BioMed CentralEnvironmental Health

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Open AcceReviewAncillary human health benefits of improved air quality resulting from climate change mitigationMichelle L Bell*1, Devra L Davis2, Luis A Cifuentes3, Alan J Krupnick4, Richard D Morgenstern4 and George D Thurston5

Address: 1School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA, 2Graduate School of Public Health, University of Pittsburgh, CNPAV 435, Pittsburgh, PA 15260, USA, 3Industrial and Systems Engineering Department, P. Catholic University of Chile, Engineering School, Santiago, Chile, 4Resources for the Future, Washington, DC 20036, USA and 5School of Medicine, New York University, Tuxedo, NY 10987, USA

Email: Michelle L Bell* - michelle.bell@yale.edu; Devra L Davis - dld20@pitt.edu; Luis A Cifuentes - lac@ing.puc.cl; Alan J Krupnick - krupnick@rff.org; Richard D Morgenstern - morgenstern@rff.org; George D Thurston - george.thurston@nyu.edu

* Corresponding author

AbstractBackground: Greenhouse gas (GHG) mitigation policies can provide ancillary benefits in terms ofshort-term improvements in air quality and associated health benefits. Several studies have analyzedthe ancillary impacts of GHG policies for a variety of locations, pollutants, and policies. In this paperwe review the existing evidence on ancillary health benefits relating to air pollution from variousGHG strategies and provide a framework for such analysis.

Methods: We evaluate techniques used in different stages of such research for estimation of: (1)changes in air pollutant concentrations; (2) avoided adverse health endpoints; and (3) economicvaluation of health consequences. The limitations and merits of various methods are examined.Finally, we conclude with recommendations for ancillary benefits analysis and related research gapsin the relevant disciplines.

Results: We found that to date most assessments have focused their analysis more heavily on oneaspect of the framework (e.g., economic analysis). While a wide range of methods was applied tovarious policies and regions, results from multiple studies provide strong evidence that the short-term public health and economic benefits of ancillary benefits related to GHG mitigation strategiesare substantial. Further, results of these analyses are likely to be underestimates because there area number of important unquantified health and economic endpoints.

Conclusion: Remaining challenges include integrating the understanding of the relative toxicity ofparticulate matter by components or sources, developing better estimates of public health andenvironmental impacts on selected sub-populations, and devising new methods for evaluatingheretofore unquantified and non-monetized benefits.

Published: 31 July 2008

Environmental Health 2008, 7:41 doi:10.1186/1476-069X-7-41

Received: 4 April 2008Accepted: 31 July 2008

This article is available from: http://www.ehjournal.net/content/7/1/41

© 2008 Bell et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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BackgroundAverting the course of climate change would result inhuman health benefits directly associated with lessenedglobal temperature changes and associated impacts, butwould also bring ancillary health benefits from reducedground-level air pollution in the short-term [1-5]. Manyfossil-fuel combustion processes that generate greenhousegases (GHG) also emit other harmful air pollutants. Sev-eral measures aimed at reducing GHG emissions can alsoimprove local air quality, most commonly particulatematter (PM) and ozone (O3) precursors. Further, whereasthe benefits from climate change mitigation would mate-rialize far in the future, co-benefits, or ancillary benefits,would occur in the short-term.

Figure 1 describes the relationships among the health con-sequences of climate change and air quality policies andthe general framework of how these responses can beassessed. Air quality policies are routinely evaluated interms of the estimated health outcomes avoided and theireconomic impact [6,7]. However, assessment of thehealth impacts of GHG strategies often considers onlyconsequences in the far future (i.e., left side of Figure 1),without integration of the short-term benefits of relatedpolicies [8]. Well-informed public health and environ-mental strategies require full consideration of conse-quences, including co-benefits and potential ancillaryharms.

A broad array of tools to evaluate the health-related ancil-lary costs and benefits of climate change is currently avail-able, and some examples are provided in italics in Figure1. The general structure for most assessments involvesthree key steps: (1) estimating changes in air pollutantconcentrations, comparing levels in response to GHG mit-igation to concentrations under a baseline "business-as-usual" scenario; (2) estimating the adverse health impactsavoided from reduced air pollution; and (3) for somestudies, estimating the monetary benefit from theseaverted health consequences, often with comparison tothe cost of the climate change mitigation measure. Thefirst step is sometimes accomplished through emissionsscenarios and information regarding how emissionstranslate into pollutant concentrations, such as with airquality modeling systems. The second step usually relieson concentration-response functions from existing epide-miological studies on ambient air pollution and health.The third stage utilizes a variety of techniques to translatehealth benefits into monetary terms, such as contingentvaluation (CV). Additional steps include sensitivity analy-sis, such as applying multiple climate change scenarios orconcentration-response functions for health effects.

This paper aims to illuminate the weight of evidence onthe ancillary health benefits of GHG policies, provide a

framework for such analysis, and critique relevant meth-ods. We focus on the effects of air quality; however a fullassessment of the complete ancillary consequences wouldconsider other factors such as the cost of mitigation meas-ures and ecological impacts. We close with recommenda-tions on the appropriate role of ancillary health benefitsand costs in the climate change mitigation debate. As partof these recommendations, we identify a number of pub-lic health and economic related research topics thatrequire clarification in order to promote more effectiveancillary benefits assessments with respect to GHG miti-gation policies.

Studies of ancillary benefitsA variety of studies have been conducted to estimate thehealth and air pollution ancillary benefits from GHGreduction, with a wide range of methods and study areas.Energy scenarios, emission inventories, and global changeand regional air quality modeling systems have beenlinked to estimate the short-term incremental changes inpublic health and the environment that could result fromvarious GHG mitigation policies [9,10].

Recently, the Stern Review [11] addressed a wide range ofglobal benefits and costs associated with climate change,including air pollution co-benefits. Citing a study by theEuropean Environmental Agency, the Review notes thatlimiting global mean temperature increase to 2°C wouldlead to annual savings in the implementation of existingEuropean air pollution control measures of €10 billionand additional avoided annual health costs of €16–46billion. Even larger co-benefits are estimated in develop-ing countries, including via the substitution of modernfuels for biomass. The Stern Review also recognizes someof the trade-offs between climate change objectives andlocal air quality gains. For instance, switching from petrolto diesel reduces carbon dioxide (CO2) emissions butincreases particles with aerodynamic diameter ≤ 10 μm(PM10) and nitrogen oxides (NOx) emissions. Increasingcombustion temperatures of aircraft engines reduces CO2while increasing NOx, as well as water vapor, which canintensify warming effects. Other GHG mitigating actionspresent fewer environmental trade-offs (e.g., reductions inaircraft weight can decrease CO2 emissions and simultane-ously improve local air quality).

