Modeling Development and Emission Scenario Analysis in China

Jiang Kejun, Hu Xiulian, Liu Qiang, Zhu Songli, Zhuang Xing Energy Research Institute

1. Background Climate change is a major issue for human being now. It is well understood that human society must respond to possible climate changes caused by increasing greenhouse gas (GHG) emissions from fossil fuel use and other sources. The strong policy question mainly comes from cost and benefit analysis. Due to very complex process of climate change, integrated assessment is essential to answer the question. Started from end of 1970s, the integrated assessment model was initially developed in developed countries. After more then 20 years, the study activities in developing countries is still very limited. Capacity building on integrated assessment modeling was emphasized in many occasions. By collaboration with international research teams, Integrated Policy Assessment Model for China was developed step by step. One of major research activities of IPAC is to develop energy and emission scenarios for China. In order to describe future possible GHG emission trajectories in both of nonintervention and intervention terms and the costs of responses for GHG emission reduction, various emission scenarios must be analyzed to answer questions posed by researchers and policymakers. Over the next hundred years, global socioeconomic development may progress in various ways. The developing countries, with the majority of the world’s population, may experience high economic growth, making them a major growth center in the global economy, which already occurred in the Asia-Pacific region. Many developing countries share problems that arise from rapid industrialization, population growth, and concentration of people in cities.

Future emission scenarios are mostly dependent on the regional development pattern, and each region has a wide range of development path options. This means that future GHG emissions may diverge depending on the future development path. Recognition of such divergent nonintervention and intervention scenarios is highly important in assessing policy options to respond to climate change, because the reduction level of GHG emissions is dependent not only on the target climate stabilization level but also on the baseline scenario of the nonintervention increase in GHG emissions.

Many emissions scenarios have already been quantified or published. The most popular scenarios are the IS92 scenarios published by IPCC in 1992 (Alcamo et al., 1995; Morita et al., 1994; Matsuoka et al, 1996). However, very few of these scenarios have been explicitly analyzed from the viewpoint of future alternative development path in developing regions (Parikh, 1992; Zhou et al, 1997; Bruce et al, 1996). Only some scenarios have clarified the relationship between

development patterns and emissions at the global level (Lashof et al, 1990; WEC, 1993). Moreover, the analysis of intervention scenarios is limited. In order to contribute to the analysis for both nonintervention and intervention emission scenarios, we developed the AIM-Linkage model. A group of nonintervention emissions was quantified based on the IPCC Special Report on Emission Scenarios (SRES) and reported (Jiang et al, 2000). This paper discusses the quantification of intervention scenarios based on the non-intervention emission scenarios by the AIM-Linkage model. Based the this project design, ERI IPAC modeling team worked on following activities: 1) Designed the model framework for application on China energy scenario.

Recent energy data in China shows a bound back of energy use and production in China, giving much more discussion on future energy demand scenario. IPAC modeling team started to design the analysis framework on energy scenario. IPAC-AIM/technology model was selected to be used for the scenario analysis, together a CGE model in IPAC family(IPAC-SGM or IPAC-AIM/Material). The focus of this study will be energy activities and energy intensive products scenario.

2) Modeling framework for global mitigation scenario. By supporting recently started IPCC activities and EMF-22, IPAC modeling team started to extend IPAC-emission model to cover demand from the global scenario analysis. More option such as hydrogen, carbon sequestration will be included more detailed in the model.

3) Modeling analysis for Beijing Transport Scenario and China Transport Scenario. As a

important driving force for energy use in China, transport development is a key topic for research. IPAC modeling team made transport analysis one of the major study points. Technology data were collected and inputted to the model, policy options were revised based on relative studies, which would be used as scenario definition.

4) IPAC modeling team started to join international and domestic research activities. IPAC team

members attended several meetings organized by various international organizations and research institutes (e.g., International Modeling Workshop in Beijing, Forecasting workshop in Bonn, Germany, organized by UNFCCC, EMF-21 research activities).

2. Energy and Environment in China

In China, due to rapid economic growth, total primary energy consumption increased from 400 Mtoe in 1978 to nearly 1520 Mtoe in 2005, with an annual average rate of increase of 4.7% (see Figure 1)(China Energy Year Book 2006, 2007; China Year Book 2006, 2006). Coal is the major energy source, providing 70.7% in 1978 and 69% in 2004 of total primary energy use (see Figure 2). Recent years have witnessed a dramatic surge in the rate of increase of energy use in

China and widespread energy shortages.

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Figure 1 Energy production and consumption in China

Figure 2 Primary energy use in China by energy type

China is the largest coal-producing and -consuming country in the world. Between 1980 and 2004

From 1980 to 2004, total installed capacity of electricity power generation increased from 66 GW

Between 1980 and 2004, total crude oil output increased from 106 Mt to 175 Mt (average annu

Energy efficiency improvement and energy conservation are given high priority in the

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, total raw coal output increased from 620 Mt to more than 1900 Mt, with an average annual growth rate of 4.8% per year. Prior to 2000, the share of coal use in total energy use decreased, but it increased again from 66% in 2000 to 72% in 2004. The heavy dependence on coal has led to serious environmental problems and represents a burden for the transportation system.

(of which hydropower is 20 GW, accounting for 31%) to 440 GW (of which hydropower is 100 GW, accounting for 23%). In the same period, electricity output increased from 300 TWh (of which hydropower is 58 TWh, accounting for 19%) to 1870 TWh (of which hydropower is 220 TWh, accounting for 12%). In 2004, newly installed capacity reached 50 GW, and newly installed capacity in 2005 and 2006 is expected be around 60 to 70 GW (Power Industry Information, 2005).

al growth rate is 2.1%). In 2002, 149 Mt was produced on land and 18 Mt was produced offshore. Crude oil output in China accounts for 4.7% of the world total.

energ

Figure 3 Technology progress and energy efficiency improvement in steel making industry urnace

Rapid energy demand increases in recent years have led to many discussions on future energy ema

O2 emission is given in figure 4.

y development strategy in China, as is the efficient and clean use of coal and other fossil energy sources. The objective of developing clean coal technology is to improve coal utilization efficiency, to reduce environmental pollution and to promote economic development. High efficiency and clean technology will be crucial for China to achieve a low-emission development path. Figure 3 illustrates the way in which energy efficiency improvements in the steel-making industry have been driven by advanced technology diffusion.

Unit Energy Use in S tee l M aking Industry

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E F

Note: EF- electric furnace, TRT-Top Gas Pressure Recovery Turbine, DCF-Direct Current F d nd, which could have much difference with previous energy scenario study output. Energy intensive industry developed in a very rapid way in last two or three years. The purpose of the study presented in this paper is to provide energy demand scenarios up to 2030 by reflecting recent development trends, especially energy intensive products. Comparing with previous scenario studies, major change in this study is assumption for sector outputs, which is significantly higher than assumption given before. This study also tries to identify possible energy import in China by using global energy model, which is not much reported in previous studies. China being a member of the World Trade Organization (WTO), this study also reflects expectations about China’s industrial transformation resulting from its role in global markets (Lu, et al., 2003). Discussion on policy options is also provided based on the scenario study and relative studies on policy assessment. C

CO2 EMission in China

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Figure 4 CO2 emission in China 3. Model development In order to response the demand from energy and environment policy making and researchers, Integrated Policy Assessment model for China(IPAC) was developed. IPAC is a model family which include several models focusing on different research topics. Development of IPAC model is benefit from several international collaboration, including the project collaborated with National Institute for Environment Studies(NIES) on AIM, Pacific Northwest National Laboratory(PNNL) on SGM, and RIVM on TIMER. In order to answer different questions from various audience, IPAC model included models from bottom model to top-down model. IPAC-AIM/technology model is a bottom-up type technology assessment model which can provide detailed policy option on energy and environment. IPAC-SGM model is a CGE type top-down model which can analyze whole economy activities linked with energy and environment. IPAC-Emission model is a global model focus on energy and GHG emission scenario analysis. IPAC-AIM/Local model concentrates on regional energy and environment analysis which covers various sectors and detailed technologies, it can go further looking at local perspectives. Now the IPAC model is developed toward to air quality model and health impact model.

