6-1 

CHAPTER 6: ESTIMATED HUMAN HEALTH BENEFITS AND CLIMATE COBENEFITS 

6.1 Introduction 

As discussed in Chapter 4, this action proposes to update CSAPR to reduce interstate 

transport of EGU ozone season NOX emissions that contribute significantly to nonattainment or 

interfere with maintenance of the 2008 ozone NAAQS. The EPA proposes to implement the 

proposed EGU NOX reductions by setting emissions budgets that are implemented through the 

CSAPR NOX ozone-season allowance trading program. Implementing this proposal to update 

CSAPR is expected to reduce emissions of NOX during the summer ozone season and provide 

ancillary annual NOX and carbon dioxide (CO2) benefits (i.e., co-benefits). This chapter 

describes the methods used to estimate the monetized ozone-related air quality health benefits, 

fine particulate matter (PM2.5)-related air quality health co-benefits from reductions in NOX

emissions and climate co-benefits from reductions of CO2 emissions. These health benefits are 

associated with reducing exposure to ambient ozone and PM2.5 by reducing emissions of 

precursor pollutants (i.e., NOX). Data, resource, and methodological limitations prevent the EPA 

from monetizing several important co-benefits from reducing emissions of these several 

pollutants, including those from reducing direct exposure to NO2, ecosystem effects and 

visibility impairment as well as co-benefits from ancillary reductions changes in other pollutants, 

such as hazardous air pollutants (HAP). We qualitatively discuss these unquantified benefits in 

this chapter. 

This chapter provides estimates of the monetized air quality health benefits and climate cobenefits associated with emission reductions for the regulatory control alternatives across several 

discount rates. The estimated benefits associated with these emission reductions are beyond 

those achieved by previous EPA rulemakings, including the 2011 CSAPR1

 and the finalized 

Clean Power Plan (CPP). 

 

1

 As discussed in Chapters 1, 3 and 5, the 2017 baseline EGU emissions for this proposal include impacts from 

CSAPR issued on July 6, 2011. As discussed and elaborated on in Chapter 1, Bbecause 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 associated with the CSAPR NOX 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 NOX. In 

the presence of sunlight, NOX and VOCs can undergo a chemical reaction in the atmosphere to 

form ozone. Reducing NOX emissions also reduces human exposure to ozone and the incidence 

of ozone-related health effects, though this depends partly on local levels of volatile organic 

compounds (VOCs). The proposal would also reduce emissions of NOX throughout the year. 

Because NOX is also a precursor to formation of ambient PM2.5, reducing these emissions would 

also reduce human exposure to ambient PM2.5 throughout the year and would reduce the 

incidence of PM2.5-related health effects.2 This RIA does not quantify PM2.5-related benefits 

associated with SO2 emission reductions. As discussed in Chapter 3, the EPA does not estimate 

significant SO2 emission reductions as a result of this proposal. Furthermore, these reductions 

would reduce ozone and PM2.5 concentrations in regions other than those with nonattainment 

monitors that are the subject of this proposal, and the benefits of reducing these pollutants in 

those regions are assessed in this Chapter. Although we do not have sufficient data to quantify

these impacts in this analysis, rReducing emissions of NOX would also reduce ambient exposure 

to NO2 and its associated health effects, though we do not quantify these effects because we have 

not developed a reduced-form technique for estimating NO2 impacts. In this section, we provide 

an overview of the monetized ozone-related benefits and PM2.5-related co-benefits estimated for 

the proposed updated CSAPR EGU NOX 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 incidence 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 NOX emissions budgets for the ozone season in 

23 eastern states may reduce ambient ozone and PM2.5 concentrations below the National 

 

2 This RIA does not quantify PM2.5-related benefits associated with SO2 emission reductions. As discussed in 

Chapter 3, the EPA does not estimate significant SO2 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 PM2.5-related 

benefits may be underestimated by a relatively small amount.
6-3 

Ambient Air Quality Standards (NAAQS) in some areas and assist other areas with attaining the 

ozone and PM2.5 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 different sources reflecting the application of identified 

and unidentified controls, incremental to implementation of existing regulations and controls 

needed to attain the NAAQS that currently is in effect. In short, NAAQS RIAs hypothesize, but 

do not predict, the strategies that States may choose 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 and benefits from NAAQS RIAs are not 

intended to be added to the costs and benefits of other regulations that result in specific costs of 

control and prescribe specific emission reductions. For example, some of the emissions 

reductions estimated to result from implementing the 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 reductions 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 those areas to, meet the 2008 ozone NAAQS. 

Specifically, the anticipated reductions in ozone concentrations from this rule would help areas 

attain and maintain the 2008 ozone NAAQS and achieve some of the air quality improvements 

from the hypothesized attainment strategy from the 2008 ozone NAAQS RIA. These ozone 

improvements would similarly achieve some of the air quality improvements assumed in the 

baseline (i.e., 2008 ozone NAAQS attainment projection) for the 2015 ozone NAAQS RIA. 

As discussed in Chapter 4, the 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 strategies analyzed in NAAQS RIAs described 

above, all of the emission reductions for the illustrative compliance modeling for this proposal 

would occur in one well-characterized sector (i.e., the EGU sector). The EPA is more confident 

in the magnitude and location of the emission reductions for this proposal because it imposes a 

specific requirement that limits emissions from a specific sector. Emission reductions achieved 

under this rule, if finalized, will ultimately be reflected in the baseline of future NAAQS 
6-4 

analyses and , which would lower the emissions reductions needed to attain revised future 

NAAQS. For more information on the relationship between illustrative analyses, such as for the 

NAAQS, and its associated implementation rules, please see the Ozone NAAQS RIA (U.S. EPA, 

2015)[Note, insert reference to 2015 ozone NAAQS RIA when/if finalized]. 

6.2.1 Health Impact Assessment for Ozone and PM2.5

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 mortality and a variety of morbidity effects 

associated with acute and chronic exposures. Similarly, the Integrated Science Assessment for 

Particulate Matter (PM ISA) (U.S. EPA, 2009b) identified the human health effects associated 

with ambient PM2.5 exposure, which include premature mortality and a variety of morbidity 

effects associated with acute and chronic exposures. Table 6-1 identifies the quantified and 

unquantified benefit and co-benefit categories captured in the EPA’s health benefits estimates for 

reduced exposure to ambient ozone and PM2.5. Although the table below does not list 

unquantified health effects, such as those associated with exposure to NO2 and mercury, nor 

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 complete. 
6-5 

Table 6-1. Human Health Effects of Ambient Ozone and PM2.5

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 ISA1

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 ISA1

Other respiratory effects (e.g., premature aging of

lungs) — — Ozone ISA2

Cardiovascular and nervous system effects — — Ozone ISA2

Reproductive and developmental effects — — Ozone ISA2,3

Reduced incidence of 

premature mortality 

from exposure to 

PM2.5

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 PM2.5

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 ISA1

Emergency room visits for cardiovascular effects 

(all ages) — — PM ISA1

Strokes and cerebrovascular disease (age 50-79) — — PM ISA1

Other cardiovascular effects (e.g., other ages) — — PM ISA2

Other respiratory effects (e.g., pulmonary function, 

non-asthma ER visits, non-bronchitis chronic 

diseases, other ages and populations) 

— — PM ISA2

Reproductive and developmental effects (e.g., low 

birth weight, pre-term births, etc.) — — PM ISA2,3

Cancer, mutagenicity, and genotoxicity effects — — PM ISA2,3

1

 We assess these co-benefits qualitatively due to data and resource limitations for this analysis, but we have quantified them in 

sensitivity analyses for other analyses. 

2

 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods. 

3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other significant 

concerns over the strength of the association. 

We follow a “damage-function” approach in calculating benefits, which estimates changes 

in individual health endpoints (specific effects that can be associated with changes in air quality) 
6-6 

and assigns values to those changes assuming independence of the values for those individual 

endpoints. Because the EPA rarely has the time or resources 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, which 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 from other contexts to the context of this 

regulation, we also use a “benefit-per-ton” approach to estimate the ozone and PM2.5 benefits in 

this RIA. Benefit-per-ton approaches apply an average benefit per ton derived from modeling of 

benefits of specific air quality scenarios to estimates of emissions reductions for scenarios where 

no air quality modeling is available. Thus, to develop estimates of benefits for this RIA, we are 

transferring both the underlying health and economic information from previous studies and 

information on air quality responses to emissions reductions from other air quality modeling. 

This section describes the underlying basis for the health and economic valuation estimates that 

inform the benefit-per-ton estimates, and the subsequent section provides an overview of the 

benefit-per-ton estimates, which are described in detail in the appendix to this chapter. 

The benefit-per-ton approach we use in this RIA relies on estimates of human health 

responses to exposure to ozone and PM obtained from the peer-reviewed scientific literature. 

