Health benefits valuation of regulatory intervention for air pollution control in thermal power plants in Delhi, India

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Health benefits valuation of regulatory intervention for air pollution control inthermal power plants in Delhi, IndiaArun Kansal a; Mukesh Khare b; Chandra Shekhar Sharma c

a TERI University, New Delhi, India b Department of Civil Engineering, Indian Institute of Technology (IIT),New Delhi, India c Shri Ram College of Commerce, University of Delhi, Delhi, India

Online Publication Date: 01 October 2009

To cite this Article Kansal, Arun, Khare, Mukesh and Sharma, Chandra Shekhar(2009)'Health benefits valuation of regulatoryintervention for air pollution control in thermal power plants in Delhi, India',Journal of Environmental Planning andManagement,52:7,881 — 899

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Health benefits valuation of regulatory intervention for air pollution

control in thermal power plants in Delhi, India

Arun Kansala*, Mukesh Khareb and Chandra Shekhar Sharmac

aTERI University, 10 Institutional Area, Vasant Kunj, New Delhi, India-110 070;bDepartment of Civil Engineering, Indian Institute of Technology (IIT), Hauz Khas, New Delhi,India-110 016; cShri Ram College of Commerce, University of Delhi, Delhi, India-110 007

(Received 17 October 2008; final version received 24 February 2009)

This study estimates minimum marginal health benefits (morbidity reductiononly) of air pollution control and total health benefits arising from regulatoryintervention regarding the adoption of the World Bank emission guidelines(WBEG) for thermal power plants (TPPs) in Delhi. The Industrial SourceComplex-Short-Term Version–3 (ISCST3) model has been used to estimate thecontribution to air pollution from TPPs. The household health productionfunction (avertive behaviour) has been used to value health benefits of airpollution control. The study revealed that the ambient air pollution due to TPPsis reduced by between 62.17% to 83.45% by adopting the WBEG. Annualmarginal benefit due to reduction in exposure to air pollution by 1 mg m73 isestimated to be US$0.353 per person. Total annual health benefits for adoptingthe WBEG for TPPs are estimated at US$235.19 million. This study provides anovel methodology to evaluate health benefits of regulatory intervention.

Keywords: air pollution dispersion modelling; health benefits valuation;regulatory intervention; thermal power plants

1. Introduction

Amongst infrastructure project investments, the power sector has the highest priorityin India (CEA 2002). With growing energy demands, many TPPs are likely to bebuilt near urban areas, threatening the ambient air quality with negativeenvironmental costs. Amongst these, health cost is most significant (Brandon et al.1995). For example, a previous study conducted in India observed that air pollutionhas a significant impact on the health cost of the people (an additional financialburden of Rs. 600 to Rs. 1500 (1 US$ ffi Indian Rs. 45) per resident per year)(Saksena and Dayal 1997). Therefore, comprehensive health impact assessments arerequired to ensure that the process of energy development does not place anadditional burden on people. However, in India, the health impact of energydevelopment receives little attention in environmental appraisal studies (Pachauriand Sridharan 1998). The formulation of environmental policies in developingcountries has been dominated by the command and control regime wherein settingofficial standards for products, process or emission has been the major approach.

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

Journal of Environmental Planning and Management

Vol. 52, No. 7, October 2009, 881–899

ISSN 0964-0568 print/ISSN 1360-0559 online

� 2009 University of Newcastle upon Tyne

DOI: 10.1080/09640560903180933

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However, there is a growing realisation that both regulatory and economicapproaches need to be used to ensure cost-effective management of the environment.

One of the regulatory measures to reduce air pollution from TPPs is the adoptionof stringent emission standards such as those proposed by the World Bank in theform of the WBEG for TPPs (Table 1). Adoption of the WBEG may imposesignificant costs on existing TPPs in India and therefore require economicjustification for adoption. However, there are many empirical and conceptualchallenges in the process of estimating values, especially in the context of developingcountries. Information on the relationship between the damage costs and the controlcosts (essential for such an evaluation) are not available at a satisfactory level(Saksena and Dayal 1997). Environmental policy changes are generally evaluatedusing the ‘benefit transfer method’ (Kandlikar and Ramachandran 2000). However,there are several sources of bias inherent in benefit transfers because only a feworiginal studies have used methods that are transferable in terms of site, region andpopulation characteristics (Navrud and Pruckner 1997). It is therefore important toundertake studies using primary data to obtain an accurate estimate of benefits fromenvironmental quality changes.

The current study aims to estimate the possible air quality improvement in Delhiassuming the adoption of the WBEG for air emissions by TPPs, and thecorresponding benefits in terms of health improvements (morbidity reductiononly). The study uses the United States Environment Protection Agency’s (EPA)Gaussian based - ISCST3 air pollution dispersion model to estimate the impact onair quality from emission sources in 14 administrative zones of Delhi. An emissioninventory was carried out from July 2004 to June 2005. The household healthproduction function (avertive behaviour method) was used to value the healthbenefits of air pollution reduction (Gerking and Stanley 1986, Harrington andPortney 1987, Bresnahan et al. 1997, Murthy et al. 2003).

