References - INFLIBNETshodhganga.inflibnet.ac.in/.../6306/14/14_references.pdf · 2015-12-04 ·...

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Söderholm P, and Sundqvist T. 2003 Pricing environmental externalities in the power sector: ethical limits and implications for social choice. Ecological Economics 46: 333-350 Srivastava A K, Shrestha R M, Shrestha R, and Paul D. 2003 Power sector development in India with CO2 emission targets: effects of regional integration and role of clean technologies. International Journal of Energy Research 27: 671-685 SRS. 2004 Percentage distribution of estimated population by age group , sex and residence. Sample Registration System Statistical Report 2004 New Delhi, India: Office of the Registrar General of India. [Report No.1 of 2006 ] Sterner T. 2007 Fuel taxes: an important instrument for climate policy. Energy Policy 35: 3194-3202 Streets D G. 2003 Environmental benefits of electricity grid interconnections in Northeast Asia. Energy Policy 28: 789-807 Sverrisson F Li J, Kittell M, and Williams E. 2003 Electricity restructuring and the environment: lessons learned in the United States Washington D C: Centre for Clean Air Policy. www.ccap.org/pdf/2003-Apr-16ElectricityRestructuring_and_the_Environment.pdf [23rd December 2005] Swisher J N, and Mc Alpin C M. 2006 Environmental impact of electricity regulation. Energy 31: 1067-1083 TERI. 1992 Risk-benefit analysis of large-scale methods of generating electricity. Report submitted to the Ministry of Environment and Forest New Delhi, India: Tata energy Research Institute [Now The Energy and Resources Institute]. TERI. 1998 Looking Back to think ahead: GREEN INDIA 2047. New Delh, India: Tata energy Research Institute [Now The Energy and Resource Institute]. TERI. 2000 Investigation on respirable particulate and trace elements with source identification in air environment of Korba. New Delhi, India: Tata energy Research Institute [Now The Energy and Resources Institute]. [Report No. 97EE65] TERI. 2008 Health impact of particulate matter on mortality in Delhi-PAPA project (unpublished study). New Delhi, India: The Energy and Resources Institute.

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Thanh T and Lefevre D B. 2000 Assessing health impacts of air pollution from electricity generation: the case of Thailand. Environmental Impact Assessment Review 20: 137-158 Thakur T, Deshmukh S G, Kaushik S C, and Kulshreshtra M. 2005 Impact assessment of the Electricity Act 2003 on the Indian power sector. Energy Policy 33: 1187-1198 Turner B D. 1994 Workbook of atmospheric dispersion estimates: An introduction to dispersion modeling. Boca Raton, Ann Arbor, London, Tokyo: Lewis USEPA 1989 Air/Superfund National technical guidance study series. Volume III-Estimation of air emissions from cleanup activities at superfund sites, Interim final report. [EPA-450/1-89-003] URBAIR. 1992 Urban air quality management strategy in Asia. Greater Mumbai Report. Eds. Shah J J, Nagpal T. [World Bank paper no.381] USEPA. 1995 Compilation of air pollutant emission factors AP-42- fifth ed. Vol. 1: Stationary point and area sources. Research Triangle Park, NC: . United States Environmental Protection Agency. US EPA. 1995a Screen 3-model user guide. Office of Air Quality Planning and Standards Emissions, Monitoring, and Analysis Division, U.S. Environmental Protection Agency Research Triangle Park, N C. [EPA-454/B-95-004] USEPA. 1995b Compilation of air pollutant emission factors AP-42- fifth ed. Volume I Chapter 13: Miscellaneous Sources, Section 13.2.5: Industrial wind erosion Research Triangle Park, NC: United Nation Environment Protection Agency. http://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0205.pdf USEPA . 1995c User guide for Fugitive Dust Model (FDM), Vol.1, User’s Instructions Seattle,W.A: United Nation Environment USEPA. 1997 The Benefits and Costs of the Clean AirAct, 1970 to 1990. Research Trianglepark, N C: UnitedNations Environment Protection Agency. [EPA 410-R-97-002] http://www.epa.gov/oar/sect812/copy.html

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USEPA. 1999 The benefit and cost of the Clean Air Act,1990-2010. United States: United States Environment Protection Agency. [EPA 410-R-99-001] http://www.epa.gov/oar/sect812/1990-2010/chap1130.pdf [30th January 2009] USEPA. 2000 Green house gases from small scale combustion devices in developing countries: phase IIA house holds stoves in India Research Triangle Park,N C: Prepared by National Risk Management, Research Laboratory. United Nations Environment Protection Agency. [EPA-600/R-00-052] USEPA. 2005 Appendix W to part 51-guidelines on air quality models. Federal register 70(216) www.epa.gov/scram001/guidance/guide/appw_05.pdf [27th January 2006] USEPA. 2006 Compilation of air pollutant emission factors (AP-42), Fifth Edn . Volume I Chapter 13: Miscellaneous Sources, section 13.2.1-Paved Roads Research triangle park, NC: United Nations Environment Protection Agency. http://www.epa.gov/ttn/chief/ap42/ch13/final/c13s0201.pdf [27th August 2004] Vadakan V N, Vajanapoom N, and Ostro B. 2008 The Public health and air pollution in Asia (PAPA) project: estimating the mortality effects of Particulate matter in Bangkok, Thailand. Environmental Health Perspectives 116: 1179-1180 Varma C V J. 1997 Impact of government regulatory role on the power development in India. India Power Jan-Mar: 39-46 Wang X, and Mauzerall L D. 2006 Evaluating impacts of air pollution in China on Public health: Implications for future air pollution and energy policies. Atmospheric Environment 40: 1706-1721 WHO. 1992 International Statistical Classification of Diseases and Related Health Problems 10th Revision, Volume 1A. Geneva: World Health Organization. WHO. 1996 A methodology for estimating air pollution health effects. Geneva: World Health Organisation. [WHO/EHG/96.5] WHO. 2004 Meta analysis of time series studies and panel studies of Particulate Matter (PM) and Ozone (O3). Report of a WHO task group Geneva: World Health Organisation. http:// www.euro.who.int/document /e82792.pdf

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WHO. 2006 Air quality guidelines: Global update 2005, Particulate Matter, Ozone, Nitrogen Dioxide and Sulphur dioxide. Copenhagen: World Health Organisation. Watcharejyothin M and Shrestha R M.2009 Effects of cross-border trade between Laos and Thailand: Energy security and environmental implications Energy Policy 37: 1782-1792 Willmott C J. 1982 Some comments on evaluation of model performance. Bulletin American Meteorological Society 63: 1309-1313 World Bank. 1999 India Environment Issues in Power Sector. World Bank, United Nations Development Program, Energy Sector Management Assistance Program. [Report No. 205/98] Wong C M, Vadakan N V, Kan H, Qian Z, and PAPA. 2008 Public Health and Air Pollution in Asia (PAPA): A Multicity Study of Short-Term Effects of Air Pollution on Mortality. Environmental Health Perspectives 116: 1195-1202 WRAP. 2006 WRAP fugitive dust handbook. Western Regional Air Partnership’s Denver, Colorado: Prepared for: Western Government Association. Prepared by: Countess Environmental Westlake Village. www.wrapair.org/forums/dejf/fdh/content/FDHandbook_Rev_06.pdf Zhang Y F, Parker D, and Kirkpatrick C. 2008 Electricity sector reform in developing countries: an econometric assessment of the effects of privatization, competition and regulation. Journal of Regulatory Economics 33: 159-178 Zeiss C, and Lefsurd L. 1995 Developing host community siting packages for waste facilities. Environment Impact Assessment Review 12: 157-178 Zhu F, Zheng Y, Guo X, and Wang S. 2005 Environmental impacts and benefits of regional power grid interconnections for China. Energy Policy 33: 1797-1805

