs being implemented by the Department of Rural Development are as

follows:

4.1.4.1 NRLM (National Rural Livelihood Mission)

The Maharashtra State Rural Livelihoods Mission (MSRLM) was launched in July 2011 as a

registered organization under the aegis of the National Rural Livelihoods Mission (NRLM), also

known as Aajeevika. The program endeavors to impact rural poverty through a range of

comprehensive and strategic livelihood interventions in a time bound manner. Initially, 10 districts

were selected for the project based on various criterions which included ranking as per the HDI, IAP

(Integrated Action Plan) districts, geographical location and so on. The pilot districts include

Gadchiroli, Wardha, Yavatmal, Osmanabad, Jalna, Ratnagiri, Nandurbar, Solapur, Thane and Gondia.

The impact of the program has been studied12

in 2011-12 and one of the major criterions to assess the

impact was to check number of Self Help Groups (SHGs) formed in the selected districts and whether

they have taken up Income Generating Activities (IGAs). About 10.2% of villages have SHGs where

no economic activities have been taken up.

4.1.4.2 Eco-village Scheme

The Department for Rural Development is implementing the Eco-village scheme since October 2012,

for environment protection. Under the program, villages are given funds to plant trees, eradicate open

defecation, ensure solid waste management and promote use of non-conventional sources of energy

such as solar, wind and biogas. In the first phase, the districts of Pune, Kolhapur, Thane and Raigad

are being covered. Till April 6, 2012, out of 27,920 villages in the State, the eco-village program is

being implemented in 12193 villages.13

4.1.4.3 National Biogas and Manure Management Program (NBMMP)

Under the aegis of the Ministry of New and Renewable Energy (MNRE), the Maharashtra Energy

Development Authority (MEDA) is implementing the Biogas program is Maharashtra. Three types of

biogas plants are supported under the scheme:

12Regular Monitoring of Rural Development Programs, Phase 1, 2012, Ministry of Rural Development, GoI 13 Department for Rural Development, Government of Maharashtra

42

i. Community biogas plants (CBP): These are suitable for Gram panchayats and village

development institutes, with an output range of 15-85 m³ per day. Seventy two such

systems have been installed till now (MEDA, n.d.a).

ii. Institutional biogas plants (IBP): They have a typical capacity of 15-35 m³/day and are

suitable for dairies etc (32 installations).

iii. Night Soil Based Biogas Plants: This setup is linked to community toilets and is one of

the best ways to ensure management of bio-waste. It kills the harmful bacteria and solves

the important problem of sanitation (324 installations till now)9.

4.1.4.4 National Program for Improved Cookstoves (NPIC)

The implementation of this MNRE initiated program has two components, namely, Research and

Development and target fulfillment. For Maharashtra, the research and development (R&D)

component is being handled by an NGO named ARTI (promoters of the ‗Laxmi‘ Stove), while the

target fulfillment component is being handled by state government agencies. Useful contributions

from R&D organizations have led to several changes in policy including emphasis on the

entrepreneurship development program. The program has helped create R&D infrastructure, skilled

manpower, and technology disseminators and entrepreneurs in the state (Hanbar & Karve, 2002).

4.1.4.5 Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY)

The RGGVY was launched in April 2005, for electrifying all villages and households. The program

includes free electricity connection for BPL families. The state nodal agency for the implementation

of the program is Maharashtra State Electricity Distribution Co. Ltd. (MAHADISCOM). As on 30th

April 2013, a total of 39337 villages in the state have been covered as part of the intensive

electrification program under this scheme. Also, a total of 23,94,241 BPL connections were provided

(RGGV, n.d.).

4.1.4.6 Rajiv Gandhi Grameen LPG Vitaran Yojana (RGGLVY)

RGGLVY was launched in 2009 and aims at setting up small scale LPG distribution agencies to

increase rural penetration and cover remote areas. Under this scheme, Maharashtra has been awarded

100 dealerships by every petroleum company.

4.1.4.7 Rural Village Electrification

MNRE has initiated this program aimed at electrification of remote census villages and hamlets of

electrified census villages through non-conventional energy sources such as solar energy, small hydro

power, wind, biomass, and hybrid systems. MEDA, the state nodal agency for the program has

carried out a brief survey of the un-electrified villages in the state. The technologies proposed for the

sanctioned villages are SPV domestic lighting systems, SPV street lighting systems and SPV power

plants. Maharashtra has also formed a state policy for rural village electrification in accordance with

the national policy. The achievements of the scheme so far are:

43

i. Total number of villages electrified as on 31st October, 2013 stood at 39762.

ii. In addition to this, work order for electrification of 28 hamlets has already been

placed. The survey work in 116 villages and 143 hamlets which are un-electrified is in

progress (MEDA n.d.b; MEDA n.d.c).

MEDA has also installed a total of 56 kW of SPV power plants in 6 villages. From these power

plants, two lights have been provided to each house in the village.

4.1.4.8 Village Energy Security Program14

MEDA is also acting as the nodal agency for the Village Energy Security Program initiated by the

MNRE. This program gives importance to social development as it is aimed at providing total energy

security (Electricity, cooking and motive power) for households in remote villages. The program is

applicable for small size villages with a total number of households in the range of 25-200, that will

not be electrified till 2012 by conventional means. The technology options supported by the program

are biogas plants, biomass gasifiers coupled with gas engine and diesel generators run on straight

vegetable oil (SVO) or bio-diesel.

4.2 Rajasthan

Located in the north-western part of the Indian subcontinent, Rajasthan is India‘s largest state,

covering an area of 342,239 sq. km, which is about 11 percent of the total geographical area of the

country. The state lies between the latitudes 23°30‗N to 30°11‘N and 69°29‘ to 78°17‘ E longitude.

Pakistan borders the state on the north-west and the Indo-Pakistan international border stretches to

about 1070 km and touches major districts including Barmer, Bikaner, Ganganagar and Jaisalmer

(Maps of India, 2011).

Rajasthan has 33 districts with Jaipur as the state capital. It is known as the Desert state of India due

to the presence of the largest deserts of the country, the Thar Desert. It encompasses about 70 percent

of the state and is spread over an area of 2,00,000 km of the total landmass of Rajasthan. Also known

as ‗Maru- Kantar‘ – it attracts tourists from across the globe.

4.2.1 Geography

The state is split into two geographical zones by the Aravalli Hills, with one side of it covered by the

desert and the other by a forest belt. The types of soil available in Rajasthan are mostly sandy, saline,

alkaline and chalky. Chambal river – one of the two major rivers in Rajasthan – holds great economic

importance in Rajasthan‘s growth and development. It is a major source of water for the agricultural

areas of Rajasthan, channeled through the dam built over it near Kota district. Physiographically the

state can be divided into four units, viz, Aravalli Hill ranges, Eastern Plains, Western Sandy Plain and

Sand Dunes, and Vindhyan Scrap Land and Deccan Lava Plateau.

14 MEDA (n.d.c)

44

4.2.2 Demographic Profile

According to the census of 2011, the population of Rajasthan stood at 68,548,437 compared to

56,507,188 in 2001 showing an increase of 21.31 percent approximately, which is higher than the

national average. Its total urban population stands at 17,048,085 and rural population at 51,500,352

(2011). The figure clearly shows that nearly 75.13 percent of the total population of Rajasthan lives in

rural areas. The following table shows the population trend over the period 1961-2011 (Government

of Rajasthan, n.d.)

Table 4: Urban – Rural Distribution over the decades

Census year Urban

population

Rural

population

Total

Population Urban% Rural%

Percentage of Population

To India

1961 3,281,478 16,874,124 20,155,602 16.2 83.72 4.6

1971 4,543,761 21,222,045 25,765,806 17.63 82.36 4.7

1981 7,210,508 27,051,354 34,261,862 21.04 78.95 5.0

1991 10,067,113 33,938,877 44,005,990 22.87 77.12 5.2

2001 13,214,375 43,292,813 56,507,188 23.85 76.14 5.5

2011 17,048,085 51,500,352 68,548,437 24.87 75.12 5.7

Source: (Government of Rajasthan, n.d.)

The share of the rural population to the total population of Rajasthan stood at 83.72 percent and that

of the urban at 16.2 percent only. As per the 2011 census, Rajasthan‘s population stands at a 5.7

percent to the total population of India. In the 2001-11 decade, the percentage change of rural

population has been 29.01 percent and that of the urban population has been 18.95 percent. This

shows that the percentage increase of population has been more in the rural population as compared

to that in the urban. Table 5 shows the decadal growth rate of population starting from the year 1961-

71 to 2001-11 for both Rajasthan and India as a whole.

Table 5: Population Growth Rates

Year Population Growth Rates

Rajasthan (%) India (%)

1961-1971 27.83 24.80

1971-1981 32.97 25.00

1981-1991 28.44 23.85

1991-2001 28.41 21.35

2001-2011 21.31 17.64

Source: (Government of Rajasthan, n.d.)

4.2.3 Socio-economic Profile of Rajasthan

Rajasthan‘s economy has primarily been agricultural and pastoral in nature. Rajasthan has achieved

the status of being the largest edible oil producer in India and also the largest producer of oilseeds.

Most of the industries in Rajasthan are agricultural in nature. The state is a leading contributor to the

textile industry and is the second largest producer of polyester fibre in India. It is also the biggest

45

wool producer in India. Several prominent and well-known engineering institutes and companies are

located in the city of Kota.

The population density of the state is less than 200km per sq. km. Jaisalmer and Barmer top the chart

with 32.22 percent and 32.55 percent growth rate respectively while Ganganagar stands with the

lowest population growth rate of 10.06 percent. The male population stands at 51.86 percent and

female population stands at 48.13 percent of the total population of Rajasthan according to the 2011

census (Government of Rajasthan, n.d.).

4.2.4 Administrative Division of Rajasthan

The formation of Panchayati Raj Institutions (PRIs) has made the process of planning, decision-

making, implementation and delivery easier due to direct and greater involvement as well as quicker

in terms time taken. There in all 33 districts, 237 blocks, 9188 gram Panchayats within Rajasthan. To

make administrative work easier the state is divided into seven broad divisions which are further

divided into administrative blocks namely Ajmer, Bharatpur Bikaner, Jaipur, Jodhpur, Kota and

Udaipur. These divisions are further classified into 33 administrative districts (including the new

district of Pratapgarh).

The literacy rate has increased from 18.12 percent in year 1961 to 67 percent in the year 2011. But

the increase has not been able to reach the targeted literacy rate of 85 percent set by the Planning

Commission for the 11th

five year plan (Planning Commission, 2008). The urban literacy rate has

been growing at a much faster pace and is closer to achieving the targeted growth than the rural

population. But the rural–urban disparity in literacy rates is seen decreasing. According to the 2001

census report, Rajasthan had recorded the largest rise in the literacy rate, a jump from 38.55 percent

in 1991 to 60.41 percent in 2001. But this trend has not persisted in the following years. The

consecutive changes in female literacy rate both in Rajasthan and India as a whole is seen to be at a

lower rate than the growth seen in male literacy rates with the former being 52.7 percent in Rajasthan

– which is among the lowest in India – and 65 percent in India as a whole.

The decade 1991-2001 had seen a tremendous increase in literacy rate, a change in literacy marked by

a 25 percent. Rajasthan was ranked 20th out of 29 Indian states on HDI with value of 0.434 (2007-

08). The female literacy rate remains a major concern to the policy makers. This is because of the

27.85 percent gap in literacy rates between the two genders, as per the 2011 census. Rajasthan also

suffers from a low sex ratio of 926 females per thousand males. Similarly child sex ratio is also very

low at 883 females per thousand male borns. Compared to the rapidly growing population Rajasthan

has a very low per capita income of Rs 23,669 as compared to the national average of Rs 33,731.

According to the Tendulkar Committee report of 2009, about 34.4 percent of the state population lies

below the poverty line and about 62.8 percent of the state population is poor.

4.2.5 State Policies on Energy and Development

Some of the central and state schemes operated by the government of Rajasthan to facilitate rural

development include:

Swarnjayanti Gram Swarozgar Yojana (SGSY)

46

Sampoorn Gramin Rozgar Yojana (SGRY)

The National Rural Employment Guarantee Scheme (NREGS)

Indira Awas Yojana (IAS)

Integrated Watershed Management Program (IWMP)

Dang Area Development

Mid-Day Meal Program

Rajasthan Grameen Aajeevika Vikas Parishad

4.2.6 Policy initiatives for fulfilling the energy requirements of Rajasthan

Energy is important determinant for development. Therefore the government of Rajasthan has put in

place some policy measures that will facilitate the development of infrastructure that is concerned

with energy distribution and generation from the most basic to advanced forms of energy needs.

4.2.6.1 Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY)

This was launched with Rural Electrification Corporation (REC) as the nodal agency by merging it

into one scheme in April 2005. This program was mainly funded by the central government with 10

percent by the state governments through either their own resources or as a loan from the

REC/financial institutions. This scheme of rural electrification continued upto the 11th

plan and

provided access to electricity to all households. Under this plan approximately 1.15 lakh un-

electrified villages were provided with electricity in which 2.34 crore BPL households were

electrified by 2009. This scheme guaranteed a minimum daily supply of 6-8 hours of electricity at a

subsidized tariff as required under the electricity Act.

4.2.6.2 The status of the village electrification in the state

The village-wise achievement after the implementation of RGGVY for the 10th

and 11th

plan within

the Jaipur Discom block consisted of 358 un-electrified villages and 4201 electrified ones. Among six

discoms the lowest BPL families were part of the Karouli district and the highest belonged to the

Alwar. In Udaipur the numbers of electrified villages were 4180 and un-electrified ones were 309.

The BPL families were given a sum of Rs. 9416.83 lakhs as disbursement on the basis of number of

BPL families which were numbered to be 122464. Jodhpur discom consisted of the highest number of

un-electrified villages (760) as recorded under the 10th

plan with 148975 BPL families who were

provided with a disbursement of Rs. 10441.05 lakhs. Including Bhilwara and Jhalawra power grid,

there were 1647 un-electrified villages and 14931 electrified ones in Rajasthan, with a total

disbursement of state being Rs. 40327.65 lakhs.

Rural energy Programs

This policy was conceptualized during the 6th

five year plan and launched as a centrally sponsored

scheme in the 7th

plan. Under this program, many other projects and policies were formulated w.r.t

energy generation and distribution. IREP was transferred in 1994-95 from the planning commission

47

to the MNRE. The objectives of the program were to fulfill the underlying requirements to establish a

well-established energy driven country.

The policies initiated were as follows:

National electricity policy of 2005: This aims at achieving access to electricity to all households

within five years of its formation.

National Rural Electrification Policy (2006): Goals included electricity access to all households by

2009, quality and reliable power supply at reasonable rates and minimum life line consumption of 1

unit/ household/ day as a merit good by 2012. For those inhabitants where grid connectivity cannot be

provided, or is not cost effective, state governments should within 6 months prepare and notify, a

rural electrification plan which should map and detail the electrification delivery mechanism. It is

under this programme the Rajiv Gandhi Grameen Vidyutikaran Yojana was formulated and initiated.

4.2.6.3 Remote Village Electrification Program (RVE)

This program meant to electrify all the remote census villages and remote hamlets through non-

conventional energy sources such as solar energy, small hydropower, biomass, wind energy, hybrid

system etc. Rs. 2152.74 lakhs was released for the implementation of this program. As of June

2013,the total number of villages for which the project was sanctioned reached 340 of which 292

villages were provided with electricity. The total hamlets sanctioned were 90 and all 90 were

completed (Deloitte, 2013).

4.2.6.4 National Biomass cookstoves initiative (NBCI)

This program works in close relation to the National biogas and Manure Management Programme

(NBMMP) as it aims at using non- conventional sources of energy for cooking. The NBCI was

launched by the Ministry of New and Renewable Energy on 2nd

December 2009. The primary aim of

this policy is to enhance the availability of clean and efficient energy for the energy deficient and

poorer sections of the country.

4.2.6.5 Renewable and solar Policy of Rajasthan

The Government of Rajasthan has a policy to promote the generation of power through non-

conventional energy sources, which was enacted in 1999 was and later updated in the year 2000,

2003 and 2004. The government also issued a Rajasthan Solar Policy in 2011 to promote solar energy

(Government of Rajasthan, 2011).

The objectives of this policy include:

Develop solar power plants for meeting renewable purchase obligation of Rajasthan as

well as other states.

Promote off-grid applications of solar energy and the development of solar park with

various policy initiatives including allotment of Government land at 10 percent District

48

Level Committee (DLC) rate, 1766 MW, wind farms and 106 MW of biomass plants are

already in operation.

Incorporation of nodal agencies: The government of Rajasthan established the Rajasthan Renewable

Energy Corporation limited (RRECL) in the year 2003 by merging erstwhile Rajasthan Energy

Development Agency (REDA) and the Rajasthan State Power Corporation limited to act as state

Nodal agency for the single window clearance of the Renewable energy Projects.

4.2.6.6. Jawaharlal Nehru National Solar Mission (JNNSM)

This program launched by the Government of India to propagate the use of solar energy. Out of the

1100 MW new projects undertaken by JNNSM, Rajasthan had received a share of 873 MW (i.e.79.36

percent of all-India allocations) through competitive bidding of the 1st phase of Jawaharlal Nehru

mission. To encourage the solar sector, the Rajasthan Electricity Regulatory Commission (RERC)

issued orders in 2008 imposing solar specific renewable procurement obligation (RPO) for the

electricity discoms in the state. The state government approved solar projects for 11 private sector

developers for setting up 66 MW capacity systems utilizing all available technologies in solar

photovoltaic technology. This is already commissioned under the migration scheme of National Solar

Mission, while the solar thermal plants of 30 MW are under implementation.

4.2.6.7 National Biogas and Manure Management Scheme:

This scheme mainly caters to setting up of family type biogas plants which have been under

implementation since 1981-82. Its objective is to provide clean bio-gaseous fuel mainly for cooking

purposes and also for other application for reducing the use of LPG and other conventional fuels.

4.3 Goa

Goa is the smallest state in India, covering an area of 3,702 km². It lies between the latitudes

14°53'54" N and 15°40'00" N and longitudes 73°40'33" E and 74°20'13" E. Goa has a coastline of

about 101 km, and the eastern part is characterized by the Western Ghats, which separate it from the

Deccan Plateau.

There are nine rivers in the state of which six originate and flow exclusively within the state

boundaries- namely Baga, Sal, Saleri, Talpona and Galgibag. However, rivers such as Terekhol and

Chapora originate in Maharashtra while the river Mandovi originates in Karnataka State. Mandovi

and Zuari are the largest rivers and drain about 70 percent of the runoff generated in the state. The

total navigable length of Goa's rivers is 253 km (157 miles). The soil of Goa is lateritic and is reddish

in colour. However, alluvial and loamy soils can be observed along the river banks. The soil is

conducive for agriculture.

Administratively, the state is organized into two districts; North Goa comprising seven talukas

namely Pernem, Bardez, Bicholim, Tiswadi , Ponda, Sattari, Dharbandora (new taluka ) with a total

area of 1,736 sq. kms, and South Goa comprising five talukas namely Canacona, Mormugao,

Salcette, Sanguem and Quepem with an area of 1,966 sq. kms. There are two Zilla Panchayats, one

49

each at the district level. The North Goa Zilla Panchayat comprises 30 elected members and the South

Goa Zilla Panchayat comprises 20 elected members. In all, there are 189 village panchayats and 359

villages of which 213 are in North Goa district and 146 in South Goa district. There are 44 towns of

which 14 are municipalities (13 councils and 1 corporation) and remaining are census towns (Census,

2011b).

4.3.1 Geography

The State comprises three main physical divisions or ecozones: the mountainous region of the

Sahyadris in the east, the middle level plateaus in the centre and the low-lying river basins, and the

coastal plains. The Sahyadris has an area of about 600 sq km and average elevation of 600 metres.

This area is a part of the Western Ghats and is the catchment area covered with thick forest. The

Western Ghats, which form most of eastern Goa, have been internationally recognized as one of the

biodiversity hotspots of the world. Being an important ecosystem, it helps to conserve water and are

prime sources of major rivers. According to FSI 2009, the recoded government owned forest area in

the state is 1224km2, which is 33.06 percent of the geographical area. Of this, the reserved forest

constitutes 20.67 percent, protected forest 69.04 percent and un-classed forests 10.29 percent of the

total forest area. The central portion consists of plateaus at varying levels, not exceeding about 100

metres and not less than 30 metres in height which has ecological and cultural characteristics. The

third ecozone is coastal region, which can be divided into two coastal regions - proper and flood

plains which are formed by the alluvial deposits carried away by the rivers from Sahyadris along their

banks. Sand dune complexes also feature prominently along 5 main regions viz. Querim – Morjim,

Chapora – Sinquerim, Caranzelem – Miramar, Talpona – Galgibaga (Mascarenhas, 1999). The most

well-known part of Goa is the coastal belt which runs from North Goa to South Goa. The beaches,

predominantly sandy, occupy about 4000 ha of area along the north-south coastline.

4.3.2 Demographic profile

As per the 2011 census, Goa's population was 14, 57,723 which was 0.2% of the total population in

India. Goa has seen an 8.17 percent rise in population since 2001 (Directorate of Planning, Statistics

and Evaluation, 2014).

50

Table 6: Population of Goa

Year

Total population

Goa ( Nos) India ( Crores)

Rural Urban Total

1961 502668 87329 589997 43.9

1971 591877 203243 795120 54.8

1981 684964 322785 1007749 68.5

1991 690041 479752 1169793 84.6

2001 677091 670577 1347668 102.7

2011 551414 906309 1457723 121.0

Source: DPSE, n.d.

Table 6 shows that there has been a decline (-18 percent) in the rural population in the decade 2001-

11 and a rise (35 percent) in the urban population during the same period. In Goa, the coastal belt

supports most of its population. It is in the four coastal talukas of Mormugao, Salcette, Bardez and

Tiswadi that the bulk of the population resides, giving rise to various regional imbalances and

straining the state‘s coastal resources.

4.3.3 Socio-Economic Profile

According to the 2011 census, the population density of Goa increased from 364 persons per sq km in

2001 to 394 persons per sq km in 2011. Figure 2 shows the map of the state with the population

density. The population density in Goa is higher than the neighboring states of Karnataka and

Maharashtra indicating incrementally rising pressure on available land resources. The density of

population of North Goa is higher than that of South Goa. North Goa has a density of population of

471 individuals per sq. km whereas south has 326 individuals per sq. km. Of the total population

males constituted 7,40,711 (50.81 percent) while the remaining (49.19 percent ) 7,17,012 were

females.

The growth rate of GSDP at constant prices in the years 2008-09 and 2009-10 was almost constant at

around 10 percent, thereafter in the subsequent two years it registered an upward trend and stood at

16.89 percent in 2010-11 and 22.10 percent in 2011-12. However, the ban on mining impacted the

GSDP in 2012-13 and the growth rate slowed to 8.47 percent. Sector-wise composition of GSDP at

current prices, as per the estimates for 2012-13 are as follows: primary sector (agriculture, forestry

and fishing; mining and quarrying) accounted for 12.28 percent; secondary sector (manufacturing;

electricity, gas and water supply; construction) accounted for 30.90 percent and tertiary sector (trade,

hotels and restaurant; transport, storage and communication; financing, insurance, real estate and

business services ; community, social and personal services) accounted for 53.87 percent.

4.3.4 Overview of state policies related to rural development

District Rural Development Agency (DRDA) came into being in 1980. Later DRDA North was

established by further dividing it into two agencies, one in North and other in South Goa. Since then

DRDA has implemented and executed the various schemes of central government as well as state

51

government for economic upliftment of rural poor by generating employment in rural areas. Some of

these schemes are detailed in following sections15

.

4.3.4.1 National Rural Livelyhood Mission (NRLM)

The Swarnjayanti Gram Swarozgar Yojana (SGSY) was designed to uplift families living below the

poverty line, by covering them under all aspects of self- employment, such as organizing the poor to

form self-help groups for starting any economic activity of their choice and by providing them

training, credit, technology, infrastructure and marketing support. In 2013, upto 379 families

benefited from the scheme while during 2010-2011 about 432 families have been assisted under the

programme. But the scheme will be discontinued from this year and replaced by NRLM scheme.

NRLM ensures that at least one member from each identified rural poor household, especially a

women is brought under SHG network in time-bound manner. Both women and men would be

organized for addressing livelihoods issues. Under this scheme 100 percent BPL families would be

covered, with such that 50 percent of the beneficiaries are SC/STs, 15 percent are minorities and 3

percent are people with disability. For this purpose, NRLM will undertake community based process

through participatory identification of poor (PIP). PIP will be carried out using sound methodology

and tools such as social mapping, well-being categorization and deprivation indicators. Once the

households have been identified as poor through PIP process, the list would be vetted and approved

byGram Sabha and the respective Panchayat thereof. Further NRLM schemes would focus on

developing and strengthening the institutions of poor women including SHGs and their federations at

village and higher level.

4.3.4.2 Rural Housing Scheme

A) Indira Awaas Yojana (IAY)

This scheme is being implemented by the Government of India with an aim to provide shelter to the

people living below poverty line and financial assistance is provided for construction of new houses

and upgradation of the existing houses. It is funded on cost sharing basis between the Government of

India and Government of Goa with a ratio of 75:25. An amount of Rs 55,000 is provided for

construction of new houses and Rs 15,000 for up gradation of existing ones. During 2013-14 (upto

December,2013) 1025 houses have been completed and 723 houses are still in progress.

B) Credit cum Subsidy Scheme

The objective of this scheme is to cover those rural families that have not been covered under IAY.

All rural household having Rs 32,000 as annual income and residing away from town are eligible for

this scheme. The funding is based on ratio of 75:25 between the central and the state government

respectively. However this scheme has been on freeze since 2014.

15 Secondary data collected from DRDA, Goa.

52

4.3.4.3 National Social Assistance Programme (NSAP)

A) National Old Age Pension Scheme (NOAPS)

The program is meant for providing pension (monthly Rs 300) for individuals from the age of 64

years till the age of 84. Thereafter the pension is increased to 500. During the year 2013-14 (upto 31st

December, 2013), 2136 pensioners have been covered.

B) National Family Benefit Scheme:

Under the scheme, an assistance of Rs 20,000 is provided to the family on the death of its primary

bread winner in the age group of 18 to 59 years. About 453 beneficiaries have been covered during

the year 2013-14 (upto 31st December, 2013).

4.3.4.4 Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS)

Under the scheme, 661 Job Cards were issued till December, 2013 and 0.51 lakh person days were

generated.

4.3.4.5 Goa Gram Samrudhi Yojana (GGSY)

GGSY scheme is supplementary to SGRY scheme and it helps to provide infrastructure assets in the

rural areas such as panchayat ghars, village community halls, crematoriums, rural roads etc. The

state funds the scheme entirely. Under this scheme, 17 projects have been completed as on 30th

November, 2013.

4.3.4.6 Goa Grameen Urja Yojana (GGUY)

The objective of GGUY scheme is to provide installation of domestic new LPG connection to

minimize the use of firewood for families below poverty line. Every BPL family would be provided

with LPG connection and 2 cylinders, one gas regulator with accessories and 2 burners one gas stove.

A financial assistance of maximum Rs 4000 is provided under this scheme.

4.3.4.7 Integrated Rural Energy Programme (IREP)16

IREP is an area-based program with the rural block as a unit of planning and is implemented by Goa

Energy Development Agency (GEDA). Currently six blocks - Quepem, Sanguem, Sattari, Pernem,

Canacona and Bicholim - are being covered under the program. Importance is laid upon training,

demonstration and dissemination of information of the various types of non-conventional energy

devices. Further, energy saving devices like Compact Fluorescent Lamps, Pressure Cookers and

Kerosene Stoves are sold at subsidized rates and gadgets like Solar Cookers, Sarai Cookers, Solar

Home Lighting Systems, Solar Water Heating Systems are being supplied under subsidized local rate.

16 Implemented by Goa Energy Development Agency

53

4.3.4.8 Atal Gram Yojana

Atal Gram Yojana is being implemented by Directorate of Social Welfare department. During the

2013 budget, the Government of Goa announced the ―Atal Gram Yojana‖ with a view to take

development to the remotest villages of the state. For this purpose, Netravali village was selected as a

model village as it constitutes nearly 62 percent of tribal population and wherein the majority of the

population are economically poor. Under this scheme latest techniques in agriculture and allied

sectors have been demonstrated through extension activities and training programs including site

visits for about 195 dairy farmers were taken to modern farms in nearby by states. There they were

exposed to the modern practices adopted for increasing the milk yield. The immediate effect of these

efforts was seen in 6 months with an increase in milk production from 2000 litres per day to 3500

litres per day. Further subsidies were also provided for green fodder cultivators, poultry farmers, and

coconut cultivators and also to cashew growers. Infertility camps for milch animals were organized

wherein a complete check-up was held in the village using a modern Ultra Sound Machine arranged

from the neighbouring state. Under this programme, soil testing was also carried out where in 1440

soil samples were collected on census basis and the reports were distributed to the respective farmers.