A study of three Latin American cities identified signifi-cant health benefits from reducing GHG, including about64,000 cases of avoided premature mortality over a 20-year period [12]. Reducing methane concentrations by20% starting in 2010 was estimated to lower troposphericO3 levels, averting over 30,000 deaths worldwide in 2030alone [13]. Country-wide assessments of GHG mitigationpolicies on public health have been produced for Canada[14] and selected energy sectors in China [15,16], under

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differing baseline assumptions. A synthesis of research onco-benefits and climate change policies in China con-cluded that China's Clean Development Mechanismpotentially could save 3,000–40,000 lives annuallythrough co-benefits of improved air pollution [17]. Sev-eral studies investigated the links between regional airpollution and climate policy in Europe [18-20]. The tablein additional file 1 summarizes key examples of co-bene-fits studies and briefly describes the methods used foreach step of analysis.

Results from co-benefits studies are typically difficult tocompare, even if study area and target year are identical,due to variations in study design. Major differences existin the methodology used to estimate benefits, as demon-strated in the table in additional file 1. Whereas somestudies implement sophisticated modeling systems toestimate altered air quality, capturing regional differencesin pollutant levels [13], others use simple target valueswith uniform pollution reductions across all spatial areas[12]. Likewise, some studies estimate changes in healthimpacts based on a single or small number of concentra-tion-response functions, capturing only a portion of thehealth impacts and at times assuming that concentration-response functions derived from one area are applicable

in others [21], while other analyses select locally devel-oped concentration-response functions where availableand consider a wide range of health impacts [12]. Eachapproach depends on different underlying conjectures.Even with the widely varying methods, results consistentlyindicate significant ancillary health benefits from GHGpolicies. Similarly, estimates of the social cost of air pollu-tion policies were found to be quite insensitive to choicesin the uncertainties of costs and benefits [22].

Estimation of changes in air pollutant concentrationsReductions in local air pollutants resulting from GHGpolicies (step 1 in Figure 1) can be calculated based on theresulting pollutant levels under a baseline and climatemitigation scenarios. Research designs differ not only bythe policy studied but the choice of a baseline "business-as-usual" scenario. Options range from assumptions thatemissions or pollutant concentrations remain at currentlevels, perhaps adjusted for population growth, to aggres-sive air pollution control policies regardless of actionstaken to affect GHGs. A review of studies of ancillary ben-efits concentrating on the energy sector found that choiceof baseline scenario greatly impacted results, especially forstudies assuming lower pollution levels as directed by the1990 Clean Air Act Amendments (CAAA) in comparisonto those omitting the CAAA [23].

Uncertainties in climate change predictions and estima-tion of regional parameters can be considerable, espe-cially for highly disaggregated assessments with long-termprojections [24-26]. However, assessment of ancillarybenefits requires estimates of pollution levels a few yearsinto the future, not several decades, and thus is notmarred by uncertainties that plague many other forms ofclimate-related research. The longest projections for stud-ies in Additional file 1 are about 20 years.

Approaches to estimate changes in air pollution rangefrom complex modeling systems to a simple pollution tar-get, assuming a pollutant's levels will be at a specified con-centration or meet a certain absolute or relative reductionby a given date. Existing emissions inventories and source-receptor matrices can be used to connect changes in emis-sions to changes in specific pollutants [27-31]. Backwardstrajectory modeling has been used to determine pollutantsources and locations [32-36], and this information canthen be used to estimate how changes in pollutant emis-sions will affect concentrations at various locales.Regional air quality modeling systems, such as theUSEPA's Models-3/Community Multi-Scale Air Quality(CMAQ) model in conjunction with meteorological mod-els, link data on meteorology, emissions, and land-use togenerate gridded estimates of pollutants, including O3and PM at various size fractions [37]. Such modeling hasbeen used to estimate how changes in emissions scenarios

Relationship between climate change and air quality policiesFigure 1Relationship between climate change and air quality policies.

Climate change policiesAim: reduce GHG emissions.

Regional, national, andinternational efforts.

(e.g., Carbon tax)

Air quality policiesAim: reduce pollutant levels.Regional and national efforts.

(e.g., changes in public transportation use and

vehicle fleet)

Greenhouse gas levels

Air pollutant levels(e.g., PM, O3,SO2, NO2, etc.)

Human health response(e.g., premature mortality,

frequency of asthma attacks)

Future Short-term

Economic assessmentValuation of avoided

adverse health outcomes, cost of policy implementation

E.g., air quality modeling,

source-receptor matrix

E.g., concentration-response functions from epidemiology

E.g., willingness-to-pay, cost-of-illness

E.g., explicit target,

modeling systems

E.g., Estimate of cost of purchase, installation, and maintenance of

air pollution control

technology

E.g., Evaluation of mitigation

costs by sector

1

2

3

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affect ambient concentrations [38,39] and similarly canbe applied to estimate future changes in ambient pollut-ants from climate change measures [40] and futureimpacts on human health [41].

The choice of method to ascertain future pollutant levelsdepends on what pollutants and regions are consideredand the spatial and temporal resolution desired. Forexample, a recent study [13] applied a sophisticated airquality modeling system to estimate O3 levels across dif-ferent regions of the world, whereas other approaches[12,42] applied uniform reductions assuming equal per-cent reductions in pollutants across all areas. The moreadvanced approach using modeling systems is betterequipped to capture spatial variability and transport ofpollution and precursors; however some pollutants aremore easily modeled than others.

