sdarticle[1]

Low-carbon energy policy and ambient air pollution in Shanghai,

China:A health-based economic assessment

Changhong Chen a ,Bingheng Chen b ,Bingyan Wang a ,Cheng Huang c ,Jing Zhao c ,

Yi Dai c ,Haidong Kan b,?

a

Shanghai Academy of Environmental Sciences,Shanghai 200233,China

b

Department of Environmental Health,School of Public Health,Fudan University,Shanghai 200032,China

c

East China University of Science and Technology,Shanghai 200237,China Received 28May 2006;received in revised form 12November 2006;accepted 18November 2006

Available online 3January 2007

Abstract

Energy and related health issues are of growing concern worldwide today.To investigate the potential public health and economic impact of ambient air pollution under various low-carbon energy scenarios in Shanghai,we estimated the exposure level of Shanghai residents to air pollution under various planned scenarios,and assessed the public health impact using concentration-response functions derived from available epidemiologic studies.We then estimated the corresponding economic values of the health effects based on unit values for each health outcome.Our results show that ambient air pollution in relation to low-carbon energy scenarios could have a significant impact on the future health status of Shanghai residents,both in physical and monetary https://www.360docs.net/doc/315399584.html,pared with the base case scenario,implementation of various low-carbon energy scenarios could prevent 2804–8249and 9870–23,100PM 10-related avoidable deaths (mid-value)in 2010and 2020,respectively.It could also decrease incidence of several relevant diseases.The corresponding economic benefits could reach 507.31–1492.33and 2642.45–6192.11million U.S.dollars (mid-value)in 2010and 2020,respectively.These findings illustrate that a low-carbon energy policy will not only decrease the emission of greenhouse gases,but also play an active role in the reduction of air pollutant emissions,improvement of air quality,and promotion of public health.Our estimates can provide useful information to local decision-makers for further cost-benefit analysis.

?2006Elsevier B.V .All rights reserved.

Keywords:Low-carbon development;Air pollution;Public health;Economic evaluation

1.Introduction

Energy and related health issues are of growing concern worldwide today.Fossil fuels,the primary source of world energy,are the greatest source of

ambient air pollution,producing particulate matter (PM),nitrogen oxides (NO 2)and sulfur oxides (SO 2).These pollutants have been related with increased mortality and morbidity from cardiorespiratory diseases (Brunekreef and Holgate,2002).The burning of fossil fuels is also the major source of carbon dioxide (CO 2),a primary contributor to global warming (Cifuentes et al.,2001).Coal has been China's long-term primary energy source.The relatively poor energy technology currently in use in China has caused high emissions of both local

air

Science of the Total Environment 373(2007)13–

21

?Corresponding author.Current address:Epidemiology Branch,National Institute of Environmental Health Sciences,P.O.Box 12233,Mail Drop A3-05,Research Triangle Park,NC 27709,US.

E-mail address:haidongkan@https://www.360docs.net/doc/315399584.html, (H.Kan).0048-9697/$-see front matter ?2006Elsevier B.V .All rights reserved.doi:10.1016/j.scitotenv.2006.11.030

pollutants(LAP)and CO2during energy consumption. With the high speed of economic growth,energy demand will increase concomitantly with economic development. Clearly,today's decisions regarding energy policies in China will have a significant effect on future CO2 emission,air pollution and the health status of local people.

Shanghai,the largest city in China,is in the leading position of economic development in the country. Emissions of CO2and LAP per unit of gross domestic product(GDP)in Shanghai are much higher than those in developed countries.Clearly,a low-carbon development strategy is beneficial for the city because it can reduce the emissions of CO2and LAP,while simultaneously meeting the energy demand of economic growth.In the present study,we strive to evaluate the public health impact of ambient air pollution under various low-carbon energy scenarios in Shanghai,and to put monetary values on the estimated health effects.Our results can provide useful information to local decision-makers for further cost-benefit analysis of low-carbon development policy.

2.Methods

2.1.Development of low-carbon energy scenarios

In this study,a LEAPs(long-range energy alter-natives planning system)model was used to study the effect of low-carbon energy scenarios on the emissions of CO2and LAP.In contrast with other optimization tools,such as MARKet ALlocation(MARKAL), LEAPs is an accounting tool that balances production and consumption of energy in an energy system.As opposed to the MARKAL model,LEAPs has fewer degrees of freedom,and usually has only one solution to the proposed problems.Both LEAPs and MARKAL models have been successfully applied in energy scenario analysis in developing countries(ERC,2006).