148

Annexure I

State and power plant wise emission load in Tonnes/year (2002-03)

State S. No Power plant name Emission

SPM Emission

NOx Emission

SO2 Total emission load

Delhi 1 Badarpur,NTPC 4265 26655 30088 61008 2 Rajghat(DVB) 846 5288 5969 12102 3 Ipstation(DVB) 596 3728 4208 8532 5707 35670 40264 81641

Haryana 4 Faridabad 1056 6600 7450 15106 5 Panipat 4462 27885 31477 63823 5518 34485 38927 78929

Punjab 6 Bhatinda,GNTP 2116 13223 14926 30264 7 Ropar 6502 40635 45869 93005

8 GHTP (Lehra Mohabbat) 2184 13650 15408 31242

10801 67508 76202 154511 Rajasthan 9 Kota 4997 31230 35252 71479

10 Suratgarh 5245 32783 37005 75033 10242 64013 72257 146512

Uttar Pradesh 11 Singrauli,NTPC 12256 76598 86463 175316 12 Rihand,NTPC 5744 35903 40527 82174 13 Anpara 9689 60555 68354 138598

14 NCTPS-Dadri NTPC 4806 30038 33906 68750

15 Panki 1194 7463 8424 17080

16 Unchahar,NTPC 4984 31148 35159 71290 17 Obra 6679 41745 47122 95546 18 Harduaganj 966 6038 6815 13819 19 Tanda,NTPC 2388 14925 16847 34160 20 Paricha 1016 6353 7171 14540 49722 310763 350789 711273

Gujarat 21 Gandhinagar 4273 26708 30147 61128 22 Utran 0 30147 30147 23 Wanakbori 8700 54375 61379 124454 24 Sabarmati 1844 11528 13012 26384

Annexure I



SPM Emission

NOx Emission

SO2 Total emission load 25 Kutch lignite 784 8082 16531 25397 26 Ukai 4242 26513 29927 60682 27 Sikka 860 5378 6070 12308 20704 132582 126920 280205

Madhyapradesh 28 Vindyachal,NTPC 12680 79253 89460 181393 29 Birsinghpur 4913 30705 34660 70278 30 Amaraknath 1334 8340 9414 19089 31 Satpura 7910 49440 55808 113158 26838 167738 189342 383918

Maharashtra 32 Dahanu 2608 16298 18397 37302 33 Khaperkheda 5874 36713 41441 84028 34 Nasik 4156 25973 29318 59446 35 Koradi 5488 34298 38715 78500 36 Chandrapur 13079 81743 92271 187092 37 Bhusawal 2159 13493 15230 30882 38 Parli 3713 23205 26194 53112 39 Paras 271 1695 1913 3880 37346 233415 263479 534240

Chattisgarh 40 Korba,NTPC 13766 86040 97122 196928 41 Korba East 2159 13493 15230 30882 42 Korba west 5158 32235 36387 73779 21083 131768 148739 301589

Tamil Nadu 43 Ennore 1994 12465 14070 28530 44 Tutikorin 6061 37883 42762 86705 45 Mettur 7675 47970 54149 109794 46 North Chennai 3931 24570 27735 56236 47 Neyveli 11524 119920 243081 374525

31186 242808 381796 655790

Andrapradesh 48 Ramagundam, NTPC 12542 78390 88487 179419

49 Ramagundam B 326 2040 2303 4669 50 Vijaywada 8671 54195 61175 124042 51 Nellore 188 1178 1329 2695 52 Rayalseema 2760 17250 19472 39482 53 Simhadri 4114 25710 29021 58845 54 Kothagundam 4600 28748 32450 65797

Annexure I



SPM Emission

NOx Emission

SO2 Total emission load 33202 207510 234237 474949 Karnataka 55 Raichur 7684 48023 54208 109914

7684 48023 54208 109914 Bihar 56 Muzaffarpur 317 1980 2235 4532

57 Kahalgaon,NTPC 5430 33938 38309 77676 58 Barauni 350 2190 2472 5012 6097 38108 43016 87220 Jharkhand 59 Patratu 1441 9008 10168 20616

60 Bokaro- B, DVC 2882 18015 20335 41233

61 Chandrapura, DVC 1282 8010 9042 18333 62 Tenughat 1140 7125 8043 16308 6745 42158 47587 96490

Orissa 63 Talcher(N),NTPC 5375 33593 37919 76887 64 Ibvalley 2680 16748 18905 38332

65 Talcher(old),NTPC 2341 14633 16517 33491 10396 64973 73341 148709 West Bengal 66 Farraka,NTPC 9025 56408 63673 129105

67 Budge-Budge CESC 2404 15023 16957 34383

68 Mejia(DVC) 2730 17063 19260 39053

69 Southern,CESC 682 4260 4809 9750 70 Bakreshwar 2616 16350 18456 37422 71 Durgapur,DVC 944 5903 6663 13510

72 New Kossipore,CESC 623 3893 4394 8909

73 Durgapur projects Ltd. 1250 7815 8822 17887

74 Santaldih 1008 6300 7111 14419 75 Kolaghat 5760 36000 40637 82397 76 Bandel 1320 8250 9313 18883

77 Titagarh CESC 1188 7425 8381 16994 29550 184688 208475 422713

151

Annexure II

Equation used in Industrial Source complex short term

model version 3 (ISCST 3)

The US EPA developed model was used to predict SPM concentration in

ambient air. The model has various options including the capability to handle

polar or Cartesian coordinates, simulating point, line, area sources and volume

sources. The data pertaining to source characteristics, meteorological

parameters and receptor network required as input to the model include (i)

source data : for point source physical dimensions (stack location, stack height,

stack top inner diameter) as well as exit velocity and temperature of gas and

pollutant emission rate are required; and for area source emission in gs-1m-2 ,

source’s south west coordinates and source dimensions are required(ii) hourly

meteorological data for the simulation period: wind speed, wind direction ,

ambient temperature , stability class and mixing height and (iii) receptor co

ordinates and height of receptor.

The model uses steady state Gaussian plume equations for continuous point and

area sources as presented below1:

I ) Equation for point source:

(1.1)

Where is concentration at receptor location

Q is the source strength or emission rate ( gs-1)

su is mean wind velocity at stack height (ms-1)

y is crosswind distance (m)

K is scaling coefficient to convert calculated concentrations to

desired units

1 Source: USEPA . 1995 USER’s guide for the Industrial Source Complex (ISC3) dispersion model. Vol.2, Description of model algorithm [EPA-454/B-95-003b] Research Triangle Park, NC: United Nation Environment Protection Agency.

2

2exp2 2s y z y

QKVD yu

Annexure II


y z are horizontal and vertical dispersion parameters (m)

respectively. Dispersion parameters are determined by Pasquill

Gifford curves for rural mode and by Briggs formulae for urban

mode.

Estimation of su : In equation 1.1 su is governed by power law which is used to

adjust the observed wind speed refu from a reference measured height refz to the

stack height sh

(1.1)

p is a function of stability category. Default values of “p” with respect to stability

classes are specified.

Estimation of Stack-tip Downwash: In order to consider stack tip down wash,

modification of physical stack height is performed. The modified physical stack

height 'sh is found from

for 1.5sv (1.3)

or

's sh h for 1.5sv

where sh is physical stack height in meters.

sv is stack gas exit velocity in (m/s)

sd is inside stack top diameter (m)

Estimation of vertical term V: In equation (1.1) V is the term for vertical

distribution of Gaussian plume and D is the decay term accounting for pollutant

removal by physical or chemical processes. The expression for V in equation is

given by

(1.4)

p

ss ref

ref

hu uz

' 2 1 .5ss s s

s

vh h du

2 2 2 2 2

31 2

1

2

4

exp 0.5 0.5 0.5 0.5 0.5

0.5 ,

r e r e

iz z z z z

z

z h z h HH HV

H

Annexure II


where,

e sh h h

1 2r i eH z iz h

2 2r i eH z iz h

3 2r i eH z iz h

4 2r i eH z iz h

z is the distance in the vertical direction above the ground (m),

sh is physical stack height (m)

rz is receptor height above the ground (m)

eh is effective stack height (m)

h is plume rise (m)

iz is mixing height (m)

The equation 1.3 which defines vertical term includes plume rise which is

governed by Briggs plume rise equations when plume is unaffected by building

wakes. Following are the equations to estimate plume rise.