Uncertainties in the translation of a given climate policyto changes in pollutant concentrations vary by themethod used, but include: (1) the choice of "baseline"scenario; (2) translation of a policy into emissionschanges in various sectors; (3) physical transformation ofthe pollutant (e.g., agglomeration of particles to a largersize); (4) chemical transformation of pollutants (e.g.,non-linear transformation of O3 precursors, conversion ofgaseous pollutants such as NOx to particles); and (5) spa-tial and temporal distribution of impacts, as a function ofthe preceding factors. Both the baseline scenario and cli-mate change mitigation policies are assumed to have uni-form or otherwise known spatial and temporaldistribution in pollution levels. This can be particularlyimportant if emissions trading is included, such as SO2cap and trade programs, which set a maximum value foremissions but allow large heterogeneity in emissions thatcan change with time. The level of uncertainty may differby pollutant depending on their spatial heterogeneity. Forexample, within-city gradients have been observed forPM2.5 [43]. While most ancillary studies to date haveexamined policies at the federal level, in theory analysiscould examine the impacts of other mitigation actionssuch as those conducted at the local level [44,45] or evenpersonal choice and household level actions [46,47] thataggregated lead to lower GHG emissions.

Estimation of human health impactsStudies of the health effects potentially avoidable by cli-mate change mitigation strategies have been based almostexclusively on concentration-response functions derivedfrom published epidemiological studies (step 2 of Figure1). Common urban air pollutants likely to be impacted byGHG policy (e.g., PM) have been associated with a widerange of harmful health impacts including increased fre-quency of hospital admissions and increased risk of mor-tality [48]. Table 1 provides the health outcomes and

sources of concentration-response coefficients employedfor the subset of studies in additional file 1 that estimatedhealth impacts. Because mortality dominates benefitsanalyses, additional detail is given on the pollutants andtimeframe of exposure (i.e., acute or chronic) for mortal-ity.

In this context, the method involves applying a mathe-matical relationship between pollution levels associatedwith various types of health endpoints, with an under-standing of the relationships between the health effectand individual (or social) preferences for reducing the riskor incidence of this effect. The use of a concentration-response function without adjustment assumes that theunderlying relationship between air pollution and healthwhen and where the function was derived will hold in thefuture, perhaps in a different location. This integrationinvolves matching as closely as possible the starting pointof the valuation analysis to the endpoint provided byhealth science, that is a measure of pollution (e.g., ambi-ent levels as a surrogate for exposure) to a health response(e.g., increased risk in hospitalization). In addition, theapproach requires knowledge of the population bycohorts that map to the health endpoints (e.g., asthmaticsor those >65 years) and assumptions regarding baselinehealth responses.

Critical differences in this stage of analysis are choice ofpollutants, health effects, time scale (e.g., acute versuschronic), epidemiological studies, and assumptions (e.g.,baseline mortality rate). Almost all studies in Table 1 esti-mated averted mortality for PM, however a variety ofexposure-response coefficients were used, and severalstudies made assumptions regarding conversion of onepollution form to another (e.g., equal toxicity for nitratesand PM10 [23], PM2.5/PM10 = 0.6 [49], PM10/TSP = 0.5[15]).

Criteria for selection of health endpoints and epidemio-logical studies were not consistent across the studies, how-ever common themes were: (1) use of locally conductedstudies where possible; (2) health endpoints with a con-sistent literature demonstrating a relationship with airpollution; and (3) emphasis on peer-reviewed research,although some studies applied non-peer-reviewed work.As the epidemiological literature grows, integrated assess-ments that incorporate these findings also evolve. Forexample, earlier studies estimating averted mortality fromlowered O3 levels were based on epidemiological researchof a single city (e.g., a Los Angeles study [50] applied toLatin America [12,42]), whereas more recent work usesmulti-city epidemiological studies to generate global esti-mates (e.g., a 95-city study [51] applied worldwide [13]).Concentration-response functions derived from numer-ous cities have advantages over single-city studies as they

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are less subject to sample size concerns and city-specificresults can be combined to generate an overall estimateaccounting for within-city and between-city statisticaluncertainty [52]. The choice of location of the epidemio-logical studies used may be based on selecting a city orregion matching or similar to that of the ancillary benefitsassessment. If only non-local single-city studies are avail-able, options are to perform a meta-analysis to generatean average that accounts for the uncertainty of each city-specific relative rate or to select an existing multi-citystudy.

An alternative to identifying epidemiological studiesthrough literature review is to apply an existing databaseor model of concentration-response functions, many ofwhich also include economic valuation tools. The FastEnvironmental Regulatory Evaluation Tool (FERET) is acost-benefit template developed by Carnegie Mellon Uni-versity and the University of Washington to evaluate howpolicy changes affect air-related health outcomes and theirassociated economic impacts [53]. The Benefits Mappingand Analysis Program (BenMAP), developed by USEPA,estimates population-level exposures, changes in healthendpoints, and economic values [54]. The Ozone RiskAssessment Model (ORAM) uses air quality modeling topredict changes in O3 levels and associated healthresponse [55]. These systems can be used to estimatechanges in health and their monetary value, or as a sourceof concentration-response and economic value functions[55-57].

A number of key uncertainties characterizes the use ofpopulation-based research on air pollution and health forancillary benefits studies. These include [58,59]:

CausalityThe precise physiological mechanism(s) by which air pol-lution could cause the health effects indicated in epidemi-ologic studies is not always fully understood. As a resultcausal inferences are generally developed based on con-sistent evidence across multiple epidemiological studiesincluding different areas and study designs, and resultsfrom toxicological and human exposure studies in con-junction with the criteria of biological plausibility.

Other pollutants and pollutant mixturesOften co-pollutants are included in integrated assess-ments separately and their health or economic conse-quences summed. This may underestimate oroverestimate actual damages. The true harmful agent maynot be the pollutant under study but a related pollutant orgroup of pollutants with similar sources and/or formationpathways. For example, O3 can be considered a marker foran array of photochemical pollutants. Nitrates and sul-fates are related to PM as they contribute to secondary par-

ticles. Interaction between multiple pollutants is not wellunderstood, and most results are presented for an individ-ual pollutant, although air pollution is experienced as amixture.

Toxicity relating to PM chemical compositionWhile a substantial literature provides consistent evidencethat particles are detrimental to health and a limitednumber of population-based studies have examined PMeffects by chemical composition [60], the differential tox-icity of various forms of the PM mixture is unidentified.Differential effects have been demonstrated based on par-ticle size, however chemical composition also appears toplay a role as the same size distribution provides differenteffect estimates based on region [61,62]. In current analy-sis of ancillary benefits, all particles of a given size (e.g.,PM2.5) may be treated with equivalent toxicity, however iffor example sulfates are more harmful than other parti-cles, technologies that reduce emissions of particles fromcoal combustion may result in greater health benefits thanother technologies. If, for example, elemental carbon isidentified to be more detrimental to health, transporta-tion technologies may be more effective.