Framework of Integrated Policy Model for China (IPAC)

ERI, ChinaERI, China

IPAC-SGM

IPAC-AIM/tech

IPAC-Emission

IPAC/Tech(Power/Transport)IPAC/SE, IPAC/EAlarm

IPAC-TIMER

IPAC/AIM-Local

Energy demand and supplyPrice/investmentEconomic impactMedium/long-term analysis

Medium/short term analysisTechnology assessmentDetailed technology flow

Region analysisMedium/short analysisEnergy demand and supplyTechnology policy

IPAC-AIM/MATERIAL

Energy demand and supplyFull range emissionPrice, resource, technologyMedium-long term analysisEconomic impact

Environment industryPollutant emissionMedium/long-term analysis

Technology developmentEnvironment impactTechnology policy

AIM-air IPAC-health

Energy demand and supplyPrice/investmentMedium/long-term analysis

Short term forecast/ energy early warning

Climate Model

IPAC/Gains-Asia

Figure 5 Framework of IPAC

The IPAC-Emission model is a global model developed for the study of greenhouse gas (GHG) emission scenarios (Jiang et al., 2000a; IPCC, 2001b). It divides the world into 9 regions covering United States (US), Pacific OECD (OECD-P), Europe OECD and Canada (OECD-W), Eastern Europe and Former Soviet Union (EFSU), Middle East (ME), China, other Asia (S.E.Asia), Africa, and Latin America (LA). Major emission sources including energy activities, industries, land use, agriculture, and forests, can be simulated in the model framework. The model consists of three modules: (i) macro-economic module, (ii) end-use module and (iii) land-use module. The macro-economic module was developed based on the Edmonds-Reilly-Barns (ERB) model (Edmonds and Reilly, 1983; Edmonds et al., 1996), a macroeconomic, partial-equilibrium model, which forecasts energy demand over the long term. It uses GDP and population as future development drivers, combined with other energy-related parameters, to forecast energy demand based on the supply and demand balance.

The end-use module was originally part of the Asia-Pacific Integrated Model (AIM), a bottom-up, energy-technology model developed by the National Institute for Environment Studies and Kyoto University (Japan). The land-use module was developed from the Agriculture Land Use Model developed by Pacific-Northwest National Lab (PNNL) to model GHG emissions from land use(Edmonds et al., 1996).

The IPAC-AIM/Technology model is a single region model for China, developed based on

AIM/enduse model (AIM Project team, 1996; Hu et al., 1996; Hu et al., 2001;Jiang et al., 1998). This model includes three modules (i.e., energy service demand projection, energy efficiency

estimation and technology selection). The demand is divided among the industrial, agricultural, service, residential, and transportation sectors, and these sectors are further divided into sub-sectors. For both demand and supply side, more than 400 technologies are considered, including existing as well as advanced technologies that may be used in the future. The model searches for the least-cost technology mix to meet the given energy service demand. The most up-to-date information on these technologies were collected from large number of printed sources, as well as by consulting experts directly.

IPAC-SGM was one model from IPAC model family. It is a CGE type of model. CGE model has the advantage to understand the overall economy activities by implantation of policies or countermeasures. CGE model plays important role in the world for policy assessment. Many modeling teams used CGE model for simulation on economic activities and policy implementation. IPAC-SGM is basically extended from SGM(Second Generation Model), which is developed by Pacific North-West National Laboratory(PNNL) of United State. The SGM is a computable general equilibrium(CGE) economic model that projects economic activity, energy consumption, and carbon emissions for twelve world regions. IPAC-SGM is the extended by establishment of data for China and some non-market based sector such as biomass and unclear, hydro power etc were revised in IPAC-SGM. The model has nine producing sectors, eleven consuming sectors, with focus on energy production detail, vintaged capital stocks, and a suite of anthropogenic greenhouse gases (table 3). The model was developed with recognition that energy production and use is the most important set of human activities associated with greenhouse gas emissions. Some of the sectors, such as electricity generation, also contain sub-sectors. Each production sector in the IPAC-SGM represents a unique product with its own unique equilibrium price. Sub-sectors within a sector represent different ways of producing the same product. For example, there are many technologies for generating electricity, represented by the several electricity sub-sectors. 4. Energy and Emission scenario for China The IPAC-Emission model and IPAC-AIM/Technology model – components of the Integrated Policy Assessment Model for China (IPAC) – were used to perform the quantitative scenario and policy option analysis. The models project future energy and pollutant emissions. Linking these two models provides both detailed analyses of various sectors and a global analysis of China’s energy future. The same scenarios and related model assumptions were used for both models. Energy demand for China was basically given by the IPAC-AIM/technology model by calculating demand from sectors with detailed technology information; whereas energy price and energy import data are derived from the IPAC-Emission model. The global energy analysis is given based on SRES B2 scenario(IPCC, 2001b), while the part for China is revised in

this study. Figure 6 presents the link between two models.

Common parameter for China:GDP, Population, sector activities,

domestic energy supply

IPAC-emission model

IPAC-AIM/technology model

Energy priceEnergy import

Technology mixTechnology efficiency improvement

IPCC SRES B2 scenario

Figure 6 Link between models

The major assumptions used in this study (including population, GDP growth and mix) are

given in the following tables. The assumptions for population come from other studies. The assumed GDP growth rate is consistent with government targets and research by the Development Research Center (Zheng et al.,2004; Tan et al., 2002; Qu, 2003; Liu et al., 2002)

Table 1 Population assumption, million 2000 2010 2020 2030 Population 1284 1393 1472 1539 Urban 465 641 780 908 Rural 819 752 692 631

Note: Assumptions by authors, based on review of relevant studies Table 2 GDP growth in China

2000-2010 2010-2020 2020-2030 Annual GDP Growth Rate 7.8% 6.7% 5.6%

In order to analyze energy trading, we used the IPCC SRES B2 scenario as a global scenario

(Jiang et al.,2000a). The IPCC SRES scenario is a scenario family developed by the Intergovernmental Panel on Climate Change in 2001, which includes seven scenario groups. The B2 scenario reflects a world with good intentions, which it is not always capable of implementing. This storyline is most consistent with current national and international developments. On balance, the B2 world is one of central tendencies that can be characterized as neutral progress among SRES scenarios. Human welfare, equality and environmental protection all have high priority, but the world proves unable to tackle these concerns at a global level and resolves them as best it can regionally or locally. Generally, high educational levels promote both development and environmental protection. Education and welfare programs are widely pursued, leading to reductions in mortality and to a lesser extent fertility. This results in a central population projection of about 10.4 billion people by 2100, consistent with the United Nations median projection. Gross World Product (GWP) grows at an intermediate growth rate of 2 percent per year, reaching about US$ 235 trillion in 2100. The B2 storyline also presents a generally favorable

climate for innovation and technological change, especially in view of high educational levels compared to today and relatively efficient markets at the regional level. B2 is a world of “regional stewardship” that, in some regions, is particularly frugal with energy and many other natural resources. Consequently, energy system structures differ among the regions. Overall high priority is given to environmental protection, although global policies prove elusive and regional polices vary widely. Major assumptions are given in Tables 3 to 5.

For the Developing Asia-Pacific region, the B2 scenario assumes that economic develop

All of China’s emission scenarios were developed under the IPCC SRES B2 scenario. In IPAC

Table 3 Key Scenario Drivers Assumed for the Developing Asia-Pacific and the World in

Assumptions

ment utilizes resources so as to maintain equity for the future, while maintaining balance among regions as well as between urban and rural areas. Such an approach is introduced based on the recognition of environmental issues and sustainable development. This scenario can be described as regional stewardship from a global perspective, based on a natural evolution of the present institutional policies and structures. It is characterized by limited population growth, medium economic growth, inequality reduction, weak global governance but strong national and regional governance, a strong de-urbanization trend, strong pursuit of environmental improvement, and encouragement of renewable energy use. It is a low per capita economic development scenario. In this scenario, the per capita GDP in the region is only 1/5 that of the OECD countries by 2100.

-emission model, international energy trade was included in the study based on the resource cost effective availability (Jiang et al., 2000b; Jiang et al., 1999).

IPAC-Emission model Item

Asia-Pacific ation 4.7 billion in 2050Popul 5.0 billion in 2100

Asia-Pacific Annual GDP 0, Growth Rate

5.7% from 1990 to 2053.8% from 2050 to 2100

World Population 11.7 billion in 2100 World GDP $250 trillion in 2100 GDP/ capita trends

ECD becomes 7 times of Disparity remains GDP/capita of Onon-OECD (now 13 times).

AEEI 1.0%-1.2% International Trade ross regions Low trade ac

High trade cost Urbanization veloping world before 2050, Increase in de

decrease in developed world

Table 4 Assumptions for B2 Scenario for the Developing Asia-Pacific and the world Item Assumptions

Resource availabil Oil/gas: mediity um; Biomass: high

Energy exploitation cost Medium

Non-carbon renewable energy cost nuclear, medium for solar and High for others

Biomass availability Medium End-use technology efficiency improvement

Medium

Social efficiency improvement Medium Transport conservation High Dematerialization trend Medium Land-use productivity improvement Medium Meat-oriented food habit Low Desulphurization degree High

Table 5 Factors influenced by key driving forces

Policies to promote the Change Driving Sectors Factors forces

Industry Value added change by

ing

ustry

roducts structure

Various policies relative to value added

iented policies, national

sub-sectors within the sector(as service demand of some sub-sectors includmachinery, other chemical, other mining, other indsector etc.) Pchange within one sector(as service demand in most industrial sectors)

such as price policy, national plan for key industry, promote well working market Market ordevelopment policies.

Residential

mercial

nge

Public education, price policies and Com

Energy activity chawithin the sector(such as change of use of heating, cooling; useof more efficient electric appliancesetc.)

