Energy Policy 30 (2002) 409–424

The potential contribution of renewable energy in airpollution abatement in China and India

J.C. Boudria, L. Hordijka, C. Kroezea,*, M. Amannb, J. Cofalab, I. Bertokb, Li Junfengc,Dai Linc, Zhen Shuangc, Hu Runquingc, T.S. Panward, S. Guptad, D. Singhd,

A. Kumard, M.C. Vipradasd, P. Dadhichd, N.S. Prasadd, L. Srivastavad

aEvironmental Systems Analysis Group, Wageningen University, Department of Environmental Sciences, Diedenweg 18,

6703 GW Wageningen, P.O. Box 9101, 6700 Wageningen, Netherlandsb International Institute for Applied Systems Analysis (Iiasa), Laxenburg, Austria

cEnergy Research Institute (ERI), Beijing, People’s Republic of ChinadTata Energy Research Institute (TERI), New Delhi, India

Abstract

The potential for the use of renewable sources of energy in China and India and their cost effectiveness in air pollution abatement

in Asia is studied. This is done through an integrated assessment of the costs and the environmental impacts of several types of

renewables, in comparison with fossil fuels. Results for different scenarios for fuel use in China and India for the period 1990–2020

are presented. The acidification model RAINS-ASIA is used to analyze environmental impacts (exceedance of critical loads for

acidification) and to perform an optimization analysis, aiming at minimizing abatement costs. The costs of sulfur dioxide (SO2)

emission-control through the switch to renewable energy sources are analyzed and compared with the costs of controlling the

emissions from fossil fuels (e.g. through flue gas desulfurization). For the environmental targets analyzed in this study an increased

use of renewable energy could cut SO2 emission-control costs in China by 17–35%, and in India by more than two thirds. r 2002

Elsevier Science Ltd. All rights reserved.

Keywords: Renewable energy; Air pollution; China; India

1. Introduction

Fossil fuels are a source of sulfur dioxide (SO2) andcarbon dioxide (CO2). These compounds contribute toacidification and climate change. As a result of rapideconomic growth, the use of fossil fuels, and theconsequent emission of air pollutants, has been increas-ing in Asia and may do so in the coming decades. As aresult, SO2-emissions may increase fast in the future,and critical loads for acidifying deposition may beexceeded for a range of ecosystems in large parts of Asia(Foell et al., 1995).

In Europe and North America, countries havedeveloped strategies to reduce acidification by emis-sion-control. In Asia, such policies have only recently

received attention and focus mainly on technologies tocontrol SO2-emissions like fuel and flue gas desulfuriza-tion. Replacing fossil fuels by renewable energy sourcesmay be an alternative to these technical measures. Thismay also reduce CO2-emissions.

Recently, a consistent inventory of future use ofrenewable energy sources at a sub-national level inChina and India has become available, including aBusiness-as-Usual and Policy scenario (Li et al., inpreparation; Panwar et al., in preparation; TERI, ERI,WAU and IIASA, 1999). Here we use this database toanalyze the cost-effectiveness of replacing fossil fuels byrenewables in SO2 emission reduction in China andIndia. To this end, the acidification model RAINS-ASIA was used and further developed. We comparedcosts of emission-control through application of renew-able energy sources to costs of individual controltechnologies for fossil fuels. Next we applied anoptimization procedure, aiming at minimizing costs for

*Corresponding author. Tel.: +31-317-484812; fax: +31-317-

484839.

E-mail address: carolien.kroeze@wimek.cmkw.wau.nl (C. Kroeze).

0301-4215/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved.

PII: S 0 3 0 1 - 4 2 1 5 ( 0 1 ) 0 0 1 0 7 - 0

reaching a defined protection (in terms of critical loadsexceedance for acidification) of the environment.

2. Future use of renewables in China and India: two

scenarios

We used a recently developed renewable energydatabase, including estimates for the use of 13 types ofrenewable energy in different end-use sectors in 47regions in China (excluding Hong Kong) and India. Thedatabase includes two different scenarios for the period1990–2020: a Business as Usual (BAU) and Policy(POL) scenario (see Li et al., in preparation; Panwaret al., in preparation; TERI, ERI, WAU and IIASA,1999).

The Business as Usual scenario is based on theextension of short term policies and trends to 2020including considerations on sustainability, existinginstitutional barriers, and economic constraints. ThePolicy scenario reflects the potential use of renewableenergy sources as constrained by technical limits, supplylimits and sustainability. The Policy scenario does nottake into account institutional barriers, existing (short-term) policies and cost constraints. It reflects the amountto which renewables could replace fossil fuels (coal,coal-based electricity or liquid fuels) in China and Indiain the power plant and district heating sector, theindustrial sector, and the residential sector. The renew-ables include all significant, both traditional and non-traditional, energy sources: fuelwood, agricultural re-sidues, biogas, bagasse, dung, solar thermal, solar PV,wind, geothermal energy, and hydropower plants (bothsmall and large).

The projections for renewables were incorporated inscenarios for total primary energy demand, provided byChinese and Indian experts and scaled down by IIASAto RAINS-ASIA sectors and regions using structuralinformation contained in original RAINS-ASIA scenar-ios (Foell et al., 1995). This resulted in new, compre-hensive regional energy scenarios for China and Indiaincluding renewable and fossil fuels (Table 1), that havebeen implemented in the acidification model RAINS-ASIA (Amann et al., in preparation; TERI, ERI, WAUand IIASA, 1999).1

The total energy demand in China increases from37EJp in 1990 to 82–83EJp in 2020 in the BAU andPOL-scenario. In India, the energy demand increasesfrom 16EJp in 1990 to 38–39EJp in 2020. In the newscenarios the use of renewable fuels increases consider-

ably, although the total energy demand will still belargely met by fossil fuels. In 1990, the total renewableenergy input was 9.5 EJp in China and 8.3 EJp in India.In 2020 in China, this number is estimated to be13.8 EJp (45% increase) in the BAU-scenario and17.5 EJp (85% increase) in the POL-scenario (Boudriet al., 2000a; Li et al., in preparation; TERI, ERI WAUand IIASA, 1999). Similarly, in India, this number willbe 9.0 EJp (9% increase) in the BAU-scenario and13.1 EJp (57% increase) in the POL-scenario (Boudriet al., 2000a; Panwar et al., in preparation; TERI, ERIWAU and IIASA, 1999).

