6-1

CHAPTER 6: ESTIMATED HUMAN HEALTH BENEFITS AND CLIM ATE COBENEFITS

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 nonatta inment 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 i mplemented through the
CSAPR NO X ozone-season allowance trading program. Implementi ng 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 monetize d 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 p revent the EPA from monetizing several important co-benefits from reducing emissions of these several pollutants, including those from reducing direct ex posure to NO 2 , ecosystem effects and visibility impairment as well as co-benefits from ancillary reductions c hanges in other pollutants, such as hazardous air pollutants (HAP) . We qualitatively discuss these unquantified benef its in this chapter.

This chapter provides estimates of the monetized ai r quality health benefits and climate cobenefits associated with emission reductions for th e regulatory control alternatives across several
discount rates. The estimated benefits associated w ith these emission reductions are beyond those achieved by previous EPA rulemakings, includi ng 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 p roposal include impacts from CSAPR issued on July 6, 2011. As discussed and elaborated on in Chapter 1, Bb ecause the modeling for the proposal was 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 ass ociated with the CSAPR NO X ozone-season phase 2 emissions budgets.6-2

6.2 Estimated Human Health Benefits
The 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 at mosphere to form ozone. Reducing NO X emissions also reduces human exposure to ozone and the incidence of ozone-related health effects, though this depend s partly on local levels of volatile organic compounds (VOCs). The proposal would also reduce em issions 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 year 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, th e 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 no nattainment monitors that are the subject of this proposal, and the benefits of reducing these pollutants in those regions are assessed in this Chapter. Although we do not have sufficient data to quantify these impacts in this analysis, rR educing emissions of NO X would also reduce ambient exposure
to NO 2 and its associated health effects , though we do not quantify these effects because w e have not developed a reduced-form technique for estimati ng NO 2 impacts . In this section, we provide
an overview of the monetized ozone-related benefits 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 apply and the tools we employ to quantify the in cidence of these effects may be found in the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2015) [Note, insert reference to 2015 ozone NAAQS RIA when/if finalized ] . The estimated benefits associated with
these emissions reductions are additional to those achieved by 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 reduce ambient ozone and PM 2.5 concentrations below the National

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. Additionally, this RIA does not estimate changes in emissions of directly emitted particles. As a result, quantified PM 2.5 -related benefits may be underestimated by a relatively smal l amount.6-3

Ambient Air Quality Standards (NAAQS) in some areas and assist other areas with attaining the
ozone and PM 2.5 NAAQS. The 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 analytical 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 strategies for differen t sources reflecting the application of identified and unidentified controls, incremental to implement ation of existing regulations and controls needed to attain the NAAQS that currently is in eff ect. In short, NAAQS RIAs hypothesize, but do not predict, the strategies that States may choo se to enact when implementing a revised NAAQS. Setting a NAAQS does not directly result in costs or benefits, and as such, the EPA’s NAAQS RIAs are illustrative. The estimated costs an d benefits from NAAQS RIAs are not
intended to be added to the costs and benefits of o ther regulations that result in specific costs of control and prescribe specific emission reductions. For example, some of the emissions reductions estimated to result from implementing th e proposal to update CSAPR may achieve some of the air quality improvements that resulted from the hypothesized attainment strategies presented in the NAAQS RIAs. The emissions reductio ns from implementing the proposal to update CSAPR will decrease the remaining amount of emissions reductions needed in nonattainment areas to, and reduce the costs to tho se areas to, meet the 2008 ozone NAAQS. Specifically, the anticipated reductions in ozone c oncentrations from this rule would help areas attain and maintain the 2008 ozone NAAQS and achiev e some of the air quality improvements from the hypothesized attainment strategy from the 2008 ozone NAAQS RIA. These ozone improvements would similarly achieve some of the ai r quality improvements assumed in the baseline (i.e., 2008 ozone NAAQS attainment project ion) for the 2015 ozone NAAQS RIA.

As discussed in Chapter 4, the IPM modeling showing compliance with the regulatory
control alternatives for which emission reductions are estimated in this RIA is also illustrative in nature. However, unlike the illustrative control st rategies analyzed in NAAQS RIAs described above, all of the emission reductions for the illus trative compliance modeling for this proposal would occur in one well-characterized sector (i.e., the EGU sector). The EPA is more confident in the magnitude and location of the emission reduc tions for this proposal because it imposes a specific requirement that limits emissions from a s pecific sector. Emission reductions achieved under this rule, if finalized, will ultimately be r eflected in the baseline of future NAAQS6-4

analyses and , which would lower the emissions reductions needed to atta in revised future NAAQS. For more information on the relationship bet ween illustrative analyses, such as for the NAAQS , and its associated implementation rules, please see the Ozone NAAQS RI A (U.S. EPA, 2015) [Note, insert reference to 2015 ozone NAAQS RIA whe n/if finalized]

.
6.2.1 Health Impact Assessment for Ozone and PM 2.5
The Integrated Science Assessment for Ozone and Related Photochemical Oxidants (Ozone ISA) (U.S. EPA, 2013b) identified the human health effects associated with ambient ozone exposure, which also include premature mortal ity and a variety of morbidity effects associated with acute and chronic exposures. Simila rly, the Integrated Science Assessment for
Particulate Matter

(PM ISA) (U.S. EPA, 2009b) identified the human he alth effects associated
with ambient PM 2.5 exposure, which include premature mortality and a v ariety of morbidity effects associated with acute and chronic exposures . Table 6-1 identifies the quantified and unquantified benefit and co-benefit categories capt ured 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
, such as those associated with exposure to NO 2 and mercury, n 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 benefits categories is not exhaustive, nor is quantification of each effect co mplete.6-5

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

Been Monetized More

Information Improved Human Health Reduced incidence of mortality 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 morbidity 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 ISA 2 Cardiovascular and nervous system effects

— — Ozone ISA 2 Reproductive and developmental effects

— — Ozone ISA 2,3 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 morbidity 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 ISA 2 Other respiratory effects (e.g., pulmonary function
, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations) — — PM ISA 2
Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc.) — — PM ISA 2,3 Cancer, mutagenicity, and genotoxicity effects — — PM ISA 2,3

1 We assess these co-benefits qualitatively due to d ata 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 d ata or methods. 3 We assess these co-benefits qualitatively because c urrent 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 calculati ng benefits, which estimates changes in individual health endpoints (specific effects th at can be associated with changes in air quality)6-6

and assigns values to those changes assuming indepe ndence of the values for those individual endpoints. Because the EPA rarely has the time or r esources to perform new research to measure directly either health outcomes or their values for regulatory analyses, our estimates are based on the best available methods of benefits transfer, wh ich is the science and art of adapting primary
research from similar contexts to estimate benefits for the environmental quality change under analysis. In addition to transferring information f rom other contexts to the context of this regulation, we also use a “benefit-per-ton” approac h to estimate the ozone and PM 2.5 benefits in this RIA. Benefit-per-ton approaches apply an avera ge benefit per ton derived from modeling of benefits of specific air quality scenarios to estim ates of emissions reductions for scenarios where no air quality modeling is available. Thus, to deve lop estimates of benefits for this RIA, we are transferring both the underlying health and economi c information from previous studies and information on air quality responses to emissions r eductions from other air quality modeling. This section describes the underlying basis for the health and economic valuation estimates that inform the benefit-per-ton estimates, and the subse quent section provides an overview of the benefit-per-ton estimates, which are described in d etail in the appendix to this chapter.

The benefit-per-ton approach we use in this RIA rel ies 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 popula tion data, baseline health information, air quality data and economic valuation information to conduct health impact and economic benefits assessments. These assessments form the key inputs to calculating benefit-per-ton estimates. The next sections provide an overview of the health imp act assessment (HIA) methodology and additional details on several key elements.

The HIA quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to ozone and PM 2.5 . We use the environmental Benefits Mapping and Analysis Program – Community Edition (BenMAP-CE ) (version 1.1) to systematize health impact analyses by applying 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 effe cts that are directly 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. E pidemiological studies generally provide6-7

estimates of the relative risks of a particular hea lth 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 pollut ion (e.g., ozone) to changes in the incidence of a
health effect. We refer the reader to the Ozone NAA QS RIA (U.S. EPA, 2015) [Note, insert reference to 2015 ozone NAAQS RIA when/if finalized ] and PM NAAQS RIA (U.S. EPA, 2012a) for more information regarding the epidemiol ogy studies and risk coefficients applied in this analysis, and we briefly elaborate on adult pr emature mortality below. The size of the mortality effect estimates from epidemiological stu dies, the serious nature of the effect itself, and the high monetary value ascribed to reducing risks of premature death make mortality risk reduction the most significant health endpoint quan tified in this analysis.

6.2.1.1 Mortality Effect Coefficients for Short-ter m Ozone Exposure The overall body of evidence indicates that there i s likely to be a causal relationship between short-term ozone exposure and premature dea th. The 2013 ozone ISA concludes that the evidence suggests that ozone effects are independen t of the relationship between PM and mortality. (U.S. EPA, 2013a). However, the ISA note s that the interpretation of the potential confounding effects of PM on ozone-mortality risk e stimates 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 by PM (U. S. EPA 2013a). 3

In 2006, the EPA requested an NAS study to answer t he following four key questions regarding ozone-related mortality: (1) How 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-rel ated mortality risk and life expectancy?