Women belonging to the Scheduled Tribes are also being encouraged to take up self-employment

activities by developing their entrepreneurial skills.

4.3.4.9 Central Rural Sanitation

This scheme includes management of liquid and solid waste disposal; environmental hygiene and

construction of latrines thereby preventing diseases. But this scheme has been replaced by Nirmal

Bharat Abiyan Yojana. In Goa, a total of 45,323 individual household latrines (IHHL) (BPL: 17935

and APL: 27388) have been sanctioned under the scheme. About 150 Sanitary complexes for women

(SCW), 731 and 547 school toilets and anganwadi toilets respectively with 3 Rural Sanitary Marts

(RSM)/Production Centres (PC) have been sanctioned (Ministry of Drinking Water and Sanitation,

2014).

4.4 Karnataka

Karnataka is the eighth largest state and occupies a geographical space of 190.50 lakh hectares

(Government of Karnataka, n.d.). Bengaluru, the capital of Karnataka, also called the Silicon Valley

of India, is the IT hub of Asia. It is among the fastest growing cities in the world. With a population

of 6,10,95,297, Karnataka is the 9th

most populous state in India. Out of the total increase in the

population from 1901 onwards, about 84 percent of the increase occurred in the last 50 years.

The state is one of the richest in biodiversity. The region accounts for about six percent of India‘s

surface water resources which is close to 17 lakh million cubic meters. About 40 percent of this is

available in the east flowing rivers and the remaining from the west flowing rivers. It is situated in the

western part of the Deccan Peninsular region, spread across the land mass where the Western and

Eastern Ghats converge into the Nilgiri hill complex. For administrative purposes, Karnataka state

has been divided into four revenue divisions: Bengaluru, Gulmarg, Belgaum and Mysore. These

revenue divisions are constituted of 30 districts. The 30 districts are sub-divided into 176 sub-districts

(Taluks), 347 Towns including 127 Census towns and 220 statutory towns, 29,340 villages (including

54

1943 un-inhabited villages) (Government of India, 2014). The local self-governing mechanisms work

through PRIs all across in India. In Karnataka 30 Zila Panchayats, 176 Taluka Panchayats, and 5631

Grama Panchayats are established.

4.4.1 Geography

Karnataka is constituted by two well-defined macro regions of India; the Deccan Plateau and the

Coastal plains and Islands (Karnataka Tourism, n.d.). The State can be divided into four

physiographic regions: The Northern Karnataka Plateau, the Central Karnataka Plateau, the Southern

Karnataka Plateau and the Karnataka Coastal Region. Karnataka has a forests cover an area of about

38724 sq kms, i.e. around 20 percent of Karnataka‘s geographical land. The percentage of forest area

to geographical area in the state is less than the all-India average of about 23 percent, and 33 percent

prescribed in the National Forest Policy. Karnataka possesses a rich mineral resource base. It is an

important source of gold and silver. The two major mines located in Kolar and Raichur produce

around 3000 kilograms of gold, which is around 84 percent of the country‘s production per annum.

The estimated reserves of high-grade iron ore are 8,798mn tonnes. The iron-ores of Bellary-Hospet

region are considered to be among the best in the world. The state is endowed with rich deposits of

asbestos, bauxite, chromite, dolomite, kaolin, limestone, magnesite, manganese, ochre, quartz and

silica sand. It is also the sole producer of felsite, moulding sand (63 percent) and fuchsite quartzite

(57 percent) (The Statesman, 2013; KLA, n.d.).


As per the 2011 census Karnataka with a population of (Government of India, 2014) 6,10,95,297

accounts for 5.05 percent of India‘s total population. The population of the state increased fourfold in

the last century. In the 2011 census, a declining trend is seen and the population stood close to 15.67

percent, implying that although the population is steadily growing, but the pace of growth is

declining. With this average, the state‘s population growth rate stands to be less than national decadal

population growth rate of 17.64 percent. Variation in the decadal growth rate of the population in

Karnataka as compared to the national rate is reported in Table 7.

Table 7: Population and Its Growth Rate: Karnataka (1961-2011)

Census Years Population

(Karnataka)

Decadal Growth

Rate (in %)

Population

(India)

Decadal

Growth Rate

(in %)

1961 2,35,86,772 - 43,92,34,771

1971 2,92,99,014 24.22 54,81,59,652 24.80

1981 3,71,35,714 26.75 68,33,29,097 24.66

1991 4,49,77,201 21.12 84,63,87,888 23.86

2001 5,28,50,562 17.51 1,027,015,247 21.34

2011 6,11,30,704 15.67 1,210,569,573 17.64

Source: Census of Karnataka, n.d.

55

While the rural population grew at the rate of 7.40 %, the urban population registered a growth rate of

31.54 percent. Bengaluru is the most populous district with a share of 15.69 percent. Kodagu district

ranks the lowest with a share of just 0.91 percent, and is preceded by Bengaluru rural district which

has a small share of 1.61 percent in the population.


Out of the nearly 6.1 crore people, 61.33 percent are residents of rural Karnataka, while 38.67 percent

are urban residents. District wise, Bengaluru is the most urbanized district with 90.94 percent of its

population residing in urban areas. The population density is 319 per sq km, which is lower than the

national average of 382 per sq km. Both, the national and state averages have increased considerably

as compared to the 2001 census. The sex ratio in the state increased from 964 to 973 in the 2001-2011

decade. The ratio is well above the national average of 943. In absolute terms, the number of males

stand at 3,10,57,742 while the number of females are 3,00,72,962. The sex ratio for rural population

increased from 977 in 2001 to 979 in 2011. For the urban population, the sex ratio registered a

spectacular increase from 942 to 963 in the 2001-2011 decade. The literacy rate at 75.36 percent is

slightly higher than the national level of 74.04 percent. The literacy rate in rural areas 68.73%, while

in urban areas is 85.78%. The male literacy rate increased from 76.1 percent in 2001 to 82.47 percent

in 2011. The female literacy rate experienced a higher jump in the same decade, from 56.87 percent

to 68.08 percent. Table 8 highlights the increase in literacy rate of the state from the year 1961 till

2011.

Table 8: Literacy Rate in Karnataka (in percent)

Gender 1961 1971 1981 1991 1996 2001 2011

Male 42.29 48.51 58.73 67.26 73.75 76.29 82.47

Female 16.70 24.56 33.17 44.34 52.65 57.45 68.08

Total 29.80 36.83 46.21 56.04 63.42 67.04 75.36

Source: Census, 2001, 2011

The Scheduled Caste population constitutes 17.15 percent of the total population of the state. The

proportion of the Scheduled Tribe population to the total state population is 6.95 percent.

The BPL families in rural Karnataka are 37.5 percent, while the BPL families in urban Karnataka

account to 26 percent. The Human Development Report of United Nations Development Programme

(UNDP) in 2010 introduced a new Multidimensional Poverty Index (MPI) in 2010 (Government of

Karnataka, 2011). This new international measure of poverty analyses poverty not only from an

income-based perspective but also by reflecting on the multiple deprivations that people face at the

same time. The multiple factors considered included basic living standards, access to school, clean

water and health care. Karnataka fairs better than India with nearly 46 percent of the population

falling in the multi-dimensional poor index as against 55.4 % at the all-India level.Currently, share of

agriculture in the state is lowest while that of the service sector is the highest, 13.22 percent and 59.44

percent. The industrial sector has a nominal share of 27.34 percent.

56

4.4.4. Overview of State and Central Government Policies for rural development

4.4.4.1 Karnataka Rural Employment Guarantee Scheme (KREGS)

This scheme provides one hundred days of guaranteed wage employment in a financial year to every

household in rural areas. KREGS is being implemented since 2006 and covers all the 30 districts of

the state. The adult members of the household are supposed to volunteer to do unskilled manual work

subject to the conditions stipulated in the Act and notified in the Scheme.

4.4.4.2 Karnataka State Rural Livelihood Mission (KSRLM)

Karnataka State Rural Livelihoods Mission (KSRLM) has been entrusted with the task of

implementing National Rural Livelihoods Mission in the state. The program is implemented with the

help of a registered society called the Karnataka State Rural Livelihood Promotion Society

(KSRLPS). Sanjeevini aims at reduction of poverty in rural Karnataka through provision of wage and

self-employment opportunities. It aims enhance the livelihood opportunities for the rural poor.

In addition, the poor would be facilitated to achieve increased access to their rights, entitlements and

public services, diversified risk and better social indicators of empowerment.

4.4.4.3 Suvarna Gramodaya Yojna

Launched in November 2006 this scheme aims at developing 1000 villages every year in the state

with the help of vibrant village communities. The specific objectives of the scheme include

improvement of the village environment, the village education system and overall upgrading lifestyle

of the villagers. Development of roads, supply of clean drinking water, identification of families

living below and above poverty line for effective implementation in more backward areas, providing

electricity to all houses and community centres, promoting education and information technology,

disposal of garbage, construction of toilets are also included in the scheme. According to state

government reports published in 2010-11, 3173 villages were selected for implementation of the

scheme. The selection of villages was based on a criteria with due weightage in the allocation of

funds. The state government granted Rs.2418 crore for the same.

4.4.4.4 Rajiv Gandhi Grameen Vidyutikaran Yojna (RGGVY)

The initiative aims at electrifying all villages and habitations in rural India. It seeks to provide free

electricity to BPL families. In Karnataka, 27 projects have been sanctioned under RGGVY at a total

cost of Rs. 100427 lakhs. Projects are being implemented by five DISCOMs namely CESCOM,

BESCOM, HESCOM, MESCOM, GESCOM and one Co-operative Society namely Hukeri Co-

operative Society.

The scheme directs the setting up of:

Rural Electricity Distribution Backbone (REDB) with 33/11 KV (or 66/11 KV) sub-station of

adequate capacity in blocks where these do not exist

Village Electrification Infrastructure (VEI) with provision of distribution transformer of

appropriate capacity in villages/habitations

57

Decentralized Distributed Generation (DDG) Systems based on conventional & non-

conventional energy sources where grid supply is not feasible or cost-effective

4.4.4.5 National Biogas and Manure Management Program (NBMMP)

The program has the following objectives:

i. To provide clean bio-gaseous fuel mainly for cooking purposes and also for other applications

for reducing use of LPG and other conventional fuels

ii. To meet ‗lifeline energy‘ needs for cooking as envisaged in ‗Integrated Energy Policy‘

iii. To provide bio-fertilizer/ organic manure to reduce use of chemical fertilizers

iv. (iv)To mitigate drudgery of rural women, reduce pressure on forests and accentuate social

benefits

v. To improve sanitation in villages by linking sanitary toilets with biogas plants

vi. (vi)To mitigate Climate Change by preventing black carbon and methane emissions

The nodal agency in Karnataka responsible for implementation of the scheme is the Rural

Development &Panchayati Raj Department. A target of setting up of 1.06 lakh biogas plants was

fixed for the year 2013-14. Around 10,300 plants were targeted to be set up in Karnataka, out of

which 5032 plants have been set up in the same financial year. The Indian Institute of Journalism and

New Media reported in 2012 that the state‘s rural development department miserably failed to

achieve the targets set by the MNRE since the allotted subsidies did not reach the concerned areas.

Only 14 percent of the total subsidies allotted by the central government for installing biogas plants

reaches rural Karnataka due to lack of coordination between the central and state governments.

4.4.4.6 Nirantara Jyothi Yojana

The scheme intends to supply make 24-hour three-phase quality power to rural areas. This program

envisages feeder separation in the rural areas, with the feeders having specially designed transformers

to supply power to farmers residing in scattered farm houses. The rural electricity usage is bifurcated

into agricultural and non-agricultural load, based on the purpose for which energy is being consumed.

With a project cost of Rs. 2122.6 crores (GoK Energy Department, n.d.), the program was

implemented in Karnataka in two phases in December 2011. It covered around 126 taluks. As a pilot

program, the scheme was first implemented in Malur, Malavalli and Bailhongal taluks. According to

the reports of the Energy Department of the Government of Karnataka, the scheme has yielded good

results with respect to tail end voltages, limited interruption period and reduced number of

interruptions.

4.5 Himachal Pradesh

Himachal Pradesh is largely a mountainous state accounting for around 1.7 percent of India‘s total

area. Nearly 90 percent of its population is settled in rural areas. The state capital, Shimla, is

relatively urbanized with nearly 25 percent population living in urban areas of the district. Although

the economy of the state is predominately governed by the agriculture sector, Himachal Pradesh

figures among the states with the lowest poverty ratios.

58

For administrative purposes, Himachal Pradesh is divided into twelve districts: Bilaspur, Chamba,

Hamirpur, Kangra, Kinnaur, Kullu, Lahaul-Spiti, Mandi, Shimla, Sirmaur, Solan and Una. Owing to

the immensely varied topographic conditions, the districts vary a lot in terms of development,

population, urbanization and industrialization. The local self-governing mechanisms work through

PRIs all across in India. At present there are 3,243 Gram Sabhas, 77 Panchayat Samitis and 12 Zila

Parishads in the state.

4.5.1 Geography

Located within 30°22‘ and 33°12‘ north latitude and 75°47‘ and 79°4‘ east latitude, Himachal

Pradesh is formed by a hilly and mountainous terrain. There are considerable differences in climate

and rainfall between sub-regions because of the wide variations in topography (Planning

Commission, n.d.). From north to south, topographically, the state can be divided into three zones:

The Shivaliks or Outer Himalayas; Inner Himalayas or mid-mountains and the Alpine zone or the

great Himalayas

Biodiversity

The state is extremely rich in biodiversity. Close to two thirds of the area is covered by forests. They

are an important source of income, raw material, fodder for livestock, herbs for drugs, resources for

the tillers etc.

Water Resources

Five mighty rivers run through the state: Beas, Chenab, Ravi, Satluj and Yamuna. All rivers are

perennial in nature, and yet scope for irrigation is limited because of the steep terrains. The

government has however, engaged the private sector for harnessing hydel power.


As per the 2011 census, the population of Himachal Pradesh (Department of Economics and

Statistics, 2013), is 68,64,602. It ranks 21 in terms of population among all the states in India. The

decadal growth of population was highest in the 1971-1981 decade (23.70%). The 2011 census shows

a declining trend of about 12.9% much below the national decadal growth Rate of 17.64 percent

Variation in the decadal growth rate of the population in Himachal Pradesh is reported in Table 9.

Kangra is the most populous district with a share of 15.69 percent. Lahaul-Spiti district ranks the

lowest with a share of just 0.46 percent, and is preceded by Kinnaur district which has a small share

of 1.23 percent in the population. Interestingly, Lahaul-Spiti is area wise the largest district of the

state.

59

Table 9: Decennial Growth Rate in Himachal Pradesh

Sl. No Years Population (in lakhs) Decennial Growth Rate

1 1951 23.86 -

2 1961 28.12 17.87

3 1971 34.60 23.04

4 1981 42.81 23.70

5 1991 51.71 20.79

6 2001 60.78 17.54

7 2011 68.65 12.94

Source: Census 1951, 1961, 1971, 1981, 1991, 2001 & 2011

4.5.3 Socio Economic Profile

Primarily a rural economy, close to 90 percent of the total population resides in rural areas, with the

remaining 10 percent constituting the urban population. The percentage share of urban population has

been increasing continuously over the previous years starting from 7.61 percent in 1981, 8.69 percent

in 1991 and 9.80 percent in 2001 to 10.00 percent in 2011 census. Shimla is the most urbanized

district with 25 percent of its population being urban. Nearly one-fourth of the total population is

constituted of the scheduled castes, while the scheduled tribes have a share of just a little over 5

percent. Birth rate and death rate have been decreasing both in rural and urban areas over the years.

The birth rate in 2011 for rural areas was 17.1 as compared to 11.2 in urban areas. The total marital

fertility rate was 1.2 as compared to all India 3.2 in 2011.

The population density is 123 per sq km, which is lower than the national average of 382 per sq km.

The population density of the state remains low as compared to most other Indian states. The trends

in sex ratio reflect massive improvement in attitudes towards birth of girl child and female

empowerment. The successive census data reflect an increase in the number of females. The sex ratio

in the state is recorded at 972 in the 2011 census, which is low, but well above the national average of

943. In absolute terms, the number of males is 34, 81, 873 while the number of females is 33,82,729.

According to the 2011 census, the overall literacy rate of the state was 82.80 percent, which is higher

than the national level of 74.04 percent. Male literacy rate was recorded at 89.53 percent and female

literacy rate at 75.93 percent. Literacy percentage among Scheduled Castes has increased from 70.3

percent in 2001 to 78.92 percent in 2011, while that among Scheduled Tribes has increased from

65.5% in 2001 to 73.64 percent in 2011.The total State Domestic Product for the year 2012-13 was

44,480 crore against 41,908 crore in 2011-12, thereby registering a growth of 6.1 percent at constant

prices (2004-05). Currently, the share of primary sector is 19.72 percent, secondary sector is 38.35

percent and tertiary sector is 41.93 percent in the Gross State Domestic Product. Close to 8.06 percent

of the population lies below the poverty line in Himachal. The percentage of people in rural Himachal

that are placed below the defined poverty line is 8.48, while those in urban areas of the state account

for 4.33 percent (Planning Commission, 2013).

60

4.5.4. Overview of the Central and State policies undertaken for rural development in

Himachal

When Himachal Pradesh came into being in 1971, almost half of the rural population in the state lay

below the defined poverty line. With the aim of accelerating rural development, the government

undertook certain measures. It included various new initiatives and strategies in its budgets. These

strategies were evolved to increase agricultural production, initiate creation of rural infrastructure,

start poverty alleviation programs to reduce the massive rural poverty, launch schemes for

empowerment of women and children and strengthen PRIs in the villages.

4.5.4.1 National Rural Livelihood Mission (NRLM) or Aajeevika

The Mission aims at creating efficient and effective institutional platforms for the rural poor enabling

them to increase household income through sustainable livelihood enhancements and improved

access to financial services. The Rural Development Department of the state government has been

entrusted with the task of implementing the NRLM in Himachal Pradesh.

4.5.4.2 Swarnajayanti Gramin Swarozgar Yojna (SGSY)

The focus of SGSY is on vulnerable groups among the rural poor. The agenda of the scheme is to

improve the quality of life of BPL households in the state, to organize the rural poor in self-help

groups, to provide income-generating assets to the beneficiaries, to train the beneficiaries or the

swarozgaries and to help them rise above the defined poverty line. SCs and STs constitute 50 percent

of the total swarozgaries, women constitute 40 percent and disabled persons constitute 3 percent. The

scheme is a credit-cum-subsidy scheme, with the subsidy being uniform for all; i.e 30 percent of the

project cost subject to a maximum limit of Rs. 7,500. The same extends to 50 percent for the

depressed classes and disabled persons. For the SHGs too, the subsidy is 50 percent of the project

cost subject to per capita subsidy of Rs. 10,000 or Rs. 1.25 lakhs, whichever is less. 75 percent of the

project cost is borne by central government, while remaining 25 percent is borne by the state

government.The state government also undertakes some special projects under the SGSY. These

include marketing of rural goods to cities, installation of hydrams, milch livestock improvement,

dairy development, setting up of Grameen Labs for skill development of rural youth and promoting

diversification in agriculture for rural development. Also, a project entitled Gold mines was launched

under SGSY in 2000. Sanctioned by the Ministry of Rural Development the project received grants in

parts from the central and state governments, and a part from banks as a loan. Activities identified

under the Project are floriculture, mushroom cultivation and sericulture. Project Green Gold with

similar objectives is also being implemented in the district of Chamba.

4.5.4.3 Indira Aawas Yojna (IAY)

Thisis a centrally sponsored scheme for construction of new houses for the BPL households. With

effect from 2008, the scheme grants Rs. 38,500 per beneficiary. The Gram Sabha undertakes the

procedure of identifying the beneficiaries. The scheme is shared on a 75:25 cost basis between the

centre and the state respectively.

61

4.6 Odisha

Odisha, situated in the east coast of India, lies between 17° 49‘ and 22° 34‘ North latitudes and 81°

27‘ and 87° 29‘ East longitudes. It is bounded by the Bay of Bengal to the east and shares borders

with Chhattisgarh to the west. Jharkhand lies to its North, West Bengal to the North-Eastwhile

Andhra Pradesh bounds it towards the south. Its approximately 450 kilometers long coast line runs

along its eastern border.

For administrative purposes, Odhisha has been divided into 30 districts and 476 sub-districts. There

are 223 towns, 107 statutory towns and 116 census towns. The number of villages according to the

2011 census data is 51,313. From the year 2009-10, Odisha prepares its annual plans for all 30

districts one year in advance. Odisha was the first and only state to incorporate all district plans in the

State Plan, 2010-11.

4.6.1 Geography

The state is broadly divided into four physiographic zones on the basis of homogeneity in certain

specific features. These zones are: the Odhisha Coastal Plain in the East, the Middle Mountainous and

Highlands Region, the Central Plateaus and the Western rolling uplands and the major flood plains.

a) Odisha Coastal Plains: The Odisha Coastal plains are at a slight elevation from the sea level.

These occupy 26 percent of Odhisha‘s total land area. This region extends from the West

Bengal border, i.e from the river Subarnarekhain the north to river Rushikulya to the south.

The districts of Cuttack, Puri and Balasore constitute this zone.

b) The Middle Mountainous and Highlands Region:This region covers almost 32 percent of the

total area of the state. It mostly comprises of the hills and mountains of the Eastern Ghats

formed by parts of Ganjam, Phulbani (except Boudh), Koraput and Kalihandi districts. The

general elevation of the region is 900 meters above the mean sea level.

c) The Central Plateaus: Parts of Bolangir, Sambalpur, Boudh and Dhenkanal form the Central

plateaus of Odhisha. The elevation of this zone varies between 300-500 meters. The region

covers 24 percent of the total land area under the state.

d) The Western Rolling Uplands: With an elevation varying between 150 to 300 meters, the

Western Rolling Uplands are lower in heights than the other three physiographic divisions.

The climate of the state is tropical; generally marked by features like high temperature, high

humidity, medium to high rainfall and a mild winter. The state receives 1450 mm of rainfall between

June and September.


The total population of Odisha stands at 4,19,74,218. It ranks 11th among Indian states and UTs and

accounts for 3.47 percent to India‘s total population of 1.2 billion. The decadal growth rate of the

62

state according to the 2011 census is 14 percent. Although the trends show a decline in the growth

rate, but in absolute terms it implies that the population is steadily growing and it is only the rate of

growth that is declining. With this average, the state‘s population growth rate stands to be less than

national decadal population growth rate of 17.64 percent. The urban population grew at a rate of 26.9

percent in the 2001-11 decade, while the rural growth rate was recorded as 11.8 percent. Variation in

the decadal growth rate of the population in Odisha as compared to the national rate is reported in

Table 10.

Table 10: Population and its Growth Rate (1961-2011)

Census Years Odisha Decadal

Growth Rate (in %) IndiaDecadal Growth Rate (in %)

1961 - -

1971 25.05 24.80

1981 20.17 24.66

1991 20.06 23.86

2001 15.94 21.34

2011 14 17.64

Source: Census, 2011b


Out of the 41 million people, more than 83.31 percent of the total population resides in rural areas of

Odhisha, while only the remaining minor percentage of people are settled in the urban

agglomerations.

In 2001, literacy rate in Odisha stood at 63.08 percent of which male and female were 71.28 percent

and 50.51 percent literate respectively. Literacy rate in Odisha has seen upward trend as compared to

the previous year records. Close to 72.9 percent of the population is literate against the all India

literacy percent of 74 as per census 2011. Of that, male literacy stands at 81.6 percent while female

literacyis recorded to be 64 percent. The population density is 270 per sq km, which is lower than the

national average of 382 per sq km. The sex ratio in the state increased from 972 to 979 in the 2001-

2011 decade. The ratio is well above the national average of 943. In absolute terms, the number of

males is 17,586,203 while the number of females is 17,384,359. Interestingly, the sex ratio in rural

Odisha is much higher (989) than that of urban Odisha (932).The Scheduled Caste population

constitutes 17.1 % of the total population of the State. The proportion of the Scheduled Tribe

population to total population of the State is 22.8 %.Agriculture provides employment to more than

60 percent of the people. Orissa has made impressive achievements in terms of economic growth and

poverty reduction.The State grew at a rate of 9.51% per annum during the 10th Five Year Plan

against the targetof 6.20% and in comparison to 4.12% during the period from 1995-96 to 1999-2000.

4.6.4 Overview of Central and State policies for Rural Development in Odisha

The department for Rural Development in the state came into operation on 1st July 1990 to tackle

areas including minor irrigation and lift irrigation (later transferred toWater Resources Department in

63

1996), rural road, rural water supply and sanitation sectors. Currently, the Rural Development

Department consists of two organizations, (i) Rural Works and (ii) Rural Water Supply & Sanitation.

The Rural Work Organisation carries out development works in sectors including road, building and

electrification. Under rural connectivity, major schemes and programmes under implementation

include (Rural Development Department, 2014):

1. Pradhan Mantri Gram SadakYojana (PMGSY) at providing all weather connectivity to all

unconnected habitations having population 250 and above for IAP districts and population of

500 or more in general area and population of 250 or more in schedule area for non-IAP

districts.

2. Rural Infrastructure Development Fund (RIDF) that was made operational by the

NABARD for financing of the ongoing as well as the new infrastructure projects in rural areas

for upliftment of rural economy and eradication of poverty

3. Biju Setu Yojana (BSY) or construction of road bridges launched on October 9, 2011 for

construction of new bridges on RD roads and important P.S. roads to provide all-weather

connectivity to the rural areas of the State. 600 bridges are targeted for construction during

2011-12 and 2012-13 and 2013-14.

4. Constituency-wise Allotment (CWA) to take up critical projects in rural areas in consultation

with the local representatives.

5. Finance Commission Award Work (Roads & Buildings)

6. Special Repair (Roads & Buildings) for maintenance and upkeep of these roads and CD

works.

Under the construction and maintenance of government buildings in the state, the Rural Work

Organisation maintains 12,48,133 square meter plinth area of 12002 residential buildings and 36,

21,788 square meter (plinth area) of 15827 Non-Residential buildings.

The National Rural Drinking Water Programme (NRWDP) came into force in April 1, 2009 with

a vision "Safe Drinking Water for all, at all times in rural areas." Given the limited coverage of rural

population by Public Water Supply scheme, the "Jalamani" scheme was launched to provide value &

quality addition to the ongoing rural water supply programme in Schools. 3460 Schools of the State

have been provided with safe and potable drinking water in the year 2011-12 under this programme.

There have also been sufficient interventions to improve the availability of drinking water in rural

areas through regular rural water quality monitoring and surveillance. Infrastructure development at

District level laboratories has taken place with computerization, providing new equipment and

furniture and other necessary facilities.

Nirmal Bharat Abhiyan, Total Sanitation Campaign (TSC) was launched in 1999-2000 to design

strategies to motivate individual households so that they realize the need for good sanitation practices.

The campaign was implemented in collaboration with PRIs at all levels, through social mobilization

for construction of toilets and also maintain clean environment by way of safe disposal of wastes.

Initiatives as part of the campaign include:

64

- Behavior Change Communication (BCC) Activities

- Capacity Building

- School Sanitation & Hygiene Education

- Nirmal Gram Puraskar (NGP)

4.6.5 Policy initiatives for fulfilling the energy requirements of Odisha

Following are the ongoing scheme and projects towards strengthening the energy scenario as well as

ensuring stable and quality power at a reasonable cost:

1. Rajiv Gandhi Grameen Vidyutikaran Yojana (RGGVY)

2. Biju Saharanchala Vidyutikaran Yojana (BSVY)

3. Biju Gram Jyoti Yojana(BGJY)

4. Energy Conservation Measures

5. Samuka Beach Project

6. Electrification of IIT at Argul, Bhubaneswar

7. Special Project for KBK districts (RLTAP Scheme)

8. Small / Mini / Micro Hydro Power Development

9. Capital Expenditure (CAPEX) Programme for improvement of T&D System .

10. Special Programme for improvement in distribution system in Komna & Nuapara.

11. Underground Caballing System for grand road and Puri Temple Area.

12. System Strengthening for Elephant Corridor.

13. Capacity addition in Energy Sector through IPPs, UMPPS & NTPC.

14. Development Programmes for Energy System Improvement (DESI).

65

5. State Level Analysis – NSS and TERI data

The most comprehensive secondary data source available on energy parameters is the NSS and

Census data provided by the Ministry of Statistics and Programme Implementation, Government of

India. While the NSS and Census surveys are primarily consumption surveys, they also include data

on household fuel consumption and currently is the most comprehensive data source available on

household fuel use patterns. This chapter deals with analysis of NSS data over time, specifically

comparing the 55th

Round (1999-2000), 61st Round (2004-05) and 66

th Round (2009-10).