3. Environmental consequences of the BAU- and

POL-scenarios

3.1. Rains-Asia

The regional air pollution INformation and simula-tion (RAINS)-model, developed at the InternationalInstitute for Applied Systems Analysis (IIASA, Aus-tria), provides a consistent framework for the analysis ofemission reduction strategies on a continental scale(Alcamo et al., 1990; Amann et al., 1999a, b). RAINS-ASIA includes modules for calculating, for the period1990–2030, regional emission of SO2, costs of emission-control, atmospheric dispersion and environmentalimpact (Foell et al., 1995; Shah et al., 2000). Atmo-spheric transport over Asia is modeled based on theATMOS model (Arndt and Carmichael, 1995; Arndtet al., 1998). Databases on critical loads for acidification(reflecting deposition levels below which harmful effectsto ecosystems are assumed to not occur) were compiledby the National Institute for Public Health andEnvironmental Protection (RIVM, The Netherlands;Hettelingh et al., 1995).

RAINS-ASIA can be used for scenario analysis andoptimization analysis. In scenario analysis, environmen-tal impacts and the emission-control costs are analyzedon the basis of assumptions on future fuel use andemission-control. Optimization analysis is performed toidentify cost-optimal allocations of emission-control inorder to achieve specified deposition/concentrationtargets throughout the study domain (Amann et al., inpreparation). In RAINS-ASIA, emissions and controlcosts are calculated for 94 regions in 25 countries. Chinaand India include 28 and 20 regions, respectively.Atmospheric deposition and environmental impactsare calculated on a grid of 11 longitude by 11 latitude.

The following terms concerning environmental im-pact are used:

* x percentile critical load: the maximum level of sulfurdeposition that ensures (100�x) % of the ecosystemswithin each grid cell to remain unaffected.

1No new estimates for the use of renewables have been made for

Hong Kong. However, Hong Kong is included in the RAINS energy

scenarios for China. For this reason, the number of regions mentioned

in the text is sometimes 47, sometimes 48, depending on whether Hong

Kong is included or not.

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424410

* Excess S deposition: the amount of sulfur depositionexceeding the critical load (percentile to be specified).

* EJp, PJp, or GJp: ExaJoule (1018 J), PetaJoule(1015 J), or GigaJoule (109 J) on a primary energyequivalent. In this article all energy dates are relatedto the energy input on the basis of primary energyequivalents, which refers to* the net calorific input of fossil fuels or biomass* an equivalent amount of primary energy for

processes without direct fuel input (wind,geothermal, hydro, nuclear, etc.), in accordancewith the convention adopted in United NationsStatistics. For electricity producing facilities thisis: 1 PJ of primary energy equivalent=0.38 PJ ofgross electricity produced. For heat producingfacilities this is: 1 PJ of primary energy equiv-alent=1PJ of gross heat produced.

3.2. Future trends in acidification in Asia (scenarioanalysis)

A RAINS emission scenario combines an energyscenario, describing future trends in regional fuel use,with a control-strategy, describing the emission-controloptions implemented. In this paper, we combined thetwo energy scenarios (BAU and POL) with threecontrol-strategies for SO2, comprising no control

(NOC), current legislation (CLE) and maximum feasiblereduction (MFR) (Amann et al., 2000; Boudri et al.,2000b). These strategies refer to end-of-pipe technolo-gies to reduce SO2 only, and do not consider renewablesources of energy as an option for emission-control. Inthe BAU-scenario without emission-control (BAU-NOC), sulfur-emissions in China increase by 50%between 1995 and 2020, and in India even by 160%.In the scenario accounting for current legislation (BAU-CLE) these numbers are 23% (China) and 146%(India). Current emission-control policies in India focusmainly on the transport sector. The maximum feasiblereduction scenarios (BAU-MFR) indicate that end-of-pipe technologies may reduce emissions considerably,but at high costs (Tables 2 and 3).

In the POL-scenarios emissions are lower than in thecorresponding BAU-scenarios as a result of an increaseduse of renewable energy (replacing mainly coal). In thePOL-CLE scenario in 2020, the SO2-emissions arealmost 20% (India) and 4.7% (China) lower than inthe BAU-CLE scenario.

The occurrence and intensity of excess sulfur deposi-tion (25 percentile) are represented in Figs. 1, 2a and b.In 1990, the deposition rates exceed these critical loadsin large areas in the Southeastern part of China, whilefor India little excess deposition is calculated (Fig. 1).Assuming current legislation (BAU-CLE), an increase in

Table 1

Demand for primary energy (EJp/year) and CO2-emissions for China (including Hong Kong) and India, in the Business-as-Usual (BAU) and Policy

(POL) scenario as implemented in RAINS-ASIA (Amann et al., 2000; Boudri et al., 2000b)

BAU POL

Fuel 1990 1995 2000 2010 2020 2000 2010 2020

China Coal 21.5 28.1 33.5 38.2 42.7 33.2 36.2 38.7

Biomass 8.3 8.6 7.9 7.3 6.7 8.1 7.8 8.0

Oil 4.6 6.5 8.7 12.0 15.8 8.7 12.0 15.8

Gas 1.0 1.9 2.0 4.9 8.0 2.0 4.9 8.0

Hydro 0.8 1.8 1.7 3.0 4.7 1.8 3.6 5.6

Nuclear 0.0 0.1 0.1 1.1 2.2 0.1 1.1 2.2

Other 0.4 0.5 0.7 0.9 2.3 0.9 1.7 4.0

Total China 36.6 47.5 54.6 67.4 82.5 54.6 67.3 82.3

GDP, (1995=100) n.a. 100 147 290 518 147 290 518

Population, million 1146 1209 1300 1400 1500 1300 1400 1500

CO2, Mt C 643 859 1040 1267 1500 1030 1216 1396

India Coal 4.4 6.0 7.2 10.5 15.7 5.8 7.5 11.3

Biomass 7.8 8.3 7.2 7.0 6.8 7.7 7.9 8.4

Oil 2.4 3.1 4.5 7.3 10.7 4.3 7.0 10.2

Gas 0.5 0.7 1.0 1.9 3.5 1.0 1.9 3.5

Hydro 0.6 0.7 0.7 1.4 2.2 1.1 2.5 3.2

Nuclear 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.0

Other 0.0 0.0 0.0 0.1 0.2 0.4 0.9 1.7

Total 15.6 18.9 20.7 28.2 39.1 20.4 27.8 38.4

GDP, (1995=100) n.a. 100 131 236 419 131 236 419

Population, million 850 937 1021 1166 1297 1021 1166 1297

CO2, Mt C 164 224 285 440 663 250 367 544

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424 411

acidification in large parts of China cannot be avoided,while for India deposition rates may exceed critical loadsin 2020 in Bihar, Bengal, Uttar Pradesh and along thesea coast (Fig. 2a). For the hypothetical maximumfeasibility scenario (BAU-MFR) deposition rates willnot exceed critical loads in India, while in Chinaexceedances would not be higher than 500 acid eq/ha/year (Fig. 2b).