In 2008, the NAS (NRC, 2008) issued a series of rec ommendations to the EPA regarding the quantification and valuation of ozone-related s hort-term mortality. Chief among these was

3 Consequently, as noted later in this section, whil e we have used a single-pollutant model in generati ng short-term ozone-related mortality benefits for this RIA, we h ave included a co-pollutants model as a sensitivity analysis to address potential uncertainty associated with this assumption of an independent ozone mortality effect

.6-8

that “...short-term exposure to ambient ozone is like ly 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 N AS also recommended that “...the greatest emphasis be placed on the multi-city and NMMAPS stu dies without exclusion of the metaanalyses” (NRC, 2008). In addition, NAS recommended that EPA “should give little or no weight to the assumption that there is no causal as sociation between estimated reductions in premature mortality and reduced ozone exposure” (NR C, 2008). In 2010, the Health Effects Subcommittee of the Advisory Council on Clean Air C ompliance Analysis, 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 m ortality benefits (U.S. EPA-SAB, 2010a).

In view of the findings of the ozone ISA, the NAS p anel, the SAB-HES panel, and the CASAC panel, we estimate ozone-related premature mo rtality for short-term exposure in the core health effects analysis using effect coefficie nts from the Smith et al. (2009) NMMAPS analysis and the Zanobetti and Schwartz (2008) mult i-city study with several additional studies as sensitivity analyses. This emphasis on newer mul ti-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 Zanobet ti and Schwartz (2008) studies for the ozone HREA (U.S. EPA-SAB, 2012, 2014), and these are mult i-city studies published more recently (as compared with other multi-city studies or meta- analyses included in the sensitivity analyses – 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 explored the sensitivity of the mortality effect to different model specifications including (a) region al versus national Bayes-based adjustment, 4

4 In Bayesian modeling, effect estimates are “updated ” from an assumed prior value using observational d ata. In the Smith et al. (2009) approach, the prior values are either a regional or national mean of the individua l effect estimates obtained for each individual city. The Ba yesian adjusted city-specific effect estimates are then calculated by updating the selected prior value bas ed on the relative precision of each city-specific estimate and the variation observed across all city-specific ind ividual effect estimates. City-specific estimates a re pulled towards the prior value if they have low precision and/or if there is low overall variation across est imates. City-6-9

(b) co-pollutant models considering PM 10 , (c) all-year versus ozone-season based estimates, and (d) consideration of a range of ozone metrics, incl uding the daily 8-hour max. In addition, the Smith et al. (2009) study did not use the trimmed m ean approach employed in the Bell et al. (2004) study in preparing ozone monitor data. 5 In selecting effect estimates from Smith et al. (2009), we focused on an ozone-only estimate for no n-accidental mortality using the 8-hour max
metric for the warmer ozone season. For the sensitivity analysis, we included a co-poll utant model (ozone and PM 10 ) from Smith et al. (2009) for all-cause mortality, using the 8-hour max ozone metric for the ozone season. Using a single p ollutant model for the core analysis and the co-pollutant model in the sensitivity analysis refl ects our concern that the reduced sampling frequency for days with co-pollutant measurements ( 1/3 and 1/6) could affect the ability of the study to characterize the ozone effect. This choice is consistent with the ozone ISA, which concludes that ozone effects are likely to be indep endent of the relationship between PM and mortality (U.S. EPA, 2013a).

The Zanobetti and Smith (2008) study evaluated the relationship between ozone exposure (using an 8-hour mean metric for the warm season Ju ne-August) and all-cause mortality in 48 U.S. cities using data collected between 1989 and 2 000. The study presented single pollutant CR 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 t he strongest effect and tighter confidence
interval. We converted the effect estimate from an 8-hour mean metric to an equivalent effect
estimate based on an 8-hour ma x to account for the period of the day in which mos t individuals are exposed to ozonex . To do this, we used the ozone metric approach whe rein the original effect estimate (and standard error) is multiplied by the appropriate ozone metric adjustment ratio. 6

specific estimates are given less adjustment if the y are precisely estimated and/or there is greater o verall variation across estimates. 5 There are a number of concerns regarding the trimm ed mean approach including (1) the potential loss o f temporal variation in the data when the approach is used (th is could impact the size of the effect estimate), a nd (2) a lack of complete documentation for the approach, which prev ents a full reviewing or replication of the techniq ue. 6 These adjustment ratios are created by (a) obtaini ng summary air quality (composite monitor values) f or each urban study area/ozone season combination reflected in the original epidemiology study, (b) calculatin g the ratio of the 8-hour max to the study-specific air metric (for each of the urban study areas), and (c) taking the average of these urban-study area ratios. Ratio adjustment of the effect estimate does introduce uncertainty into the benefits estimates generated using these adjusted effect est imates; however, adjustments of relatively similar metrics (e.g.,6-10

6.2.1.2 PM

2.5 Mortality Effect Coefficients for Adults and Infan ts A substantial body of published scientific literatu re 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, t oxicology, and clinical studies. The PM ISA completed as part of the most recent review of the PM NAAQS, which was twice reviewed by the SAB-CASAC (U.S. EPA-SAB, 2009a, 2009b), conclud ed 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 red uction the most significant health endpoint quantified in this analysis.

Researchers have found statistically significant as sociations between PM 2.5 and premature mortality using different types of study designs. Time-series methods have been used to relate short-term (often day-to-day) changes in PM 2.5 concentrations and changes in daily mortality rates up to several days after a period o f exposure to elevated PM 2.5 concentrations. Cohort methods have been used to examine the potent ial relationship between community-level
PM 2.5 exposures over multiple years (i.e., long-term exp osures) and community-level annual mortality rates that have been adjusted for individ ual level risk factors. When choosing between using short-term studies or cohort studies for esti mating mortality benefits, cohort analyses 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 e xposures, as well as some fraction of shortterm exposures (Kunzli et al., 2001; NRC, 2002). Th e National Research Council (NRC) stated
that “it is essential to use the cohort studies in benefits analysis to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). T he NRC further noted that “the overall effect estimates may be a combination of effects from long -term exposure plus some fraction from short-term exposure. The amount of overlap is unkno wn” (NRC, 2002, p. 108-9). To avoid

8-hour max and 8-hour mean), as is the case with th e Zanobetti and Schwartz (2008) study, are likely t o introduce less uncertainty than adjustments for more disparat e ratios (e.g., 24-hour or 1-hour max ratios to 8-h our max equivalents).6-11

double counting, we focus on applying the risk coef ficients from the long-term cohort studies in estimating the mortality impacts of reductions in P M 2.5
. Over the last two decades, several studies using “p rospective cohort” designs have been published that are consistent with the earlier body of literature. Two prospective cohort 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 Soci ety” or “ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004; Krewski et al ., 2009), provide the most extensive analyses of
ambient PM 2.5 concentrations and mortality. These studies have f ound consistent relationships between fine particle indicators and premature mort ality across multiple locations in the United States. The credibility of these two studies is fur ther enhanced by the fact that the initial published studies (Pope et al., 1995; Dockery et al ., 1993) were subject to extensive reexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effects Institute (HEI) and by a Special Pan el of the HEI Health Review Committee (Krewski et al., 2000). Publication of studies conf irming and extending the findings of the 1993 Six Cities Study and the 1995 ACS study using more recent air quality data and a longer followup period for the ACS cohort provides additional va lidation of the findings of these original studies (Pope et al., 2002, 2004; Laden et al., 200 6; Krewski et al., 2009; Lepeule et al., 2012). The SAB-HES also supported using these two cohorts for analyses of the benefits of PM reductions, and concluded, “the selection of these cohort studies as the underlying basis for PM mortality benefit estimates [is] a good choice. The se are widely cited, well studied and extensively reviewed data sets” (U.S. EPA-SAB, 2010 a). 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 reanalysis of these cohorts (Krewski et al., 2009; Lepeule et al., 2012). Presenting results using bot h ACS and Six Cities is consistent with other recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 2011c). The PM ISA concludes that the ACS and Six Cities cohorts provide the strongest evidence of th e association between long-term PM 2.5 exposure and premature mortality with support from a number of additional cohort studies (described below).

The extended analyses of the ACS cohort data (Krews ki et al., 2009) refined the earlier ACS studies by (a) extending the follow-up period b y 2 years to the year 2000, for a total of 186-12

years; (b) incorporating almost double the number o f urban areas; (c) addressing confounding by spatial autocorrelation by incorporating ecological , or community-level, co-variates; and (d) performing an extensive spatial analysis using land use regression modeling in two large urban areas. These enhancements make this analysis well-s uited for the assessment of mortality risk from long-term PM 2.5 exposures for the EPA’s benefits analyses.

