6 - 1

**CHAPTER 6**  **: ESTIMATED HUMAN HE ALTH BENEFITS AND CL**  **IMATE CO**   ** BENEFITS** **6 .1**  **Introduction** As discussed in Chapter 4, this action proposes to update CSAPR to reduce interstate transport of EGU ozone season NO X emissions that contribute significantly to nonattainment or interfere with maintenance of the 2008 ozone NAAQS. The EPA proposes to implement the proposed EGU NO X reductions by setting emissions budgets that are implemented through the CSAPR NO X ozone - sea son allowance trading program. Implementing this proposal to update CSAPR is expected to reduce emissions of NO X during the summer ozone season and provide ancillary annual NO X and carbon dioxide (CO 2 ) benefits (i.e., co - benefits). This chapter describes the methods used to estimate the monetized ozone - related air quality health benefits , fine particulate matter (PM 2.5 ) - related air quality health co - benefits from reductions in NO X emissions and climate co - benefits from reductions of CO 2 emissions. These health benefits are associated with reducing exposure to ambient ozone and PM 2.5 by reducing emissions of precursor pollutants (i.e., NO X). Data, resource, and methodological limitations prevent the EPA from monetizing several important co - benefit s from reduc ing emissions of several pollutants , including those from reducing direct exposure to NO 2 , ecosy stem effects and visibility impairment . We qualitatively discuss these unquantified benefits in this chapter. This chapter provides estimates of the monetized air quality health benefits and climate co benefits associated with emission reductions for the regulatory control alternatives ac ross several discount rates. The estimated benefits associated with these emission reductions are beyond those achieved b y previous EPA rulemakings, including the 2011 CSAPR 1 and the finalized Clean Power Plan (CPP ).

1 As discussed in Chapters 1, 3 and 5, the 2017 baseline EGU emissions for this proposal include impacts from CSAPR issued on July 6, 2011. As discussed and elaborated on in Chapter 1, b ecause the modeling for the proposal wa s performed prior to the D.C. Circuit’s issuance of EME Homer City II , 11 that modeling assumed in its baseline for all states the emission reductions associated with the CSAPR NO X ozone - season phase 2 emissions budge ts.6 - 2

**6 .2**  **Estimated Human Health Benefits** T he proposal to update CSAPR is expected to reduce emissions of ozone season NO X . In the presence of sunlight, NO X and VOCs can undergo a chemical reaction in the atmosphere to form ozone. Reducing NO X emissions also reduce s human exposure to ozone and the incidence of ozone - related health effects , though this depends partly on local levels of volatile organic compounds (VOCs) . The proposal would also reduce emissions of NO X throughout the year. Because NO X is also a precursor to formation of ambient PM 2.5 , reducing these emissions would also reduce human exposure to ambient PM 2.5 throughout the ye ar and would reduce the incidence of PM 2.5 - related health effects. 2 This RIA does not quantify PM 2.5 - related benefits associated with SO 2 emission reductions. As discussed in Chapter 3, the EPA does not estimate significant SO 2 emission reductions as a result of this proposal. Furthermore, these reductions would reduce ozone and PM 2.5 concentrations in regions other than those with nonattainment monitors that are the subject of this proposal, and the benefits of reducing these pollutants in those regions are assessed in this Chapter . R educing emissions of NO X would also reduce ambi ent exposure to NO 2 and its associated health effects , though we do not quantify these effects because we have not developed a reduced - form technique for estimating NO 2 impacts . In this section, we provide an overview of the monetized ozone - related benefit s and PM 2.5 - related co - benefits estimated for the proposed updated CSAPR EGU NO X ozone season emissions budgets and for the more and less stringent alternatives. A full description of the epidemiological studies we use, the methods we appl y and the tools we employ to quantify

the incidence of these effects may

be found in the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2015 ) . The estimated benefits associated with these emi ssion s reductions are additional to those achieved b y previous EPA rulemakings, including the finalized CPP . Implementing these updated CSAPR EGU NO X emissions budgets for the ozone season in 23 eastern states may

reduc e ambient ozone and PM 2.5 concentrat ions below the National Ambient Air Quality

Standards (NAAQS) in some areas and assist other areas with attaining the ozone and PM 2.5 NAAQS. T he NAAQS RIAs (U.S. EPA, 2008 , 2012a , 2015 ) also calculated the benefits of attaining alternate ozone and PM NAAQS , and so differences in the design and

2 Additionally, this RIA does not estimat e changes in emissions of directly emitted particles.6 - 3

anal yt ical objectives of each RIA are worth noting here . The NAAQS RIAs illustrate the potential costs and benefits of attaining a revised air quality standard nationwide based on an array of emission reduction strate gies for different sources reflecting the application of identified and unidentified controls, incremental to implementation of existing regulations and controls needed to attain the NAAQS that current ly is in effect . In short, NAAQS RIAs hy pothesize, but do not predict, the strategies that States may choose to enact when implementing a revised NAAQS. S etting a NAAQS does not directly result in costs or benefits, and as such, the EPA’s NAAQS RIAs are illustrative . T he estimated costs and benefits from NAAQS RIAs are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and prescribe specific emission reductions. For example, s ome of the emissions reductions estimated to result from implementing the p roposal to update CSAPR may achieve some of the air quality i mprovements that resulted from the h ypothesized attainment strategies presented in the NAAQS RIAs. The emissions reductions from implementing the proposal to update CSAPR will decrease the remaining amount of emi ssions reductions needed in non attainment areas to, and reduce the costs to those areas to, meet the 2008 ozone NAAQS. Specificall y, the anticipated reductions in ozone concentrations from this rule would help areas attain and maintain the 2008 ozone NAAQS and achieve some of the air quality improvements from the h ypothesized attainment strategy from the 2008 ozone NAAQS RIA. These ozone improvements would similarly achieve some of the air quality improvements assumed in the baseline (i.e., 2008 ozone NAAQS attainment projection) for the 2015 ozone NAAQS RIA. As discussed in Chapter 4, the IP M modeling showing compliance with the regulatory control alternatives for which emission reductions are estimated in this RIA is also illustrati ve in nature . However, unlike the illustrative control strategies anal yz ed in NAAQS RIAs described above , all of the emission reductions for the illustrative compliance modeling for this proposal would occur in one well - characterized sector (i.e., the EGU sector). T he EPA is more confident in the magnitude and location of the emission reductions for this proposal because it imposes a specific require ment that limits emission s from a specific sector . E mission reductions achieved under th is rule, if finalized , will ultimately be reflected in the baseline of future NAAQS anal ys es and would lower the emissions reductions needed to attain revised future NAAQS. For more information on the relationship between illustrative anal ys es, such as for the NAAQS and its associated implementation rules, please see the Ozone NAAQS RIA (U.S. EPA, 2015) .6 - 4

6. 2 .1 Health Impact Assessment for Ozone and PM 2.5 The Integrated Science Assessment for Ozone and Related Photochemical Oxidants (Ozone I SA) (U.S. EPA, 2013b) identified the human health effects associated with ambient ozone exposure, which also include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. Similarly , the Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b) identified the human health effects associated with ambient PM 2.5 exposure, which include premature mortality and a variety of morbidity effects associated w ith acute and chronic exposures. Table 6 - 1 identifies the quantified and unquantified benefit and co - benefit categories captured in the EPA’s health benefits estimates for reduced exposure to ambient ozone and PM 2.5 . Although the table below does not list unquantified health effects or welfare effects , such as acidification and nutrient enrichment, these effects are described in detail in Chapters 5 and 6 of the PM NAAQS RIA (U.S. EPA, 2012a) and summarized later in this chapter. It is important to emphasize that the list of unquantified benefit s categories is not exhaustive, nor is quantification of each effect complete.6 - 5

**Table 6**  **- 1**  **. Human Health Effects of Ambient Ozone**  **and PM**  **2.5** Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More

Information Im proved Human Health Reduced incidence of mo rtality from exposure to ozone Premature mortality based on short - term study estimates (all ages)   Ozone ISA Premature mortality based on long - term study estimates (age 30

– 99) — — Ozone ISA 1

Reduced incidence of mo rbidity from exposure to ozone Hospital admissions

— respiratory causes (age > 65)   Ozone ISA Hospital admissions

—

respiratory causes (age <2)   Ozone ISA Emergency department visits for asthma (all ages)   Ozone ISA Minor restricted - activity days (age 18

–65)   Ozone ISA School absence days (age 5

– 17)   Ozone ISA Decreased outdoor worker productivity (age 18

– 65)

— — Ozone ISA 1 Other respiratory effects (e.g., premature aging of lungs) — — Ozone ISA2 Cardiovascular and nervous system effects

— — Ozone ISA2 Reproductive and developmental effects

— — Ozone ISA2,3 Reduced incidence of premature mortality from exposure to PM 2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)   PM ISA Infant mortality (age <1)   PM ISA

Reduced incidence of mo rbidity from exposure to PM 2.5 Non - fatal heart attacks (age > 18)   PM ISA Hospital admissions

—

respiratory (all ages)   PM ISA Hospital admissions

—cardiovascular (age >20)   PM ISA Emergency room visits for asthma (all ages)   PM ISA Acute bronchitis (age 8 - 12)   PM ISA Lower respiratory symptoms (age 7 - 14)   PM ISA Upper respiratory symptoms (asthmatics age 9 - 11)   PM ISA Asthma exacerbation (asthmatics age 6 - 18)   PM ISA Lost work days (age 18 - 65)   PM ISA Minor restricted - activity days (age 18 - 65)   PM ISA Chronic Bronchitis (age >26)

— — PM ISA 1 Emergency room visits for cardiovascular effects (all ages) — — PM ISA 1 Strokes and cerebrovascular disease (age 50 - 79)

— — PM ISA 1 Other cardiovascular effects (e.g., other ages)

— — PM ISA2 Other respiratory effects (e.g., pulmonary function, non - asthma ER visits, non -bronchitis chronic diseases, other ages and populations) — — PM ISA2 Reproductive and developmental effects (e.g., low birth weight, pre - term births, etc .) — — PM ISA2,3 Cancer, mutagenicity, and genotoxicity effects