In addition, CO2-emissions were estimated for theBAU and POL energy scenarios (Table 1). In the BAU-scenario, CO2-emissions are by 2020 two times (China)to four times (India) as high as in 1990. In the POL-scenario, as a result of increased use of renewable energy(replacing mainly coal), the decrease in 2020 relative tothe BAU-scenario in India and China is 18% and 5%,respectively.

Table 2

Comparison of SO2-emissions for China (including Hong Kong) and India in 1990 and 1995 and in 2020 for the Business-as-Usual (BAU) and Policy

(POL) scenario, while assuming no emission-control (NOC), current legislation for emission-control (CLE) and maximum feasible reductions

(MFR), respectively. (Amann et al., 2000; Boudri et al., 2000b)

SO2-emissions (million tons/yr)

2020

1990 1995 BAU_NOC POL_NOC BAU_CLE POL_CLE BAU_MFR POL_MFR

China 20.8 23.9 36.6 33.7 29.6 28.2 6.1 5.8

India 3.7 5.0 13.0 10.5 12.3 9.9 2.0 1.9

Total 24.5 28.9 49.6 44.2 41.9 38.1 8.1 7.7

Table 3

Costs of SO2 emission control by technical options in China (including Hong Kong) and India in 1990 and 1995 and in 2020 for the Business-as-

Usual (BAU) scenario, assuming no emission-control (NOC), current legislation for emission-control (CLE) and maximum feasible reductions

(MFR), respectively. (Amann et al., 2000; Boudri et al., 2000b)

Costs of SO2 emission-control (billion US$95/year)

2020

1990 1995 BAU_NOC BAU_CLE BAU_MFR

China F F F 2.7 32.1

India F F F 1.4 21.0

Total F F F 4.1 53.1

Fig. 1. Excess S deposition (25th percentile) in China and India in 1990. Units: acid equivalents/ha/yr (results from RAINS-ASIA; Amann et al.,

2000).

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424412

4. A method to evaluate the cost-effectiveness of

renewables in air pollution control (optimization analysis)

Section 3 showed that maximally avoiding futureacidification by applying end-of-pipe technologies forsulfur control is too expensive to be realistic. Below wewill discuss to what extent the use of renewables may bea more cost-effective solution than end-of-pipe technol-ogies.

For this aim we explored the cost effectiveness ofrenewables to reduce air pollution, focusing on environ-mental targets that lie between the environmentalimpact of BAU-CLE and BAU-MFR. We used theoptimization module of RAINS-ASIA (Amann et al., inpreparation). Our goal function is to minimize total

costs of SO2 emission reduction in Asia, subject to pre-defined deposition constraints. Based on regional cost-curves, RAINS can be used to identify cost-optimalregional emission levels, taking into account atmo-spheric transport of pollutants and the sensitivity ofecosystems to pollution.

We performed optimization-runs for the BAU energyscenario for the year 2020. The RAINS optimizationmodule requires three types of inputs.

* Environmental targets for RAINS grid cells. Wedefined three sets of environmental targets for Chinaand India (see Section 6). The model identifies costeffective emission reductions strategies to meet the

Fig. 2. (a) Excess S deposition (25th percentile) in China and India, for the Business-as-Usual energy scenario while taking into account current

legislation for SO2 emission-control (BAU-CLE) for 2020. Units: acid equivalents/ha/year (results from RAINS-ASIA; Amann et al., 2000). (b) As

Fig. 2a but assuming maximum feasible reduction of SO2-emissions by end-of-pipe technologies, excluding the use of renewable sources of energy

(BAU-MFR).

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424 413

deposition targets specified for China and India,including emission reductions in other countries.

* Policy constraints, or emission ceilings (constraints)for each region, simulating the effects of ‘‘CurrentLegislation’’ for sulfur-control. The optimized solu-tion will only include measures that are additional tocurrent legislation, while returning to the uncon-trolled situation is not allowed.

* Cost-curves, including information on costs of redu-cing SO2-emissions from the 94 RAINS-ASIAregions (‘‘area’’ sources) and more than 250 largepoint sources (LPS). Costs differ by abatement-options, fuel type and sector. If a certain abate-ment-option replaces another option,2 marginal costscan be calculated, defined as the cost of controllingthe last unit of SO2 at a given emission level. Byranking the available abatement-options according totheir marginal costs, emission reduction cost-curvesresult, providing for each source the least costs toachieve a certain emission reduction (Cofala and Syri,1998). Cost-curves are energy scenario and yearspecific.

We used two types of cost-curves. The first set ofcalculations has been performed with ‘‘conventional’’RAINS-ASIA cost-curves only including add-on tech-nologies like fuel cleaning and flue gas desulfurization(Foell et al., 1995). We refer to these optimization-runsas the BAU-runs. For the second set of optimization-runs (the BAUR-runs) we used new cost-curves thatinclude replacement of fossil fuels by renewables as anoption for emission-control. Ideally, optimization of thefuel mix would be based an optimization of the fullenergy system. RAINS-ASIA, however, does notinclude an energy model that allows for such analyses.We therefore treated renewable options mathematicallyas though they were end-of-pipe control-technologiesand developed new regional cost-curves for China andIndia for the year 2020 that include end-of-pipetechnologies as well as switches from fossil to renewablefuels as control-options for SO2 (Boudri et al., 2000b,Annex 4). We assumed that renewables only replacefossil fuels in the RAINS-ASIA ‘‘area’’ sources (includ-ing part of the power plants) in China and India. Forother Asian countries and large point sources theexisting RAINS-ASIA cost-curves were used. Usingthese new cost-curves we explored to what extent cost-optimal strategies to reduce sulfur deposition in Asiainclude a shift from fossil fuels to renewables. We

consider this a novel approach to evaluate the costeffectiveness of renewable energy in air pollutionabatement.

In short, the new cost-curves were constructed asfollows:

1. Starting point is the difference between the BAUand POL-scenarios for renewables in 2020, reflectingthe potential increase in use of renewables. Weidentified for each renewable energy type or technol-ogy which fossil fuel (or fossil fuel based electricityor heat) in which sector and application it couldreplace.

2. Next, we quantified substitution ratios, reflecting theunits (PJp) of renewable energy sources needed toreplace one unit (PJp) of the conventional energycarrier in a particular sector and application, assum-ing an equal energy service.

3. We then calculated the unit costs of SO2-abatementby a shift to renewables on the basis of an analysis ofthe costs of renewables compared to costs offossil fuels, and their potentials to reduce sulfur-emissions.3.1. For each renewable energy source, both unit

costs and emissions of SO2 and CO2 wereestimated per unit of primary energy input. Costestimates include investment costs (discounted ata rate of 4%), fuel costs, fixed and variableoperating and maintenance costs, and transmis-sion and distribution costs.