In 2009, the SAB-HES again reviewed the choice of m ortality risk coefficients for benefits analysis, concluding that “[t]he Krewski e t 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 support for including this study in this RIA. For example, the PM ISA (U.S. EP A, 2009b) included this study among the key mortality studies. In addition, the risk assess ment supporting the PM NAAQS (U.S. EPA, 2010b) used risk coefficients drawn from the Krewsk i et al. (2009) study, the most recent reanalysis of the ACS cohort data. The PM risk asse ssment 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 period of observa tion, the rigorous examination of model forms and effect estimates, the coverage for ecolog ical variables, and the large dataset with over 1.2 million individuals and 156 MSAs (U.S. EPA, 201 0b). 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, 2 010b), which was reviewed by the CASAC (U.S. EPA-SAB, 2009a, b), we use the all-caus e mortality risk estimate based on the random-effects Cox proportional hazard model that i ncorporates 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 t he confidence interval around the Krewski et al. (2009) risk estimate is tighter.6-13

In the most recent Six Cities study, which was publ ished 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 exposures. The authors found significant associations
between PM 2.5 exposure and increased risk of all-cause, cardiova scular and lung cancer mortality. The authors also concluded that the C-R relationship was linear down to PM 2.5 concentrations of 8 μ g/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 monetized benefits associated with PM 2.5 are driven largely by reductions in premature mortality, it is important to characteriz e the uncertainty in this endpoint. In order to do so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 (Roman et al., 2008; IEc, 2006). The results of tha t expert elicitation can be used as a characterization of uncertainty in the C-R function s . The co-benefits results derived from expert elicitation is discussed in Appendix 6B (section 6B .4)

. In addition to the adult mortality studies describe d above, several studies show an association between PM exposure and premature morta lity in children under 5 years of age. 7 The PM ISA states that less evidence is available regar ding the potential impact of PM 2.5 exposure on infant mortality than on adult mortality and the re sults of studies in several countries include a range of findings with some finding significant ass ociations. Specifically, the PM ISA concluded that evidence exists for a stronger effect at the p ost-neonatal period and for respiratory-related mortality, although this trend is not consistent ac ross all studies. In addition, compared to avoided premature deaths estimated for adult mortal ity, avoided premature deaths for infants are significantly smaller because the number of infants in the population is much smaller than the

7 For the purposes of this analysis, we only calcula te benefits for infants age 0–1, not all children u nder 5 years old.6-14

number of adults and the epidemiology studies on in fant mortality provide smaller risk coefficients associated with exposure to PM 2.5
.
In 2004, the SAB-HES noted the release of the WHO G lobal Burden of Disease Study focusing on ambient air, which cites several recent ly published time-series studies relating daily PM exposure to mortality in children (U.S. EPA-SAB, 2004). With regard to the cohort study conducted by Woodruff et al. (1997), the SAB-HES no ted several strengths of the study, including the use of a larger cohort drawn from a l arge 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 ma ternal smoking status). Based on these findings, the SAB-HES recommended that the EPA inco rporate infant mortality into the primary benefits estimate and that infant mortality be eval uated using an impact function developed from the Woodruff et al. (1997) study (U.S. EPA-SAB, 200 4).

In 2010, the SAB-HES again noted the increasing bod y of literature relating infant mortality and PM exposure and supported the inclusi on of infant mortality in the monetized benefits (U.S. EPA-SAB, 2010a). The SAB-HES general ly supported the approach of estimating infant mortality based on Woodruff et al. (1997) bu t also noted that a more recent study by Woodruff et al. (2006) continued to find associatio ns 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.” Consiste nt with The Benefits and Costs of the Clean Air
Act 1990 to 2020 (U.S. EPA, 2011a), we continue to rely on the earl ier 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 impa cts, we estimate the economic value of these avoided impacts. Reductions in ambient concen trations of air pollution generally lower the risk of future adverse health effects by a small am ount 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 admission s, WTP estimates are generally not available, so we use the cost of treating or mitigating the ef fect. These cost-of-illness (COI) estimates generally (although not necessarily in every case) understate the true value of reductions in risk6-15

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 analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a).

For this proposed rule Aa voided premature deaths account for over 90 percent of monetized ozone-related benefits and 98 percent of monetized PM-related co-benefits. The economics literature concerning the appropriate met hod 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 di scussion within the economics and public policy analysis community. Following the advice of the SAB’s Environmental Economics Advisory Committee (SAB-EEAC), the EPA currently us es the value of statistical life (VSL) approach in calculating estimates of mortality bene fits, because 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, 200 0). The VSL approach 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 va luing mortality risk reductions, and the Agency consulted several times with the SAB-EEA C on this issue. Until updated guidance is available, the Agency determined that a single, pee r-reviewed estimate applied consistently, best
reflects the SAB-EEAC advice it has received. There fore, the EPA has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014) 8 while the Agency continues its efforts to update i ts guidance on this issue. This approach calculates a mean value across VSL es timates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 9 We then adjust this VSL to account for the currenc y year and to

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 fur ther updates to the mortality risk valuation guidan ce would be forthcoming. 9 In 1990$, this base VSL is $4.8 million.6-16

account for income growth from 1990 to the analysis year. Specifically, the VSL s applied in this analysis in 2011$ after adjusting for income growth are is $9.9 million for 2017.

The Agency is committed to using scientifically sou nd, appropriately reviewed evidence in valuing mortality risk reductions and has made sign ificant progress in responding to the SABEEAC’s specific recommendations. In the process, th e Agency has identified a number of important issues to be considered in updating its m ortality 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 re commendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). The EPA is taki ng SAB’s recommendations under
advisement.

In valuing PM 2.5 -related premature mortality, we discount the value of premature mortality occurring in future years using rates of 3 percent and 7 percent (OMB, 2003). We assume that there is a “cessation” lag between changes in PM ex posures and the total realization of changes in health effects. Although the structure of the la g is uncertain, the EPA follows the advice of the SAB-HES to assume a segmented lag structure charact erized by 30 percent of mortality reductions in the first year, 50 percent over years 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 la g 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 analysis year, the estimated
ozone-related co-benefits are identical for all dis count rates.

6.2.3 Benefit-per-ton Estimates for Ozone
We used a “benefit-per-ton” approach to estimate th e ozone benefits 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 tota l monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of NO X (an ozone precursor). We generated benefit-per-ton estimates for ozone based on air quality modeling for the illustrative control case described in Chapter 3 of this RIA. The estimates correspond to NO X emissions from U.S. EGUs during the ozone-season (M ay to September). Because we estimate6-17

ozone health impacts from May to September only, th is approach underestimates ozone benefits in areas with a longer ozone season such as Texas. These estimates assume that EGU-attributable ozone formation at the regional-level is due to NOx NO X alone. Because EGUs emit little VOC
relative to NO X emissions, it is unlikely that VOCs emitted by EGU s would contribute substantially to regional ozone formation. We provi de more detailed information regarding the generation of these estimates in the appendix to th is chapter.

As noted below in the characterization of uncertain ty, all benefit-per-ton estimates have inherent limitations. Specifically, 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 local variability in population den sity, meteorology, exposure, baseline health incidence rates, or other local factors for any spe cific location. The benefit-per-ton estimates derived for use in this RIA, although less subject to these types of uncertainties than national estimates, still should be interpreted with caution

.
6.2.4 Benefit-per-ton Estimates for PM 2.5 We used a “benefit-per-ton” approach to estimate th e PM 2.5 co-benefits in this RIA, which represent the total monetized human health co-benef its (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 analysis, w e multiplied the benefit-per-ton estimates by the corresponding emission reductions that were generat ed from air quality modeling of the illustrative control case. The appendix to this cha pter provides additional detail regarding these calculations. All benefit-per-ton estimates have i nherent limitations and should be interpreted with caution. Even though we assume that all fine p articles have equivalent health effects, the benefit-per-ton estimates vary between precursors d epending 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 provides the benefit-per-ton estimates fo r the analysis year : 2017 ; because the modeled change in SO 2 levels was very small, we did not quantify the SO 2 -related PM 2.56-18

benefits . Table 6-3 provides the emission reductions estima ted to occur in the analysis year. 10
Table 6-4 summarizes the national monetized ozone-r elated and PM-related health benefits estimated to occur for regulatory control alternati ves for the 2017 analysis year, by precursor pollutant using discount rates of 3 percent and 7 p ercent. Table 6-5 provides national summaries
of the reductions in estimated health incidences as sociated with the proposal and more and less stringent alternatives for the 2017 analysis year. 11 Figure 6-1 provides a visual representation of the range of estimated ozone and PM 2.5 -related benefits using benefit-per-ton estimates b ased on concentration-response functions from different stu dies 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 tT he Illustrative Control Case in 2017 (2011$)*

Pollutant Discount Rate National (from emission reductions in 23 states) 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 epid emiology studies for avoided premature mortality fo r ozone and PM 2.5 . All estimates are rounded to two significant figu res. The monetized co-benefits do not include reduc ed health effects from direct exposure 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 loc ation and magnitude of their impact on PM 2.5 concentrations, which drive population exposure. T he monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-t on 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. Confidenc e intervals are unavailable for this analysis becau se 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 perce nt of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012).