— — PM ISA2,3 1 We assess these co - benefits qualitatively due to data and resource limitations for this analysis, but we have quantified them in sensitivity analyses for other analyses. 2 We assess these co - benefits qualitatively because we do not have sufficient confidence in available data or methods. 3 We assess these co - benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association. We follow a “damage - function” approach in calculating benefits, which estimates changes in individual health endpoints (specific effects that can be associated with changes in air quality)6 - 6

and assigns values to thos e changes assuming independence of the values for those individual endpoints. Because the EPA rarel y has the time or resources to perform new research to measure directly either health outcomes or their values for regulatory anal ys es, our estimates are bas ed on the best available methods of benefits transfer, which is the science and art of adapting primary research from similar contexts to estimate benefits for the environmental quality change under anal ys is. In addition to transferring information from other contexts to the context of this regulation, we also use a “benefit - per - ton” approach to estimate the ozone and PM 2.5 benefits in this RIA. Benefit - per - ton approaches appl y an average benefit per ton derived from modeling of benefits of specific air quality scenarios to estimates of emissions reductions for scenarios where no air quality

modeling is available. Thus, to develop estimates of benefits for this RIA, we are transferring both the underlying health and economic information from previous studies and information on air quality responses to emissions reductions from other air quality modeling. This section describes the underl yi ng basis for the health and economic valuation estimates that inform the benefit - per - ton estimates, and the subsequent sec tion provides an overview of the benefit - per - ton estimates, which are described in detail in the appendix to this chapter. The benefit - per - ton approach we use in this RIA relies on estimates of human health responses to exposure to ozone and PM obtained from the peer - reviewed scientific literature. These estimates are used in conjunction with population data, baseline health information, air quality data and economic valuation information to conduct health impact and economic benefits assessments. These ass essments form the key inputs to calculating benefit - per - ton estimates. The next sections provide an overview of the health impact assessment (HIA) methodology and additional details on several key elements. The HIA quantifies the changes in the incidence o f adverse health impacts resulting from changes in human exposure to ozone and PM 2.5 . We use the environmental Benefits Mapping and Anal ys is Program

– Community Edition (BenMAP - CE) (version 1.1) to sy stematize health impact anal ys es b y appl ying a database of key input parameters, including population projections, health impact functions, and valuation functions (Abt Associates, 2012). For this assessment, the HIA is limited to those health effects that are directl y linked to ambient ozone and PM 2.5 concentrations. There may be other indirect health impacts associated with reducing emissions, such as occupational health exposures. Epidemiological studies generall y provide6 - 7

estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 ppb for ozone or μg/m 3 for PM 2.5 ). These relative risks can be used to develop risk coefficients that relate a unit reduction in pollution (e.g., ozone) to changes in the incidence of a health effect. We refer the reader to the O zone NAAQS RIA (U.S. EPA, 2015) and PM NAAQS RIA (U.S. EPA, 2012a) for more information regarding the epidemiology studies and risk coefficients applied in this analy sis, and we briefl y elaborate on adult premature mortality below. The size of the mortality

effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to reducing risks of premature death make mortality risk re duction the most significant health endpoint quantified in this analy sis. 6. 2 .1.1 Mortality Effect Coefficients for Short- term Ozone Exposure The overall body

of evidence indicates that there is likely to be a causal relationship between short - term ozone exposure and premature death. The 2013 ozone ISA concludes that the evidence suggests that ozone effects are independent of the relationship between PM and mortality . (U . S . EPA , 2013a). However, the IS A note s that the interpretation of the potential confounding effects of PM on ozone - mortality risk estimates requires caution due to the PM sampling schedule (in most cities) which limits the overall sample size available for evaluating potential confounding of the ozone effect b y PM (U . S . EPA 2013a). In 20 06, the EPA requested an NAS study to answer the following four key questions regarding ozone - related mortality : (1) H ow did the epidemiological literature to that point improve our understanding of the size of the ozone - related mortality effect? ; (2) How best can EPA quantify

the level of ozone - related mortality i mpacts from short - term exposure? ; (3) How might EPA estimate the change in life expectancy? ; and (4) What methods should EPA use to estimate the monetary

value of changes in ozone - related mortality risk and life expectancy? In 2008, the NAS (NRC, 2008) issued a series of recommendations to the EPA regarding the quantification and valuation of ozone - related short - term mortality . Chief among these was that “...short - term exposure to ambient ozone is li kely to contribute to premature deaths” and the committee recommended that “ozone - related mortality be included in future estimates of the health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest emphasis be placed on the multi - city and NMMAPS studies without exclusion of the meta -6 - 8

anal ys es” (NRC, 2008). In addition, NAS recommended that EPA “should give little or no weight to the assumption that there is no causal association between estimated reductions in premature mortal ity

and reduced ozone exposure” (NRC, 2008). In 2010, the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance Anal ys is, while reviewing EPA’s The Benefits and Costs of the Clean Air Act 1990 to 2020 (U.S. EPA, 2011a), also confirmed the NAS recommendation to include ozone mortality benefits (U.S. EPA - SAB, 2010a). In view of the findings of the ozone I SA, the NAS panel, the SAB - HES panel, and the CASAC panel, we estimate ozone - related premature mortality for short - term exposure in the core health effects analy sis using effect coefficients from the Smith et al. (2009) NMMAPS anal ys is and the Zanobetti and Schwartz (2008) multi - city study with several additional studies as sensitivity anal ys es. This emphasis on newer multi - city studies is consistent with recommendations provided by the NAS in their ozone mortality report (NRC, 2008). CASAC supported using the Smith et al. (2009) and Zanobetti and Schwartz (2008) studies for the ozone HREA (U.S. EPA - SAB, 2012 , 2014 ) , and these are multi - city studies published more recentl y (as compared with other multi - city studies or meta - anal ys es included in the sensitivity anal ys es – see discussion below). Smith et al. (2009) reanalyzed the NMMAPS dataset , evaluating the relationship between short - term ozone exposure and mortality . While this study reproduces the core national - scale estimates presented in Bell et al. (2004), it also explore d the sensitivity of the mortality

effect to differe nt model specifications including (a) regional versus national Bayes - based adjustment, 4 (b) co - pollutant models considering PM 10 , (c) all - ye ar versus ozone - season based estimates, and (d) consideration of a range of ozone metrics, including the dail y 8 - h our max. In addition, th e Smith et al. (2009) study

did not use the trimmed mean approach emplo ye d in the Bell et al. 4 In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In the Smith et al . (2009) approach, the prior values are either a r egional or national mean of the individual effect estimates obtained for each individual city. The Bayesian adjusted city - specific effect estimates are then calculated by updating the selected prior value based on the relative precision of each city - specif ic estimate and the variation observed across all city - specific individual effect estimates. City - specific estimates are pulled towards the prior value if they have low precision and/or if there is low overall variation across estimates. City specific esti mates are given less adjustment if they are precisely estimated and/or there is greater overall variation across estimates.6 - 9

(2004) study

in preparing ozone monitor data. 5 Inselecting effect estimates from Smith et al. (2009), we focused on an ozone - only estimate for non - accidental mortality using the 8 - h our max metric for the warmer ozone season. The Zanobe tti and Smith (2008) study evaluated the relationship between ozone exposure (using an 8 - h our mean metric for the warm season June - August) and all - cause mortality in 48 U.S. cities using data collected between 1989 and 2000. The study presented single pollutant C R functions based on shorter (0 - 3 day) and longer (0 - 20 day) lag structures, with the comparison of effects based on these different lag structures being a central focus of the study . We used the shorter day

lag based C - R function since this had th e strongest effect and tighter confidence interval. We co nvert ed the effect estimate from an 8 - h our mean metric to an equivalent effect estimate based on an 8 - h our max to account for the period of the day in which most individuals are exposed to ozone . To do this, we used the ozone metric approach wherein the original effect estimate (and standard error) is multiplied by the appropriate ozone metric adjustment ratio. 6. 2 .1.2 PM

2.5 Mortality Effect Coefficients for Adults and Infants A substantial body

of published scientific literature documents the association between elevated PM 2.5 concentrations and increased premature mortality (U.S. EPA, 2009b). This body of literature reflects thousands of epidemiology , toxicology , and clinical studies. The PM I SA c ompleted as part of the most recent review of the PM NAAQS , which was twice reviewed b y the SAB - CASAC (U.S. EPA - SAB, 2009a, 2009b ), concluded that there is a causal relationship between mortality and both long - term and short - term exposure to PM 2.5 based on the entire body of scientific evidence (U.S. EPA, 2009b). The size of the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reducti on the most significant health endpoint quantified in this analy sis. Researchers have found statistically significant associations between PM 2.5 and premature mortality

using different types of study designs. Time - series methods have been used

5 There are a number of concerns regarding the trimmed mean approach including (1) the potential loss of temporal variation in the d ata when the approach is used (this could impact the size of the effect estimate) , and (2) a lack of complete documentation for the approach , wh ich prevents a full reviewing or replication of the technique.6 - 10

to relate s hort - term (often day- to - day) changes in PM 2.5 concentrations and changes in dail y mortality rates up to several day s after a period of exposure to elevated PM 2.5 concentrations. Cohort methods have been used to examine the potential relationship between co mmunity- level PM 2.5 exposures over multiple years (i.e., long - term exposures) and community- level annual mortality rates that have been adjusted for individual level risk factors. When choosing between using short - term studies or cohort studies for estimat ing mortality benefits, cohort anal ys es are thought to capture more of the public health impact of exposure to air pollution over time because they

account for the effects of long - term exposures , as well as some fraction of short term exposures (Kunzli et al., 2001; NRC, 2002). The N ational R esearch C ouncil (NRC) stated that “it is essential to use the cohort studies in benefits analy sis to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). The NRC further note d that “ the overal l effect estimates may

be a combination of effects from long - term exposure plus some fraction from short - term exposure. The amount of overlap is unknown” (NRC, 2002, p. 108 - 9). To avoid double counting, we focus on applying the risk coefficients from the l ong - term cohort studies in estimating the mortality

impacts of reductions in PM 2.5 . Over the last two decades, several studies using “prospective cohort” designs have been published that are consistent with the earlier body of literature. Two prospective c ohort studies, often referred to as the Harvard “Six Cities Study” (Dockery et al., 1993; Laden et al., 2006; Lepeule et al., 2012) and the “American Cancer Society” or “ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004; Krewski et al., 2 009), provide the most extensive analy ses of ambient PM 2.5 concentrations and mortality. These studies have found consistent relationships between fine particle indicators and premature mortality across multiple locations in the United States. The credibil ity

of these two studies is further enhanced by the fact that the initial published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive reexamination and reanaly sis by an independent team of scientific experts commissioned by the He alth Effect s Institute (HEI) and b y a Special Panel of the HEI Health Review Committee (Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 Six Cities Study

and the 1995 ACS study using more recent air quality data and a longer follow up period for the ACS cohort provides additional validation of the findings of these original studies (Pope et al., 2002, 2004; L aden et al., 2006; Krewski et al., 2009; Lepeule et al., 2012). The SAB - HES also supported using these two cohorts for anal ys es of the benefits of PM6 - 11

reductions, and concluded, “the selection of these cohort studies as the underlying basis for PM mortality benefit estimates [is] a good choice. These are widel y cited, well studied and extensively

reviewed dat a sets” (U.S. EPA - SAB, 2010a). As both the ACS and Six Cities studies have inherent strengths and weaknesses, we present benefits estimates using relative risk estimates from the most recent extended reanal ys is of these cohorts (Krewski et al., 2009; Lepeu le et al., 2012). Presenting results using both ACS and Six Cities is consistent with other recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 2011c ). The PM I SA concludes that the ACS and Six Cities cohorts provide the strongest evidence of the association between long - term PM 2.5 exposure and premature mortality with support from a number of additional cohort studies (described below). The extended anal ys es of the ACS cohort data (Krewski et al., 2009) refined the earlier ACS studies by