Comparisons are also made between the NSS data and the TERI survey data for the six states

surveyed as part of the study.

5.1 NSS data analysis17

As per the latest round (NSSO 2010; 66th Round), assuming that all the firewood reported is used for

cooking, about 29.50 kg of firewood and chips (per capita per month) are consumed in rural

households as compared to about 2.31 kg of LPG per capita for cooking purposes. Access to clean

energy fuels is a challenge among households particularly in the rural areas. The graph below

indicates the proportion of households having access to different fuel types in both rural and urban

areas. As can be noted, access to cleaner fuels is limited and the use of traditional biomass fuels is

predominant among rural households. The primary need for energy at the domestic level can broadly

be categorized under cooking and lighting needs.

The graph below (Figure 5) gives a picture of the distribution of fuel use across rural and urban India.

Among rural households, almost 76% households are still dependent on the most polluting traditional

biomass fuels to meet their cooking fuel requirements. The cleaner cooking fuels such as LPG have

very little coverage (about 12%) among rural households. In comparison, almost 65% urban

households indicate use of LPG as a cooking fuel.

Figure 5: Graph of Penetration rates of cooking fuel for 66th round

Source: NSSO 66th Round, 2009-2010

17 Detailed report for this section is available at http://www.teriin.org/projects/nfa/pdf/Working_paper4.pdf

75.92

12.09 6.11

0.79 2.46 1.53

17.56

64.6

1.38 6.38

0.92 6.55

01020304050607080

Fire

wo

od

an

dC

hip

s

LPG

Du

ngc

ake

Ker

ose

ne

Oth

ers

No

co

oki

ng

arra

nge

men

t

Pe

rce

nta

ge H

Hs

rep

ort

ing

pri

mar

y co

oki

ng

fue

l

Rural Urban

http://www.teriin.org/projects/nfa/pdf/Working_paper4.pdf

66

Figure 6: Graph of penetration rates of lighting fuel for 66th round (2009-10)

Source: NSSO 66th Round, 2009-2010

The disparity in case of lighting is shown in Figure 2. There is considerable difference among rural

and urban households in terms of access to cleaner energy sources, i.e. electricity. Only 65% of rural

households indicate electricity as their primary source of lighting. However, even this figure may not

be a true reflection of access to electricity as other factors such as regularity of supply also need to be

considered while drawing conclusions. In urban areas, in comparison, more than 90% households

have access to electricity as a primary source of lighting.

If we look at the story so far in India, the access situation while showing clear signs of improvement

in urban areas; is still a challenge in rural India. In order to be able to appreciate the challenge of

providing energy access in rural India, it is important to understand how energy transition has

occurred in rural India over the past decade. The figures below give a snapshot view of the number of

households reporting a particular fuel as the primary lighting or cooking fuel. In the case of cooking,

the fuels considered are firewood and LPG, whereas for lighting, kerosene and electricity have been

considered as they are the most common fuels used.

33.54

65.61

0.28 0.28 4.85

93.83

0.54 0.25 0

10

20

30

40

50

60

70

80

90

100

Kerosene Electricity No lighting arrangement Others

Pe

rce

nta

ge H

Hs

rep

ort

ing

pri

mar

y lig

hti

ng

fue

l

Rural Urban

67

Figure 7: Penetration rates of cooking fuels over time and across income classes

Source: 55th, 61st & 66th Rounds of NSSO (1999-2000, 2004-2005, 2009-2010)

Both Figures 3 and 4 indicate the percentage of households in rural India reporting a particular fuel as

the primary cooking fuel (firewood and LPG) and primary lighting fuel (Electricity and Kerosene)

respectively. The graphs plot the trends over time and across income classes18

. If we see the case for

cooking (Figure 3), we find that the intersection point between the graph for firewood and LPG

occurs only at the higher MPCE classes. This indicates that the switch from firewood to LPG is

occurring only among the higher income classes. Thus, in the case of cooking fuels, we find that there

is no real transition to in terms of access to modern cooking fuels.

Figure 4 indicates the penetration rates or the number of households per 1,000 households reporting a

particular fuel as the primary lighting fuel, in this case, kerosene and electricity. it can be noted that

while in the 55th round (1999-2000) the intersection point between the graph of kerosene and

electricity occurred around the 8th MPCE class; in the 61st round (2004-05) the intersection point

occurred around the 6th MPCE class; and, in the 66th round (2009-10) the intersection point occurred

around the 3rd MPCE class. This means that the switch (denoted by the intersection point of the

graph of kerosene and electricity) to modern lighting fuels is occurring at lower income classes over

time, indicating improved access to modern lighting fuels and a clear transition path.

18 Monthly Per Capita Expenditure (MPCE) classes have been taken as proxy for income by the NSSO survey for each household and

these are used to categorize the income classes with 1 being the lowest and 12 the highest. The data has been used from three NSSO

rounds: 55th Round (1999-2000); 61st Round (2004-05); and, 66th Round (2009-10).

68

Figure 8: Penetration rates of lighting fuels over time and across income classes

Source: 55th, 61st & 66th Rounds of NSSO (1999-2000, 2004-2005, 2009-2010)

The success in the case of lighting can be attributed to the national level Rural Electrification

Program (Rajiv Gandhi Grameen Vidyutikaran Yojana or RGGVY) that aims to provide electricity to

all villages in India. As of May 2012, Government of India had spent about Rs. 28, 265 Crores on

rural electrification under the RGGVY Program, covering over 3 lakh villages.

Clearly, the transition witnessed in lighting fuel usage is not replicated in the case of cooking fuels.

Providing access to modern cooking fuels and more importantly, effecting a transition towards

modern cooking fuels will remain a challenge in terms of energy access at the household level. With

respect to use of cooking fuels, the factors governing a household‘s decision to use a particular fuel

are very different from the case of lighting and these factors also differ from one region to another,

making the goal of energy access more challenging.

5.1.1 Energy Consumption Patterns across rural households

Energy consumption patterns differ for households across regions and income groups as well as over

time. To capture household energy transitions, it is very critical to understand the choices different

households belonging to different income groups vary with changes in the environment.

In this section, energy consumption patterns across income groups and across agro-climatic zones

would be analysed so as to get valuable insights into household energy choices which would help

inform policy.

66 61

55

69

The table below provides a snapshot of changes in consumption patterns in rural India of three

important fuels, namely, firewood, LPG, kerosene and electricity that are used predominantly for

cooking and lighting among rural households. In the case of firewood, we find that there is an

increase in household consumption from 1999-2000 to 2004-05 and then a slight decline in 2009-10.

It should be noted that the actual quantity of reported consumption level in 2009-10 was still higher

than that reported in 1999-2000. The overall consumption of firewood has in fact, increased in the

past decade by about 7.5%. In the case of electricity, there has been an increase in electricity

consumption by almost 25 – 30% overall. While for LPG, though there have been marginal changes

in consumption over time, it has remained more or less constant. (Refer Figure 9).We can categorize

the households into three different income groups namely: Low Income, Medium Income, and High

Income. The table (Table 11) below summarizes the changes in physical consumption of different

fuels over time.

Table 11: Fuel consumption patterns over time in Rural India

Low Income Medium Income High Income

Firewood Increase from 1999-00 to

2004-05 (95kg – 113kg);

Decline from 2004-05 to

2009-10 (113kg – 102kg) but

overall increase in the

decade

Significant increase from

1999-00 to 2004-05 (107kg -

125kg); lower mid-income

remain same from 2004-05 to

2009-10 (~120kg); high mid-

income indicates decline

from 2004-05 to 2009-10

(124kg – 115kg)

Drastic increase from 1999-00 to

2004-05 (112kg – 124kg) and

marginal decline in 2009-10

(~120kg); richer have higher

consumption

Electricity Increase from 1999-00 to

2004-05 (33kWh – 37kWh);

marginal increase from 2004-

05 to 2009-10

Increase over time (40kWh –

47kWh); low mid-income

indicate greater increase in

consumption (~6kWh) than

high mid-income

Increase over time (54kWh –

80kWh); significant increase from

2004-05 to 2009-10 (61kWh –

80kWh); for highest income group,

2004-05 and 2009-10 consumption

values converge (~80kWh)

LPG Increase from 1999-00 to

2004-05 (6kg to 8kg);

marginal change from 2004-

05 to 2009-10

Increase in consumption by

lower mid-income (7kg to

10kg); high mid-income

constant from 2004-05 to

2009-10 (~11kg)

No change from 2004-05 to 2009-10

(~10-11kg)

Kerosene Marginal decrease from

1999-00 to 2004-05 (3.17lts to

2.83lts); constant (~2.82lts)

from 2004-05 to 2009-10 ;

Similar trends for market

purchased kerosene

Significant decline in PDS

kerosene consumption from

1999-00 to 2004-05 (3.62lts to

3.20lts); further decline in

PDS kerosene consumption

from 2004-05 to 2009-10

(3.20lts to 2.98 lts); Similar

trends for market purchased

kerosene

Drastic decline in PDS kerosene

consumption from 1999-00 to 2004-

05 (4.29lts to 3.35lts) and further

decline from 2004-05 to 2009-10

(3.35lts to 3lts); Similar trends for

market purchased kerosene

Source: NSS Rounds 55th, 61st and 66th (1999-2000, 2004-2005, 2009-2010)

70

Figure 9: Consumption of Firewood, LPG and Electricity over time across income classes

Source: 55th, 61st and 66th Rounds of NSSO (1999-2000, 2004-2005, 2009-2010)

Figure 10: Consumption of Kerosene over time and across income classes

Source: 55th, 61st and 66th Rounds of NSSO (1999-2000, 2004-2005, 2009-2010)

Firewood consumption

0

10

20

30

40

50

60

70

80

90

0

20

40

60

80

100

120

140

1 3 5 7 9 11

Ele

ctri

city

(kW

h)

Fire

wo

od

an

d L

PG

(kg

s)

MPCE Class

FW55

FW61

FW66

LPG55

LPG61

LPG66

Elec55

Elec61

Elec66

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12

Ke

rose

ne

(lit

res)

MPCE Class

sko_PDS_66

sko_PDS_61

sko_PDS_55

sko_MKT_66

sko_MKT_61

sko_MKT_55

71

Among the Low Income groups we find that per household consumption has gone up from 95 kg per

month (55th Round, NSSO) to about 102 kg per month (66th Round, NSSO). Among the medium

income households, we find that there is a significant increase from 107 kg per month (55th Round,

NSSO) to about 125 kg per month (61st Round, NSSO) and then a decline to about 115 kg per month

by the 66th Round of NSSO. Among the Medium Income category, those in the lower income

bracket indicate a more or less constant consumption level of firewood per month over the three

rounds whereas for those in the upper income brackets in this category, there has been a decrease

from about 124 kg per month (61st Round, NSSO) to about 115 kg per month (66th Round, NSSO).

In the High Income group, there has been an increase in consumption over time from about 112 kg

per month (55th Round, NSSO) to about 120 kg per month (66th Round, NSSO). The households in

the higher end of the spectrum of this category indicate higher levels of consumption.

Electricity Consumption

There has been an increase in consumption of electricity across all income groups over the three

rounds analysed. Households belonging to the Low Income category saw a 12% increase in electricity

consumption, i.e. from 33 KWh (55th Round NSSO) to 37KWh (66th Round, NSSO) per household

per month. Among the Medium Income households, there was a significant increase in electricity

consumption over time. Households in the lower spectrum of this category saw a 15% increase while

households in the higher income spectrum of the Medium Income category saw a 21% increase in

electricity consumption. As for the High Income category, there has been a considerable jump in

electricity consumption with households in this category indicating an increase by almost 48%.

Though, from the 55th to the 61st Round, there was only a marginal change in electricity

consumption; the last 5 years (from the 61st to the 66th Round) have seen a significant increase of

almost 28%. Also, for the highest income households, they seem to be already consuming an

optimum of 80KWh per month as the trend lines of the all the three rounds converge at that point.

Though there has been an increase in consumption of electricity across all income classes, the

measure has not been much for the low income households. The impact of improved electricity

access is seen more among the high income and middle income households.

LPG Consumption

Consumption of LPG has some interesting observations if we look at the NSSO rounds over time. All

the income groups indicate no increase in LPG consumption from the 55th to the 61st while from the

61st to the 66th round there is a decline in LPG consumption. Assuming sampling bias, it still means

that there is no increase in consumption over time or rather, household consumption of LPG has more

or less remained constant over the past decade. Unlike the other fuels, penetration of LPG has also

not increased significantly, thus bringing up the issues of access and service delivery. If we see the

Lower Income households, their LPG consumption is constant at about 6 – 8kg per month; the

medium income households consume about 10kg per month; and, the high income households

consume about 10kg per month. There has been no significant change in LPG consumption which is a

cleaner fuel. Unlike electricity, which shows considerable improvement over the years, the transition

to LPG has not occurred as expected.

72

Kerosene Consumption

Consumption of kerosene is seen to significantly fall over time across all income groups. This could

be attributed to the increasing access to electricity and LPG; more so with the access to electricity.

Consumption of kerosene falls drastically among the higher income groups. Kerosene is available to

consumers through the PDS which provides a fixed amount at a subsidized rate every month as well

as the market where the rates are higher. There is more than a 50% subsidy on kerosene supplied

through the PDS mechanism.

Since kerosene is mainly used to meet lighting needs at the household level in rural areas, improved

electricity access will lead to lower kerosene consumption by virtue of it being a close substitute to

electricity. There is a significant decrease in kerosene consumption of about 30% and 41% among

higher income households over time (1999-00 to 2009-10) for both PDS and Non-PDS kerosene

respectively. The low income households report a decrease in PDS kerosene consumption of about

10% while in contrast the Non-PDS kerosene consumption indicates a decline of about 30% in the

period 1999-00 to 2009-10. Among the medium income households, the reported decline in PDS

kerosene is about 17% while the reported decline in Non-PDS kerosene consumption is about 20%

during the same period.

5.2 Comparison of NSS and TERI surveys

The table below indicates the comparisons of average fuel consumption figures across the states

surveyed in the course of the project and the corresponding observations from the National Sample

Survey (66th Round, 2009-10).

Table 12: Average Fuel Consumption (as per NSSO 66th Round, 2009-10 and TERI Survey, 2013)

Avg. MPCE

(RS) FW (kg)

Electricity

(kWh)

Kerosene

PDS (ltr)

Kerosene

Non-PDS (ltr)

LPG

(kg)

National Average

(Rural)

1019 115 42 2.21 0.46 2.11

Maharashtra (NSS) 1083 94.6 42.8 3 0.47 1.9

Maharashtra (TERI) 957 57.19 45.2 1.88 0.35 2.83

Rajasthan (NSS) 1062 177.85 57 2.9 3.2 7.9

Rajasthan (TERI) 1620 229.097 129 2.36 0.19 3.26

Goa (NSS) 1592 167.7 136 3.9 3.86 12.02

Goa (TERI) 1643 197.66 167.2 0.49 1.32 9.29

Karnataka (NSS) 870 206.4 41 2.96 3.9 10.7

Karnataka (TERI) 1109 195 41.8 2.48 0.4 10

Himachal Pradesh

(NSS)

1560 202 113.76 3.91 6.4 7.55

73

Avg. MPCE

(RS)

FW (kg) Electricity

(kWh)

Kerosene

PDS (ltr)

Kerosene

Non-PDS (ltr)

LPG

(kg)

Himachal Pradesh

(TERI)

2179 168.23 103.81 0.97 0.06 5.68

Odisha (NSS) 950 159.687 73.61 2.59 1.9 10.9

Odisha (TERI) 1445 156.19 47.03 2.28 0.46 8.63

Source: NSS Rounds 66th (2009-10); TERI Survey 2013

5.2.1 Maharashtra

The NSS (66th

Round) data for rural households in Maharashtra indicates an average firewood

consumption of about 95kg per household per month; average PDS kerosene consumption of 3 litres

per household per month as compared to 2 litres as per the TERI Survey; average market kerosene

consumption of about 0.5 litres per household per month; average LPG consumption of about 2kg

(1/7th

of a cylinder) per household per month as compared to 2.83kg as per the TERI Survey; and, an

average electricity consumption of about 43kWh per household per month. The average rural per

capita income is about Rs. 1083 per month (NSS 66th

Round) while the TERI survey indicated an

average MPCE of Rs. 957 which was found to be statistically significant.

5.2.2 Himachal Pradesh

The average MPCE as per the NSS (66th

Round) in the case of Himachal Pradesh is Rs. 1560, which

is lower than the Rs. 2179 from TERI‘s survey. NSS data indicates that consumption of firewood is

202kg per household per month, consumption of kerosene is approximately 4 litres per household per

month from PDS while it is 6.4 litres from elsewhere, and that of LPG is 7.55 kg per household per

month. The corresponding numbers from the TERI Survey for firewood, PDS kerosene, non-PDS

kerosene and LPG are 169kg, 1 litre, 0.06 litres and 5.7 kg respectively. The consumption of

electricity is 114 kWh per household per month as per NSS, while the TERI survey revealed it was

104 kWh.

5.2.3 Goa

The NSS (66th

Round) revealed that Goa‘s average MPCE was Rs. 1592, compared to Rs. 1643 as per

TERI‘s survey. The firewood consumption is 168 kg per household per month as per the NSS,

compared to the 198 kg as per TERI‘s survey. The consumption of PDS kerosene and non-PDS

kerosene are both 3.9 litres per household per month as per the NSS, while they are 0.5 and 1.3 litres

respectively according to TERI‘s survey. The electricity consumption is 139 kWh per household per

month and 167 kWh respectively for the two surveys.

5.2.4 Karnataka

Karnataka‘s average MPCE is Rs. 870 as per the 66th

Round of NSS, compared to Rs. 1109 as per

TERI‘s survey. Firewood consumption in the state is at 206 kg per household per month and 195 kg

as per the two surveys respectively. PDS kerosene consumption is 3 litres per household per month as

74

per NSS while it is 2.5 litres as per TERI‘s survey. The corresponding numbers for non-PDS

kerosene is 3.9 and 0.5 litres respectively. The NSS 66th

Round and TERI‘s survey reveal similar

levels of LPG and electricity consumption in the state of Karnataka. LPG consumption is 10.7 kg per

household per month and 10 kg while electricity consumption is 41 kWh and 41.8 kWh per

household per month respectively across the two studies.

5.2.5 Rajasthan

Rajasthan‘s average MPCE as per NSS‘ 66th

Round was Rs. 1062 compared to Rs. 1620 in TERI‘s

Survey. Firewood consumption in the two surveys is 178 kg and 229 kg per household per month

respectively. Kerosene sold via PDS is at 2.9 litres per household per month according to the NSS

survey while it was 2.4 litres in the TERI Survey. The figures for non-PDS kerosene for the two

surveys are 3.2 and 0.2 litres per household per month respectively. The consumption of LPG in

Rajasthan is 7.9 kg per household per month as per NSS, while it was 3.2 kg in the TERI survey.

Corresponding numbers in the case of electricity are 57 kWh and 129 kWh per household per month

respectively.

5.2.6 Odisha

NSS‘ 66th

Round indicated that Odisha‘s average MPCE was Rs. 950 compared to Rs. 1445 in

TERI‘s survey. The consumption per household per month of firewood, PDS kerosene, non-PDS

kerosene and LPG is 160 kg, 2.6 litres, 1.9 litres and 11 kg respectively in the case of the NSS.

TERI‘s survey numbers for these fuels were 156 kg, 2.3 litres, 0.46 litres and 8.63 kg per household

per month respectively. The consumption of electricity is 73.6 kWh as per the NSS while it is 47 per

TERI‘s survey.

5.3 Energy Transitions

5.3.1 Maharashtra

Cooking energy consumption patterns and transitions

As per the Census data (2011), firewood remains the main source of cooking for about 70% rural

households in Maharashtra, while including use of dung-cake and crop residue, almost 80% rural

households are dependent on biomass as the primary source for cooking. About 18% report LPG as

the primary cooking fuel while biogas is reported as a primary cooking fuel by 2% households.

If we look at gross consumption figures, we find that rural households in Maharashtra are reporting

about 95kg per household per month (NSS 66th

Round) while the TERI survey indicates about 58kg

per household per month. In the TERI survey, household consumption of firewood, agriculture

residue and dung-cake have been distinguished and the data has been collected accordingly. In terms

of calorific value, we find that both the NSS as well as the TERI estimate of biomass consumption are

similar, thus indicating the possibility of a difference in the manner of reporting in the NSS as

compared to the TERI survey.

75

The fuels used for cooking as reported in the survey conducted are firewood, agricultural residue,

dung-cake, LPG, kerosene from market and PDS. The share of fuel mix (TERI Survey, 2013) shows

that biomass (firewood, agricultural residue and dung-cake) still remains the major cooking fuel for

majority of the households in Maharashtra.

Figure 11: Average share of cooking fuels in Maharashtra

Source: TERI Survey, 2013

Figure 12 provides a snapshot view of the number of households reporting a particular fuel as the

primary cooking fuel. The fuels considered are firewood and LPG as they are the most common fuels

used and in terms of cooking energy transitions, an ideal transition to clean energy would be one from

firewood to LPG.

Figure 12: Cooking Energy Transitions in rural Maharashtra over time

Source: NSS 55th, 61st and 66th Rounds (1999-2000, 2004-2005, 2009-2010)

FW

54%

AGRIRES

18%

DUNG 12%

LPG

13%

SKO-PDS

3%

SKO-MKT

0%

FW

AGRIRES

DUNG

LPG

SKO-PDS

SKO-MKT

0

100

200

300

400

500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Nu

mb

er o

f H

ou

seh

old

s p

er 1

00

0

MPCE Class

FW-55

FW-61

FW-66

LPG-55

LPG-61

LPG-66

76

Figure 13: Cooking Energy Transitions in rural Maharashtra over time (based on TERI Survey)


Figures 12 and 13 indicate the percentage of rural households in Maharashtra reporting firewood or

LPG as the primary cooking fuel. The graphs represent the trends over time and across income

classes19

. Examining the case for cooking (Figure 12), we find that the intersection point between the

graph for firewood and LPG occurs only at the higher MPCE classes. This indicates that the switch

from firewood to LPG is occurring only among the higher income classes. Though, it should be

pointed out here that as compared to overall rural India figures, rural Maharashtra performs better. It

can be seen from the above figure that in rural Maharashtra, the transition towards LPG was

occurring at the richest income class (MPCE class 12) in 1999-00, while, from 2004-05 onwards, this

switch is occurring around MPCE class 10 and 11. There has been no significant shift in the transition

since 2004-05 as the transition point as per 2009-10 data also occurs around MPCE class 10 and 11.

Thus, in the case of cooking fuels, we find that there is no real transition to in terms of access to

modern cooking fuels.

Lighting energy consumption patterns and transitions

As per the Census (2011) data, about 74% rural households indicate electricity as their primary

source of lighting. About 24% report kerosene as the primary lighting fuel while use of other fuels is

reported as a primary lighting fuel by 2% households.

19 Monthly Per Capita Expenditure (MPCE) classes have been taken as proxy for income by the NSSO survey for each household and

these are used to categorize the income classes with 1 being the lowest and 12 the highest. The data has been used from three NSSO

rounds: 55th Round (1999-2000); 61st Round (2004-05); and, 66th Round (2009-10).

0

100

200

300

400

500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Nu

mb

e o

f h

osu

eh

old

s p

er 1

00

0

MPCE Classes

FWc

LPGc

FW5

LPG5

FW10

LPG10

77

If we look at gross consumption figures, we find that rural households in Maharashtra are reporting

consumption of about 43 units (kWh) per household per month of electricity (NSS 66th

Round) while

the TERI survey indicates about 45 units (kWh) per household per month.

The fuels used for lighting reported in the survey conducted are electricity and kerosene from market

as well as PDS. The share of fuel mix (TERI Survey, 2013) indicates electricity as the primary

lighting fuel for majority of the households in rural Maharashtra.

Figure 14 below provides a snapshot view of the number of households reporting a particular fuel as

the primary lighting fuel. The fuels considered are kerosene and electricity as they are the most

common fuels used and in terms of lighting energy transitions, an ideal transition to clean energy

would be one from kerosene to electricity.

Figure 14: Lighting Energy Transitions in rural Maharashtra over time


0

100

200

300

400

500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Nu

mb

er o

f H

ou

seh

old

s p

er

10

00

MPCE Class

SKO_66

SKO_61

SKO_55

ELEC_66

ELEC_61

ELEC_55

78

Figure 15: Lighting Energy Transitions in rural Maharashtra over time (based on TERI Survey)


Figure 14 and 15 indicate the percentage of rural households in Maharashtra reporting kerosene or

electricity as the primary lighting fuel. The graphs represent the trends over time and across income

classes. Examining the case for lighting (Figure 14), we find that the intersection point between the

graph for kerosene and electricity occurs at the lower MPCE Classes. This indicates that the switch

from kerosene to electricity is occurring among the lower income classes. It can be seen from the

above figure that in rural Maharashtra, the transition towards electricity was occurring at MPCE class

2 in 1999-00 and 2004-05, while beyond that this switch is occurring around MPCE class 1. In terms

of lighting transition from kerosene to electricity, in Maharashtra, electricity access has been provided

to the poorest of the poor. Thus, in the case of lighting fuels, we find that the transition to modern

lighting fuels has been successful.

79



Figure 16: Average share of cooking fuels in Himachal Pradesh


TERI Survey (2013) shows that biomass (firewood, agricultural residue and dung-cake) is the

primary cooking fuel for majority of the households in Himachal Pradesh, with firewood alone being

consumed by 76% of the households. LPG on the other hand was consumed by 18% of the

households. TERI‘s survey data also showed that the average firewood consumption was 168kg per

household per month, while it was 202 kg per household per month as per NSS 66th

Round. The

corresponding figures for LPG in the two surveys were 5.7 kg and 7.5 kg per household per month.

Figures 17 and 18plot the number of households across the MPCE classes reporting a particular fuel

as the primary cooking fuel. The fuels considered are firewood and LPG as they are the most

common fuels used and in terms of cooking energy transitions, an ideal transition to clean energy

would be one from firewood to LPG.

FW

76%

Dung cake

5%

LPG

18%

Kerosene

1%

FW

Dung cake

LPG

Kerosene

80

Figure 17:Cooking Energy Transitions in rural Himachal Pradesh over time (NSS Data)


Figure 18: Cooking Energy Transitions in rural Himachal Pradesh over time (based on TERI Survey)


Data from NSS survey shows that there is a switch of fuels at the higher MPCE classes in Himachal

Pradesh. The switch from firewood to LPG is occurring in MPCE class 12 in all the three NSS rounds

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in consideration, however, the point of intersection moves marginally towards the lower end of the

12th

MPCE class between 2004-05 and 2009-10. This indicates that not enough progress has been

made in energy use transitions from traditional to modern fuels in the case of cooking. TERI‘s survey

data on the other hand does not show an intersection between the lines representing the number of

households per 1000 whose primary fuels are LPG and firewood. This indicates that no transition

takes place even at higher MPCE classes, even though the lines begin to converge after MPCE class

10. While this may be a result of sampling, but the overall trend in the case of NSS and TERI survey

is similar.


TERI‘s survey data indicates that the electricity consumption in Himachal Pradesh is 113.8 kWh per

household per month, which is higher than the national rural average of 42 per kWh per month.

Further, the figures below (Figures 19 and 20) represent the number of households reporting

kerosene and electricity as the primary lighting fuel. Both NSS 66th

round and the TERI data as of

2013-14 reveal that electricity is the primary lighting fuel for most households, while very few

households report kerosene as the primary lighting fuel in their households. In the previous rounds of

NSS – from the 61st and the 55

th rounds – there were also no points of intersection between the graphs

representing kerosene and electricity, indicating that more houses considered electricity their primary

lighting fuel at every MPCE class. However, the number of households that consider electricity as

their primary fuel has increased across all income classes over the years, indicating that the state has

made progress in improving access to electricity.