2. Site description

Delhi, with an area of 1485 km2, is inhabited by approximately 13.78 million people,of which 12.82 million are in urban areas (Census of India 2001). The decadal growthrate of the region is 46.31% (urban growth is 51.33%). Such a high growth rate isprimarily attributable to large and uncontrolled in-migration from the surroundingstates. It is amongst the most polluted cities in India, with major contributions fromvehicular and industrial sources including the power plants (Kandlikar 2007). Delhihas three coal-based TPPs, namely, Rajghat, Indraprastha (IP) and Badarpur, andtwo natural-gas (NG) based TPPs – Indraprastha Gas Turbine (IGT) andPragati Power. Table 2 shows the characteristics of TPPs in Delhi. There areapproximately 126,000 industrial units in Delhi (GNCTD 1999). Approximately 8%of the total registered vehicles in India are in Delhi (MoST 1996). Figure 1 shows thelocation of administrative zones in Delhi and Table 3 gives the characteristics ofthese zones.

3. Methodology

The first step was to estimate the impact of TPPs on ambient air quality and thechange in ambient air quality from the intervention (adoption of the WBEG) using adeterministic air pollution dispersion model (ISCST3) ceteris paribus. For this,

882 A. Kansal et al.


Table

1.

IndianstandardsandtheWBEG

forTPPs(TERI1998).

Pollutant

Capacity

Indianstandards

WBEG

Controloptions

TSP

Lessthan200/210MW

350mgNm

73

50mgNm

73

Existingtechnologies(ESPorbag

housesto

achieveem

issionbelow

50mgNm

73

200/210MW

&above

150mgNm

73

Ifnotachievable,99.9%

removaleffi

ciency

Coalcleaning

SO

2Lessthan200MW

H¼

14Q

0.8m

2000mgNm

73

Use

ofgasorlow

sulphurfuels

200MW

andless

than500MW

220m

stack

height

Maxim

um

level

0.2

TPD

per

MW

upto

500MW

plus

Furnace

sorbentinjection(30–60%

removal)

500MW

andabove

275m

stack

height

0.1

TPD

per

MW

foreach

additionalMW

over

500MW

Dust

injection,dry

orwet

scrubbers

(upto

95%

removal),orfluidised

bed

combustion(upto

95%

removal)

NO

2Allexistingunits

150ppm

at15%

excess

oxygen

750mgNm

73(coal)

460mgNm

73(oil)

320mgNm

73(G

as).

Low

NO

xburnerswithorwithout

other

combustionmodifications

New

units4400MW

Naturalgas–50ppm

Naphtha–100ppm

NA

NA

100–400MW

Naturalgas–75ppm

Naphtha–100ppm

NA

Reburning,Water/steam

injection

5100MW

Naturalgas–100ppm

Naphtha–100ppm

NA

Selectivecatalyticornoncatalytic

reduction

Notes:TSP–totalsuspended

particulate;SO

2–sulphurdioxide;NO

2–nitrogen

oxides;ESP–electrostaticprecipitator;Q

–em

issionrate

ofSO

2in

kgh71;ppm

–partsper

million;TPD

–tonsper

day;MW

–megawatt;H

–stack

height(inmeters).

Journal of Environmental Planning and Management 883


ambient air concentration of the pollutants and a source emission inventory of thekey pollutants (sulphur dioxide (SO2), nitrogen dioxide (NO2), and total suspendedparticulates (TSP)) was prepared. The selection of SO2, NO2, and TSP as keypollutants was based on the rationale that: (1) these are the significant pollutantsemitted from TPPs; (2) Indian standards for TPPs and the WBEG addressed thesepollutants only; (3) these are monitored by TPPs and regulatory agencies. This wasfollowed by an assessment of mitigating and averting responses of the population forair pollution and an estimation of the number of persons affected. Finally, the healthbenefits of regulatory intervention were calculated.

Table 2. Characteristics of TPPs in Delhi.

Nameof TPP

Capacity(MW) Fuel

Specific fuelconsumption Stack details

Badarpur 705 Coal 0.84 kg kWh71 2, each 150m height and 6.1m diameter,IP 247.5 Coal 0.79 kg kWh71 3, each 61m height and 3.96m diameter,Rajghat 135 Coal 0.8 kg kWh71 2, each 160m height and 6m diameter,IGT 282 NG 0.3 kg kWh71 3, each 30m height and 3.25m diameter,Pragati 330 NG 0.213 m3 kWh71 2, each 70m height and 6 m diameter,

Notes: kWh – Kilowatt-hours; NG – natural gas.

Figure 1. Administrative zones and TPPs in Delhi.Note: Refer to Tables 2 and 3 for details.



3.1. Impact of TPPs

Kansal (2006) has demonstrated that the ISCST3 can be satisfactorily applied topredict ambient air pollution concentrations for given meteorological and sourceconfigurations for Delhi. The results of the study are based on the monthly‘predicted’ and ‘observed’ values for TSP, SO2 and NO2 at seven-receptor locationsspread throughout Delhi. In the business-as-usual (BAU) scenario, monthlyemissions from TPPs during the study period were obtained from Central ElectricityAuthority (CEA), Delhi Pollution Control Committee (DPCC) and individual TPPs(Table 4). At Rajghat and IP TPPs, only TSP and SO2 were monitored, whereas ingas-based TPPs, only NO2 was monitored. At Badarpur TPP, all the threepollutants, i.e. TSP, SO2 and NO2 were monitored. The NO2 emissions from other

Table 4. Average emission rate from TPPs.