(a) extending the follow - up period by 2 ye ars to the ye ar 2000, for a total of 18 ye ars; (b) incorporating almost double the number of urban areas ; (c) addressing confounding b y spatial autocorrelation by incorporating ecological, or community- level, co - variates; and (d) performing an extensive spatial anal ys is using land use regression modeling in two large urban areas. These enhancements make this analy sis well - suited for the assessment of mortality risk from long - term PM 2.5 exposures for the EPA’s benefits anal ys es. In 2009, the SAB - HES again reviewed the choice of mortality risk coefficients for benefits anal ys is, concluding that “[t]he Krewski et al. (2009) findings, while informative, have not yet undergone the same degree of peer review as have the aforementioned studies. Thus , the SAB - HES recommends that EPA not use the Krewski et al. (2009) findings for generating the Primary Estimate” (U.S. EPA - SAB, 2010a). Since this time, the Krewski et al. (2009) has undergone additional peer review, which we believe strengthens the suppo rt for including this study in this RIA. For example, the PM I SA (U.S. EPA, 2009b) included this study among the key mortality studies. In addition, the risk assessment supporti ng the PM NAAQS (U.S. EPA, 2010b ) u s ed risk coefficients drawn from the Krewski et al. (2009) study, the most recent reanal ys is of the ACS cohort data. The PM risk assessment cited a number of advantages that informed the selection of the Krewski et al. (2009) study as the source of the core effect estimates, including the extended p eriod of observation, the rigorous examination of model forms and effect estimates, the coverage for ecological variables, and the large dataset with over6 - 12

1.2 million individuals and 156 MSAs (U.S. EPA, 2010b ). The CASAC also provided extensive peer review of the PM risk assessment and supported the use of effect estimates from this study (U.S. EPA - SAB, 2009a, b , 2010b). Consistent with the PM risk assessment (U.S. EPA, 2010b ), which was reviewed b y the CASAC (U.S. EPA - SAB, 2009 a, b ), we use the all - cause mortality risk estimate based on the random - effects Cox proportional hazard model that incorporates 44 individual and 7 ecological covariates (RR=1.06, 95% confidence intervals 1.04

– 1.08 per 10 μg/m 3 increase in PM 2.5 ). The relative risk estimate (1.06 per 10 μg/m 3 increase in PM 2.5 ) is identical to the risk estimate drawn from the earlier Pope et al. (2002) study , though the confidence interval around the Krewski et al. (2009) risk estimate is tighter. In the most recent Six Cities study , which was publish ed after the last SAB - HES review, Lepeule et al. (2012) evaluated the sensitivity of previous Six Cities results to model specifications, lower exposures, and averaging time using eleven additional years of cohort follow - up that incorporated recent lower e xposures. The authors found significant associations between PM 2.5 exposure and increased risk of all - cause, cardiovascular and lung cancer mortality . The authors also concluded that the C - R relationship was linear down to PM 2.5 concentrations of 8 μg/m 3 and that mortality rate ratios for PM 2.5 fluctuated over time, but without clear trends, despite a substantial drop in the sulfate fraction. We use the all - cause mortality risk estimate based on a Cox proportional hazard model that incorporates 3 individual covariates. (RR=1.14, 95% confidence intervals 1.07

– 1.22 per 10 μg/m 3 increase in PM 2.5 ). The relative risk estimate is slightly smaller than the risk estimate drawn from Laden et al. ( 2006), with relatively

smaller confidence intervals. Given that moneti zed benefits associated with PM 2.5 are driven largely by reductions in premature mortality , it is important to characterize the uncertainty in this endpoint. In order to do so, we utilize the results of an expert elicitation sponsored b y the EPA and comple ted in 2006 (Roman et al., 2008; IEc, 2006). The results of that expert elicitation can be used as a characterization of uncertainty in the C - R functions .6 - 13

In addition to the adult mortality studies described above, several studies show an association between PM exposure and premature mortality in children under 5 years of age. 7 The PM I SA states that less evidence is available regarding the potential impact o f PM 2.5 exposure on infant mortality

than on adult mortality

and the results of studies in several countries include a range of findings with some finding significant associations. Specificall y, the PM I SA concluded that evidence exists for a stronger effe ct at the post - neonatal period and for respiratory- related mortality , although this trend is not consistent across all studies. Inaddition, compared to avoided premature deaths estimated for adult mortality , avoided premature deaths for infants are signif icantl y smaller because the number of infants in the population is much smaller than the number of adults and the epidemiology studies on infant mortality provide smaller risk coefficients associated with exposure to PM 2.5 . In 2004, the SAB - HES noted the release of the WHO Global Burden of Disease Study focusing on ambient air, which cites several recently published time - series studies relating daily PM exposure to mortality in children (U.S. EPA - SAB, 2004 ). With regard to the cohort study conducted b y Woodruff et al. (1997), the SAB - HES note d several strengths of the study , including the use of a larger cohort drawn from a large number of metropolitan areas and efforts to control for a variety of individual risk factors in infants (e.g., maternal educational level, maternal ethnicity, parental marital status, and maternal smoking status). Based on these findings, the SAB - HES recommended that the EPA incorporate infant mortality into the primary benefits estimate and that infant mortality be evaluated using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA - SAB, 2004 ). In 2010, the SAB - HES again noted the increasing body of literature relating infant mortality and PM exposure and supported the inclusion of infant mortality in the mone tized benefits (U.S. EPA - SAB, 2010a). The SAB - HES generall y supported the approach of estimating infant mortality

based on Woodruff et al. (1997) but also noted that a more recent study b y Woodruff et al. (2006) continued to find associations between PM 2.5 and infant mortality in California. The SAB - HES also noted, “when PM 10 results are scaled to estimate PM 2.5 impacts, the results yield similar risk estimates.” Consistent with The Benefits and Costs of the Clean Air

7 For the purposes of this analysis, we only calculate benefits for infants age 0

– 1, not all children under 5 years old.6 - 14

Act 1990 to 2020 (U.S. EPA, 2011a), we continue to rely on the earlier 1997 study in part due to the national

– scale of the earlier study. 6.2.2 Economic Valuation for Health Benefits After quantifying the change in adverse health impacts, we estimate the economic value of these avoided impacts. Reductions in ambient concentrations of air pollution generall y lower the risk of future adverse health effects b y a small amount for a large population. Therefore, the appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. For some health effects, such as hospital admissions, WTP estimates are generall y not available, so we use the cost of treating or mitigating the effect. These cost - of - illness (COI) estimates generall y (although not necessarily in every case) un derstate the true value of reductions in risk of a health effect. They

tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect. The unit values applied in this anal ys is are provide d in Table 5 - 9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a). For this proposed rule avoided premature deaths account for over 90 percent of monetized ozone - related benefits and 98 percent of monetized PM - related co - benefits . The economic s literature concerning the appropriate method for valuing reductions in premature mortality risk is still developing. The adoption of a value for the projected reduction in the risk of premature mortality is the subject of continuing discussion within the economics and public policy anal ys is community . Following the advice of the SAB’s Environmental Economics Advisory Committee (SAB - EEAC), the EPA currentl y uses the value of statistical life (VSL) approach in calculating estimates of mortality

benefits, be cause we believe this calculation provides the most reasonable single estimate of an individual’s willingness to trade off money for reductions in mortality risk (U.S. EPA - SAB, 2000). The VSLapproach is a summary measure for the value of small changes in mortality

risk experienced by

a large number of people. The EPA continues work to update its guidance on valuing mortality risk reductions, and the Agency

consulted several times with the SAB - EEAC on this issue. Until updated guidance is available, the Age ncy

determined that a single, peer - reviewed estimate applied consistently , best reflects the SAB - EEAC advice it has received. Therefore, the EPA has decided to appl y the VSL6 - 15

that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic A nalyses (U.S. EPA, 2014) 8 while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSLacross these studies is $6.3 million (2000$). 9 We then adjust this VSLto account for the currency year and to account for income growth from 1990 to the anal ysis year. Specifically , the VSL applied in this anal ys is in 2011$ after adjusting for income growth is $9.9 million for 2017 . The Agency

is committed to using scientificall y s ound, appropriatel y reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB EEAC’s specific recommen dations. In the process, the Agency has identified a number of important issues to be considered in updating its mortality risk valuation estimates. These are detailed in a white paper, “Valuing Mortality Risk Reductions in Environmental Policy” (U.S. EPA, 2010c), which recently underwent review by

the SAB - EEAC. A meeting with the SAB on this paper was held on March 14, 2011 and formal recommendations were transmitted on July 29, 2011 (U.S. EPA - SAB, 2011). The EPA is taking SAB’s recommendations under advis ement. In valuing PM 2.5 - related premature mortality , we discount the value of premature mortality occurring in future ye ars using rates of 3 percent and 7 percent (OMB, 2003). We assume that there is a “cessation” lag between changes in PM exposures and th e total realization of changes in health effects. Although the structure of the lag is uncertain, the EPA follows the advice of the SAB - HES to assume a segmented lag structure characterized by 30 percent of mortality reductions in the first year, 50 percent over ye ars 2 to 5, and 20 percent over the years 6 to 20 after the reduction in PM 2.5 (U.S. EPA - SAB, 2004c). Changes in the cessation lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Because short - term ozone - related premature mortality occurs within the anal ys is year, the estimated ozone - related co - benefits are identical for all discount rates.