These estimates are used in conjunction with population data, baseline health information, air 

quality data and economic valuation information to conduct health impact and economic benefits 

assessments. These assessments form the key inputs to calculating benefit-per-ton estimates. The 

next sections provide an overview of the health impact assessment (HIA) methodology and 

additional details on several key elements. 

The HIA quantifies the changes in the incidence of adverse health impacts resulting from 

changes in human exposure to ozone and PM2.5. 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 effects that are directly linked to ambient ozone 

and PM2.5 concentrations. There may be other indirect health impacts associated with reducing 

emissions, such as occupational health exposures. Epidemiological studies generally provide 
6-7 

estimates of the relative risks of a particular health effect for a given increment of air pollution 

(often per 10 ppb for ozone or μg/m3 for PM2.5). These relative risks can be used to develop risk

coefficients that relate a unit reduction in pollution (e.g., ozone) to changes in the incidence of a 

health effect. We refer the reader to the Ozone NAAQS 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 epidemiology studies and risk coefficients applied in 

this analysis, and we briefly elaborate on adult premature mortality below. The size of the 

mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and 

the high monetary value ascribed to reducing risks of premature death make mortality risk 

reduction the most significant health endpoint quantified in this analysis. 

6.2.1.1 Mortality Effect Coefficients for Short-term Ozone Exposure 

The overall body of evidence indicates that there is likely to be a causal relationship 

between short-term ozone exposure and premature death. The 2013 ozone ISA concludes that the 

evidence suggests that ozone effects are independent of the relationship between PM and 

mortality. (U.S. EPA, 2013a). However, the ISA notes that the interpretation of the potential 

confounding effects of PM on ozone-mortality risk estimates requires caution due to the PM 

sampling schedule (in most cities) which limits the overall sample size available for evaluating 

potential confounding of the ozone effect by PM (U.S. EPA 2013a).3

 

In 2006, the EPA requested an NAS study to answer the 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 impacts from short-term exposure?; (3) How 

might EPA estimate the change in life expectancy?; and (4) What methods should EPA use to 

estimate the monetary value of changes in ozone-related mortality risk and life expectancy? 

In 2008, the NAS (NRC, 2008) issued a series of recommendations to the EPA regarding 

the quantification and valuation of ozone-related short-term mortality. Chief among these was 

 

3

 Consequently, as noted later in this section, while we have used a single-pollutant model in generating short-term 

ozone-related mortality benefits for this RIA, we have 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 likely to contribute to premature deaths” and the 

committee recommended that “ozone-related mortality be included in future estimates of the 

health benefits of reducing ozone exposures...” The NAS also recommended that “...the greatest 

emphasis be placed on the multi-city and NMMAPS studies without exclusion of the metaanalyses” (NRC, 2008). In addition, NAS recommended that EPA “should give little or no 

weight to the assumption that there is no causal association between estimated reductions in 

premature mortality and reduced ozone exposure” (NRC, 2008). In 2010, the Health Effects 

Subcommittee of the Advisory Council on Clean Air Compliance 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 mortality benefits (U.S. EPA-SAB, 

2010a). 

In view of the findings of the ozone ISA, the NAS panel, the SAB-HES panel, and the 

CASAC panel, we estimate ozone-related premature mortality for short-term exposure in the 

core health effects analysis using effect coefficients from the Smith et al. (2009) NMMAPS 

analysis and the Zanobetti and Schwartz (2008) multi-city study with several additional studies 

as sensitivity analyses. This emphasis on newer multi-city studies is consistent with 

recommendations provided by the NAS in their ozone mortality report (NRC, 2008). CASAC 

supported using the Smith et al. (2009) and Zanobetti and Schwartz (2008) studies for the ozone 

HREA (U.S. EPA-SAB, 2012, 2014), and these are multi-city studies published more 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) regional versus national Bayes-based adjustment,4

 

4

In Bayesian modeling, effect estimates are “updated” from an assumed prior value using observational data. In the 

Smith et al. (2009) approach, the prior values are either a regional or national mean of the individual effect 

estimates obtained for each individual city. The Bayesian adjusted city-specific effect estimates are then 

calculated by updating the selected prior value based on the relative precision of each city-specific estimate and 

the variation observed across all city-specific individual effect estimates. City-specific estimates are pulled 

towards the prior value if they have low precision and/or if there is low overall variation across estimates. City-
6-9 

(b) co-pollutant models considering PM10, (c) all-year versus ozone-season based estimates, and 

(d) consideration of a range of ozone metrics, including the daily 8-hour max. In addition, the 

Smith et al. (2009) study did not use the trimmed mean 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 non-accidental mortality using the 8-hour max 

metric for the warmer ozone season. For the sensitivity analysis, we included a co-pollutant 

model (ozone and PM10) from Smith et al. (2009) for all-cause mortality, using the 8-hour max 

ozone metric for the ozone season. Using a single pollutant model for the core analysis and the 

co-pollutant model in the sensitivity analysis reflects 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 independent 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 June-August) and all-cause mortality in 48 

U.S. cities using data collected between 1989 and 2000. 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 the 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 max to account for the period of the day in which most individuals 

are exposed to ozonex. To do this, we used the ozone metric approach wherein the original effect 

estimate (and standard error) is multiplied by the appropriate ozone metric adjustment ratio.6 

 

specific estimates are given less adjustment if they are precisely estimated and/or there is greater overall variation 

across estimates. 

5

 There are a number of concerns regarding the trimmed mean approach including (1) the potential loss of temporal 

variation in the data when the approach is used (this could impact the size of the effect estimate), and (2) a lack of 

complete documentation for the approach, which prevents a full reviewing or replication of the technique. 

6

 These adjustment ratios are created by (a) obtaining summary air quality (composite monitor values) for each 

urban study area/ozone season combination reflected in the original epidemiology study, (b) calculating 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 estimates; however, adjustments of relatively similar metrics (e.g., 
6-10 

6.2.1.2 PM2.5 Mortality Effect Coefficients for Adults and Infants 

A substantial body of published scientific literature documents the association between 

elevated PM2.5 concentrations and increased premature mortality (U.S. EPA, 2009b). This body 

of literature reflects thousands of epidemiology, toxicology, 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), concluded that there is a causal relationship 

between mortality and both long-term and short-term exposure to PM2.5 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 reduction the most significant health endpoint 

quantified in this analysis. 

Researchers have found statistically significant associations between PM2.5 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 PM2.5 concentrations and changes in daily 

mortality rates up to several days after a period of exposure to elevated PM2.5 concentrations. 

Cohort methods have been used to examine the potential relationship between community-level 

PM2.5 exposures over multiple years (i.e., long-term exposures) and community-level annual 

mortality rates that have been adjusted for individual level risk factors. When choosing between 

using short-term studies or cohort studies for estimating 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 exposures, as well as some fraction of shortterm exposures (Kunzli et al., 2001; NRC, 2002). The 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). The 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 unknown” (NRC, 2002, p. 108-9). To avoid 

 

8-hour max and 8-hour mean), as is the case with the Zanobetti and Schwartz (2008) study, are likely to introduce 

less uncertainty than adjustments for more disparate ratios (e.g., 24-hour or 1-hour max ratios to 8-hour max 

equivalents). 
6-11 

double counting, we focus on applying the risk coefficients from the long-term cohort studies in 

estimating the mortality impacts of reductions in PM2.5. 

Over the last two decades, several studies using “prospective cohort” designs have been 

published that are consistent with the earlier body of literature. Two prospective 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 Society” 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 PM2.5 concentrations and mortality. These studies have found consistent relationships 

between fine particle indicators and premature mortality across multiple locations in the United 

States. The credibility of these two studies is further enhanced by the fact that the initial 

published studies (Pope et al., 1995; Dockery et al., 1993) were subject to extensive 

reexamination and reanalysis by an independent team of scientific experts commissioned by the 

Health Effects Institute (HEI) and by a Special Panel of the HEI Health Review Committee 

(Krewski et al., 2000). Publication of studies confirming and extending the findings of the 1993 

Six Cities Study and the 1995 ACS study using more recent air quality data and a longer followup period for the ACS cohort provides additional validation of the findings of these original 

studies (Pope et al., 2002, 2004; Laden et al., 2006; 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. These are widely cited, well studied and 

extensively reviewed data sets” (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities studies 

have inherent strengths and weaknesses, we present benefits estimates using relative risk 

estimates from the most recent extended reanalysis of these cohorts (Krewski et al., 2009; 

Lepeule et al., 2012). Presenting results using both ACS and Six Cities is consistent with other 

recent RIAs (e.g., U.S. EPA, 2010c, 2011a, 2011c). The PM ISA concludes that the ACS and Six 

Cities cohorts provide the strongest evidence of the association between long-term PM2.5

exposure and premature mortality with support from a number of additional cohort studies 

(described below). 