3.2. Similarly, unit costs and emissions were calcu-lated for the corresponding fossil fuels. In caseonly part of the fossil fuel technology is replaced,costs were only estimated for the replacedcomponents.

3.3. On the basis of 2, 3a, and 3b, unit replacementcosts were calculated, reflecting the costs ofreplacement of conventional fuels by renewables.In addition, the unit cost of SO2 and CO2

emission reduction were calculated, reflecting thecosts of emission reduction by replacing conven-tional fuels by renewables.

4. Next, the unit replacement costs for all renewable andend-of-pipe reduction-options were compared tocalculate marginal costs. This resulted in sets ofmarginal costs for each control-option, relative toother options compared to which it is cost effective.

5. For each RAINS region, the SO2 emissioncontrol options (renewables and end-of-pipetechnologies) were ranked according to their cost-effectiveness in reducing sulfur-emissions (marginalcosts). Application of more than one option to acertain sector and fuel is allowed. In this way, cost-optimal combinations of control-measures are deter-mined for each emission-reduction level for all sectorsand fuels.

2Abatement-options are defined in such a way that, in a selected

sector and for a selected fuel, options can only replace other options,

and are never added on top of each other. Thus, coal washing and flue

gas desulfurization hypothetically results in three options: both options

separately, and the combined application of both coal washing and flue

gas desulfurization, defined as one individual option. Of course,

options that are applied in different sectors or to different fuels are

independent and therefore can be added on top of each other.

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424414

5. The costs of using renewable fuels in China and India

We identified for each renewable fuel that is applied inthe POL-scenario, the fossil fuel that it replaces, andestimated the associated costs roughly referring to theperiod 2010–2020 (Table 4a and b). The ranges in costsand emission factors reflect the range of valuescalculated for different regions in India and China.The calculated unit costs per kg of SO2 avoided dependon the costs of fossil and renewable fuels, substitutionratios, and the sulfur-emissions per unit of energy. Insome cases, renewables are cheaper than conventionalfuels, resulting in negative replacement costs.

Replacement of fossil fuels by renewables does notalways result in a reduction of SO2-emissions. Increasedemissions are calculated for direct burning of fuel wood,crop residues and dung for cooking and/or heatingpurposes in India. In China electricity production frommunicipal waste fuel combustion causes higher sulfur-emissions than coal-based electricity in some regions.Likewise, biogas from organic waste may increase sulfuremission from industry. Finally, the use of dung forresidential cooking may decrease sulfur-emissions inTibet, Qinghai, and Xinjiang Uygur, but increase themin inner Mongolia, where coal is low in sulfur. All otherrenewables result in a reduction in SO2-emissions whenthey replace fossil fuels. In the following some results arediscussed by sector.

5.2.1. Power sectorIncreased use of renewables, as assumed in the POL-

scenario, may reduce the need for new coal-fired powerplants. The estimated unit costs of electricity deliveredto the grid from new coal-fired power plants (excludingcogeneration) in 2020 are 2.6–4.1US$95/GJ thermalinput for the different regions in India, and are3.6US$95/GJ thermal input for China.

In India electricity from bagasse, wind turbines, smallhydropower and large hydropower may replace coal-based electricity. Wind-based electricity in the POL-scenario is almost three times the BAU level (an increaseof 252 PJp), while electricity production by large hydroturbines is about 65% (1051 PJp) higher, and energyfrom bagasse about 29% (962 PJp). Estimated unit costsfor large hydro, wind and bagasse are in the same rangeas for fossil fuels, while the unit costs for agriculturalresidues and small hydropower are at least twice as high.For the case of bagasse, see also (Boudri et al., 2000b).For some regions, negative costs were calculated forSO2-control through a shift from coal to wind turbinesand large hydropower. The estimated replacement costsfor other renewables range from very low to 15US$95 per kg SO2 avoided.

In China, electricity from wind is in the 2020 POL-scenario 15 times the BAU level (1246 PJp), while large

and small hydro turbines produce 21% (845 PJpprimary) and 11% (89 PJp) more electricity, respec-tively. In addition, municipal solid waste and bagassecould replace coal in cogeneration, while geothermalsources replace coal used for heat production only.Sulfur-emissions of coal-based cogeneration is higherthan that of bagasse, and in many regions also ofmunicipal solid waste. Negative replacement costs(per kg SO2 avoided) were calculated for small andlarge hydropower for all regions. The costs for otherrenewables range between very low to 49 US$95 per kgSO2 avoided.

5.2.2. Residential and commercial sectorIn India, several renewables may replace LPG used

for cooking. However, only biogas from dung and thesolar box type cooker may reduce sulfur-emissions. Thecosts of biogas are 2.5US$95 per kg SO2 avoided. Thereplacement costs of the solar box type cooker are veryhigh. In addition, solar heating and solar PV mayreplace electrical geysers and diesel generators, atnegative costs and at about 33 US$95 per kg SO2

avoided, respectively.In rural China, coal stoves used for cooking may be

replaced by biofuels, biogas and biomass gasification, atunit replacement costs ranging from very low to 75US$95 per kg SO2 avoided. Solar heating and solar PVmay replace coal boilers and electricity from the grid,respectively. Solar heating is calculated to have negativereplacement costs, while those for solar PV are between5 and 50 US$95 per kg SO2.

In the POL-scenario for China, geothermal heat inputis 19% (228 PJp) higher than in the BAU-scenario. Lowtemperature heat may replace coal boilers, at negativeunit replacement cost, while high temperature resourcesmay be used for district heating at relatively high costs,due to geographical and demographical conditions.

5.2.3. IndustryIn Indian industry, solar thermal heating by flat plate

collectors are assumed not to replace boilers, but only toreduce coal consumption. The unit replacement coststherefore are rather high, at 13–16 US$95 per kg SO2

avoided.In the POL-scenario for China, use of fuel wood is

4% (19 PJp) higher than the BAU level by 2020,assuming that sustainable wood production will increaseand replace part of the coal used in brick making. Theunit replacement costs are 3.6–23US$95 per kg of SO2

avoided. In addition, biogas production is about 150%(26 PJp) higher in the POL than in the BAU-scenario,replacing coal. The unit costs of biogas and coal boilersare 4 and 6 US$95 per GJp, respectively. In someregions with low-sulfur coal a switch to biogas wouldincrease sulfur-emissions. In other regions, the unitreplacement costs are negative.