Although LEAPs is basically an accounting frame-work,users can go beyond simple accounting and perform sophisticated simulations.Details of LEAPs are available from the Stockholm Environmental Institute—Boston(LEAPs,2001).LEAPs uses the concepts of sector,sub-sector,end use and devices,and therefore analysis of demand and resource at each stage,from end use to primary energy level,may be carried out.Our study forecasts the future energy demand in Shanghai according to the driving factors of economic growth,city develop-ment scale,industry structure and vehicle population,and estimates LAP and CO2emission according to energy consumption.Details of the application of the LEAPs model in Shanghai have been discussed previously(Wang et al.,2004).

Scenarios related to this study included base-case (BC)and various low-carbon scenarios.The base-case scenario only considers the economic growth in Shanghai,assuming that GDP growth rate is be9–11%during2000–2005,with a0.5%–1%decline of GDP growth rate on the base of9–11%for every five years afterward between2005and2020.The low-carbon scenarios consist of three sub-components: energy efficiency improvement(EE),expanding natural gas use for final sectors(GAS),and wind electricity generation(WIND).

In the EE scenario,an annual average improvement of2%is assumed in final energy use sectors from2000 to2020.

In the GAS scenario,direct burning of hard coal and coal gas is switched to natural gas.The detailed settings are as follows:

?Primary industry:The share energy derived from hard coal falls by5%each year.

?Other industry:In industry sectors apart from primary industry,the dependence on hard coal falls by0.5%each year from2001to2005and by13% each year after2005.

?Commercial use:The need for hard coal falls by5% each year,and the dependence on coal gas will drop to zero after2005.

?Residential use:For the urban area of Shanghai,the use of hard coal falls by5%each year from2001to 2005and by10%after2005;while coal gas utilization decreases by5%yearly.In the rural areas,the use of hard coal falls by5%yearly from 2001to2005and by10%yearly after2005.

The WIND scenario sets the capacity of power generation according to“Eleventh Five-Year Plan of Electricity Supply in Shanghai”.The detailed settings are shown in Table1.

The detailed elements of various scenarios are summarized in Table2.

2.2.Concentrations of ambient air pollutant

Emissions of particulate matter less than10μm (PM10)and SO2provided by the LEAPs model were summarized in Table3.

Based on the principle of transfer matrix,one type of air quality model\the exposure level model was developed to link emission scenarios of the LEAPs model and air pollutant concentrations.

The fundamental matrix was input through a long-range transport and deposition model(ATMOS model)

14 C.Chen et al./Science of the Total Environment373(2007)13–21

for primary PM 10and SO 2.The ATMOS model is a Lagrangian parcel model with three vertical layers (Calori and Carmichael,1999).For the Shanghai project,the ATMOS model provided a 4km×4km resolution for the concentration of primary PM 10and SO 2.The total area of Greater Shanghai,6341km 2,was divided into 487grids.Two transfer matrices for use in exposure level prediction were produced:a region-to-grid matrix for the area sources,and a large point source-to-grid matrix for the elevated point sources.Based on matrix output of the ATMOS model,the Shanghai Exposure Level model was developed in Excel to link the emission prediction of the LEAPs model and provide exposure levels for the health impact analysis.The year 2000was selected as the base period in this analysis.Air quality changes in 2010and 2020were estimated under the following scenarios:BC,EE,GAS and WIND.2.3.Human exposure level to PM 10

In the present assessment,PM 10was selected as an indicator of air pollution to estimate the relevant health effects,since there has been strong epidemiologic evidence to support its association with adverse health effects among all air pollutants.All people living in Greater Shanghai were considered as the exposed population in this analysis.An estimate of the number

of Shanghai residents in each 4km×4km grid cell was then made for the assessment based on the population data collected from the Shanghai Bureau of https://www.360docs.net/doc/315399584.html,bining the PM 10level and population number in each cell,we estimated the population exposure level to outdoor air pollution under various scenarios in 2010and 2020in Shanghai.

2.4.Estimation on health effects

To develop estimates of public health impact of air pollution,we relied on published peer-reviewed studies on air pollution and health,using concentration-response (C-R)coefficients derived from studies con-ducted in China or worldwide.