The extended analyses of the ACS cohort data (Krewski et al., 2009) refined the earlier 

ACS studies by (a) extending the follow-up period by 2 years to the year 2000, for a total of 18 
6-12 

years; (b) incorporating almost double the number of 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-suited for the assessment of mortality risk 

from long-term PM2.5 exposures for the EPA’s benefits analyses. 

In 2009, the SAB-HES again reviewed the choice of mortality risk coefficients for 

benefits analysis, concluding that “[t]he Krewski et al. (2009) findings, while informative, have 

not yet undergone the same degree of peer review as have the aforementioned studies. Thus, the 

SAB-HES recommends that EPA not use the Krewski et al. (2009) findings for generating the 

Primary Estimate” (U.S. EPA-SAB, 2010a). Since this time, the Krewski et al. (2009) has 

undergone additional peer review, which we believe strengthens the support for including this 

study in this RIA. For example, the PM ISA (U.S. EPA, 2009b) included this study among the 

key mortality studies. In addition, the risk assessment supporting the PM NAAQS (U.S. EPA, 

2010b) used risk coefficients drawn from the Krewski et al. (2009) study, the most recent 

reanalysis of the ACS cohort data. The PM risk assessment cited a number of advantages that 

informed the selection of the Krewski et al. (2009) study as the source of the core effect 

estimates, including the extended period of observation, the rigorous examination of model 

forms and effect estimates, the coverage for ecological variables, and the large dataset with over 

1.2 million individuals and 156 MSAs (U.S. EPA, 2010b). The CASAC also provided extensive 

peer review of the PM risk assessment and supported the use of effect estimates from this study 

(U.S. EPA-SAB, 2009a, b, 2010b). 

Consistent with the PM risk assessment (U.S. EPA, 2010b), which was reviewed by the 

CASAC (U.S. EPA-SAB, 2009a, b), we use the all-cause mortality risk estimate based on the 

random-effects Cox proportional hazard model that incorporates 44 individual and 7 ecological 

covariates (RR=1.06, 95% confidence intervals 1.04–1.08 per 10 μg/m3

 increase in PM2.5). The 

relative risk estimate (1.06 per 10 μg/m3 increase in PM2.5) is identical to the risk estimate drawn 

from the earlier Pope et al. (2002) study, though the confidence interval around the Krewski et 

al. (2009) risk estimate is tighter. 
6-13 

In the most recent Six Cities study, which was published 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 PM2.5 exposure and increased risk of all-cause, cardiovascular and lung cancer 

mortality. The authors also concluded that the C-R relationship was linear down to PM2.5

concentrations of 8 μg/m3

 and that mortality rate ratios for PM2.5 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/m3

 increase in PM2.5). 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 PM2.5 are driven largely by reductions in 

premature mortality, it is important to characterize the uncertainty in this endpoint. In order to do 

so, we utilize the results of an expert elicitation sponsored by the EPA and completed in 2006 

(Roman et al., 2008; IEc, 2006). The results of that expert elicitation can be used as a 

characterization of uncertainty in the C-R functions. The co-benefits results derived from expert 

elicitation is discussed in Appendix 6B (section 6B.4). 

In addition to the adult mortality studies described above, several studies show an 

association between PM exposure and premature mortality in children under 5 years of age.7

 The 

PM ISA states that less evidence is available regarding the potential impact of PM2.5 exposure on 

infant mortality than on adult mortality and the results of studies in several countries include a 

range of findings with some finding significant associations. Specifically, the PM ISA concluded 

that evidence exists for a stronger effect at the post-neonatal period and for respiratory-related 

mortality, although this trend is not consistent across all studies. In addition, compared to 

avoided premature deaths estimated for adult mortality, 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 calculate benefits for infants age 0–1, not all children under 5 years old. 
6-14 

number of adults and the epidemiology studies on infant mortality provide smaller risk 

coefficients associated with exposure to PM2.5. 

In 2004, the SAB-HES noted the release of the WHO Global Burden of Disease Study 

focusing on ambient air, which cites several recently published time-series studies relating daily 

PM exposure to mortality in children (U.S. EPA-SAB, 2004). With regard to the cohort study 

conducted by Woodruff et al. (1997), the SAB-HES noted several strengths of the study, 

including the use of a larger cohort drawn from a large number of metropolitan areas and efforts 

to control for a variety of individual risk factors in infants (e.g., maternal educational level, 

maternal ethnicity, parental marital status, and maternal smoking status). Based on these 

findings, the SAB-HES recommended that the EPA incorporate infant mortality into the primary 

benefits estimate and that infant mortality be evaluated using an impact function developed from 

the Woodruff et al. (1997) study (U.S. EPA-SAB, 2004). 

In 2010, the SAB-HES again noted the increasing body of literature relating infant 

mortality and PM exposure and supported the inclusion of infant mortality in the monetized 

benefits (U.S. EPA-SAB, 2010a). The SAB-HES generally supported the approach of estimating 

infant mortality based on Woodruff et al. (1997) but also noted that a more recent study by 

Woodruff et al. (2006) continued to find associations between PM2.5 and infant mortality in 

California. The SAB-HES also noted, “when PM10 results are scaled to estimate PM2.5 impacts, 

the results yield similar risk estimates.” Consistent with The Benefits and Costs of the Clean Air 

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

the national–scale of the earlier study. 

6.2.2 Economic Valuation for Health Benefits

After quantifying the change in adverse health impacts, we estimate the economic value of 

these avoided impacts. Reductions in ambient concentrations of air pollution generally lower the 

risk of future adverse health effects by a small amount for a large population. Therefore, the 

appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. 

For some health effects, such as hospital admissions, WTP estimates are generally not available, 

so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates 

generally (although not necessarily in every case) understate the true value of reductions in risk 
6-15 

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 Aavoided 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 method for valuing reductions in premature 

mortality risk is still developing. The adoption of a value for the projected reduction in the risk of

premature mortality is the subject of continuing discussion within the economics and public 

policy analysis community. Following the advice of the SAB’s Environmental Economics 

Advisory Committee (SAB-EEAC), the EPA currently uses the value of statistical life (VSL) 

approach in calculating estimates of mortality benefits, 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, 2000). 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 valuing mortality risk reductions, and 

the Agency consulted several times with the SAB-EEAC on this issue. Until updated guidance is 

available, the Agency determined that a single, peer-reviewed estimate applied consistently, best 

reflects the SAB-EEAC advice it has received. Therefore, 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 its guidance on this issue. 

This approach calculates a mean value across VSL estimates derived from 26 labor market and 

contingent valuation studies published between 1974 and 1991. The mean VSL across these 

studies is $6.3 million (2000$).9

 We then adjust this VSL to account for the currency 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 further updates to the mortality risk valuation guidance 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 VSLs 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 sound, appropriately reviewed evidence in 

valuing mortality risk reductions and has made significant progress in responding to the SABEEAC’s specific recommendations. In the process, the Agency has identified a number of 

important issues to be considered in updating its mortality risk valuation estimates. These are 

detailed in a white paper, “Valuing Mortality Risk Reductions in Environmental Policy” (U.S. 

EPA, 2010c), which recently underwent review by the SAB-EEAC. A meeting with the SAB on 

this paper was held on March 14, 2011 and formal recommendations were transmitted on 

July 29, 2011 (U.S. EPA-SAB, 2011). The EPA is taking SAB’s recommendations under 

advisement. 

In valuing PM2.5-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 exposures and the total realization of changes 

in health effects. Although the structure of the lag is uncertain, the EPA follows the advice of the 

SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality 

reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20 

after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). Changes in the cessation lag assumptions 

do not change the total number of estimated deaths but rather the timing of those deaths. Because 

short-term ozone-related premature mortality occurs within the analysis year, the estimated 

ozone-related co-benefits are identical for all discount rates. 

6.2.3 Benefit-per-ton Estimates for Ozone 

We used a “benefit-per-ton” approach to estimate the 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 total monetized human health benefits (the 

sum of premature mortality and premature morbidity) of reducing one ton of NOX (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 NOX

emissions from U.S. EGUs during the ozone-season (May to September). Because we estimate 
6-17 

ozone health impacts from May to September only, this 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 NOX alone. Because EGUs emit little VOC 

relative to NOX emissions, it is unlikely that VOCs emitted by EGUs would contribute 

substantially to regional ozone formation. We provide more detailed information regarding the 

generation of these estimates in the appendix to this chapter. 

As noted below in the characterization of uncertainty, all benefit-per-ton estimates have 

inherent limitations. 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 density, meteorology, exposure, baseline health 

incidence rates, or other local factors for any specific 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 PM2.5

We used a “benefit-per-ton” approach to estimate the PM2.5 co-benefits in this RIA, which 

represent the total monetized human health co-benefits (the sum of premature mortality and 

premature morbidity), of reducing one ton of PM2.5 (or PM2.5 precursor such as NOX) from a 

specified source. Specifically, in this analysis, we multiplied the benefit-per-ton estimates by the 

corresponding emission reductions that were generated from air quality modeling of the 

illustrative control case. The appendix to this chapter provides additional detail regarding these 

calculations. All benefit-per-ton estimates have inherent limitations and should be interpreted 

with caution. Even though we assume that all fine particles have equivalent health effects, the 

benefit-per-ton estimates vary between precursors depending on the location and magnitude of 

their impact on PM2.5 levels, which drive population exposure. 