Figure 19: Lighting Energy Transitions in rural Himachal Pradesh over time


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Figure 20: Lighting Energy Transitions in rural Himachal Pradesh over time (based on TERI Survey)


5.3.3 Goa


Figure 21: Average share of cooking fuels in Goa


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2%

LPG

30%

Kerosene

1%

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Agri Residue

Dung cake

LPG

Kerosene

83

TERI‘s survey data reveals that biomass is consumed in 60% of all households surveyed in the state

of Goa, while LPG constitutes 30% of the share. TERI‘s survey data also showed that the average

firewood use in the state was 197.7 kg per household per month, which is higher than the national

average of 115kg. Similarly, LPG use in Goa is on average 9.3 kg per household per month, which is

more than the national average of 2.1 kg. Firewood and LPG consumption according to the 66th

round

of NSS in Goa is 168 kg and 12 kg per household per month respectively.

Figures 22 and 23 below provide a glimpse into the number of households reporting firewood and

LPG as their primary cooking fuel. Only firewood and LPG are considered as they are the most

common fuels used and an ideal transition to clean energy would be one from firewood to LPG.

Figure 22: Cooking Energy Transitions in rural Goa over time (NSS Data)


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Figure 23: Cooking Energy Transitions in rural Goa over time (based on TERI Survey)


NSS data from Goa shows that switching from firewood to LPG has moved from the 10th

MPCE class

during the 55th

round (1999-2000) to 8th

-9th

MPCE class during the 66th

round (2009-10). This shows

that energy transitions from traditional to modern fuels have moved to lower MPCE classes.

However, no such switching was revealed in TERI‘s survey, with firewood or LPG or both being the

primary fuel across all MPCE classes. In spite of no such evident switch, LPG use in Goa remains

high across all MPCE classes and one of the highest among all states.


In Goa, NSS 66th round data showed that the average consumption of electricity was 57 kWh per

household per month. TERI‘s survey data indicated that the consumption was 129 kWh per

household per month. The national rural average happens to be 42 kWh per household per month.

In the figures below (Figure 24 and 25), which represent the number of households per 1000 that

report a particular fuel as the primary lighting fuel, we see that electricity is considered as the primary

fuel across all MPCE classes in both the NSS and TERI data. TERI‘s data in particular reveals that

among the higher MPCE classes, nearly all households reported that electricity is their primary fuel.

Further, the lines representing kerosene and electricity move further apart over the years, indicating

that the access to electricity has improved over time.

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Figure 24: Lighting Energy Transitions in rural Goa over time


Figure 25: Lighting Energy Transitions in rural Goa over time (based on TERI Survey)


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FW

86%

Agri Residue

6%

Dung cake

3%

LPG

2%

Kerosene

3%

FW

Agri Residue

Dung cake

LPG

Kerosene

5.3.4 Karnataka


Figure 26: Average share of cooking fuels in Karnataka


In Karnataka, 86% of the households consumed firewood, compared to only 2% consuming LPG,

according to the TERI survey. The survey also showed that the average consumption of firewood in

the state was 195 kg per household per month, while according to the NSS (66th

round), it was 206

kg. This is far higher than the national average of 115 kg. On the other hand, the average LPG

consumption in Karnataka was 10 kg and 11 kg per household per month according to the TERI and

NSSO surveys, which is higher than the national average of 2.1 kg.

87

Figure 27: Cooking Energy Transitions in rural Karnataka over time (NSS Data)


Figure 28: Cooking Energy Transitions in rural Karnataka over time


Further, data from NSS shows that fuel switching – from firewood to LPG – took place in the lower

11th

MPCE class in 2009-10 (66th

Round), which was a marginal improvement from the switch taking

place in the upper 11th

MPCE class in 2004-05 (61st round). In 1999-2000 (55

th Round), on the other

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88

hand, there is no intersection, implying that no fuel switching was taking place. However, TERI‘s

data shows that no fuel switching has been taking place in Karnataka all through 2003-04, 2008-09

and 2013-14, which were the three time periods in consideration in the survey.


In Karnataka, the average consumption of electricity was 42 kWh per household per month as per the

TERI survey, which is similar to the national rural average of 42 kWh.

In the analysis of the proportion of households which use kerosene and electricity as their primary

fuels, it is revealed that in both the NSS survey and the TERI survey, electricity is considered the

primary fuel over kerosene across all MPCE classes. Further, the lines from the different NSS rounds

move towards greater use of electricity across all MPCE classes over time, implying that energy

access has improved over the years. Both the NSS and TERI data indicate similar trends in the case of

lighting transitions.

Figure 29: Lighting Energy Transitions in rural Karnataka over time


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Figure 30: Lighting Energy Transitions in rural Karnataka over time (based on TERI Survey)


5.3.5 Rajasthan


The cooking energy basket (Figure 31) for sample households surveyed in Rajasthan is dominated by

firewood, followed by dung cake and then LPG. The low share of kerosene (both PDD and Market) is

on account of the irregular availability across the state.

Figure 31: Average share of cooking fuels in Rajasthan


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66%

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25%

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8%

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1%

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0%

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DUNG

LPG

SKO-PDS

SKO-MKT

90

Figure 32 provides the cooking energy consumption pattern across MPCE classes. Firewood

constitutes above 60% of the energy mix for all class up to 10, with a similar share of dung cake

throughout. Only in the higher MPCE classes of 11 and 12 does firewood get replaced by an

increases LPG share.

Figure 32: Cooking Energy Transitions in rural Rajasthan over time (NSS Data)


Figure 33: Cooking Energy Transitions in rural Rajasthan over time (based on TERI Survey)


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91

The above graphs indicating the percentage of households that report to be using firewood or LPG as

per NSS and TERI survey respectively. However, in neither of the figures do the graphs for firewood

and LPG intersect to suggest any shift from firewood to LPG among any income class. In comparison

to the rural India consumption pattern, Rajasthan perform worse, as the number of households

consuming firewood continues to be above LPG consumers across all income classes. While

households consuming LPG increases and those consuming firewood decreases with increasing

income classes, the respective graphs corresponding to every round moves closer as we move to

recent years. Thispoints to the reducing gap between the household using two fuels.

In the graph describing NSS cooking energy patterns (Figure 32), not much change was seen in the

households consuming firewood in the 55th (1999-2000) and 61st (2004-05) round, while a moderate

increase in the number of LPG consumers was observed. However, a noticeable shift towards LPG

and away from firewood was seen among the highest income classes (MPCE class 11 and 12) for the

66th (2009-10) round. The TERI survey, mapping the cooking energy consumption for Rajasthan

over the past 5-10 years, show that while households reporting to consume LPG has marginally

increased over the years and with increasing income classes, those consuming firewood saw a drastic

fall in number in 2013 as compared to previous five years. This fall is more in the higher income

classes. Overall, the state has not seen a significant change in the share of households opting for LPG

and at the same time opting out of firewood.


As per the NSS 66th round, the electricity consumption in Rajasthan averaged at 57 kWh per

household per month, while the same as per TERI Survey stood at 129 kWh per household per

month. Figure 34 (below) indicates that a switch (from kerosene to electricity as primary lighting

fuel) in the proportion of households is seen to be achieved at lower income classes as we move from

the 55th (1999-2000) and 61st (2004-05) to the 66th (2009-10) NSS round. That is the point of

intersection in 1999-2000 and 2004-05 was in the 8th MPCE class, while for 2009-10 the intersection

takes places in the 4th MPCE class. This clearly indicates in the successful efforts in ensuring

electricity for all by the state government. Post the point of switch, it is evident that with increasing

income classes the proportion of households consuming electricity increases replacing increasingly

kerosene quantities.

92

Figure 34: Lighting Energy Transitions in rural Rajasthan over time


The consumption pattern extracted from TERI survey reveals a slightly different picture in terms of

the switch to a cleaner fuel that takes place between those consuming electricity and kerosene. While

5 to 10 years, number of households consuming kerosene was higher than those consuming electricity

across all income classes. Interestingly, as per current lighting fuel consumption trend, the proportion

of those consuming electricity remained higher than those consuming kerosene, which is seen to

increase after the 8th MPCE class. A major increase in electricity access took place over the past five

years from 2008-09 such that all household underwent the switch in fuel use.

Figure 35: Lighting Energy Transitions in rural Rajasthan over time (based on TERI Survey)


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5.3.6 Odisha


Figure 36: Average share of cooking fuels in Odisha


TERI‘s survey showed that 81% of households in Odisha consumed firewood, while LPG was

consumed in only 4% of the households. Average consumption of firewood was 156 kg per

household per month, which is higher than the national average. The consumption of LPG, at 8.6kg

per household per month, is also higher than India‘s national average of 2.11 kg, even though the

percentage of houses using LPG is low.

Figure 37: (above) Cooking Energy Transitions in rural Odisha over time (NSS Data)

FW

81%

Agri Residue

4%

Dung cake

9%

LPG

4%

Kerosene

2%

FW

Agri Residue

Dung cake

LPG

Kerosene

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Figure 38: Cooking Energy Transitions in rural Odisha over time (based on TERI Survey)


NSS data shows that in Odisha, no fuel switching – from firewood to LPG – took place in 2009-10

and in 1999-2000, as was revealed by the 66th

and the 55th

Round of NSS respectively. However, fuel

switching did take place in the 10th

MPCE in 2004-05, as revealed by the 61st Round of NSS. The

NSS data shows a temporary transition to increased LPG use, but one that could not be sustained,

thus, in effect, a reverse transition of sorts. However, TERI‘s survey does not reveal any fuel

switching between firewood and LPG in the three years in consideration. The TERI survey data also

indicates a sudden reduction in firewood consumption in the period corresponding to 2008-09. A look

at the data indicates that firewood during this time was substituted by many households by

agricultural residue and other locally available lower grade biomass fuels due to increasing prices of

firewood. Thus, we see that cooking transition is lagging behind significantly in Odisha.


In Odisha, electricity consumption is on average 73 kWh per household per month as per the NSS

66th

round, while it is 47 kWh per household per month as per TERI‘s survey. The national rural

average is 42 kWh per household per month.

Further, NSS data reveals that the lines that represent the proportion of households which consider

electricity and kerosene as their primary fuels intersect at the 4th

MPCE class in 2009-10. This point

of intersection was in the 6th

MPCE in 2004-05 and in the 9th

MPCE class in 1999-2000. These points

of intersection represent the points at which households switch from one fuel to another: in this case,

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from kerosene to electricity, which is a cleaner and modern source of energy. It goes to show that

efforts to improve energy access over the years by the state government have proven to be successful.

Since the graph lines diverge with increasing incomes after the point of intersection, it can also be

inferred that as households move to higher income brackets, they are able to replace kerosene with

electricity more effectively, leading to fuller transitions towards the cleaner fuel. The TERI survey

indicates that there is a switch from kerosene to electricity between the 2nd

and 3rd

MPCE classes, a

further improvement over the 66th

NSS round.

Figure 39: Lighting Energy Transitions in rural Odisha over time


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Figure 40: Lighting Energy Transitions in rural Odisha over time (based on TERI Survey)


5.4Energy Inequalities

While rigorous econometric models are useful in explaining the determinants of household fuel

choices, it is also important to understand the inequalities in energy consumption, the insights of

which when combined with other forms of analysis including the econometric models will give a

holistic picture of the challenges of achieving universal energy access and also help inform policy and

planning.

We have used the Gini coefficient to estimate both income and energy inequality across the pilot

sites. The energy inequality has been looked at separately for biomass fuel consumption (firewood,

dung cake and crop residue), petroleum fuel consumption (kerosene and LPG), and electricity

consumption. The figures below plot the energy inequality measures for different fuel types across

income classes.

5.4.1 Maharashtra

Figure 41: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc)


The above graph (Figure 41) shows that the inequality in consumption of biomass fuels increases for

households which are transitioning across income groups, that is, those in income classes 3, 6 and 9

wherein, income class 3 corresponds to households transitioning from low-income to middle income;

income class 6 corresponds to households transitioning from low-middle income to high-middle

income; and, income class 9 corresponds to households transitioning from middle income to high

income.

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GINI_Bz

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It has been established that higher income households have wider choices unlike lower income

households as found in the analysis20

of the National Sample Survey data (2009-10), i.e. it was found

that as incomes increased, the GINI coefficient also increased indicating that higher income

households were using biomass more out of choice rather than compulsion as compared to lower

income households.

In the case of the Maharashtra, it was found from the data collected from the survey, that households

in a transitionary phase in terms of income indicated greater variation in use of biomass fuels for

cooking, thus indicating that every time a household moved from low income to middle income or

middle income to high income, there was a shift in the energy basket towards fuels other than

biomass.

Figure 42: Inequality in LPG consumption (GINI_Pz) and income (GINI_Inc)


In the case of LPG consumption for Maharashtra (Figure 42), it was found that the inequality in

consumption of petroleum fuels decreases with increase in incomes thus indicating that higher

income households converged to similar consumption patterns of LPG, unlike the trends indicated by

National Sample Survey data wherein it was found that as incomes increased, the GINI coefficient

also increased indicating higher inequalities at higher incomes.

20 TERI-NFA Working Paper 4

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Figure 43: Inequality in electricity consumption (GINI_Elec) and income (GINI_Inc)


Similarly, considering the GINI for electricity consumption across income groups, the National

Sample Survey data indicated that as incomes increased, the inequality in electricity consumption

decreased with households converging to similar patterns of consumption, thus also implying that

electricity access was a function of the household‘s ability to pay, in other words, its economic status.

In the case of Maharashtra, the survey data indicates that the inequality levels in electricity

consumption remains more or less the same across income classes, indicating that the level of access

across income groups is almost similar. This means that the state government of Maharashtra has

been able to provide similar access to electricity across all income groups, a positive sign in terms of

policy effectiveness and implementation. Additionally, the state also exhibits low levels of income

inequality overall, as can be seen in the Lorenz Curve below.

Figure 44: Lorenz curve for Income Inequality


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0.00 0.01 0.06 0.13 0.16 0.26 0.31 0.44 0.52 0.68 0.79 1.00

REFLINE

INCSHARE

99


Figure 45: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) in Himachal Pradesh


TERI data shows that there is relatively high inequality in biomass consumption in the 3rd

, 7th

, 9th

and

10th

, and the 12th

MPCE classes. Again, this shows that there is high inequality when households are

transitioning to higher income groups. The 3rd

MPCE class corresponds to the transitioning from the

lower income to middle income classes; the 7th

class corresponds with the movement from lower

middle to higher middle income class, while the 9th

and 10th

correspond with the movement to the

higher income class. Households in income transition phase indicate greater variation in the use of

biomass for cooking, indicating that there is a shift in the energy basket towards fuels other than

biomass. Further, since the highest income classes have a wider array of choices, they are likely to

consume biomass out of choice rather than in compulsion. Thus, the inequality in the consumption of

biomass will be high in the highest income classes.

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In the case of petroleum based fuels used in cooking (LPG and kerosene), we see a fall in inequality

with the rise in income. LPG in particular, which is a costlier fuel than biomass, is less likely to be

used in the lower income classes, leading to higher inequalities in the use of petroleum fuels in the

lower income classes. However, as incomes rise, households are likely to consistently use certain

amounts of such fuels (while possibly still consuming biomass based fuels complimentarily), leading

to low inequality in the use of petroleum fuel in general among higher income classes.

Figure 47:Inequality in electricity consumption (GINI_ELEC) and income (GINI_Inc)


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I

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101

When it comes to electricity consumption, there are very low levels of inequality across all MPCE

classes. This is due to the fact that across all income groups in Himachal Pradesh, nearly all

households consider electricity as their primary lighting fuel (as seen in 5.3.2) and the data also

indicates a significant rise in electricity consumption from the 2009-10 (NSS 66th

Round) to 2013-24

(TERI Survey), thus leading to a lower GINI value as most households are converging to similar

consumption patterns across income classes.

Additionally, the state also exhibits low levels of income inequality overall, as can be seen in the

Lorenz Curve below.



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0.06 0.11 0.28 0.31 0.43 0.51 0.60 0.70 0.83 0.90 0.95 1.00

INCSHARE

REFLINE

102

5.4.3 Goa

Figure 49: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) in Goa


In Goa, inequality in the consumption of biomass, as measured by the GINI index, marginally

increases with the increase in MPCE classes, with the exception of the 11th

MPCE class. Inequality

increases with income as households with higher incomes have a wider range of cooking fuel options

and are likely to use biomass out of choice and not just out of compulsion.



0

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103

When it comes to petroleum fuels, we see a fall in the inequality of its use with the rise in income

classes. Petroleum fuels are less likely to be used in the lower income households, leading to higher

inequalities in such classes. As discussed previously, as incomes rise, households are likely to

consistently use certain amounts of such fuels, leading to low inequality in the use of petroleum fuel

in general among higher income classes. However in the case of Goa, we also notice that after falling

from MPCE class 1, inequality remains low and stable until class 11, after which it falls to negligible

levels in MPCE class 12. As mentioned earlier, in Goa, 30% of all households use LPG, which is the

highest among the states in this study. After households reach the middle income levels, their

consumption of petroleum fuels becomes consistently high, leading to low inequality in petroleum

fuel consumption.



The inequality in the consumption of electricity is constant in the case of Goa, marginally falling in

the highest three MPCE classes. This is because nearly all households at the highest income levels

consume electricity for all their lighting purposes, implying very low consumption levels of alternate

lighting fuels such as kerosene.

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GINI_INC

104



5.4.4 Karnataka

Figure 53: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) in Karnataka


0.00

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INCSHARE

REFLINE

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GINI_INC

105

In Karnataka, the inequality in the consumption of biomass is low compared to other states, and this

inequality increases only marginally with the increase in income brackets. Higher incomes have the

advantage of being able to afford a far wider range of fuels, therefore are likely have a diverse energy

consumption basket. Higher inequality is observed in the 3rd

MPCE class in the case of Karnataka as

it is a transition income group, where households move from lower to middle income group. This

inequality arises as there is a shift in the energy basket towards fuels other than biomass.



The inequality in the use of petroleum fuels in Karnataka is high across all MPCE classes, although it

falls marginally in the 6th

and 8th

MPCE classes, eventually falling steeply in the 12th

MPCE class.

Given only 2% of households use LPG in the state and that consumption of these fuels is low across

the MPCE classes as per the TERI survey (as seen in section 5.3.4), very high variation in the use of

these fuels is expected across all income groups, leading to such high inequality. Inequality falls in

the 12th

MPCE class as it is the highest MPCE category: households in this income class on average

are able to consistently afford certain amounts of LPG and kerosene for their cooking needs.

0

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GINI_INC

106



In Karnataka, inequality in the consumption of electricity is nearly constant, except for a marginal

rise in the 8th

MPCE class, and an eventual fall in MPCE class 12. Since MPCE class 8 is a

transitionary income class, representing a shift from middle to upper income groups, the high

inequality represents greater variation in the lighting fuel energy basket. This could be possible due to

the fact that any additional income for households at that level might be spent on other priorities

rather than on increasing electricity consumption. Inequality falls in MPCE class 12 as with high

incomes, households predominantly start using electricity for their lighting needs.

The state also exhibits low levels of income inequality overall, as can be seen in the Lorenz Curve

below.



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REFLINE

INCSHARE

107

5.4.5 Rajasthan

Figure 57: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) in Rajasthan


TERI‘s survey reveals that in Rajasthan, inequality in the consumption of biomass is high in the 1st

and 5th

MPCE classes among lower and middle income groups, and then the inequality begins to rise

after the 6th

MPCE class, reaching a peak in the 12th

MPCE class. In this case, the 3rd

and 5th

MPCE

classes represent transitionary income classes, where households move from low income groups to

the middle income groups. In such income groups, there is a greater variation in the use of biomass

for cooking as there is a shift in energy baskets towards fuels other than biomass. Further, inequality

rises after the middle income groups as with greater income, households have wider choices and may

choose to use biomass out of choice rather than compulsion.

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108



In the case of petroleum based fuels used in cooking, we see a fall in inequality with the rise in

income, as has been the case with the other states. LPG in particular, which is a costlier fuel than

biomass, is less likely to be used in the lower income classes, leading to higher inequalities. As

incomes rise, however, households are likely to consistently use certain amounts of such fuel, leading

to low inequality in such fuel use in the higher MPCE classes. In Rajasthan‘s case, it can also be

noted that the fall in inequality is steeper after the 8th

MPCE class, indicating that there is more

consistent use of LPG and kerosene after the households move from the middle income to the higher

income groups.



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109

Inequality in the consumption of electricity in Rajasthan rises with rising income classes, with

intermediary peaks at the 4th

, 8th

and 11th

MPCE classes. The peaks indicate that at these income

levels, some households either increase their demand of electricity by acquiring new machines and

gadgets, or that their energy consumption baskets include varying amounts of consumption of other

lighting fuels as it is a transitory income class.



5.4.6 Odisha

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110

Figure 61: Inequality in biomass energy consumption (GINI_Bz) and income (GINI_Inc) in Odisha


In Odisha, inequality in the consumption of biomass is high across all but three expenditure classes,

namely the 1st, the 7

th and the 10

th MPCE. In this state, 91% of the households use biomass. In

Odisha, it was found that households were using other lower grade biomass fuels such as agri-residue

along with firewood for cooking. In this case, a movement from low-grade biomass to firewood

would also be a step towards more efficient fuels. The fall in inequality in the 1st, 7

th and 10

th MPCE

classes indicates that these households are using more firewood and lesser of other biomass fuels.



Like in Karnataka, the inequality in the use of petroleum fuels in Odisha is high across all MPCE

classes, eventually falling steeply in the 12th

MPCE class. In the state, only 4% of households use

LPG and this consumption is low across the MPCE classes as per the TERI survey. Therefore, high

variation in the use of these fuels is expected across all income groups, leading to such high

inequality. Inequality falls in the 12th

MPCE class since households on average are able to

consistently afford the use of LPG and kerosene for their cooking needs.

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111

Figure 63: Inequality in electricity consumption (GINI_ELEC) and income (GINI_Inc)


The inequality of electricity consumption in Odisha is high across all expenditure classes except

MPCE classes 1, 7, 11 and 12. Low inequality at the highest income classes indicates that households

are able to afford the consumption of certain amount of electricity every billing cycle. The

consumption of other lighting fuels at these income classes drop.

Figure 64: Lorenze curve for Income Inequality


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INCSHARE

REFLINE

112

6. Results from the Pilot Survey

This chapter examines results from the Pilot Survey conducted as part of the project as a precursor to

the main household survey conducted in other states. The pilot study aimed to draw lessons from the

field to better understand the determinants of current energy use patterns, causes for variations,

designing appropriate methodologies to measure the impacts of these factors, and most importantly,

arrive at useful policy recommendations. The Pilot study also served as a benchmark for testing the

primary hypotheses, survey questionnaire and the robustness of the analysis methodology.

6.1 Background and Profile of Survey Sites

Madhya Pradesh (MP) lies in central India; it is the second largest state in the country by area and

sixth largest state in India by population. Population of MP is 7, 25, 97, 565 comprising 3, 76, 12, 920

males and 3, 49, 84, 645 females, contributing 6 percent to India‘s total population (Census, 2011b).

According to Census 2011, MP along with the other eight Empowered Action Group (EAG) states21

has low literacy levels with high population growth.

MP is home to a large tribal population, which has been largely cut-off from mainstream

development. This makes MP one of the least developed states in India, with an HDI value of 0.375

(IAMR, 2011), which is below the national average of 0.467 (IHDR, 2011). The state's per-capita

gross state domestic product (nominal GDP) is the fourth lowest in the country.22

A primary survey was conducted in four districts chosen from four divisions of Madhya Pradesh. The

districts for chosen for the pilot survey includes:

Bhopal Division: Raisen district

Jabalpur Division: Mandla district

Narmadapuram Division: Betul district

Ujjain Division: Ratlam district

Village Profile and Data Collection

The districts for the pilot study were selected in consultation with the partner NGO‘s and government

officials to get a good mix in the sample. While Betul and Mandla primarily represent tribal

populations, Ratlam and Raisen are fairly developed towns. Ratlam is primarily an industrial town

and Raisen being very near to the state capital, Bhopal, is also developed. Geographically all the four

districts lie in different regions and represent different divisions with differing terrains.

The map of the state of Madhya Pradesh below (Map 1) indicates the districts surveyed for the pilot

study.

21 The Government of India had constituted an Empowered Action Group (EAG) under the Ministry of Health and Family Welfare

following 2001 census to stabilise population in eight states (called EAG states) that were lagging in containing population. EAG sates

include Bihar, Jharkhand, Uttar Pradesh, Uttarakhand, Rajasthan, Madhya Pradesh, Chhattisgarh and Odisha. 22Gross State Domestic Product (GSDP) at Current Prices (as on 15-03-2012), Planning Commission of India.

http://en.wikipedia.org/wiki/Tribals_in_Madhya_Pradesh

http://en.wikipedia.org/wiki/Human_Development_Index

http://en.wikipedia.org/wiki/Gross_state_product

http://planningcommission.nic.in/data/datatable/0904/tab_104.pdf

113

Map 1: District Map of Madhya Pradesh, India

Source: www.etradeservices.com

Data was collected for 200 households across the four districts on various indicators ranging from

primary cooking fuel, primary activity for men and women, education level of the household, social

status, economic status, and other related variables. The data collected at the household and village

level was based on interviews with the village residents with the help of a pre-designed questionnaire.

Two Blocks from each district were covered and two villages from each block were surveyed to

maintain a good sample of the households.

6.2 Analysis

There could be various reasons apart from income that may be impacting the expenditure patterns on

fuels for households. Through the field experiences during the pilot survey and secondary literature it

has been observed that levels and forms of fuel consumed by the household sector depend not only on

incomes but also on various other factors such as size of settlements, households, geographic

location, price of fuels, the availability and accessibility of modern commercial fuels, the efficiency

of the end-use equipment and the socio-cultural environment that people live in which to a large

extent drive household consumption patterns. Thus, given the vast size of the country and the myriad

cultures and social constructs that exist, it is critical that these factors are addressed at various levels

in the economy i.e. national, regional, district and household level, which may influence household

energy choices as desirable.

These have important policy implications, i.e. it indicates that variations exist in energy use and these

are not driven primarily by income, thus making it imperative to understand in detail the causes for

these differences so as to facilitate appropriate policy design and effective implementation.

114

6.2.1 Logit Regression

A logit model was set up to assess which household factors influence the choice of primary cooking

fuel, in this case, fuel wood or LPG.

The dependent variable constructed for this study is the primary cooking fuel used by the household.

It is taken as a binary variable, with value ‗0‘ if the household uses biomass fuels for cooking and

value ‗1‘ if the household uses LPG as a cooking fuel. In our sample, we have considered only two

categories for the dependent variable, as given the small sample sizes, there were not sufficient

number of households to classify into further categories. Thus, to avoid any biased results and

spurious relations from the model, the dependent variable has been taken as a binary variable. It is

important to note that in rural India, households use a mix of fuels for meeting their cooking energy

demands and thus in the analysis of the final survey data, given the large sample size, we will group

households into multiple categories based on the different combination of fuels used for cooking

purposes.

The independent variables taken in this model are described below.

Dependent Variable

Primary Cooking fuel: The variable takes the value ‗0‘ if the household uses biomass fuel for

cooking and value ‗1‘ if the household uses LPG for cooking.

Independent Variables

1) Primary Occupation of Males (primarylivelihoodmen): This variable captures the primary

activity of the male member which reflects the major earnings of the household. This variable

is a categorical variable and takes values from ‗0‘ to ‗6‘ with the lower values indicating

informal jobs and the higher values indicating more formal and stable jobs. (Refer to

Annexure 1 for details on categorical values)

2) Social Status (socialstatus): This variable is a categorical variable. It captures the different

social categories that people are divided into. This variable is expected to play a crucial role as

social status very often defines the access to common property resources (in this case, biomass

fuels). More importantly, social status defines the way a person lives and the manner in which

they are treated by the rest of the community. It is also to be noted that many government

benefits are due to an individual or household based on their social status making the

inclusion of this variable all the more important. The categories included are Scheduled

Castes (SC), Scheduled Tribes (ST), Other Backward Classes (OBC) or General (Gen). (Refer

to Annexure Table 48for details on categorical values)

115

3) MPCE class23

(mpceclass): MPCE class is taken as a proxy for income of the household.