Since most of the epidemiologic studies linking air pollution and health endpoints are based on a relative risk model in the form of a Poisson regression,the cases at a given concentration C ,could be given by:E ?exp eb ?eC ?C 0TT?E 0

e1T

In Eq.(1),C and C 0are the PM 10concentration under one specific scenario and baseline scenario,respectively,and E and E 0are the corresponding health effect cases under the concentration of C and C 0.The health effect (benefit/damage)under the scenario with respect to baseline scenario is the difference between E

Table 2

Elements of the scenarios

Economic growth Energy

efficiency improvement Natural gas availability Wind electricity

generation

Base-case (BC)

√

Low carbon scenarios EFF √√GAS √√√Wind

√√

√

√

Table 3

Emissions of PM 10and SO 2for selected scenarios in Shanghai (unit:1000ton/year)Scenarios Pollutants 200020102020BC PM 10144247386SO 24617451111EE PM 10144216293SO 2461666871GAS PM 10144168169SO 2461544569WIND

PM 10144154154SO 2

461

503

530

Table 1

Electricity generation capacity in WIND scenario (unit:10,000kW)

Year

Coal Natural gas Wind Total Power plant

2006699.95020769.92010805.95020875.920301165.950601275.9Combination of heat and power (CHP)2006447.51500597.52010747.51500897.520302347.515002497.5Total a

20061147.4200201367.420101553.4200201773.42030

3513.4

200

60

3773.4

a

Combination of power plant and

CHP.

Fig.1.Model to derive number of cases under different scenarios.

15

C.Chen et al./Science of the Total Environment 373(2007)13–21

and E0.The value could be obtained if the following data components are available:exposure-response functions(β),population exposure levels(C and C0), and baseline rate(E0)(Fig.1).

Exposure-response functions(β)link air quality changes and heath outcomes.The preference for this analysis was to select C-R functions from Chinese studies whenever they were available.Only when the selected endpoints could not be found in Chinese literature,the results of international peer-reviewed studies were used.If there were several studies describing the C-R function for the same health endpoint,we used the pooled estimate to get the mean and95%confidence interval(CI)of the coefficient.This meta-analysis method was based on the variance weighted average across the results of studies with available quantitative effect estimates(coefficients or relative risks).Studies with lower standard errors had more weight in the resulting joint estimate.

Although PM10was selected as the indicator of air pollution in this analysis,some studies depended on other measures of particulate matter[e.g.total sus-pended particles(TSP),or particulate matter less than 2.5μm(PM2.5)]for exposure assessment.Therefore,if

Table4

Percent of population exposure to PM10level under different scenarios in2010and2020(%)

PM10 level (μg/m3)20102020

BC EE GAS WIND BC EE GAS WIND

0–100.85 1.62 3.83 5.060.030.20 5.09 6.15 10–208.4010.6018.5019.69 2.32 5.3219.5220.07 20–3011.2312.969.989.60 5.279.4210.1711.23 30–407.85 6.5819.2833.17 6.509.7941.6058.39 40–50 5.20 6.8246.5832.467.87 4.9421.99 4.07 50–60 6.0924.08 1.810.02 5.17 3.85 1.610.09 60–7023.3637.330.02– 3.40 5.740.02–

70–8036.410.02–– 2.9614.84––

80–900.62––– 4.7936.72––

90–100–––– 4.319.16––

100–110––––11.480.02––

110–120––––30.25–––

120–130––––15.63–––

130–140––––0.02–––Total100100100100100100100100 Table5

Exposure-response coefficients and baseline rate(per person)used in the analysis

Health outcome

(age group)

Mean(95%CI)Reference Frequency Reference

Long-term mortality (adult≥30)0.00430(0.00260,0.00610)Dockery et al.(1993),Pope et al.(1995)0.01077Shanghai Municipal Bureau

of Public Health(2002)

Chronic bronchitis (all ages)0.00450(0.00127,0.00773)Ma and Hong(1992),Jin et al.(2000)0.01390China Ministry of

Health(1998)

Respiratory hospital admission(all ages)0.00130(0.00010,0.00250)Zmirou et al.(1998),Wordley et al.(1997)0.01240Shanghai Municipal Bureau

of Public Health(2002)