Table 6-3. Emission Reductions of Criteria Polluta nts for the Proposal and More and

Less Stringent Alternatives in 2017 (thousands of s hort tons)*

Proposal More Stringent Alternative Less Stringent Alternative Ozone Season NO X 85,000 87,000 24,000 All Year NO X 90,000 93,000 24,000 All Year SO 2 380 430 300

10 See Chapter 5 of this RIA for more information reg arding the expected emission reductions used to cal culate the health co-benefits in this chapter. Chapter 4 also provides more information regarding the illustrativ e plan approach.

11 Incidence estimates were generated using the same “per ton” approach as used to generate the dollar b enefit per ton values. See Appendix 46 -A for details.6-19

*All emissions shown in the table are rounded, so r egional emission reductions may appear to not sum t o national total. Table 6-4. Summary of Estimated Monetized Health B enefits for the Proposal and

More and Less Stringent Alternatives Regulatory Con trol Alternatives for 2017 (millions of 2011$) *

Pollutant Proposal More Stringent Alternative Less Stringent

Alternative NOx (as Ozone) $490 to $790 $500 to $820 $140 to $220 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 $340

7% Discount Rate $650 to $1,200 $670 to $1,200 $180 to $330 * All estimates are rounded to two significant figu res 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 est imated monetized co-benefits do not include climate benefits or reduced health effects from direct exposure to NO 2 , SO 2 , ecosystem effects, or visibility impairment. All fi ne particles are assumed to have equivalent health eff ects, but the benefit-per-ton estimates vary depend ing on the location and magnitude of their impact on PM 2.5 levels, which drive population exposure. The moneti zed co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM 2.5 precursors are based on regional benefit-per-ton es timates. Co-benefits for ozone are based on ozone season NOx NO X emissions. Ozone co-benefits occur in analysis year , so they are the same for all discount rates. Conf idence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, th e 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 f unction used. For this RIA, we did not estimate changes in emissi ons of directly emitted particles. As a result, qua ntified PM 2.5 related benefits are underestimated by a relatively small amount. In the proposal RIA, the benefits from reductions in directly emitted PM 2.5 were less than 10 percent of total monetized healt h co-benefits across all scenarios and years.6-20

Table 6-5. Summary of Avoided Health Incidences fr om Ozone-Related and PM 2.5 -

Related Benefits 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 23 Avoided Morbidity Hospital admissions—respiratory causes (ages > 65) 79 81 22 Emergency room visits for asthma (all ages) 320 330 90 Asthma exacerbation (ages 6-18) 93,000 95,000 26,000 Minor restricted-activity days (ages 18-65) 240,000 240,000 67,000 School loss days (ages 5-17) 77,000 79,000 22,000 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 Acute bronchitis (age 8–12) 31 32 8.1 Lower respiratory symptoms (age 7–14) 390 400 100 Upper respiratory symptoms (asthmatics age 9–11) 560 580 150 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) 26 25 6.6 Pooled estimate of 4 studies 2.8 2.7 0.71 * All estimates are rounded to whole numbers with t wo significant figures. Co-benefits for PM 2.5 precursors are based on regional incidence-per-ton estimates for a ll precursors . 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 interval for the health impa ct function alone ranges from approximately ±30 percent for mortality incidence b ased on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). The confidence intervals around the ozone mortalit y estimates are on the order of +/- 60 percent depending on the concentration-response function us ed.6-21OzonePM2.5

Figure 6-1. Monetized Health Benefits of Proposal for 2017 *

*The PM 2.5 graphs show the estimated PM 2.5 co-benefits at discount rates of 3% and 7% using ef fect 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 mortali ty (Roman et al. , 2008). The results shown are not the direct results from the studies or expert elicitation; rat her, the estimates are based in part on the concent ration-response functions provided in those studies. Ozone co- benefits occur in the analysis year, so they are th e same for all discount rates. These estimates do not include bene fits from reductions in CO 2 . The monetized co-benefits do not include climate benefits from changes in NO 2 and SO 2 or reduced health effects from direct exposure to N O 2 , SO 2 , ecosystem effects, or visibility impairment. For this RIA, we did not estimate changes in emiss ions of directly emitted particles. As a result, quantified PM 2.5 related benefits are underestimated by a relativel y small amount. In the proposal RIA, the benefits from reductions in d irectly emitted PM 2.5 were less than 10 percent of total monetized health co-benefits across all scenarios and years.6-22

6.2.6 Characterization of Uncertainty in the Estim ated Health bB enefits In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. This analysis includes many data sources as inputs, including emi ssion inventories, air quality 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 an d would affect the estimate of benefits. When the uncertainties from each stage of the analysis a re 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 analyses based directly on air quality modeling. Therefore, the estimates of benefits shou ld be viewed as representative of the general magnitude of benefits of the regulatory control alt ernatives for the 2017 analysis year, rather than the actual benefits anticipated from implement the proposal.

This RIA does not include the type of detailed unce rtainty assessment found in the Ozone NAAQS RIA (U.S. EPA, 2015) [Note, insert reference to 2015 ozone NAAQS RIA whe n/if finalized] or the PM NAAQS RIA (U.S. EPA, 2012a) because we l ack the necessary air quality modeling input and/or monitoring data to run the be nefits model. However, the results of the quantitative and qualitative uncertainty analyses p resented in the Ozone NAAQS RIA and PM NAAQS RIA can provide some information regarding th e uncertainty inherent in the estimated benefits results presented in this analysis. For ex ample, sensitivity analyses 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 perc ent by between 10 percent and –27 percent and that alternative income growth adjustme nts could change the PM 2.5 -related mortality benefits by between 33 percent and −14 percent. Alt hough we generally do not calculate confidence intervals for benefit-per-ton estimates as they can provide an incomplete picture about the overall uncertainty in the benefits estim ates, the PM NAAQS RIA provides an indication of the random sampling error in the heal th impact and economic valuation functions using Monte Carlo methods. 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 estimates6-23

based on Krewski et al. (2009) and Lepeule et al. (2012). The 95 th percentile confidence interval for the health impact function alone ranges from ap proximately ±30 percent for mortality incidence
based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012).

Unlike RIAs for which the EPA conducts scenario-spe cific air quality modeling, we do not have information on the specific location of the ai r quality changes associated with the proposal. Instead, wW e applied benefit-per-ton estimates, which reflect specific geographic patterns of emissions reductions and specific air quality and b enefits modeling assumptions. For example, these estimates may not reflect local variability i n population density, meteorology, exposure, baseline health incidence rates, or other local fac tors that might lead to an over-estimate or
under-estimate of the actual co-benefits of control ling PM and ozone precursors. As such, it is not feasible to estimate the proportion of co-benef its occurring in different locations , such as designated nonattainment areas . Use of these benefit-per-ton values to estimate b enefits may lead to higher or lower benefit estimates than if benefi ts were calculated based on direct air quality modeling. Great care should be taken in applying th ese estimates to emission reductions occurring in any specific location, as these are al l based on a broad emission reduction scenario and therefore represent average benefits-per-ton ov er the entire region. The benefit-per-ton for emission reductions in specific locations may be ve ry different than the estimates presented here. To the extent that the geographic distribution of t he emissions reductions achieved by implementing the proposal is different than the emi ssions in the air quality modeling of the illustrative control case, the benefits may be unde restimated or overestimated.

Our estimate of the total monetized benefits is bas ed on the EPA’s interpretation of the
best available scientific literature and methods an d supported by the SAB-HES and the National Academies of Science (NRC, 2002). Below are key ass umptions underlying 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 th eir chemical composition, are equally potent in causing premature mortality. This is an i mportant assumption, because PM 2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estim ates by particle type. The PM ISA concluded that “many constituents of PM 2.5 can be linked with multiple health effects, and6-24

the evidence is not yet sufficient to allow differe ntiation of those constituents or sources that are more closely related to specific outcomes” (U.S. EPA, 2009b).

2. We assume that the health impact function for fine particles is log-linear without a threshold. Thus, the estimates include health co-be nefits 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 in attai nment, down to the lowest modeled concentrations.

3. We assume that there is a “cessation” lag between t he change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM 2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the a dvice of the SAB-HES (U.S. EPASAB, 2004c), which affects the valuation of mortali ty 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 o bserved PM concentrations in the epidemiological studies that are used to estimate t he benefits. Likewise, we are less confident in
the risk we estimate from simulated PM 2.5 concentrations that fall below the bulk of the obs erved
data in these studies. Concentration benchmark anal yses (e.g., lowest measured level [LML], one standard deviation below the mean of the air qualit y data in the study, etc.) allow readers to determine the portion of population exposed to annu al mean PM 2.5 levels at or above different concentrations, which provides some insight into th e level of uncertainty in the estimated PM 2.5 mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and one standard deviation below the mean) that have be en 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 whi ch our confidence in reported associations becomes appreciably less, and the scientific eviden ce provides no clear dividing line. However,
the EPA does not view these concentration benchmark s as a concentration threshold below which we would not quantify health benefits of air quality improvements. 12 Rather, the cobenefits estimates reported in this RIA are the bes t estimates because they reflect the full range

12 For a summary of the scientific review statements regarding the lack of a threshold in the PM 2.5 -mortality relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Th reshold in the Concentration-Response Function for PM 2.5 -related Mortality (U.S. EPA, 2010b).6-25

of air quality concentrations associated with the r egulatory control alternatives. The PM ISA 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 estim ate PM-related long-term mortality (U.S. EPA, 2009b).