Use of ambient monitorsThe vast majority of epidemiological studies applied inancillary benefits studies use ambient monitoring data asa surrogate for individual or community-level exposure.The relationship between personal exposure and ambientmonitoring data varies by pollutant, typically with bettercorrelation for particles than for O3 [63,64]. Use of ambi-ent monitors increases the possibility of exposure misclas-sification, which if non-differential would generally driveeffect estimates towards the null, resulting in underesti-mates. This issue has particular importance for the extrap-olation of concentration-response functions from onearea to another, as the relationship between ambientmonitors and exposure, and thereby health, is a functionof indoor pollution and indoor/outdoor activity patterns,which may vary widely across populations.

Shape of concentration-response functionsMany concentration-response functions applied in ancil-lary benefits studies assume a log-linear relationshipbetween exposure and risk. If the true shape differs, incor-rect estimates could be obtained. If the assessmentincludes pollutant levels above those used to generate theconcentration-response function, results will be distortedif the log-linear or otherwise assumed function does nothold. If there exists a safe level below which pollutiondoes not adversely impact health, calculations based onfunctions assuming no threshold would be incorrect forpollutant levels below the threshold value. Some studieshave examined the shape of the concentration-responsecurve, however such analysis does not exist for all pollut-

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ants and health outcomes. Several recent US-based studiesfound no evidence of a threshold at typical concentrationsfor the relationship between mortality and O3 [65] or PM[66].

Temporal or spatial extrapolationPollution and health relationships developed in one areamay not be applicable in another location due to differ-

ences in the underlying population and pollutant charac-teristics [67]. Efforts are often made to apply locally-derived studies [49], however concentration-responsefunctions do not exist for many outcomes and pollutantsfor much of the world. Therefore US and European studiesare generally employed, although a growing number ofepidemiological studies are underway in Asia and LatinAmerica [68-71]. Uncertainties introduced by such extrap-

Table 1: Concentration-response functions used in the assessments listed in Additional file 1

Mortality Morbidity

Aaheim et al. 1999 [153]PM: Adult and infant [160] Lung-cancer, acute and chronic respiratory symptoms, pseudo-croup,

asthma [160]

Aunan et al. 2004 [75]PM10 (chronic): modified version of Pope et al. 1995 [160] Outpatient visits, emergency room visits, hospital admissions, work loss

days, acute respiratory symptoms in children and adults, chronic respiratory symptoms in children and adults, asthma attacks [161]

Burtraw et al. 2003 [23]PM10 and nitrates (acute) [162] NOx: respiratory symptoms, eye irritation days, phlegm days [163]

Cifuentes et al. 2001 [12,42]PM10 (acute and chronic): Respiratory hospital admissions [171,172], emergency department visits

[173], chronic adult bronchitis [174], acute bronchitis in children [56], asthma attacks [175], work loss days [176], restricted activity days (RAD) [177-179], respiratory symptom days [180]

Adults [164-169]Infants [84,170]O3 (acute) [50]

Dessus and O'Connor 2003 [155]PM10 (acute): Based on previously conducted literature reviews [181] Respiratory hospital admissions, emergency room visits, RAD, MRAD,

clinic visits for bronchitis for children <15 years, respiratory symptoms for adults and children, chronic bronchitis, chest discomfort, eye irritation, headaches. Based on previously conducted literature reviews [181,182]

Dudek et al. 2003 [156]Did not apply concentration-response functions. Estimated changes in mortality based on baseline burden.

Respiratory disease and neoplasm. Did not apply concentration-response functions. Estimated changes in morbidity based on baseline burden.

Mazzi and Dowlatabadi 2007 [157]PM2.5 (chronic) [167,183,184] Respiratory and cardiovascular (CVD) hospitalizations [185]

McKinley et al. 2005 [49]PM10 (acute and chronic) [167,183,186] O3 (acute) [187] Chronic bronchitis [188], MRAD [176,178], emergency room visits and

hospital admissions: previously conducted review [189]

Wang and Smith 1999 [15]PM (acute and chronic) [190-192] Respiratory hospital admissions [193], emergency room visits [194],

RAD >16 years [177], acute bronchitis <16 years [195], asthma attacks per asthmatic [175,196], respiratory symptoms [180], chronic bronchitis >16 years [174]

West et al. 2006 [13]O3 (acute) [51]

Note: References for health endpoints refer to the concentration-response function applied.

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olation include differences in indoor/outdoor activity pat-terns, population characteristics, household characteris-tics that relate to exposure, and the pollution mixture.Likewise, the study of ancillary benefits involves futuresocieties that may have dissimilar housing, populations,health care systems, and pollutant mixtures compared tothe present day or the timeframe of the epidemiologicalresearch.

Chronic and acute effects and exposuresAir pollution exposure can be categorized as short-term(i.e., a few days) or longer term (i.e., a few months oryears). Health impacts can be classified as those that takeplace immediately or a short time after exposure, or thosethat have a gradual or much-delayed response, such ascancer and neurological disease. Cohort studies of PM,which evaluate long-term exposure, generally providehigher estimates for mortality than do time-series studies,which evaluate short-term exposure [72,73]. Often moreinformation is available regarding health impacts ofshort-term exposure because such exposure estimates aremore readily available. However, the use of only acute-exposure impacts may underestimate the total mortalityburden from air pollution [74]. Co-benefits studies haveused different approaches to address chronic and acutehealth impacts. Whereas one study [75] included esti-mates of chronic mortality, excluding acute mortalityeffects, another [23] incorporated acute mortality only.

Unknown health endpointsWhile air pollution has been quantitatively linked tomany health consequences, there are other health events,including several pediatric and neurological endpoints,for which concentration-response functions have not yetbeen developed. Some of these health responses are lesssevere than the more commonly studied effects. Howeveras a counter example, recent studies elucidated the linkbetween O3 and mortality [51,76-78]. Although lesssevere health endpoints have lower monetary valuationsthan more severe impacts, they often occur in larger num-bers. Thus, the more grave outcomes such as death andhospital admissions are best viewed as indicators of themuch broader spectrum of adverse health effects resultingfrom air pollution.