Cardiovascular hospital admission(all ages)0.00130(0.00070,0.00190)Wordley et al.(1997),Prescott et al.(1998)0.00850Shanghai Municipal Bureau

of Public Health(2002)

Outpatient visits-internal medicine(all ages)0.00034(0.00019,0.00049)Xu et al.(1995) 3.26000Shanghai Municipal Bureau

of Public Health(2002)

Outpatient visits-pediatrics(all ages)0.00039(0.00014,0.00064)Xu et al.(1995)0.30000Shanghai Municipal Bureau

of Public Health(2002)

Acute bronchitis

(all ages)

0.00550(0.00189,0.00911)Jin et al.(2000)0.39000Wang et al.(1994)

Asthma attack (children b15years)0.00440(0.00270,0.00620)Roemer et al.(1993),Segala et al.

(1998),Gielen et al.(1997)

0.06930Ling et al.(1996)

Asthma attack (adults≥15years)0.00390(0.00190,0.00590)Dusseldorp et al.(1995),Hiltermann et al.

(1998),Neukirch et al.(1998)

0.05610Ling et al.(1996)

16 C.Chen et al./Science of the Total Environment373(2007)13–21

necessary,the following conversions were applied for different particulate matter indicators:PM 10?TSP ?0:65and PM 10?PM 2:5=0:6

(Teng et al.,1999)

The final results of this analysis were given as the comparison of health effects under one specific scenario with respect to BC scenario in 2010and 2020,respectively.

2.5.Economic valuation of the health effects Generally there are two ways to estimate the economic cost of health effects due to air pollution:one is the direct cost approach,the other is the general equilibrium approach.Given the difficulty and uncer-tainty associated with projecting future economic and technological changes in the general equilibrium approach,we used the direct cost approach.Although the general equilibrium approach could provide many insights that the direct cost approach cannot,it also introduces a significant level of additional uncertainty.Our choice of approach for economic assessment is also in line with other similar assessment (US EPA,1999).The direct cost approach in our analysis was mainly based on the concept of willingness to pay (WTP),while cost of illness (COI)was also employed as an alternative choice for some morbidity endpoints that could not be valued based on existing WTP literature.The effect of air pollution on mortality was assessed by using the value of a statistical life (VOSL).The literature on the VOSL,or on willingness to pay to avoid a statistical premature death,however,is mainly from the United States.Due to unavoidable constrains such as limited time and budget,our analysis relied on a contingent valuation study (CVM),which was conducted in Chongqing,China (Wang et al.,2001),for the estimate of Shanghai VOSL.In the Chongqing study,Wang et al.reported an average WTP for saving a statistical life being US $34,750.The marginal effect of income on WTP value was also reported as:with annual income increase of $145.80,the marginal increase for saving a statistical life was $14,550.Therefore,taking the annual income differences between Chongqing and Shanghai residents into account,which were $495.70and $1234.50respectively in 2000,we did a conversion based on Chongqing's coefficient between marginal WTP and income,and got an estimate for the VOSL in Shanghai.

For different endpoints of morbidity,since no WTP studies on these endpoints are available in China,we used an alternative approach to infer the value from those used by the U.S.EPA after conversion (US EPA,1999).The ratio for conversion was based on the per capita income of US and Shanghai residents,and the income elasticity was assumed to be 1.The COI was also calculated for the endpoint of hospital admissions and outpatient visits,using actual data from Shanghai.No existing studies based on WTP are available on the endpoints in Shanghai.

Table 6

Health benefits in different scenarios with respect to BC Scenario in Shanghai in 2010(mean and 95%CI)

EE

GAS

WIND

Premature death 2804(1766,3811)7452(4754,9994)8249(5274,11040)Chronic bronchitis

5828(2048,9069)15,450(5558,23,500)17,100(6175,25,900)Respiratory hospital admission 1570(124,2934)4269(341,7906)4745(380,8774)Cardiovascular hospital admission 796(507,1080)

2169(1385,2935)

2412(1541,3262)

Outpatient visits (internal medicine)111,300(61,860,158,400)304,600(169,500,433,100)339,000(188,700,481,900)Outpatient visits (pediatrics)11,540(4201,18,970)31,590(11,520,51,820)35,150(12,820,57,650)Acute bronchitis 186,100(0,334,700)493,700(0,858,500)546,400(0,944,500)Asthma attack

3652(3162,4134)