We report also the key assumptions associated with our analysis of ozone-related effects:

• Key assumption and uncertainties related to modelin g of ozone-related premature mortality : Ozone-related short-term mortality represents a s ubstantial proportion of total monetized benefits (over 94% of the ozone-related-b enefits), and these estimates have the following key assumptions and uncertainties. We uti lize a log-linear impact function without a threshold in modeling short-term ozone-re lated mortality. However, we acknowledge reduced confidence in specifying the na ture of the C-R function in the range
of ≤ 20ppb and below (ozone ISA, section 2.5.4.4). Thus, the estimates include health benefits from reducing ozone in areas with varied c oncentrations 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 premature mortality according to baseline p ollutant concentrations : We recognize that, in estimating short-term ozone-rela ted mortality, we are less confident in specifying the shape of the C-R function at lower a mbient ozone concentrations (at and below 20 ppb, ozone ISA, section 2.5.4.4). Quantita tive uncertainty analyses completed for the Ozone NAAQS RIA (U.S. EPA, 2015) found almost 1 00% of mortality reductions occurred above 20 ppb, where we are more confident in specifying the nature of the ozonemortality effect (ozone ISA, section 2.5.4.4). Howe ver, as discussed in section 6B.7 of that RIA, care must be taken in interpreting these resul ts since the ambient air metric used in modeling this endpoint is the mean 8-hour max valu e in each grid cell (and not the full distribution of 8-hour daily max values). Had the l atter been used, then the distribution would have likely been wider.6-26

For this analysis, policy-specific air quality data is not available, and the control scenarios are illustrative of what states may choose to do. H owever, we believe that it is still important to characterize the distribution of exposure to baseli ne 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 mo deling used to calculate the benefit-per-ton estimates for this RIA using 12 km grid cells acros s the contiguous U.S. 13 It is 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 qualit y. In other words, the percentage of the population exposed to air pollution below the LML i s 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 quant ify without rule-specific air quality modeling, is the shift in exposure anticipated by i mplementing the proposal. Therefore, caution is warranted when interpreting the LML assessment in t his RIA because these results are not consistent with results from RIAs that had air qual ity modeling.

Figure 6-3 shows a bar chart of the percentage of t he population exposed to various air quality levels, including the LML concentration ben chmarks in the illustrative control case modeling, and Figure 6-4 shows a cumulative distrib ution function of the same data. Both figures identify the LML for each of the major cohort studi es.

13 As described in Chapter 3, the baseline for the ai r quality modeling used to calculate the benefit-pe r-ton values differs from the baseline used to estimate the bene fits, costs, and impacts of this rulemaking. See Ch apter 3 for more details about the differences between the two basel ines.6-27

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

the Baseline Illustrative Modeling (uu sed for the Air Quality Analysis in Chapter 3to generate the benefit-per-ton estimates) Among 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

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

PM 2.5 Exposure in the Baseline used for the Air Quality Analysis in Chapt er 3Illustrative Modeling (used to generate the benefi t-per-ton estimates)

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 t he economic valuation of CO 2 using the Social Cost of Carbon (SC-CO 2 ), a metric that estimates the monetary value of im pacts associated with marginal changes in CO 2 emissions in a given year. Table 6-8 summarizes th e quantified and unquantified climate benefits in thi s analysis. Among 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) study6-29

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

Monetized More

Information Improved 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 ISA 2 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 est imated within each integrated assessment model as part of the calculat ion 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 effect s of quantified changes in CO 2 emissions.
2 We assess these co-benefits qualitatively because we do not have sufficient confidence in available d ata or methods. There are several important considerations in asses sing the climate-related benefits for an ozone air quality-focused rulemaking. First, the es timated health benefits do not account for any climate-related air quality changes (e.g., increase d ambient ozone associated with higher temperatures). Excluding climate-related air qualit y changes may underestimate ozone-related
health benefits. It is unclear how PM 2.5 -related health benefits would be affected by exclu ding climate-related air quality changes since the scien ce is unclear as to how climate change may
affect PM 2.5 exposure. Second, the estimated health benefits als o do not consider temperature
modification of PM 2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 20 08a, 2008b). Third, the estimated climate co-benefits reported 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 qualit y-related health benefits likely leads to underestimation of quantified health benefits (Anen berg et al, 2009, Jhun et al, 2014). Fourth, as noted earlier, we do not estimate the climate co-be nefits associated with reductions in PM and O 3

ozone precursors.

6.3.1 Climate Change Impacts
Through the implementation of CAA regulations, the EPA addresses the negative externalities caused by air pollution. In 2009, the EPA Administr ator found that elevated concentrations of greenhouse gases in the atmosphere may reasonably b e anticipated both to endanger public health and to endanger public welfare. For health, these include the increased likelihood of heat waves, negative impacts on air quality, more intens e hurricanes, more frequent and intense6-30

storms and heavy precipitation, and impacts on infe ctious and waterborne diseases. For welfare, these include reduced water supplies in some region s, increased water pollution, increased occurrences of floods and droughts, rising sea leve ls and damage to coastal infrastructure, increased peak electricity demand, changes in ecosy stems, and impacts on indigenous
communities. The preamble also summarizes new scientific assessm ents and recent climatic observations. Major scientific assessments released since the 200 9 Endangerment Finding have improved scientific understanding of the climate, and provid e even more evidence that GHG emissions endanger public health and welfare for current and future generations. The National Climate Assessment (NCA), in particular, assessed the impac ts of climate change on human health in the United States, finding that Americans will be affec ted 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 groups such as children, the elderly and low income households. Furthermore, the assessments present an improved understanding of th e impacts of climate change on public welfare, higher projections of future sea level ris e 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 a cidification. The impacts of GHG emissions will be realized worldwide, independent of their lo cation 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 R egulatory 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 Executi ve Order 12866 , Interagency Working Group on Social Cost of Carbon, with participation by Council of Economi c Advisers, Council on Environmental Quality, Depar tment of Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental Protection Agency, National Economic Council, Offic e of Energy and Climate Change, Office of Managemen t and6-31

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 associ ated with marginal changes in CO 2 emissions in a given year. It includes a wide range of antici pated climate impacts, such as net changes in agricultural productivity and human health, propert y damage from increased flood risk, and changes in energy system costs, such as reduced cos ts for heating and increased costs for air conditioning. It is typically used to assess the av oided damages as a result of regulatory actions (i.e., benefits of rulemakings that lead to an incr emental reduction in cumulative global CO 2 emissions).

The SC-CO 2 estimates used in this analysis were developed ove r many years, using the best science available, and with input from the pub lic. Specifically, an interagency working group (IWG) that included the EPA and other executi ve branch agencies and offices used three integrated assessment models (IAMs) to develop the SC-CO 2 estimates and recommended four global values for use in regulatory analyses. The S C-CO 2 estimates were first released in February 2010 and updated in 2013 using new version s of each IAM. As discussed further below, the IWG published two minor corrections to t he 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 liter ature with the ability to estimate the SC-CO 2
. A key objective of the IWG was to draw from the ins ights of the three models while respecting the different approaches to linking GHG emissions a nd monetized damages taken by modelers in the published literature. After conducting an exten sive literature review, the interagency group selected three sets of input parameters (climate se nsitivity, socioeconomic and emissions trajectories, and discount rates) to use consistent ly in each model. All other model features were left unchanged, relying on the model developers’ be st estimates and judgments, as informed by the literature. Specifically, a common probability distribution for the equilibrium climate sensitivity parameter, which informs the strength o f climate’s response to atmospheric GHG concentrations, was used across all three models. I n addition, a common range of scenarios for the socioeconomic parameters and emissions forecast s were used in all three models. Finally, the

Budget, Office of Science and Technology Policy, an d Department of Treasury (May 2013, Revised July 20 15). Available at: <https://www.whitehouse.gov/sites/def ault/files/omb/inforeg/scc-tsd-final-july-2015.pdf> Accessed 7/11/2015.6-32

marginal damage estimates from the three models wer e estimated 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 u ncertainties, and the current TSD for the
latest estimates. 15