6.2.5 Estimated Health Benefits Results 

Table 6-2 provides the benefit-per-ton estimates for the analysis year: 2017; because the 

modeled change in SO2 levels was very small, we did not quantify the SO2-related PM2.5
6-18 

benefits. Table 6-3 provides the emission reductions estimated to occur in the analysis year.10

Table 6-4 summarizes the national monetized ozone-related and PM-related health benefits 

estimated to occur for regulatory control alternatives for the 2017 analysis year, by precursor 

pollutant using discount rates of 3 percent and 7 percent. Table 6-5 provides national summaries 

of the reductions in estimated health incidences associated 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 PM2.5-related benefits using benefit-per-ton estimates based on 

concentration-response functions from different studies and expert opinion for the proposal 

evaluated for 2017. 

Table 6-2. Summary of Ozone and PM2.5 Benefit-per-Ton Estimates Based on Air 

Quality Modeling from tThe Illustrative Control Case in 2017 (2011$)* 

Pollutant Discount Rate National 

(from emission reductions in 23 states)

NOX (as Ozone) N/A $5,700 to $9,400 

NOX (as PM2.5) 3% $2,100 to $4,700 

7% $1,900 to $4,300 

* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone and 

PM2.5. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced 

health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are 

assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and 

magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton 

estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in the analysis year, so 

they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the 

benefit-per-ton methodology. In general, the 95th percentile confidence interval for monetized PM2.5 benefits 

ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) 

and Lepeule et al. (2012). 

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

Less Stringent Alternatives in 2017 (thousands of short tons)* 

Proposal 

More Stringent Alternative Less Stringent 

Alternative 

Ozone Season NOX 85,000 87,000 24,000 

All Year NOX 90,000 93,000 24,000 

All Year SO2 380 430 300 

 

10 See Chapter 5 of this RIA for more information regarding the expected emission reductions used to calculate the 

health co-benefits in this chapter. Chapter 4 also provides more information regarding the illustrative plan approach. 

11 Incidence estimates were generated using the same “per ton” approach as used to generate the dollar benefit per 

ton values. See Appendix 46-A for details. 
6-19 

*All emissions shown in the table are rounded, so regional emission reductions may appear to not sum to national 

total. 

Table 6-4. Summary of Estimated Monetized Health Benefits for the Proposal and 

More and Less Stringent Alternatives Regulatory Control Alternatives for 2017 

(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 PM2.5) 

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 figures so numbers may not sum down columns. The health benefits 

range is based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. 

(2012) with Zanobetti and Schwartz (2008)). The estimated monetized co-benefits do not include climate benefits or 

reduced health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine 

particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the 

location and magnitude of their impact on PM2.5 levels, which drive population exposure. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles and ozone. Co-benefits for PM2.5 

precursors are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

NOX emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence 

intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 95th percentile 

confidence interval for monetized PM2.5 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 +/- 60 percent depending on the concentration-response function used. 

For this RIA, we did not estimate changes in emissions of directly emitted particles. As a result, quantified PM2.5

related benefits are underestimated by a relatively small amount. In the proposal RIA, the benefits from reductions 

in directly emitted PM2.5 were less than 10 percent of total monetized health co-benefits across all scenarios and 

years.
6-20 

Table 6-5. Summary of Avoided Health Incidences from Ozone-Related and PM2.5-

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 

PM2.5-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 two significant figures. Co-benefits for PM2.5 precursors are 

based on regional incidence-per-ton estimates for all 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 95th percentile confidence interval for the health impact function alone ranges from 

approximately ±30 percent for mortality incidence based on Krewski et al. (2009) and ±46 percent based on Lepeule 

et al. (2012). The confidence intervals around the ozone mortality estimates are on the order of +/- 60 percent 

depending on the concentration-response function used.

 
6-21 

Ozone PM2.5

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

*The PM2.5 graphs show the estimated PM2.5 co-benefits at discount rates of 3% and 7% using effect coefficients 

derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 12 effect coefficients 

derived from EPA’s expert elicitation on PM mortality (Roman et al., 2008). The results shown are not the direct 

results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response 

functions provided in those studies. Ozone co-benefits occur in the analysis year, so they are the same for all 

discount rates. These estimates do not include benefits from reductions in CO2. The monetized co-benefits do not 

include climate benefits from changes in NO2 and SO2 or reduced health effects from direct exposure to NO2, SO2, 

ecosystem effects, or visibility impairment. For this RIA, we did not estimate changes in emissions of directly 

emitted particles. As a result, quantified PM2.5 related benefits are underestimated by a relatively small amount. In 

the proposal RIA, the benefits from reductions in directly emitted PM2.5 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 Estimated Health bBenefits 

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 emission 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 and would affect the estimate of benefits. When 

the uncertainties from each stage of the analysis are compounded, even small uncertainties can 

have large effects on the total quantified benefits. In addition, the use of the benefit-per-ton 

approach adds additional uncertainties beyond those for analyses based directly on air quality 

modeling. Therefore, the estimates of benefits should be viewed as representative of the general 

magnitude of benefits of the regulatory control alternatives for the 2017 analysis year, rather than 

the actual benefits anticipated from implement the proposal. 

This RIA does not include the type of detailed uncertainty assessment found in the Ozone 

NAAQS RIA (U.S. EPA, 2015)[Note, insert reference to 2015 ozone NAAQS RIA when/if 

finalized] or the PM NAAQS RIA (U.S. EPA, 2012a) because we lack the necessary air quality 

modeling input and/or monitoring data to run the benefits model. However, the results of the 

quantitative and qualitative uncertainty analyses presented in the Ozone NAAQS RIA and PM 

NAAQS RIA can provide some information regarding the uncertainty inherent in the estimated 

benefits results presented in this analysis. For example, sensitivity analyses conducted for the 

PM NAAQS RIA indicate that alternate cessation lag assumptions could change the estimated 

PM2.5-related mortality co-benefits discounted at 3 percent by between 10 percent and –27 

percent and that alternative income growth adjustments could change the PM2.5-related mortality 

benefits by between 33 percent and −14 percent. Although 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 estimates, the PM NAAQS RIA provides an 

indication of the random sampling error in the health impact and economic valuation functions 

using Monte Carlo methods. In general, the 95th percentile confidence interval for monetized 

PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates 
6-23 

based on Krewski et al. (2009) and Lepeule et al. (2012). The 95th percentile confidence interval 

for the health impact function alone ranges from approximately ±30 percent for mortality incidence 

based on Krewski et al. (2009) and ±46 percent based on Lepeule et al. (2012). 

Unlike RIAs for which the EPA conducts scenario-specific air quality modeling, we do not 

have information on the specific location of the air quality changes associated with the proposal. 

Instead, wWe applied benefit-per-ton estimates, which reflect specific geographic patterns of 

emissions reductions and specific air quality and benefits modeling assumptions. For example, 

these estimates may not reflect local variability in population density, meteorology, exposure, 

baseline health incidence rates, or other local factors that might lead to an over-estimate or 

under-estimate of the actual co-benefits of controlling PM and ozone precursors. As such, it is 

not feasible to estimate the proportion of co-benefits occurring in different locations, such as 

designated nonattainment areas. Use of these benefit-per-ton values to estimate benefits may lead 

to higher or lower benefit estimates than if benefits were calculated based on direct air quality 

modeling. Great care should be taken in applying these estimates to emission reductions 

occurring in any specific location, as these are all based on a broad emission reduction scenario 

and therefore represent average benefits-per-ton over the entire region. The benefit-per-ton for 

emission reductions in specific locations may be very different than the estimates presented here. 

To the extent that the geographic distribution of the emissions reductions achieved by 

implementing the proposal is different than the emissions in the air quality modeling of the 

illustrative control case, the benefits may be underestimated or overestimated. 

Our estimate of the total monetized benefits is based on the EPA’s interpretation of the 

best available scientific literature and methods and supported by the SAB-HES and the National 

Academies of Science (NRC, 2002). Below are key assumptions underlying the estimates for 

PM2.5-related premature mortality, which accounts for 98 percent of the monetized PM2.5 health 

co-benefits. 