Social EfficienChange

cy

Transport e of transport mode(more public transport, non-mobilty etc.) Traffic volume conservation(use less private car)

Transport development policies, public education

Chang

Technology progress

ll sectors

or

vement)

more nergy and

licies, international collaboration Market oriented policies, environmental regulation National energy industry policies, import & export policies, tax system

For a Efficiency progress ftechnology(unit energy use improTechnology mixchange(, more advanced technologies) Fuel mix change(renewable enuclear)

Technology R&D promotion, market oriented po

3.2. Scenarios In order to analyze future and emissions in China, we consider three scenarios. Considering the uncertai products demand with impact of WTO accession, a baseline scenario and rio were given. Another one is policy scenario. The three scenarios are de lows:

scenario: This scenario gives a basic trend to describe future economic activities. There will be better international trading and China’s economy will be part of global

center for manufacturing following WTO accession, which will bring more energy-intensive product production to China, such as

- w nd environmental constraints.

The

same. Se Tab

io

energy demandnty for energy intensive a high demand scenafined as fol

- Baseline

economy. Therefore China could rely on international markets and energy resource imports to meet part of its energy supply needs.

- High demand scenario: This scenario presents a high demand for energy in the future. The major driving force is China's assumed role as a

steel, non-ferrous products and building materials. At the same time, more technology transfer and R&D on high efficiency energy use technologies is also assumed. Policy scenario: Various energy and emission control policies are assumed for this lodemand scenario, which reflects energy supply a

basic assumptions for the three scenarios, such as population and GDP growth, are the ctor service output for the three scenarios is given in Table 6.

le 6 energy intensive products assumption in the model Baseline scenario/Policy

scenario High demand scenar Unit 2002

2020 2030 2020 2030 Steel Mt 182.4 380 320 430 380 Copper Mt 1.63 4.5 5.2 5.2 5.8 Aluminum Mt 4.51 10 14 12 18 Ethylene Mt 5.43 12 16 14 20 Ammo a Mni t 36.75 47 49 50 56 Chemical Mt 37.9 48 50 52 58

fertilizer Cement Mt 725 1000 900 1100 1100Glass Million

s

case234.4 480 530 520 560

Vehicles Million 3.25 11 12 15 17

tions to be consid the po scenario ven in le 7. Th policy opti ed based on policy potential in China and technology trends (Qu, 2003; Liu et al., C 2001 02 Table 7 Policy option n the modeling study Poli xplanatio

Policy opons were defin

ered in licy are gi Tab ese

2002; IPC a; IPCC 20 )

s used icy options E n

Tec logy promotion policy End use technology efficiency increase by hnousing new technologies

Energy efficiency standard for buildings New buildings reach 75% increase standard in 2030

Renewable energy development policy Promote use of renewable energy Energy tax Introduce vehicle tax by 2005, and energy tax

by 2015 Public transport policies In cities public transport in 2030 will take 1

15% higher share than 200 to

00. Transport Efficiency Improvement High fuel efficiency vehicles widely used,

including hybrid vehicle, compact cars, advanced diesel car

Power Generation Efficiency 030

Efficiency of coal fired power plants increase to40% by 2

Nature Gas Incentive Enhance natural gas supply, localization of technology to reduce cost

Nuclear power development National promotion program by setting up target

ed using the IPAC

8.

in the baseline scen .7

billion toe in 2030. The annual growth ra 2000 to 2030 is 3.6%, while energy elasticity of GDP is 0.58. Coal will be the major component energy in China (1.5 billion toe

demand in China, with its share in total primary energy use increasing from 4% in 2000 to ge annual growth rate: 10%).

Energy demand is calculat -Emission model, Baseline scenario results are

given in Figures 7 and

Primary energy demand ario could go to 2.1 billion toe in 2020 and 2te from

in 2030), with a 58% share in total energy demand. There is a rapid increase for natural gas

17.3% in 2030 (avera

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Figure 7 Primary energy demand in China for baseline scenario

Wit ct to final energy use, electricity and natural gas increase rapidly. Electricity demand creases from 112 million toe in 2000 to 478 million toe in 2030. Natural gas demand increases from 21 million toe in 2000 to 437 million toe in 2030. Coal and oil demand increase slowly. Coal use in the residential sector will generally decrease and be replaced by gas and electricity; coal for oil products used for transport will increase quickly, with the rapid growth of vehicles in China. Oil use in tran

e scenario

For the is 2.9 billion toe, which is 250

million t ns higher than the baseline scenario. Of the total primary energy demand, coal provides 59.1%, l 16.1%, natural gas 17.8%, and nuclear 1.2%. Because this scenario assumes better integration in international markets, there is greater reliance on imported energy such as natural

gas and oil(see fig

h respe in

will be mainly used in large equipment such as boilers. Demand

sport will increase from 74 million toe in 2000 to 320 million toe in 2030.

RenewableNuclearN.GasOilCoal

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Figure 8 Final energy demand in China for baselin

high demand scenario, primary energy demand in 2030 ooi

ure 9 and 10).

igure 10 Final energy demand in high demand scenario

This study also simulated future energy production in China. Figures 11 and 12 give energy

producti in the baseline and high demand scenarios. Coal production could reach 1.31 billion toe by 2020 and 1.48 billion toe by 2030. Chinese coal industry experts estimate an upper bound of coal pro ore, could exceed domestic coal producti n tons in 2020 and 175 million tons in 2030. This is within the forecast of experts from the oil industry, which range from 180 to 200 3 3

in 2

Figure 9 Primary energy demand in high demand scenario

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duction of 1.2 billion toe by 2020. Coal demand, therefon in China. Oil production is projected to be 190 millio

million tons in 2020. Natural gas production will be 133 billion m in 2020 and 312 billion m 030. The production of natural gas is within the range of natural gas production forecast by

energy experts, which ranges from 130 to 150 billion m3 in 2020. Nuclear power generation will increase quickly in future, but still represents a small share, because of its high cost. The model results shows that nuclear power generation could reach 256 TWh in 2020 and 344 TWh in 2030, compared with 16.7 TWh in 2000. The installed capacity will be 39,400 MW in 2020 and 53,030 MW in 2030. Hydropower output will increase from 224 TWh in 2000 to 555 TWh in 2020 and 722 TWh in 2030, with capacity reaching 154 GW in 2020 and 201 GW in 2030.

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Figure 11 Energy production in baseline scenario

hydr oNucl earN. GasOi lCoal

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Figure 12 Energy production in high demand scenario

ccording to the energy demand and production, we can calculate the need for future energy

imports (see Figures 13 and 14). In baseline scenario, future fossil energy imports could reach 375

million toe annually in 2020 and 562 million toe in 2030 (for comparison, in 2000, the USA imported to be imported: oil imports will

A

870 million tce). Oil will be the major energy source

reach 230 million tons in 2020 and 300 million tons by 2030. Natural gas imports will amount to 154

y assuming the adoption of energy and environmental policy measures, the policy scenario

sults are described in Figures 15 and 16. Compared to the baseline scenario, there is nearly 245

billion and 183 billion m3 in 2020 and 2030, respectively. Even coal will be imported after 2020, with 129 million tons of coal needed annually by 2030.

In the high demand scenario, energy imports are much bigger. Total fossil energy import will be 445 million toe in 2020 and 680 million toe in 2030. There will be more coal import in this scenario, reaching 189 million toe in 2030.

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Figure 13 Energy imports in the baseline scenario

N.GasOilCoal

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Figure 14 Energy imports in the high demand scenario

B

re

million toe energy demand reduction in 2020, 280 mtoe in 20we found there is big pressure to apply these policy optio

30. By exploring the policy options, n in order to reach the lower energy

emand scenario, and also need to be introduced at early time because of long life span of energy

With the calculation of energy demand, several pollutant emissions were calculated. Figure 17

9 give SO2, NOx, TSP and CO2 emission from energy activities. SO2 emission will keep

coal use in China. After 2010, more and more desulphurization technologies will be used and therefore SO2 emission will be reduced

will big challenge for government target. Because of lack of policy to control NOx, its

dtechnologies.