The plume rise is used in the calculation of the vertical term. The Briggs plume

rise equations are described below. For estimation of plume rise the value of

bF i.e. buoyancy flux and mF i.e. momentum flux is required which is estimated

by following equations

Buoyancy flux parameter

(1.5)

where, g is acceleration due to gravity, sv is stack gas exit velocity sd is stack top

inner diameter, and T = ST - aT , ST is stack gas exit temperature and aT is

ambient temperature

Momentum Flux parameter

(1.6)

Estimation of plume rise varies according to the stability condition of the

atmosphere.

2

4b s sS

TF gv dT

2 2

4a

m s ss

TF v dT

Annexure II


Case 1 : For unstable or neutral crossover between Momentum and Buoyancy

following equations is used.

In case of unstable or neutral conditions, if stack gas temperature is greater than

or equal to ambient temperature, it must be determined whether the plume rise

is dominated by momentum or buoyancy. The crossover temperature T c is

determined by following equations.

(1.7)

(1.8)

In case, T exceeds or equals cT , the plume rise is dominated by buoyancy

otherwise plume rise is assumed to be momentum dominated.

For buoyancy dominated plume rise, plume rise equation under unstable or

neutral condition will be:

(1.9)

(1.10)

For momentum dominated plume rise, plume rise equation under unstable or

neutral condition is:

(1.11)

35

'

55

38.71

b

be s

s

F

Fh hu

1/3

2/3

55

0.0297

b

sc

s

For F

vTd

2/3

1/3

55

0.00575

b

sc s

s

For F

vT Td

3/4'

55

21.425

b

be s

s

FFh hu

' 3 se s s

s

vh h du

Annexure II


Case 2: Stable crossover between Momentum and buoyancy

In case of stable atmospheric conditions, stack gas temperature greater than or

equal to ambient temperature, it must be determined whether the plume rise is

dominated by momentum or buoyancy. The crossover temperature T c is

determined by following equations.

( ) .019582c s sT T v s (1.12)

In case, T exceeds or equal cT , the plume rise is dominated by buoyancy

otherwise plume rise is assumed to be momentum dominated.

For estimation of buoyancy plume rise under stable condition following

equation is used:

(1.13)

Where, s is stability parameter and is estimated by following equation

(1.14)

For stability class E , z is taken as 0.020 K/m and for class F it is taken as

0.035 K/m

For estimation of momentum plume rise under stable condition, following

equation is used:

(1.15)

Estimation of D: The expression for D in equation 1.1 is given by

exp 0xD foru

1 0for (1.16)

where, is decay coefficient (s-1) ( a value of 0 means decay is not considered),

x is downwind distance (m)

1/3' 2.6 b

e ss

Fh hu s

a

zs gT

1/3

' 1.5 me s

s

Fh hu s

Annexure II


II Equation used for area source

(1.17)

Where,

AQ is area source emission rate (gm-2s-1)

V the vertical term is given by equation (1.3)

exp 0.52

A

y z yx y

Q K VD yx dy dxu

157

Annexure III

Estimation of Years of life lost

Table III.I : Estimation of deaths as a result of pollution in each age group

*Source: SRS .2006. Sample registration system statistical report 2004. Report No.1 of 2006. Office of the registrar general of India. New Delhi Note: CRF used to estimate deaths as a result of pollution is adopted from long term exposure study of Pope III. 2002 . The value of CRF is 3%.

Age group 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+ Total % of population in each group ( Chattisgarh rural 2004)* 12.4 12.3 11.6 9.8 8.4 7.5 7.3 6.7 5.4 3.9 4.5 3.1 2.8 2 1.3 0.5 0.4 0.2 100

Population in each group (Korba, district) 125466 124454 117371 99159 84993 75887 73863 67792 54638 39461 45532 31367 28331 20236 13154 5059 4047 2024 1012835 Age specific death rates * 20.7 3 0.7 1.3 1.9 3.4 2.2 4 3.5 4.9 10.3 9.9 17.1 34.5 32.2 97.1 185.8 119.3 8.2 Deaths as a result of pollution 8 1 0 0 0 1 0 1 1 1 1 1 1 2 1 1 2 1 25

Annexure III


Table III.II: Estimation of Years of Life Lost per person

*Source:

Indiastat.2005. Age group wise life expectancy in Madhya Pradesh (Compiled from statistic released by: Office of Registrar general India). http://www.indiastat.com/default.aspx Note: Life expectancy of infants (i.e. less than one year) is assumed to be similar of 1-5 age groups because percentage of population of infants was not available separately. It is included in 0-4 age group in the above table.

Age group 1-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70+ Total Life expectancy in each age group, Madhya Pradesh* 61.6 61.8 57.6 53 48.7 44.3 40 35.7 31.4 27.1 22.9 19.1 15.5 12.2 9.5 Deaths as a result of pollution 8 1 0 0 0 1 0 1 1 1 1 1 1 2 5 Total years of Life lost 480 69 14 20 24 34 19 29 18 16 32 18 23 26 48 870 Years of Life Lost per person 0.0008586

159

ANNEXURE IV

Table IV.I: Estimation of total years of life lost in Korba

Grid number Population in

each grid

Average RSPM (µg m-3) in each grid

as a result of power plants i.e. power plants stacks and ash ponds

Years of life lost in each grid as a result of power

generation 7 149 2 0 8 1389 3 3 9 2994 2 5

10 6302 2 10 11 22173 2 40 12 828 2 2 13 956 2 2 14 336 2 1 15 14 2 0 24 1648 3 4 25 659 2 1 26 2221 3 6 27 3221 3 7 28 5224 2 11 29 10635 2 22 30 2326 3 5 31 1575 2 3 32 691 2 1 33 2541 2 4 34 0 2 0 41 2010 4 7 42 4205 4 13 43 1933 3 6 44 2809 3 8 45 4576 3 12 46 2973 3 7 47 4329 3 11 48 3873 3 11 49 2285 2 5 50 1875 2 4

Annexure IV


Grid number Population in

each grid

Average RSPM (µg m-3) in each grid

as a result of power plants i.e. power plants stacks and ash ponds

Years of life lost in each grid as a result of power

generation 51 982 2 2 52 557 1 1 53 0 1 0 59 260 4 1 60 2924 5 12 61 3266 5 13 62 2533 4 10 63 4713 4 16 64 4039 4 13 65 3952 4 14 66 4809 4 16 67 3809 3 9 68 2607 3 6 69 1004 2 2 70 826 1 1 71 1308 1 2 77 232 6 1 78 3862 5 17 79 5633 6 30 80 5315 6 27 81 16481 6 84 82 5426 4 21 83 4222 5 19 84 5042 5 20 85 13837 3 40 86 2570 3 7 87 1712 2 3 88 1805 2 3 89 1657 1 2 90 928 1 1 95 1235 9 10 96 5153 9 40 97 6090 8 40 98 4076 8 27