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424 415

Table 4

Overview of the application of renewable energy sources in India (a) and (b) China as assumed in the POL-scenario and as used for constructing cost-curves, including assumptions on replacement

of fossil fuels, the range of unit costs per unit of energy input in the sub-national regions (in US$95/GJ thermal input), SO2-emission factors (in kg SO2/GJ thermal input); substitution ratios, and

unit cost of SO2-reduction (in US$95per kg SO2 reduced). The energy data is reported on a primary energy equivalent basis. a[see text for further details. (Boudri et al., 2000b)]

Renewableenergy source

Renewabletechnology

Application(energyservice)

Unit costs ofrenewableapplicationper GJ

SO2 emissionfactor

Sector Fuel replaced Conventionaltechnologyreplacedb

Unit costs offuel replacedper GJ

SO2 emissionfactor of fuelreplacedc

Substitutionratiod

Unit cost per kg SO2

(a) Bagasse Cogeneration Heat andpower

2.46 0 Power sector Hard coal Pulverizedcoal powerplant

2.63–4.06 0.570 0.684 5.50–8.02

Agriculturalresidue,mainly ricehusk

Biomass gasi-fier

Power 7.61 0 1.84 12.6–15.2

Wind Wind turbine Power 3.91 0 0.920 �0.82–1.70Water Small hydro

power turbinePower 10.3 0 0.920 9.45–12.0

Water Large hydropowerturbines

Power 1.95 0 0.939 �3.92–�1.40

Fuel wood Directburning

Cookingheating

6.39 0.06 Residentialcooking

LPG Stove 4.97 0.0228 4.80 n.r.

Crop residue Directburning

Cooking,heating

3.34 0.06 4.80 n.r.

Dung Directburning

Cooking 1.95 0.30 4.80 n.r.

Dung Biogas plant Cooking 10.5g 0 1.09 (5.58)f 285 (2.53)f

Solari Box typecookere

Cooking 41.2 0 0.600 865

Solar Photovoltaiccells

Lighting 28.2 0 Residential,commercial

Diesel Generator 14.7 0.233 0.789 32.8

Solar Flat plate col-lector

Heating 14.3 0 Residential Electricity Electricalgeyser

13.7–18.1 0.0570 0.850 �3.39–�0.871

Solar Flat plate col-lector

Heating 10.4 0 Industry,commercial

Hard coal Boiler 0.895–2.27h 0.450 0.800 13.4–16.4

(b) Municipalsolid waste/constructionwaste

Waste incin-eration

Power/heat 3.68 0.858 Power sector Hard coal different com-binations of

0.401–4.02 1.109 o0;–39.2j

Bagasse Cogeneration Power/heat 4.92 0.129 (1) Pulverizedcoal powerplant

3.61 1.560 0.89–17.1

(2) Cogenera-tion plant

2.03

(3) Boiler fordistrict heating

5.71

J.C.Boudrietal./EnergyPolicy

30(2002)409–424

416

Wind Large scalewind turbine

Power 4.52 0 Power sector Hard coal Pulverizedcoal powerplant

3.61 0.401–4.02 0.944 0.079–0.79

Water Small hydropower station

Power 3.40 0 0.944 �1.64– �0.163

Water Large hydro Power 3.88 0 0.944 �0.604–�0.0602

Geothermal directutilization

(district) heat 34.3 0 Residential Hard coal Boiler for dis-trict heating

5.71 0.401–4.02 0.760 4.93–49.4

Geothermal Directutilization

Heat 5.76 0 Commercial(district heat-ing) and in-dustry

Hard coal Boiler 5.71 0.292–1.59 0.700 �5.74–1.06

Solar Breadbox/flatplate collector/vacuum tubecollector

Heating 4.19 0 Residential Gas/hardcoal

Gas waterheater/coalboiler

12.5 (gas)/5.71 (coal)

0.010 (gas)/0.292–1.59(coal)

0.600 (gas)/0.700 (coal)

�972 (gas)/�9.49–�1.75

Solar Photovoltaiccell/light bulb

Lighting 25.3 0 Residential Electricity Light bulb 15.5 1.24–12.4 2.63 5.36–53.7

Fuel wood Stove Cooking 7.30 0.0585 Residential Hard coal Stove 1.50 0.292–1.59 2.000 8.90–74.8Agriculturalresidue(straw)

Stove Cooking 3.37 0.0639 2.000 3.58–31.9

Agriculturalresidue

Biomass gasi-fication/stove

Cooking 6.90 0 0.550 1.44–7.85

Animal andhuman faeces

Biogas plant/stove

Cooking 9.28k 0.0813 0.500 2.03–12.5

Dry dung ofcattle (cakes)

Stove builtof dry mud

Cooking(herdingareas)

0.00 0.303 3.000 o0–41.4j

Industrialorganic waste

Biogas plant/stove orboiler

Cooking 9.28k 0.0813 0.500 2.03–12.5

Heat 4.22k 0.0813 Industry Hard coal Boiler 5.71 0–1.92 0.897 �6.81–�1.04l

Fuel wood Directburning

Brick making 8.38 0.0583 Industry Hard coal Brick kilns 2.60 0.355–1.92 1.111 3.63 to 23.1

aSee for a definition of primary energy equivalent Section 3.1.bRenewables only replace conversion technologies without sulfur-control, but they may include control of particulate matter in power plants and cogeneration plants.c In cases where electricity is the fuel replaced, the SO2 emission factor is related to the electricity input on the basis of coal fueled power plants.dUnits of renewable energy source that replaces one unit of conventional energy carrier in a given application.eThis option is not included in the cost-curve because of missing information.fNumber between brackets is including the extra demand and less cost, respectively, owing to having less dung available for direct burning (conversion ratio: 1 PJp of dung gives 0.282PJp of

biogas).gRelated to input of biogas.hOnly costs of coal input.i In the optimization-runs, it has been assumed that solar heating replaces coal boilers due to the higher sulfur-emissions of coal compared to gas.j In the case of waste combustion and direct burning of dry dung, negative costs indicate an increase of sulfur-emissions.kRelated to input of biogas.l In some regions (Guangdong, Hainan, Hebei, Henan, Anhui, Inner-Mongolia, Heilongjiang, Jilin, Liaoning, Shaanxi, Gansu, Tibet, Qinghai, Xinjiang Uygur) coal consumed in the industrial

sector has very low sulfur contents, resulting in increased sulfur-emissions.

n.r. no reduction: replacement of fossil fuel by renewable results in increase of sulfur emission.