TPPTemperaturea

(8K)Exit velocitya

(m s71)TSP

(g s71)SO2

(g s71)NO2

(g s71)

RajghatStack 1 401 4.27 24.20a 78.10a 20.54b

Stack 2 402 4.57 24.60a 82.40a 20.15b

IGTc 384 1.6 0.347b 0.040b 11.78a

IPStack 1 397 12.85 30.26a 94.18a 48.91b

Stack 2 403 6.48 5.83a 18.17a 21.27b

Stack 3 408 6.54 12.17a 31.96a 21.97b

BadarpurStack 1 396 24.38 294.77a 1406.21a 516.23a

Stack 2 398 25.96 206.87a 1107.51a 327.40a

Pragatic 372 2.3 0.68b 0.081b 23.80a

Notes: avalues taken from DPCC and CEA records for actual on-field monitoring.bvalues derived from emission factors and specific fuel consumption for the given month.cother stack(s) have similar values.

Table 3. Characteristics of administrative zones of Delhi.

Zone no. Name of the zone Area (km2) Population

1 City 31.12 547,1242 Central 96.87 1,290,4853 Civil Lines 51.89 953,1304 Delhi Cantonment 42.97 124,4525 Karol Bagh 32.73 610,4496 Najafgarh 445.90 1,772,2357 Rohini 64.63 1,377,0078 NDMC 42.74 294,7839 Sadar Paharganj 10.96 378,49010 Shahdara North 37.28 1,716,56911 Shahdara South 54.50 1,507,18312 South 158.55 1,118,11313 West 63.10 1,580,97714 Narela 349.82 532,115



coal-based TPPs were estimated using the emission factor for coal as 2.64 kg ton71,whereas NO2 emissions from fuel oil consumed were estimated using an emissionfactor of 7.5 kg ton71 (CPCB 1994). For gas-based TPPs, the emission factors forTSP, SO2 and NO2 were taken as 0.008 g m73, 0.0096 g m73 and 2.8 g m73,respectively (TERI 1992).

In the WBEG scenario, the emissions from TPPs conforming to the WBEG(Table 1) were assumed. The stack emission characteristics (e.g. stack diameter,release height, velocity, temperature etc.) and the power generation and fuelconsumption patterns were equivalent to existing emissions.

3.2. Valuation of health benefits of air pollution

Pearce et al. (1994) broadly classified valuation methods as ‘direct’ and ‘indirect’methods. The former reveals the choices by an individual for a good or servicethrough experiments, questionnaires and surveys. The latter is based on the actualexpenditures on goods or services in the conventional market or through surrogatemeans where the measurable goods or services may be the complement or substituteto the one concerned. The conventional market approach values environmentaldamage through inferred prices and includes dose-response technique (Ostro 1994,Pearce 1996, El-Fadel and Massoud 2000) and replacement cost (Gregory et al.1996). The surrogate market techniques are hedonic (property and/or wage) pricingand household productions (avertive behaviour and travel cost). The propertypricing approach reflects the willingness of a buyer to pay for a particular attributeof a property such as environmental quality (Ridker and Henning 1967), whereasdifferential wage (the higher wage paid to a worker to work in a polluted area) is thebasic premise of hedonic wage pricing (Moore and Viscusi 1988, Viscusi 1992, 1993).Household production function is based on the ‘behaviour’ of the consumer in themarket (Gerking and Stanley 1986, Bartik 1987, Bresnahan and Dickie 1995).‘Avertive behaviour’ examines actual expenditures incurred by individuals to avoidenvironmental damage (Cropper 1981). The travel cost method estimates theeconomic use values associated with sites used for recreation (Kahneman andKnetsh 1992).

The health value measurement is defined as the value associated with loss ofquality of life that includes costs associated with avoiding adverse health effects toreduce medical costs, work or leisure time loss, discomfort, inconvenience or effortsto avoid or treat them etc. Valuation techniques analyse impacts of an activity, adescription of the pathway and determination of the associated costs. However, eachtechnique has its associated assumptions. Therefore, selection of a technique for aparticular policy objective is an indispensable task because it requires assessmentof theoretical validity, data availability, advantages and limitations. Table 5summarises relative strengths, limitations and applicability of the valuationtechniques (Kansal 2006).

3.2.1. Econometric mathematical model

The avertive behaviour method was used to estimate the benefits of air pollutioncontrol. The method is a form of the household production function and is based onthe behaviour of the consumer in the market, i.e. behaviour that is revealed throughhis expenditures on various goods and purchases in the market. The technique



Table

5.

Comparisonofhealthbenefits

valuationtechniques

andtheirapplicabilityin

developingcountries.

Valuation

technique

Cost

anddata

requirem

ents

Strengths

Lim

itations

Applicability

Contingent

valuation

Expensive

Fairly

highdata

requirem

entto

be

collectedthrough

questionnairesurvey.