8 In the updated Guidelines for Preparing Economic Analyses (U.S. EPA, 2010e), the EPA retained the VSL endorsed by the SAB with the understanding that further updates to the mortality risk valuation guidance would be forthcoming. 9 In 1990$, this base VSL is $4.8 million.6 - 16

6. 2 .3 Benefit-

per- ton Estimates for Ozone W e used a “benefit - per - ton” approach to estimate the ozone bene fits in this RIA. The EPA has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011c, 2012b, 2014a ; 2015 ). These benefit - per - ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of NO X (an ozone precursor). W e generated benefit - per - ton estimates for ozone based on air quality modeling for the illustrative control case described in Chapter 3 of this RIA. T he estimates correspo nd to NO X emissions from U.S. EGUs during the ozone - season (May to September). Because we estimate ozone health impacts from May to September only , this approach underestimates ozone benefits in areas with a longer ozone season such as Texas. These estimat es assume that EGU - attributable ozone formation at the regional - level is due to NO X alone. Because EGUs emit little VOC relative to NO X emissions, it is unlikely that VOCs emitted by

EGUs would contribute substantially

to regional ozone formation. We p rovide more detailed information regarding the generation of these estimates in the appendix to this chapter. As noted below in the characterization of uncertainty , all benefit - per - ton estimates have inherent limitations. Specificall y, the benefit - per - ton estimates reflect the geographic distribution of the modeled illustrative control case, which may not match the geographic distribution of emission reductions anticipated for compliance with the regulatory control alternatives , and they may not reflect lo cal variability in population density , meteorology , exposure, baseline health incidence rates, or other local factors for any specific location. 6. 2 .4 Benefit-

per- ton Estimates for PM 2.5 We used a “benefit - per - ton” approach to estimate the PM 2.5 co - benefits in this RIA , which represent the total monetized human health co - benefits (the sum of premature mortality and premature morbidity), of reducing one ton of PM 2.5 (or PM 2.5 precursor such as NO X) from a specified source. Specifically , in this anal ys is, we multiplied the benefit - per - ton estimates by the corresponding emission reductions that were generated from air quality modeling of the illustrative control case . The appendix to this chapter pro vides additional detail regarding these calculations. A ll benefit - per - ton estimates have inherent limitations and should be interpreted with caution. Even though we assume that all fine particles have equivalent health effects, the6 - 17

benefit - per - ton estimat es vary between precursors depending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure. 6. 2 .5 Estimated Health Benefits Results Table 6 - 2 provide s the benefit - per - ton estimates for the anal ys is year 2017 ; becau se the modeled change in SO 2 levels was very small, we did not quantify the SO 2 - related PM 2.5 benefits . Table 6 - 3 provide s the emission reductions estimated to occur in the anal ys is year. Table 6 - 4 summarize s the national monetized ozone - related and PM - related health benefits estimated to occur for regulatory control alternatives for the 2017 anal ys is year, by precursor pollutant using discount rates of 3 percent and 7 percent. Table 6 - 5 provides national summaries of the reductions in estimated health i ncidences associated with the proposal and more and less stringent alternatives for the 2017 anal ys is year . 11 Figure 6 - 1 provides a visual representation of the range of estimated ozone and PM 2.5 - related b enefits using benefit - per - ton estimates based on con centration - response functions from different studies and expert opinion for the proposal evaluated for 2017 . **Table 6**  **- 2**  **. Summary of**  **Ozone and PM**  **2.5 Benefit**  **- per**  **- Ton Estimates Based on Air** 

**Quality Modeling from t**  **he Illustrative Control Case in 20**  **17 (2011$)**  *****

Pollutant Discount Rate National NO X (as Ozone) N/A $5,700 to $9,400 NO X (as PM 2.5 ) 3% $2,100 to $4,700 7% $1,900 to $4,300 * The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone and PM 2.5 . All estimates are rounded to two significant figures. The monetized co - benefits do not include reduced health effects from direct expos ure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the benefit - per - ton estimates vary depending on the location and magnitude of their impact on PM 2.5 concentrations, which drive population exposure. The monetized co - benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit - per - ton estimates for ozone are based on ozone season NO X emissions. Ozone co - benefits occur in the analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit - per - ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximatel y - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

11 Incidence estimates were generated us ing the same “per ton” approach as used to generate the dollar benefit per ton values. See Appendix 6 - A for details.6 - 18

**Table 6**  **- 3**  **. Emission Reductions of Criteria Pollutants for the**  **P ropos**  **al and**  **More and**

**L ess**  **S tringent**  **A lternatives**  **in 2017**  **(thousands of short tons)** * Proposal More Stringent Alternative Less Stringent Alternative Ozone Season NO X 85,000 87,000 2 4 ,000 All Year NO X 90,000 93,000 24,000 All Year SO 2 380 430 300 *All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national total. **Table 6**  **- 4**  **. Summary of Estimated Monetized Health Benefits for the**  **Proposal and**

**More and Less Stringent Alternatives Regulatory Control Alternatives for 2017**  **( m**  **illions of 2011$) *** Pollutant Proposal More Stringent Alternative Less Stringent

Alternative NOx (as Ozone) $490 to $790 $500 to $820 $140 to $2 2 0 NOx (as PM 2.5 ) 3% Discount Rate $190 to $430 $190 to $440 $49 to $110

7% Discount Rate $170 to $380 $170 to $390 $45 to $100 **Total** 3% Discount Rate $670 to $1,200 $690 to $1,300 $190 to $3 4 0

7% Discount Rate $6 5 0 to $1,200 $670 to $1,200 $ 180 to $ 330 * All estimates are rounded to two significant figures so numbers may not sum down columns. The health benefits range is based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) with Zanobetti and Schwartz (2008)). The estimated monetized co - benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effec ts, but the benefit - per - ton estimates vary depending on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The monetized co - benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co - benefits for ozone are based on ozone season NO X emissions. Ozone co - benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit - per - ton methodology. In general, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The confidence intervals around the ozone mortality estimates are on the order of +/ - 6 0 percent depending on the concentration - response function used .6 - 19

**Table 6**  **- 5**  **. Summary of Avoided Health Incidences from Ozone**  **- Related**  **and PM**  **2.5 -** 

**Related B**  **enefits for the Proposal and More and Less Stringent Alternatives for**  **2017** ***** **Ozone -**  **related Health Effects** Proposal More Stringent Alternative Less Stringent

Alternative Avoided Premature Mortality Smith et al. (2009) (all ages) 48 50 14 Zanobetti and Schwartz (2008) (all ages) 81 83 2 3 Avoided Morbidity Hospital admissions

—

respiratory causes (ages > 65) 79 81 2 2 Emergency room visits for asthma (all ages) 320 330 9 0 Asthma exacerbation (ages 6 - 18) 93,000 95,000 2 6 ,000 Minor restricted - activity days (ages 18 - 65) 240,000 240,000 67 ,000 School loss days (ages 5 - 17) 77,000 79,000 2 2 ,000 **PM 2.5**  **- related Health**  **Effects** Avoided Premature Mortality Krewski et al. (2009) (adult) 21 22 5.6 Lepeule et al. (2012) (adult) 48 50 13 Woodruff et al. (1997) (infant) <1 <1 <1 Avoided Morbidity Emergency department visits for asthma (all ages) 12 12 3.1 Ac ute bronchitis (age 8

– 12) 31 32 8.1 Lo wer respiratory symptoms (age 7

– 14) 390 400 100 Upper respiratory symptoms (asthmatics age 9

– 11) 560 5 8 0 150 Minor restricted - activity days (age 18

– 65) 16,000 16,000 4,200 Lost work days (age 18

– 65) 2,700 2,700 700 Asthma exacerbation (age 6

– 18) 580 600 150 Hospital admissions

—

respiratory (all ages) 6.4 6.5 1.7 Hospital admissions

—cardiovascular (age > 18) 7.8 8.0 2.1 Non -Fatal Heart Attacks (age > 18) Peters et al. (2001) 2 6 2 5 6.6 Pooled estimate of 4 studies 2. 8 2. 7 0.7 1 * All estimates are rounded to whole numbers with two significant figures. . Co - benefits for ozone are based on ozone season NOx emissions. Confidence intervals are unavailable for this analysis because of the incidence - per - ton methodology. In general, the 95 th percentile confidence i nterval for the health impact function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). The confidence intervals around the ozone mortality estimates are on the order of +/ - 60 percent depending on the concentration - response function used.6 - 20OzonePM2.5

**Figure 6**  **- 1.**  **Monetized Health B**  **enefits of Proposal for 2**  **0 17**  *****

*T he PM 2.5 graphs show the estimated PM2.5 co - benefits at discount rates of 3% and 7% using effect coefficients derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 12 effect coefficients derived from EPA’s expert elicitation on PM mortality (Roman et al. , 20 08). The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration - response functions provided in those studies. Ozone benefits occur in the analysis year, so they are t he same for all discount rates. These estimates do not include benefits from reductions in CO2 . The monetized co - benefits do not include climate benefits from changes in NO 2 or reduced health effects from direct exposure to NO 2 , ecosystem effe cts, or visibility impairment. 6 . 2 .6 Characterization of Uncertainty in the Estimated Health B enefits In any complex anal ys is using estimated parameters and inputs from numerous models, there are likel y to be many sources of uncertainty. This analysis is no exception. This analysis6 - 21

includes many

data sources as inputs, including emission inventories, air q uality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions regarding the future state of the world (i.e., regulations, technology , and human behavior). Each of these inputs may be uncertain and would affect the estimate of benefits. When the uncertainties from each stage of the anal ys is are compounded, even small uncertainties can have large effects on the total quantified benefits. In addition, the use of the benefit - per - ton approach adds additional uncertainties beyond those for analy ses based directly on air quality modeling. Therefore, the estimates of benefits should be viewed as representative of t he general magnitude of benefits of the regulatory control alternatives for the 2017 analy sis year , rather than the actual benefits anticipated from implement the proposal . This RIA does not include the ty pe of detailed uncertainty assessment found in the Ozone NAAQS RIA (U.S. EPA, 2015 ) or the PM NAAQS RIA (U.S. EPA, 2012a) because we lack the necessary air quality

modeling input and/or monitoring data to run the benefits model. However, the results of the quantitative and qualitative uncertainty analy ses presented in the Ozone NAAQS RIA and PM NAAQS RIA can provide some information regarding the uncertainty inherent in the estimated benefits results presented in this analy sis. For example, s ensitivity anal ys es conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions could change the estimated PM 2.5 - related mortality co - benefits discounted at 3 percent by between 10 percent and