Degree of mortality displacementThe public health burden of mortality associated with airpollution depends not only on the increased risk of death,but also on the length of life shortening. Several recentstudies provide evidence that short-term mortality dis-placement of a few days or less does not account for theobserved PM mortality effect estimates [79-83]. Past eval-uations of air pollution's effect on life expectancy gener-ally considered only deaths among adults above 30 yearsof age, but some studies [84-86] suggest that infants may

be among the sub-populations particularly affected bylong-term PM exposure, which would indicate a muchlarger reduction in life expectancy. Currently, considera-ble uncertainty remains as to the amount of life-shorten-ing associated with air pollution.

Economic valuation of avoided adverse health outcomesTo help decision-makers assess policies with a wide arrayof consequences, outcomes are often converted into com-parable formats. Several multi-criteria decision-makingtechniques have been applied in the context of climatechange policy [87-90]. Another widely used approach is toconvert health outcomes into economic terms to allowdirect comparison of costs and benefits. Underlying eco-nomic valuation of health is the concept that individualshave preferences that extend over environmental quality,market goods, and other non-market goods. If thisassumption is accepted, in principle it is possible todeduce how individuals tradeoff health by measuringhow much in the way of other services individuals arewilling to forego to enjoy health benefits. Expression ofthese values in monetary terms is used as a surrogate forwhat people are willing to give up in alternative real con-sumption opportunities. The notion that such individualtradeoffs well describe society's interest in environmentalquality is by no means universally accepted, and contro-versy surrounds economic valuation and benefit-costanalysis in particular [91]. For a summary of the economicargument see [92].

Approaches for economic valuation of healthWe identified several approaches for economic valuationof averted health consequences (step 3 of Figure 1): COI;human capital; a variety of WTP methods; and quality-adjusted life year (QALY) approaches.

Cost of illnessThe COI method totals medical and other out-of-pocketexpenditures and has been used for acute and chronichealth endpoints. For instance, separate models of cancerprogression and respiratory disease were used to estimatemedical costs from these diseases over one's lifetime [93].COI incorporates direct medical costs, such as for physi-cians' visits and medications, and indirect costs, includinglost income from work loss days. However the approachdoes not capture other consequences of illness such aspsychological suffering, physical pain, transportation tomedical appointments, dietary restrictions, and expendi-tures for friends or family acting as caretakers. Theapproach can have a welfare theoretic basis, but does notreflect the full damage of illness, hence results usuallyunderestimate costs and should be considered a lowerbound. Some COI studies assign a medical expenditurebased on primary diagnosis [94].

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Human capital approachEarly attempts to value mortality risk reductions appliedthe human capital approach, which estimates the "valueof life" as lost productivity. This method is generally rec-ognized as problematic and not based on modern welfareeconomics, where preferences for reducing death risks arenot captured. Another limitation is incorporation ofracial- or gender-based discrimination in wages. Thismethod assigns value based solely on income, withoutregard to social value, so unpaid positions such as home-maker and lower paid positions such as social workerreceive lower values. Because data are often available forsuperior alternatives, this approach is rarely used in healthbenefit studies.

Willingness to payWTP generates estimates of preferences for improvedhealth that meet the theoretical requirements of neoclas-sical welfare economics, by aiming to measure the mone-tary amount persons would willingly sacrifice to avoidnegative health outcomes. Complications arise in analysisand interpretation because changes in environmentalquality or health often will themselves change the realincome (utility) distribution of society. A valuation proce-dure that sums individual WTP does not capture individ-ual preferences about changes in income distribution.Another complication is that the value of avoided healthrisk may differ by type of health event and age. Forinstance, in one study WTP to reduce cancer was about athird larger than that for a similar chronic, degenerativedisease [95]. VSL estimates can be adjusted based on exist-ing health condition or age, or by the use of a value perlife-year saved [96]. Use of the value of a statistical life year(VSLY) is very controversial, however, because it impliesthat age and WTP are proportionally and inversely related,although the literature does not support this assumption.Estimates for children are very limited; however VSLs aregenerally higher for children [97] and the empirical liter-ature suggests that children's values are approximatelytwice the value for an adult. WTP measures are theoreti-cally superior to the "supply-side" measures of healthdamage because they can capture the complete value ofhealth, including pain and suffering.

The hedonic labor market WTP approach relates wage dif-ferentials to health risk differences across occupations andindustrial/commercial sectors, under the theory that incompetitive labor markets, workers in risky jobs shouldreceive wage premiums equal to the value they place onavoiding health risks [98,99]. Such studies can ask work-ers their perception of health risks to address differencesbetween perceived and actual risk. These studies arenumerous and form the foundation for most VSL esti-mates. However, they are problematic for application tohealth effects of pollution, because of less directly relevant

behavioral contexts and/or the populations. In particular,reducing air pollution may lower some health risks dis-proportionately for older persons who are not in the labormarket. These benefits, furthermore, may be more likelyfor people with chronic heart or lung disease and mayhave a delayed effect, all of which would not be capturedin the labor market studies.

A small literature of consumer preference studies esti-mates WTP to reduce health risks from purchases or otheractual consumer decisions (e.g., purchase of smoke detec-tors [100], driving behavior under different speed limits[101]). These studies typically find lower VSLs than otherapproaches [101]. A difficulty about these studies is statis-tically separating the health risk-reducing attribute fromother valued attributes. A large body of literature applies ahedonic property value approach [102], which provides arevealed WTP for air pollution reductions but is depend-ent on housing market perceptions about pollution andlinks to non-health effects.

The stated preference WTP approaches, of which CV andchoice experiments are most prominent, are survey meth-ods presenting hypothetical choices (e.g., willingness topay some amount or prefer one set of attributes overanother) to recover preferences for health risk reductions.Results can be sensitive to question wording and ordering,and cognition difficulties when understanding smallchanges in probabilities are required. However thesemethods can be molded to a particular population or con-text. Respondents can be tested for their cognition andunderstanding of the survey's concepts.

Some of the best known stated preference studies for mor-tality examine traffic fatalities [103,104] and fewer studiesare available for air pollution contexts [105-108]. A CVsurvey found that WTP was higher when death risk reduc-tion takes place now rather than later in life or if the indi-vidual was mentally healthy [109]. Age had a relativelyminor effect on VSL, and physical health status had noeffect. These results are consistent with those from a studyof adults in the US and Canada, which did not find strongevidence that WTP is lower for older persons or for thosewith chronic heart or lung conditions or cancer [110]. Arecent WTP study of three countries also found that VSL isnot significantly lower for older populations, howeverpersons admitted to the hospital or emergency room forCVD or respiratory causes had higher VSL [111]. The firststudy to investigate WTP for increased life expectancy(one year in expectation) added between ages 75 to 85years found implied VSLs to range from $70,000 to$110,000, but did not provide indication of whetherrespondents understood the complex scenario, andoffered respondents an unrealistically large reduction inrisk [106].