1. We assume that all fine particles, regardless of their chemical composition, are equally 

potent in causing premature mortality. This is an important assumption, because PM2.5 

varies considerably in composition across sources, but the scientific evidence is not yet 

sufficient to allow differentiation of effect estimates by particle type. The PM ISA 

concluded that “many constituents of PM2.5 can be linked with multiple health effects, and 
6-24 

the evidence is not yet sufficient to allow differentiation 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-benefits from reducing fine particles in 

areas with varied concentrations of PM2.5, including both areas that do not meet the fine 

particle standard and those areas that are in attainment, down to the lowest modeled 

concentrations. 

3. We assume that there is a “cessation” lag between the 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 PM2.5 exposures occur in a distributed fashion 

over the 20 years following exposure based on the advice of the SAB-HES (U.S. EPASAB, 2004c), which affects the valuation of mortality co-benefits at different discount 

rates. 

In general, we are more confident in the magnitude of the risks we estimate from simulated 

PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the 

epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in 

the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed 

data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML], one 

standard deviation below the mean of the air quality data in the study, etc.) allow readers to 

determine the portion of population exposed to annual mean PM2.5 levels at or above different 

concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 

mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and 

one standard deviation below the mean) that have been incorporated into recent RIAs and the 

EPA’s Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties 

inherent in identifying any particular point at which our confidence in reported associations 

becomes appreciably less, and the scientific evidence provides no clear dividing line. However, 

the EPA does not view these concentration benchmarks as a concentration threshold below 

which we would not quantify health benefits of air quality improvements.12 Rather, the cobenefits estimates reported in this RIA are the best estimates because they reflect the full range 

 

12 For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality 

relationship, see the TSD entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b). 
6-25 

of air quality concentrations associated with the regulatory control alternatives. The PM ISA 

concluded that the scientific evidence collectively is sufficient to conclude that the relationship 

between long-term PM2.5 exposures and mortality is causal and that, overall, the studies support 

the use of a no-threshold log-linear model to estimate PM-related long-term mortality (U.S. EPA, 

2009b). 

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

• Key assumption and uncertainties related to modeling of ozone-related premature 

mortality: Ozone-related short-term mortality represents a substantial proportion of total 

monetized benefits (over 94% of the ozone-related-benefits), and these estimates have the 

following key assumptions and uncertainties. We utilize a log-linear impact function 

without a threshold in modeling short-term ozone-related mortality. However, we 

acknowledge reduced confidence in specifying the nature of the C-R function in the range 

of ≤20ppb and below (ozone ISA, section 2.5.4.4). Thus, the estimates include health 

benefits from reducing ozone in areas with varied concentrations of ozone, including both 

areas that do not meet the ozone standard and those areas that are in attainment, down to the 

lowest modeled concentrations. 

• Avoided premature mortality according to baseline pollutant concentrations: We 

recognize that, in estimating short-term ozone-related mortality, we are less confident in 

specifying the shape of the C-R function at lower ambient ozone concentrations (at and 

below 20 ppb, ozone ISA, section 2.5.4.4). Quantitative uncertainty analyses completed for 

the Ozone NAAQS RIA (U.S. EPA, 2015) found almost 100% of mortality reductions 

occurred above 20 ppb, where we are more confident in specifying the nature of the ozonemortality effect (ozone ISA, section 2.5.4.4). However, as discussed in section 6B.7 of that 

RIA, care must be taken in interpreting these results since the ambient air metric used in 

modeling this endpoint is the mean 8-hour max value in each grid cell (and not the full 

distribution of 8-hour daily max values). Had the latter been used, then the 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. However, we believe that it is still important to 

characterize the distribution of exposure to baseline concentrations. As a surrogate measure of 

mortality impacts, we provide the percentage of the population exposed at each PM2.5

concentration in the baseline of the air quality modeling used to calculate the benefit-per-ton 

estimates for this RIA using 12 km grid cells across 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 quality. In other words, the percentage of the 

population exposed to air pollution below the LML is not the same as the percentage of the 

population experiencing health impacts as a result of a specific emission reduction policy. The 

most important aspect, which we are unable to quantify without rule-specific air quality 

modeling, is the shift in exposure anticipated by implementing the proposal. Therefore, caution is 

warranted when interpreting the LML assessment in this RIA because these results are not 

consistent with results from RIAs that had air quality modeling. 

Figure 6-3 shows a bar chart of the percentage of the population exposed to various air 

quality levels, including the LML concentration benchmarks in the illustrative control case 

modeling, and Figure 6-4 shows a cumulative distribution function of the same data. Both figures 

identify the LML for each of the major cohort studies. 

 

13 As described in Chapter 3, the baseline for the air quality modeling used to calculate the benefit-per-ton values 

differs from the baseline used to estimate the benefits, costs, and impacts of this rulemaking. See Chapter 3 for more 

details about the differences between the two baselines. 
6-27 

Figure 6-2. Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in 

the Baseline Illustrative Modeling (uused for the Air Quality Analysis in 

Chapter 3to generate the benefit-per-ton estimates)

Among the populations exposed to PM2.5 in the baseline:

88% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study 

47% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study 
6-28 

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

PM2.5 Exposure in the Baseline used for the Air Quality Analysis in Chapter 

3Illustrative Modeling (used to generate the benefit-per-ton estimates)

6.3 Estimated Climate Co-Benefits from CO2

A co-benefit of this proposal is reducing emissions of CO2. In this section, we provide a 

brief overview of the 2009 Endangerment Finding and climate science assessments released 

since then. We also provide information regarding the economic valuation of CO2 using the 

Social Cost of Carbon (SC-CO2), a metric that estimates the monetary value of impacts 

associated with marginal changes in CO2 emissions in a given year. Table 6-8 summarizes the 

quantified and unquantified climate benefits in this analysis. 

Among the populations exposed to PM2.5 in the baseline: 

88% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study 

47% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study 
6-29 

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 CO2 —1  SCC TSD 

Climate impacts from ozone and black 

carbon (directly emitted PM) 

— — Ozone ISA, PM 

ISA2

Other climate impacts (e.g., other GHGs 

such as methane, aerosols, other impacts)

— — IPCC2

1 The global climate and related impacts of CO2 emissions changes, such as sea level rise, are estimated within each 

integrated assessment model as part of the calculation of the SC-CO2. The resulting monetized damages, which are 

relevant for conducting the benefit-cost analysis, are used in this RIA to estimate the welfare effects of quantified 

changes in CO2 emissions.

2

 We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or 

methods. 

There are several important considerations in assessing the climate-related benefits for an 

ozone air quality-focused rulemaking. First, the estimated health benefits do not account for any 

climate-related air quality changes (e.g., increased ambient ozone associated with higher 

temperatures). Excluding climate-related air quality changes may underestimate ozone-related 

health benefits. It is unclear how PM2.5-related health benefits would be affected by excluding 

climate-related air quality changes since the science is unclear as to how climate change may 

affect PM2.5 exposure. Second, the estimated health benefits also do not consider temperature 

modification of PM2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 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 quality-related health benefits likely leads to 

underestimation of quantified health benefits (Anenberg et al, 2009, Jhun et al, 2014). Fourth, as 

noted earlier, we do not estimate the climate co-benefits associated with reductions in PM and O3

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 Administrator found that elevated concentrations of 

greenhouse gases in the atmosphere may reasonably be anticipated both to endanger public 

health and to endanger public welfare. For health, these include the increased likelihood of heat 

waves, negative impacts on air quality, more intense hurricanes, more frequent and intense 
6-30 

storms and heavy precipitation, and impacts on infectious and waterborne diseases. For welfare, 

these include reduced water supplies in some regions, increased water pollution, increased 

occurrences of floods and droughts, rising sea levels and damage to coastal infrastructure, 

increased peak electricity demand, changes in ecosystems, and impacts on indigenous 

communities. 

The preamble also summarizes new scientific assessments and recent climatic observations. 

Major scientific assessments released since the 2009 Endangerment Finding have improved 

scientific understanding of the climate, and provide even more evidence that GHG emissions 

endanger public health and welfare for current and future generations. The National Climate 

Assessment (NCA), in particular, assessed the impacts of climate change on human health in the 

United States, finding that Americans will be affected 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 the impacts of climate change on public 

welfare, higher projections of future sea level rise than had been previously estimated, a better 

understanding of how the warmth in the next century may reach levels that would be 

unprecedented relative to the preceding millions of years of history, and new assessments of the 

impacts of climate change on permafrost and ocean acidification. The impacts of GHG emissions 

will be realized worldwide, independent of their location of origin, and impacts outside of the 

United States will produce consequences relevant to the United States. 