–27 percent and that alternative income growth adjustments could change the PM 2.5 - related mortality benefits by

between 33 percent and −14 percent. Although we generall y do not calculate confidence intervals for benefit - per - ton estimates as they can provide an incomplete picture about the overall uncertainty in the benefits estimates, the PM NAAQS RIA provides an indication of the random sampling error in the health impact and economic valuation functions using Monte Carlo methods. Ingeneral, the 95 th percentile confidence interval for monetized PM 2.5 benefits ranges from approximately - 90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and L epeule et al. (2012). The 95 th percentile confidence interval for the health imp act function alone ranges from approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).6 - 22

W e applied benefit - per - ton estimates, which reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling a ssumptions. For example, these estimates may

not reflect local variability in population density , meteorology , exposure, baseline health incidence rates, or other local factors that might lead to an over- estimate or under - estimate of the actual co - benefits of controlling PM and ozone precursors. As such, it is not feasible to estimate the proportion of co - benefits occurring in different locations . Use of these benefit - per - ton values to estimate benefits may lead to hi gher or lower benefit estimates than if benefits were calculated based on direct air quality modeling. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on a broad emiss ion reduction scenario and therefore represent average benefits - per - ton over the entire region. The benefit - per - ton for emission reductions in specific locations may be very different than the estimates presented here. To the extent that the geographic dis tribution of the emissions reductions achieved b y implementing the proposal is different than the emissions in the air quality

modeling of the illustrative control case , the benefits may be underestimated or overestimated. Our estimate of the total moneti zed benefits is based on the EPA’s interpretation of the best available scientific literature and methods and supported by the SAB - HES and the National Academies of Science (NRC, 2002 ). Below are key assumptions underl yi ng the estimates for PM 2.5 - related premature mortality , which accounts for 98 percent of the monetized PM 2.5 health co - benefits. 1. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality . This is an important assumption, because PM 2.5 varies considerabl y in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM I SA concluded that “man y constituents of PM 2.5 can be li nked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closel y related to specific outcomes” (U.S. EPA, 2009b). 2. We assume that the health impact function for fine pa rticles is log - linear without a threshold. Thus, the estimates include health co - benefits from reducing fine particles in areas with varied concentrations of PM 2.5 , including both areas that do not meet the fine particle standard and those areas that are i n attainment, down to the lowest modeled concentrations.6 - 23

3. We assume that there is a “cessation” lag between the change in PM exposures and the total realization of changes in mortality effects. Specificall y, we assume that some of the incidences of premature mortality related to PM 2.5 exposures occur in a distributed fashion over the 20 ye ars following exposure based on the advice of the SAB - HES (U.S. EPA SAB, 2004c), which affects the valuation of mortality co - benefits at different discount rates. In general, we are more confident in the magnitude of the risks we estimate from simulated PM 2.5 concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies that are used to estimate the benefits. Likewise, we a re less confident in the risk we estimate from simulated PM 2.5 concentrations that fall below the bulk of the observed data in these studies. Concentration benchmark analy ses (e.g., lowest measured level [LML ], one standard deviation below the mean of the air quality data in the study, etc.) allow readers to determine the portion of population exposed to annual mean PM 2.5 levels at or above different concentrations, which provides some insight into the level of uncertainty in the estimated PM 2.5 mortality b enefits. In this anal ys is, we appl y two concentration benchmark approaches (LML and one standard deviation below the mean) that have been incorporated into recent RIAs and the EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably

less, and the scientific evidence provides no clear dividing line. However, the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health benefits of air quality improvements. 12 Rather, the co benefits estimates reported in this RIA are the best estimates because they reflect the full range of air quality concentrations associ ated with the regulatory control alternatives . The PM I SA concluded that the scientific evidence collectively is sufficient to conclude that the relationship between long - term PM 2.5 exposures and mortality is causal and that , overall , the studies support the use of a no - threshold log - linear model to estimate PM - related long - term mortality (U.S. EPA, 2009b). We report also the key

assumptions associated with our anal ys is of ozone - related effects:

12 F or a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 - mo rtality relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration -Response Function for PM 2.5 - related Mortality (U.S. EPA, 2010b).6 - 24

 **Key assumption and uncertainties related to modeling of ozon e**  **- related premature**  **mo rtality**  : Ozone - related short - term mortality represents a substantial proportion of total monetized benefits (over 94% of the ozone - related - benefits), and these estimates have the following key

assumptions and uncertainties. We utilize a log - linear impact function without a threshold in modeling short - term ozone - related mortality . However, we acknowledge reduced confidence in specifying the nature of the C - R function in the range of ≤20ppb and below (ozone I SA, section 2.5.4.4). Thus, the estimates include health benefits from reducing ozone in areas with varied concentrations of ozone, including both areas that do not meet the ozone standard and those areas that are in attainment, down to the lowest modeled concentrations.

 **Avoided pre ma**  **ture mortality according to baseline pollutant concentrations** : We recognize that, in estimating short - term ozone - related mortality , we are less confident in specifying the shape of the C - R function at lower ambient ozone concentrations (at and below 20 p pb, ozone I SA, section 2.5.4.4). Q uantitative uncertainty anal ys es completed for th e Ozone NAAQS RIA (U . S . EPA, 2015) found almost 100% of mortality reductions occurred above 20 ppb, where we are more confident in specifying the nature of the ozone mortality effect (ozone ISA, section 2.5.4.4). H owever, as discussed in section 6B.7 of that RIA , care must be taken in interpreting these results since the ambient air metric used in modeling this endpoint is the mean 8 - hour max value in each grid cell (and not the full distribution of 8 - hour daily max values). Had the latter been used, then the distributi on would have likel y been wider . For this anal ys is, policy- specific air quality data is not available, and the control scenarios are illustrative of what states may choose to do. However, we believe that it is still important to characterize the distribution of exposure to baseline concentrations. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 concentration in the baseline of the air quality modeling used to calculate the benefit - per - ton6 - 25

estimates for this RIA using 12 km gri d cells across the contiguous U.S. 13 Itis important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population and change in air quality . In other words, the percentage of the populati on exposed to air pollution below the LMLis not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction policy . The most important aspect, which we are unable to quantify without rule - specific air quality modeling, is the shift in exposure anticipated by implementing the proposal . Therefore, caution is warranted when interpreting the LML assessment in this RIA because these results are not consistent with results from RIAs that had air quality m odeling. Figure 6 - 3 shows a bar chart of the percentage of the population exposed to various air quality levels , including the LMLconcentration benchmarks in the illustrative control case modeling, and Figure 6 - 4 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.

13 As described in Chapter 3, the baseline for the air quality modeling used to calculate the b enefit - per - ton values differs from the baseline used to estimate the benefits, costs, and impacts of this rulemaking. See Chapter 3 for more details about the differences between the two baselines .6 - 26

**Figure 6**  **- 2**  **. Percentage of Adult Population (age 30+) by Annual Mean PM**  **2.5 Exposure in** 

**the Baseline**  **u sed**  **for the Air Quality Analysis in Chapter 3 Am**  **ong the populations exposed to PM 2.5**  **in the baseline:** 88 % are exposed to PM 2.5 levels at or above the LML of the Krew ski et al. (2009) study 47 % are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study6 - 27

**Figure 6**  **- 3**  **. Cumulative Distribution of Adult Population (age 30+) by Annual Mean** 

**PM 2.5**  **Exposure in the B**  **aseline used for the Air Quality Analysis in Chapter 3**  **6 .3**  **Estimated Climate Co -**  **Benefits from CO 2**  A co - benefit of this proposal is reducing emissions of CO 2 . In this section, we provide a brief overview of the 2009 Endangerment Finding and climate science assessments released since then. We also provide information regarding the economic valuation of CO 2 using the Social Cost of Carbon (SC - CO 2 ), a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 emissions in a given ye ar. Tabl e 6 - 8 summarizes the quantified and unquantified climate benefits in this analysis. **Am ong the populations exposed to PM**  **2.5 in the baseline:**  88 % are exposed to PM **2.5** levels at or above the LML of the Krewski et al. (2009) study 47 % are exposed to PM 2.5 levels at or above the LML of the Lepeule et al. (2012) study6 - 28

**Table 6 -**  **8 .**  **Climate Effects** Benefits Category Specific Effect Effect Has Been Quantified Effect Has Been

Monetized More

Information Im proved Environment Reduced climate effects Global climate impacts from CO 2 — 1  SCC TSD

Climate impacts from ozone and black carbon (directly emitted PM) — — Ozone ISA, PM ISA2 Other climate impacts (e.g., other GHGs such as methane, aerosols, other impacts) — — IPCC 2 1 The global climate and related impacts of CO 2 emissions changes, such as sea level rise, are estimated within each integrated assessment model as part of the calculation of the SC - CO 2 . The resulting monetized damages, which are relevant for conducting the benefit - cost analysis, are used in this RIA to estimate the welfare effects of quantified changes in CO 2 emissions. 2 We assess these co - benefits qualitatively because we do not have sufficient confidence in available data or methods. There are several important considerations in assessing the climate - related benefits for an ozone air quality- focused rulemaking. First, the estimated health benefits do not account for any climate - related air quality changes (e.g., increased ambient ozone associated with higher temperatures). Excluding climate - related air quality changes may

underestimate ozone - related health benefits. It is unclear how PM 2.5 - related health benefits would be affected by excluding climate - related air quality changes since the science is unclear as to how climate change may affect PM 2.5 exposure. Second, the estimated health benefits also do not consider temperature modification of PM 2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, the estimated climate co - benefits repo rted in this RIA reflect global benefits, while the estimated health benefits are calculated for the contiguous U.S. only . Excluding temperature modification of air pollution risks and international air quality- related health benefits likely leads to underestimation of quantified health benefits (Anenberg et al, 2009, Jhun et al, 2014). Fourth, as noted earlier, we do not estimate the climate co - benefits associated with reductions in PM and ozone precursors. 6. 3 .1 Climate Change Impacts Through the implementation of CAA regulations, the EPA address es the negative externalities caused b y ai r pollution. In 2009, the EPA Administrator found that elevated concentrations of greenhouse gases in the atmosphere may reasonably

be anticipated both to endanger public health and to endanger public welfare. For he alth, these include the increased likelihood of heat waves, negative impacts on air quality , more intense hurricanes, more frequent and intense6 - 29

storms and heav y precipitation, and impacts on infectious and waterborne diseases. For welfare, these include re duced water supplies in some regions, increased water pollution, increased occurrences of floods and droughts, rising sea levels and damage to coastal infrastructure, increased peak electricity demand, changes in ecosystems, and impacts on indigenous communities. The preamble also summarizes new scientific assessments and recent climatic observations. Major scientific assessments released since the 2009 Endangerment Finding have improved scientific understanding of the climate, and provide even more eviden ce that GHG emissions endanger public health and welfare for current and future generations. The National Climate Assessment (NCA), in particular, assessed the impacts of climate change on human health in the United States, finding that Americans will be a ffected by “increased extreme weather events, wildfire, decreased air quality , threats to mental health, and illnesses transmitted by food, water, and disease - carriers such as mosquitoes and ticks.” These assessments also detail the risks to vulnerable gro ups such as children, the elderly and low income households. Furthermore, the assessments present an improved understanding of the impacts of climate change on public welfare, higher projections of future sea level rise than had been previously estimated, a better understanding of how the warmth in the next century may reach levels that would be unprecedented relative to the preceding millions of years of history , and new assessments of the impacts of climate change on permafrost and ocean acidification. Th e impacts of GHG emissions will be realized worldwide, independent of their location of origin, and impacts outside of the United States will produce consequences relevant to the United States. 6. 3 .2 Social Cost of Carbon We estimate the global social benefits of CO 2 emission reductions expected from the final emission guidelines using the SC - CO 2 estimates presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (May 2013, Revised July 2015) (“current TSD”). 14 We refer to these estimates,