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Two studies applied choice experiments to examine WTPto reduce risks of chronic respiratory disease [112,113].Subjects chose between two cities for residence, both pre-ferred to their present city and differing in risk of develop-ing chronic bronchitis or respiratory disease and in oneother characteristic: the probability of dying in an auto-mobile accident or cost of living. Several studies evaluatedthe WTP to reduce cancer morbidity risks [114,115].

Three of the first CV studies for acute health responsesused bidding procedures to elicit values for respiratory-symptom days, with average estimates from $5 to $25depending on the symptom, its severity, and whether acomplex of symptoms is experienced [116-118]. CV tech-niques have advanced since these studies, however theyoffer consistent ranges of WTP estimates. In one of the fewEuropean studies of this type, over 1,000 Norwegianswere interviewed to ascertain WTP to avoid various ac utehealth effects (e.g., one more day over their usual annualfrequency). The values for avoiding symptoms are slightlysmaller than those found in older US studies, but the asth-matic values are far larger [119]. A survey of 832 Taiwan-ese investigated WTP to avoid participants' most-recentepisode of acute respiratory illness [120]. Statistical tech-niques are used to relate these values to the duration andseverity of the episode and other variables.

Another approach is the averting-behavior method, whichinfers WTP by observing and placing values on behaviorused to avoid adverse health outcomes. For instance, ifsomeone stays indoors with the air conditioner onbecause of high air pollution, the added electricity costsmight relate to WTP to avoid health impacts. Defensibleestimates under this approach require stringent assump-tions, and in practice the method is rarely used, particu-larly in an acute-health context.

Quality-adjusted life yearThe QALY approach attempts to account for the quality oflife lost by adjusting for time "lost" from disease or death.This method is welfare-theoretic only under very restric-tive assumptions, so it is difficult to conceptualize the sig-nificance of any particular QALY score. The estimates maybe very insensitive for distinguishing among differentseverities and types of acute morbidity. See the recentInstitute of Medicine report [121] for a full review of thisapproach as it could be applied in a regulatory, cost-ben-efit analysis setting.

A QALY analysis of USEPA's Heavy Duty Engine/DieselFuel regulations found that for situations in which mor-tality dominates other health outcomes, QALY and WTPmethods can provide similar results [122]. If morbidityand non-health consequences are predominant, resultsfrom QALY and WTP analysis may differ. Another use of

QALYs investigated over 230 WTP estimates, finding thatvariation in WTP values is affected by QALY estimates ofillness severity, illness duration, income, and age [123].There also exists literature providing QALY estimates forchronic diseases, for example for various severities ofasthma [124].

Applications of economic valuationValuations of mortality risk reductions associated withenvironmental policies are usually the largest category ofbenefits, both among health responses and compared toother attributes. For instance, a USEPA analysis of theClean Air Act estimated a value of $100 billion annuallyfor reduced premature mortality out of $120 billion intotal benefits, compared to costs of approximately $20billion [7]. European and Canadian studies similarlyfound that mortality risk dominates analysis of pollutionreductions [125,126]. Next to mortality, reductions in theprobability of developing a chronic respiratory diseasehave been estimated to be the most valued, recognizingthat values for other types of diseases are sparse. Reduc-tions in acute effects are lower valued.

Table 2 provides a sample of values typically used by prac-titioners of health benefits analyses from several majorstudies or models: the USEPA's BenMAP, which is used inRegulatory Impact Analyses of Regulations [7,54]; theExternE model [125], which is used by the EuropeanUnion (EU) in its regulatory analyses, taken from itsClean Air For Europe (CAFÉ) Program (AEA) [127]; theAir Quality Valuation Model (AQVM) for Canada [126];the Australian Bureau of Transport and Regional Econom-ics (BTRE) assessment of transportation-related pollutantsin Australia [128]; and a study of the benefits of environ-mental improvement in New Zealand [129]. Within thetable, health values are converted to common, compara-ble currency using purchasing power parity (PPP) andconstant 2000 dollars. The WTP for reducing risks of mor-tality and chronic morbidity is expressed, for convenience,as VSL and the value of a statistical case (VSC) of chronicdisease. This term is merely shorthand for the WTP for agiven risk reduction divided by that risk reduction. Thisrelationship is useful because VSLs or VSCs can be multi-plied by estimates of the "lives saved" or "chronic casessaved" to obtain benefits.

The table shows a fairly wide range of VSL values, with thehighest in the US. Rank ordering of values across the otherhealth endpoints is very similar across studies, althoughsome different sets of health endpoints are consideredand there are many blank cells (i.e., categories for whichinformation is unknown or not incorporated) outside ofthe US and EU. The relatively close agreement between theUS and EU likely results from reliance on a common poolof studies, results, and interpretations as well as the social

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cost of electricity studies in the US and the ExternE effortin Europe, which benefited from close collaborationbetween the participating researchers in both efforts[130,131]. In addition, the Canadian studies wereinformed by the AQVM developed by researchers active inthe US social costing debate [132].

Credibility of economic valuation estimatesWe evaluated economic valuation methods on three crite-ria: (i) the degree to which methods are based on prefer-ences for such health improvements, which we took to bein agreement with welfare economics principles; (ii) thenumber of studies following the technique, which is animperfect measure of degree of consensus and attractive-ness of the technique to researchers; and (iii) additionalmajor limitations, serving to capture other issues, such asdata shortcomings. Based on this admittedly subjectivejudgment, we then rated the reliability of the differentapproaches from A (very reliable) to D (unreliable). Theassessment is intended to provide comparison amongapproaches, rather than an absolute assessment of accu-racy.

As a first step of the evaluation, we compared theoreticalpredictions and empirical results of economic valuation

studies for mortality (Table 3). Under the theoreticalframework, WTP should increase with the size of the riskchange. The life cycle model also implies lower WTP whenrisk change is further in time. Persons facing higher base-line risks should have higher WTP for a given risk reduc-tion (the "dead anyway" effect) [133]. Higher incomes orwealth should relate to higher WTP. With borrowingagainst future earnings, the relationship between WTPand age should be an inverted U-shape according to lifecycle models. Finally, these models do not make a predic-tion regarding health status.