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Figure 15 Primary energy demand in policy scenario

Figure 16 Final energy demand in policy scenario

OilCoal

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to 1

increasing before 2010 with the rapid increase of

from fossil fuel use. Compared with high demand scenario, SO2 emission for baseline scenario in 2010 is 4.5million tons lower, but still increase 9.45million ton from 2000. This

beemission keeps going up. Same trend is seen for TSP emission.

roductsCoal

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ion

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Figure 16 SO2 emission in China

Figure 17 NOx emission in China

Figure 18 TSP emission in China

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Figure 19 CO2 Emission in China

If we look at the effects for policy options used in the policy scenario, by comparing with

aseline scenario and high demand scenario, we found there are a package of policy options could e adopted now to reduce the growth rate of energy demand. For example policy to promote

hig echnologies(see table 8), fiscal energy and environment ubsidies for renewable energy, emission taxes, resource

xes etc., and policy to promote public involvement, are important for China to go to a low erg

bbpenetration rate of h energy efficiency tpolicies including vehicle fuel taxes, staen y demand scenario. Table 8 Technologies contributing to Energy saving and GHG emission reduction in short and middle-term Sector Technologies Steel Industry Large size equipment (Coke Oven, Blast furnace, Basic oxygen

furnace ,etc.), Equipment of coke dry quenching, Continuous casting machine, TRT

Furnace gas recovery , DC-electric arc furnace Continuous rolling machine, Equipment of coke oven gas, OH gas and Blast

Chemical Industry ment for Chemical Production, Waste Heat Recover g

Paper Making or , Continuous distillation system

Textile Non-ferrous metal em, QSL for lead and zinc

Building Materials ith

Machinery eat Preservation Furnace l

building and energy efficient windows ,

ficient

Car,

Use Technology

Large size equipSystem, Ion membrane technology, Existing Technology ImprovinCo-generation System, facilities of residue heat utilization, Black liqurecovery systemCo-generation System, Shuttleless loom, High Speed Printing and Dyeing Reverberator furnace, Waste Heat Recover Systproduction Dry process rotary kiln with pre-calciner, Electric power generator wresidue heat, Colburn process, Hoffman kiln, Tunnel kiln High speed cutting, Electric-hydraulic hammer, H

Residentia Cooking by gas, Centralized Space Heating System, Energy Saving Electric Appliance, High Efficient Lighting, Solar thermal for hot water, insulation of

Service Centralized Space Heating System, Centralized Cooling Heating SystemCo-generation System, Energy Saving Electric Appliance, High EfLighting

Transport Hybrid vehicle, advanced diesel truck, Low Energy Use Car, ElectricFuel cell vehicle, Natural Gas Car, Electric Railway Locomotives, public transport development

Common High Efficiency Boiler, Fluid Bed Combustion Technology, High Efficiency Electric Motor

Speed Adjustable Motor, Centrifugal Electric Fun, Energy Saving Lighting Power generation

ydropower, biomass based power generation

Super critical unit, Natural Gas Combined Cycle, Pressured Fluid Bed Combustion Boiler, Wind turbine, Integrated Gasification Combined Cycle, Smaller Scale H

5. Energy tax assessmen

Because of rapid e ed from 400Mtce in 1978 to ajor energy in all energy use, taking share of 70.7 in 1978 and 70% in 2003 in total primary energy use.

ecently rapid increase of energy use in China already caused various problems including rgy security, production safety problems etc. People started to worry

bout the energy future in China. With the big pressure from environment and energy supply, a sustai

e in this study we will develop a mode

nd IPAC-SGM model are used. Both

ities level could be calculated by IPAC-SGM model, such as steel output, cement output etc., could be input into IPAC-AIM/technology

alculated by IPAC-SGM model, and then input into

- d input into IPAC-AIM/technology

The lin iven in figure 20.

t

conomy growth, total primary energy consumption in China increas 1300Mtoe in 2004, at an annual average rate of 4.3%. Coal is the m

Renvironment, transport, enea

nable way for energy development in China is necessary. There is a large demand for policy making research with the hot issue of energy demand.

Recently there are several important energy related policies announced. It is time for research groups to think about further energy policies, even though some policies maybe be thought hard to accept before, for example energy taxes, emission caps etc.

In order to answer growing questions from policy makers, it is important for research groups to provide further information on relative studies. Her

ling framework for quantified assessment on Fiscal and Financing Mechanisms for Energy Systems in China. This is a quite weak part now.

In this study, IPAC-AIM/technology model a model will use same package of scenario parameters, such as population, GDP,

technology efficiency, energy resource, energy price, sector output, to keep the two models in line with same analysis framework. Because these two models are different in analysis mechanisms and have different input and output parameters, it is useful to use each other’s data.

In order to better use the function of two models, soft linkage is established by pass data in following ways: - technology progress rate could be calculated by IPAC-AIM/technology

model after adoption of policies including taxes, and then the progress rate will be input into IPAC-SGM model.

- Sector activ

model. - Energy Price could be c

IPAC-AIM/technology model. Subsidy for energy efficient technology and renewable energy could be simulated by IPAC-SGM model anmodel.

kage is g

imate r to simulate

ffects of tax and subsidy, the models need to be specially extended. So far following nctions were extended:

ax is levied based on energy, but transport fuel tax only focus on vehicle fuel. IPAC-SGM model was revised to be suitable for vehicle fuel tax.

TOP-DOWN MODELIPAC-SGM

Demand of Non-Energy Goods

Demand ofEnergy

Market Equilibrium

IPAC-AIMBOTTOM-UP MODEL

Production in Each Sector

Energy Price

Supply of Non-Energy Goods

Supply ofEnergy

Technology Choice in Energy Demand Sectors

Energy Efficiency Improvement

Technology Choice in Energy Supply Sectors

Production Function

Figure 20 Linkage between two models.

Because these two models are originally designed for energy system analysis and clchange mitigation analysis, there are still some limitations for this study. In ordeefu

Vehicle fuel tax. In IPAC-SGM model t-

- Tax neutral. Tax neutral is widely used in tax analysis, therefore we also need to keep this in mind. The model was revised to do this.

- Subsidy use. Originally in IPAC-SGM model government revenue from tax was used as normal purpose. But this study need to use some of the budget for subsidy on energy saving technologies and renewable energy development. Therefore the model need to be

- cent energy price change.

m

lack counat di e between energy tax and fuel tax. This work is

n(Jian

able 9 Vehicle fuel tax rate, Yuan/liter

extended for government budget use. International energy price. International energy price, especially oil price, was setup to follow re

So e key parameters for this study used in the models are given in tables 9 to 12. Because of of study on energy tax in China, energy tax rates were given based on experience in other tries, by looking at the share of energy tax in energy price. And now this study is looking fferent tax which did not consider linkag

bei g done in following research projects. The baseline scenario is given in other papers g et al., 2006).

T

2006 2010 2020 2030 Gasoline 1.1 2.4 3.6 4.6 Diesel 1 2.1 2.7 3.4 GTL 2.1 2.7 3.4 EthaMet anol

1 1 1 nol/ 1 h

Bio-Diesel 1 1 1 T ba le 10 Ene x rate, Yu of coal equivale

2006 2010 2020 2030

rgy ta an/tce (tonne nt)

Coal 0 50 80 120 Oil 0 50 0 70 10N. Gas 0 50 60 80 Hydro 0 0 0 0 Renewable 0 0 0 0

Table Carbon t , Yuan/t-

2005 2010 2020 2030 11 ax rate C

Carbon tax 0 rate

100 150 200

Table 12 International energy price, US$/GJ

2005 2010 2020 2030 OIL 5.02 5.87 8.16 9.66 GAS 1.63 1.6 1.81 2.09 SOLIDS 51. 1.51 1.53 1.48

By applying mention ove, mo results seline scenario are given in 21 to 28.

enario. Note: “Oil Prod.” Mean

taxes ed ab deling for ba

figure figure

Figure 21 Final energy demand by energy source in baseline scs “petroleum derivatives”.

Figure 22 Final energy demand by sector in baseline scenario

Final energy demand by sector in China

500

200025003000

2000 2010 2020 2030Year

0

10001500

Mtc

e

RuralUrbanTransportServiceIndustryAgriculture

Final Energy Demand in China

0

500

1000

1500

2000

2500

3000

2000 2010 2020 2030Year

Mtc

e

HeatElectricityN.gasOil Prod.Coal

Primary energy demand in China

Figure 23 Primary energy demand by energy source in baseline scenario

ic output, % GDP loss ompared to baseline scenario.

0

1000

2000

3000

4000

5000

2000 2010 2020 2030Year

Mtc

eWindBiomassNuclearHydroN.gasCrude OilCoal

GDP l oss by Car bon Tax

0. 0%

0. 1%

0. 2%

0. 3%

0. 4%

0. 5%

2000 2005 2010 2015 2020 2025 2030Year

%

Figure 24 Impact of carbon tax (as shown in Table 6) on economc

GDP l oss by Ener gy Tax

0. 0%

0. 1%

0. 2%

0. 3%

0. 4%

0. 5%

2000 2005 2010 2015 2020 2025 2030Year

%

Figure 25 Impact of energy tax (as shown in Table 5) on economic activity, % GDP loss compared to baseline scenario.

Figure 26 Impact of energy tax on energy demand, % reduction compared to baseline

Figure 27 Impact on energy demand by using carbon tax.

Ener gy Use I mpact by Ener gy Tax

0%

5%

10%

15%

20%

2000 2005 2010 2015 2020 2025 2030Year

%

scenario for the year shown. .