14 Docket ID EPA - HQ - OAR - 2013 - 0495, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 , Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and6 - 30

which were developed by the U.S. government, as “SC - CO 2 estimates.” The SC - CO 2 is a metric that estimates the monetary value of impacts associated with marginal changes in CO 2 em issions in a given ye ar. It includes a wide range of anticipated climate impacts, such as net changes in agricultural productivity

and human health, property damage from increased flood risk, and changes in energy

s ys tem costs, such as reduced costs for he ating and increased costs for air conditioning. It is ty

pically used to assess the avoided damages as a result of regulatory actions (i.e., benefits of rulemakings that lead to an incremental reduction in cumulative global CO 2 emissions). The SC - CO 2 estim ates used in this analysis were developed over many ye ars, using the best science available, and with input from the public. Specificall y, an interagency working group (IWG) that included the EPA and other executive branch agencies and offices used three i ntegrated assessment models (IAMs) to develop the SC - CO 2 estimates and recommended four global values for use in regulatory anal ys es. The SC - CO 2 estimates were first released in February 2010 and updated in 2013 using new versions of each IAM. As discussed further below, the IWG published two minor corrections to the SC - CO 2 estimates in July 2015. The SC - CO 2 estimates were developed using an ensemble of the three most widely cited integrated assessment models in the economics literature with the ability to estimate the SC - CO 2 . A key objective of the IWG was to draw from the insights of the three models while respecting the different approaches to linking GHG emissions and monetized damages taken by modelers in the published literature. After conducting an e xtensive literature review, the interagency group selected three sets of input parameters (climate sensitivity , socioeconomic and emissions trajectories, and discount rates) to use consistently in each model. All other model features were left unchanged, rel yi ng on the model developers’ best estimates and judgments, as informed by the literature. Specifically , a common probability distribution for the equilibrium climate sensitivity

parameter, which informs the strength of climate’s response to atmospheric GHG concentrations, was used across all three models. In addition, a common range of scenarios for Budget, Office of Science and Technolo gy Policy, and Department of Treasury (May 2013, Revised July 201 5 ). Available at: < https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc - tsd - final - july - 2015.pdf

> Accessed 7/11/2015.6 - 31

the socioeconomic parameters and emissions forecasts were used in all three models. Finall y, the marginal damage estimates from the three models were estimat ed using a consistent range of discount rates, 2.5, 3.0, and 5.0 percent. See the 2010 TSD for a complete discussion of the methods used to develop the estimates and the key uncertainties, and the current TSD for the latest estimates. 15 The SC - CO 2 estimate s represent global measures because of the distinctive nature of climate change, which is highl y unusual in at least three respects. First, emissions of most GHGs contribute to damages around the world independent of the country in which they

are emitted. The SC - CO 2 must therefore incorporate the full (global) damages caused by GHG emissions to address the global nature of the problem. Second, the U.S. operates in a global and highl y interconnected econom y, such that impacts on the other side of the world c an affect our econom y. This means that the true costs of climate change to the U.S. are larger than the direct impacts that simply

occur within the U.S. Third, climate change represents a classic public goods problem because each country ’s reductions benef it everyone else and no country can be excluded from enjo yi ng the benefits of other countries’ reductions, even if it provides no reductions itself. In this situation, the only

way to achieve an economically efficient level of emissions reductions is for c ountries to cooperate in providing mutuall y beneficial reductions beyond the level that would be justified only

by their own domestic benefits. In reference to the public good nature of mitigation and its role in foreign relations, thirteen prominent academics noted that these “are compelling reasons to focus on a global SCC” in a recent article on the SCC (Pizer et al., 2014). In addition, as noted in OMB’s Response to Comments on the SCC, there is no bright line between domestic and global damages. Advers e impacts on other countries can have spillover effects on the United States, particularl y in the areas of national security, international trade, public health and humanitarian concerns. 16 The 2010 TSD noted a number of limitations to the SC - CO 2 analy sis, including the incomplete way

in which the integrated assessment models capture catastrophic and non - 15 See https://www.whitehouse.gov/omb/oira/social - cost - of - carbon for both TSDs. 16 See Endangerment and Caus e or Contribute Findings for Greenhouse Gase s Under Section 202(a) of the Clean Air Act , 74 Fed. Reg. 66,496, 66,535 (Dec. 15, 2009) and National Research Council 2013a.6 - 32

catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, a nd assumptions regarding risk aversion. Currently

integrated assessment models do not assign value to all of the important phy sical, ecological, and economic impacts of climate change recognized in the climate change literature due to a lack of precise inf ormation on the nature of damages and because the science incorporated into these models understandabl y lags behind the most recent research. 17 The limited amount of research linking climate impacts to economic damages makes the modeling exercise even more difficult. These individual limitations do not all work in the same direction in terms of their influence on the SC - CO 2 estimates, though taken together they suggest that the SC - CO 2 estimates are likely conservative. In particular, the IPCC Fourth Assessment Report (2007), which was the most current IPCC assessment available at the time of the IWG’s 2009 - 2010 review, concluded that “It is very likel y that [SC - CO 2 estimates] underestimate the damage costs because they

cannot include many

non - quantifiable imp acts.” Since then, the peer reviewed literature has continued to support this conclusion. For example, the IPCC Fifth Assessment report observed that SC - CO 2 estimates continue to omit various impacts that would likely increase damages. The 95th percentile estimate was included in the recommended range for regulatory

impact analy sis to address these concerns. The EPA and other agencies have continued to consider feedback on the SC - CO 2 estimates from stakeholders through a range of channels, including public comments on rulemaking s that use the SC - CO 2 in supporting analyses and through regular interactions with stakeholders and research anal ys ts implementing the SC - CO 2 methodology used b y the interagency

working group. In addition, OMB’s Office of Information and Regulatory Affairs issued a separate request for public comment on the approach used to develop the estimates. 18 After careful evaluation of the full range of comments submitted to OMB’s Office of Information and

17 Climate change impacts and SCC modeling is an area of active research. For example, see: (1) Howard, Peter, “Omitted Damages: What’s Missing from the Social Cost of Carbon.” March 13, 2014, http://costofcarbon.org/files/Omitted_Damages_Whats_Missing_From_the_Social_Cost_of_Carbon.pdf ; and (2) Electric Power Research Institute, “Underst anding the Social Cost of carbon: A Technical Assessment,” October 2014, www.epri.com . 18 See https://www.federalregister.gov/articles/2013/11/26/2013 - 28242/technical - support - document - technical update - of - the - social - cost - of

- carbon - for - regulatory - im pact6 - 33

Regulatory

A ffairs, the IWG continues to recommend the use of these SC - CO 2 estimates in regulatory

impact anal ys is. With the release of the response to comments 19 , the IWG announced plans to obtain expert independent advice from the National Academies of Sciences, Engi neering, and Medicine (Academies) to ensure that the SC - CO 2 estimates continue to reflect the best available scientific and economic information on climate change. 20 The Academies’ process will be informed by the public comments received and focus es on the technical merits and challenges of potential approaches to improving the SC - CO 2 estimates in future updates. 21 Concurrent with OMB’s publication of the response to comments on SC - CO 2 and announcement of the Academies process, OMB posted a revised TSD that includes two minor technical corrections to the current estimates. One technical correction addressed an inadvertent omission of climate change damages in the last ye ar of anal ys is (2300) in one model and the second addressed a minor indexing error in another model. On average the revised SC - CO 2 estimates are one dollar less than the mean SC - CO 2 estimates reported in the November 2013 revision to the May

2013 TSD. The change in the estimates associated with the 95th percentile estimates when using a 3% disc ount rate is slightly larger, as those estimates are heavil y influenced b y the results from the model that was affected b y the indexing error. The four SC - CO 2 estimates are as follows: $1 2 , $4 1 , $6 3 , and $120 per metric ton of CO 2 emissions in the year 20 17 (2011$). 22 The first three values are based on the average SC - CO 2 from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SC - CO 2 estimates for several discount rates are included because the literature shows that the SC - CO 2 is quite

19 See https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc - response - to - comments - final - july - 2015.pdf 20 See https://www.whitehouse.gov/blog/2015/07/02/estimating - benefits - carbon - dioxide - emissions - reductions . 21 See http://sites.nationalacademies.org/DBASSE/BECS/CurrentProjects/DBASSE_167526?utm_source=All%20DBASS E%2 0New sletters&utm_campaign=e84c13e8c4 - New _Project_the_Social_Cost_of_Carbon&utm_medium=email&utm_term=0_e16023964e - e84c13e8c4 - 267347161 for more information about the National Academies process and the status of the project. 22 The current version of the TSD is available at: https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc tsd - final - july - 2015.pdf

. The 2010 and 2013 TSDs present SC - C O 2 in 2007$ per metric ton. The unrounded estimates from the current TSD we re adjusted to 2011$ using GDP Implicit Price Deflator (1.061374), http://www.bea.gov/iTable/index_nipa.cfm . The estimates presented here have been rounded to two significant digits.6 - 34

sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context (where costs and benefits are incurred b y different generations). The fourth value is the 95 th percentile o f the SC - CO 2 from all three models at a 3 percent discount rate. It is included to represent higher- than - expected impacts from temperature change further out in the tails of the SC - CO 2 distribution (representing less likel y, but potentially

catastrophic, o utcomes). Table 6 - 9 presents the gl obal SC - CO 2 estimates in metric tons for the ye ars 2015 to 2050. In order to calculate the dollar value for emission reductions, the SC - CO 2 estimate for each emissions year would be applied to changes in CO 2 emissions for that year, and then discounted back to the analy sis year using the same discount rate used to estimate the SC - CO 2 . 23 , 24 The SC CO 2 increases over time because future emissions are expected to produce larger incremental damages as ph ys ical and economic s ys tems become more stressed in response to greater climate change. Note that the interagency group estimated the growth rate of the S C - CO 2 directly using the three integrated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. Table 6 - 10 report s the incremental climate co - benefits from CO 2 emission impacts estimated for the proposal and more and less stringent alternatives for the 2017 anal ys is year.