These theoretical predictions are not always matched byempirical results, and Table 3 demonstrates that no sim-ple consistent relationship exists between WTP for mortal-ity and other factors listed, other than income. This couldbe due to differences in the underlying approaches used tosolicit results, or indication of a more complicated system(e.g., age's impact on VSL may further depend on otherfactors). Our subjective evaluation of the valuation meth-ods for mortality, chronic morbidity, and acute morbidityare provided in Tables 4, 5, and 6, respectively. No singlemethod is fully satisfactory. Due to the array of methodsavailable for estimating the economic impact of health

Table 2: Sample of typically used values for PM-related health impacts (mean estimates) ($2000 PPP-adjusted [197])

Health Effects US EU Canada Australia New Zealand

Mortality: 1,042 1,296,552 (premature death) [198]

VSL: Adults 6,300,000 2,247,191 3,480,000 1,439,394 1,717,241 (1,724,138) [198]

VSL: Children 2 × adult 4088764 (infant)VSLY 134,831 70,455 118,621Morbidity: 1929.55 (average cost/

separation) [199]Morbidity: children 2 × adultsChronic bronchitis 340,000 213,483Chronic asthma 39,000Respiratory hospital admission

14,000 2,247 1,032 2,069

CVD hospital admission

21,000 2,247 1,052 2,759

Emergency room visit 300 (asthma) 541 (respiratory) 562 (CVD)

Doctor's visit 60RAD 106 92 (working age) 78

(young, elderly)22 53

MRAD 50 43Acute respiratory symptom

3–24

Use of respiratory medication

1.12

Asthma day 32–74 43 15References: [54] [127] [200] [128]* [129]

Note: *: VSL derived from population-weighted values in the Australian Bureau of Transport and Regional Economics (BTRE) assessment, Table 3 [128]. Population data from the Australian Bureau of Statistic.

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and the limitations of any single approach, we recom-mend the application of multiple methods.

In addition to the issues of credible economic evaluationof the benefits and costs of climate change policies, a cen-tral issue in comparing these values is the discount rateapplied [134]. Selection of the discount rate, whichaccounts for differential value of costs and benefits occur-ring in the far future compared to those taking place in thepresent or near feature, can greatly alter results of cost ben-efit analysis, such as for climate change. In fact, a recentdisagreement regarding climate change policy analysis bytwo leading economists centered largely on the use of adifferent discount rate [11,134]. While some aspects ofbenefit/cost analysis are well-suited to monetary terms,the issue of an appropriate discount rate carries ethicalimplications regarding the relative impacts on variouspopulations.

DiscussionEstimating the ancillary public health consequences ofGHG policies is a challenging task drawing upon expertisein economics, emission inventories, air pollution mode-ling, and public health. However, to date most assess-ments have focused more heavily on one aspect of theframework (i.e., a portion of Figure 1), whether it be esti-mation of changes in air pollutant concentrations, healthresponse, or economic analysis (see Table in additionalfile 1). We have summarized the limitations in the healthand economics estimations, however other uncertaintiesexist for the selection of policy alternatives and estimationof changes in air quality. In spite of differences inapproaches, choice of climate change policy, etc., thewealth of evidence from multiple studies provides a broadconsensus that ancillary health benefits from improvedair quality are substantial, which can be useful informa-tion for the policy debate about the scope, design, andtiming of climate policy.

Results from current ancillary benefits studies may beunderestimates due to unquantified benefits, as only asubset of the health consequences from air pollution haveadequate exposure-response relationships [59,135-137].A USEPA evaluation of the Clean Air Interstate Rule(CAIR) noted numerous unquantified health impactssuch as chronic respiratory damage for O3, pulmonaryfunction for PM, and lung irritation for NOx [135]. Thenature of unquantified effects is continually evolving.Some pollution and health relationships consideredunquantifiable by USEPA in 1999 [7] have since beenidentified, such as for acute O3 exposure and mortality[51,76-78] and air pollution's association with lung can-cer [138,139]. Further some endpoints may be includedin one analysis, but regarded as too uncertain for another,perhaps due to a different study location or differences inresearchers' judgment. One approach to address healthendpoints with uncertain concentration-response func-tions is to include these effects qualitatively in discussionof unquantified benefits. Another is to incorporate theseeffects in sensitivity analysis.

Similarly, some economic costs may not be easily quanti-fiable, even if the health response to air pollution isunderstood. For example, the USEPA's CAIR analysisidentified several unquantifiable costs including employ-ment shifts as workers become reemployed, administra-tion costs in state and federal governments, and somepermitting costs [135]. Only a limited number of studiesare available regarding the value of children's health, suchas several that estimated the cost of children's asthma[140-143]. Valuing reduced mortality risks for newbornsor children is challenging because children are generallynot the key decision-makers over their own health. Tech-niques to transfer adult monetary valuations to childrenhave been explored [144].

This work has focused primarily on health benefits fromimproved air quality resulting from climate change miti-

Table 3: Theoretical predictions and empirical results of studies estimating value of mortality risk reductions. Source: Hammitt and Graham (1999) [103]

Study Size of Risk Change

Future Risk Change

Baseline Risk Income (or proxies)

Age Health Status

Life cycle model: Theory

+, proportional - +a + -b, + then -c indeterminate

Empirical StudiesCompensating Wage + N/A -d + - N/AOther Revealed Preference

+ N/A Unknown + + N/A

CV +, not proportional - Varies + + then -, 0, - No effect, +

a. Small "dead anyway" effect: Higher value to benefits while alive than for a bequest [133].b. With borrowing against future earnings.c. Inverted U with no borrowing.d. Self selection by risk tolerant workers

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gation, however a full assessment of the short-term conse-quences of climate change policies would incorporatetradeoffs that may in fact be negative or for which thedirection of impact is difficult to predict. Policies mightalter unemployment rates and income levels, which havebeen linked to increased suicides [145,146], domestic vio-lence [147,148], depression [149], and mental health[150,151]. The relationships between low income orunemployment and health are not fully understood andsomewhat controversial. Still, changes in employment orincome from climate policies have the potential to intro-duce another set of health-related ancillary benefits orcosts.