Households are categorized into different income groups based on their level of expenditure.

This has been calculated in the same manner as the NSS data to maintain comparability.

Households have been divided into 6 classes, with ‗1‘ being the poorest and ‗6‘ being the

richest. (Refer to Annexure 1 for details on categorical values)

4) Highest education level attained by the male member in the household (logedumale): This

variable is a continuous variable and reflects the educational status among the male members

of the household. The variable is calculated as the maximum level of education achieved by

any male member as on the date surveyed. The variable takes the value based on the level of

education, for example, if the highest education attained among male members in a household

is Standard X, then the variable takes the value ‗10‘ and so on.

5) Highest education level attained by the female member in the household (logedufemale): This

variable is a continuous variable and reflects the educational status among the female

members of the household. The variable is calculated as the maximum level of education

achieved by any female member as on the date surveyed. The variable takes the value based

on the level of education, for example, if the highest education attained among female

members in a household is Standard X, then the variable takes the value ‗10‘ and so on.

6) Household Size (loghhsize): The size of the household is expected to affect the cooking

energy demand and also have a bearing on the income distribution. Higher household size

would mean greater energy demand. This variable takes the value of the number of family

members in the household.

7) Price of Firewood (logpfw): Price of firewood will influence a household‘s decision in fuel

choice. The price of firewood for all households within a village has been assumed the same.

The price has been taken to be the average price of firewood being sold in the nearest market

on a date closest to the date of survey. It should be noted that the price of firewood is difficult

to capture as the markets are informal and the prices are determined by the seller based on

their perception of the quality of wood being sold. Also, the scarcity of firewood determines

the average level of the price of firewood in the local market.

8) Price of Kerosene (logpsko): Price of kerosene will influence a household‘s decision in fuel

choice. The price of kerosene has been assumed to be the same for all households belonging

to a village as the price of PDS kerosene is determined by the distribution centre, whereas the

price of market kerosene is determined by the market rates. Again, the data has been collected

at the market level as well.

23 Monthly per capita Expenditure (MPCE) class is taken as a proxy for income of the household. Households are categorized into

different income groups based on their level of expenditure. This has been calculated in the same manner as the NSS data to maintain

comparability.

116

9) Price of LPG (logpLPG): Price of LPG will influence a household‘s decision in fuel choice.

LPG is an expensive option for households and we would expect a household to choose LPG

as a primary cooking fuel only if it is made affordable or the household has sufficient income

to afford a cylinder. The price of LPG was determined for the village based on the prices that

were quoted by consumers from that village.

10) Land Size (logland): This reflects ownership of land, as land availability will determine

availability of freely accessible firewood. Moreover, land is an asset which can generate

substantial income for the household thus playing an important role in determining fuel

choice. This is a continuous variable measured as the log of land holding size reported.

11) Electricity Access (elecaccess): Electricity access can influence a household‘s fuel choice

significantly. The presence of reliable and good quality supply of electricity allows a

household to take up other activities even after sunset, thus prolonging the number of hours

available for productive work in the day. This can impact household incomes significantly

resulting in changing lifestyles and thus lead to changes in household expenditure patterns and

possibly fuel choices as well. It is very important to focus on how we define ‗electricity

access‘. In most of rural India, while there is provision for electricity supply, the supply hours

are very erratic and very often people end up paying for electricity that has no use for them.

For example, supply of electricity for 3 hours in the day from 10AM to 1PM has no use for

the household members as all are out working, whereas the same three hours of supply from

6PM to 9PM or 7PM to 10PM would enable the household to take up productive activities or

allow children to study and so on. Thus, given short hours of electricity supply, ‗access‘ to a

household is really defined as the when the value of the payment they make for an ‗energy

service‘ (in this case, electricity) is fully realised by productive use of the duration of supply.

Thus, in this case, we have defined ‗electricity access‘ for a household as a binary variable

which takes the value ‗1‘, if, the household receives electricity supply anytime between 6PM

to 10PM for at least 20 days a month; and, if it does not, then the variable take the value ‗0‘.

12) Time spent by women cooking and working (timelive): This variable is a combination of the

average time spent by women for cooking per day which is a continuous variable and the

primary occupation of the woman which is a discrete or categorical variable. This variable

was constructed on the premise that if the woman spends more time in income generating

activities, and particularly in a regular salaried job, it could have a significant impact on the

time that a woman allocates to domestic chores including cooking. We would expect that with

a formal job which also translates into higher incomes than casual labour, there would be

higher probability that a woman would be willing to choose cleaner fuels such as LPG for

cooking. But given social and cultural factors also play a role in determining household fuel

choice, there could be a possibility that a woman could continue using biomass fuels for

cooking irrespective of the occupation type and the income generated, thus the combined

effect of cooking time and occupation type of women in the household has been taken as a

variable in the model to be tested.

Thus, the final model (Table 13) to be tested is specified below:

117

Logit Model(1)

Predicted Logit (lfp=1/0) (Choice of Primary cooking fuel) = α + β1(social status) + β2(primary

livelihood for men) + β3(mpce class) + β4log(edumale) + β5log(edufemale) + β6(timelive) +

β7log(hhsize) + β8log(pLPG) + β9log(psko) + β10log(pfw) + β11log(land) + β12(elecaccess)

Table 13: Estimated Coefficients of the logit model(1)

Primary cooking fuel Robust Coef. Std. Err. z P>|z| [95% Conf. Interval]

Social Status -0.1432212 0.801239 -0.18 0.858 -1.713621 1.427178

Primary livelihood of men 2.018577 0.885418 2.28 0.023** 0.2831907 3.753964

MPCE Class 0.8372557 0.413984 2.02 0.043* 0.0258613 1.64865

Education of male (Log)24 2.393379 2.112356 1.13 0.257 -1.746763 6.533521

Education of female (Log) 1.726696 0.973183 1.77 0.076 -0.180708 3.634099

Timelive -3.242136 1.241909 -2.61 0.009*** -5.676233 -0.808039

Household size (Log) 2.145111 2.08919 1.03 0.305 -1.949626 6.239848

Price of LPG25 (Log) -156.4151 35.97687 -4.35 0.000*** -226.9285 -85.90176

Price of Kerosene (Log) 62.84464 56.72915 1.11 0.268 -48.34246 174.0317

Price of Firewood (Log)26 15.45975 37.37725 0.41 0.679 -57.79832 88.71782

Land Size (Log) 2.163311 0.964814 2.24 0.025** 0.2723105 4.054312

Electricity access27 7.249156 2.177322 3.33 0.001*** 2.981684 11.51663

Constant 760.6622 . . . . .

Source: TERI Survey 2013

The results show that primary occupation of men, MPCE class, timelive (a joint variable of women‘s

cooking time and the occupation type of women), price of LPG (log), land ownership (log of land

holding size) and electricity access are found to be significant (at 95% confidence intervals) in

influencing a household‘s decision in its choice of primary cooking fuel. These variables explain the

likelihood of households to use LPG over firewood (biomass fuels) as the primary cooking fuel. The

marginal effect of the ‗primary livelihood activity of men‘ on the probability of using LPG as a

primary cooking fuel is 0.000000356. This indicates that a unit increase in primary activity of men

will increase the probability of the household to switch to a cleaner fuel (LPG) by 0.000000356. The

odds ratio can be interpreted as the probability of switching/moving/substituting to cleaner form of

energy to the probability of not switching/moving/substituting. This implies that, for a unit change in

24 The positive relation between education and fuel switching is as expected. As education levels increase, we expect that it would lead

to improved livelihoods, better incomes, greater awareness – all of which could lead to a transition to cleaner fuels at the household

level. 25 We expect that as the price of LPG decreases, there would be greater chances of its uptake and usage, which is corroborated with the

negative sign. 26 Given that firewood is available at very low prices, very often at no cost in rural India, there will be a positive coefficient. This would

change when the price of firewood becomes higher and closer to the price of LPG, i.e., there is a threshold price for firewood, after

which the pattern of consumption would change. Thus, the fact that the firewood and LPG are substitutes is indicated by the opposite

signs the coefficients have. 27 Electricity access is expected to have an impact on fuel choice to the extent that with improved electricity access, there would be a

greater chance of its productive use and thus leading to improved incomes which may lead to a transition to cleaner fuels such as LPG.

Thus, the impact of electricity access though not direct, is critical, but it behaves like an instrumental variable in this case (as seen in

Model 2) because as of now, the consumption levels are still quite low, thus not allowing for any productive use of electricity.

118

primary livelihood activity for men, the odds of choosing cleaner cooking fuels are expected to

change by a factor of 7.52, ceteris paribus. It can also be interpreted as, for every unit increase in

primary livelihood activity for men, the odds of choosing a cleaner cooking fuel is expected to

increase by about 652% [100*(7.52 – 1)], ceteris paribus.

The results also show that electricity access to the household and price of LPG play an important role

in switching/moving/substituting to a cleaner fuel. This may be due to the fact that the households

who have access to electricity reside closer to towns or are located centrally and hence, have greater

chances of moving higher up the energy ladder. Secondly, the price of LPG influences a household‘s

decision since rural households have income constraints which can be measured as the elasticity of

fuel expenditure with respect to total expenditure of the household, i.e. priority of fuel as expenditure

for a household.

While the results of the pilot survey have given some valuable insights on the challenges to energy

access and household fuel choices which are discussed in detail in the subsequent section, the

significance of ‗electricity access‘ in determining primary cooking fuel choice was rather interesting.

Electricity access is considered to be an important driver for household transitions towards cleaner

energy forms as it impacts incomes and living styles of people, thus impacting the energy basket or

fuel basket of households. But for this, the reliability and quality of electricity supply is very critical.

In most studies28, electricity has been included as a variable to test whether it impacts household fuel

choices and it has been found to be significant. In the Indian context, while many households in the

rural areas have provision for electricity supply, the reliability and quality is questionable. We have

still not reached a situation where electricity access has begun to significantly impact incomes of

households or rather the marginal benefit of one unit of electricity supplied has not yet exceeded the

marginal cost of receiving that electricity. Given that most households did not have ‗electricity

access‘ as defined for the purposes of our study, we wanted to test the overall fit of the model if

‗elecaccess‘ was dropped from the model as an explanatory variable. Thus, another logit model was

set-up and run without ‗electricity access‘ as an explanatory variable. The model to be tested (Table

14) is specified below:

Logit Model(2)

Predicted Logit (lfp=1/0) (Choice of Primary cooking fuel) = α + β1(social status) + β2(primary

livelihood for men) + β3(mpce class) + β4log(edumale) + β5log(edufemale) + β6(timelive) +

β7log(hhsize) + β8log(pLPG) + β9log(psko) + β10log(pfw) + β11log(land)

Table 14: Estimated Coefficients of logit model(2)

Primary cooking fuel Coef. Robust Std. Err. z P>|z| [95% Conf. Interval]

Socialstatus 0.436772 0.4505063 0.97 0.332 -0.4462 1.319748

Primarylivelihoodmen 0.874493 0.476518 1.84 0.066 -0.05947 1.808451

Mpceclass 0.370944 0.2529367 1.47 0.142 -0.1248 0.866691

Education of male (Log) 2.884985 1.803539 1.6 0.11 -0.64989 6.419856

Education of female (Log) 0.77118 0.6645606 1.16 0.246 -0.53134 2.073694

28 Insert references of the papers multi country fuel choice and Sri Lanka and couple others with logit/probit models

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Primary cooking fuel Coef. Robust Std. Err. z P>|z| [95% Conf. Interval]

Timelive -1.47028 0.5833322 -2.52 0.012** -2.61359 -0.32697

Loghhsize -0.22916 1.319293 -0.17 0.862 -2.81493 2.356608

logpLPG -103.023 12.38131 -8.32 0.000*** -127.29 -78.7559

Logpsko 36.70411 19.78822 1.85 0.064 -2.08008 75.48831

Logpfw -0.98839 13.12615 -0.08 0.94 -26.7152 24.73838

Logland 1.531942 0.5480256 2.8 0.005*** 0.457831 2.606052

_cons 531.8787 . . . . .


In logit model(2), we find that ‗timelive‘, ‗price of LPG‘ (log) and ‗land ownership‘ (log of land

holding size) are found to be significant (at 95% confidence intervals) in influencing a household‘s

decision in its choice of primary cooking fuel. If we test at 10% significance level (at 90% confidence

intervals), we find that the variables found to significant become even more significant and

additionally the ‗primary livelihood activity for men‘ and price of kerosene (log) also become

significant. The model estimates such as the log likelihood and the AIC and BIC do move in

favourable directions but the changes are not very large to know whether electricity access is

significant to impact the fit of the model.

Since the sample size is not very large and there are significantly large set of explanatory variables,

we have used two basic guidelines in selecting the explanatory variables: First, including all possible

set of variables to make the model useful for theoretical purposes and to obtain good predictive

power; Second, to keep the model simple, as a counterbalance to the first goal. The other effect of

having extra variables in the model that add little predictive power, perhaps because of overlapping a

lot with the other variables, has disadvantages which may lead to multi-collinearity. The model may

be more difficult to interpret, having many more parameters to be estimated. This can result in inated

standard errors of the parameter estimates, and may make it impossible to assess the partial

contributions of variables that are important theoretically. Thus, to avoid multi-collinearity we have

tried to build a simple but comprehensive model since the data set is small. This model will further

develop as the data set increases with the survey being carried out across different regions in India.

6.2.2 Key Findings from the Model

Thus the pilot analysis provides us with the following preliminary findings:

Affordability

There is an important role of income in determining household fuel choices, which is evident from

the econometric model which indicates that both the income class to which the household belongs as

well as the primary livelihood activity of men are significant determinants.

Prices of cleaner fuels will play an important role as well in determining fuel choices. In the case of

pilot study, we find the price of LPG (Log) is significant in determining the choice of primary

cooking fuel. We also expect LPG to be a substitute to firewood which is also indicated by the model

with the coefficients of the prices of both fuels being inversely related.

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Availability

The model indicates land holding size to be a significant determinant of primary cooking fuel choice.

The ownership of land impacts household fuel choices in two ways. Firstly land is an asset and can

significantly impact household income thus impacting energy choices. Secondly, if there exist

ownership of land, greater is the access to biomass fuels which are freely available to the household;

and thus, it could have a negative impact on the probability of the household‘s willingness to shift to

cleaner fuels.

Gender

Most often it is the women of the household that are expected to spend time on collecting firewood

and cooking. We would expect that if the women were able to engage themselves in income

generating activities that would offset the opportunity cost of collecting firewood then it would

improve the probability of the household transitioning to cleaner fuels. This would happen because as

the women contribute greater shares of income to the household they would prefer spending lesser

time on domestic chores like cooking, thus increasing the possibility of a shift towards cleaner fuels

such as LPG which also take lesser time to cook than traditional biomass fuels such as firewood.

6.3 Energy Inequality

While econometric models are important in explaining the determinants of household fuel choices, it

is also important to understand the inequalities in energy consumption, the insights of which when

combined with other forms of analysis including the econometric models will give a holistic picture

of the challenges of achieving universal energy access and also help inform policy and planning.

We have used the Gini coefficient to estimate both the income and energy inequality across the pilot

sites. The energy inequality has been looked at separately for biomass fuel consumption (firewood,

dung cake and crop residue), petroleum fuel consumption (kerosene and LPG), and electricity

consumption. The figures below plot the energy inequality measures for different fuel types across

income classes.

The graph below plots the Lorenz curve for income inequality and calculates the Gini Coefficient29 as

a measure of inequality. The Lorenz curve30

, L, for a cumulative income distribution F with mean μ is

defined by

We obtain a Gini coefficient of 0.44 which indicates fairly high inequality.

Given that income level is a significant determinant of energy choices, it would be useful to look at

the relation between income and energy inequality.

29 G is a measure of inequality, defined as the mean of absolute differences between all pairs of individuals for some measure. The

minimum value is 0 when all measurements are equal and the theoretical maximum is 1 for an infinitely large set of observations where

all measurements but one has a value of 0, which is the ultimate inequality (Stuart and Ord, 1994) 30 Aaberge R, & Mogstad, M. (2011).

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Figure 65: Lorenz curve for income inequality among pilot sites sample data


The graphs below plot the energy inequality for biomass fuels (Figure 66) and petroleum products

(Figure 66). They are plotted along with the income Gini as well to see the relationship between

income and energy consumption. In the case of both biomass and petroleum fuel consumption, we

find that there is an inverse relation between income inequality and energy inequality, i.e. where there

is high income inequality, there is lower energy inequality and vice-versa. This pattern is similar to

that found in the NSS data as well. This indicates that household fuel choice is not just determined by

income but by factors other than income which includes certain socio-cultural factors. These socio-

cultural factors are not always quantifiable and at times, very subjective. Thus, it is important to

carefully analyse these variables and their impacts on household energy choices.

Figure 66:Biomass and Petroleum fuels inequality across income groups


GINI = 0.44

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6.4 Conclusion

The pilot survey in the four districts of Madhya Pradesh has provided us with useful insights. Though

there are differences across the four districts in terms of social and cultural aspects, but due to the

limited sample size, we have carried out the analysis inclusive of all the districts. Based on the

detailed analysis, we have tested the following hypothesis

Significant changes in income flows impact energy use patterns

The analysis suggests that there is an important role of income in determining

household fuel choices, which is evident from the econometric model which indicates

that both the income class to which the household belongs as well as the primary

livelihood activity of men are significant determinants.

In the given districts, land holding size was found to be a significant determinant of

primary cooking fuel choice since the ownership of land impacts household fuel

choices. As land is an asset, contributing to the higher household income and secondly

providing the households with easy access to biomass fuel at almost negligible costs.

Higher the value of women‘s labour, lower the probability of collecting biomass fuels, and

thus, lower the chance of using biomass fuels for cooking

The joint variable „timelive‟ in the logit model which represents the trade-off between

the time spent by women in cooking and in income generating activities. High level of

significance shows that if the women contribute significant income shares to the

household, there is higher probability of the household switching to cleaner energy

options.

Availability and the Prices of the fuels play an important role in determining household choice

for the type of fuel.

Prices of cleaner fuels play an important role in determining fuel choices. As we find

the price of LPG (Log) is significant in determining the choice of primary cooking

fuel.

Observations from the pilot survey in Madhya Pradesh and the NSS data show that there exist

variations with respect to the fuel used within the state. Madhya Pradesh is home to a large tribal

population, which has been cut-off from mainstream development and has a mix of various ethnic

groups and tribes, castes and communities, including the indigenous tribes and relatively more recent

migrants from other states. Mandla and Betul districts have large tribal communities and are located

far from urban centres and thus have different issues and concerns as compared to Ratlam and Raisen

that are industrial towns and fairly urbanized as they were located near to the main urban. The NSS

data too indicates significant regional variations. Thus, to address the issue of energy transitions,

issues at the local level will have to be taken into consideration, further stressing the importance and

role of local government institutions, so as to ensure effective policy making.


organizations as well as the intended beneficiaries in the planning process. While policies at the

national level may provide important guidelines and the necessary framework, the implementation

strategies need to be designed at the local level. Communities also differ in their essential fabric.

There are areas where community based solutions will be successful and others where these won‘t.

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Identifying the key services where interventions may be more successful particularly those that

contribute to livelihood enhancement are essential. As mentioned earlier, to address issues of

availability, structural changes and improvements in the supply chain of the product/energy service

would need to be ensured so as to create reliable and quality supply. Last but not the least, awareness

building is essential for informing people about the various options available to them so that

households can make informed energy choices that best suit their needs.

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7. Regression Analysis: State-wise and Overall

The model has been setup to analyze the impact of various factors on the probability that a household

would make a transition to a cleaner cooking fuel basket. For this purpose, the dependent variable has

been defined as ―transition‖. The dependent variable is a multinomial variable and takes values as 1, 2

or 3, depending on the share of LPG (p_lpg) in the total cooking energy basket of the household. The

variable ―transition‖ is defined as follows:

transition = 1, if p_lpg=0;

= 2, if 0 < p_lpg ≤ 31%;

= 3, if p_lpg > 31%

Where, p_lpg is the share of LPG in the cooking fuel basket of the household.

This definition of cooking energy transition has been considered after having carefully analysed the

various existing definitions available from a comprehensive literature review. The above definition of

cooking energy transition has been chosen taking into account various factors in terms of data

availability, simplicity of the definition, dynamic in terms of allowing for periodic revisions and so

on. The above considered definition can be revised periodically based on the median LPG

consumption across households as measured by the NSS and Census surveys which can measure

transitions in cooking energy in a more accurate manner.

The independent variables have been selected very carefully after a detailed understanding of

household structure and the factors impacting their choices. The variables are explained as follows:

1) Social Status: This variable is a categorical variable and takes values from ‗0‘ to ‗3‘. It

captures the different social categories that people are divided into. This variable is expected

to play a crucial role as social status very often defines access to common property resources

(in this case, biomass fuels). More importantly, social status defines the way a person lives

and the manner in which they are treated by the rest of the community. It is also to be noted

that many government benefits are due to an individual or household based on their social

status making the inclusion of this variable all the more important. The categories included are

Scheduled Castes (SC), Scheduled Tribes (ST), Other Backward Classes (OBC) or General

(GEN).

2) Expenditure Class (MPCE)31

: Monthly per capita Expenditure (MPCE) class is taken as a

proxy for income of the household. Households are categorized into different income groups

based on their level of expenditure. This has been calculated in the same manner as the

31 Monthly per capita Expenditure (MPCE) class is taken as a proxy for income of the household. Households are categorized into

different income groups based on their level of expenditure. This has been calculated in the same manner as the NSS data to maintain

comparability.

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National Sample Survey data to maintain comparability. Households have been divided into 6

classes, with ‗1‘ being the poorest and ‗6‘ being the richest.

3) Land size: This reflects ownership of land and is a continuous variable measured as the log of

land holding size reported. The ownership of land impacts household fuel choices in two

ways. Firstly, land is an asset and can significantly impact household income thus impacting

energy choices. Secondly, if there exist ownership of land, greater is the access to biomass

fuels which are freely available to the household; and thus, it could have a negative impact on

the probability of the household‘s willingness to shift to cleaner fuels.

4) Price of Firewood: Price of firewood will influence a household‘s decision in fuel choice with

a higher price possibly leading to a shift to alternative fuels. The price of firewood for all

households within a village has been assumed the same. The price has been taken to be the

average price of firewood being sold in the nearest market on a date closest to the date of

survey. It should be noted that the price of firewood is difficult to capture as the markets are

informal and the prices are determined by the seller based on their perception of the quality of

wood being sold. Also, the scarcity of firewood for a level of demand determines the average

level of the price of firewood in the local market.

5) Price of Kerosene: Price of kerosene will influence a household‘s decision in fuel choice. The

price of kerosene has been assumed to be the same for all households belonging to a village as

the price of PDS kerosene is determined by the distribution centre, whereas the price of

market kerosene is determined by the market rates. Again, the data has been collected at the

market level as well.

6) Price of LPG: Price of LPG will influence a household‘s decision in fuel choice. LPG is an

expensive option for households and we would expect a household to choose LPG as a

primary cooking fuel only if it is made affordable or the household has sufficient income to

afford a cylinder. The price of LPG was determined for the village based on the prices that

were quoted by consumers from that village. (LPG is considered as a substitute to firewood

which is also indicated by the model with the coefficients of the prices of both firewood and

LPG being inversely related.)

7) Highest education32

level attained by the male member in the household (Education of

Males): This variable is a discrete variable and reflects the educational status among the male

members of the household. The variable is calculated as the maximum level of education

achieved by any male member of the household as on the date surveyed. The variable takes

the value based on the level of education, for example, if the highest education attained among

male members in a household is Standard X, then the variable takes the value ‗10‘ and so on.

32 It is expected that Education of the household head increases the demand for more modern energy in both rural and urban India and

hence, the end-use energy and energy expenses.

126

8) Highest education level attained by the female member in the household (Education of

females): This variable is a discrete variable and reflects the educational status among the

female members of the household. The variable is calculated as the maximum level of

education achieved by any female member as on the date surveyed. The variable takes the

value based on the level of education, for example, if the highest education attained among

female members in a household is Standard X, then the variable takes the value ‗10‘ and so

on.

9) Electricity Access: Electricity access can influence a household‘s fuel choice significantly.

The presence of reliable and good quality supply of electricity allows a household to take up

other activities even after sunset, thus prolonging the number of hours available for productive

work in the day. In this case, we have defined ‗electricity access‘ for a household as a binary

variable which takes the value ‗1‘, if, the household receives electricity supply at least for an

hour between 6PM to 9PM for at least 20 days a month; and, if it does not, then the variable

take the value ‗0‘.

10) Time spent by women cooking and working (timelive): This variable is a combination of the

average time spent by women for cooking per day which is a continuous variable and the

primary occupation of the woman which is a discrete or categorical variable. This variable

was constructed on the premise that if the woman spends more time in income generating

activities, and particularly in a regular salaried job, it could have a significant impact on the

time that a woman allocates to domestic chores including cooking. We would expect that with

a formal job which also translates into higher incomes than casual labour, there would be

higher probability that a woman would be willing to choose cleaner fuels such as LPG for

cooking. But given that social and cultural factors also play a role in determining household

fuel choice, there could be a possibility that a woman could continue using biomass fuels for

cooking irrespective of the occupation type and the income generated, thus the combined

effect of cooking time and occupation type of women in the household has been taken as a

variable in the model to be tested.

11) Kitchen location: The location of the kitchen, whether it is located inside the house or

outside, determines to an extent the uptake of a particular cooking technology option at the

household level. The household may choose an improved cooking technology if it perceives

the benefits of the technology to outweigh the impacts of the existing cooking practice. This is

a binary variable which takes the value ‗1‘ if the kitchen is located inside the house and value

‗2‘ if the kitchen is located outside the house.

12) Kitchen window: When the cooking activities are carried out inside the house, especially

using biomass fuels, the smoke generated has significant health impacts on not only the

household member carrying out the cooking activities but also on other household members as

the smoke remains inside. With a window in the kitchen, there exists an outlet for the smoke

to go out of the house, thus reducing the health impact that may arise out of using biomass

fuels. This may affect the choice of cooking fuel that a household makes.

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13) Distance to collect firewood: Till a few years ago, biomass fuels especially firewood were

easily available nearer to households. With dependence on biomass, slowly it has led to

depletion of forests, thus leading to individuals walking longer distances to get firewood for

household energy needs. As the distances increase and firewood becomes a scarce resource,

households will begin to find alternatives. When the marginal cost of getting firewood for

cooking becomes equal or more than the marginal cost of purchasing an LPG cylinder or any

other clean cooking energy source, there will be a shift in the household cooking energy

basket.

14) Interventions: Various intervention programs have been designed and implemented to

provide improved energy services to the rural poor. These interventions may be in the form of

direct energy interventions such as providing improved cookstoves or solar home lighting

systems. Other interventions which aim at improved livelihoods or health benefits may also

have indirect impacts on household energy choices. Different communities respond differently

to intervention programs. Thus, the variable intervention takes the value ‗1‘ if the household

is a beneficiary of any type of intervention and takes the value ‗0‘ if not.

15) Female Decision-making: This variable takes the value ‗1 if the women of the household are

involved in decision making at the household level for daily expenses. It takes the value ‗0‘,

otherwise.

16) Location/District: This is a dummy variable which takes values from 1 to 6 as representative

of which district the sample household belongs to. The significance of this variable will

indicate the presence of regional factors in determining household energy choices.

For the purpose of this model, households have been categorized as Labour households, agriculture

households and salaried households. These categories have been created taking into account both

male and female occupations within the household, i.e. given the occupations of the male and female

members in a household, whichever occupation type provides a sustained and higher income stream,

would be the primary occupation of that household. Thus, for example, if the working members of a

household were all casual or daily wage earners, then the household would be a labour household,

whereas, if among the working members of the household, some were daily wage earners and some

had formal jobs, then such households would count as salaried households. Similarly, households

with their primary income stream from agriculture would be categorized as agriculture households.

The model was run for each of these categories of household type, thus taking into account income

streams of households as well as occupation types of both males and females.