6.3.2 Social Cost of Carbon 

We estimate the global social benefits of CO2 emission reductions expected from the final 

emission guidelines using the SC-CO2 estimates presented in the Technical Support Document: 

Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive 

Order 12866 (May 2013, Revised July 2015) (“current TSD”).14 We refer to these estimates, 

 

14 Docket ID EPA-HQ-OAR-2013-0495, Technical Support Document: Technical Update of the Social Cost of 

Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost 

of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management and 
6-31 

which were developed by the U.S. government, as “SC-CO2 estimates.” The SC-CO2 is a metric 

that estimates the monetary value of impacts associated with marginal changes in CO2 emissions 

in a given year. It includes a wide range of anticipated climate impacts, such as net changes in 

agricultural productivity and human health, property damage from increased flood risk, and 

changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is typically used to assess the avoided damages as a result of regulatory actions 

(i.e., benefits of rulemakings that lead to an incremental reduction in cumulative global CO2

emissions). 

The SC-CO2 estimates used in this analysis were developed over many years, using the 

best science available, and with input from the public. Specifically, an interagency working 

group (IWG) that included the EPA and other executive branch agencies and offices used three 

integrated assessment models (IAMs) to develop the SC-CO2 estimates and recommended four 

global values for use in regulatory analyses. The SC-CO2 estimates were first released in 

February 2010 and updated in 2013 using new versions of each IAM. As discussed further 

below, the IWG published two minor corrections to the SC-CO2 estimates in July 2015. 

The SC-CO2 estimates were developed using an ensemble of the three most widely cited 

integrated assessment models in the economics literature with the ability to estimate the SC-CO2. 

A key objective of the IWG was to draw from the insights of the three models while respecting 

the different approaches to linking GHG emissions and monetized damages taken by modelers in 

the published literature. After conducting an extensive literature review, the interagency group 

selected three sets of input parameters (climate sensitivity, socioeconomic and emissions 

trajectories, and discount rates) to use consistently in each model. All other model features were 

left unchanged, relying on the model developers’ best estimates and judgments, as informed by 

the literature. Specifically, a common probability distribution for the equilibrium climate 

sensitivity parameter, which informs the strength of climate’s response to atmospheric GHG 

concentrations, was used across all three models. In addition, a common range of scenarios for 

the socioeconomic parameters and emissions forecasts were used in all three models. Finally, the 

 

Budget, Office of Science and Technology Policy, and Department of Treasury (May 2013, Revised July 2015). 

Available at: <https://www.whitehouse.gov/sites/default/files/omb/inforeg/scc-tsd-final-july-2015.pdf> Accessed 

7/11/2015. 
6-32 

marginal damage estimates from the three models were 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 uncertainties, and the current TSD for the 

latest estimates.15 

The SC-CO2 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-CO2 must therefore incorporate the full (global) damages caused by GHG emissions to 

address the global nature of the problem. Second, the U.S. operates in a global and highly 

interconnected economy, such that impacts on the other 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, climate change represents a classic public goods 

problem because each country’s reductions benefit everyone else and no country can be excluded 

from enjoying the benefits of other countries’ reductions, even if it provides no reductions itself. 

In this situation, the only way to achieve an economically efficient level of emissions reductions 

is for countries to cooperate in providing mutually beneficial reductions beyond the level that 

would be justified only by their own domestic benefits. In reference to the public good nature of 

mitigation and its role in foreign relations, thirteen prominent academics noted that these “are 

compelling reasons to focus on a global SCC” in a recent article on the SCC (Pizer et al., 2014). 

In addition, as noted in OMB’s Response to Comments on the SCC, there is no bright line 

between domestic and global damages. Adverse impacts on other countries can have spillover 

effects on the United States, particularly in the areas of national security, international trade, 

public health and humanitarian concerns.16

The 2010 TSD noted a number of limitations to the SC-CO2 analysis, including the 

incomplete way in which the integrated assessment models 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 Clean 

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 climate 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 limitations do not all work in the same direction in 

terms of their influence on the SC-CO2 estimates, though taken together they suggest that the 

SC-CO2 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-CO2 estimates] underestimate the damage 

costs because they cannot include many non-quantifiable impacts.” Since then, the peerreviewed literature has continued to support this conclusion. For example, the IPCC Fifth 

Assessment report observed that SC-CO2 estimates continue to omit various impacts that would 

likely increase damages. The 95th percentile estimate was included in the recommended range 

for regulatory impact analysis to address these concerns. 

The EPA and other agencies have continued to consider feedback on the SC-CO2 estimates 

from stakeholders through a range of channels, including public comments on rulemakings that 

use the SC-CO2 in supporting analyses and through regular interactions with stakeholders and 

research analysts implementing the SC-CO2 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-CO2 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 Cost of Carbon.” March 13, 2014, 

http://costofcarbon.org/files/Omitted_Damages_Whats_Missing_From_the_Social_Cost_of_Carbon.pdf; and (2) 

Electric Power Research Institute, “Understanding the Social Cost of carbon: A Technical Assessment,” October 

2014, www.epri.com. 

18 See https://www.federalregister.gov/articles/2013/11/26/2013-28242/technical-support-document-technicalupdate-of-the-social-cost-of-carbon-for-regulatory-impact 
6-34 

regulatory impact analysis. With the release of the response to comments19, the IWG announced 

plans to obtain expert independent advice from the National Academies of Sciences, 

Engineering, and Medicine (Academies) to ensure that the SC-CO2 estimates continue to reflect 

the best available scientific and economic information on climate change.20 The Academies’ 

process will be informed by the public comments received and focuses on the technical merits 

and challenges of potential approaches to improving the SC-CO2 estimates in future updates.21 

Concurrent with OMB’s publication of the response to comments on SC-CO2 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-CO2

estimates are one dollar less than the mean SC-CO2 estimates reported in the November 2013 

revision to the May 2013 TSD. The change in the estimates associated with the 95th percentile 

estimates when using a 3% discount rate is slightly larger, as those estimates are heavily 

influenced by the results from the model that was affected by the indexing error. 

The four SC-CO2 estimates are as follows: $12, $41, $63, and $120 per metric ton of CO2

emissions in the year 2017 (2011$).22 The first three values are based on the average SC-CO2

from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SC-CO2 estimates 

for several discount rates are included because the literature shows that the SC-CO2 is quite 

sensitive to assumptions about the discount rate, and because no consensus exists on the 

 

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

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

21 See 

http://sites.nationalacademies.org/DBASSE/BECS/CurrentProjects/DBASSE_167526?utm_source=All%20DBASS

E%20Newsletters&utm_campaign=e84c13e8c4-

New_Project_the_Social_Cost_of_Carbon&utm_medium=email&utm_term=0_e16023964e-e84c13e8c4-

267347161 for more information about the National Academies process and the status of the project. 

22 The current version of the TSD is available at: https://www.whitehouse.gov/sites/default/files/omb/inforeg/scctsd-final-july-2015.pdf. The 2010 and 2013 TSDs present SC-CO2 in 2007$ per metric ton. The unrounded 

estimates from the current TSD were adjusted to 2011$ using GDP Implicit Price Deflator (1.061374), 

http://www.bea.gov/iTable/index_nipa.cfm. The estimates presented here have been rounded to two significant 

digits. 
6-35 

appropriate rate to use in an intergenerational context (where costs and benefits are incurred by 

different generations). The fourth value is the 95th percentile of the SC-CO2 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-CO2 distribution (representing less likely, 

but potentially catastrophic, outcomes). 

Table 6-9 presents the global SC-CO2 estimates in metric tons for the years 2015 to 2050. 

In order to calculate the dollar value for emission reductions, the SC-CO2 estimate for each 

emissions year would be applied to changes in CO2 emissions for that year, and then discounted 

back to the analysis year using the same discount rate used to estimate the SC-CO2.

 23, 24 The SCCO2 increases over time because future emissions are expected to produce larger incremental 

damages as physical and economic systems become more stressed in response to greater climate 

change. Note that the interagency group estimated the growth rate of the SC-CO2 directly using 

the three integrated assessment models rather than assuming a constant annual growth rate. This 

helps to ensure that the estimates are internally consistent with other modeling assumptions. 

Table 6-10 reports the incremental climate co-benefits from CO2 emission impacts estimated for 

the proposal and more and less stringent alternatives for the 2017 analysis year. 

 

23 Emission impacts for this rulemaking are shown for the year 2017 and are calculated in short tons. Therefore, the 

conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in the year 2017. 

24 This analysis considered the climate impacts of only CO2 emission change. As discussed below, the climate 

impacts of other pollutants were not calculated for the proposed guidelines. While CO2 is the dominant GHG 

emitted by the sector, we recognize the representative facilities within these comparisons may also have different 

emission rates for other climate forcers that will serve a minor role in determining the overall social cost of 

generation. 
6-36 

Table 6-9. Social Cost of CO2, 2015-2050 (in 2011$ per metric ton)* 

Year 

Discount Rate and Statistic 

5% Average 3% Average 2.5% Average 3% (95th percentile) 

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-CO2 values are stated in $/metric ton and rounded to two significant figures. The estimates vary 

depending on the year of CO2 emissions and are defined in real terms, i.e., adjusted for inflation using the GDP 

implicit price deflator. 