23 Emission impacts for this rulemaking are shown for the year 2017 and are calculated in short tons. Therefore, the conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in the year 2017. 24 This analysis considered the climate impacts of only CO 2 emission change. A s discussed below , the climate im pacts of other pollutants were not calculated for the proposed guidelines. While CO 2 is the dominant GHG emitted by the sector, we recognize the representative facilities within these comparisons may also have different emission rates for other climate forcers that will serve a minor role in determining the overall social cost of generati on.6 - 35

**Table 6**  **- 9**  **. Social Cost of CO**  **2 , 2015**  **- 2050 (in 2011$ per**  **metric ton )** * Year Discount Rate and Statistic

5% Average 3% Average 2.5% Average 3% (95th percentile) 2015 $1 2 $3 8 $5 9 $1 10 2017 $12 $41 $63 $120 2020 $1 3 $4 5 $6 6 $1 3 0 2025 $1 5 $4 9 $ 72 $1 5 0 2030 $1 7 $ 53 $7 7 $1 6 0 2035 $1 9 $5 8 $ 83 $1 8 0 2040 $2 2 $ 64 $8 9 $1 9 0 2045 $2 4 $6 8 $ 94 $ 2 10 2050 $2 8 $ 73 $ 100 $2 3 0 * These SC - CO 2 values are stated in $/metric ton and rounded to two significant figures. The estimates vary depending on the year of CO 2 emissions and are defined in real terms, i.e., adjusted for inflation using the GDP im plicit price deflator.

**Table 6**  **- 1**  **0 .**  **Estimated Global Climate Co**  **- b**  **enefits of CO 2**  **Reductions for the Proposal** 

**and More and Less Stringent Alternatives for 20**  **17 (m illions of 2011$)** * Discount rate and statistic **Proposal More Stringent**  **Alternative Less Stringent** 

**Alternative** Thousands of s hort tons of CO 2 reduced ** 610 61 0 720

5% (average) $6.5 $6.5 $7.6 3% (average) $23 $23 $27 2.5% (average) $35 $35 $41 3% (95 th percentile) $66 $66 $78 * The SC - CO 2 values are dollar - year and emissions - year specific. SC - CO 2 values represent only a partial accounting of climate impacts. ** Emission impacts for this rulemaking are shown for the year 2017 and are calculated in short tons. The conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in the year 2017. It is important to note that the climate co - benefits presented above are associated with changes in CO 2 emissions only . Implementing th is proposal , however, will have an impact on the emissions of other pollutants that would affect the climate. Both predicting reductions in emissions and estimating the climate impacts of these other pollutants, however, is complex. The climate impacts of these other pollutants have not been calculated for this proposal . 25

25 The SC - C O 2 estimates used in this analysis are designed to assess the climate benefits associated with changes in CO 2 emissions only.6 - 36

The other emissions potentially reduced as a result of the proposal to update CSAPR include other greenhouse gases (such as methane), aerosols and aerosol precursors such as black carbon, organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as nitrogen oxides and volatile organic carbon compounds. Changes in emissions of these pollutants (both increases and decreases) could directl y result from changes in electricity generati on, upstream fossil fuel extraction and transport, and/or downstream secondary market impacts. Reductions in black carbon or ozone precursors are projected to lead to further cooling, but reductions in the other aerosol species and precursors are projected to lead to warming. Therefore, changes in non - CO 2 pollutants could potentially augment or offset the climate co benefits calculated here. These pollutants can act in different way s and on different timescales than carbon dioxide. For example, aerosols ref lect (and in the case of black carbon, absorb) incoming radiation, whereas greenhouse gases absorb outgoing infrared radiation. In addition, these aerosols are thought to affect climate indirectly by altering properties of clouds. Black carbon can also dep osit on snow and ice, darkening these surfaces and accelerating melting. In terms of lifetime, while carbon dioxide emissions can increase concentrations in the atmosphere for hundreds or thousands of years, man y of these other pollutants are short lived a nd remain in the atmosphere for short periods of time ranging from day s to weeks and can therefore exhibit large spatial and temporal variability . **6 .4**  **Combined Health Benefits and Climate Co -**  **Benefits Estimates** In this analy sis, we were able to monetize the estimated benefits associated with the reduced exposure to ozone and PM 2.5 and co - benefits of d ecreased emissions of CO 2 , but we were unable to monetize the co - benefits associated with reducing exposure to mercury , carbon monoxide, and NO 2 , as well as e cos ys tem effects and visibility impairment. In addition, there are expected to be unquantified health and welfare impacts associated with changes in hydrogen chloride. Specifically , we estimated combinations of health benefits at discount rates of 3 percent and 7 percent (as recommended b y the EPA’s Guidelines for Preparing Economic Analyses [U.S. EPA, 2014] and OMB’s Circular A - 4 [OMB, 2003]) and climate co - benefits at estimates of the SC - CO 2 (average SC - CO 2 at each of three discount rates

— 5 percent, 3 percent, 2.5 percent

— and the 95 th percentile SC - CO 2 at 3 percent) (as recommended b y the IW G ).6 - 37

Different discount rates are applied to SC - CO 2 than to the health benefit estimates because CO 2 emissions are long - lived and subsequent damages occur over many years. Moreover, several rates are applied to SC - CO 2 because the literature shows that it is sensitive to assumptions about discount rate and because no consensus exists on the appropriate rate to use in an intergenerational context. The SC - CO 2 interagency group centered its attention on the 3 percent discount rate but emphasized the importance of considering all four SC - CO 2 estimates. 26 The EPA has evaluated the range of potential impacts by combining all SC - CO 2 values with health benefits values at the 3 perc ent and 7 percent discount rates. Combining the 3 percent SC - CO 2 values with the 3 percent health benefit values assumes that there is no difference in discount rates Table 6 - 11 provide s the combined health and climate benefits for the proposal and more and less stringent alternatives for the 2017 analy sis year.

26 See the 2010 SCC TSD. Docket ID EPA - HQ - OAR- 2009 - 0472 - 114577 or http://www. whitehouse.gov/sites/default/files/omb/i nforeg/for - agencies/Social - Cost - of - Carbon - for - RIA.pdf for details.6 - 38

**Table 6**  **- 11**  **. Combined Health Benefits and Climate Co**  **- Benefits for**  **the Proposal and**

**More and Less Stringent Alternatives for 20**  **17 (**  **m illions of 2011$)** * **SC -**  **C O**  **2 Discount Rate**  ****** **Health and Clim**  **ate Benefits**  **(Discount Rate Applied to Health Co -**  **Benefits) Climate**  **Co**   ** Benefits Only** **3% 7%**  Proposal 5% $ 680 to $ 1200 $ 660 to $ 1200 $ 6.5 3% $ 700 to $ 1200 $ 680 to $ 1200 $ 23 2.5% $ 710 to $ 1300 $ 690 to $ 1200 $ 35 3% (95 th percentile) $ 740 to $ 1300 $ 720 to $ 1200 $ 66 More Stringent Alternative 5% $ 700 to $ 1300 $ 680 to $ 1200 $ 6.5 3% $ 720 to $ 1300 $ 700 to $ 1200 $ 23 2.5% $ 730 to $ 1300 $ 710 to $ 1200 $ 35 3% (95 th percentile) $ 760 to $ 1300 $ 740 to $ 1300 $ 66 Less Stringent Alternative 5% $ 190 to $ 340 $ 190 to $ 330 $ 7.6 3% $ 210 to $ 360 $ 210 to $ 350 $ 27 2.5% $ 230 to $ 380 $ 220 to $ 370 $ 41 3% (95 th percentile) $ 260 to $ 410 $ 260 to $ 400 $ 78 *All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO 2 emissions. Co - benefits are based on regional benefit - per - ton estimates. Co - benefits for ozone are based on ozone season NOx emissions. Ozone benefits occur in analysis year, so they are the same for all discount rates. The health benefits reflect the sum of the PM 2.5 and ozone co - benefits and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. ( 2009 ) to Lepeule et al. (2012) with Zanobetti and Schwartz ( 2008 )). The mo netized health co - benefits do not inc lude reduced health effects from direct exposure to NO 2 and HAP; ecosystem effects; or visibility impairment. **As discussed in section 6.3, the SC - CO 2 estimates are calculated with four different values of a one ton reduction . **6 .5**  **Unquantified Co -**  **benefits** The monetized co - benefits estimated in this RIA reflect a subset of co - benefits attributable to the health effect reductions associated with ambient fine particles and ozone. Data, time, and resource limitations pr evented the EPA from quantifying the impacts to, or monetizing the co benefits from several important benefit categories, including co - benefits associated with exposure to several HAP and NO 2 , as well as ecosystem effects, and visibility impairment due to the absence of air quality modeling data for these pollutants in this analysis. This does not imply that there are no co - benefits associated reductions in exposures to NO 2 . In this section, we provide a qualitative description of these benefits, which are listed in Table 6 - 1 2. **Table 6**  **- 1**  **2. Unquantified Health and Welfare Co**  **- benefits Categories**  Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized Mo re Information6 - 39 Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized Mo re Information Improved Human Health

Reduced incidence of morbidity from exposure to NO 2 Asthma hospital admissions (all ages)

— — NO 2 ISA 1 Chronic lung disease hospital admissions (age > 65) — — NO 2 ISA 1 Respiratory emergency department visits (all ages)

— — NO 2 ISA 1 Asthma exacerbation (asthmatics age 4

– 18)

— — NO 2 ISA 1 Acute respiratory symptoms (age 7

– 14)

— — NO 2 ISA 1 Premature mortality

— — NO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — NO 2 ISA2,3

Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65)

— — SO 2 ISA 1 Asthma emergency department visits (all ages)

— — SO 2 ISA 1 Asthma exacerbation (asthmatics age 4

– 12)

— — SO 2 ISA 1 Acute respiratory symptoms (age 7

– 14)