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7.1 Maharashtra

Table 15: Generalized Ordered Logit Model Results

Y=Transition Labour HHs Agriculture HHs Salaried HHs

Coefficient P>|z| Coefficient P>|z| Coefficient P>|z|

Social status -0.71904 0.042** -0.47618 0.027** -0.30863 0.305

MPCE class 0.20701 0.052* 0.03087 0.536 0.14211 0.222

Timelive -0.06916 0.032** -0.00564 0.772 -0.02919 0.416

Price of kerosene (log) 0.05170 0.750 0.08966 0.432 0.07222 0.683

Price of Firewood (log) -0.16363 0.160 -0.08558 0.342 -0.45041 0.001***

Price of LPG (log) 1.60996 0.000*** 1.58665 0.000*** 1.91245 0.000***

Education level of males (log) 0.32467 0.350 -0.74428 0.010*** -0.03228 0.942


(log)

-0.22158 0.465 0.15028 0.491 -0.26925 0.457

Land Size (log) -0.16301 0.779 -0.05742 0.741 -0.54533 0.145

Electricity Access 0.44914 0.554 0.83147 0.092* -1.25918 0.257

Kitchen window 1.19968 0.067* -0.40376 0.321 -0.85485 0.210

Location of kitchen 0.06921 0.936 -0.60734 0.335 2.49313 0.200

Distance to collect firewood 0.03704 0.600 0.01192 0.757 -0.03098 0.601

Female decision-making 0.46633 0.655 0.19013 0.800 0.47228 0.613

District -0.10945 0.623 0.38418 0.001*** 0.67955 0.002***

Intervention 1.95660 0.005*** 0.43790 0.242 -1.97106 0.006***

Constant -6.61337 0.033 -2.37350 0.194 -7.79301 0.105

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Key findings from the model

The table below provides the findings from the model for cooking choices.

Table 16: Generalized Ordered Logit Model findings

Labour Households Agriculture Households Salaried Households

Inferences Labour HH's energy choices

are subject to achieving a basic

minimum income threshold,

the social class the price of

LPG, the presence of a kitchen

window and that they are

beneficiaries of welfare

schemes or livelihood

interventions

Agri HH's energy choices are

driven by their social status,

price of LPG, education of

male members, the access to

electricity during productive

hours and the region to which

they belong to.

For Salaried HHs, energy

choices seem to driven by

fuel prices, the region

they belong to, and the

fact that they are not

beneficiaries of welfare

schemes or livelihood

interventions

Labour Households

Among labour households, the probability of completely dependent households shifting to LPG

increases as the income level crosses Rs 10000 per month. Further, the probability rises as the

electricity consumption crosses 150 kWh per month. For households that consume positive quantities

of LPG but have an LPG share of less than 31% in the cooking fuel basket, increasing income levels

increase the propensity of the households to spend more on LPG, while the propensity increases up

till the electricity consumption of 140 kWh per month. For households with LPG share more than

31% of the cooking energy basket, up to an income level of Rs 8000 per month, the probability to

increase LPG share rises, while the same falls after 150 kWh of monthly electricity consumption. It is

found that as electricity consumption increases, those dependent only on biomass for cooking indicate

a higher probability of shifting to LPG (clean energy) than those whose energy baskets already

include a certain share of LPG. This could be due to the fact that households whose LPG

consumption is already greater than ‗0‘, would prefer spending any additional money towards

acquiring other household appliances rather than increasing the share of LPG.

Agriculture Households

Households who are completely dependent on biomass indicate an increasing probability of moving

towards a cleaner energy basket after the household income goes beyond Rs. 32000 per month.

Among such households, once the electricity consumption crosses about 125 kWh per month, they

indicate an increasing probability of the willingness to spend on LPG. In comparison, those

households already using a mix of fuels with the share of LPG in the cooking energy basket less than

31% indicate a positive probability of increasing the share of LPG. This probability increases as

household incomes increase up to around Rs. 40000 per month after which it becomes constant at a

probability level of about 0.6.

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Those households who use a mix of fuels with the share of LPG in the cooking energy basket greater

than 31% indicate a decreasing probability of reducing their LPG consumption beyond a household

income level of Rs. 28000 per month itself. As compared to biomass dependent households, those

with a positive share of LPG in their cooking energy baskets indicate that once electricity

consumption crosses about 125 kWh, the probability of increased expenditure on LPG decreases. The

probability of increased LPG expenditure decreases at a slower rate for households with a LPG share

of less than 31% but greater than zero than for households with a LPG share of greater than 31%.

Salaried Households

Households who are completely dependent on biomass indicate an increasing probability to shift

towards a cleaner energy basket after the household income goes beyond Rs. 25000 per month. For

such households, once electricity consumption crosses about 140 kWh, the probability of expenditure

on LPG increases.

HHs already using mix of fuels with LPG share less than 31% indicates a positive probability of

further increasing the share of LPG with an increase in household income up to Rs. 40000 per month

with maximum probability of 0.6. Households using mix of fuels with LPG share greater than 31%,

indicate a drastically decreasing probability of reducing LPG consumption only beyond a household

income level of Rs. 15000 per month. The probability of an increased LPG expenditure increases at

an increasing rate for households with a LPG share of greater than zero but less than 31%.As

compared to biomass dependent households, those with a positive share of LPG in their cooking

energy baskets indicate that once electricity consumption crosses about 90 kWh, the probability of

increased expenditure on LPG decreases.

Intervention Targeting for Maximum Impact in cooking energy transitions

Table 17: Appropriate targeting of population for interventions in cooking33

Transition

stage Labour HHs Agri HHs Formal HHs

T1 Target HHs above income

of Rs.12000 per month

Target HHs above Rs.

33000/month

Target HHs with monthly

income above Rs. 25000

T2 About 20-30% prob. that

HHs with income of Rs.

8000/month or less will

shift to an energy basket

with LPG share > 31%

60% prob. that HHs with income of Rs. 40000/month or more will

shift to an energy basket with LPG share > 31%

33Table 17 summarizes which sections of the population would benefit the maximum from the interventions aimed at improved cooking

practices. The two categories T1 and T2 in the first column indicate the stage of transition of the household with respect to its cooking

energy basket. While T1 corresponds to households completely dependent on biomass fuels for cooking; T2 corresponds to households

that use LPG for cooking such that the share of LPG in the total cooking energy basket is less than 31 percent.

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The table below indicates the policy intervention type which would have the highest probability of

effecting a change in household cooking practices. Among labour households, intervention programs

aimed at livelihood security and natural resource management indicate the highest odds of a positive

change towards cleaner cooking practices followed by fuel prices and not having a kitchen window.

For agriculture households, the price of LPG has the greatest impact followed by the access to

electricity in the productive hours of from 6-9 pm as well as the location of the household which

indicates that regional differences have a role to play especially for such households as land quality is

an important factor to ensure livelihood security. As for salaried households, fuel prices have the

maximum impact in terms of promoting a change in cooking fuel use followed by the location of the

household.

Table 18: Key parameters for intervention planning for cooking transitions


Changing

Odds

Livelihood + Natural Resource

Management interventions have

the greatest impact with being a

beneficiary increasing the odds of

transitioning by 7 times followed

by price of LPG wherein an

increase in LPG price would

decrease the probability of

transitioning by 5 times. Lack of

ventilation in the kitchen increases

the probability of LPG uptake by 3

times

Price of LPG has the greatest

impact with an increase in the

price of LPG decreasing the

odds of transitioning by 4

times. The access to electricity

during the productive hours of

6-9 pm increases the odds of

LPG uptake by 3 times. The

district in which the household

is changes the odds of

transitioning by about 1.5

times.


impact with a 10% increase in

the price of LPG decreasing

the odds of transitioning by

7 times followed by the

district in which the

household is changing the

odds of transitioning by

about 2 times.

132



Y = Transition Labour HHs Agriculture HHs Salaried HHs


Social status 1.06188 0.085* 0.30709 0.076* 0.65752 0.012**

MPCE class -0.18454 0.309 -0.09502 0.189 -0.02764 0.747

Timelive -0.12871 0.067** -0.00812 0.806 -0.03254 0.236

Price of kerosene (log) 0.11169 0.606 -0.09669 0.527 0.15670 0.313

Price of Firewood (log) -1.42933 0.409 -0.22512 0.238 -0.22819 0.150

Price of LPG (log) 5.66461 0.000*** 1.84803 0.000*** 2.01548 0.000***

Education level of males (log) 0.93174 0.369 -0.37465 0.347 0.32783 0.440

Education level of females (log) -1.08282 0.092** 0.01748 0.951 -0.22150 0.561

Land Size (log) -0.00902 0.99 0.19017 0.449 -0.96705 0.024**

Electricity Access 8.98078 0.715 0.83453 0.525 0.67390 0.649

Kitchen window -8.04126 0.903 -0.22069 0.901 0.74890 0.451

Location of kitchen -2.54839 0.027** -0.03388 0.947 -0.13940 0.803

Distance to collect firewood -0.12788 0.372 -0.11007 0.038** 0.06430 0.466

Female decision-making 4.76805 0.044** 1.63389 0.121 2.57210 0.029**

District -0.61853 0.195 0.06428 0.646 0.52168 0.008***

Intervention 0.24681 0.907 1.51080 0.004*** 2.16668 0.006***

Constant -4.95942 0.944 -4.40293 0.158 -9.23939 0.002





Inferences Labour HH's energy

choices are subject to

employment security of

as well as the education

level of women in the

household, kitchen

dimension, social status

and the price of LPG.


driven by the price of LPG,

whether they are beneficiaries of

an energy intervention, and

female decision-making, i.e., with

women having a say in fuel

choices of the household,

combined with an intervention,

the willingness for uptake of LPG

is higher.


choices seem to be driven by

their social status, LPG prices,

land ownership, the district

they belong to, and the fact that

they are beneficiaries of any

energy intervention.

133

Labour Households

Among labour households in Himachal Pradesh, it is found that as incomes increase, the propensity

for LPG uptake increases across households who are completely biomass dependent once the

household income crosses Rs. 15000 per month. On the other hand, Households with a positive share

of LPG indicate a decreasing probability of increasing LPG consumption any further once household

income crosses Rs.15000 per month. The probability of LPG uptake increases further when the

increase in income is due to an increase in the earnings of the working women in the household.

While households completely dependent on biomass indicate an increasing probability of

transitioning to a cleaner cooking energy basket with an increase in electricity consumption, the same

does not hold for households for whom the share of LPG in the cooking basket is greater than zero.

This indicates that for only biomass dependent households, an increased access to electricity could

result in productive use of the units consumed thus, indirectly impacting cooking energy baskets

through increased income. Given that labour households are probably the poorest - those who are

already using a certain amount of LPG for cooking would increase their LPG consumption to a

certain threshold and then prefer to spend any additional income on meeting aspirations.

Agricultural Households

Households who are completely dependent on biomass as well as those who use a mix of fuels with

the share of LPG in the cooking energy basket greater than 31%, both indicate an increasing

probability of moving towards a cleaner energy basket after the household income goes beyond Rs.

40000 per month and Rs. 20000 per month respectively, i.e. completely biomass dependent

households indicate a transition at higher incomes. Among the completely biomass dependent

households, once the electricity consumption crosses about 300 kWh per month, they indicate an

increasing probability of the willingness to spend on LPG. In comparison, those households already

using a mix of fuels with the share of LPG in the cooking energy basket less than 31% indicates an

increasing probability of increasing the share of LPG. This probability increases as household

incomes increase up to around Rs. 30000 per month and electricity consumption increases to about

300 kWh per month to about 0.7 after which it decreases to about 0.5.

This indicates that for households who use LPG such that its share is less than 31% in the cooking

energy basket, they indicate a decreasing probability once incomes increase beyond a certain

threshold, whereas with increasing incomes, the probability of transition increases for households

who are either completely biomass dependent or whose cooking energy basket includes LPG

consumption with a share greater than 31%. Similarly, the productive use of electricity results in

higher probability of transition for biomass dependent households and those with LPG share of

greater than 31%, whereas, in the case of households with LPG share greater than 0 but below 31%

indicate that any additional income would not be spent on clean fuels but on other priorities.

Salaried HHs



134

such households, once electricity consumption crosses about 225 kWh, the probability of expenditure

on LPG increases.

Households already using mix of fuels with LPG share less than 31%, indicate a positive probability

of further increasing the share of LPG with an increase in household income up to Rs. 25000 per

month with maximum probability of 0.85 after which it decreases to 0.65. Households using mix of

fuels with LPG share greater than 31%, indicate an increasing probability of LPG consumption as

incomes increase. Both biomass dependent households as well as those with a positive share of LPG

in their cooking energy baskets such that the LPG share is less than 31% indicate that once electricity

consumption crosses about 100–200kWh, the probability of increased expenditure on LPG decreases.

The probability of increased LPG expenditure decreases for households with a LPG share of greater

than 31% once the electricity consumption crosses 175kWh.


Table 21: Appropriate targeting of population for interventions in cooking

Transition

stage Labour HHs Agriculture Households Salaried HHs

T1 Target HHs above income


Target HHs above Rs.

40000/month and less than 2.2

acres land


income above Rs. 25000

T2 50% prob. that HHs with

income of Rs.

15000/month or less will



70% prob. that HHs with

income of around Rs.

40000/month shift to an energy

basket with LPG share > 31%

Over 80% prob. that HHs with

income of Rs. 25000/month or

below shift to an energy basket



effecting a change in household cooking practices. Among labour households, the price of LPG and

the involvement of women members in fuel-related household decision indicate the highest odds of a

positive change towards cleaner cooking practices followed by a higher social standing. For

agriculture households, the price of LPG and being a beneficiary of an energy intervention has the

greatest impact followed by the social standing of the household. As for salaried households, the

women‘s participation in decision making in fuel purchases as well as being a beneficiary of an

energy intervention have the maximum impact in terms of promoting a change in cooking fuel use

followed by the price of LPG, district location where the households reside and their social standing.

135



Changing

Odds

Price of LPG and the female

contribution in fuel-related decision

making has the greatest impact on

transition by changing the odds of

transition by 288 and 118 times

respectively. The improvement in

the social status of the household

changes the odds of transitioning

by about 3 times and if women are

employed in formal jobs with a

regular salary, the odds change by

about 0.8 times.


impact with a decrease in the

price of LPG increasing the odds

of transitioning by 6 times. If the

household is a beneficiary of any

form of energy intervention, it

increases the odd s of

transitioning by 4 times,

followed by an improvement n

the social status of the

households increase in odds of

transitioning by about 1.4 times.

If the woman has a major

role in finalizing decision

on fuel purchases, it

changes the odds in favour

of transitioning maximum

by 12 times. Also if a

household is a beneficiary

of an energy intervention or

if there is a decrease in the

LPG price, the odds of

transitioning increase 9 and

8 times respectively,

followed by the district in

which the household is

located and an

improvement in social

status that increase the

probability of transition to

clean fuels by 2 times.

136

7.3 Goa

Table 23: Regression Model Results



Table 24: Model findings





working women in the

household, the price of

LPG and firewood,

education level of males,

and location of kitchen.


driven by the price of LPG,

education of male members, the

region to which they belong

which drives both land quality

and agricultural benefit schemes,

decision-making power of female

members and whether they are

beneficiaries of any energy

intervention.


choices seem to be driven by

fuel prices, decision-making

power of women, and the

distance travelled to collect

firewood.

Labour HHs Agriculture HHs Salaried HHs (Tobit)

Y = Transition Coefficient P>|t| Coefficient P>|z| Coefficient P>|t|

Social status -0.09387 0.753 0.02022 0.983 0.04996 0.139

MPCE class -0.33944 0.025** -0.10720 0.599 0.00012 0.991

Timelive -0.08140 0.072* -0.06732 0.200 -0.00274 0.470

Price of kerosene (log) -0.20846 0.358 0.33957 0.311 0.04165 0.015**

Price of Firewood (log) -0.34172 0.035** -4.15235 0.291 -0.05513 0.000***

Price of LPG (log) 3.52913 0.025** 5.29777 0.039** 0.23558 0.000***

Education level of males (log) 0.70155 0.093* -2.42248 0.007*** 0.02329 0.551

Education level of females (log) 0.56666 0.165 -0.66175 0.229 0.03472 0.294

Land Size (log) -0.00915 0.946 -0.05319 0.860 -0.02664 0.308

Electricity Access 0.23479 0.983 0.61821 0.772 0.09482 0.653

Kitchen window -1.25522 0.133 -1.83264 0.416 -0.01180 0.865

Location of kitchen -3.34352 0.043** -1.20026 0.372 -0.09659 0.380

Distance to collect firewood 0.16984 0.419 1.82609 0.001*** -0.01721 0.082*

Female decision-making 0.12430 0.881 3.16537 0.041** 0.19549 0.014**

District -1.05490 0.156 -3.31493 0.081* -0.01364 0.836

Intervention -4.82779 0.438 3.70698 0.084* -0.02355 0.873

Constant 1.47310 0.906 -7.01546 0.574 0.89023 0.022

137

Labour HHs

Both, households that are completely biomass dependent as well as those with a share of LPG greater

than 31% in their cooking energy basket, indicate that as incomes increase, the propensity for LPG

uptake increases across households once the household income crosses Rs. 25000 per month. For

households with a positive share of LPG but less than 31% of the cooking energy basket, the

probability of increasing LPG consumption goes up to 0.7 and then decreases once the household

monthly income crosses Rs. 25000. For households completely dependent on biomass, with an

increase in electricity consumption, the probability of transitioning to a cleaner cooking energy basket

decreases and tends to zero once the household electricity consumption crosses about 570kWh, thus,

indicating that an increase in LPG uptake is purely income driven among such households. For

households with a positive share of LPG in the cooking energy basket, they indicate that with

increasing electricity consumption, especially beyond 200kWh per month, the probability of higher

LPG consumption increases. This indicates that for households already using some amount of LPG,

an increased access to electricity could result in productive use of the units consumed thus, indirectly

impacting cooking energy baskets through increased income.

Agricultural HHS

Households who are completely dependent on biomass as well as those with an LPG share of greater

than 31% in their cooking basket indicate a decreasing probability of moving towards a cleaner

energy basket after the household income goes beyond Rs. 10000 per month, with the probability

tending to zero beyond a household income of Rs. 40000 per month. For completely biomass

dependent households, once the electricity consumption crosses about 600 kWh per month, they

indicate an increasing probability of the willingness to spend on LPG. In comparison, those

households already using LPG with its share in the cooking energy basket less than 31% indicate a

positive probability of increasing the share of LPG. This probability increases as household incomes

cross Rs. 10000 per month and the monthly electricity consumption keeps increasing. For households

with LPG share greater than 31% in the cooking energy basket, the probability of increased

expenditure on LPG decreases after the electricity consumption crosses 400 kWh per month.

Salaried HHs



such households, once electricity consumption crosses about 500kWh, the probability of expenditure

on LPG decreases. Households already using mix of fuels with LPG share less than 31% indicate a

positive probability of further increasing the share of LPG with an increase in household income up

to Rs. 30000 per month after which it decreases. Once household monthly income crosses Rs. 50000

and electricity consumption crosses 800kWh per month, households show a decreasing propensity to

spend more on LPG, but at the same time indicate that they would sustain their existing consumption

levels.

138



Transition

stage Labour HHs Agricultural HHs Salaried HHs

T1 Target HHs above

income of Rs.25000 per

month

Target HHs below Rs.

15000/month and less than 2.4

acres land


income below Rs. 30000

T2 60-70% prob. that HHs

with income of leading to

Rs. 25000/month will shift

to an energy basket with

LPG share > 31%

70% prob. that HHs with




Target HHs with income of Rs.

30000/month or more to sustain

a shift to an energy basket with

LPG share > 31%


effecting a change in household cooking practices. Among labour households, the fuel price followed

by intervention programs aimed at improving the affordability of cleaner fuels and ensuring that male

members are well educated indicate the highest odds of a positive change towards cleaner cooking

practices. For agriculture households, interventions aimed at livelihood security of women or energy

interventions along with improving LPG affordability have the greatest impact. As for salaried

households, fuel prices and the decision-making power of women in the household have the

maximum impact in terms of promoting a change in cooking fuel use.



Changing

Odds

A decrease in the price of LPG

would increase the odds of

transitioning towards cleaner

fuels by 34 times followed by

an increase in the level of

education of male members

impacting the odds by 2 times.

Energy interventions and

livelihood schemes focused on

women have a significant

impact with being a beneficiary

increasing the odds of

transitioning by 40 and 23 times

respectively, followed by price

of LPG wherein an increase in

LPG price would decrease the

probability of transitioning by

200 times, while an increase in

the distance to collect firewood

will increase the probability of

transition by 6 times.


impact with a 10% decrease

in the price of LPG

increasing the probability of

transitioning by 23%.

Greater female decision

making in the household

increases the probability of

transitioning by 19%, while

an increase in firewood

prices increases the

probability of transitioning

by 5%.

139

7.4 Karnataka

Table 27: Tobit Model Results

Y = Transition

Labour HHs Agriculture HHs Salaried HHs

Coefficient P>|t| Coefficient P>|z| Coefficient P>|t|

Social status 0.00397 0.342 0.00484 0.159 0.0166241 0.335

MPCE class 0.00053 0.748 0.00148 0.150 0.001851 0.769

Timelive -0.00055 0.219 -0.00065 0.018** -0.0005029 0.790

Price of kerosene (log) 0.00241 0.688 0.01902 0.000*** 0.0209761 0.318

Price of Firewood (log) -0.00121 0.531 -0.00174 0.273 -0.0078459 0.332

Price of LPG (log) 0.19114 0.000*** 0.18838 0.000*** 0.189826 0.000***


(log)

-0.01000 0.020** -0.00003 0.993 -0.00313 0.840


(log)

-0.00019 0.965 0.00429 0.157 -0.006284 0.699

Land Size (log) -0.01174 0.048** -0.00878 0.021** -0.0099631 0.673

Electricity Access 0.00091 0.951 -0.00260 0.769 0.0343929 0.440

Kitchen window 0.00385 0.647 -0.00872 0.215 -0.033436 0.332

Location of kitchen 0.00977 0.473 -0.00485 0.692 -0.0002077 0.997

Distance to collect

firewood

0.00006 0.954 -0.00025 0.888 0.0126 0.361

Female decision-making 0.00544 0.569 0.01993 0.014** 0.0289044 0.507

District -0.00031 0.899 0.00034 0.817 -0.0030927 0.723

Intervention -0.00013 0.995 -0.00929 0.365 -0.0483746 0.507

Constant 0.98184 0.000 0.93334 0.000 0.9157666 0.000



Table 28: Tobit Model findings



choices depend on price

of LPG, asset ownership

and the level of

education of the male

member


driven by role of women in HH

decision making and the

cooking-working trade off, by

fuel prices and extent of land

ownership

For Salaried HHs, energy choices

seem to driven majorly by the

price of LPG

140

Labour HHs

Among labour households, an increase in the income levels results in falling probability of any shift

to LPG. As majority of the households are heavily biomass-dependent, any additional income will not

translate into better fuel choice but will be directed towards upgradation of living standards other than

fuels. Therefore, there is no income effect to a HH opting for LPG as a cooking fuel as against or in

addition to firewood. It is thus important to understand what the other needs and aspirations of this

section of households are keeping income apart. At the same time, it is found that as electricity

consumption increases, those dependent only on biomass for cooking indicate an increasing

probability of shifting to LPG (clean energy). Beyond 18 kWh of monthly electricity consumption,

this probability falls. However, it is seen that for labour HHs, at any level of electricity consumption,

the HH does not transition to consuming a positive amount of LPG.

However, an increase in the size of land owned by a labour HH, results in the HH purchasing LPG,

displaying the income effect of asset ownership. That is, a HH considers land more as a higher

income source as against a higher biomass source.

Agri HHS


towards a cleaner energy basket after the household income goes beyond Rs. 39000 per month.

Among such households, the increase in monthly electricity consumption indicates an increasing

probability of the willingness to spend on LPG. The electricity consumption of over 150 kWh is

likely to push HHs to the mid-transitioned stage with positive LPG consumption.

Salaried HHs


towards a cleaner energy basket up until the household income reaches Rs. 25000 per month. Beyond

this income level, the probability to shift falls at the same rate. For such households, the probability

of expenditure on LPG increases as the electricity consumption goes beyond 97 kWh. A further

increase in the supply is likely to enable the HH to shift to higher levels of LPG consumption.

It must however be noticed that even at an income of Rs 25000 per month, the households do not

consume positive quantities of LPG. As with further increases in income, the households do not

necessarily opt for LPG, any income driven approach will not ensure the expected transition.

Characteristics other than income will need to be targeted for such households. No other factor apart

from the price of LPG enables transitioning for the salaried households.


The table below summarizes which sections of the population would benefit the maximum from the

intervention aimed at improved cooking practices. As the tobit model was run to test the factors, the

stages of transitions are implicit in the fitted values taken by the dependent variable.

141


Transition Labour HHs Agricultural HHs Salaried HHs

Target HHs above income

of Rs.25000 per month or

electricity consumption

below 20 kWh


40000/month and less than 2

acres land




effecting a change in household cooking practices. Among labour households, the price of LPG has

the greatest impact followed by ownership of land assets. For agriculture households as well, the

price of LPG has the greatest impact followed by the role of women in household decision making

with regard to fuel purchases. As for salaried households, only the LPG price has an impact in terms

of promoting a change in cooking fuel use.



Marginal

Effects



price decreasing the probability

of transitioning by 20%,

followed by the size of land

owned and the education

attainment by the male

members wherein any increase

also increases the probability of

transitioning 1%.


impact as with an increase in

the price of LPG decreasing the


20% followed by the price of

kerosene that however

decreases the probability only

by 1%. The female member

participation in decision

making on HH fuel purchases

increases the probability of

transitioning by about 2%.

Only the Price of LPG has an

impact increasing

probability of transitioning

by 20 %

142

7.5 Rajasthan

Table 31: Regression Model Results

Y=Transition Labour HHs (Tobit) Agriculture HHs Salaried HHs


Social status 0.00013 0.977 0.29258 0.671 0.24431 0.460

MPCE class 0.00006 0.975 -0.06252 0.744 0.11139 0.354

Timelive -0.00063 0.229 -0.14126 0.077* -0.11135 0.005***

Price of kerosene

(log)

0.00135 0.709 0.15474 0.636 0.02716 0.889

Price of Firewood

(log)

0.00330 0.067* -0.23223 0.349 0.16007 0.169

Price of LPG (log) 0.19064 0.000*** 3.43022 0.000*** 3.04080 0.000***

Education level of

males (log)

-0.00185 0.747 0.05593 0.946 1.20254 0.106

Education level of

females (log)

0.00334 0.512 0.72861 0.302 0.01467 0.965

Land Size (log) -0.00929 0.246 -1.20847 0.072* -0.32898 0.371

Electricity Access 0.00973 0.354 1.86397 0.165 -0.21266 0.739

Kitchen window -0.00581 0.509 0.44193 0.387 0.87533 0.059*

Location of kitchen 0.01016 0.189 0.73428 0.196 -0.06884 0.922

Distance to collect

firewood

-0.00179 0.337 0.12020 0.669 -0.39731 0.088*

Female decision-

making

(omitted) (omitted) (omitted)

District 0.00305 0.048** -0.02002 0.929 0.53101 0.000***

Intervention (omitted) -2.00929 0.951 (omitted)

Constant 0.98427 0.000 -9.75089 0.015 -13.02497 0.000



Table 32: Regression Model findings



choices are subject to the

price of LPG and firewood,

and the region that the

belong to


driven by fuel prices, the

trade-off between working and

cooking for women, and extent

of land ownership


choices seem to driven majorly

by the price of LPG

143

Labour HHs

Among labour households, as income increases for the purely biomass-dependent the likelihood of

the HH shifting to LPG for cooking increases up to Rs 35000 per month, beyond which this

possibility declines. Thus, in order to facilitate any HH to continue looking at an LPG purchase even

after Rs 35000, other factors other than income will need to be targeted. At the same time, any

increase in the electricity consumption by such households also provides a positive outlook for

transitioning till 800 kWh of consumption per month, after which this probability falls relatively. In

both cases, we see that even as the probability to transition increases with increasing electricity

consumption and income, no household with peaking income or electricity consumption are LPG

consumers.

Agricultural HHS

Households who are completely dependent on biomass indicate a decreasing probability of moving

towards a cleaner energy basket with increasing income levels. Even in case of electricity, as the

electricity consumption per month increases for such households, they indicate a decreasing

probability of the willingness to spend on LPG up to 500 kWh per month after which the willingness

marginally increases. Contrarily, those households already using a mix of fuels with the share of LPG

in the cooking energy basket less than 31% indicate a positive probability of increasing the share of

LPG. This probability increases as household incomes increase up to around Rs. 45000 per month.