9585(8242,10,890)

10,590(9093,12,040)

Table 7

Health benefits in different scenarios with respect to BC Scenario in Shanghai in 2020(mean and 95%CI)

EE

GAS

WIND

Premature death 9870(5911,14,170)22,210(13720,30,860)23,100(14,300,32,010)Chronic bronchitis

20,700(6636,35,400)46,330(15,730,74,980)48,160(16,410,77,610)Respiratory hospital admission 5057(387,9782)12,030(941,22,770)12,560(985,23,730)Cardiovascular hospital admission 2537(1600,3477)

6075(3856,8273)

6347(4030,8639)

Outpatient visits (internal medicine)348,600(193,000,498,300)844,200(468,700,1,204,000)882,700(490,200,1,258,000)Outpatient visits (pediatrics)36,210(130,90,59,950)87,620(31,810,144,400)91,620(33,270,150,900)Acute bronchitis 659,700(0,1,363,000)1,478,000(0,2,819,000)1,537,000(0,2,913,000)Asthma attack

13,580(12,040,15,130)

29,590(25,870,33,270)

30,700(26,800,34,540)

17

C.Chen et al./Science of the Total Environment 373(2007)13–21

The economic value of a change in the incidence of a given adverse health outcome was calculated as the change in incidence(e.g.the number of avoidable deaths)multiplied by the unit monetary value(the value of a single case avoided).To deal with the inherent uncertainty in the health and economic impact assess-ment,the uncertainties in the effect estimates were quantified and the results were given as a range(mean and95%CI).Since both health outcomes and unit values are distributions rather than constants,we performed the Monte Carlo simulation to calculate the economic values on the Analytic?environment(Lumina Corp,2006). 3.Results

3.1.Exposure assessment to PM10of general population in Shanghai

Table4summarizes the percent of the population exposed to different levels of PM10under various scenarios in2010and2020,respectively.It should be emphasized that the PM10levels in Table4are much lower than the actual concentrations in Shanghai, because in the present study,only the PM10from the source of energy consumption was assessed.Those from other sources,such as natural sources,construction sites etc.,were not included.

3.2.Estimation of health effect

Table5summarizes the PM10exposure-response coefficients(mean and95%CI)and the baseline rates of selected health outcomes in the analysis.

The excess cases in each scenario,with respect to BC scenario,are computed based on the change of population exposure levels to PM10under each scenario, exposure-response functions,and baseline rates for the health outcomes.Tables6and7show the health benefits in different scenarios with respect to the BC scenario in Shanghai in2010and2020,respectively.It is clear that energy scenarios could have a significant impact on the health status for Shanghai residents in the future. Compared with the BC scenario,implementation of various energy scenarios in Shanghai could prevent 2804–8249and9870–23,100PM10-related avoidable deaths(mid-value)in2010and2020,respectively. 3.3.Economic valuation of the estimated health effects

Table8summarized the unit values(mean and95% CI)for various endpoints in2000in Shanghai,and the specific approach used in deriving them.

Combining the health benefits and unit values described above,we compute the economic benefits under different scenarios with respect to the BC scenario.Tables9and10 show the results in2010and2020,respectively.

4.Discussion

Our findings illustrate that low-carbon strategies could play an active role in the reduction of air pollutant emissions,improvement of air quality,and public

Table8

Summary of unit value for various endpoints in2000

Endpoint Mean(95%CI)Approach

Premature death108,500(101,900,

115,100)

WTP

Chronic bronchitis6050(807,20,130)WTP

Respiratory hospital admission710?COI

Cardiovascular hospital admission1043?COI

Outpatient visits(internal medicine)14?COI

Outpatient visits(pediatrics)14?COI

Acute bronchitis7.2(2.6,11.9)WTP

Asthma attack 5.3(2.3,8.3)WTP

?The available data in Shanghai did not provide the distribution of

the values.