Annexure IV


Grid number Population in each

grid

Average RSPM (µg m-3) in each grid as a

result of power plants i.e. power plants stacks and ash ponds

Years of life lost in each grid as a result of power generation

99 14473 9 115

100 6647 8 44

101 3643 7 23

102 7182 6 37

103 8575 4 32

104 2498 4 9

105 1508 2 3

106 1420 2 2

107 621 1 1

108 0 1 0

114 2609 14 31

115 6013 15 79

116 12861 14 151

117 17354 14 212

118 17293 13 198

119 7335 14 87

120 8162 11 77

121 3053 8 22

122 2795 4 10

123 468 2 1

124 1304 1 2

125 266 1 0

126 0 1 0

127 699 10 6

128 1736 11 17

129 1416 13 15

130 622 15 8

131 283 17 4

132 4044 19 67

133 5023 22 93

134 13801 28 328

135 17354 28 424

136 17354 26 392

137 15952 28 378

Annexure IV



grid




138 4476 50 190

139 1548 94 126

140 1051 5 4

141 686 3 1

142 338 2 1

143 111 2 0

144 625 1 1

145 1319 10 12

146 5324 12 55

147 3041 14 36

148 1198 16 17

149 3683 20 62

150 4633 25 101

151 4542 37 143

152 9813 60 506

153 19680 198 3350

154 15722 50 671

155 7087 35 216

156 1817 106 165

157 1829 16 25

158 301 6 2

159 586 4 2

160 362 2 1

161 293 2 1

162 2120 2 3

163 1511 12 15

164 4986 13 55

165 4117 14 51

166 5354 16 76

167 6948 20 117

168 8112 25 171

169 12536 31 338

170 36560 39 1233

171 39263 46 1555

172 32444 41 1134

Annexure IV



grid




173 2794 23 56

174 1473 13 16

175 307 10 3

176 50 6 0

177 0 4 0

178 213 3 1

179 984 3 2

182 981 11 9

183 3014 13 33

184 5135 15 67

185 5719 17 86

186 5980 20 101

187 5290 22 99

188 16366 25 344

189 24422 34 721

190 18414 65 1030

191 2957 29 74

192 276 10 2

198 0 2 0

201 1753 11 17

202 2391 12 24

203 4747 12 50

204 4372 12 46

206 2914 13 33

207 3467 13 39

208 2490 13 27

209 953 11 9

214 36 4 0

215 223 3 1

216 0 2 0

219 219 8 2

220 1282 9 9

221 1888 8 13

222 2628 8 19

223 2070 8 14

Annexure IV



grid




224 520 7 3

225 5758 7 34

226 5875 7 36

227 1807 7 11

232 887 3 2

233 356 3 1

234 0 2 0

237 312 6 2

238 1543 6 8

239 2487 6 13

240 2119 5 10

241 1492 5 6

242 1200 5 5

243 1613 5 7

244 933 5 4

245 156 4 1

250 1452 3 3

255 1999 5 8

256 2078 5 9

257 2260 4 8

258 1464 4 5

259 1111 4 4

260 1606 4 5

261 495 4 2

262 434 4 2

269 0 2 0

273 436 4 1

274 4910 3 14

275 2664 4 8

276 1272 3 4

277 2019 3 6

278 861 3 2

279 1127 3 3

280 1302 4 4

Annexure IV



grid




281 192 3 0

293 3944 3 10

294 878 2 2

295 1812 2 4

296 1783 3 4

297 1327 3 3

298 673 3 2

299 1060 3 2

300 395 2 1

301 751 3 2

302 240 3 1

303 40 2 0

311 354 2 1

312 1450 2 3

313 1696 2 3

314 2125 2 4

315 1139 2 2

316 870 3 2

317 969 2 2

318 1272 2 2

319 1635 3 4

320 1260 3 3

321 91 2 0

Total Years of life lost 17252

Abbreviations

AT&C Aggregate Technical and Commercial loss

BALCO Bharat Aluminium Company Ltd.

CO2 Carbon di oxide

(CO) Carbon monoxide

CEA Central Electricity Authority

CPCB Central Pollution Control Board

CSEB E Chattisgarh State Electricity Board Power plant-East

CSEBW Chattisgarh State Electricity Board Power plant-West

CRF Concentration Response Function

DALY Disability Adjusted Life Years

$ Dollar

DRFs dose response functions

E Eastern region

FAC2 Factor of two

FB Fractional Bias

FDM Fugitive Dust Model

MG Geometric Mean

VG Geometric Variance

GWh Gigawatt Hour

GoI Government of India

GDP Gross Domestic Product

HEI Health Effect Institute

ISCST2 Industrial source complex short term model version 2

ISCST3 Industrial source complex short term model version 3

ICD International standard diagnostic classification of diseases

kg t-1 kilograms per tonne

kg yr-1 kilograms per year

kWh Kilowatt hour

LLE Life Expectancy

MSL mean sea level

MW Megawatt

m Meter

µgm-3 Micro gram per cubic meter

MoP Ministry of Power

MST modified sigma method

NTPC National Thermal Power Corporation

NO2, Nitrogen di oxide

NMSE Normalised Mean Square Error

NERC North American Electricity Reliability Council

NE North-Eastern

N Northern region

O3 Ozone

PM Particulate Matter

PREMIERE Primary Regional Environment Model in Electricity

Restructuring

PAPA Public Health and Air Pollution in Asia

PWD Public Works Department

QUALY Quality Adjusted Life Years

REBs Regional Electricity Boards

RRs relative risk

PM10 or RSPM Respirable Particulate Matter

SLIM3 Sector average Limited Mixing Mesoscale Model

SECL South Eastern Coal Field Ltd.

S Southern region

sq km Square Kilometer

SEBs State Electricity Boards

SO2, Sulphur di oxide

SPM Suspended Particulate Matter

SPM Suspended Particulate Matter

TPPs thermal power plants

t yr-1 tonne per year

T&D Transmission and Distribution

UMPP Ultra Mega Power Projects

US United States

VKT Vehicle Kilometer Traveled

VOLY Value of Life Year Lost

DT/DZ Vertical temperature gradient

W Western region

WTA willingness to accept

WTP Willingness to Pay

WHO World Health Organisation

YOLL Years of Life lost

RESEARCH ARTICLE

The impact of electricity transfer on the distribution of pollution loads: An

Indian case study

Nemika Relhana* and T.S. Panwarb

aCentre for Policy and Regulatory Research, TERI University, Darbari Seth Block, India HabitatCentre, Lodhi Road, New Delhi, India 110003; bEnergy Environment Policy Division, The Energy andResources Institute (TERI), Darbari Seth Block, India Habitat Centre, Lodhi Road, New Delhi, India110003

(Received 8 January 2008; final version received 21 April 2008)

The interconnection of electricity grids, establishment of central sector power plants andconstruction of coal-based power plants at pitheads have resulted in greater powertransfer from one region to another in India. This enhanced power transfer can result indistributional implications in terms of pollution loads between communities located ingeneration vis-a-vis consumption regions, especially in the case of coal-based powerplants. This paper assesses the impact of electricity transfer on the spatial distribution ofpollution loads in India. A three step methodology is adopted: a) identification ofelectricity importing regions, b) development of emission inventory from coal-basedpower plants and, c) estimation of additional pollution loads due to power transfer.Uttar Pradesh, Chattisgarh, West Bengal, Andhra Pradesh and Madhya Pradeshemerged as the top five electricity-exporting states having significant coal-basedgeneration. On the other hand, Maharashtra, Delhi, Haryana, Gujarat and Punjab arethe top five electricity-importing states. It has been estimated that in 2002–2003, theadditional pollution loads of suspended particulate matter (SPM) SO2 and NOx in thetop five exporting states were 47,112, 332,379 and 294,453 tonnes respectively due toelectricity generated for export. Besides, certain pockets in the exporting regions arehighly polluted. Therefore, it will be argued, there is an essential need to assess theenvironmental degradation in these areas as well as to formulate appropriate policies toremove the environmental inequity by protecting the right to clean air of the people inthe power generating regions.