Among these households, up to electricity consumption of 400 kWh per month, they indicate an

increasing probability to about 0.4 to shift beyond which its declines. Interestingly, at the income

level of Rs 28,000 the biomass-dependent households as well as those consuming LPG but with less

than 31 % share in this energy basket, have similar level of probabilities of consuming LPG. After

this level, those with increasing probability continue to increase, and those with decreasing continue

to decrease.

The probability of households who use a mix of fuels with the share of LPG in the cooking energy

basket greater than 31% remains more or less unchanged with increasing income, implying that after

a household employs a certain share (≥ 31%) of LPG in its cooking energy share their willingness to

spend further falls drastically. Among these households, with increasing electricity consumption, the

probability to shift to LPG increases after 240 kWh per month.

Salaried HHs


towards a cleaner energy basket after the household income crosses Rs. 33000 per month. For such

households, once electricity consumption crosses about 420 kWh, the probability of expenditure on

LPG increases.

HHs already using mix of fuels with LPG share less than 31% indicate a positive probability of

further increasing the share of LPG with an increase in household income up to Rs. 31000 per month

with maximum probability of 0.83 after which it declines. Households using mix of fuels with LPG

share greater than 31%, indicate a probability of increasing LPG consumption as the monthly income

144

increases, however, this probability does not exceed 0.2. As the electricity consumption increases for

such households, the probability to shift increases marginally up till 350 kWh per month, beyond

which it falls. As compared to biomass dependent households, those with a positive share of LPG in

their cooking energy baskets indicate that once electricity consumption crosses around 400kWh, the

probability of increased expenditure on LPG decreases. The probability of increased LPG expenditure

decreases at a slower rate for households with a LPG share of greater than 31% than for households

with a LPG share of less than 31% but greater than zero. This highlights the highsensitivity of the

households that are LPG consumers, yet depend-heavily on biomass.


The table below summarizes which sections of the population would benefit the maximum from the

intervention aimed at improved cooking practices. A tobit model was run to test the factors for

Labour Households, while the agricultural and salaried households were tested through a generalized

order logit model.


Transition

stage Labour HHs

Transition

stage Agricultural HHs Salaried HHs

Target HHs below

income of Rs.35000 per

month

T1 Target HHs beyond Rs.

40000/month with over 40%

prob. and 500 kWh of

monthly electricity

consumption

Target HHs with

monthly income over

Rs. 33000

T2 Nearly 60% prob. that HHs

with income of Rs.

40000/month or more will



Target HHs below

income of Rs 30000



the greatest impact distantly followed by firewood price and the region of stay. For agriculture

households only the price of LPG has any significant impact. As for salaried households, the LPG

price has the greatest impact followed by the placement of a kitchen window.

145



Marginal

Effects



price decreasing the probability

of transitioning by 20%.


impact as with an increase in

the price of LPG decreasing the

odds of transitioning by 30

times.

The price of LPG has an

impact increasing the odds

of transitioning 20 times

followed by a window not

being there in the kitchen

that increases the odds of

taking up LPG by 2.

7.6 Odisha

Table 35: Tobit Model Results

Y = Transition Labour HHs Agriculture HHs Salaried HHs

Coefficient P>|t| Coefficient P>|t| Coefficient P>|t|

Social status 0.00819 0.048** 0.01504 0.000*** 0.01007 0.237

MPCE class 0.00063 0.718 0.00103 0.540 0.00083 0.814

Timelive -0.00034 0.448 -0.00074 0.081* -0.00111 0.239

Price of kerosene (log) -0.00739 0.078* -0.00866 0.074* -0.00287 0.754

Price of LPG (log) 0.24649 0.000*** 0.24059 0.000*** 0.22082 0.000***

Price of firewood (log) -0.00177 0.365 -0.00497 0.011** -0.00198 0.567


(log)

0.00585 0.323 0.00438 0.491 0.01426 0.359


(log)

-0.00223 0.679 -0.00412 0.463 -0.01183 0.310

Land Size (log) 0.01629 0.172 -0.00870 0.230 -0.04578 0.017**

Electricity Access -0.01786 0.114 -0.00992 0.303 -0.00761 0.755

Location of kitchen -0.01319 0.184 -0.00733 0.446 0.01913 0.398

Kitchen window -0.00756 0.563 -0.01936 0.102 -0.00179 0.943

Distance to collect firewood -0.00259 0.000*** -0.00213 0.001*** -0.00127 0.190

Female decision-making 0.01830 0.149 -0.00990 0.432 0.02567 0.331

District -0.00583 0.133 -0.00321 0.345 0.00079 0.931

Intervention 0.03227 0.238 -0.00303 0.914 -0.05097 0.447

Constant 1.06447 0.000 1.07996 0.000 0.98436 0.000



Table 36: Tobit Model findings

146



choices depend on their

social status, the

distance travelled to

collect firewood as well

as fuel prices (LPG and

Kerosene).


driven by their social status, the

type of occupation women are

involved in, prices of primary

fuels and the distance travelled

to collect firewood.


are subject to the price of LPG in

the market and the land

ownership by the households

Labour HHs

Among labour households, as the social status of the households improves, the likelihood of

switching to LPG increases. Better social standing could indicate higher income-earning capacity as

well as better influence in the society, thus increasing the probability of including a higher share of

LPG in the cooking fuel basket. Beyond an income level of Rs 18000 per month, the households

purely dependent on biomass are likely to take up LPG. It is also found that as electricity

consumption increases, households indicate an increasing probability of shifting to LPG (clean

energy) up till 60 kWh of monthly consumption. Beyond this level, households probably are not able

to convert the additional supply of electricity towards any productive use, thus resulting in a

decreasing possibility for LPG use. Moreover, even for households indicating an increasing

probability, the transition value doesn‘t go beyond 1.3, that is the households still stay completely

biomass dependent.

Agricultural HHs

Among Agricultural households as well, an improvement in the social status indicates an increasing

willingness to spend on LPG. In terms of income, the households indicate an increasing probability of

shift to LPG up to a monthly income of Rs. 20,000. Beyond this level, the probability falls sharply to

the extent of no uptake at Rs. 46,000 per month. However, in either case, the households dominantly

remain biomass-dependent (transition value below 2). Conversely, an increase in electricity

consumption increases the probability of moving towards a cleaner energy basket. Even beyond 200

kWh, the households indicate a high probability of taking up LPG in their fuel basket. Therefore, in

this case, access to electricity and productive use of electricity has a greater impact on clean cooking

fuel choices rather than just an increase in income.

Salaried HHs

As expected, with increasing incomes, salaried households indicate a higher propensity to consume

LPG in their daily cooking activities. Beyond an income level of Rs 48,000 per month, households

indicate a tendency to increase the LPG uptake in their cooking energy basket, especially from being

completely biomass-dependent to including a positive use of LPG (share of LPG in energy basket

147

>0). At the same time, for these households, increasing electricity consumption is probably resulting

in productive use and as a result a higher use of LPG. However, in the current sample, despite the

increasing probability to take up LPG, with any increase in electricity levels the households, the

impact is marginal and households indicate a tendency to remain biomass-dependent.



Transition Labour HHs Agricultural HHs Salaried HHs

Target HHs with income

below Rs 19000/month

and electricity

consumption below 60

kWh/month

Target HHs with income below

Rs. 20000/month and improve

electricity supply





the greatest impact indicating the highest odds of a positive change towards cleaner cooking practices

followed by intervention programs aimed at livelihood improvement through income and asset

management. For agriculture households, the price of LPG has the greatest impact followed by

improvement in social status and higher female participation in HH fuel purchase decision. As for

salaried households, LPG fuel has the greatest impact followed by female participation in HH

decision making.



Marginal

Effects

The price of LPG has the

greatest impact at decreasing

the probability of transitioning

by 25% followed by

intervention programs aimed

at livelihood improvement

through income and asset

management which increases

the probability of transitioning

by over 1%.



price of LPG decreasing the


24%. The improvement in social

status increase transitioning

probability by 2% while

increased female participation

improves the chances of

transitioning by 1%.


impact with decreasing the

likelihood of transitioning by

22% followed by increased

female participation that

improves the chances of

transitioning by over 2%.

148

7.7 All States


Y=Transition Labour HHs Agriculture HHs Salaried HHs


Social status 0.11854 0.324 0.013631 0.879 0.16696 0.098*

MPCE class 0.031869 0.517 0.000565 0.986 -0.00168 0.965

Timelive -0.03488 0.015** -0.00739 0.471 -0.03004 0.012**

Price of kerosene (log) -0.05037 0.504 -0.0305 0.606 0.097617 0.129

Price of firewood (log) 2.09485 0.000*** 1.970246 0.000*** 2.117473 0.000***

Price of LPG (log) -0.13862 0.012** -0.14886 0.012** -0.24153 0.000***

Education level of

males (log)

0.070733 0.683 -0.45996 0.002*** 0.236676 0.142

Education level of

females (log)

0.010911 0.943 0.057588 0.605 -0.11122 0.392

Land Size (log) -0.11296 0.381 0.082857 0.304 -0.34054 0.021**

Electricity Access -0.0054 0.989 0.257321 0.345 -0.21958 0.552

Location of kitchen 0.109964 0.724 -0.08091 0.687 -0.02852 0.437

Kitchen window -0.14017 0.692 -0.08389 0.770 -0.17214 0.558

Distance to collect

firewood

-0.01559 0.532 -0.01185 0.574 -0.01562 0.653

Female decision-

making

0.083693 0.723 0.068212 0.698 -0.04064 0.834

District -0.00297 0.211 0.0005 0.857 0.002856 0.160

Intervention 0.563361 0.149 0.805641 0.002*** -0.04942 0.897

Agro-climatic zone 0.15252 0.103 0.087547 0.210 0.104886 0.129

Maharashtra 2.532405 0.000*** 0.493452 0.520 3.227106 0.000***

Himachal Pradesh 1.545264 0.183 Omitted 2.090462 0.050**

Goa Omitted -0.66732 0.454 1.302895 0.127

Karnataka -0.21811 0.815 -2.09394 0.016** Omitted

Rajasthan 0.42984 0.460 -1.7619 0.047** 0.437158 0.593

Odisha 2.023576 0.004*** 0.070986 0.929 1.341372 0.135

Constant -8.14796 0.000 -4.70598 0.000 -7.19328 0.000



149






working women in the

household, the price of

LPG and firewood, and

the fact that they are

residents of

Maharashtra or Odisha.


driven by the price of LPG and

firewood, education of male

members, the region to which

they belong which drives both

land quality and agri-benefit

schemes, and whether they are

beneficiaries of any intervention.


seem to be driven by social status,

employment security of women,

fuel prices, and the fact that they

are residents of Maharashtra and

Himachal Pradesh.

Labour HHs

Households that are completely biomass dependent, indicate that as incomes increase, the propensity

for LPG uptake increases across households once the household income crosses Rs. 33000 per month.

For households with a positive share of LPG but less than 31% of the cooking energy basket, the

probability of increasing LPG consumption goes up to 0.5 and then decreases once the household

monthly income crosses Rs. 30000. For households with LPG share of greater than 31%, the

probability of a positive transition is very marginal. For households completely dependent on

biomass, with an increase in electricity consumption, the probability of transitioning to a cleaner

cooking energy basket increases once the household electricity consumption crosses about 500kWh.

For households with a positive share of LPG in the cooking energy basket, they indicate that with

increasing electricity consumption, especially beyond 500kWh per month, the probability of higher

LPG consumption decreases. This indicates that for households already using some amount of LPG,

an increased access to electricity could result in no productive use of the units consumed.

Agricultural HHS



completely biomass dependent households, once the electricity consumption crosses about 500 kWh

per month, they indicate an increasing probability of the willingness to spend on LPG. In comparison,

those households already using LPG with its share in the cooking energy basket less than 31%

indicate a positive probability of increasing the share of LPG up till a household income of around Rs

40000 per month. This probability decreases as household incomes cross Rs. 40000 per month and

the monthly electricity consumption goes beyond 5000 kWh. For households with LPG share greater

than 31% in the cooking energy basket, the probability of transition towards LPG increases with

rising income levels and decreases after the electricity consumption crosses 500 kWh per month.

150

Salaried HHs



such households, once electricity consumption crosses about 500kWh, the probability of expenditure

on LPG decreases.

Households already using mix of fuels with LPG share less than 31% indicate a positive probability

of further increasing the share of LPG with an increase in household income up to Rs. 30000 per

month after which it decreases. Once the household‘ electricity consumption crosses 600kWh per

month, its probability to shift to LPG declines. Households with LPG share greater than 31% in their

cooking energy basket, show a decreasing propensity to spend more on LPG after an income level of

Rs 30000 per month and beyond an electricity consumption of 500 kWh per month.



Transition

stage Labour HHs Agricultural HHs Salaried HHs

T1 Target HHs below income



50000/month and less than 3

acres land



T2 50% prob. that HHs with

income of Rs.

30000/month or below

will shift to an energy

basket with LPG share >

31%

50-60% prob. that HHs with




Target HHs with income of Rs.

30000/month or less to sustain a

shift to an energy basket with

LPG share > 31%


effecting a change in household cooking practices. Among labour households the price LPG

significantly indicates a positive change towards cleaner cooking practices, followed by the fact that

the households reside in Maharashtra or Odisha. For agriculture households, interventions aimed at

livelihood security or energy interventions along with improving LPG affordability have the greatest

impact. As for salaried households, fuel prices and the fact that the households reside in Maharashtra

or Himachal Pradesh have the maximum impact in terms of promoting a change in cooking fuel use.

151



Changing

Odds

A decrease in the price of LPG

would increase the odds of

transitioning towards cleaner

fuels by 8 times. If the

household is a resident of

Maharashtra or Odisha, the

odds of a positive transition

increase by 12 and 7 times

respectively.

Energy and livelihood

interventions have a significant

impact with being a beneficiary


transitioning by 2 times,

followed by price of LPG

wherein an increase in LPG

price would decrease the


7 times.

Price of LPG has a significant

impact with a 10% decrease

in the price of LPG


transitioning by 8 times. If

the household is a resident

of Maharashtra or Himachal

Pradesh, the odds of

transitioning increase by 25

and 8 times respectively.

152

8. Gender Roles in Energy Transitions

Energy needs, roles and responsibilities as well as resource access and control differ among men and

women members of a households, thus highlighting the need to focus on the role and impact of

gender in making household energy choices. While women direct the use of cooking energy fuels

(biomass and LPG) in the household, it is the men who often take decisions with regard to purchasing

domestic energy or ensuring fuel access to the household. The role of women in energy use involves

taking out time in the day to travel kilometres and gather firewood, collect and processing agricultural

residue and preparation of dung cakes and finally putting the fuels to use. As biomass-based fuel

sources are used in traditional cookstoves, it often exposes them to IAP causing respiratory health

issues among other risk-related concerns. In addition, cooking practises that adopt traditional fuels

tend to take more time than cleaner and more efficient fuels. This limits women‘s time in the day that

could‘ve otherwise been engaged in income generating activities. Men on the other hand, are mainly

responsible for financial matters relating to fuels in households where women do not engage in

income generating activities and at times do play a role in collection and transportation of

firewood/crop residue and LPG.

With the aim of identifying and studying the extent and impact of the participation of men and

women in facilitating a fuel transition to clean, more efficient alternatives, the study incorporates

gender specific characteristics that include education, occupation and household decision-making. As

income is a one of the key driver to transition, the source of income among the earning member in the

households is captured by the classification of information as per labour, agriculture and salaried

households. The level of education attainted by the male and female members of the household

characterizes the level of understanding and general awareness on household matters, livelihood and

energy schemes and village development. Women‘s involvement in the household decision making

for fuel purchases as well the trade-off they face in working in a formal job and the hours left in a day

for cooking meals signify the impact that the role of women would have on facilitating shifts to

cleaner fuels.

It is widely understood that in most rural households (labour, agricultural or salaried), it is the male

member, who is often also the head of the households, that makes key decisions with regard to

expenditures, investments, children education, and employment among others. The inclusion of LPG

in the cooking basket by such households indicates a level of understanding of the costs and more so

the additional benefits of using LPG as against biomass-based fuels. While more formal forms of

employment would cause such shifts owing to the regular income streams, only a certain level of

education among the men in labour and agricultural households would increase the probability of the

same. These trends are visible in Maharashtra, Goa and Karnataka labour and agricultural households

implying a growing understanding of the benefits of clean energy sources among men.

All states highlight some male or female-specific factor impact on the probability of transitioning.

Among labour households in Maharashtra, an increase in the value of female labour such that women

employed in daily wage or agricultural labour activities as against being a housewife or unemployed

increases the probability of the household including for LPG in their cooking fuel basket. This is due

to the fact that as women engage in income generating activities outside home, their time available

for cooking reduces, thus compelling them to choose a more efficient and convenient fuel. A similar

153

relationship in the women working and the likelihood in transitioning to LPG has been observed

across all states majorly among labour and agricultural households (refer Table 47 in Annexure I). In

certain households, an income generating role also gives women a participatory power in the

household decision making process. As per the TERI survey data, states where the female

involvement has come out as a significant factor in testing for an shift have seen a positive influence

of women‘s say in any fuel purchases for the household. In Himachal Pradesh, among the labour

households the significance of female involvement in fuel-related decision making could be linked to

the additional income that she brings from agricultural and daily wage labour. Similar results are

observable for the agricultural households in Karnataka where nearly 50% per the women members

are involved in self-owned agricultural land work and daily wage labourer. However, in case of

salaried households in HP and Goa where majority of the women are housewives, the female impact

on fuel purchases can be attributed to the fact that it is the woman of the household that takes all

decision related to cooking and surrounding kitchen activities.

The education level of women in the households has not come out as a major variable for impacting

transition to LPG, except in case of labour households in Himachal Pradesh. This further emphasises

the fact that irrespective of the level of education attained by the female member, unless they

contribute to the households expenses via income, they are less likely to impact the fuel choices made

in the household.

154

9. Willingness to Pay

Household perception and aspirations towards provisioning of improved energy services

The household survey questionnaire dealt with a section on ‗Willingness to Pay‘ wherein respondents

were informed about the benefits of using modern fuels, the time savings involved and so on in the

context of cooking while a similar exercise was conducted for lighting as well. A brief of the

information enumerated to the respondents during the survey has been provided below followed by

an overview of the responses obtained.

Cooking

Over time, firewood has become difficult to get and there has been a need to travel longer distances to

procure it. In the near future, as forest cover reduces, the availability of firewood will become

increasingly difficult and there will be a need to buy it from the market. Also, the smoke from

burning of firewood has negative impacts on your health, making you prone to diseases including

respiratory diseases and lung cancer, thus reducing your ability to work and in turn your wages. An

improved cooking appliance may have greater benefits. The benefits of using alternate cookstoves

include use of lesser firewood, less smoke, and thus lesser medical expenses. Also, with requirement

of lesser firewood, you would save considerable time from collection of firewood and can use that

time for other productive purposes that lead to enhancement of your livelihood.

Benefits of using an Improved Cookstove

1. An improved cookstove is an alternate cooking option which is similar to the traditional

cookstove. It also uses firewood as the main cooking fuel but has a chimney attached to it

which protects you from the smoke. It uses lesser firewood than the traditional cookstove as

the design of the stove burns the firewood better. You can reduce your firewood consumption

by 1/3rd.

2. The improved cookstove also comes as a single burner or double burner stove. The cookstove

can be made out of mud or from stainless steel.

3. The life of an improved cookstove made out of steel ranges from 12-15 years while the one

made out of mud lasts for 6-8 years with minor repairs needed.

4. The improved cookstove involves only a one-time cost of Rs.1200 to buy it in the beginning

after which the only costs involved are that of procuring the firewood.

Benefits of using an LPG Gas Stove

1. The benefits of using an LPG stove include no use of firewood, no smoke, lesser medical

expenses, no respiratory diseases, safe fuel, and lesser pressure on natural resources.

2. The LPG Gas Stove involves an upfront cost of Rs.1400 for the gas connection and about

Rs.1500 for the stove. After this initial payment, the costs involved are that of procuring the

gas cylinder on a regular basis wherein each cylinder costs about Rs.450.

Lighting

For lighting, there are various alternate forms of lighting systems available which would consume

lesser electricity. By using these appliances, you would save your expenditure on electricity in terms

of lesser electricity bills. While many do not have metered connections, soon meters will be installed

155

across all villages. Thus, moving to alternate lighting appliances that are more efficient and would

help save electricity costs would be useful for you. Some of the options are listed below.

1. Tubelight

a. The benefits of using a tubelight are that it lasts about 4 times longer than an ordinary

bulb and gives more light. It is also three times more efficient than an ordinary bulb.

b. The cost of one tubelight is about Rs.50 for a 40Watt tubelight.

2. CFL Bulb

a. The benefits of using a CFL Bulb over a tubelight and ordinary bulb are that it uses

1/4th the electricity consumption of an ordinary bulb and lasts about 5 times longer

than an ordinary bulb.

b. The cost of one CFL bulb is Rs. 120 for 100W bulb.

3. LED Bulb

a. The benefits of using an LED Bulb over an ordinary bulb are that it uses 1/10th the

electricity consumption of an ordinary bulb and lasts about 8 times longer.

b. The cost of one LED bulb is Rs. 300 for a 100W bulb.

4. Solar Lantern

a. The solar lantern charges from sunlight which is free. It comes with a small solar panel

for charging and an attached LED bulb which gives sufficient light. Once purchased,

the solar lantern has no costs involved and can be used for a lifetime. The only

additional costs would be to replace the battery every 1.5 years which costs Rs. 220

and some small components that cost Rs. 40-50; the bulb has a life of 10 years and

costs around Rs. 120-150.

b. The benefits of using a solar lantern include no electricity consumption, enough light

to read, write and take up other activities, the brightness can be adjusted, lasts for 5

years with guarantee of 1 year and regular maintenance. It can reduce your expenditure

on kerosene as a lighting fuel.

5. It can also be carried around while walking or going to the field in the evening or when some

family member goes to the forest during nightfall.

6. A solar lantern costs about Rs.1500.

Based on the responses of households towards cooking and lighting preferences, a summary of

findings for each state has been provided in the sections below.

156

9.1 Maharashtra

Cooking Preferences

• 65% households prefer a LPG stove if given a choice, and among these 78% household are

not willing to pay more than Rs. 400 per month for the cylinder

• 15% households are satisfied with traditional cookstove

• 20% households are willing to switch to improved cookstove but not pay more than Rs.800

for the stove

• 86% households that are using LPG are satisfied with the delivery in the village

• Out of 800 households, about 35% households have tasted food cooked on LPG of which

• 45% households feel that food cooked on LPG is better

• 30% households feel its tastes same

• 25% households feel that food cooked on firewood is better

• 70% of households are unwilling to pay the connection cost of Rs. 3000 to procure LPG

• 66% households are willing to pay Rs. 1500 or below to get LPG connection

Solar Energy

7% households indicate use of a solar

appliance

Maximum households (~98%) use it for

lighting and reading.

Households have paid Rs 350 for solar torch and Rs 625 for solar lantern on average

Biogas

2% households are using biogas for

cooking and have individual units at

home

40% biogas users installed it based on government initiative

The cost incurred in setting up a biogas is between Rs 10,000-

15,000

90% of the households are satisfied with the

operations of the biogas plant

Lighting

CFL bulb is preferred by 73% households

Solar lantern is preferred by 20%

households

Willingness to pay is about Rs. 45 per

month rental for its usage and to buy the lantern the willingness to pay is Rs 250-300.

157

• LPG cylinder – 80% households are willing to pay Rs. 400 or below per month and the

average willingness to pay is around Rs. 200-250 per month


Cooking Preferences




• 14% households are willing to switch to improved cookstoves but not pay more than Rs.1000

for the stove







Solar Energy


appliance



Obtained Free/donation

Biogas

<1% households are using biogas for


home

Installed it based on government initiative


15,000

90% of the households are

satisfied with the operations of the

biogas plant

Lighting



households


month or Rs.7/day rental for its usage

and to buy the lantern the

willingness to pay is Rs 400-850.

158

• 63% households are willing to pay Rs. 1500 or below to get an LPG connection



9.3 Karnataka

Cooking Preferences





for the stove






Solar Energy


appliance



Households have paid Rs Rs 850 for solar lantern on average

Biogas



home



15,000

96% of the households are

satisfied with the operations of the

biogas plant

Lighting



households


month or Rs.2/day rental for its usage

and to buy the lantern the

willingness to pay is Rs 250-600.

159





9.4 Goa

Cooking Preferences

• 9% households prefer a LPG stove if given a choice and among these 42% households are not

willing to pay more than Rs. 400 per month for the cylinder


• 55% households are willing to switch to improved cookstove and are willing to pay more

between Rs.800 - 2000 for the stove





Solar Energy


appliance



Households have paid Rs 280 for solar torch and Rs 2500 for solar lantern on average

Biogas



home



15,000

80% of the households are satisfied with the

operations of the biogas plant

Lighting



households

To buy lantern the willingness to pay is

Rs 800 - 850.

160






9.5 Rajasthan

Cooking Preferences





for the stove




Solar Energy

<1% households indicate use of a solar

appliance

Main use is lighting

Households have obtained free.

Biogas

No households are using biogas

Lighting



households

To buy lantern the willingness to pay is

Rs 200 - 500. On rental basis, HHs willing to pay Rs.

4/day or Rs. 90/month.

161







9.6 Odisha

Cooking Preferences





for the stove



Solar Energy

3% households indicate use of a solar appliance



Households have paid Rs 500-600 for solar lantern on average

Biogas

<1% households are using biogas for


home

All biogas users installed it based on government initiative


7,000

Households are satisfied with the operations of the

biogas plant

Lighting



households

To buy lantern the willingness to pay is Rs 200 - 500. For rental, daily basis WTP is Rs. 4/day

while monthly basis is Rs. 120.

162








163

10. Lighting Index

The fundamental prerequisite for lighting is access to electricity. Various efforts have been made to

measure access to energy services for households. Most of these measures are composite in terms of

looking at cooking and lighting together.

The most recent definite index that has been developed is the Multidimensional Energy Poverty Index

(MEPI) by Nussbaumer, et al. (2012), focusing on the set of energy deprivations that may affect a

person. This approach takes into account the factors related to access, affordability and greater issues

of ―capability deprivation‖ and indoor air pollution. The index assigns weights to indicators of use of

modern fuel, indoor air pollution, electricity access along with use of appliances which is of utmost

importance to the household.

Adopting a similar methodology as the MEPI, an index for electricity access has been created. The

Index under the study has been calculated using the weighted sum method for variables looking at

electricity access, use of lighting appliances and other basic household appliances. These include

indicators for Electricity Access (v1), ownership of lighting appliances (v2), ownership of TV/radio

(v3) and ownership of mobile phones (v4). The variables used in the index have been defined below.

Defining Electricity Access

Electricity access can influence a household‘s way of living significantly. Electricity access measures

the overall availability of electricity to the household for various in-house uses such as lighting,

entertainment and device charging. The presence of reliable and good quality supply of electricity

allows a household to take up other activities even after sunset, thus prolonging the number of hours

available for productive work in the day. This can impact household incomes significantly resulting

in changing lifestyles and thus lead to changes in household expenditure patterns and possibly fuel

choices as well. It is very important to focus on how ―electricity access‖ is defined.

In the paper by Nussbaumer, et al. (2012), a household is considered deprived of electricity if it has

no amount of electricity supply coming in. In most of rural India, while there is provision for

electricity supply, the supply hours are very erratic and very often people end up paying for electricity

that has no use for them (TERI Survey, 2013). For example, supply of electricity for 3 hours in the

day from 10AM to 1PM has no use for the household members as all are out working, whereas the

same three hours of supply from 6PM to 9PM or 7PM to 10PM would enable the household to take

up productive activities or allow children to study and so on. Thus, given short hours of electricity

supply, ―access‖ to a household is really defined as the point when the value of the payment they

make for an ―energy service‖ (in this case, electricity) is fully realized by productive use of the

duration of supply. Thus, in this case, ―electricity access‖ for a household is defined as electricity

supply anytime between 6PM to 10PM for at least 20 days a month.

In the Integrated Energy Policy (2006), by the Planning Commission, Government of India, it has

been suggested that the minimum threshold electricity required by a household would be around

30kWh so as to meet minimum energy needs.

Thus, taking into account both the findings from the survey on redefining electricity access and the

threshold electricity requirement for households as per the IEP, the deprivation index for electricity

access has been defined as in the table below.