The SC-CO 2 estimates represent global measures because of the distinctive nature of climate change, which is highly 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) damag es caused by GHG emissions to address the global nature of the problem. Second, t he U.S. operates in a global and highly interconnected economy, such that impacts on the ot her side of the world can affect our economy. 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, cl imate change represents a classic public goods problem because each country’s reductions benefit e veryone else and no country can be excluded from enjoying the benefits of other countries’ redu ctions, even if it provides no reductions itself. In this situation, the only way to achieve an econo mically efficient level of emissions reductions is for countries to cooperate in providing mutually beneficial reductions beyond the level that would be justified only by their own domestic benef its. In reference to the public good nature of mitigation and its role in foreign relations, thirt een prominent academics noted that these “are compelling reasons to focus on a global SCC” in a r ecent 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. Adverse impact s on other countries can have spillover effects on the United States, particularly in the a reas of national security, international trade, public health and humanitarian concerns. 16

The 2010 TSD noted a number of limitations to the S C-CO 2 analysis, including the incomplete way in which the integrated assessment m odels capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change,

15 See https://www.whitehouse.gov/omb/oira/social-cost-of- carbon for both TSDs.

16 See Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Cl ean Air Act, 74 Fed. Reg. 66,496, 66,535 (Dec. 15, 2009 ) and National Research Council 2013a.6-33

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Currently integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of clima te change recognized in the climate change literature due to a lack of precise information on the nature of damages and because the science incorporated into these models understandably 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 limi tations 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 likely that [SC-CO 2 estimates] underestimate the damage costs because they cannot include many non-quantifi able impacts.” Since then, the peerreviewed literature has continued to support this c onclusion. For example, the IPCC Fifth Assessment report observed that SC-CO 2 estimates continue to omit various impacts that wo uld likely increase damages. The 95th percentile estima te was included in the recommended range for regulatory impact analysis to address these con cerns.
The EPA and other agencies have continued to consid er feedback on the SC-CO 2 estimates from stakeholders through a range of channels, incl uding public comments on rulemakings that
use the SC-CO 2 in supporting analyses and through regular interac tions with stakeholders and research analysts implementing the SC-CO 2 methodology used by 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 Regulatory Affairs, the IWG continues to recommend the use of these SC-CO 2 estimates in

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 Co st 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, “Understanding t he 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-technicalupdate-of-the-social-cost-of-carbon-for-regulatory- impact6-34

regulatory impact analysis. With the release of the response to comments 19 , the IWG announced plans to obtain expert independent advice from the National Academies of Sciences, Engineering, and Medicine (Academies) to ensure tha t the SC-CO 2 estimates continue to reflect
the best available scientific and economic informat ion on climate change. 20 The Academies’ process will be informed by the public comments rec eived and focuses 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 t o 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 year of analysis (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 est imates associated with the 95th percentile estimates when using a 3% discount rate is slightly larger, as those estimates are heavily influenced by the results from the model that was a ffected by the indexing error.

The four SC-CO 2 estimates are as follows: $12, $41, $63, and $120 per metric ton of CO 2 emissions in the year 2017 (2011$). 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 sensitive to assumptions about the discount rate, a nd because no consensus exists on the

19 See https://www.whitehouse.gov/sites/default/files/omb/ inforeg/scc-response-to-comments-final-july-2015.pd f

20 See https://www.whitehouse.gov/blog/2015/07/02/estimati ng-benefits-carbon-dioxide-emissions-reductions

.

21 See http://sites.nationalacademies.org/DBASSE/BECS/Curr entProjects/DBASSE_167526?utm_source=All%20DBASS E%20Newsletters&utm_campaign=e84c13e8c4- New_Project_the_Social_Cost_of_Carbon&utm_medium=em ail&utm_term=0_e16023964e-e84c13e8c4- 267347161 for more information about the National A cademies process and the status of the project.

22 The current version of the TSD is available at: ht tps://www.whitehouse.gov/sites/default/files/omb/in foreg/scctsd-final-july-2015.pdf. The 2010 and 2013 TSDs pr esent SC-CO 2 in 2007$ per metric ton. The unrounded estimates from the current TSD were adjusted to 201 1$ using GDP Implicit Price Deflator (1.061374), http://www.bea.gov/iTable/index_nipa.cfm . The estimates presented here have been rounded t o two significant digits.6-35

appropriate rate to use in an intergenerational con text (where costs and benefits are incurred by different generations). The fourth value is the 95 th percentile of 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 likely, but potentially catastrophic, outcomes).

Table 6-9 presents the global SC-CO 2 estimates in metric tons for the years 2015 to 205 0.
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 analysis year using the same discount r ate used to estimate the SC-CO 2
. 23, 24 The SCCO 2 increases over time because future emissions are e xpected to produce larger incremental damages as physical and economic systems become mor e stressed in response to greater climate change. Note that the interagency group estimated t he growth rate of the SC-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 c onsistent with other modeling assumptions. Table 6-10 reports the incremental climate co-benef its from CO 2 emission impacts estimated for the proposal and more and less stringent alternativ es for the 2017 analysis year.

23 Emission impacts for this rulemaking are shown for the year 2017 and are calculated in short tons. T herefore, the conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in t he year 2017.

24 This analysis considered the climate impacts of on ly CO 2 emission change. As discussed below, the climate impacts of other pollutants were not calculated for the proposed guidelines. While CO 2 is the dominant GHG emitted by the sector, we recognize the representat ive facilities within these comparisons may also ha ve different emission rates for other climate forcers that will serve a minor role in determining the overall socia l cost of generation.6-36

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 percent ile) 2015 $12 $38 $59 $110 2017 $12 $41 $63 $120 2020 $13 $45 $66 $130 2025 $15 $49 $72 $150 2030 $17 $53 $77 $160 2035 $19 $58 $83 $180 2040 $22 $64 $89 $190 2045 $24 $68 $94 $210 2050 $28 $73 $100 $230 * These SC-CO 2 values are stated in $/metric ton and rounded to t wo significant figures. The estimates vary depending on the year of CO 2 emissions and are defined in real terms, i.e., adj usted for inflation using the GDP implicit price deflator.

Table 6-10. Estimated Global Climate Co-benefits o f CO 2 Reductions for the Proposal

and More and Less Stringent Alternatives for 2017 ( millions of 2011$)*

Discount rate and statistic Proposal More Stringent Alternative Less Stringent

Alternative Thousands of short tons of CO 2 reduced** 610 61 04 720

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 fo r 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 t he year 2017.

It is important to note that the climate co-benefit s presented above are associated with changes in CO 2 emissions only. Implementing this proposal, howeve r, 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 the se other pollutants, however, is complex. The climate impacts of these other pollutants have not been calculated for this proposal. 25

25 The SC-CO 2 estimates used in this analysis are designed to as sess the climate benefits associated with changes i n CO 2 emissions only.6-37

The other emissions potentially reduced as a result of the proposal to update CSAPR include other greenhouse gases (such as methane), a erosols 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 compoun ds. Changes in emissions of these pollutants (both increases and decreases) could directly resul t from changes in electricity generation, 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 precurs ors are projected to lead to warming. Therefore, changes in non-CO 2 pollutants could potentially augment or offset the climate cobenefits calculated here. These pollutants can act in different ways and on different timescales than carbon dioxide. For example, aerosols reflect (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 indire ctly by altering properties of clouds. Black carbon can also deposit on snow and ice, darkening these surfaces and accelerating melting. In terms of lifetime, while carbon dioxide emissions c an increase concentrations in the atmosphere for hundreds or thousands of years, many of these o ther pollutants are short lived and remain in the atmosphere for short periods of time ranging fr om days to weeks and can therefore exhibit large spatial and temporal variability.

6.4 Combined Health Benefits and Climate Co-Benefi ts Estimates
In this analysis, we were able to monetize the esti mated benefits associated with the reduced exposure to ozone and PM 2.5 and co-benefits of decreased 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 ecosystem effects and visibility impai rment. In addition, there are expected to be unquantified health and welfare impa cts associated with changes in hydrogen chloride. Specifically, we estimated combinations o f health benefits at discount rates of 3 percent and 7 percent (as recommended by 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 perce nt, 2.5 percent—and the 95 th percentile SC-CO 2 at 3 percent) (as recommended by the IWG).6-38

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 oc cur 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 th e 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 cons idering 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 percent and 7 percent disc ount rates. Combining the 3 percent SC-CO 2 values with the 3 percent health benefit values ass umes that there is no difference in discount

rates

Table 6-11 provides the combined health and climate benefits for the proposal and more and less stringent alternatives for the 2017 analys is year.

26 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-04 72-114577 or http://www.whitehouse.gov/sites/default/files/omb/i nforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.p df for details.6-39

Table 6-11. Combined Health Benefits and Climate C o-Benefits for the Proposal and

More and Less Stringent Alternatives for 2017 (mill ions of 2011$)*

SC-CO 2 Discount Rate** Health and Climate Benefits (Discount Rate Applied to Health Co-Benefits) Climate CoBenefits 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 figur es. Climate benefits are based on reductions in CO 2 emissions. Co-benefits are based on regional benefit-per-ton e stimates. Co-benefits for ozone are based on ozone season NOx emissions. Ozone benefits occur in analysis year, s o they are the same for all discount rates. The hea lth benefits reflect the sum of the PM 2.5 and ozone co-benefits and reflect the range based o n 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 monetized health co-benefits do not include reduced health effects from directly emitted PM 2.5 , direct exposure to NO 2 , SO 2 , and HAP; ecosystem effects; or visibility impairmen t. **As discussed in section 6.3, the SC-CO 2 estimates are calculated with four different value s of a one ton reduction.