— — SO 2 ISA 1 Premature mortality

— — SO 2 ISA 1,2,3 Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations) — — SO 2 ISA 1,2 Improved Environment Reduced visibility impairment Visibility in Class 1 areas

— — PM ISA 1 Visibility in residential areas

— — PM ISA 1 Reduced effects on materials Household soiling

— — PM ISA 1,2 Materials damage (e.g., corrosion, increased wear)

— — PM ISA2 Reduced effects from PM deposition (metals and organics) Effects on Individual organisms and ecosystems

— — PM ISA2

Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation

— — Ozone ISA 1 Reduced vegetation growth and reproduction

— — Ozone ISA 1 Yield and quality of commercial forest products and crops — — Ozone ISA 1 Damage to urban ornamental plants

— — Ozone ISA2 Carbon sequestration in terrestrial ecosystems

— — Ozone ISA 1 Recreational demand associated with forest aesthetics — — Ozone ISA2 Other non - use effects Ozone ISA2 Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf - gas exchange, community composition) — — Ozone ISA2

Reduced effects from acid deposition Recreational fishing

— — NO x SO x ISA 1 Tree mortality and decline

— — NO x SO x ISA2 Commercial fishing and forestry effects

— — NO x SO x ISA2 Recreational demand in terrestrial and aquatic ecosystems — — NO x SO x ISA2 Other non - use effects NO x SO x ISA2 Ecosystem functions (e.g., biogeochemical cycles)

— — NO x SO x ISA2 Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA2 Coastal eutrophication

— — NO x SO x ISA26 - 40 Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized Mo re Information Recreational demand in terrestrial and estuarine ecosystems — — NO x SO x ISA2 Other non - use effects NO x SO x ISA2 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA2 Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA2 Injury to vegetation from NO x exposure — — NO x SO x ISA2 1 We assess these co - benefits qualitatively due to data and resource limitations for this RIA. 2 We assess these co - benefits qualitatively because we do not have sufficient confidence in available data or methods. 3 We assess these co - benefits qualitatively because current evidence is only suggestive of causality or there are other signific ant concerns over the strength of the association. 6.5.1 HAP Impacts Due to methodology

and resource limitations, we were unable to estimate the impacts associated with changes in emissions of the hazardous air pollutants in this anal ys is. The EPA’s SAB - HES concluded that “the challenges for assessing progress in health improvement as a result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure - response functions, uncertainties in emissions inventories an d background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of tracking health progress for diseases, such as cancer, that have long latency periods” (U.S. EPA - SAB, 2008 ). In 2009, the EPA convened a workshop to address the inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations from this workshop included identifying research priorities, focusing on susceptible and vulnerable populations, and improving dose - response relationships (Gwinn et al. , 2011 ).

6.5.2 Additional NO2 Health Co -Benefits In addition to being a precursor to PM 2.5 and ozone, NOx emissions are also linked to a variety of adverse health effects associated with direct exposure. We were unable to estimate the health co - benefits associated with reduced NO 2 exposure in this analysis. Therefore, this anal ys is only quantified and monetized the PM 2.5 and ozone co - benefits associated with the reductions in NO 2 emissions.6 - 41

Following a comprehens ive review of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Nitrogen

— Health Criteria (NOx I SA) (U.S. EPA, 2008c) concluded that there is a likel y causal relationship between respiratory health e ffects and short - term exposure to NO 2 . These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory

s ymptoms, airway

hy perresponsiveness, airway inflammation, and lung function. The NOx I SA also concluded that the relationship between short - term NO 2 exposure and premature mortality was “suggestive but not sufficient to infer a causal relationship,” because it is difficult to attribute the mortality

risk effects to NO 2 al one. Although the NOx I SA stated that studies consistently

reported a relationship between NO 2 exposure and mortality , the effect was generall y smaller than that for other pollutants such as PM. 6.5.3 Additional SO2 Health Co -Benefits In addition to bein g a precursor to PM 2.5 , SO 2 emissions are also linked to a variety of adverse health effects associated with direct exposure. We were unable to estimate the health co benefits associated with reduced SO 2 in this analy sis because we do not have air quality modeling data available. Therefore, this anal ys is only quantifies and monetizes the PM 2.5 co benefits associated with the reductions in SO 2 emissions. Following an extensive evaluation of health evidence from epidemiologic and laboratory studies, the Inte

grated Science Assessment for Oxides of Sulfur

— Health Criteria (SO 2 IS A) concluded that there is a causal relationship between respiratory health effects and short - term exposure to SO 2 (U.S. EPA, 2008a). The immediate effect of SO 2 on the respiratory s ys t em in humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease. A clear concentration response relationship has been demonstrated in laboratory studies fo llowing exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversel y affected. Based on our review of this information, we identified three short - term morbidity endpoints that the SO 2 IS A identified as a “causal relationship”: asthma exacerbation, respiratory- related emergency department visits, and respiratory- related hospitalizations. The differing evidence and associated strength of the evidence for these different effect s is described in detail in the SO 2 IS A. The SO 2 IS A also6 - 42

concluded that the relationship between short - term SO 2 exposure and premature mortality was “suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to S O 2 alone. Although the SO 2 IS A stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality , there was a lack of robustness of the observed associations to adjustment for other pollutants. We did not quantify these co - benefits due to data constraints. 6.5.4 Additional NO2 and SO2 Welfare Co -Benefits As described in the In tegrated Science Assessment for Oxides of Nitrogen and Sulfur

— Ecological Criteria (NOx/SOx I SA) (U.S. EPA, 2008d), SO 2 and NOx emissions also contribute to a variety of adverse welfare effects, including those associated with acidic deposition, visibility impairment, and nutrient enrichment. Deposition of nitrogen causes acidification, which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic ecos ys tems, as well as a decline in sensitive tree species, such as red spr uce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial ecos ystems. In the northeastern U.S., the surface waters affected b y acidification are a source of food for some recreational and subsistence fishermen and for other consumers and support s everal cultural services, including aesthetic and educational services and recreational fishing. Biological effects of acidification in terrestrial ecos ys tems are generall y linked to aluminum toxicity , which can cause reduced root growth, restricting the a

bility of the plant to take up water and nutrients. These direct effects can, in turn, increase the sensitivity

of these plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canop y trees. Terrestrial acidification affects several important ecological services, including declines in habitat for threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating). (U.S. EPA, 2008d) Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of estuaries can disrupt an important source of food production, particularl y fish and shellfish production, and a variety of cultural ecosystem services, including water - based recreational and aesthetic services. Terrestrial nutrient enrichment is associated with changes in the ty pes and6 - 43

number of species and biodiversity in terrestrial sy stems. Excessive nitrogen deposition upsets the balance between native and nonnative plants, changing the ability of an area to support biodiver sity . When the composition of species changes, then fire frequency and intensity can also change, as nonnative grasses fuel more frequent and more intense wildfires. (U.S. EPA, 2008d) Reductions in emissions of NO 2 and SO 2 will improve the level of visibil ity throughout the United States because these gases (and the particles of nitrate and sulfate formed from these gases) impair visibility

by scattering and absorbing light (U.S. EPA, 2009). Visibility is also referred to as visual air quality (VAQ), and it directly

affects people’s enjoy

ment of a variety

of daily activities (U.S. EPA, 2009). Good visibility increases quality

of life where individuals live and work, and where they travel for recreational activities, including sites of unique public value, su ch as the Great Smoky

Mountains National Park (U. S. EPA, 2009). 6.5.5 Ozone Welfare Co -Benefits Exposure to ozone has been associated with a wide array of vegetation and ecosy stem effects in the published literature (U.S. EPA, 2013b). Sensitivity to ozon e is highl y variable across species, with over 65 plant species identified as “ozone - sensitive”, many of which occur in state and national parks and forests. These effects include those that damage or impair the intended use of the plant or ecos ys tem. Such effects can include reduced growth and/or biomass production in sensitive plant species, including forest trees, reduced yi eld and quality of crops, visible foliar injury , species composition shift, and changes in ecos ys tems and associated ecos ys tem servi ces. 6.5.7 Visibility Impairment C o -Benefits Reducing secondary

formation of PM 2.5 would improve levels of visibility in the U.S. because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009b). Fine particles with significant light - extinction e fficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people’s enjoy ment of dail y activities and their overall sense of wellbeing. Good visibility increases the quality

of l ife where individuals live and work, and where they engage in recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern6 - 44

U.S. and particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularl y during winter (U.S. EPA, 2009b). Previous analyses (U.S. EPA, 2011a) show that visibility co - benefits can be a significant welfare benefit category . Without air quality

modeling, we are unable to estimate visibility- related benefits, and we are also unable to determine whether the emission reductions associated with the final emission guidelines would be likely to have a significant impact on visibility in urban areas or Class I areas. **6 .6**  **References** 40 CFR Chapter I [EPA

–HQ

–OAR

–2009

–0171; FRL

– 9091

– 8] RI

N 2060

–ZA14, “Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act,” Federal Register, Vol. 74, No. 239, Tuesday , December 15, 2009, Rules and Regulations. Abt A ssociates, Inc. 2012. “BenMAP User’s Manual Appendices,” prepared for U.S. Research Triangle Park, NC: U. S. Environmental Protection Agency , Office of Air Quality Planning and Standards. Available at: <http://www.epa.gov/air/benmap/models/BenMAPAppendices Oct2012.pdf>. Accessed June 6, 2015. Anenberg SC, West IJ, Fiore AM, Jaffe DA, Prather MJ, Bergmann D, Cuvelier K, Dentener FJ, Duncan BN, Gauss M, Hess P, Jonson JE, Lupu A, Mackenzie IA, Marmer E, Park RJ, Sanderson MG, Schultz M, Shindell DT, Szopa S, V ivanco MG, Wild O, Zeng G. 2009. Intercontinental impacts of ozone pollution on human mortality. Environmental Science and Technology . 43(17): 6482 - 7. Bell, M.L., A. McDermott, S.L . Zeger, J.M. Sarnet, and F. Dominici . 2004. “Ozone and Short Term Mortality in 95 U.S. Urban Communities, 1987 - 2000.” Journal of the American Medical Association. 292(19):2372 - 8. Bell, M.L., F. Dominici, and J.M. Samet. 2005. “A Meta - Analy sis of Time - Series Studies of Ozone and Mortality

with Comparison to the National Morbidity , Mortality , and Air Pollution Study .” Epidemiology . 16(4):436 - 45. Docket ID EPA - HQ - OAR - 2009 - 0472 - 114577, Technical Support Document: Social Cost of Carbon for Regulatory

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