CO2 Emission Reduction by Car bon Tax

0. 0%5. 0%

10. 0%15. 0%20. 0%25. 0%30. 0%

2000 2005 2010 2015 2020 2025 2030Year

%

Transport energy demand by fuel tax

0

100

200

300

400

500

600

700

2000 2010 2020 2030

Year

Mtc

e

Indirect EffectsDirect EffectsBaseline

Figure 28 Impact of vehicle fuel tax on energy demand. The Figure shows energy use considering direct effects only, as well as considering both direct and indirect effects.

fBeijing Urban Transport scenarios 6.1 Transport in Beijing

Beijing is the capital city of People’s Republic of China with a history of 5000 years old, and it is the political and cultural center of China. It is located at very northwest of North China Plain, close to Bo Sea at southeast. Total area of Beijing is 16800 km2, with a permanent resident population of 11.336 million (2002). From 1990 to 2002, the annual of population growth rate is about 0.8%. Gross domestic product (GDP) of Beijing is 321.27 billion RMB (2002), about 3.04 times of that in 1990 and annual growth rate was as high as 9.72% calculated by constant price.

High economic growth rate has stimulated the growth of vehicle stock. Vehicle stock (not including motorcycles) in 2002 has amounted to be 1.77 million, with an average growth rate of 14% compared with that in 1980. Recent information shows that the auto stock has been over 2 million in august in 2003.

Private car grows dramatically. In 1980, only 22 cars were registered for private purpose and this number amounted to be 458 thousand by the end of 2002. Average of annual growth rate is as high as 57.14% from 1995 to 2002,. Car ownership, 11 cars per 100 households in 2002, shows that Beijing has been a society of motorization. This kind of astonishing growth rate brings heavy pressure on the limited road network whose growth rate is much lower, oil supply and environment emissions. China become a net oil-importing country in 1994 and 30% of oil consumed now is imported abroad. Furthermore, urban transport becomes one of three major emitters of pollutants such as CO, NOx and HC. 6.2 Scenarios for Beijing

In order to understand possible future for energy use and environment emission from urban transport in Beijing, analysis on the transport scenario for Beijing was conducted. Three scenarios were defined to describe possible future development trends in urban

transport in Beijing. These are described below.

Government promotion (BaU): Present transportation policies will continue; present vehicles with lower energy use will become gradually more popular. This is given based on review for the policies adopted and planed by local government. Technology Progress (TG): Additional environment-friendly policies such as emission standard, public transport promotion policies will be introduced, and present vehicles with lower energy use such as high fuel economy gasoline vehicle and new diesel vehicles will become rapidly more popular. Clean future (CF): Additional policies and countermeasures will be introduced to increase the environment performance of transportation, such as the introduction of advanced cars (e.g. hybrid cars, fuel-cell cars), policies to encourage mini cars, integrated design of transportation system and city function area, top priority given to public transit, use of bio-gasoline, and public involvement.

Major assumptions for the three scenarios are given in table 13. In the table, based year data comes from Beijing Year Book 2003, and calculated by authors. The future data is given based on author’s judgment, by following government planning and relative researches.

Table 13. major assumptions for the three scenarios

SPO applied BaU TG CF GDP, billion Yuan

2000: 248 2020 1500

same same

POP 2000: 12.78million 2020: 18million

same Same

GDP per Capita

2002: 27746CNY 2020: 85500

same Same

Vehicle Stock 2000: 1.37million 2020: 5.9million

2000: 1.37million 2020: 5.6 million

2000: 1.37million 2020: 5.6million

Total passenger traffic volume(billion person-km)

2000: 23.4 2020: 36.7

Same Same

Total freight Traffic volume(billion ton-km)

2000: 8.73 2020: 13.36

2000: 8.73 2020: 13.36

2000: 8.73 2020: 13.36

Share of public transport in total passenger

2000: 31.7% 2020: 32%

2000: 31.7% 2020: 44%

2000: 31.7% 2020: 44%

MRT, km

Rail-Based mass rapid transit

2000: 56 2008: 300 2020: 1000

Same Same

Number of Buses

2000: 130002020: 23000

2000: 13000 2020: 23000

2000: 13000 2020: 24000

Clean fuel bus

Promotion of Alternative Fuel

2000: 3680 2020: 10000

2000: 3680 2020:

2000: 3680 2020: 12000

Vehicles 12000 Emission Standard

Vehicle emission standard and Inspection/Maintenance

2007: EURO III

2005: EURO III 2010: EURO IV

2005: EURO III 2010: EURO IV

Parking fee Increase Increase Public Transport Oriented Policy

Bus lane Better interchange

Bus lane Better interchange

ITS Finish by 2006

Finish by 2006

Clean fuel Promotion of Alternative Fuel Vehicles

2007: M15%

High efficiency car

Promotion of high efficiency vehicles

2004:Hybrid car 2005: new diesel car 2013: fuel cell car

Mini Car Promotion of high efficiency vehicles

Incentive policies

Integrated transport

Promotion of special lanes for walking and biking

Public interchange

Public interchange Bicycle

Public involvement

Travel awareness initiative for wise use of automobiles

Public transport Energy saving driving

Transport demand reduction

Information Technology based communication and services to reduce transportation need

Tele-conference, on-line shopping, nearby service

Data source: Beijing Year Book, 2003, and calculated by author

8.3. Data assumptions

IPAC-AIM/technology model was used in the simulation. Major assumption for the modelling study is given as following.

6.3.1 transport mode parameter

In the model analysis, transport was divided into several mode in order to provide provision in detail based on technology classification. In each model, sub-mode was given to make further classification, and then technologies were provided. The transport mode and technology is given in table 14. Energy and emission is calculated based on use of each technology in targeted year. Then transport model parameter is given in table 15, which is the basis to calculate traffic volume for major technologies, and emission factor.

Table 14 transport mode of urban transport Transport mode Sub-mode Technology Public transport Subway bus Gasoline bus New gasoline bus Diesel bus New diesel bus CNG bus LPG bus Private transport Private car Gasoline car Low energy use car Diesel car New diesel car Mini-car Hybrid car Fuel car Taxi LPG taxi Gasoline taxi Low energy use

gasolinetaxi Diesel taxi New diesel taxi Small bus Gasoline mini-bus New gasoline mini-bus Diesel mini-bus New diesel mini-bus LPG mini-bus Motor cycle Motor cycle New motor cycle Bicycle Freight transport Large truck Gasoline truck New gasoline truck Diesel truck New diesel truck

Medium and small truck Gasoline truck New gasoline truck Diesel truck New diesel truck Railway Passenger Diesel locomotive Electric locomotive Freight Diesel locomotive Electric locomotive Air transport Passenger Existing air plane New air plane(20%

energy saving) Freight Existing air plane New air plane(20%

energy saving) Alternative fuel Gasoline Diesel Bio-gasoline Bio-diesel ITS Waiting time display Engine shut down Wise transport Table 15 parameter for transport mode

Transport

mode

fuel Average travel

distance(1000km/year)Average

load(person/vehicle) CO2

emission factor(g/km)

Gasoline 884.59 Diesel 817 CNG 831 LPG/gasoline 910.74

bus

Electricity

65.7

49

624.40 Gasoline 165.86

Taxi LPG/gasoline 93.15 0.975

136.95 Gasoline 221.15 Diesel 81 electricity 112.11

Car Hybrid

12

1.8

100.04 MRT electricity 42.7 164.5 1734.45

6.3.2 traffic volume Traffic volume is the driving force for future transport in the model, given as exogenous

parameter. By following development plan of Beijing and other relative studies, based on GDP growth, vehicle ownership, and relative policies, future traffic volume is given in table 16 and 17.

Table 16 number and traffic volume(baseline scenario and technology progress scenario) 2000 2002 2010 2020

Vehicle, 1000 1365 1765 3900 5900MRT,km 54.0 75.0 340 1000Bus,1000 13 14 20 23Private car,1000 321 458 2426 4106Truck,1000 214 184 300 350Motor cycle,1000 332 343 400 430Bicycle,1000 9887 11010 11000 11000Road passenger Traffic volume, billionperson-km 74.9 81.2 139.4 166.5Share of public transport(%) 31.3% 30.1% 32.7% 32.8%Share of private transport Car 33.3% 39.9% 62.2% 71.1%Taxi 10.1% 9.1% 5.3% 4.4%Bicycle 31.2% 27.1% 14.6% 7.9%Business bus 20.7% 19.7% 14.9% 14.1%Motor cycle 4.6% 4.2% 3.0% 2.5%Railway freight traffic volume,billionton-km 20 21.9 25.5 31.0Railway passenger traffic

volume,billion person-km 6.27 7.6 9.5 14.0Air passenger traffic volume, billionperson-km 19.8 24 51.5 101.3Air freight traffic volume, billion ton-km 1.7 2.3 3.6 7.1 Table 17 number and traffic volume(clean future)

2000 2002 2010 2020 Vehicle, 1000 136.5 176.5 350 560MRT, km 54.0 75.0 340 1200Bus,1000 1.3 1.4 2.1 2.4Private car,1000 32.1 45.8 203.8 388.1Truck,1000 21.4 18.4 30 35Motor cycle,1000 33.2 34.3 40 43Bicycle,1000 988.7 1101.0 1100 1100Road passenger Traffic volume, billionperson-km 74.9 81.2 138.9 165.1Share of public transport(%) 31.3% 30.1% 38.0% 36.7%Share of private transport Car 33.3% 39.9% 54.1% 60.8%Taxi 10.1% 9.1% 5.8% 4.8%Bicycle 31.2% 27.1% 20.6% 16.6%