164

Table 43: Deprivation cut-off for Electricity access

Deprivation cut – off Deprived/Not deprived

Electricity access but quantity of electricity

consumed in a month is less than 30KwH

Deprived

Electricity access but quantity of electricity

consumed in a month is more than or equal to

30KwH

Not deprived

No Electricity access Deprived

Deprivation of modern appliances that use electricity

The other three variables that have been considered are related to ownership of lighting appliances,

entertainment appliances and communication appliances. Lighting appliances include electricity

consumption by appliances including tubelights, incandescent bulbs and compact fluorescent lamps

(CFL) that are regularly used by rural households. Electricity used for TV/radio and mobile phone

charging provide for additional indices thereby accounting for a more comprehensive basket of

electricity consumption. The inclusion of other appliances apart from basic lighting needs takes into

account the larger issue of electricity access, affordability of energy services and productive use of

available energy. Electricity access alone doesn‘t cater to the needs of the end user if they do not have

the financial means to invest in appliances that deliver the desired energy service. (Nussbaumer, et

al., 2012).

The definition of deprivation has been adopted from the OPHI paper, which states deprivation in

appliance ownership is equal to 1 if the household owns the appliance and 0, if not. A household is

considered deprived in lighting if it does not own any form of lighting appliance that runs on

electricity. In terms of entertainment services, a household is considered deprived if it doesn‘t own a

radio or a television. Presence of either is considered as having access to entertainment appliances.

Deprivation of communication services is considered if a household owns no mobile phone. The

detailed methodology of the index is given in the next section.

Methodology for Index calculation

Formally, the index measures electrical energy poverty in d variables across a population of n

individuals. Y =[ yij ] represents the n x d matrix of achievements for i persons across j variables. yij>

0 therefore denotes the individual i achievement in the variable j. Thus, each row vector yi = (yi1, yi2,

…, yid) represents the individual i achievements in the different variables, and each column vector yj

= (y1j, y2j, …, ynj) gives the distribution of achievements in the variable j across individuals. The

methodology allows weighting the indicators unevenly if desired. A weighting vector w is composed

of the elements wj corresponding to the weight that is applied to the variable j. Nussbaumer, et al.

(2012), defined ∑d

j=1 wj= 1.

They define zj as the deprivation cut-off in variable j, and then identify all individuals deprived in

anyvariables. Let g = [ gij ] be the deprivation matrix whose typical element gij is defined by gij = wj

when yij < zj and gij = 0 when yij ≥ zj. In the case of the index, the element of the achievement

165

matrix being strictly non-numeric in nature, the cut-off is defined as a set of conditions to be met. The

entry ij of the matrix is equivalent to the variable weight wj when a person i is deprived in variable j,

and zero when the person is not deprived. Following this, they construct a column vector c of

deprivation counts, where the ith entry ci = ∑d

j=1 gij represents the sum of weighted deprivations

suffered by person i. They then identify the persons multi-dimensionally poor in terms of electricity

access by defining a cut-off k > 0 and applying it across the column vector, and consider a person as

‗energy poor‘ if his/her weighted deprivation count ci exceed k. Therefore, ci (k) is set to zero when ci

≤ k and equals ci when ci > k. Thus, c (k) represents the censored vector of deprivation counts, and it

is different to c in that it counts zero deprivation for those not identified as multi-dimensionally

‗energy poor‘.

Finally, they compute the headcount ratio H, which represents the proportion of people that are

considered ‗energy poor‘. With q as the number of ‗energy poor‘ people (where ci > k) and n the

total, we have H = q / n, which represents the incidence of multi-dimensional ‗energy poverty‘. The

average of the censored weighted deprivation counts ci (k) represents the intensity of multi-

dimensional ‗energy poverty‘, A. More formally, they calculate A = Ʃ n

i=1 ci(k) / q .

The Indexcaptures information on both the incidence and the intensity of electrical energy poverty,

the variables for which are derived directly from the data of energy deprivation at the micro-level,

and is defined as I = H * A.

Thus, the methodology has a virtue of decomposability allowing for a wide range of analysis focusing

of sub-groups (e.g. MPCE class). Also, the methodology respects the constraint of dimensional

(variable) monotonicity. That is, both if an additional person becomes poor and if a person considered

multi-dimensionally poor becomes poor in an additional variable, the aggregate value of the index

increases.

Table 44: Index parameters and weights

Dimension Indicator (weight) Variable Deprivation cut-

off (poor if..)

Lighting Electricity Access

(0.34)

Has access to electricity FALSE

Lighting appliances Appliance

Ownership (0.22)

Has an incandescent,

tubelight or CFL bulb

FALSE

Education/Entertainment Appliance

Ownership (0.22)

Has a TV/Radio FALSE

Communication Appliance

Ownership (0.22)

Has a mobile phone FALSE

The results of Index of deprivations are provided in the table 45.

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Table 45: Electricity Access Index results

State Index

MAHARASHTRA 0.28

HIMACHAL PRADESH 0.02

GOA 0.06

RAJASTHAN 0.34

KARNATAKA 0.35

ODISHA 0.42

The index has been generated for the six states which have been surveyed as part of this study. As

indicated in the table, the state of Odisha has the highest index value of 0.42 while Himachal Pradesh

has the lowest index value of 0.02. We find that Goa and Himachal Pradesh have the lowest incidence

of ‗energy poverty‘ among the selected states whereas Odisha has the highest incidence of energy

poverty in terms of access to electricity and its productive use.

In comparison to a benchmark cut-off of 0.3 for the index value, we find that Rajasthan, Karnataka

and Odisha are above the cut-off measure with considerable attention to be directed towards these

three states in terms of ensuring access to electricity in a reliable manner.

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11. Case Studies

11.1 Rajasthan: Gender and Energy Transitions

Santosh is a middle aged anganwadi worker and a homemaker, with three children. The primary

source of income for the family is through a grocery shop run by her husband. For lighting purposes

the household is well connected to the metered grid and they have been getting electricity for the past

25 years. The power in the house is consumed by appliances including the water motor pump,

washing machine, electric heater and lights and fans. The grocery shop is also connected to the grid.

While electricity is available for most part of the day, load shedding occur for 1-2 hours and in such

situations, they resort to the use of kerosene lamps and chargeable torches in the house as well as the

shop.

For cooking purposes, until a decade back, Santosh relied solely on traditional fuels and kerosene and

used the conventional chulah. Now with the shift to cleaner fuel like LPG the conventional chulah

and the hazards and drudgery of collecting fuel wood is a thing of the past. ―LPG is more convenient

and cleaner and moreover it does not blacken and dirty the house like the fuel wood‖ expressed

Santosh. She also opined - ―the upfront cost of Rs 5000 for the regulator, connection and the cook

stove is worth the money and I can comfortably pay for the cylinders from the salary I get as an

anganwadi worker‖. She does not want to own an improved cook stove even if the running cost is less

than an LPG. Santosh personally did not come across any major barrier in carrying out this shift in

energy usage as affordability and access was not a major concern. She has also tried to spread the

awareness regarding the use of LPG for cooking in the village, though there are some preconceived

notions of fear and safety regarding the usage of LPG.

Analysis of rural energy transition from a gendered lens clearly shows that there is sufficient evidence

that women have more propensity and willingness to use LPG as cooking fuels than men. When

households‘ transitions to LPG use women will be spared from their responsibility and drudgery of

provisioning for biomass based fuels and can use the available time for more productive activity and

leisure. Switch to cleaner fuels like LPG will also have positive impact on the health of women.

Income is also important determinant of quantum of various kinds of cooking fuels used by a

household.

11.2 Himachal Pradesh: Innovation in cooking practices

In the districts of Lahul and Spiti, Himachal Pradesh, which is cut-off from the mainland due to heavy

snow-fall for six months in a year, households have been using a different kind of chulhas for many

years. The chulhas are customised and made for each household, depending on their needs in terms of

the size of the vessels and number of vessels. On an average the chulhas have a life span of 10-15

years. The communities opined that their chulhas saves firewood as compared to other traditional or

improved chulhas and served the twin purpose of cooking and heating.

The chulhas are placed centrally in the room so that its heat could be used to keep the place warm

while allowing for simultaneously carrying out cooking activities, and has a chimney attached to it

allowing for the smoke to leave the house. It is also considered safe for children and patients

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suffering from asthma and eye diseases. There are two families located in the town of Keylong, who

manufacture these improved cookstoves and have been doing so for the past 40 years and they get

regular orders from across the district as they have managed to improvise the design to suit the needs

of the local people. The chulhas costs in the range of Rs.500 to 2500 depending on the different sizes

and purposes. Some of the households also use LPG during the months of May-November when road

is better.

11.3 Madhya Pradesh: Redefining Energy Access

As we walked across the barren fields, we reached the humble, two-room house of Sonabai who lives

with her husband in Dindori district of Madhya Pradesh, India. Sonabai was tending to her

grandchildren while her husband and sons had gone to work in the fields. Our field workers

introduced us to Sonabai and told her that we had come to research on issues pertaining to energy

access focusing on cooking and lighting. She listened silently and then began with recounting her

struggle to make ends meet. The little land they owned gave them just enough for subsistence. The

other income flows came from daily wages as agricultural labourers and selling firewood. Most of the

time, firewood was exchanged in return for household cooking items such as masala and spices. She

walks about 10km each day to the forest to bring back one bundle of firewood (approximately 10kg)

of which half is used at home and the remaining is sold or exchanged. Their woes do not end here –

they had taken a loan of Rs.6000 about 10 years ago which they are still struggling to repay. Their

major expenses are on food and health which amount to about Rs.1200 every month.

As we sat on a sack of grains, Sonabai continued, ―Electricity comes for only one hour a day in

total….the bulb is always on….It suddenly lights up in the middle of the night or in the day… what is

the use….it was better without electricity….We still spend Rs.100 every month on kerosene and now

we have to pay another Rs.60 for electricity which is of no use.‖Sonabai‘s husband and sons returned

home from the fields and joined the conversation. ―I cook twice a day and each time it takes at least 2

hours. But I have no choice…. Firewood is the only the option. LPG is too expensive‖, she continued.

We told her that maybe she could use the improved chulha for cooking. ―Even Rs.100 for a cookstove

is too much. I know that my wife sits for hours in the smoke while cooking, but we have no choice‖,

said her husband.

She left us with a pertinent question, ―What is access?‖ and while we pondered over that, Sonabai

went back to making her mud cookstove. With seasonal income flows, no substantial savings, no

access to basic services, there are many like Sonabai who make us realize that energy access and

development are deeply interconnected.

11.4 Maharashtra: Case of Reverse Transitions

In the Panchghar Village of Thane district, Maharashtra, efforts to enable a positive move towards

modern use of fuel did not turn out as expected. With nearly 88 households, primarily BPL, the

primary occupation is either that of daily wage labour or of agricultural activities. The village has a

government-operated primary school with classes up to the 4th

grade. The village is also

activelyinvolved in forest related activities with an established Joint Forest Committee as well as a

‗Paristhikiya Sanstha‖ established under forest department. The Paristhikiya Sanstha receives Rs. 10

169

lakh per year for undertaking developmental activities. These include watershed development,

construction of bund walls, livestock for fodder and LPG distribution.

The fuel consumption in the village is primarily biomass-based for cooking purposes and kerosene

for household lighting. LPG supply is over a year old with about 30 households having connections.

The initial connection cost is Rs. 1300 with a per cylinder cost of Rs. 450. There are no transmission

lines present in the village for electricity supply. Addressing the absent electricity scenario in the

village, a large private sector company installed solar home systems in 2007 in every house as part of

its Corporate Social Responsibility (CSR) project. The project was undertaken for a period of 5 years.

This supply of electricity via solar panels was used for lighting and running a fan in the house. At the

time of the TERI primary survey, the solar home system was in the fourth year of operation. Despite

operation and maintenance training in the village, the systems were found to function improperly.

Further, the costs incurred on part of the households tended to be high for their monthly expenses. As

a result of these shortfalls in the initiative, the households effectively transitioned back to a situation

of no electricity. Moreover, households too poor to even purchase kerosene were forced to use

candles for lighting purposes.

While the initiative undertaken by the private sector was favourable in bringing solar technology to

the village, a more programmatic approach in implementing the project would have proved successful

in the long term. Possible solutions in this regard could be a establishing a public-private partnership

such that the private sector could have borne the initial installation and operational cost for 5 years

and then subsequently transferring the ownership to the village or the local government.

Alternatively, a public – private – community partnership could be a plausible scenario, wherein, the

private party builds the system, the community operates and the government maintains it further on.

11.5 Odisha: Role of Local Government

Across the surveyed districts of Odisha, the overall level of LPG uptake remained low. The initial

cost of the connection was quoted as a major deterring factor in households not opting for LPG as a

primary cooking fuel. However, some initiatives have been undertaken to facilitate at least a minimal

use of LPG in hosueholds. The Govardhanpura village of Baleswar District is located at the banks of

Subarnarekha River and is 8 km from Jalweswar block town. With a population of 250 households,

the villagers are primarily occupied in farming or as agricultural labourers. There are also certain

households with members working in the garment business. The literacy among the male folk goes up

to graduation, while women are educated up to grade 8-10.The cooking fuel mix among households

comprised of firewood, crop residue and LPG. While firewood was available from nearby forest areas

and common village land, during lean month, the wood was also sold in local village markets. As in

case of LPG, the distribution outlet for the same was nearly 8 km away from the village. As

mentioned before, one of the major reasons that curbed sufficient uptake of LPG was the initial

connection cost of Rs 5000 per connection. In an effort of address this concern, the Local Panchayat

of Govardhanpura along with 4 other Panchayats, waived initial cost of LPG connection for all BPL

households. This cost was borne by the Pachayatthat directly paid the distribution agency around the

village Bharat Gas.

The initiative on part of the local government helped eliminate the initial cost-burden that limited

households from taking up LPG in the first place. Further, with the cost waived for BPL households,

it encouraged low -income households (toughest to transition) to transition to cleaner fuel. As a result,

the overall uptake of LPG increased in the 5 Panchayats.

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12. Setting the Policy Context

The analysis approach followed in this report indicates that to ensure a sustained and effective

transition to cleaner energy forms while at the same time achieving the dual goals of livelihood and

energy security, it is important to understand the target population and prioritize the delivery

mechanism to ensure maximum coverage. The distinction made in this analysis between labour,

agriculture and salaried households allows a comparison to other datasets such as the National

Sample Survey or Census data.

One of the key insights from this analysis is that till a cost-effective and scalable alternative to LPG,

in terms of a cleaner fuel, is found, it is imperative that the right-type of policy innovations are made

so that the available options are made affordable to people. To this effect, a proposal of an additional

LPG connection subsidy of Rs. 1400 can go a long way in ensuring significant changes in household

energy baskets. The additional subsidy would bring down the household cost for a connection to

around Rs. 1500, which would lead to a greater uptake of LPG among rural households in India.

Improved supply streams to reduce the cost of entry to LPG, and a public education campaign, are

necessary if LPG is to have a role in displacing biomass dependence. Biogas from existing

agricultural, livestock, or sewage waste streams, has the potential to fill this niche (Gwavuya et al.,

2012; Lee, 2013).

The analysis indicates that interventions such as the eco-village program in Maharashtra, have

significant positive impacts on the current status of households in terms of cleaner cooking choices

but such programs need to be up-scaled to ensure sustained long-term impacts on household energy

transitions. Integration of energy services within the architecture of current development schemes will

be very critical to ensure both goals of universal energy access as well as ensuring productive use of

energy services towards enhanced livelihoods, which is also a core objective of the National Rural

Livelihoods Mission of the Government of India. Along with this, expanding the coverage of Self

Help Group‘s needs to be actively pursued as the ability of women to generate additional income has

a significant impact on household energy choices.


organizations as well as the intended beneficiaries in the planning process. Communities also differ in

their essential fabric. There are areas where community based solutions will be successful and others

where these may not be the best solution.

Electricity access (including decentralized energy options), as defined in this report, will have

significant impacts as the model results indicate an exponential increase in the probability of

switching to modern fuels with improved availability of electricity allowing for extra time during

day-light for monetary activities, thus resulting in greater purchasing power of the household.

The bandwagon effect of interventions is not seen yet as a strong factor. Greater emphasis on

awareness programs highlighting the importance of clean energy use are needed to push energy as a

development priority for households.

171

Setting regional or national policies targeting controllable factors, specifically, education, income,

and public infrastructure, can achieve the objective of facilitating a switch to modern and cleaner

cooking fuels, considering the positive effects these variables generally have on fuel switching

(Leach, 1992; Jingchao and Kotani, 2012; Lee, 2013; Sehjpal et al., 2014).

Finally, in the Indian context, two critical findings from this study have been identified that indicate

an overarching impact on energy choices and access among rural households. Firstly, social status,

which in this study has been defined as the caste identities of the household, has been found to be

significant in impacting access to energy options, not only in the quantitative analysis but also from

field experiences. For example, in certain areas, it was found that given low coverage of distributors

of LPG, preferences were given to households who belonged to the same caste identity as the

distributor. Such instances reduce the access of households to modern fuels including those who show

a willingness and ability for uptake. Secondly, coordination between line departments within the state

as well as between the Centre and states prove to be determinants of supply infrastructure, both in the

case of lighting and cooking.

The table below provides a policy context to possibly effecting transitions to clean energy based on

the analysis of the data collected from the survey in the six states of Maharashtra, Goa, Himachal

Pradesh, Odisha, Karnataka and Rajasthan.

Table 46: Policy linkages


1. Interventions indicate significant impacts on current

status of households but need to be up-scaled to see any

significant impacts on household energy transitions

• Change in livelihood choices needs to be more

pronounced

• Integration of energy services within the architecture

will be very critical to ensure both goals of universal

energy access as well as ensuring productive use of

energy services towards enhanced livelihoods (a core

objective of NRLM)

• SHG/Grassroot institutions need wider coverage– by

way of banking linkages as well as skill development

programmed

• Housing scheme grants should be conditional to

inclusion of a window in cooking area

Skill development programmes

should be carried out based on

available local employment

opportunities. These needs to be

actively pursued as the ability of

women to generate additional

income has a significant impact on

household energy choices

Just as inclusion of toilets in

household structure are

mandatory under central housing

grants, inclusion of a window in

cooking area will help reduce IAP

impacts for households that are

biomass dependent by

compulsion.

2. LPG availability and accessibility must be improved to

ensure sufficient uptake

• Reallocation of unutilized subsidy resources from

cylinder-based subsides to subsidizing new LPG

connection cost to increase uptake.

• Subsidy reform through DBT program as well as

providing alternate (for example: CFL and Solar)

With the initial cost for procuring

an LPG connection subsidized,

immediate fiscal burden on

household budget will be reduced

Unwanted divergence of kerosene

will fall overtime, facilitating a

shift to alternate and more efficient

172


lighting sources to reduce dependency on kerosene

for lighting

• Widen LPG distributer coverage and ease the process

of procuring an LPG connection

fuels for cooking and lighting

Increased distributor (delivery)

coverage will reduce

transportation costs for households

and simplification of procuring

connection will encourage uptake

3. Incentivizing higher enrollment ratios at the school

Specific schemes to promote Girl Child enrollment

in schools. (For example, cash transfer scheme,

Ladli scheme)

Inclusion of basic knowledge on energy efficiency in

school curriculum to spread awareness

An increase in male education

level with increase awareness both

in terms of benefits of modern fuel

as well as improve social cohesion.

Female education indicates

positive impacts on uptake of

modern fuels

4. Re-defining Electricity Access

Improving Access to Electricity during post sunset

hours

Measurement of access to electricity to include not

just availability of physical infrastructure but also

reliability and quality of supply

Improvement of supply infrastructure

Upgrading grid infrastructure to allow for greater

number of users

Increase coverage of decentralized energy options

such as smart/micro grids and rooftop SPV by

way of innovation financing mechanisms

Decentralized energy options have

significant potential as analysis

indicates exponential increase in

probability of switching to modern

fuels with improved availability of

electricity between 6 – 9 PM

allowing for extra time during day-

light in monetary activities34

5. Designing an appropriate Intervention

Replication of successful delivery models after

ensuring that the design and implementation are

made context-specific to the region in which it is

being targeted

A successful model in particular

location bound to have spill-over

effects in terms of increased

awareness in neighboring regions

as well.

To convert this new knowledge

increased usage, location specific

factors need to be accounted for.

34 For detailed information on electricity access refer to Chapter 6.

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Figure 67: Energy and Development Linkages

174

Annexures

Annexure I

Table 47: Variable significance and its impact on the probability of transition

Y=Transition Maharashtra Himachal Pradesh Goa Karnataka (Tobit) Rajasthan Odisha (Tobit)

Lab Agri Sal Lab Agri Sal Lab Agri Sal

(T) Lab Agri Sal

Lab

(T) Agri Sal Lab Agri Sal

Social status - - + + + + +

MPCE class + -

Timelive - - - - - - -

Price of kerosene - - + +

Price of LPG - - - - - - - - - - - - - - - - - -

Price of firewood + + + - +

Education level of males + - + +

Education level of females +

Land Size + + + + +

Electricity Access

Location of kitchen -

Kitchen window + +

Distance to collect firewood - + - - - -

Female decision-making + + + + +

District + + + - + +

Intervention + - + + +

Source: TERI Survey, 20`13

Key for table 47

Identifying Labour Households

Identifying Agricultural Households

Identifying Salaried Households

+ sign The variable has a positive impact on the household transitioning to LPG

- sign The variable has a negative impact on the household transitioning to LPG

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Annexure II

Table 48:Categorical variables as defined for the regression analysis in the Pilot Survey

Economic Status CODE PRIMARY COOKING CODE BPL 0 Biomass (FW, Dung) 0 APL 1 Petroleum Products (Kerosene) 1 Social Status CODE LPG 2 GEN 0 LPG + Biomass 3 SC 1 PRIMARY LIGHTING FUEL CODE ST 2 Kerosene 0 OBC 3 Electricity 1 Gender CODE Kerosene + electricity 2 MALE 0 Solar + electricity 3 FEMALE 1 District CODE Occupation CODE Betul 1 AGRICULTURE 0 Mandla 2 DIALY WAGE/CASUAL 1 Raisen 3 SELF EMPLOYMENT 2 Ratlam 4 SERVICES 3 Village CODE RENT FROM LAND 4 Chilkapur 1 HOUSEWIFE/UNEMPLOYED 5 Dhondi 2 STUDENT 6 Nayegaon 3 House Characteristics CODE Umbada 4 KUCCHA 0 Chiraidongri 1 PUCCA 1 Chiraidongri 1 OWN 0 Dungria 2 RENTED 1 Dungria (B) 2 Income characteristics CODE Limrua 3 400-1000 0 Tharka 4 1000-1500 1 Ghat Kamariya 1 1500-3000 2 Kokalpur 2 3000-6000 3 Mehgua 3 6000-12000 4 Sagauni 3 Greater than 12000 5 Semrikala 4 Baga Kheda 1 Kalmoda 2 Karamdi 3 Sejawta 4

176

Annexure III

The following table summarizes the factors and hypotheses of a few other studies which deal with

issues related to energy poverty, accessibility and transition.

Table49: Summarization of Literature on Energy Poverty, Accessibility and Transition.

Category Factor(s) Researcher Hypothesis

Economic

characteristics

Household income Elias and Victor, 2005 and

Fitzgerald et al., 1990

There is strong positive

correlation between income and

the amount of final energy used.

Household

characteristics

Household size,

gender, age,

composition and

education

ESMAP, 2000,

UNDP/ESMAP, 2003,

Farsi et al., 2007, Gupta

and Kohlin, 2006,

Heltberg, 2005, Leiwen

and O’Neill, 2004 and

Sathaye and Tyler, 1991,

Barnes et al., 2005,

UNDP/ESMAP, 2003,

Heltberg, 2004 and

Heltberg, 2005

Household size, gender, age,

composition and education

influence energy use. Larger

households have greater absolute

consumption but lower per capita

consumption, also larger

households have different income

profiles impacting energy use and

have a higher probability for fuel

stacking than fuel switching.

Behavioural and

cultural characteristics

Food tastes,

lifestyles and

cooking practices

ESMAP, 1991, Fitzgerald

et al., 1990, Heltberg, 2005

and IEA, 2006, Masera et

al, 2000

Food tastes and cooking practices

also influence the choice of energy

system

Locational

characteristics

Geography and

location

Elias and Victor, 2005,

Jiang & O’Niell 2004,

Bhatt and Sachan, 2004

People living in colder regions

consume more energy than

people living in warmer climates

Government policies

and regulations

Subsidies, pricing,

cross-subsidies,

lifeline tariffs

ESMAP, 2000, ESMAP,

2004 and Jiang & O’Niell,

2004

Pricing policies of the govt. such

as lifeline tariffs, subsidies etc

influence the energy consumption

patterns of the households.

Regulations such as caps on

production and distribution also

impact energy consumption

Energy supply

characteristics

Access, availability,

reliability and

affordability

Barnes et al., 2005,

Cecelski and Elizabeth,

2002, ESMAP, 2002,

Fitzgerald et al., 1990 and

Leach, 1992, Chaurey et

al,2004

Access, availability, reliability and

affordability affect fuel choice

particularly in rural households.

People may continue using

inefficient fuels due to high initial

cost of efficient alternative

http://www.sciencedirect.com/science/article/pii/S0301421511004861#bib25



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Annexure IV

Table 50: Basic Household Characteristics of LPG and Biomass users

LPG Users (p_lpg>0) Non-LPG users/ Bio-mass users

(p_lpg==0)

Labour Agricultural Salaried Labour Agricultural Salaried

Rajasthan

Income (average monthly, Rs.) 8129 8876 14189 5577 6455 7176.78

Household Size 5.5 5 5 5 5 5

No. of Children 2 1 5 2 1 4

No. of women 2 2 2 2 2 2

Expenditure Priority for fuel in

HH expenditure

5 5 5 5 5 6

Distance from forest (km) 2.4 2.25 2.13 2.31 2 2

Land they cultivate (acres) 3.33 4.8 4.08 2.6 6.4 3.5

Livestock (Cows + Buffalos+

Poultry + Goats)

0.45 0.57 0.46 1 2 0.7

Migration 0.15% 0.11% 0% 0% 0% 0.5%

Per capita monthly income 1712.79 1796.5 2914.3 1297 1432.44 1802.45

Karnataka

Income (average monthly, Rs.) 4406 8383 12013 3879.54 4297 5578

Household Size 5 5 4 5 4 4


No. of women 2 2 2 2 2 2


HH expenditure

4 6 4 3 3 3

Distance from forest (km) 4 2 2 2.3 2 2

Land they cultivate (acres) 10 7.8 8 3.5 3.82 4.47


Poultry + Goats)

7 7 5 3 3 3

Migration 0% 0.2% 0% 0.13% 0.13% 0.1%

Per capita monthly income 986.81 1601.57 2704.8 924.05 1047.49 1660.07

Goa

Income (average monthly, Rs.) 7250 7359.21 11981 5342 6047.62 7384



No. of women 2 2 2 2 2 2


HH expenditure

2 2 2 1 2 2

Distance from forest (km) 2 2.25 2.54 3 2.33 3

Land they cultivate (acres) 1.65 3.55 3 1.82 2.72 2.28

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(p_lpg==0)



Poultry + Goats)

4 7 6 4 4 4

Migration 0.23% 0.1% 0.12% 0.5% 0.7% 0.5%


Himachal Pradesh

Income (average monthly, Rs.) 6490 9272 13005 5649 6747 7302



No. of women 2 2 2 2 2 2


HH expenditure

6 6 6 6 6 6

Distance from forest (km) 2.7 2.5 3.08 3.19 2.52 2.24

Land they cultivate (acres) 3 2.73 1.71 1.22 1.6 1.68


Poultry + Goats)

3 1 1 1 2 1

Migration 0.08% 0.20% 0.22% 0.06% 0.65% 0.10%


Maharashtra




No. of women 2 2 2 1 2 2


HH expenditure

3 3 3 3 3 3

Distance from forest (km) 2.55 2 2.6 2.83 3.24 3

Land they cultivate (acres) 3.2 4.2 3.42 3.08 2.77 3.8


Poultry + Goats)

4 3 4 4 3 3.08

Migration 1.4% 1.3% 1.3% 1.15% 1.18% 1.44%


Odisha




No. of women 2 2 2 2 2 2


HH expenditure

4 4 4 5 5 4

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(p_lpg==0)


Distance from forest (km) 4 5.41 4.07 3 2.71 3.05

Land they cultivate (acres) 3 3.15 2.8 1.87 2.16 1.61


Poultry + Goats)

1 2 1 2 3 1

Migration 0.35% 0.33% 0.39% 0.80% 0.08% 0.22%



180

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