Table9

Economic benefits in different scenarios with respect to BC Scenario in2010(millions of2000US$)(mean and95%CI)

EE GAS WIND

Premature death450.40(408.90–495.60)1197.00(1087.00–1317.00)1325.00(1203.00–1458.00) Chronic bronchitis49.45(44.89–54.41)133.10(120.90–146.50)144.90(131.50–159.40) Respiratory hospital admission 1.65(1.50–1.82) 4.49(4.07–4.94) 4.99(4.53–5.49) Cardiovascular hospital admission 1.23(1.12–1.35) 3.35(3.04–3.69) 3.72(3.38–4.10) Outpatient visits(internal medicine) 2.31(2.09–2.54) 6.31(5.73–6.95)7.03(6.38–7.73) Outpatient visits(pediatrics)0.24(0.22–0.26)0.65(0.59–0.72)0.73(0.66–0.80)

Acute bronchitis 2.00(1.82–2.21) 5.32(4.83–5.86) 5.88(5.34–6.47)

Asthma attack0.03(0.03–0.03)0.08(0.07–0.08)0.08(0.08–0.09)

Total a507.311350.301492.33

a Summing5th and95th percentile values yield a misleading estimate of the5th and95th percentile estimate of total health benefits.As a result,we only present the total mean.

18 C.Chen et al./Science of the Total Environment373(2007)13–21

health.To our knowledge,this is the first study in China to integrate the analysis of low-carbon development,air pollution and public health.Our estimates may provide supportive evidence to the implementation of low-carbon strategies in the city in that the resultant public health improvement is substantial in both physical and monetary terms.Actually some of the scenarios we proposed have been under consideration by the local decision-makers and will be implemented in Shanghai in the near future.

Quantification of the impact of air pollution on public health has increasingly become a critical component in policy decision.Analyzing the total burden of ambient air pollution on public health in a community remains challenging,given the gaps in scientific knowledge about the health effects of air pollution,and the wide range of uncertainties characterizing many parts of the process.

To assess the effects of air pollution,a complex mixture of pollutants,epidemiological studies use several indicators of exposure,(eg,NO 2,CO,PM 10,TSP,and SO 2).These pollutants,however,are correlat-ed.Hence,epidemiological studies cannot strictly allocate observed effects to single pollutants.But a pollutant-by-pollutant assessment would grossly over-estimate the impact.Therefore,we selected only one pollutant to derive the public health impact.In this context PM 10is a useful indicator of several sources of outdoor air pollution.

Our current estimation of public health impact associated with air pollution under various energy scenarios is conservative for three reasons.First,in the present analysis we only selected PM 10as an indicator of outdoor air pollution,which would probably overlook the adverse health effects due to exposure to other air pollutants,thus underestimating the health effects attributable to total air pollution.Although PM 10may be considered a good indictor of air pollution,there is clear evidence that other pollutants,such as ozone,nitrogen oxides,and sulfur dioxide etc.,may have independent health effects.In addition,we could not include estimates of synergistic effects between air pollutants and cofactors such as pollen and other allergens.Second,as we stated above,the ATMOS model we used could only simulate primary PM 10and SO 2,thus leading to underestimation of the health effects attributable to secondary PM 10,such as sulfate and nitrate.Previous study has shown that ammonium sulfate and nitrate accounted for substantial ratios of fine particles in Shanghai (Ye et al.,2003).Third,in the selection of relevant health endpoints,we only focused on those outcomes that could be quantitatively estimated and then translated into monetary values for further assessment.Some endpoints (e.g.sub-clinical symp-toms,decrease in pulmonary function)were not included in this analysis,although there is evidence for an association between them and air pollution exposure.We did not estimate effects from cancer linked to exposure to ambient air pollution,although a recent cohort study in U.S.has suggested their association (Pope et al.,2002).Fourth,we focused our analysis on ambient air pollution and we did not consider the health impact due to exposure to indoor air pollution.Actually a substantial part of indoor air pollutants might be from outdoor sources in Shanghai.Thus excluding indoor air pollution from our analysis might also underestimate the total health impact under various energy scenarios.Some of the exposure-response functions we employed in this analysis were not available in Chinese studies.So we had to rely on international studies,conducted mostly in the U.S.and Western Europe.This raises the question of transferring the results from a developed country to a developing one.For example,compared to the studies in the U.S.and Western Europe,the Chinese studies generally reported lower coefficients for the exposure-response relationships

Table 10

Economic benefits in different scenarios with respect to BC Scenario in 2020(millions of 2000US $)(mean and 95%CI)

EE

GAS

WIND

Premature death 2346.00(1934.00–2841.00)5280.00(4352.00–6394.00)5490.00(4525.00–6648.00)Chronic bronchitis