Business bus 20.7% 19.7% 16.3% 15.1%Motor cycle 4.6% 4.2% 3.3% 2.8%Railway freight traffic volume,billionton-km 200.2 255.0 255.0 310.0Railway passenger traffic

volume,billion person-km 62.7 95.0 95.0 140.0Air passenger traffic volume, billionperson-km 198.5 514.9 514.9 1012.8Air freight traffic volume, billion ton-km 16.8 36.2 36.2 71.2 6.3.3 technology parameter Vehicle based parameters used in the model are given in following tables. Mainly includes fuel efficiency, cost, emission etc. Table 6 presents the cars in market in 2004, which could be a basis for understanding on future technology development trend. Table 18 present the fuel efficiency used in the model, it is used to calculate energy use in transport sector. Table 8 gives the average fuel efficiency for cars in target years, which calculated based on the model output. Table 18 parameters for existing car

car

Fuels Fuel efficiency

Litter/100km Engine size Emission standard

Jatta Gasoline 6.6 1600EURO-II Santana Gasoline 6.8 EURO-II

Passate Gasoline

6.81800－2400

EURO-II

Polo Gasoline 4.7 1600EURO-II Auto Gasoline 4.4 1000EURO-II Elante Gasoline 6.29 1795EURO-III Corolla Gasoline 6.1 1794EURO-III Kaiyue1.8LS-AT Gasoline 6.8 1799EURO-III Bora1.8S-AT Gasoline 7 1800EURO-III SanaAT Gasoline 6.6 2000EURO-III NISSAN SunnyLS-AT Gasoline 6.2 1998EURO-III Toyota PRUIS Gasoline 4.1 EURO-IV LUPO Diesel 2.981500 EURO-IV Vehicle fuel efficiency used in the model is given in table 19.

Table 19 fuel efficiency for vehicles Fuel

efficiency(Mcal/1000person-km) Gasoline bus 0.0618 New gasoline bus 0.0525 Diesel bus 0.051 New diesel bus 0.042 CNG bus 0.0882 LPG bus 0.085 Trolley bus 0.0456 Gasoline car 0.437 High fuel efficiency car 0.271 Diesel car 0.43 New diesel car 0.27 Mini-car 0.362 Hybrid car 0.33 LPG taxi 0.594 Gasoline taxi 0.437 Diesel taxi 0.383 Gasoline mini-bus 0.28 New gasoline mini-bus 0.22 Diesel mini-bus 0.18 New diesel mini-bus 0.15 LPG mini-bus 0.27 Motor cycle 0.023 New motor cycle 0.02 Gasoline large truck 0.361 New gasoline large truck 0.34 Diesel large truck 0.31 New large diesel truck 0.28 Gasoline small truck 0.623 New gasoline small truck 0.54 Diesel small truck 0.436 New diesel small truck 0.401 Table 20 average fuel efficiency in scenarios, littler/100km

2002 2010 2020 Baseline scenario 9.73 9.62 9.25 Technology progress scenario 9.73 8.46 6.90 Clean future scenario 9.73 8.15 5.91

6.4 Results

The results of running the model are given in Figures 29 to figure 34.

From the results we can see energy use for transport will increase rapidly before

Ener gy Demand i n Bei j i ng, TG

0

1000000

2000000

3000000

4000000

5000000

2000

2002

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2006

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Gca

l

Di eselGasol i neNGSLPGEl ec

Ener gy Demand i n Bei j i ng, BaU

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1000000

2000000

3000000

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500000020

0020

0220

0420

0620

0820

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Gca

l

Di eselGasol i neNGSLPGEl ec

Ener gy Demand i n Bei j i ng, CF

0

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2000000

3000000

4000000

5000000

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2002

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l

Di eselGasol i neNGSLPGEl ec

NOx emi ssi on i n Bei j i ng f r om t r anspor t

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CO emi ssi on i n Bei j i ng f r om t r anspor t

0100002000030000400005000060000

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TGCF

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0500000

1000000150000020000002500000300000035000004000000

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t-C

BaUTGCF

Figure 29 Energy demand for Beijing Urban Transport, BaU scenario

Figure 30. NOx emission from urban transport in Beijing

Figure 31 Energy demand for Beijing Urban Transport, TG scenario

Figure 32 CO emission from urban transport in Beijing

Figure 33. Energy demand for Beijing Urban Transport, CF scenario Figure 34. CO2 emission from urban transport

in Beijing

2010. After 2010, the growth rate could be reduced and total energy use can keep stable. In BaU case, energy use in 2010 is the effects of use of new transport technology and policy options. In BaU case, the total energy use for transport in Beijing would be 4.11million toe in 2010 and 4.46million toe in 2020. Compared with BaU case, there will be 0.45million toe and 0.86million toe saved in 2010 and 2020 in technology progress scenario, 0.92million toe and 1.36million toe in clean future scenario respectively. We can observed large amount energy saving. In the mean time, Nox, CO and CO2 emission also would be reduced. Compare with BaU scenario, there would be 19%, 48% and 31% reduction for Nox, CO and CO2 emission in clean future scenario. Among the technology and policy options, energy efficient vehicle such as hybrid car and advanced diesel vehicle and policies to promote public transport can play important role.

6.5 Policy recommendations for Beijing Based on the scenario study, the following policy recommendation was made: Maintaining present transportation control policy Encouraging policy for low-energy-use vehicles:

1).encouraging development of mini cars and abolishing the limit on running of mini cars

2).encouraging low-energy-use vehicles: reducing tax, and putting out energy use standard as soon as possible 3).introducing advanced vehicles, such as PRIUS, LUPO 4).establish long-term development plan for fuel-cell vehicles

Use of ethanol gasoline Integrated design of transportation system

Integrated design for city function areas（this should be divided according to district,

not street）

Traffic control policy

1).on the condition of improvement of public transit, promoting the running cost for cars (increase the parking fee to 10-20 Yuan/h, integrated with public transit) 2).encouraging telephone meetings 3).developing electronic payment 4).nearby purchase 5).school-bus plan

Public involvement（flameout when waiting for more than 6 minutes, reduced vehicle

use, increased walking and cycling）

Develop a clean transportation system through international cooperation mechanism

Appendix I

Industry Growth and Energy Use, Local Carbon Future

Jiang Kejun, Hu Xiulian Energy Research Institute

Abstract: Recent rapid growth of energy use in China exerts great pressure on energy supply and environment. Energy use in industry, especially in energy intensive industry is the key driving force for this. This paper review the energy use in industries, policies, together scenarios of future energy development and resulting pollutant and greenhouse gas emissions, taking into account the most up-to-date data. Recent policy discussions that will affect future economic, industrial and energy supply trends also considered. To address uncertainties, especially uncertainties surrounding the level of energy intensive production in the next several decades, three scenarios were defined, which reasonably represent the range of plausible futures for energy development. This paper also discussed the possibility of energy use in industry to contribute low carbon future. The results from quantitative analysis show that energy demand in China could be as high as 2.9 billion toe in 2030, which could exceed the available energy supply. Energy use in industry still take more than half in final energy use. By using various policy options discussed in the paper, however, there is potential to reduce this high demand to 2.4 billion toe in 2030. Keywords: energy, climate change, modeling, scenario, China, industry, energy intensive production 1. Background

In China, due to rapid economic growth, total primary energy consumption increased from 400 Mtoe in 1978 to nearly 1320 Mtoe in 2004, with an annual average rate of increase of 4.7% (see Figure 1)(China Energy Year Book 2002-2003, 2004; China Year Book 2004, 2004). Coal is the major energy source, providing 70.7% in 1978 and 69% in 2004 of total primary energy use (see Figure 2). Recent years have witnessed a dramatic surge in the rate of increase of energy use in China and widespread energy shortages.

0

200

400

600

800

1000

1200

1400

1950 1970 1990 2010Year

Mto

e ConsumptionProduction

Figure 1 Energy production and consumption in China

Energy Consumption Mix in China, 1957-2005

0

20

40

60

80

100

120

1957 1970 1980 1983 1986 1989 1992 1995 1998 2001 2004Year

%

Hydro-power Natural Gas Crude Oil Coal

Figure 2 Primary energy use mix in China

Energy efficiency improvement and energy conservation are given high priority in the energy development strategy in China, as is the efficient and clean use of coal and other fossil energy sources. The objective of developing clean coal technology is to improve coal utilization efficiency, to reduce environmental pollution and to promote economic development. High efficiency and clean technology will be crucial for China to achieve a low-emission development path. Figure 5 illustrates the way in which energy efficiency improvements in the steel-making industry have been driven by advanced technology diffusion.