Canbeusedto

value

allkindsof

environmental

changes.

Only

methodfor

elicitingnon-use

values.

Reliable

resultscomefrom

caseswhererespondents

are

familiarwiththeasset

being

valued.

Embeddingproblem

and

biases.

Successfullyapplied

towatersupply

and

quality.

Peoplehaveim

perfect

knowledgeoftheeff

ects

ofairpollutionandassociatedhealth

episodes,whichmayresultin

unreliable

responsesofthepeople.

Lim

ited

applicationin

developingcountriesas

people

havelow

income,

whichbrings

constraintonWTPestimatesandethical

issues.Further,users’charges

are

practically

verylow

ornon-existent.Thecommon

methodisto

linkpollutioncontrolservices

withtaxpayment.However,less

than10%

ofthepopulationpay(oractuallyfile)

incometax,whichmayresultin

biasesin

the

responsesdueto

free-rider

problem.

Travel

cost

Expensive

Highdata

requirem

ents,

tobecollectedthrough

questionnairesurveys

andsecondary

sources.

More

accurate

when

travel

distancesare

short.

Resultsare

sensitiveto

opportunitycost

oftime.

Lim

ited

only

torecreational

site

characteristics.

Donotestimate

non-use

values.

Methodiscomplicatedwhen

thetripsare

multi-purpose.

Veryfew

studiesare

mainly

relatedto

parks

andforest

reserves.

Inmanycountriesrecreationsiteschargezero

ornegligibleprice,whichmeansthatitisnot

possible

toreliably

estimate

thedem

and.

Visitors’recordsare

diffi

cultto

accessdueto

poordata

keepingbyauthorities

inmany

countriesandinherentcomplexity.

Airpollutioncontrolmeasureshaveanim

pact

ontheentire

urbanarea(airpollution

controlregion)rather

thanaparticular

recreationsite.

(continued)



Table

5.

(Continued).

Valuation

technique

Cost

anddata

requirem

ents

Strengths

Lim

itations

Applicability

Visitsto

urbanareaare

generallymultipurpose

andtherefore

thetechniqueisnotsuitable

forassessinghealthbenefits

ofairpollution

controlmeasuresin

urbanareas.

Avertive

behaviour

Moderate

cost.

Easy

tocalculate

as

thisisbasedon

actualbehaviour.

Much

expenditure

hasjoint

outputandviolatesthe

assumptionofperfect

substitutability.

Successfullyapplied

towatersupply

and

quality.

Modestdata

requirem

entand

collectedthrough

primary

survey.

Hedonic

property

pricing

Expensive.

Substantialdata

requirem

entand

collectedthrough

primary

questionnaire

survey.

Reliable

methodfor

noiseandair

pollutionand

neighbourhood

amenities.

Thetechniqueim

plicitly

assumes

thathouseholds

continuallyre-evaluate

their

choiceoflocation,whichis

often

incorrect.

Unreliable

especiallyfordevelopingcountries

asindividualdonothaveperfect

inform

ationsuch

thatthey

canbuytheexact

property

andassociatedcharacteristics

that

they

desire.

Themethodtherefore,does

not

revealtheirperfect

dem

andfor

environmentalquality.Further,alargepart

ofthehousingmarket

isin

thepublicsector

andso

allocatedsubject

toprice

control.

Data

onpricesandfactors

determiningpricesare

often

diffi

cultto

obtain.

Hedonic

wage

pricing

Substantial.

Highdata

requirem

ent

andcollectedthrough

survey

amongst

workers.

Theoreticallysound

techniqueespecially

formortality

and

injury

risksin

occupation.

Diffi

cultto

isolate

explanatory

variables.

Few

studiesthatrelate

tofatalandinjury

risk

differentials.

Unreliable

forlow-income

countries.

Verylimited

applicabilityin

developing

countriesasmost

oftheim

pactsdueto

air

pollutionhavedelayed

effects

andthe

workershaveim

perfect

knowledgeofthe

effects

ofairpollutiononhealth.



involves analysing data from market transactions in goods and services and theestablishment of the relationship between these private goods and various measuresof environmental quality. The individual would tend to maximise ‘utility’ subject tobudget and time constraints. The model was first developed by Grossman (1972) andused by Cropper (1981), Gerking and Stanley (1986), Harrington and Portney(1987), Alberni and Krupnick (1997, 2000), Bresnahan et al. (1997) and Murthy et al.(2003). The study here is based on a similar model and is defined in Equation (1). Theequation states that marginal willingness-to-pay (dI/da) is comprised marginal lostearnings, medical expenditures, marginal cost of the averting activity and of thedisutility (discomfort) of illness using the marginal utility of income.

dI=da ¼ wdH=daþ qM @M=@aþ qA @A=@a�@U=@H

ldH=da ð1Þ

where:

I is the incomea is air quality (concentration of pollutants)H is stock of health capitalM is mitigating activities (such as medical care from which the individual deriveno direct utility)A is aversion activities (which may affect both health and utility, such asparticipation in outdoor leisure activity)U is utility function which depends on H, a, and I and consumption of privategood other than M and Aw is wage ratel denotes marginal utility of income, andqj ¼ Pj þ wtj, j ¼ A, M, denote full, time-inclusive prices of A, and M: Pj

represents the unit money price and tj represents time required to consume oneunit of good j