Keywords: emission inventory; electricity transfer; spatial distribution

1. Introduction

There have been mounting debates worldwide on the environmental and socialimplications of power transfer as a result of the interconnection of electricity grids andelectricity restructuring (Lee and Darani 1996; Hoyt et al. 1998; Burtaw et al. 2000;Sverrisson et al. 2003; Swisher and Mc Alpin 2006). The associated environmental benefitsof power transfer are: lower cost of service due to economies of scale; increased energysecurity; and generation plant siting away from urban locations (Streets 2003; Srivastavaet al. 2003; Mkhwanazi 2004; Zhu et al. 2005). However, there can be negativeenvironmental implications in terms of distribution of pollution loads. A study undertaken

*Corresponding author. Email: [email protected]; [email protected]

Environmental Sciences

Vol. 5, No. 3, September 2008, 173–189

ISSN 1569-3430 print/ISSN 1744-4225 online

� 2008 Taylor & Francis

DOI: 10.1080/15693430802141852

http://www.informaworld.com

by Palmer and Burtaw (1996) established that new opportunities from inter-regionalpower transmission could lead to changes in the regional profile of generation andemissions between the contiguous regions of the North American Electricity ReliabilityCouncil (NERC). This is mainly due to increase in low cost electricity generation fromrelatively old unutilised coal-based power plants. Bloyd et al. (2002) also projected thatthere can be changes in emissions of NOx, SO2 and CO2 due to additional transmissioncapability in the western United States. However in their recent study, Palmer and Burtaw(2006) compared their predictions with the actual experience. They concluded thatelectricity restructuring led to a small net environmental benefit as a result of increasedgeneration from natural gas based power plants. These benefits would have been more ifnatural gas prices were not raised in earlier decades. Alternatively, Sharma (2003)demonstrated that there has been an increase in carbon dioxide emissions in Australia dueto the increased share of brown coal in electricity generation ever since the introduction ofa competitive market. Thus, it can be observed that environmental implications will bedependent on the amount of generation and the fuel used for generation. Moreover, theenvironmental impact of restructuring will vary significantly across the settings accordingto the approach adopted for reforms and environmental policy (Burtaw et al. 2000; Zhanget al. 2002; Perkins 2005).

In India, electricity transfer was initiated as a result of establishment of central sectorpower plants (public sector power generation companies owned by the central government).Further, establishment of pithead power plants and national electricity grids have increasedpower transfer. These decisions were necessary for economic reasons and to overcome thedisparity in resource endowments for electricity generation (CPU 1993; CEA 2004a).Furthermore, The Electricity Act 2003 is also a major step towards facilitating power tradeby allowing open access to transmission and distribution facilities. Srivastava (2003)established that there would be net environmental benefits through integration of grids inIndia due to better utilisation of hydroelectricity potential in the country.

Though there would be economic and environmental savings associated withintegration of grids, pithead stations and reduced railway transport, the flipside of thesedecisions could be a shift in the burden of pollution, especially in the case of coal-basedgeneration i.e., electricity would be consumed widely whereas the emissions resultingfrom generation would be concentrated in certain locations. The all-India installedcapacity of electric power generating stations under utilities was 135,006.7 MW (as ofJuly 2007), of which approximately 53.28% generation is from coal-based power plants(CEA 2007). Because of the abundant coal reserves, rising natural gas prices and thelower cost associated with pithead coal consumption, coal will remain the mainstay forpower generation in the foreseeable future to ensure energy security (MoP 2001; INC2004). In the present policy framework, there is no comprehensive provision forinclusion of social costs in electricity prices. The Electricity Act 2003 does not deal withenvironmental issues explicitly (Thakur et al. 2005). So far there has been no study inIndia that investigates the distributional implications in terms of air pollution loads as aresult of power transfer from one region to another. Hence, there is a need forpolicymakers to know whether such disparity exists or not and if so, which are the sitesthat are heavily polluted.

The objective of this paper is to analyse the impact of electricity transfer on the spatialdistribution of pollution loads in India. Following a brief account of the Indian powersector, states have been categorised as electricity exporting or importing states and theamount of electricity export/import has been estimated. An emission inventory of coal-based power plants in India has also been developed in order to assess the additional

174 N. Relhan and T.S. Panwar

pollution loads generated as a result of electricity generation for transfer. Accordingly, thiswork provides a firmer basis for some meaningful analysis and concerted debate on theissue. It also adds to the literature on environmental implications of electricity transfer byproviding an illustration.

2. Background information

2.1. Intra-region power transfer

After independence, The Electricity Supply Act, 1948 was enacted to establish the StateElectricity Boards. It assigned the responsibility of energy utility development to the states.Thus, electricity was generated as well as distributed within the state administrativeboundary.

Subsequently, in the mid seventies the transfer of power from one state to anothercommenced with the Government of India’s intervention to establish the central sectoragencies for power development. It was aimed to overcome the disparity in power sectordevelopment among the states due to non-uniform distribution of power generationresources, economic conditions and constraints of infrastructure services. In 1964,Regional Electricity Boards were formed to help interstate power transfer and regionalplanning was adopted for the development of the power sector. India was divided into fiveregions for power development, viz., Eastern region (E), Western region (W), Southernregion (S), Northern region (N) and North-Eastern (NE) region. The central sector plantswere established in the mid-seventies in each region to supply electricity to states of thatparticular region. Thus, the aim was to make each region self-sufficient in terms of powergeneration (CPU 1993; Varma 1997; Rao 2004; CEA 2004a). Under this scheme largepithead coal-based power plants and large hydro power plants were commissioned. Thesestations supplied electricity to the adjacent states in the respective regions according totheir assigned allocations. The principle of global accounting1 for energy transactions wasadopted. According to this, 85% of the available capacity was allocated to different states/constituents of the region. The remaining 15% was kept under the control of the CentralGovernment. This 15% was allocated by the Ministry of Power (MoP)/Central ElectricityAuthority (CEA). Of the other 85%, a 10% share was allocated to the home state wherethe central sector thermal station was situated. The rest of the power was allocated todifferent states in the region based on their energy consumption 5 years prior to the periodwhen allocation was made (Mitra 1997).

2.2. Inter-region power transfer

The adoption of regional planning did not completely serve the purpose of making eachregion self-sufficient. It was observed that the Eastern region had substantial surpluspower throughout the year while the other regions were unable to meet the demand due toregional non-uniform distribution of power generation resources. However, there existedseasonal and off-peak surpluses in these regions as well (Mitra 1997; CEA 2004a). TheNorth East (Meghalya, Manipur, Assam, Arunachal Pradesh, Nagaland, Mizoram andSikkim) is endowed with hydro resources whereas the Eastern states (Bihar, West Bengal,Orissa and Madhya Pradesh) of the country are endowed with coal resources. Thus, thefocus shifted to inter-region transfer of power via formation of a national grid. Tofacilitate the transfer of power from one state to another, agencies like the Power Grid andPower Trading Corporation2 have been established. The transfer of power has startedthrough inter-regional links. The inter-regional exchanges increased from 9041.8 Gigawatt

Environmental Sciences 175

Hour (GWh) in 1999–2000 to 12646.7 GWh in 2002–2003 (CEA 2004a). Inter-regionalexchanges of energy for the year 2002–2003 have been shown in Figure 1.

While the interconnection of the grid is necessary to optimise the utilisation of energyfor the country’s overall development3; the associated problem could be in terms of aregional imbalance of the pollution load due to concentrated pattern of power generationespecially in regions where the power generation is through coal. The Central PollutionControl Board (CPCB 2003) reported that thermal power plants contribute 89% of SO2

and 82% of PM10 (Particulate Matter less than 10 microns in diameter) of the totalemission load generated from different categories of industries.