J.C.Boudrietal./EnergyPolicy

30(2002)409–424

417

6. The cost-effectiveness of using renewables in air

pollution abatement in China and India

We present the results of six optimization-runs,aiming at minimizing costs of achieving sulfur deposi-tion targets for Asia (Fig. 3a and b). All runs wereconducted for the ‘‘Business as Usual’’ (BAU) energyscenario for the year 2020. In short, two sets ofoptimization-runs were performed, reflected by the twolines in Fig. 3a and b. The top lines indicate the resultsof the BAU optimization-runs, that only consider‘‘conventional’’ (end-of-pipe) emission-control technol-ogies. The bottom lines reflect the results of the BAUR-runs that also consider an increased use of renewableenergy as an option to control SO2-emissions in Chinaand India. The optimization runs aim at limiting theexcess deposition (ED) of sulfur in China and India overcritical loads (25th percentile critical load, ensuringprotection of 75% of the ecosystems) for each grid cellto the level in 1990 (BAUR-ED90), 1995 (BAUR-ED95)and 2000 (BAUR-ED00).

The calculated SO2-emission control-costs are lowerfor the BAUR-runs than for the BAU-runs, implyingthat use of renewables may reduce the costs of SO2

emission reduction in China and India. For China, thepotential decrease in reduction costs caused is 17–33%,depending on the deposition target. For India the costsare reduced by at least two thirds. The optimal solutions

include a shift from fossil fuels to renewables for severalregions, implying that in these regions a switch in energysource is a more cost effective option to reduce sulfurreduction than end-of-pipe technologies (Table 5aand b).

Many of the selected renewable options appear tohave negative marginal costs, indicating that it wouldalso be cost-effective to implement these options withoutconstraints for sulfur dioxide. These renewable energytechnologies not only reduce sulfur-emissions, but arealso cheaper (per unit of energy service provided) thanconventional fuels. In India, this is the case for largehydropower and solar heating in the residential sector inmany regions, and for wind turbines in some regions.For China, this is the case for hydropower, geothermalheating (commercial and industry), solar heating (re-sidential), biogas (industry), and (to an almost negligibleamount) dung for cooking purposes in western regions.

The optimal solutions also include several renewableswith non-negative marginal costs in a number ofregions. These renewables include solar heating in theresidential sector, biogas for cooking purposes, andwind turbines in India, and wind turbines in China. Asan example we present results for Tamil Nadu, a regionin India. Fig. 4 illustrates where replacement of fossilfuels by renewables appear in the cost-curve. Forinstance, the marginal costs of SO2 reduction by aswitch to large hydropower (HYD) are negative in this

Fig. 3. (a) Results for China: cost-efficiency of SO2 emission reduction by 2020 for the Business-as-Usual scenario including current legislation for

SO2 emission-control (BAU-CLE and BAUR-CLE) and different sets of deposition targets. The upper line takes into account only end-of-pipe

technologies (BAU-runs). The lower line takes also into account renewables (BAUR-runs). Results from RAINS-ASIA. The targets limit excess

sulfur deposition (25th percentile) for each grid cell to the level calculated for 1990 (ED90), 1995 (ED95) and 2000 (ED00). (Amann et al., 2000;

Boudri et al., 2000b). (b) As Fig. 3a but for India.

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424418

region, while the use of solar thermal (STH/C) and smallhydropower (SHP) appear at the end of the cost-curve.The graph also shows the emissions of the Business-as-Usual energy scenario without emission-control (BAU-NOC), the BAU-scenario while including all renewablefuels with negative replacement costs without additionalsulfur-control (BAUR-NOC), and while includingcurrent legislation (BAUR-CLE), as well as of thedifferent optimization-runs (BAUR-ED90, BAUR-ED95 and BAU-ED00). The results indicate thatcurrent legislation in this region is not a cost-optimalway of sulfur-control. This may be caused by the factthat current legislation in India includes the use ofexpensive low-sulfur diesel, primarily aimed at thecontrol of urban air pollution.

7. Conclusions

We developed a novel approach to evaluate the costeffectiveness of renewable sources of energy in airpollution abatement in Asia. Our analyses show thatrenewable energy sources provide sulfur- and carbon-free alternatives to conventional fuel supplies. We usedthe RAINS-ASIA model to explore SO2-emissioncontrol-strategies for China and India for the coming20 years. All strategies explored in this study aim at cost-optimal allocation of emission reduction measuresacross the various emission sources in Asian countriesto achieve environmental targets. The RAINS-ASIAapproach demonstrates that optimization analysis mayhelp finding cost-effective strategies for air pollution

abatement. It should be stressed that the selection of atarget is a political, not a scientific choice.

The overall conclusions of our work are:

1. Current levels of acidifying deposition may cause aserious threat to natural ecosystems over large areasin the Southeastern part of China and currentlegislation may not avoid a further increase inacidification. For India, acidification is not a majorproblem at present, but may become a problem inthe future in many regions.

2. In a Business-as-Usual (BAU) scenario, use ofrenewables increases between 1990 and 2020 by45% in China and by 9% in India.

3. In the Policy (POL) scenario, the use of renewablesincreases between 1990 and 2020 by 85% in Chinaand by 57% in India. Both BAU and POL-scenarioshave been implemented in the RAINS-ASIA model.

4. In the BAU-scenario that accounts for currentpolicies, SO2-emissions increase by 42% (23%)between 1990 (1995) and 2020 in China, and by232% (145%) in India. CO2-emissions in 2020 aretwo times higher in China and four times higher inIndia than in 1990.

5. In the POL-scenario emissions are lower than in theBAU-scenario as a result of the increased use ofrenewable energy sources. For India, the calculated2020 CO2-emissions are 18% lower in the POL thanin the BAU-scenario, and for China 7%. The SO2-emissions for India and China are 20% and 5%lower in the POL- than in the BAU-scenario,respectively, while taking into account currentlegislation.

Fig. 4. Cost-curve for the region Tamil Nadu (India), as used in the BAUR-runs, including marginal and total costs of SO2 emission control in 2020.

Fuel abbreviations indicate where renewables replace fossil fuels in the cost-curve (HYD=large hydro, STH/C=solar thermal cooking, STH/

DH=solar thermal for domestic heating, WND=wind, BIO=biogas, BGS=bagasse). Also included are total costs and emissions in the Business-

as-Usual energy scenario without emission-control (BAU-NOC), and in the BAU-scenario while including all renewable fuels with negative costs

without additional sulfur-control (BAUR-NOC), and while including current legislation (BAUR-CLE), as well as of the different optimization-runs

(BAUR-ED90, BAUR-ED95 and BAUR-ED00). (Boudri et al., 2000b)

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424 419

Table 5

Overview by region in India (a) and China (b) of renewables that have been selected in optimal solution for three sets of environmental targets, and total costs for area sources of SO2 emission

reduction involved (in million US$/year). The targets limit excess sulfur deposition in China and India over 25th percentile critical load (ensuring protection of 75% of the ecosystems) for each grid

cell to the level calculated for 1990 (BAUR-ED90), 1995 (BAUR-ED95) and 2000 (BAUR-ED00). (Boudri et al., 2000b)