The study has focused on estimating the first three components using ‘revealedpreference data’. Due to the obvious difficulty of measuring the value of disutility ofillness, the reported estimates are lower bound. Thus, the minimum householdmarginal willingness-to-pay (MTP) for a reduction of 1 mg m73 of air pollutants hasbeen estimated using Equation (1) and is described in Equation (2)

MTP ¼ @ lost earningsð Þ=@ pollution exposureð Þ þ @ medical expensesð Þ=@ pollution exposureð Þ þ @ avertive expenditureð Þ=@ pollution exposureð Þ:

ð2Þ

The parameters are estimated using the household health production function, thehousehold demand functions for M and A. The model is defined in Equations (3)to (5).

log NSDð Þ ¼ C 1ð Þ þ C 2ð Þ � log PEð Þ þ C 3ð Þ � log NCDð Þ þ C 4ð Þ � log FSð Þþ C 5ð Þ � log SHð Þ þ C 6ð Þ � log MEð Þ þ C 7ð Þ � log AEð Þ ð3Þ



log MEð Þ ¼ C 8ð Þ þ C 9ð Þ � log PEð Þ þ C 10ð Þ � log NCDð Þ þ C 11ð Þ � log FSð Þþ C 12ð Þ � log SHð Þ þ C 13ð Þ � log HIð Þ þ C 14ð Þ � log NSDð Þþ C 15ð Þ � log AEð Þ ð4Þ

log AEð Þ ¼ C 16ð Þ þ C 17ð Þ � log PEð Þ þ C 18ð Þ � log NCDð Þ þ C 19ð Þ � log EAð Þþ C 20ð Þ � log HIð Þ þ C 21ð Þ � log IAð Þ þ C 22ð Þ � log NSDð Þþ C 23ð Þ � log MEð Þ: ð5Þ

Three endogenous variables are health status of the household in terms of number ofsick days (NSD), mitigating expenditure (ME) and averting expenditure (AE). Theexogenous variables are household air pollution exposure (PE), number of chronicdisease cases (NCD), family size (FS), smoking habits (SH), education andawareness of air pollution (EA), gross annual household income (HI), andcleanliness measures for indoor air (IA). The variables are estimated through asurvey. The coefficient of variables (C) is estimated by regression for parametricestimates of the model. Equation (3) represents the household health productionfunction expressing the health status. The NSD in a household depends on PE,NCD, FS, SH, ME and AE. High values of PE, NCD, FS and the presence of SH arepositively related to NSD, whereas an increase in AE and ME decrease NSD.Equation (4) represents the household demand functions for ME. Apart from thefactors mentioned above, ME depends on HI as more income increases the capacityof the household to spend more on ME. Equation (5) represents AE. Variablesspecific to the demand function for AE are EA and IA. EA and IA should increasethe prevention received for a given expenditure of resources.

3.2.2. Description of the variables

NSD: Number of days of sickness of each adult and child member of a household isused as a measure of health status.

ME: The medical expenses of the household are used to denote the total expenses onmitigating activities and includes expenditure on medicines, doctor fees anddiagnostic tests for each of the air quality related health problems. The reportedfigure for each household is a cumulative one, including expenses of all adult andchild members.

AE: The monetary equivalent of avertive activities is calculated on the basis of (1)extra kilometres travelled in a day to avoid polluted areas in the city; (2) use of a gasmask while travelling; and (3) switching to an expensive mode of travel such as carinstead of two wheelers. It is assessed based on the inputs from the respondents. Fortravelling behaviour, the cost is taken as Rs 4.5 km71 and 22 days of travel in amonth. For a gas mask the cost is obtained from the respondents and the life of amask is taken as one year. For vehicular choice, the differential cost is taken asRs 2.5 km71 and 22 days of travel in a month. This cost is further proportioned by afactor of a respondent attribute for the choice of expensive vehicle to avert airpollution exposure. In total 37% respondents reported travelling extra kilometres toavoid polluted areas, 0.8% respondents have used gas masks and a mere 0.06% of



respondents reported switching to an expensive mode of travel attributable to avertair pollution exposure.

PE: Pollution exposure is based on the information on the total concentration ofambient air pollutants, e.g. TSP, SO2 and NO2 throughout the study area and duringthe study period (CPCB 2005). The pollution concentration in the zone is assumed ashomogenous.

NCD: The presence of a chronic condition has a large negative impact on theproductivity of time invested in health and is therefore positively related to sick time.The number of chronic diseases is an ordered variable of the range 0–8. Chronicdiseases considered are diabetes, high blood pressure, tuberculosis, cancer,asthma, glaucoma, heart disease and anything specific. A household that has nonescores 0.

FS: The family size operates as a control variable for higher days of sickness ormedical expenses in a large sized household.

SH: The smoking habit is used as a control variable in the estimation of householdproduction function and its value is taken as ‘1’ if any member of the householdsmokes, otherwise it is ‘0’.