6.5 Unquantified Co-benefits

The monetized co-benefits estimated in this RIA ref lect a subset of co-benefits attributable
to the health effect reductions associated with amb ient fine particles and ozone. Data, time, and resource limitations prevented the EPA from quantifying the impacts to, or monetizing the cobenefits from several important benefit categories, including co-benefits associated with exposure to several HAP (including mercury), SO 2 and NO 2 , as well as ecosystem effects, and visibility impairment due to the absence of air qua lity modeling data for these pollutants in this analysis. This does not imply that there are no co- benefits associated with changes in emissions
of HAP or reductions in exposures to SO 2 and NO 2 . In this section, we provide a qualitative description of these benefits, which are listed in Table 6-12.6-40

Table 6-12. Unquantified Health and Welfare Co-ben efits Categories

Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More 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 ISA 2,3

Reduced incidence of morbidity from exposure to SO 2 Respiratory hospital admissions (age > 65) — — SO 2 ISA 1 Asthma emergency department visits (all ages) — — S O 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 ISA 2
Reduced effects from PM deposition (metals and organics) Effects on Individual organisms and ecosystems — — PM ISA 2

Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation — — Ozone ISA 1 Reduced vegetation growth and reproduction — — Ozon e ISA 1 Yield and quality of commercial forest products and crops — — Ozone ISA 1 Damage to urban ornamental plants — — Ozone ISA 2 Carbon sequestration in terrestrial ecosystems — — Ozone ISA 1 Recreational demand associated with forest aesthetics — — Ozone ISA 2

Other non-use effects Ozone ISA 2 Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf-gas exchange, community composition) — — Ozone ISA 2

Reduced effects from acid deposition Recreational fishing — — NO x SO x ISA 1 Tree mortality and decline — — NO x SO x ISA 2 Commercial fishing and forestry effects — — NO x SO x ISA 2 Recreational demand in terrestrial and aquatic ecosystems — — NO x SO x ISA 2

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles) — — NO x SO x ISA 26-41 Category Specific Effect Effect Has Been Quantified Effect Has

Been Monetized More Information

Reduced effects from nutrient enrichment Species composition and biodiversity in terrestrial and estuarine ecosystems — — NO x SO x ISA 2 Coastal eutrophication — — NO x SO x ISA 2 Recreational demand in terrestrial and estuarine ecosystems — — NO x SO x ISA 2

Other non-use effects NO x SO x ISA 2 Ecosystem functions (e.g., biogeochemical cycles, fire regulation) — — NO x SO x ISA 2 Reduced vegetation effects from ambient exposure to SO 2 and NO x Injury to vegetation from SO 2 exposure — — NO x SO x ISA 2 Injury to vegetation from NO x exposure — — NO x SO x ISA 2

1 We assess these co-benefits qualitatively due to da ta and resource limitations for this RIA.
2 We assess these co-benefits qualitatively because w e do not have sufficient confidence in available da ta or methods.
3 We assess these co-benefits qualitatively because c urrent evidence is only suggestive of causality or there are other significant concerns over the strength of the association. 6.5.1 HAP Impacts
Due to methodology and resource limitations, we wer e unable to estimate the impacts associated with changes in emissions of the hazardo us air pollutants in this analysis. The EPA’s SAB-HES concluded that “the challenges for assessin g 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 em issions inventories and background levels, the difficulty of extrapolating risk estimates to low d oses and the challenges of tracking health progress for diseases, such as cancer, that have lo ng latency periods” (U.S. EPA-SAB, 2008). In 2009, the EPA convened a workshop to address the in herent complexities, limitations, and uncertainties in current methods to quantify the be nefits of reducing HAP. Recommendations from this workshop included identifying research pr iorities, focusing on susceptible and vulnerable populations, and improving dose-response relationships (Gwinn et al. , 2011).

6.5.1.1 Mercury
Mercury in the environment is transformed into a mo re toxic form, methylmercury (MeHg). Because Hg is a persistent pollutant, MeHg accumula tes in the food chain, especially the tissue of fish. When people consume these fish, the y consume MeHg. In 2000, the NAS Study was issued which provides a thorough review of the effects of MeHg on human health (NRC, 2000). 27 Many of the peer-reviewed articles cited in this s ection are publications originally

27 National Research Council (NRC). 2000 . Toxicological Effects of Methylmercury . Washington, DC: National Academies Press.6-42

cited in the Mercury Study. 28 In addition, the EPA has conducted literature sear ches to obtain other related and more recent publications to compl ement the material summarized by the NRC in 2000.
In its review of the literature, the NAS found neur odevelopmental effects to be the most sensitive and best documented endpoints and appropriate for e stablishing a reference dose (RfD) (NRC, 2000); in particular NAS supported the use of results from neurobehavioral or neuropsychological tests. The NAS report noted that studies on animals reported sensory effects as well as effects on brain development and memory functions and supported the conclusions based on epidemiology studies. The NAS noted that their recommended endpoints for a RfD are associated with the ability of childr en to learn and to succeed in school. They concluded the following: “The population at highest risk is the children of women who consumed large amounts of fish and seafood during p regnancy. The committee concludes that the risk to that population is likely to be suffici ent to result in an increase in the number of children who have to struggle to keep up in school. ”
The NAS summarized data on cardiovascular effects a vailable up to 2000. Based on these and other studies, the NRC concluded that “Although the data base is not as extensive for cardiovascular effects as it is for other end point s (i.e., neurologic effects), the cardiovascular system appears to be a target for MeHg toxicity in humans and animals.” The NRC also stated that “additional studies are needed to bette r characterize the effect of methylmercury exposure on blood pressure and cardiovascular funct ion at various stages of life.”
Additional cardiovascular studies have been publish ed since 2000. The EPA did not develop a quantitative dose-response assessment for cardiovas cular effects associated with MeHg exposures, as there is no consensus among scientist s on the dose-response functions for these effects. In addition, there is inconsistency among available studies as to the association between MeHg exposure and various cardiovascular sy stem effects. The pharmacokinetics of some of the exposure measures (such as toenail Hg l evels) are not well understood. The studies have not yet received the review and scruti ny of the more well-established neurotoxicity data base.
The Mercury Study noted that MeHg is not a potent m utagen but is capable of causing chromosomal damage in a number of experimental syst ems. The NAS concluded that evidence that human exposure to MeHg caused genetic damage i s inconclusive; they note that some earlier studies showing chromosomal damage in lymph ocytes may not have controlled sufficiently for potential confounders. One study o f adults living in the Tapajós River region in Brazil (Amorim et al., 2000) reported a direct r elationship between MeHg concentration in hair and DNA damage in lymphocytes, as well as effe cts on chromosomes. 29 Long-term MeHg exposures in this population were believed to occur through consumption of fish, suggesting

28 U.S. Environmental Protection Agency (U.S. EPA). 1 997. Mercury Study Report to Congress , EPA–HQ–OAR– 2009–0234–3054. December. Available on the Internet at <http://www.epa.gov/hg/report.htm>.

29 Amorim, M.I.M., D. Mergler, M.O. Bahia, H. Dubeau, D. Miranda, J. Lebel, R.R. Burbano, and M. Lucotte

. 2000. Cytogenetic damage related to low levels of m ethyl mercury contamination in the Brazilian Amazon . An. Acad. Bras. Ciênc. 72(4): 497-507.6-43

that genotoxic effects (largely chromosomal aberrat ions) may result from dietary and chronic MeHg exposures similar to and above those seen in t he Faroes and Seychelles populations.
Although exposure to some forms of Hg can result in a decrease in immune activity or an autoimmune response (ATSDR, 1999), evidence for imm unotoxic effects of MeHg is limited (NRC, 2000). 30
Based on limited human and animal data, MeHg is cla ssified as a “possible” human carcinogen by the International Agency for Research on Cancer (IARC, 1994) 31 and in IRIS (U.S. EPA, 2002). 32 The existing evidence supporting the possibility o f carcinogenic effects in humans from low-dose chronic exposures is tenuous. Multipl e human epidemiological studies have found no significant association between Hg exposur e and overall cancer incidence, although a few studies have shown an association between Hg exposure and specific types of cancer incidence (e.g., acute leukemia and liver cancer) ( NRC, 2000).
There is also some evidence of reproductive and ren al toxicity in humans from MeHg exposure. However, overall, human data regarding reproductive , renal, and hematological toxicity from MeHg are very limited and are based on either studi es of the two high-dose poisoning episodes in Iraq and Japan or animal data, rather t han epidemiological studies of chronic exposures at the levels of interest in this analysi s. 6.5.1.2 Hydrogen Chloride
Hydrogen chloride (HCl) is a corrosive gas that can cause irritation of the mucous membranes of the nose, throat, and respiratory tract. Brief e xposure to 35 ppm causes throat irritation, and levels of 50 to 100 ppm are barely tolerable fo r 1 hour. 33 Concentrations in typical human exposure environments are much lower than the se levels and rarely exceed the reference concentration. 34 The greatest impact is on the upper respiratory tra ct; exposure to high concentrations can rapidly lead to swelling an d spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the

30 Agency for Toxic Substances and Disease Registry ( ATSDR). 1999. Toxicological Profile for Mercury. U. S. Department of Health and Human Services, Public Hea lth Service, Atlanta, GA.