(a) Regiona ED90 ED95 ED00

Renewables in all optimal solution

with marginal costso0

Renewables in optimal solution

with marginal costs>0

Total

costsbRenewables in optimal solution with

marginal costs>0

Total

costsbRenewables in optimal solution

with marginal costs>0

Total

costsb

Large

hydro

power

Wind

turbines

Solar

thermal

heatingc

Solar

thermal

heatingc

Biogas for

cooking

Wind

turbines

Solar

thermal

heatingc

Biogas for

cooking

Wind

turbines

Solar

thermal

heatingc

Biogas for

cooking

Wind

turbines

Andra Pradesh X X X 165.9 X X 177.6 X X 165.9

West

Bengal

X X X 481.0 X X 490.1 X X 410.3

Bihar X X 330.2 X 371.2 X 293.2

Bombay X 9.5 9.5 F3.5

Calcutta X 72.1 X 72.1 X 9.4

Delhi X 19.4 19.4 19.4

East

Himalayas:

Assam –

North-East

Highland

X �249.7 �249.7 �249.7

Gujarat X 16.3 16.3 16.3

Haryana X X �2.8 �2.8 �2.8

Karnataka–Goa X X X 71.0 X 71.0 X 11.3

Kerala X X X �154.3 �172.8 �199.0

Madras X 202.8 203.4 166.0

Maharashtra–

Dadra-Nagar

Haveli–Daman–

Diu

X X X 547.9 552.1 389.6

Madhya

Pradresh

X X 284.5 �22.8 �58.1

Orissa X 69.8 69.8 69.8

Punjab–Chandi-

garh

X X X �50.6 �50.6 �50.6

Rajasthan X X 58.8 58.8 58.8

J.C.Boudrietal./EnergyPolicy

30(2002)409–424

420

Tamil Nadu–

Pondicherry

X X X X 436.8 X 264.1 X 240.4

Uttar Pradesh X X 69.9 60.0 50.4

West Himalayas:

Jammu–Kash-

mir–Himachal

Pradesh

X X �466.7 �466.7 �466.7

Total costs for area sourcesb 1911.8 1470.0 870.4

(b) Regiona ED90 ED95 ED00

Renewables in all optimal solution with MCo0 Renew-

ables in

optimal

solution

with mar-

ginal

costs>0

Total

costs

Renewables

in

optimal

solution

with

marginal

costs>0

Total

costs

Renew-

ables in

optimal

solution

with

marginal

costs>0

Total

costs

Large hy-

dro power

Small

hydro

power

Solar

thermal

heatingc

Geo-

thermal

heating

Dung for

cooking

Biogas in

industry

Wind

turbines

Wind

turbines

Wind

turbines

Beijing X X X 128.6 61.1 20.3

Chongqing X �15.3 28.2 �15.3

Fujian X X X X X �12.4 X 42.4 X 9.1

Guangdong-

Hainan

X X X X X 303.7 X 242.8 X 168.0

Guangxi X X X X 290.4 209.4 115.3

Guangzhou X 77.0 47.2 45.6

Guizhou X X X X 241.7 68.4 �0.9

Hebei-

Anhui-Henah

X X X X X 1152.8 X 665.2 X 531.4

Hong Kongd 305.5 305.5 305.5

Hubei X X X X X 415.9 413.4 86.1

Hunan X X X X X 769.6 262.0 82.2

Inner

Mongolia:

Nei-Mongol-

Ningxia

X X X X 141.8 X 141.8 X 141.8

Jiangsu X X X X 92.3 X 273.0 X 230.3

Jiangxi X X X X X 349.8 168.7 55.1

North East

Plain: Heilong

Jiang-Jilin-

Liaoning

X X X X �32.6 X 189.5 �2.0

Shanghai X X X 207.1 X 192.1 X 178.8

Shenyang X �3.7 5.6 4.7

Shaanxi-

Gansu

X X X X X 645.0 256.4 X 186.9

J.C.Boudrietal./EnergyPolicy

30(2002)409–424

421

Table 5 (continued)

(b) Regiona ED90 ED95 ED00

Renewables in all optimal solution with MCo0 Renew-

ables in

optimal

solution

with mar-

ginal

costs>0

Total

costs

Renewables

in optimal

solution

with

marginal

costs>0

Total

costs

Renew-

ables in

optimal

solution

with

marginal

costs>0

Total

costs

Large

hydro

power

Small

hydro

power

Solar

thermal

heatingc

Geo-

thermal

heating

Dung

for

cooking

Biogas

in

industry

Wind

turbines

Wind

turbines

Wind

turbines

Shandong X X X 934.8 274.1 264.6

Shanxi X X 516.3 111.1 118.9

Sichuan X X X X 134.0 148.2 135.0

Taiyuan X 63.6 87.1 33.8

Tianjin X X X 87.6 78.4 21.9

West: Tibet–

Qinghai-Xin-

jiang-Uygur

X X X X X X �54.4 X �54.4 X �54.4

Wuhan X �2.7 �2.7 �2.7

Yunnan X X X X X �49.7 73.6 �24.0

Zheijang X X X X X X 206.0 X 213.2 X 135.8

Total costs for area sources (billion US$95)b 6898.6 4507.2 2777.7

aNames in italics represent megacities.bTotal costs, including large point sources.cReplacing electrical heating domestic sector.dNo renewables have been considered for Hong Kong.

J.C.Boudrietal./EnergyPolicy

30(2002)409–424

422

6. An increased use of renewable energy may substan-tially cut SO2-emission control-costs that wouldotherwise occur if environmental air quality targetswere to be met. Our results indicate that for theenvironmental targets analyzed here an increaseduse of renewables could reduce sulfur-control costsin China by 17–35%, and in India by at least twothirds.

7. We identified a potential for renewable energy withnegative costs per kg of SO2 avoided, meaning thatthese renewables are always cost-efficient, evenwithout taking into account their acidifying impact.In India, this is the case for wind turbines, largehydropower and solar heating in the residentialsector. For China, this is the case for small and largehydropower, low temperature geothermal heat(commercial and industry), solar heating (residen-tial), biogas (industry), and dung for cooking inwestern regions.

8. Other renewable options to reduce SO2-emissionsare currently only competitive in a limited numberof cases.

9. A few renewables do not result in a reduction ofSO2-emissions when they replace fossil fuels. Theseinclude direct burning of fuelwood, crop residuesand dung for cooking and/or heating purposes inIndia. In China these include electricity productionfrom waste in some regions, biogas in the industrialsector, and dung for cooking in inner Mongolia.