As another example, GHG mitigation might incorporatepolicies to deter suburban sprawl, which could reducetransportation-related air emissions and thereby improvehealth in the short-term. However, a fuller understandingof the consequences of such a policy would addresschanges in population-weighted air pollution exposure,which may be higher in urban areas, as well as urbancrime, and other potential impacts from higher popula-

tion density. Other examples are transition to biofuels,which could have implications for nutrition, or the use ofbikes rather than cars for transportation, which wouldlower air pollution emissions but could potentially alsoharm health if biking occurred near major roadways,increasing proximity to high pollution at an increasedventilation rate, or could improve health throughincreased exercise. Thus, while our discussion and mostresearch of ancillary consequences have focused on bene-fits, a full suite of positive and adverse consequencescould exist.

One of the most controversial aspects of ancillary benefitsanalysis is the valuation of health in non-industrializedcountries. Previous Intergovernmental Panel on ClimateChange (IPCC) assessments sparked heated debatebecause they presented non-market values for healthimprovements that some thought unethically devaluedlives in non-industrialized countries. Challenges to eco-nomic valuation of health in these regions are describedelsewhere [152]. Limited data availability, such as forwages, prohibits application of some approaches. Medical

Table 4: Credibility ratings for approaches to valuing changes in the risk of mortality

Criteria

Approach Welfare Theoretic (Y/N) Numbers of Studies (Many/Some/Few)

Other Limitations Rating

Human Capital N M (not recent) Undervalues non-workers DCOI Not usually; in principle could be if

separate estimates available for pain and suffering

M Usually underestimates C

Revealed preference: Hedonic Labor Market; others

Y M Inappropriate commodity/Population sampled

B

CV and choice experiments: health

Y S Hypothetical; hard to understand small probability change

B

QALYs N (except under very restrictive conditions)

M Monetization arbitrary C

Table 5: Credibility ratings for approaches to valuing changes in the risk of chronic morbidity

Criteria

Approach Welfare Theoretic (Y/N) Numbers of Studies (Many/Some/Few)

Other Limitations Rating

COI Not usually; hospitalization; sometimes labor productivity (which is a revealed preference approach)

M: medical cost studies F: labor productivity studies

Pricing medical services can be difficult where medical care is socialized or subsidized

C-B

Revealed preference Y Many on injury/accidents; not on morbidity

C

CV and choice experiments: health

Y F See above B

QALYs Y (under very restrictive conditions) M Arbitrary monetization C

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cost information may not reflect social opportunity costs.Hedonic labor market studies, which presume that laborand goods markets are competitive and workers have rea-sonable information on death and injury risks, may carrymore uncertainties in some regions than others. Valuationof the health of various household members, particularlychildren, may be quite different than in developed coun-tries because of children's more central role in the econ-omy. Rapid economic growth means preferences arechanging as well, raising questions about the applicabilityof indigenous studies several years hence.

A related challenge is differential effects by subpopula-tions. Epidemiological evidence supports the hypothesisthat some segments of the population (e.g., racial orsocio-economic groups) face disproportionate health bur-dens from air pollution. Current ancillary benefit analysisdoes not include separate estimation of health and eco-nomic damages by sub-groups or confront issues of envi-ronmental justice. Further information is needed on therelationship between air pollution and health and eco-nomic valuation methods with respect to subpopulations.

In order to conduct the most robust ancillary benefitsanalyses, we recommend reliance on the most defensible,transparent methods, even if they are recognized as defi-cient. Because a variety of approaches are available, noneof which are ideal, we recommend the application of mul-tiple methods and extensive sensitivity analysis consider-ing a range of changes in air pollution concentrations,spatial distribution of impacts (if considered), health end-points, epidemiological concentration-response func-tions, and economic valuation estimates.

ConclusionOverall, though still a work in progress, the present tech-niques available for the analyses of the ancillary public

health costs and benefits are adequate and appropriate forimplementation by those comparing the relative meritsand overall value of various GHG mitigation policies. Esti-mates of considerable benefits that remain after a varietyof sensitivity analyses can alleviate some concerns regard-ing limitations of individual methods or assumptions.The short-term public health changes associated withGHG mitigation strategies should be considered as a keyfactor in the choice of GHG policies.

List of abbreviationsAQVM Air Quality Valuation Model, BenMAP BenefitsMapping and Analysis Program, BTRE Bureau of Trans-port and Regional Economics, CAAA Clean Air ActAmendments, CAIR Clean Air Interstate Rule, CO2 carbondioxide, COI cost of illness, CV contingent valuation,CVD cardiovascular, EU European Union, GHG green-house gases, MRAD minor restricted activity days, NOxnitrogen oxides, O3 ozone, PM particulate matter, PM10particulate matter with an aerodynamic diameter ≤ 10μm, PM2.5 particulate matter with an aerodynamic diame-ter ≤ 2.5 μm, PPP purchasing power parity, QALY quality-adjusted life year, RAD restricted activity days, SO2 sulfurdioxide, TSP total suspended particles, USEPA US Envi-ronmental Protection Agency, VOCs volatile organic com-pounds, VSC value of a statistical case, VSL value of astatistical life, VSLY value of a statistical life year, WTPwillingness to pay.

Competing interestsThe authors declare that they have no competing interests.

Authors' contributionsAll authors made substantial contributions to the concep-tion and design of this paper, were involved in draftingand revising the manuscript. All authors have approvedthe final version.

Table 6: Credibility ratings for approaches to valuing changes in the risk of acute morbidity

Criteria

Approach Welfare Theoretic (Y/N)

Numbers of Studies (Many/Some/Few)

Other Limitations Rating

COI No M Pricing medical services can be difficult

C

Revealed preference (averting behavior)

Y (under restrictive conditions)

Many for injury and accidents; not for acute respiratory symptoms

C

CV and choice experiments: Health

Y S Old methods/studies; some ad hoc estimates; small samples

B

QALYs Y (under very restrictive conditions)

M Scores insensitive to severity of acute effects

C

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Additional file 1

Studies investigating the air pollution and health co-benefits from climate change policiesClick here for file[http://www.biomedcentral.com/content/supplementary/1476-069X-7-41-S1.doc]

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200. DSS Management Consultants Inc., RWDI Air Inc., prepared forOntario Ministry of Energy: Cost-Benefit Analysis: Replacing Ontario'sCoal-Fired Electricity Generation 2005.

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