31 International Agency for Research on Cancer (IARC) . 1994. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans and their Supplements: Beryllium, Cadmium, Mercury, and Exposures in the Glass Manufacturing Industry. Vol. 58. Jalili, H.A., and A.H. Abbasi. 1961. Poisoning by ethyl mercury tolue ne sulphonanilide. Br. J. Indust. Med. 18(Oct.):303-30 8 (as cited in NRC, 2000).

32 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on Methylmercury. National Center for Environmental As sessment. Office of Research and Development. Avail able at http://www.epa.gov/iris/subst/0073.htm.

33 Agency for Toxic Substances and Disease Registry ( ATSDR). Medical Management Guidelines for Hydrogen Chloride. Atlanta, GA: U.S. Department of Health an d Human Services. Available at http://www.atsdr.cdc.gov/mmg/mmg.asp?id=758&tid=147 #bookmark02.

34 Table of Prioritized Chronic Dose-Response Values : http://www2.epa.gov/sites/production/files/2014- 05/documents/table1.pdf6-44

skin, and narrowing of the bronchioles. Exposure to HCl can lead to Reactive Airways Dysfunction Syndrome (RADS), a chemically, or irrit ant-induced type of asthma. Children may be more vulnerable to corrosive agents than adu lts because of the relatively smaller diameter of their airways. Children may also be mor e vulnerable to gas exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classified for carci nogenic effects. 35

6.5.2 Additional NO 2 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 d irect exposure. We were unable to estimate the
health co-benefits associated with reduced NO 2 exposure in this analysis. Therefore, this analysi s only quantified and monetized the PM 2.5 and ozone co-benefits associated with the reduction s in
NO 2 emissions.

Following a comprehensive review of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Nitroge n —Health Criteria (NOx ISA) (U.S. EPA, 2008c) concluded that there is a likely causal relationship between respiratory health effects and short-term exposure to NO 2 . These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, a irway inflammation, and lung function. The NOx ISA also concluded that the relationship betwee n short-term NO 2 exposure and premature mortality was “suggestive but not sufficient to inf er a causal relationship,” because it is difficult to attribute the mortality risk effects to NO 2 alone. Although the NOx ISA stated that studies consistently reported a relationship between NO 2 exposure and mortality, the effect was generally smaller than that for other pollutants su ch as PM.

6.5.3 Additional SO 2 Health Co-Benefits
In addition to being a precursor to PM 2.5 , SO 2 emissions are also linked to a variety of adverse health effects associated with direct expos ure. We were unable to estimate the health cobenefits associated with reduced SO 2 in this analysis because we do not have air qualit y

35 U.S. Environmental Protection Agency (U.S. EPA). 1 995. “Integrated Risk Information System File of Hy drogen Chloride.” Washington, DC: Research and Development , National Center for Environmental Assessment. Thi s material is available at http://www.epa.gov/iris/su bst/0396.htm .6-45

modeling data available. Therefore, this analysis o nly quantifies and monetizes the PM 2.5 cobenefits associated with the reductions in SO 2 emissions.

Following an extensive evaluation of health evidenc e from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO 2 ISA) concluded that there is a causal relationship betwe en respiratory health effects and short-term exposure to SO 2 (U.S. EPA, 2008a). The immediate effect of SO 2 on the respiratory system 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 concentrationresponse relationship has been demonstrated in labo ratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in term s of increasing severity of effect and percentage of asthmatics adversely affected. Based on our review of this information, we identified three short-term morbidity endpoints tha t the SO 2 ISA identified as a “causal relationship”: asthma exacerbation, respiratory-rel ated emergency department visits, and respiratory-related hospitalizations. The differing evidence and associated strength of the
evidence for these different effects is described i n detail in the SO 2 ISA. The SO 2 ISA also 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 SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent i n reporting a relationship between SO 2 exposure and mortality, there was a lack of robust ness of the observed associations to adjustment for other pollutants. We did not quantify these co-benefits due to data
constraints.

6.5.4 Additional NO 2 and SO 2 Welfare Co-Benefits

As described in the Integrated Science Assessment for Oxides of Nitroge n and Sulfur — Ecological Criteria (NOx/SOx ISA) (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. Dep osition of nitrogen causes acidification, which can cause a loss of biodiversity of fishes, z ooplankton, and macro invertebrates in aquatic ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial ecosyst ems. In the northeastern U.S., the surface waters affected by acidification are a source of fo od for some recreational and subsistence6-46

fishermen and for other consumers and support sever al cultural services, including aesthetic and educational services and recreational fishing. Biol ogical effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicit y, which can cause reduced root growth, restricting the ability of the plant to take up wat er and nutrients. These direct effects can, in turn
, increase the sensitivity of these plants to stresse s, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality o f canopy trees. Terrestrial acidification affects several important ecological services, including de clines in habitat for threatened and endangered species (cultural), declines in forest aesthetics ( cultural), declines in forest productivity (provisioning), and increases in forest soil erosio n and reductions in water retention (cultural and regulating). (U.S. EPA, 2008d)

Deposition of nitrogen is also associated with aqua tic and terrestrial nutrient enrichment. In estuarine waters, excess nutrient enrichment can le ad to eutrophication. Eutrophication of estuaries can disrupt an important source of food p roduction, particularly fish and shellfish production, and a variety of cultural ecosystem ser vices, including water-based recreational and
aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and number of species and biodiversity in terrestrial s ystems. Excessive nitrogen deposition upsets the balance between native and nonnative plants, ch anging the ability of an area to support biodiversity. When the composition of species chang es, then fire frequency and intensity can also change, as nonnative grasses fuel more frequen t and more intense wildfires. (U.S. EPA, 2008d)

Reductions in emissions of NO 2 and SO 2 will improve the level of visibility throughout th e United States because these gases (and the particle s of nitrate and sulfate formed from these gases) impair visibility by scattering and absorbin g light (U.S. EPA, 2009). Visibility is also referred to as visual air quality (VAQ), and it dir ectly affects people’s enjoyment 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 ac tivities, including sites of unique public value, such as the Great Smoky Mountains National Park (U. S. EPA, 2009).6-47

6.5.5 Ozone Welfare Co-Benefits
Exposure to ozone has been associated with a wide a rray of vegetation and ecosystem effects in the published literature (U.S. EPA, 2013 b). Sensitivity to ozone is highly variable across species, with over 65 plant species identifi ed as “ozone-sensitive”, many of which occur in state and national parks and forests. These effe cts include those that damage or impair the intended use of the plant or ecosystem. Such effect s can include reduced growth and/or biomass production in sensitive plant species, including fo rest trees, reduced yield and quality of crops, visible foliar injury, species composition shift, a nd changes in ecosystems and associated ecosystem services.

6.5.6 Carbon Monoxide Co-Benefits
CO in ambient air is formed primarily by the incomp lete combustion of carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these reactions, relative to carbon dioxide (CO 2 ), is sensitive to conditions in the combustion zon e, such as fuel oxygen content, burn temperature, or m ixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad r ange of effects in multiple tissues and organ systems that depend on concentration and dura tion of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposu re to CO is “likely to have a causal relationship” with c ardiovascular morbidity, particularly in individuals with coronary heart disease. Epidemiolo gic studies associate short-term CO exposure with increased risk of emergency departmen t visits and hospital admissions. Coronary heart disease includes those who have angi na pectoris (cardiac chest pain), as well as those who have experienced a heart attack. Other subpopulations potentially at risk include individuals with diseases such as chronic o bstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as older adults or the developing young. The evidence is suggestive of a causal relationship between short-term exposure to CO and respiratory morbidity and mortal ity. The evidence is also suggestive of a causal relationship for birth outcomes and developm ental effects following long-term exposure to CO, and for central nervous system effe cts linked to short- and long-term exposure to CO.
6.5.7 Visibility Impairment Co-Benefits
Reducing secondary formation of PM 2.5 would improve levels of visibility in the U.S. because suspended particles and gases degrade visib ility by scattering and absorbing light (U.S. EPA, 2009b). Fine particles with significant light- extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soi l (Sisler, 1996). Visibility has direct significance to people’s enjoyment of daily activit ies and their overall sense of wellbeing. Good6-48

visibility increases the quality of life where indi viduals live and work, and where they engage in
recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern U.S. and particulate nitrate is an important contri butor to light extinction in California and the upper Midwestern U.S., particularly 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 esti mate visibility-related benefits, and we are
also unable to determine whether the emission reduc tions associated with the final emission guidelines would be likely to have a significant im pact on visibility in urban areas or Class I

areas.

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