260.30(214.50–315.20)580.50(478.50–702.90)613.50(505.70–742.90)Respiratory hospital admission 7.87(6.49–9.53)18.72(15.43–22.66)19.54(16.11–23.66)Cardiovascular hospital admission 5.80(4.78–7.02)13.88(11.44–16.81)14.51(11.96–17.56)Outpatient visits (internal medicine)10.69(8.82–12.95)25.90(21.35–31.36)27.08(22.32–32.79)Outpatient visits (pediatrics) 1.11(0.92–1.35) 2.69(2.22–3.26) 2.81(2.32–3.40)Acute bronchitis 10.53(8.68–12.76)23.28(19.19–28.19)24.31(20.04–29.44)Asthma attack 0.16(0.13–0.19)0.34(0.28–0.42)0.36(0.29–0.43)Total a

2642.45

5945.31

6192.11

a

Summing 5th and 95th percentile values yield a misleading estimate of the 5th and 95th percentile estimate of total health benefits.As a result,we only present the total mean.

19

C.Chen et al./Science of the Total Environment 373(2007)13–21

between air pollution and adverse health effects(Kan et al.,2005).This is probably due to different levels of air pollution,local population sensitivity,age distribu-tion and especially different air pollutant components. For instance the composition of the motor vehicle fleets in Western Europe and the U.S.,where most of the epidemiological studies were performed,differs sub-stantially from that in China.This,together with other differences such as the widespread use of coal in China, implies that the air pollution mixture differs substan-tially between China and the areas where most epidemiologic studies were conducted.Therefore, conceptually,when exposure-response functions from developed countries are applied to other regions,for example—Shanghai,they should be revised,taking into account local conditions,such as the physical (diameter,etc.)and chemical(components)character-istics of particles,social-economical status of local populations,etc.However,no reference data are available for such a revision.Until exposure-response functions derived locally become available,this will probably be the weakest part of this analysis.

As no valuation studies on the health endpoints associated with air pollution in Shanghai have been performed,we had to estimate values from previous studies of similar changes.This procedure is often termed as benefit transfer or value transfer in econom-ics.Characteristics of the concerned population,e.g.age distribution,income,health status,culture,may have contextual effects on the valuation results.If we directly transferred the U.S.VOSL into the Shanghai case after considering the income difference,a value of US $780,000would be yielded,which is much higher than that estimated according to the Chongqing study. The value would be even higher if we used purchasing power parity(PPP)as the income definition here.It is obvious that the Chongqing result is better fitted to the Shanghai estimation than the US result in terms of economic and social situation.Therefore,the present analysis tries to employ the Chinese studies wherever they are available,and attempts to provide a conserva-tive range of reasonable estimates.

A major uncertainty that complicates the application of WTP estimates from the study site to the target site in the benefit transfer arises from difference between income levels.One of the fundamental issues in valuing the reduction of risk is that WTP rises with the income. The key question is the determination of income elasticity of the relevant WTP.The literature on the income elasticity of WTP for reducing the risk of damage to health is,however,extremely sparse. Different studies estimated the income elasticity from 0.26(Loehman and De,1996)to 1.1(Viscusi and Evans,1992).It is important to note the acute sensitivity of the social costs of ill health to the value of this https://www.360docs.net/doc/315399584.html,ing an elasticity of0.4and1.1makes a difference of nearly20times in the final results.In view of the limited data source,it is considered prudent to maintain a degree of conservatism in this valuation exercise.Since there is no suggestive information that could be relied on for elasticity estimate,we have chosen to assume a higher income elasticity of1for morbidity costs estimates,so that attention is focused on difference in income.

5.Conclusions

Energy and health is one of the biggest challenges for sustainable development in Shanghai during the high economic growth.Despite of the limitations described above,our analysis still emphasis the need to consider air pollution-related health effects as an important impact of low-carbon development strategy in Shang-hai.Of course,selection of optimal low-carbon scenarios for Shanghai requires further cost-benefit analysis based on both our estimates and other analyses on the implementation cost of those scenarios.Our analyses also suggest that in a century moving toward sustainable development and health,close collaboration between public health and energy policy will enhance success in preventing avoidable health hazards.

Acknowledgement

The current project was founded by the U.S.Energy Foundation through grant G-0212-06632.The authors also appreciate the kind assistance of the NIH Fellows Editorial Board in reviewing the manuscript. References

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