Table 6-10. Estimated Global Climate Co-benefits of CO2 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 CO2

reduced** 610 6104 720 

5% (average) $6.5 $6.5 $7.6 

3% (average) $23 $23 $27 

2.5% (average) $35 $35 $41 

3% (95th percentile) $66 $66 $78 

* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting 

of climate impacts. 

**Emission impacts for this rulemaking are shown for the year 2017 and are calculated in short tons. The 

conversion factor 0.90718474 metric tons in a short ton was applied to the calculate the benefits in the year 2017. 

It is important to note that the climate co-benefits presented above are associated with 

changes in CO2 emissions only. Implementing this proposal, however, will have an impact on the 

emissions of other pollutants that would affect the climate. Both predicting reductions in 

emissions and estimating the climate impacts of these other pollutants, however, is complex. The 

climate impacts of these other pollutants have not been calculated for this proposal.25

 

 

25 The SC-CO2 estimates used in this analysis are designed to assess the climate benefits associated with changes in 

CO2 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), aerosols and aerosol precursors such as black 

carbon, organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as 

nitrogen oxides and volatile organic carbon compounds. Changes in emissions of these pollutants 

(both increases and decreases) could directly result 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 precursors are projected to lead to warming. 

Therefore, changes in non-CO2 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 indirectly 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 can increase concentrations in the atmosphere 

for hundreds or thousands of years, many of these other pollutants are short lived and remain in 

the atmosphere for short periods of time ranging from days to weeks and can therefore exhibit 

large spatial and temporal variability. 

6.4 Combined Health Benefits and Climate Co-Benefits Estimates 

In this analysis, we were able to monetize the estimated benefits associated with the 

reduced exposure to ozone and PM2.5 and co-benefits of decreased emissions of CO2, but we 

were unable to monetize the co-benefits associated with reducing exposure to mercury, carbon 

monoxide, and NO2, as well as ecosystem effects and visibility impairment. In addition, there are 

expected to be unquantified health and welfare impacts associated with changes in hydrogen 

chloride. Specifically, we estimated combinations of health benefits at discount rates of 3 percent 

and 7 percent (as recommended 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-CO2 (average SC-CO2 at each of three discount rates—5 percent, 3 percent, 2.5 

percent—and the 95th percentile SC-CO2 at 3 percent) (as recommended by the IWG). 
6-38 

Different discount rates are applied to SC-CO2 than to the health benefit estimates because 

CO2 emissions are long-lived and subsequent damages occur over many years. Moreover, several 

rates are applied to SC-CO2 because the literature shows that it is sensitive to assumptions about 

discount rate and because no consensus exists on the appropriate rate to use in an 

intergenerational context. The SC-CO2 interagency group centered its attention on the 3 percent 

discount rate but emphasized the importance of considering all four SC-CO2 estimates.26 The 

EPA has evaluated the range of potential impacts by combining all SC-CO2 values with health 

benefits values at the 3 percent and 7 percent discount rates. Combining the 3 percent SC-CO2

values with the 3 percent health benefit values assumes 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 analysis year. 

 

26 See the 2010 SCC TSD. Docket ID EPA-HQ-OAR-2009-0472-114577 or 

http://www.whitehouse.gov/sites/default/files/omb/inforeg/for-agencies/Social-Cost-of-Carbon-for-RIA.pdf for 

details. 
6-39 

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

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

SC-CO2 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% (95th 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% (95th 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% (95th percentile) $260 to $410 $260 to $400 $78 

*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. 

Co-benefits are based on regional benefit-per-ton estimates. Co-benefits for ozone are based on ozone season NOx 

emissions. Ozone benefits occur in analysis year, so they are the same for all discount rates. The health benefits 

reflect the sum of the PM2.5 and ozone co-benefits and reflect the range based on adult mortality functions (e.g., from 

Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) with Zanobetti and Schwartz (2008)). The 

monetized health co-benefits do not include reduced health effects from directly emitted PM2.5, direct exposure to 

NO2, SO2, and HAP; ecosystem effects; or visibility impairment. 

**As discussed in section 6.3, the SC-CO2 estimates are calculated with four different values of a one ton reduction. 

6.5 Unquantified Co-benefits 

The monetized co-benefits estimated in this RIA reflect a subset of co-benefits attributable 

to the health effect reductions associated with ambient fine particles and ozone. Data, time, and 

resource limitations 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), SO2 and NO2, as well as ecosystem effects, and 

visibility impairment due to the absence of air quality modeling data for these pollutants in this 

analysis. This does not imply that there are no co-benefits associated with changes in emissions 

of HAP or reductions in exposures to SO2 and NO2. 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-benefits Categories 

Category Specific Effect 

Effect Has 

Been 

Quantified 

Effect Has 

Been 

Monetized 

More Information

Improved Human Health 

Reduced incidence of 

morbidity from exposure 

to NO2

Asthma hospital admissions (all ages) — — NO2 ISA1

Chronic lung disease hospital admissions (age > 

65) — — NO2 ISA1

Respiratory emergency department visits (all 

ages) — — NO2 ISA1

Asthma exacerbation (asthmatics age 4–18) — — NO2 ISA1

Acute respiratory symptoms (age 7–14) — — NO2 ISA1

Premature mortality — — NO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — NO2 ISA2,3

Reduced incidence of 

morbidity from exposure 

to SO2

Respiratory hospital admissions (age > 65) — — SO2 ISA1

Asthma emergency department visits (all ages) — — SO2 ISA1

Asthma exacerbation (asthmatics age 4–12) — — SO2 ISA1

Acute respiratory symptoms (age 7–14) — — SO2 ISA1

Premature mortality — — SO2 ISA1,2,3

Other respiratory effects (e.g., airway 

hyperresponsiveness and inflammation, lung 

function, other ages and populations) 

— — SO2 ISA1,2

Improved Environment 

Reduced visibility 

impairment 

Visibility in Class 1 areas — — PM ISA1

Visibility in residential areas — — PM ISA1

Reduced effects on 

materials 

Household soiling — — PM ISA1,2

Materials damage (e.g., corrosion, increased 

wear) — — PM ISA2

Reduced effects from PM 

deposition (metals and 

organics) 

Effects on Individual organisms and ecosystems — — PM ISA2

Reduced vegetation and 

ecosystem effects from 

exposure to ozone 

Visible foliar injury on vegetation — — Ozone ISA1

Reduced vegetation growth and reproduction — — Ozone ISA1

Yield and quality of commercial forest products 

and crops — — Ozone ISA1

Damage to urban ornamental plants — — Ozone ISA2

Carbon sequestration in terrestrial ecosystems — — Ozone ISA1

Recreational demand associated with forest 

aesthetics — — Ozone ISA2

Other non-use effects Ozone ISA2

Ecosystem functions (e.g., water cycling, 

biogeochemical cycles, net primary productivity, 

leaf-gas exchange, community composition) 

— — Ozone ISA2

Reduced effects from 

acid deposition 

Recreational fishing — — NOx SOx ISA1

Tree mortality and decline — — NOx SOx ISA2

Commercial fishing and forestry effects — — NOx SOx ISA2

Recreational demand in terrestrial and aquatic 

ecosystems 

— — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles) — — NOx SOx ISA2
6-41 

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 — — NOx SOx ISA2

Coastal eutrophication — — NOx SOx ISA2

Recreational demand in terrestrial and estuarine 

ecosystems — — NOx SOx ISA2

Other non-use effects NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical 

cycles, fire regulation) — — NOx SOx ISA2

Reduced vegetation 

effects from ambient 

exposure to SO2 and NOx

Injury to vegetation from SO2 exposure — — NOx SOx ISA2

Injury to vegetation from NOx exposure — — NOx SOx ISA2

1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA. 

2We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods. 

3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other significant 

concerns over the strength of the association. 

6.5.1 HAP Impacts 

Due to methodology and resource limitations, we were unable to estimate the impacts 

associated with changes in emissions of the hazardous air pollutants in this analysis. The EPA’s 

SAB-HES concluded that “the challenges for assessing progress in health improvement as a 

result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack 

of exposure-response functions, uncertainties in emissions inventories and background levels, the 

difficulty of extrapolating risk estimates to low doses and the challenges of tracking health 

progress for diseases, such as cancer, that have long latency periods” (U.S. EPA-SAB, 2008). In 

2009, the EPA convened a workshop to address the inherent complexities, limitations, and 

uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations 

from this workshop included identifying research priorities, focusing on susceptible and 

vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011). 

6.5.1.1 Mercury 

Mercury in the environment is transformed into a more toxic form, methylmercury (MeHg). 

Because Hg is a persistent pollutant, MeHg accumulates in the food chain, especially the 

tissue of fish. When people consume these fish, they 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 section 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 searches to obtain 

other related and more recent publications to complement the material summarized by the 

NRC in 2000. 