3. Methodology

A three-step methodology, as described below, is followed in this study to estimate thepollution loads as a result of power generation for the purpose of export (excess over theconsumption):

(1) preparation of a matrix of importers and exporters of electricity to identify themajor exporting regions;

(2) estimation of emissions of air pollutants viz. Suspended Particulate Matter (SPM),Nitrogen oxides (NOx) and Sulphur dioxide (SO2) from power plants; and,

(3) quantification of the pollution loads due to power generated for the purpose ofexport only.

3.1. Matrix of importers and exporters

A matrix of importers and exporters of electricity can be prepared at different scales; forinstance, matrix showing electricity transactions at regional level, state level or even at theunit level.

Figure 1. Inter-regional energy exchanges (GWh) in 2002–2003. Source: CEA Annual Report(2002–2003).


This work presents a matrix showing power transaction at the state level,4 because:

(1) in region-based planning, electricity from the central sector power plants isallocated to states within the region.

(2) according to Clause 6 of the Electricity Act 2003, ‘‘the State Government has theresponsibility to supply electricity to all areas including villages and hamlets’’(CEA 2005b).

(3) according to Article 48 A of the Indian constitution ‘‘the state should endeavour toprotect and improve the environment and to safeguard the forests and wildlife ofthe country’’.5

The matrix of importers and exporters of energy generation will comprise an array ofelements that demonstrate the power transfer from one state to the other. It has beenpossible to come up with a matrix that identifies the net electricity import/ export from astate.6 The matrix has been prepared by calculating the amount of surplus/deficit in stateswith respect to the state’s own generation along with the generation from central sectorplants in that state. The surplus or deficit for each state has been calculated using theformula:

Surplus=Deficit ¼ Net generation� Consumptionþ lossesð Þ

If net generation7 were more than the consumption and losses in the state, then itwould imply that surplus energy is transferred to other states. Accordingly, statesgenerating electricity more than their consumption are exporters of electricity while thestates having generation less than their consumption are the importers of electricity. Thisgives the amount of net electricity exported or imported from each state. The matrix hasbeen compiled for the year 2002–2003. The data of consumption and losses and the datafor calculation of net generation (i.e. gross generation and auxiliary consumption) for theyear 2002–2003 are taken from CEA (2004b) and CEA (2004c), respectively.

3.2. Emission inventory

‘‘Emission inventory is the listing and description of air pollutant emitting sources,including estimated pollutant emission quantification’’ (Hammerle 1976). Directmonitoring from the source, computer simulations and rapid assessment are theestablished methodologies to estimate emissions.

Several authors have prepared an emission inventory of power plants in India using theemission factors, which is actually rapid assessment methodology (Papney 2001; Garget al. 2001a,b, 2002a,b; Reddy and Venkataraman 2002). Mittal and Sharma (2004) usedstoichiometric analysis for developing the emission inventories of gaseous pollutants;However for the estimation of SPM, they have assumed that 85% of the ash content isreleased after combustion, out of which 99% of the ash is controlled by Electrostaticprecipitator and 1% is released in the atmosphere.

Since the temporal domain of the matrix of importers and exporters of energygeneration for this study is 2002–2003, it is imperative to prepare an updated emissioninventory for the same time period. Although direct monitoring provides the moreaccurate inventory, this method is extremely time consuming and resource intensive andeven impractical for a large and complex study. In terms of the objectives of this study, weneed to estimate emissions from 78 coal-based power plants. It is unfeasible either to


monitor all these power plants or to collect the information on emissions and processparameters. Therefore, the emissions inventory is prepared in this study through a rapidsource inventory technique which requires fuel consumption data and emission factors.8 Itis convenient to use this method because of its simplicity and modest data requirementespecially when it is required to cover a large number of sources. The disadvantagehowever, is in terms of variability in emissions from the normalised emission due tovariation in fuel quality, technology, end of pipe controls and their efficiency(Economopolous 1993). A plant-wise emission inventory of coal-based power plants hasbeen developed for the year 2002–2003. The pollutants considered for the emissionsinventory are SO2, NOx and SPM. The inventory has been developed using the formula:

E ¼ Ef �Qf

E is emissions in kilograms per year (kg yr71); Ef is emission factor for pollutantin kilograms per tonne (kg t71); Qf is the quantity of coal used for each power plant(t yr71).

In this work, annual plant-wise coal consumption data for the year 2002–2003 has beencollected from CEA (2005a). Different agencies have developed emission factors, such asUSEPA (1995), EEA (2001), Economopolous (1993) and IPCC (1997). However, in thisstudy emission factors developed for Indian power plants by the Central Pollution ControlBoard (CPCB) have been used to estimate the plant-wise emissions of SPM and SO2

(CPCB 1994).For coal-based power plants these emissions are controlled (as power plants are

equipped with Electrostatic Precipitators). In the case of NOx, the emission factor has beentaken from Economopolous (1993).9 The emission factors used have been presented inTable 1. The sulphur content of Indian coals varies from 0.4% to 0.8% (CCO 1998). Forthis study, the sulphur content is assumed to be 0.5% in case of coal and 1% in case oflignite.

3.3. Estimation of additional pollution loads from power transfer

The additional pollution loads due to power export at the state level have been estimatedby multiplying the emissions per unit generation with the amount of power export in theindividual states. The emissions per unit generation were calculated by dividing the totalemissions from the power plants in the state with the gross generation from coal-basedpower plants in the respective state.

Table 1. Emission factors used for inventory preparation.

Fuel Pollutant Emission factor (Kilogram per tonne of coal)

Coal SPM 1.20SO2 16.60 Sa

NOxb 7.5

Lignite SPM 0.64SO2 13.50 SNOx

b 6.0

aS is weight % sulpher in coal.bTangentailly fired boilers for coal-based generation.

Source: CPCB (1994), Economopolous (1993).


4. Results

4.1. Exporting and importing regions

The electricity exporting and importing regions, identified as per the methodologyexplained in section 3.1, showed that there are a few states where power generation is morethan consumption10 (Table 2) and these states are exporting electricity to other states.Amongst these, Himachal Pradesh, Uttranchal, Sikkim, Manipur, Tripura, and ArunachalPradesh have mainly hydro-based electricity generation. However, except for HimachalPradesh, the export of electricity is significantly less amongst the predominantly hydro-based electricity generating states. On the other hand, the majority of the export is from thestates where generation is mainly coal-based. Amongst the major power exporting states,West Bengal and Chattisgarh have predominantly coal-based generation i.e. 98–99% of thestate’s total generation. Madhya Pradesh also has significant generation from coal (94%).In Uttar Pradesh and Andhra Pradesh, 79–80% of the state’s total generation is coal-basedand the rest is predominantly gas-based. Similarly, Orissa too has 78% of power generationfrom coal and the remaining power generation is from hydro-based plants.

There are 18 states and 4 union territories that are net importers of electricity (Table 3).The major importing states are Maharashtra, Delhi, Haryana, Gujarat, Punjab,Rajasthan, Karnataka and Kerala. States like Maharashtra, Delhi and Haryana areimporting huge amounts of electricity despite significant generation within their ownterritory.

The estimated export and import of power calculated in this paper is the net export andnet import, respectively. Under surplus conditions, an importing state can also exportpower but in totality, it imports more electricity than it exports. The net total export andimport of power includes export from the central sector power plants as well as bilateraltrading among the states.

4.2. Pollution due to thermal power generation

The emissions for the year 2002–2003 have been estimated using the methodologyexplained in section 3.2.1. It has been observed that the Indian power sector contributed

Table 2. Electricity exporting states, net export and percentage share of generation based ondifferent fuels in 2002–03.