10. In all applications, renewable energy reduces CO2-emissions (leaving aside indirect CO2-emissions).

11. Several renewables have negative replacement costs,but are not fully implemented in the BAU-scenario,indicating that there are barriers (other than costs)that prevent full implementation.

Acknowledgements

This study was funded by the European CommissionDGXII (contract number ERBIC18CT960098). Fordetails see www.dow.wau.nl/msa/renewables.

References

Alcamo, J., Shaw, R., Hordijk, L. (Eds.), 1990. The RAINS Model of

Acidification. Science and Strategies in Europe. Kluwer Academic

Publishers, Dordrecht, The Netherlands.

Amann, M., Bertok, I., Cofala, J., Gyarfas, F., Heyes, C., Klimont, Z.,

Makowski, M., Sch .opp, W., Syri, S., 1999a. Cost-effective Control

of Acidification and Ground-Level OzoneFSeventh Interim

Report to the European Commission, DGXI. Laxenburg, IIASA.,

pp. 87.

Amann, M., Cofala, J., Heyes, C., Klimont, Z., Sch .opp, W., 1999b.

The RAINS model: a tool for assessing regional emission control

strategies in Europe. Pollution Atmospherique, December 1999,

pp. 41–63.

Amann, M., Bertok, I., Cofala, J., Gyarfas, F., Lixin, F., Sch .opp, W.,

2000. In: Cost-effective scenarios of sulfur abatement in Asia and

the role of renewable energy sources in pollution-control, IIASA.

IIASA’s contribution to Work Package 2 as part of the project

Potential for use of renewable sources of energy in Asia and their

cost effectiveness in air pollution abatement. Wageningen

(the Netherlands), WIMEK, Wageningen University.

pp.53–122.

Amann, M., Cofala, J., Gyarfas, F., Sch .opp, W., Boudri, J.C.,

Hordijk, L., Kroeze, C., Junfeng, L., Lin, D., Panwar, T.S., Gupta,

S., in preparation. RAINS-ASIA: a tool for optimization analysis

of the acidification problem in Asia while taking into account the

potential for use of renewables.

Arndt, R.L., Carmichael, G.R., 1995. Long-range transport and

deposition of sulfur in Asia. Water, Air, and Soil Pollution 85 (4),

2283–2288.

Arndt, R.L., Carmichael, G.R., Roorda, J.M., 1998. Seasonal source-

receptor relationships in Asia. Atmospheric Environment 31, 1553–

1572.

Boudri, J.C., Hordijk, L., Kroeze, C., Amann, M., Cofala, J., Bertok,

I., Gyarfas, F., Lixin, F., Sch .opp, W., Junfeng, L., Lin, D., Xing,

Z., Xiaofeng, L., Runqing, H., Li, Z., Yanqin, S., Panwar, T.S.,

Gupta, S., Singh, D., Kumar, A., Vipradas, M.C., Dadhich, P.,

Prasad, N.S., Srivastava, L., 2000a. Potential for use of renewable

sources of energy in Asia and their cost-effectiveness in air

pollution abatement. Wageningen (the Netherlands), WIMEK,

WageningenUniversity. Final report of the EU-funded projectF-Potential for use of renewable sources of energy in Asia and their

cost effectiveness in air pollution abatement, pp. 77.

Boudri, J.C., Hordijk, L., Kroeze, C., Amann, M., Cofala, J., Bertok,

I., Junfeng, L., Lin, D., Shuang, Z., Runquing, H., Panwar, T.S.,

Gupta, S., Singh, D., Kumar, A., Vipradas, M.C., Dadhich, P.,

Prasad, N.S., Srivastava, L., 2000b. Cost-effective scenarios of

sulfur abatement in Asia and the role of renewable energy sources

in pollution-control. Wageningen (the Netherlands), WIMEK,

Wageningen University. Final report on Work Package 2 as part of

the EU-funded projectFPotential for use of renewable sources of

energy in Asia and their cost effectiveness in air pollution

abatement, pp. 163.

Cofala, J., Syri, S., 1998. Sulfur-emissions, Abatement Technologies

and Related Costs for Europe in the RAINS Model Database.

Laxenburg, IIASA. IR-98-35 (update 19.10.98). IIASA Interim

Report.

Foell, W., Amann, M., Carmichael, G., Chadwick, M., Hettelingh,

J.P., Hordijk, L., Dianwu, Z., 1995. RAINS-Asia: An Assessment

Model for Air Pollution in Asia. Report on the World Bank

Sponsored ProjectFAcid Rain and Emission Reductions in Asia.

No.l, Final report submitted to the bank by the project team.

Hettelingh, J.-P., Chadwick, M.J., Sverdrup, H., Zhao, D., 1995.

Assessment of Environmental Effects of Acidic Deposition. In:

Foell, W. (Ed.), Rains-Asia: an assessment model for air

pollution in Asia. Report on the World Bank Sponsored

ProjectFRAINS-ASIA: An Assessment Model for Air Pollution

in Asia. Laxenburg.

Li, J., Lin, D., Shuang, Z., Runqing, H., Panwar, T.S., Gupta, S.,

Singh, D., Kumar, A., Vipradas, M.C., Dadhich, P., Prasad, N.S.,

Srivastava, L., Amann, M., Cofala, J., Bertok, I., Boudri, J.C.,

Hordijk, L., Kroeze, C., in preparation. Potential for renewable

energy in China and associated emissions of sulfur dioxide and

carbon dioxide. To be submitted for publication.

Panwar, T.S., Gupta, S., Singh, D., Kumar, A., Vipradas, M.C.,

Dadhich, P., Prasad, N.S., Srivastava, L., Junfeng, L., Lin, D.,

Amann, M., Cofala, J., Boudri, J.C., Hordijk, L., Kroeze, C., in

preparation. Potential for renewable energy in mitigating emissions

of sulphur dioxide, carbon dioxide in India. To be submitted for

publication.

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424 423

Shah, J., Nagpal, T., Johnson, T., Amann, M., Carmichael,

G., Foell, W., Green, C., Hettelingh, J.P., Hordijk, L., Li, J.,

Peng, C., Pu, Y., Ramankutty, R., Streets, D., 2000. Integrated

analysis of acid rain in Asia: policy: implication and results of

rains-Asian model. Annual Review Energy and Environment 24,

338–375.

TERI, ERI, WAU, IIASA, 1999. Analysis of the potential demand for

renewable sources of energy in India and China. New Delhi (India),

Tata Energy Research Institute. Final report on Work Package 1 as

part of the EU-funded projectFPotential for use of renewable

sources of energy in Asia and their cost effectiveness in air pollution

abatement, pp. 222.

J.C. Boudri et al. / Energy Policy 30 (2002) 409–424424