In its review of the literature, the NAS found neurodevelopmental effects to be the most sensitive 

and best documented endpoints and appropriate for establishing 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 children 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 pregnancy. The committee concludes that 

the risk to that population is likely to be sufficient 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 available 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 points (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 better characterize the effect of methylmercury 

exposure on blood pressure and cardiovascular function at various stages of life.” 

Additional cardiovascular studies have been published since 2000. The EPA did not develop a 

quantitative dose-response assessment for cardiovascular effects associated with MeHg 

exposures, as there is no consensus among scientists 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 system effects. The pharmacokinetics of 

some of the exposure measures (such as toenail Hg levels) are not well understood. The 

studies have not yet received the review and scrutiny of the more well-established 

neurotoxicity data base. 

The Mercury Study noted that MeHg is not a potent mutagen but is capable of causing 

chromosomal damage in a number of experimental systems. The NAS concluded that evidence 

that human exposure to MeHg caused genetic damage is inconclusive; they note that some 

earlier studies showing chromosomal damage in lymphocytes may not have controlled 

sufficiently for potential confounders. One study of adults living in the Tapajós River region 

in Brazil (Amorim et al., 2000) reported a direct relationship between MeHg concentration in 

hair and DNA damage in lymphocytes, as well as effects 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). 1997. 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 methyl mercury contamination in the Brazilian Amazon. An. 

Acad. Bras. Ciênc. 72(4): 497-507. 
6-43 

that genotoxic effects (largely chromosomal aberrations) may result from dietary and chronic 

MeHg exposures similar to and above those seen in the 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 immunotoxic effects of MeHg is limited 

(NRC, 2000).30

Based on limited human and animal data, MeHg is classified 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 of carcinogenic effects in humans 

from low-dose chronic exposures is tenuous. Multiple human epidemiological studies have 

found no significant association between Hg exposure 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 renal 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 studies of the two high-dose poisoning 

episodes in Iraq and Japan or animal data, rather than epidemiological studies of chronic 

exposures at the levels of interest in this analysis. 

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 exposure to 35 ppm causes throat irritation, 

and levels of 50 to 100 ppm are barely tolerable for 1 hour.33 Concentrations in typical 

human exposure environments are much lower than these levels and rarely exceed the 

reference concentration.34The greatest impact is on the upper respiratory tract; exposure to 

high concentrations can rapidly lead to swelling and 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 Health 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 toluene 

sulphonanilide. Br. J. Indust. Med. 18(Oct.):303-308 (as cited in NRC, 2000). 

32 U.S. Environmental Protection Agency (EPA). 2002. Integrated Risk Information System (IRIS) on 

Methylmercury. National Center for Environmental Assessment. Office of Research and Development. Available 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 and 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.pdf 
6-44 

skin, and narrowing of the bronchioles. Exposure to HCl can lead to Reactive Airways 

Dysfunction Syndrome (RADS), a chemically, or irritant-induced type of asthma. Children 

may be more vulnerable to corrosive agents than adults because of the relatively smaller 

diameter of their airways. Children may also be more 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 carcinogenic effects.35

6.5.2 Additional NO2 Health Co-Benefits 

In addition to being a precursor to PM2.5 and ozone, NOx emissions are also linked to a 

variety of adverse health effects associated with direct exposure. We were unable to estimate the 

health co-benefits associated with reduced NO2 exposure in this analysis. Therefore, this analysis 

only quantified and monetized the PM2.5 and ozone co-benefits associated with the reductions in 

NO2 emissions. 

Following a comprehensive review of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Nitrogen —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 NO2. These epidemiologic and experimental studies 

encompass a number of endpoints including emergency department visits and hospitalizations, 

respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. The 

NOx ISA also concluded that the relationship between short-term NO2 exposure and premature 

mortality was “suggestive but not sufficient to infer a causal relationship,” because it is difficult 

to attribute the mortality risk effects to NO2 alone. Although the NOx ISA stated that studies 

consistently reported a relationship between NO2 exposure and mortality, the effect was 

generally smaller than that for other pollutants such as PM. 

6.5.3 Additional SO2 Health Co-Benefits 

In addition to being a precursor to PM2.5, SO2 emissions are also linked to a variety of 

adverse health effects associated with direct exposure. We were unable to estimate the health cobenefits associated with reduced SO2 in this analysis because we do not have air quality 

 

35 U.S. Environmental Protection Agency (U.S. EPA). 1995. “Integrated Risk Information System File of Hydrogen 

Chloride.” Washington, DC: Research and Development, National Center for Environmental Assessment. This 

material is available at http://www.epa.gov/iris/subst/0396.htm . 
6-45 

modeling data available. Therefore, this analysis only quantifies and monetizes the PM2.5 cobenefits associated with the reductions in SO2 emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment for Oxides of Sulfur —Health Criteria (SO2 ISA) 

concluded that there is a causal relationship between respiratory health effects and short-term 

exposure to SO2 (U.S. EPA, 2008a). The immediate effect of SO2 on the respiratory system in 

humans is bronchoconstriction. Asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease. A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms 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 that the SO2 ISA identified as a “causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for other pollutants. We did not quantify these co-benefits due to data 

constraints. 

6.5.4 Additional NO2 and SO2 Welfare Co-Benefits 

As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur —

Ecological Criteria (NOx/SOx ISA) (U.S. EPA, 2008d), SO2 and NOx emissions also contribute 

to a variety of adverse welfare effects, including those associated with acidic deposition, 

visibility impairment, and nutrient enrichment. Deposition of nitrogen causes acidification, 

which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic 

ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and 

sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern U.S., the surface 

waters affected by acidification are a source of food for some recreational and subsistence 
6-46 

fishermen and for other consumers and support several cultural services, including aesthetic and 

educational services and recreational fishing. Biological effects of acidification in terrestrial 

ecosystems are generally linked to aluminum toxicity, which can cause reduced root growth, 

restricting the ability of the plant to take up water and nutrients. These direct effects can, in turn, 

increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect 

pests, and disease leading to increased mortality of canopy trees. Terrestrial acidification affects 

several important ecological services, including declines in habitat for threatened and endangered 

species (cultural), declines in forest aesthetics (cultural), declines in forest productivity 

(provisioning), and increases in forest soil erosion and reductions in water retention (cultural and 

regulating). (U.S. EPA, 2008d) 

Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In 

estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of 

estuaries can disrupt an important source of food production, particularly fish and shellfish 

production, and a variety of cultural ecosystem services, including water-based recreational and 

aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and 

number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets 

the balance between native and nonnative plants, changing the ability of an area to support 

biodiversity. When the composition of species changes, then fire frequency and intensity can 

also change, as nonnative grasses fuel more frequent and more intense wildfires. (U.S. EPA, 

2008d) 

Reductions in emissions of NO2 and SO2 will improve the level of visibility throughout the 

United States because these gases (and the particles of nitrate and sulfate formed from these 

gases) impair visibility by scattering and absorbing light (U.S. EPA, 2009). Visibility is also 

referred to as visual air quality (VAQ), and it directly affects people’s 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 activities, 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 array of vegetation and ecosystem 

effects in the published literature (U.S. EPA, 2013b). Sensitivity to ozone is highly variable 

across species, with over 65 plant species identified as “ozone-sensitive”, many of which occur 

in state and national parks and forests. These effects include those that damage or impair the 

intended use of the plant or ecosystem. Such effects can include reduced growth and/or biomass 

production in sensitive plant species, including forest trees, reduced yield and quality of crops, 

visible foliar injury, species composition shift, and changes in ecosystems and associated 

ecosystem services. 

6.5.6 Carbon Monoxide Co-Benefits 

CO in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels 

and photochemical reactions in the atmosphere. The amount of CO emitted from these 

reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the combustion zone, 

such as fuel oxygen content, burn temperature, or mixing 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 range of effects in multiple tissues and 

organ systems that depend on concentration and duration of exposure. The Integrated Science 

Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to 

CO is “likely to have a causal relationship” with cardiovascular morbidity, particularly in 

individuals with coronary heart disease. Epidemiologic studies associate short-term CO 

exposure with increased risk of emergency department visits and hospital admissions. 

Coronary heart disease includes those who have angina 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 obstructive 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 mortality. The evidence is also suggestive of a 

causal relationship for birth outcomes and developmental effects following long-term 

exposure to CO, and for central nervous system effects linked to short- and long-term 

exposure to CO. 

6.5.7 Visibility Impairment Co-Benefits 

Reducing secondary formation of PM2.5 would improve levels of visibility in the U.S. 

because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. 

EPA, 2009b). Fine particles with significant light-extinction efficiencies include sulfates, 

nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct 

significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good 
6-48 

visibility increases the quality of life where individuals 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 contributor 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 estimate visibility-related benefits, and we are 

also unable to determine whether the emission reductions associated with the final emission 

guidelines would be likely to have a significant impact on visibility in urban areas or Class I 

areas. 

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