Export(GWh) %diesel %steam %Hydro %gas %nuclear %wind

Uttar Pradesh (N) 30324 0 79 2 14 5 0Chhatisgarh (W) 12004 0 99 1 0 0 0West Bengal (E) 8939 0 98 2 0 0 0Andhra Pradesh (S) 6601 0 80 7 13 0 0Himachal Pradesh (N) 4905 0 0 100 0 0 0Madhya Pradesh(W) 4228 0 94 6 0 0 0Orissa (E) 966 0 78 22 0 0 0Tripura (NE) 243 0 0 6 94 0 0Sikkim(E) 226 0 0 100 0 0 0Arunachal Pradesh (NE) 82 8 0 92 0 0 0Manipur (NE) 58 0 0 100 0 0 0Uttranchal(N) 28 0 0 100 0 0 0A&N island(E) 4 100 0 0 0 0 0


2.39 Mt (million tonnes) of SO2, 0.31 Mt of SPM and 2.0 Mt of NOx in 2002–2003. The10 highest pollutant-emitting power plants are represented in Figure 2. These power plantscome under the central sector, except Chandrapur (Maharashtra), Vijaywada (AndhraPradesh) and Wanakbori (Gujarat).

In case of central sector power plants high emissions can be attributed to high installedcapacity. The five hotspots in terms of emissions of SPM are Korba (Chattisgarh),Chandrapur, Vindhyachal (Madhya Pradesh), Ramagundam (Andhra Pradesh), andSingrauli (Uttar Pradesh). Likewise, in the case of SO2 and NOx, Neyvelli, Korba,Chandrapur, Vindhyachal and Ramagundam are the top five ranked power plants interms of emissions.

However, we observed that the hotspots are different with respect to specific emissionrates i.e. in terms of emission per unit of generation. These plants are Vijaywada,Khalgaon (Bihar), Rayalseema (Andhra Pradesh), Patratu (Jharkhand), and Farraka(West Bengal). These plants are consuming more coal per unit of power generation, andtherefore, measures should be taken to improve their efficiency and reduce emissions.

4.3. Pollution loads corresponding to power export

From the results of matrix of the importers and exporters of electricity generation, it canbe observed that except Himachal Pradesh, the export takes place mainly from coal-basedpower generating states. Moreover, hydro power generation does not have the sameenvironmental concerns as that of coal-based generation. There will undoubtedly be otherexternalities associated with the hydropower generation but these externalities are beingtaken care of by the government. The states that have export-oriented hydro-based power

Table 3. Electricity importing states and net import in 2002–03.

States Net import (GWh)

Maharashtra (W) 14187Delhi (N) 12040Haryana (N) 10577Gujarat (W) 6916Punjab (N) 5909Rajasthan (N) 5208Karnataka (S) 4607Kerala (S) 4346Goa (W) 2663Tamil Nadu (S) 2490Pondichery (S) 1700Jharkhand (E) 1686D&N Haveli (W) 1504Chandigarh (N) 1059Daman & Diu (W) 966Bihar (E) 742Assam (NE) 477J&K (N) 314Mizoram (NE) 268Nagaland (NE) 133Meghalya (NE) 103Lakshadweep (S) 21


plants, are receiving 12% of the electricity generated by these plants free of cost as acompensation for using the water resources available in the state. On the other hand,power exporting states where generation is predominantly coal-based, are not receivingany compensation. Therefore, in this study, electricity exporting states havingpredominantly hydro-based generation will not be considered for further analysis.

It was observed that the hydro/nuclear/gas based power stations in major exportingstates are under the state sectors, which are dedicated to supply electricity within the state(explained in section 2). Therefore, it is assumed that all the export of electricity is fromcoal-based generation. However, Uttar Pradesh is an exception where approximately 19%and 7% of the generation in 2002–2003 was from central sector export oriented gas andnuclear-based plants, respectively and export from these plants accounted for 28% of thetotal export from Uttar Pradesh (as per the data collected from CEA). Hence, to calculateadditional pollution loads in case of Uttar Pradesh, we have deducted the power exportfrom gas and nuclear based power plants from the total export of Uttar Pradesh. In case ofgas-based power plants there can be some emissions of SO2 and NOx but that would benegligible as compared with emissions released from coal-based power plants.

The additional pollutant load due to power export during the 2002–2003 in majorpower exporting states is illustrated in Figure 3. The lower portion of each bar is thepollution load due to power consumption within the state while the upper portion depictsthe pollutant load due to power export. The methodology used for the estimation ispresented in section 3.2.3. Our results showed that in 2002–03, the total additionalpollution load of SPM, SO2 and NOx were 47,112, 332,279 and 294,453 tonnesrespectively, due to export from all the power exporting states where generation isprimarily coal-based.

Figure 2. Top ten pollutant emitting power plants for the year 2002–2003.


Figure

3.

Comparisonofpollutionloadsdueto

power

consumptionwithin

thestate

andpower

export

fortheyear2002–2003.


It can be seen that the Uttar Pradesh is at the top among the power exporting states. Itexported 23,449 GWh electricity in a year to other states which was 45% of its totalgeneration. We have estimated that this additional power generation for other statesresulted in 19,672 tonnes of SPM, 122,948 tonnes of NOx and 138,784 tonnes of SO2

release in 2002–2003.This pollution load is distributed in the area around the powergeneration centre. Chattisgarh follows at the second rank with 12,003 GWh power export.It is noteworthy that this exported power is 50% of the state’s total generation i.e.Chattisgarh is exporting half of its power to the other states and as a consequence 10,520,74,225 and 65,756 tonnes of SPM, SO2 and NOx, respectively were released in Chattisgarhin a year. Other major exporting states are West Bengal, Madhya Pradesh, AndhraPradesh and Orissa. West Bengal exported approximately a quarter (i.e. 26%) of its totalelectricity generation and the remaining major power exporting states i.e. MadhyaPradesh, Andhra Pradesh and Orissa also had 13%, 15% and 9% of the pollution loadsfrom electricity generation attributable to generation for the export.

We have also compared the economic status of power exporting and importing states.The per capita net state domestic product (at 1993–1994 prices) and electricityconsumption of the top 5 electricity exporting and importing states were compared inFigure 4. It is important to note that among the major power exporting states MadhyaPradesh, Orissa, Rajasthan and Uttar Pradesh are poor income states while AndhraPradesh and West Bengal are middle income states (MoF 2000). On the other hand, richstates like Gujarat, Haryana, Maharashtra and Punjab are the major power importingstates. Therefore, it could be inferred that poor states are subsidising for the richer statesby bearing the negative externalities of power trade in India.

Figure 4. Per capita NSDP (Net State Domestic Product) and Per capita consumption of totalelectricity (i.e. utilities and non utilities) of top five electricity importing and exporting states for theyear 2002–2003. Source: CEA (2004c), MOSPI (2005).


5. Discussion and recommendations

It is evident from the analysis that there exists an imbalance of pollution loads betweenthe states as a result of transfer of electricity. Moreover there is also imbalance of pollutionloads exists within the states. The central sector, export oriented power plants are mainlycoal-based and located at pitheads. At these locations mining and ash ponds are also asource of SPM. This problem is compounded by the fact that these sites have beenattractive locations for other energy intensive2 industries and captive power plants.

Figure 5 represents the map of India with state administrative boundaries, export andimport status of electricity generated and major power plants of the exporting states. Themajor generation pockets/centres are Singrauli,11 Korba (Chattisgarh), Talcher (Orissa),Ramagundam and Simhadri (Andhra Pradesh) and Farraka (West Bengal). with installedcapacities of 6180 Megawatt (MW), 3390 MW, 1173 MW, 2100 MW,1000 MW and 700MW, respectively12 (as on March 2003). A 1000 MW plant has been added in Singrauliregion at Rihand power plant (Hindustan Times 2005). The Singrauli region will haveapproximately 9000 MW capacity in the near future including generation plants under thestate sector. Similarly in Korba, coal-based power plants having total generation capacityof 2875 MW got environmental clearance in this area (MoP 2005a and 2006a). Besides,under the central sector 2640 MW of capacity addition has been planned for the Bilaspurdistrict of Chattisgarh and 2000 MW has been commissioned at Talcher in Orissa.