Energy Use by Sector in China

0200400600800

1000120014001600

1990 1995 2000 2004

Year

Mto

e

ResidentServiceTransport(Business)Cons truct ionIndus tryAgriculture

Energy Use Mix by Sector in China

0%

20%

40%

60%

80%

100%

19 90 1995 2000 2004

Year

Mto

e

ResidentServiceTransport(Business)Construct ionIndus tryAgriculture

Figure 3 Energy use by sector in China Figure 4 Energy use mix by sector in China

Figure 5 Technology progress and energy efficiency improvement in steel making industry urnace

. Policies on energy conservation in industry sector

.1 General energy policies

istorically China is a central planning nation, government instruction strongly affects energy

Energy efficiency planning. This planning is part of national Five-Year-Plan announced

- dards. Nearly 100 efficiency standard was announced by end of

- uld be decided based on

- nergy saving building. Tax derating is given to wind

- for energy conservation project by different interest with average 30%

- energy saving by enterprise. Started from 1985, price is 8 to 10% of value by

- ergy saving stove in rural area, biogas promotion and city banquette

- portant energy conservation project by government including cement

Unit Energy Use in S tee l M aking Industry

0

50

1 00

1 50

2 00

2 50

3 00

1 97 0 19 75 198 0 1 985 19 90 199 5 2 000

Year

kgce

Large C onvertorC ontinuous C as ting

Large B las t Furnac e

C oke D ry Q uenc hing

C oal P ow der Injec tionTR T

Fluit Gas R ec overy D C F C O R E X

E F

Note: EF- electric furnace, TRT-Top Gas Pressure Recovery Turbine, DCF-Direct Current F 2 2 Hactivities. Reforming for energy industry restructure is undergoing now. Recently more and more policies and countermeasures have been announced to use standard and market based policies. Aim of these policies is to provide sufficient energy and high efficient energy industry to support economic development in China, in the same time take clean energy production and consumption to protect environment. These policies and countermeasures include:

-

every five years. In the planning, energy use per industrial output value, unit energy use for major industry products and ability for energy conservation was set up; and some targets for rural energy production, requirement for energy conservation in sector and enterprise was given. Energy efficiency stan1998, catalogue for machinery and electronic products including 1068 energy saving products and 610 products need to be disused was published. Price favourable for independent power plant investor. Price copayback of loan and profit level. Tax derating for co-generation, eturbine import, small hydro and biogass. No tax levy for wind farm in first two years in some province. Loan favourablelower. Price for energy saving. Subsidy for enproduction. Organize im

industry technology retrofit, fun and pump retrofit, World Bank/GEF project on energy conservation in China, green lighting, World Bank/GEF project on renewable energy commercialisation, forest energy project, pilot project of straw utilization, clean production plan, clean vehicle etc. Opening of energy price. Energy pr- ice could be decided by market and increased energy

- administration system of China’s oil

.2 Energy intensive sectors

Energy intensive industry is key factor in China energy conservation effort. Major policies on

Improving energy management: In most energy intensive sectors, national

- on: For example in 1990, the State

- istrations had put

Incentive Saving

price could well contribute energy saving. Started from beginning of 1990, energy price generally went to market based system from government control system. This give better situation for energy development. Now only energy use for some large power plants and residential users were controlled by government. Reforming on important energy industries. The industry has changed in correspondence with the historical transformation of the management mechanism of the oil companies. There are three major companies for oil and natural gas production in China. Power generation reforming is undergoing. Separating government function from power generation and change to companies management, separating power generation and distribution are the major activities for the reforming to provide competitive and opening power industry in China.

2

energy conservation in energy intensive sectors include: -

administrations including ministries, industry associations calling for enterprises to bring energy conservation into enterprise management, so that management is held accountable for enterprise energy statistics, exceeding the set energy-quota and transformation of energy conservation technologies. Setting graded quota for products energy-consumptiStandardization Administration set graded quota of energy consumption and statistical and calculation method for 22 major building material products suches cement, cement products, plate glass, sanitary building ceramics, sintered bricks and tiles, which are applied to energy consumption quota management of building material producing enterprises. This also happened for other energy intensive products. Discarding backward technological equipment: Sector adminregulation on discarding backward technology equipment. For example in 1999, the State Economic and Trade Commission issued Catalogue of Eliminating Backward Production Capacity and Products ( the first and second batch ), banning in clear terms and eliminating at fixed periods to discarding backward technology equipment. policies: In the 1980s, the State established a special fund for the Basic Energy

Construction as well as a special fund for renovation of energy-saving technologies. In the 1990s, the country lowered down the interest rate of loans for the basic energy-saving infrastracture construction projects 30 per cent than the one for commercial loans. An Award for Energy Saving of Enterprises was established. The State has also actively promoted clean production among enterprises, giving preference

- to its investment for these projects which could optimize energy consumption and conservation, environmental protection, and comprehensive utilization of resources. In 1993, the State Taxation Administration promulgated the Notice on Exempting some Wall Materials from Value Added Tax (VAT). In the same year, the Ministry of Finance and the State Taxation Administration jointly issued the Notice on Exempting Some Products that Utilizing Resources Comprehensively from VAT. The two documents regulate that raw building materials mixed with no less than 30 per cent of coal gangue, bone coal, flyash, and industrial slag are exempted from VAT.

- Energy conservation technological innovation: This is commonly used in many energy intensive sectors. In building material sector, fourteen energy conservation measures have been applied to vertical cement kilns. For example, comprehensive energy conservation innovation have been carried out in vertical kilns, and power-generating equipment by residual heat has been installed in the middle-hollow kilns. Some renovation has been made to rotary kilns, such as transforming wet kilns into dry or semi-dry manufacturing, and carrying out comprehensive energy conservation innovation for rotary kilns.

1.1 Policies in 11th Five Year Plan

China’s 11th Five-Year Plan for National Economic and Social Development (11th FYP) puts energy at the top of the agenda and represents a major shift in government strategy towards a “scientific approach to development.” For the first time, the Chinese Communist Party formally recognized that economic growth (measured in GDP terms) is not an adequate measure of economic development. This policy shift is reflected explicitly in the 11th FYP, which only contains two quantitative targets: - Doubling of per-capita GDP between 2000 and 2010 and - a 20% reduction in energy intensity (energy consumption per unit of GDP) over the period 2006-2010. Whereas China is well on track to attain the former target (which would continue the trend in GDP growth achieved between 1980 and 2000), the energy intensity target is more challenging. Although the energy intensity of the economy declined by over 50% during the two decades prior to 2000 (Figure 2), while GDP quadrupled, energy consumption per unit of GDP has begun to climb again in recent years as a result of more energy intensive investment, industrial and export activities. The government intend to achieve their ambitious energy intensity goal by:

adopting an economic growth model that is driven to a greater extent by domestic consumption and growth in the service and high-tech sectors (at the expense of the primary and secondary sectors);

encouraging investment in more energy efficient capital stock, equipment and products;

reforming resource prices to end-users to better reflect market conditions and environmental externalities, which will likely lead to higher energy prices. Supporting the 11th FYP is the Medium- to Long-Term Plan for Energy Conservation (MLTPEC), which was issued by the National Development and Reform Commission (NDRC) in November 2004. The Plan contains ten major energy saving programs (as well as a very detailed and quantitative target for energy consumption for major industrial products and energy consuming equipment):

Table 2. Ten Programs for Energy Efficiency in the MLTPEC

Program Potential Annual Energy Savings EJ Coal-fired industrial boilers

conversion and energy efficiency improvement

70 Mtce (conversion) 35 Mtce (efficiency)

2.05 1.03

Heat-power co-generation 5 Mt tce 0.15 Residual heat and pressure usage 2.66 Mtce (steel industry)

3 Mtce (cement industry) 1.35 Mtce (coal mining industry)

0.08 0.09 0.04

Oil conservation and substitution 35 Mt less oil consumption 1.47 Electrical machinery system

energy conservation 20 TWh electricity

Energy system optimization strive to achieve international benchmarks of energy efficiency in steel, petrochemical and

chemical industries

Construction energy conservation 50 Mtce 1.47 Green lighting 29 TWh electricity

Government organisations’ energy conservation

reduce energy consumption per capita and per area of office space by 20% in 2010, compared

to 2002

Energy conservation monitoring & technology services system

construction.

Source: NDRC (2004) In order to support the targets, a program focusing on top 1000 enterprises was set up, coordinated by NDRC. Actually this program include more than 1000 big energy users, which take more then 60% of total industry energy use. Component of this program includes energy auditing, energy monitoring, planning for energy saving. In this program it is planed to make nearly 100mollion tce energy saving in 2010, which take 6% of total energy use by that time. All the members in this program have to report their energy use annually and open for public. This will be important program to support national 20% energy intensity decrease in 11th five year plan. But right now there is no specific financial mechanism to support the program. In order to provide incentives, recently NDRC initiated more than 500 energy saving projects with 10% funding support from government, while other 90% funding raising by enterprise themselves. However nowadays energy saving in large energy users is no more low hanging fruit, the cost is increasing. More regulation and financial support need to be designed.