EA: Environmental awareness is assessed based on (1) education status of theadult members and (2) awareness of the respondent about the diseases attributableto air pollution. EA is ‘0’ where none of the two criteria is satisfied; EA is ‘1’where any one of the criteria is satisfied and the EA is ‘2’, where both criteria aresatisfied.

HI: Household income controls for the capacity of a household to spend on health.It is based on the gross annual family income of the household. The reported figurefor each household is cumulative, including the wage rate of each earning member ofthe household and the family combined asset income from interest, dividends, rentand/or capital gains. In 46% of the households actual figures could be elicited. In theremainder it was necessary to offer income brackets, and the median value of thechosen bracket is taken as total household income.

IA: Measures for indoor air quality include the use of cleaner fuels in the house, useof exhaust fans/chimneys in the kitchen and air conditioning at home. This variablehas a positive effect on the number of averting activities that a person undertakeswhen they leave the house. The presence of all good controls in a household carriesthe value of 3, and 0 when no controls are present in the household.

3.2.3. Parametric estimates of the model

Applying the order condition (Harvey 1990) in the proposed structural models, it isobserved that Equation (3) is over-identified whereas Equations (4) and (5) areexactly identified. The structural models are analysed using the Hausmanspecification test (Hausman 1976) to analyse the ‘simultaneity’ problem. Theunderlying idea of the test is to compare two sets of estimates, one of which is



consistent under both the null and the alternative hypothesis and another, which isconsistent only under the null hypothesis. A large difference between the two sets ofestimates is taken as evidence in favour of the alternative hypothesis. In the presenceof ‘simultaneity’, formulation of a simultaneous structural system is essential.Amongst various ‘system estimate’ methods, three-stage least squares (3SLS) andgeneralised method of moments (GMM) have wide acceptability (Pindyck andRubinfeld 1991). Therefore, estimates have been made using both methods.

4. Survey and data collection

A health survey was conducted to assess the problems caused by the air pollutantsand the expenditure incurred by families towards medical treatment and avertiveactivities. Since the impact of air emissions from TPPs is distributed over Delhi’sentire geographical area, households from all regions of Delhi were considered in thesampling frame. The survey was carried out in all administrative zones of Delhi fromJanuary 2005 to June 2005 through in-home personal interviews. The samplingframe chosen for the study was the computer record of household electricityconnections of Delhi Vidyut (electricity) Board. The sample size chosen was basedon Mitchell and Carson (1989). Seven hundred households were surveyed by clusterrandom sampling, taking 50 households from each of the 14 zones of Delhi. Adultmembers of the sampled household were chosen as the respondents for the survey.Questions were straightforward as the objective was to elicit the actual behaviour ofthe household. A pilot survey consisting of 25 volunteer households was undertakento pre-test the questionnaire schedule.

4.1. Description of the questionnaire schedule

The first section of the schedule included the information related to identificationsuch as name, address and telephone number of the respondent. The second sectionincluded questions on economic and demographic characteristics of the respondenthousehold such as age, education, gender of each member of the household, generalawareness of household about air pollution (based on the general awareness of thehousehold about diseases attributable to air pollution and education status), job andincome, assets owned etc. The third and fourth section elicited information about thehealth status of the household and their behavioural responses to mitigate and/oravert air pollution impacts respectively. Information about the health history and thehealth status of the household were obtained for a recall period of one year.Responses were elicited for air quality related health symptoms such as chest pain,coughing, wheezing, sore throat, cold, headaches and eye irritations. The healthsymptoms were described in a manner that people could understand and relate toand were also scientifically accurate (Tolley 1986). For these diseases, detailed datacollected consisted of number of days of sickness, number of visits to the doctor,expenditure on medicines, doctor fees and diagnostic tests, number of days stayedindoors to avoid exposure to pollution, extra miles travelled in a day to avoidpolluted areas in the city and other averting activities. Data were also collected aboutthe defensive activities such as using air conditioning, cooking gas and the exhaustfan in the kitchen to reduce indoor air pollution. Information was also collectedabout the chronic diseases in the family, and habits of family members (such assmoking) that affected their health.



5. Results and discussion

Comparative analysis of various techniques (Table 5) suggests that ‘contingentvaluation’ and ‘avertive behaviour’ methods are most appropriate to value healthbenefits of regulatory intervention for air pollution control in developing countries.Choice amongst these two methods depends upon the needs of the user(s) ofvaluation studies and institutional acceptability to support policy debate anddiscussion. For example, if a technique is to be used to value health benefits due tothe implementation of stricter standards for emissions in TPPs of a developingcountry, then the ‘avertive behaviour’ method can provide reliable estimates. Thebasic reason is that the cost to comply with stricter standards will either be metthrough internal financial resources of the TPPs or through state loans (as most ofthese are under the public sector and the price of electricity is administered) ratherthan direct collection from the population. Therefore, estimates based onwillingness-to-pay in the contingent valuation method would be unreliable due tofree-rider problem1 and also due to the unfamiliarity of the population about thelikely improvement in their health status resulting from regulatory intervention.Even though the contingent valuation method has an extensive range of applicabilityand is the only method for estimating ‘non-use values’, there are several otherlimitations of this method, which are more pronounced for air pollution relatedstudies in the developing countries. These include embedding effects2 (Kahnemanand Knetsch 1992), biases3 (Diamond and Hausman 1993), payment vehicle(Schkade and Payne 1994), strategic (Brookshire et al. 1982), and hypothetical4

(Hanley 1990). Serious criticism of the contingent valuation method has come fromDiamond and Hausman (1994), who concluded that the method is deeply flawed formeasuring non-use values. They argued that inconsistency in responses acrosssurveys implies that the survey responses are not satisfactory bases for policy studies.