The Central Pollution Control Board has identified 24 critically polluted areas all overthe country, which includes Singrauli, Korba, Talcher and Vishakhapatnam (CPCB 2000).For the year 2002, annual SPM levels were high13 in Korba and Vishakhapatnam andmedium in Talcher and Anpara. Also, critical levels of PM10 were observed in residentialareas of Vishakhapatnam and Korba and high levels of PM10 were observed at Angul andAnpara. Thus these locations need immediate attention from policy makers to preventfurther degradation of these areas. In the present policy framework, there is nocomprehensive provision for inclusion of social costs in electricity prices. The ElectricityAct, 2003 (MoLJ 2003) as well as tariff policy (MoP 2006b) does not address this issue.

The main focus of the government is to provide ‘‘Electricity to all’’ by 2012 (MoP 2001,2005b). There are no doubts that pithead power plants are required to fulfil this objectiveand to increase the overall benefits with centralisation. But local air pollution impacts arenot balanced by the overall benefits as benefits from electricity generated would beconsumed widely where as the externalities resulting from generation would beconcentrated in certain locations. The geographic inequity between the host communityvis-a-vis beneficiaries of the facilities is a source of concern. At present Orissa, one of theelectricity exporting states, has started demanding compensation for the degradation of itsnatural resources and is trying to formulate appropriate policies for thermal powergeneration (Bisowi 2005).

The Indian electricity sector is also in the process of reform. This can enhance powertrade and in turn the disparity in distribution of pollution loads can increase. Appropriateinterventions are required, or else the problem of air quality could worsen. Therefore,environmental policy must deal with the issue pertinent to increased electricity transfer.

The following can be the likely propositions: 1) the cost of electricity should reflect theenvironmental cost of generation. A shift from command and control approach to moreflexible and cost effective approaches for pollution control, such as Market BasedInstruments like taxes and permit systems can be an appropriate option. Studies should beconducted to judge the efficacy of emission taxes or permit systems. 2) the taxes collectedcan be used to subsidise renewable energy as well as for impact reduction through


management, control, mitigation and remediation. 3) To redress the additionalexternalities, too complex to be mitigated, compensation packages must be designed.These packages should be tied to the specific impact caused by the facility. Compensationcan be in the form of monetary (direct payments to individuals, grant to local government,the allocation of a proportion of facility’s employment or procurement to local residents)

Figure 5. Map of India showing demarcation for regional power system, state administrativeboundaries, state-wise export and import status of electricity, major power generation plants andtheir SO2 emissions.


or non-monetary benefits (Kunreuther and Eastering 1996). The non-monetary compensa-tion is classified as in-kind awards, contingency funds, property value guarantees, benefitassurance and economic good will incentives by Gregory et al. (1991). The planning,selection and implementation of packages should involve all stakeholders including citizensso that the communities get better off (Field et al. 1996). Zeiss and Lfsurd (1995) analysedthe existing literature and stated that compensation mechanism should be negotiated only ifresidual health and environmental risk and trigger impacts are below acceptable limits asthey are considered less effective than impact reduction, control and monitoring measures 4)Assimilative capacity studies should be carried out in the identified power exporting regionsso as to know how much new capacity can be allowed. This will also help in setting theemission cap in case the permit system is implemented. Also, Cumulative Impact assessmentshould be carried out in addition to the project level Environmental Impact assessment so asto prevent further degradation of the already polluted areas.

6. Conclusions

It can be concluded from this study that there exists an imbalance of pollution amongst thestates in India as result of power export. A number of states bear pollution loads as a resultof power generation for export to other states and ironically these electricity exporting statesare poorer than the electricity importing states. This paper also illustrates the importance ofconsidering the potential shift in environmental emissions while formulating policies.

The present analysis does not mean that importing states are cleaner in terms of airquality since some cities in the major importing states are also highly polluted. However,electricity importing states are redeeming benefits from the reduced pollution loads sincethe pollution burden has been shifted to certain locations in the power exporting states.Therefore there is a need for policy intervention to redress the existing imbalance.

Acknowledgement

The authors would like to thank the CEA, New Delhi, India for providing access to the requireddata. We would also like to acknowledge Dr. Leena Shrivastava, Dean, Faculty of Policy andPlanning, TERI University, Lodi Road, New Delhi, India for her continuous guidance and support.The authors also wish to thank colleagues (Dr. Nirmal K. Saha, Dr. Nishritha Bapana, Ms. NituSood and Ms. Susmita Sahu) at TERI University, for the help provided. We would like to thank Dr.S Sreekesh, Associate professor, JNU, Delhi for providing a digitised map of India. The authors arealso grateful to reviewers and the team involved in the publication of this paper.

Notes

1. Global accounting is a commercial accounting procedure agreed by organisation/constituents,participating in the integrated grid operation, where the booking of net energy exchanges isdone globally without identification of the source of energy.

2. Power Grid Corporation of India Limited (POWERGRID) is the Central transmission utilityof India, which possesses one of the largest transmission networks in the world. Power TradingCorporation (PTC India Ltd.) is Government of India initiated Public-Private Partnership,whose primary focus is to develop a commercially vibrant power market in the country.

3. ‘‘Generation planning studies have revealed that if the power system in the entire country isoperated as a single power system, with large block of power transmitted across regionalboundaries, then considerable economies would be realised on account of diversity based savings,economic interchange and reduction in requirements of reserve margin’’. (Source: CEA 1999)

4. The whole India is divided into 29 states and 6 union territories administered by Stategovernment and Union government, respectively.

5. Fundamental duties have been added under Article 51-A (g) by the 42nd amendment to theconstitution in 1976, Under Part IV A of the Directive Principle of the State policy.


6. Because of lack of plant-wise power transfer data in eastern and western region, a matrix thatshows exact state-to-state transaction (i.e. quantum of power transferred to a particular stateand the source state for that power) could not be compiled.

7. Where, Net generation is the difference between gross generation and auxiliary consumption.Losses includes transmission and distribution losses

8. The emission factor is defined as statistical average of the rate at which pollutant is released tothe atmosphere, as a result of certain activity such as fuel combustion to generate power.

9. In case of oxides of nitrogen, emission factors varied considerably and were not recommendedfor use.

10. There is a small difference between all India total net import and net export, which is merely0.23% of the all India total energy generation by utilities for the year 2002–2003. Thisdifference is attributed to the ambiguity in the data prevailing due to use of two different datasources.

11. Singrauli area extends in the two states i.e. in eastern part of Sidhi district in the state ofMadhya Pradesh and the adjoining southern part of Sonebhadra district in the state of UttarPradesh.

12. Total installed capacity has been calculated from the plant wise information available fromCentral Electricity Authority, New Delhi.

13. Based on Annual Mean Concentration (microgram per cubic meter of ambient air) of SO2,NOx and SPM and the Notified Ambient Air Quality Standards, the Ambient Air QualityStatus is described in terms of Low (Exceedence factor 5 0.5), Moderate (Exceedencefactor ¼ 0.5–1.0), High (Exceedence factor ¼ 1.0–1.5) and Critical (Exceedence factor 41.5) for Industrial (I), Residential and mixed use (R) areas of Cities/Towns in differentStates/Union Territories (CPCB 2004). Exceedence Factor is observed annual meanconcentration of criteria pollutant divided by its annual standard for the respective classand criteria pollutant.

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