Energy Service Companies are getting more role in energy saving in China. With support of GEF funding, three energy service companies(ESCOs) were established and they basically running well. And more important is there are several other energy service companies operated in market and have good business. This is a big potential area for energy saving widely implement in service sector, and small industries. 1. Energy and Emission scenarios 3.1 Methodology

The IPAC-Emission model and IPAC-AIM/Technology model – components of the Integrated Policy Assessment Model for China (IPAC) – were used to perform the quantitative scenario and policy option analysis. The models project future energy and pollutant emissions.

The IPAC-AIM/Technology model is a single region model for China, developed based on AIM/enduse model (AIM Project team, 1996; Hu et al., 1996; Hu et al., 2001;Jiang et al., 1998). This model includes three modules (i.e., energy service demand projection, energy efficiency estimation and technology selection). The demand is divided among the industrial, agricultural, service, residential, and transportation sectors, and these sectors are further divided into sub-sectors. For both demand and supply side, more than 400 technologies are considered, including existing as well as advanced technologies that may be used in the future. The model searches for the least-cost technology mix to meet the given energy service demand. The most up-to-date information on these technologies were collected from large number of printed sources, as well as by consulting experts directly. Linking these two models provides both detailed analyses of various sectors and a global analysis of China’s energy future. The same scenarios and related model assumptions were used for both models. Energy demand for China was basically given by the IPAC-AIM/technology model by calculating demand from sectors with detailed technology information; whereas energy price and energy import data are derived from the IPAC-Emission model. The global energy analysis is given based on SRES B2 scenario(IPCC, 2001b), while the part for China is revised in this study. Figure 4 presents the link between two models.

Common parameter for China:GDP, Population, sector activities,

domestic energy supply

IPAC-emission model

IPAC-AIM/technology model

Energy priceEnergy import

Technology mixTechnology efficiency improvement

IPCC SRES B2 scenario

Figure 6 Link between models

Created by trial version, http://www.pdf-convert.com

3.2 Model assumptions

The major assumptions used in this study (including population, GDP growth and mix) are

given in the following tables. The assumptions for population come from other studies. The

assumed GDP growth rate is consistent with government targets and research by the Development

Research Center (Zheng et al.,2004; Tan et al., 2002; Qu, 2003; Liu et al., 2002)

Table 1 Population assumption, million

2000 2010 2020 2030

Population 1284 1393 1472 1539

Urban 465 641 824 1000

Rural 819 752 648 539

Note: Assumptions by authors, based on review of relevant studies

Table 2 GDP growth in China

2000-2010 2010-2020 2020-2030

Annual GDP Growth Rate 8.9% 7.5% 6%

Table 3 Economy mix assumption

2000 2010 2020 2030

Primary industry 16.4 11.5 10 7

Secondary industry 50.2 48.5 46 44

Tertiary industry 33.4 40 44 49

3.3. Scenarios

In order to analyze future energy demand and emissions in China, we consider three

scenarios. Considering the uncertainty for energy intensive products demand with impact of WTO

accession, a baseline scenario and a high demand scenario were given. Another one is policy

scenario. The three scenarios are defined as follows:

- Baseline scenario: This scenario gives a basic trend to describe future economic activities.

There will be better international trading and China’s economy will be part of global

economy. Therefore China could rely on international markets and energy resource

imports to meet part of its energy supply needs.

- Policy scenario: Various energy and emission control policies are assumed for this low

demand scenario, which reflects energy supply and environmental constraints.

The basic assumptions for the three scenarios, such as population and GDP growth, are the

same. Sector service output for the three scenarios is given in Table 4.

Baseline scenario Policy scenario Unit 2002

2020 2030 2020 2030 Steel Mt 182.4 430 380 380 320 Copper Mt 1.63 5.2 5.8 4.5 5.2 Aluminum Mt 4.51 12 18 10 14 Ethylene Mt 5.43 14 20 12 16 Ammonia Mt 36.75 50 56 47 49 Chemical fertilizer

Mt 37.9 52 58 48 50

Cement Mt 725 1100 1100 1000 900 Glass Million

cases 234.4 520 560 480 530

Vehicles Million 3.25 15 17 11 12 Policy options to be considered in the policy scenario are given in Table 5. These policy options were defined based on policy potential in China and technology trends (Qu, 2003; Liu et al., 2002; IPCC 2001a; IPCC 2002) Table 5 Policy options used in the modeling study Policy options Explanation Technology promotion policy End use technology efficiency increase by

using new technologies Energy efficiency standard for buildings New buildings reach 75% increase standard in

2030 Renewable energy development policy Promote use of renewable energy Energy tax Introduce vehicle tax by 2005, and energy tax

by 2015 Public transport policies In cities public transport in 2030 will take 10 to

15% higher share than 2000. Transport Efficiency Improvement High fuel efficiency vehicles widely used,

including hybrid vehicle, compact cars, advanced diesel car

Power Generation Efficiency Efficiency of coal fired power plants increase to 40% by 2030

Nature Gas Incentive Enhance natural gas supply, localization of technology to reduce cost

Nuclear power development National promotion program by setting up target

Scenario up to 2050 is simulated by using IPAC-Emission model. Global and Asian-Pacific scenario is from IPCC SRES B2 scenario(AIM-Linkage model is one of the six models for IPCC SRES, IPAC-Emission model is extension of AIM-Linkage model). In IPAC-emission model, international energy trade was included in the study based on the resource cost effective availability (Jiang et al., 2000b; Jiang et al., 1999). Main assumptions are given in table 6 to 8.

Table 6 Key Scenario Drivers Assumed for the Developing Asia-Pacific and the World in IPAC-Emission model

Item Assumptions Asia-Pacific Population 4.7 billion in 2050

5.0 billion in 2100 Asia-Pacific Annual GDP Growth Rate

5.7% from 1990 to 2050, 3.8% from 2050 to 2100

World Population 11.7 billion in 2100 World GDP $250 trillion in 2100 GDP/ capita trends Disparity remains

GDP/capita of OECD becomes 7 times of non-OECD (now 13 times).

AEEI 1.0%-1.2% International Trade Low trade across regions

High trade cost Urbanization Increase in developing world before 2050,

decrease in developed world

Table 7 Assumptions for B2 Scenario for the Developing Asia-Pacific and the world Item Assumptions

Resource availability Oil/gas: medium; Biomass: high

Energy exploitation cost Medium Non-carbon renewable energy cost High for nuclear, medium for solar and

others Biomass availability Medium End-use technology efficiency improvement

Medium

Social efficiency improvement Medium Transport conservation High Dematerialization trend Medium Land-use productivity improvement Medium Meat-oriented food habit Low Desulphurization degree High

Table 8 Scenarios for 2050 scenario

Baseline scenario Policy and technology scenario

Enhanced Energy Saving

Energy Intensive Products

Annual average energy saving rate 2.7%

Annual average energy saving rate 3.6%

Building Annual average energy saving rate

Annual average energy saving rate

1.9% 3.0% Transport Annual average

energy saving rate 1.5%

Annual average energy saving rate 2.8%

Renewable energy Biomass Annual average reduction rate of cost by 3.7%

Annual average reduction rate of cost by 5.9%

Hydro 65% of technical potential by 2050

80% of technical potential by 2050

Solar/wind 0.7yuan/kWh by 2050 0.5Yuan/kWh by 2050 Carbon Capture and Sequestration

Coal fired power plants

4% by 2050 15% by 2050

Industry 1% by 2050 5% by 2050 Clean coal technology Power generation 7% by 2050 35% by 2050 Industry 5% by 2050 15% by 2050 Hydrogen Power generation Distributed power

generation system by 3% in 2050

Distributed power generation system by 8% in 2050

Transport Fuel cell vehicle 5% Fuel cell vehicle 15% Transport Vehicle Hybrid vehicle

diffusion start from 2010, 10% by 2030

Hybrid vehicle diffusion start from 2010, 70% by 2040

Policies Carbon tax No 50yuan/t-C in 2010, 200yuan/t-C in 2050

Subsidy No Power from renewable energy 0.4yuan/kWh

Investment Energy technology R&D

Annual average growth rate 4%

Annual average growth rate 6.2%

4. Results

Energy demand is calculated using the IPAC-AIM/Technology model, Baseline scenario results are given in Figures 5 and 6.

Primary energy demand in the baseline scenario could go to 2.1 billion toe in 2020 and 2.7 billion toe in 2030. The annual growth rate from 2000 to 2030 is 3.6%, while energy elasticity of

Primary Energy Mix, Baseline

0%

20%

40%

60%

80%

100%

2000 2010 2020 2030Year

BiomassPrimary ElectricityN.GasOilCoal

Primary Energy Demand in China: Baseline

0

500

1000

1500

2000

2500

3000

2000 2010 2020 2030Year

Mto

e

BiomassPrimary ElectricityN.GasOilCoal

GDP is 0.58. Coal will be the major component energy in China (1.5 billion toe in 2030), with a 58% share in total energy demand. There is a rapid increase for natural