Table 6 shows the annual average ground level concentrations (GLCs) of totalpollutants attributable to TPPs under the BAU and WBEG scenarios. Adoption ofthe WBEG reduces ambient pollution due to TPPs emissions by between 62.17% to83.45% compared to the BAU scenario. Table 7 shows the descriptive statistics of

Table 6. Average annual GLCs (mg m73) of total pollutants at different zones in Delhi due toTPPs emissions.

Name of the Zone BAU scenario WBEG scenario Reduction

City 52.26 18.92 63.80%Central 69.72 21.68 68.90%Civil Lines 33.54 10.37 69.08%Delhi Cantonment 54.10 9.10 83.18%Karol Bagh 35.24 10.85 69.21%Najafgarh 93.97 16.48 82.46%Rohini 105.31 17.43 83.45%NDMC 56.70 17.22 69.62%Sadar Paharganj 99.52 31.20 68.65%Shahdara North 35.00 10.42 70.23%Shahdara South 81.52 27.11 62.17%South 65.28 12.32 81.13%West 24.05 7.43 69.11%Narela 91.84 16.45 82.09%



the variables. The monetary value of sick days is estimated based on the wageincome. The Hausman specification test was performed and the residual terms havelarge t-values, indicating that the problem of ‘simultaneity’ is present. Therefore, theformulation of a simultaneous structural system is essential. Table 8 give the

Table 7. Descriptive statistics of the variables.

Variable Mean Median Maximum Minimum s

NSD 11.7 9 32 0 6.8ME (Rs) 2278.6 1177.54 39,700 50 3901AE (Rs) 301.8 155 1500 50 324.28PE 314.18 310.99 355.31 274.05 25.98NCD 1.09 1.0 3.0 0 0.74FS 4.188 4.0 11.0 2.0 1.37SH 0.59 1.0 1.0 0 0.49EA 1.33 1.0 2.0 0 0.6HI (Rs) 186,485.7 180,000 800,000 50,000 105,572.9IA 1.42 1.0 2.0 0 0.6Annual wage income (Rs) 171,657.1 150,000 800,000 50,000 103,787.6Annual assets income (Rs) 13,800 0 60,000 0 21,191

Table 8. Estimates of household health production function.

Log value of Expected3SLS method GMM method

the variable sign C t p C t p

Function for NSD (Equation 3)Constant 739.972 71.796 0.073 739.973 71.618 0.106PE þ 9.541 1.718 0.086 9.541 1.547 0.122NCD þ 0.366 2.280 0.023 0.366 2.42 0.016FS þ 0.961 1.637 0.101 0.961 1.546 0.122SH þ 0.101 0.816 0.414 0.101 0.741 0.459ME 7 72.520 71.294 0.196 72.520 71.174 0.241AE 7 0.794 1.269 0.205 0.794 1.176 0.24

Function for ME (Equation 4)Constant 715.286 74.474 0.000 713.011 73.443 0.0006PE þ 3.561 6.420 0.000 2.669 3.021 0.0025NCD þ 0.123 2.594 0.01 0.039 0.480 0.631FS þ 0.331 2.148 0.032 0.130 0.573 0.566SH þ 0.040 1.322 0.186 0.041 1.323 0.186HI þ 0.063 0.279 0.780 0.315 0.964 0.335NSD þ 70.282 71.514 0.130 0.174 0.427 0.669AE 7 0.270 1.607 0.108 0.091 0.389 0.697

Function for AE (Equation 5)Constant 714.354 72.075 0.038 714.382 72.099 0.036PE þ 0.940 0.738 0.460 0.943 0.742 0.458NCD þ 70.036 0.823 0.411 70.036 70.852 0.394EA þ 6.621 4.274 0.000 6.578 3.941 0.0001HI þ 1.262 5.038 0.000 1.263 5.163 0.000IA 7 76.260 74.077 0.000 76.217 73.773 0.0002NSD þ 0.598 2.145 0.032 0.598 2.222 0.0264ME 7 70.271 70.515 0.606 70.271 70.518 0.604

Notes: C’s are the coefficients in Equations (3)-(5); t is t-statistics; and p is probability.



estimates of the household health production function model using the 3SLS andGMM methods. Perusal of the Table reveals that parametric estimates derived bythe 3SLS system method of estimation are similar to the GMM estimates in terms ofmagnitude and expected signs, which indicates the stability of the coefficients by twoalternative estimation procedures.

Table 8 shows that three out of six parameters in the health function (Equation 3)are significant. The coefficients of all the variables except for AE have expected signs.The demand function for ME (Equation 4) has seven coefficients, of which twocorresponding to variables SH and HI are insignificant. HI can be insignificant forME, as also pointed out by Grossman (1972) that HI may act as a proxy fordeleterious consumption habits, for example, eating rich food, which increases therate of decay of health capital. PE and NCD are insignificant in the demand functionof avertive expenditure (Equation 5).

Based on the results (Table 8) of parametric estimates of the structural model,Equation (2) is written as

MWP ¼ 9:54� sickdays

Pollution exp osureþ 3:56�medical exp enditure

Pollution exp osure

þ 0:94� avertive exp enditure

Pollution exp osure: ð6Þ

The annual marginal benefit for a household in Delhi due to a reduction in exposureto air pollution by 1 mg m73 is estimated as Rs 66.50 (US$1.4778), which amounts toRs 15.90 (US$0.3533) per person. Total health benefits due to implementation of theWBEG in TPPs are then calculated by multiplying with the number of households atrisk in each zone, marginal value of health benefits for unit reduction in air pollution,and the reduction in ambient air pollution under the WBEG scenario in therespective zone. Table 9 shows the values of health benefits under the WBEG

Table 9. Health benefits of WBEG for air emissions in TPPs for Delhi.

Name of the zoneNumber ofhouseholdsa

Reduction in airpollution (mg m73)

Health benefits(million Rs)b

City 130,267 33.34 288.81Central 307,258 48.04 981.58Civil Lines 226,936 23.17 349.66Delhi Cantonment 29,631 45.00 88.67Karol Bagh 145,345 24.39 235.74Najafgarh 421,960 77.49 2174.39Rohini 327,859 87.88 1916.01NDMC 70,186 39.48 184.26Sadar Paharganj 90,117 68.32 409.42Shahdara North 408,707 24.58 668.06Shahdara South 358,853 54.41 1298.42South 266,217 52.96 937.57West 376,423 16.62 416.03Narela 126,694 75.39 635.17

Total 10583.79

Notes: aCensus of India 2001.b1 US$ ¼ Indian Rs. 45.



scenario in each zone of Delhi. Total health benefits of WBEG for air emissions inTPPs for Delhi are estimated at Rs 10583.79 million (US$ 235.19 million). It isapproximately 2% of the Delhi state GDP of 2004–05.

The capital cost of air pollution control measures in TPPs is of the order of $0.2million/MW (World Bank 1997; Tavoulareas and Charpentier 1995). Therefore, aninvestment of approximately $217 million is required at coal-based TPPs in Delhi tomeet the WBEG. The capital investment required by the TPPs to comply with theWBEG is found to be less than the annual health benefits obtained by this regulatoryintervention.

6. Conclusions

The regulatory intervention of adoption of the WBEG has been studied andanalysed for 14 administrative zones of Delhi. The implementation of the WBEGshows a reduction of between 62.17% to 83.45% in ambient pollution due to TPPsemissions. There is an economic rationale for the implementation of WBEG in TPPs.Capital investment required by the TPPs to meet the WBEG is less than the annualhealth benefits due to a reduction in air pollution under the WBEG scenario. Totalbenefits that include reduced material damage, improved visibility etc. will furtheraugment the case for adoption of the WBEG by TPPs. The study also providesimportant policy-relevant information by valuing air pollution abatement benefits.The valuation of the air pollution conducted in this study is based on the actualbehaviour of the individuals using primary data. Most of the valuation studiesconducted in India for environmental impact assessment and policy analysis areeither based on the benefit transfer or abatement cost methods. The benefit transfermethod has several inherent sources of bias and generally over- or under-estimatesthe damage or benefits (Navrud and Pruckner 1997). The abatement cost methoddoes not take into account the external environmental damage cost. Hence, themethod employed in this study contributes reliable information for conducting thecost-benefit analysis of air pollution control measures not only for TPPs, but also forother sources of air pollution.

Acknowledgements

The authors gratefully acknowledge the co-operation of members of surveyed households andof volunteers who collected the data, and also Dr Ramakrishnan Sitaraman, Department ofNatural Resources, TERI University, for editorial assistance.

Notes

1. A general problem in the management of public goods, whereby some individuals avoidpaying for their use of the good in question.

2. The value of a particular good as perceived by respondents is sensitive to the number ofgoods to be valued.

3. Such as aggregation.4. This may be due to the unrealistic assumption of the method that the consumer is the best

judge of his interests and that the consumer’s ability to rank preferences is rational.

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Appendix: list of symbols

A Aversion activitiesAE Averting expenditurea Air qualityC Coefficients (for variables of econometric model)EA Environmental awareness (awareness of air pollution)FS Family sizeH Stock of health capitalHI Household incomeI IncomeIA Indoor air (cleanliness measures)M Mitigating activitiesME Mitigating expenditureMTP Minimum household marginal willingness-to-payNCD Number of chronic diseasesNSD Number of sick daysp ProbabilityPE Pollution exposureSH Smoking habits3SLS Three-stage least squaret t-statisticstj Time required to consume one unit of good ‘j’U Utility functionw wage ratel Marginal utility of income



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Health benefits valuation of regulatory intervention for air pollution control in thermal power plants in Delhi, India