Reducing emissions of fine PM2.5 and SO2 emissions is expected to reduce ground-level PM2.5 concentrations. Reducing NOX emissions is expected to reduce both ground-level ozone and PM2.5 concentrations. Below we present the estimated number and economic value of these avoided PM2.5 and ozone-attributable premature deaths and illnesses. We also present the estimated monetized climate and health benefits associated with emission reductions for each of three regulatory options described in prior sections.
In addition to reporting results, this section details the methods used to estimate the benefits to human health of reducing concentrations of PM2.5 and ozone resulting from the projected emissions reductions from EGUs under this proposal. This analysis uses methods for determining air quality changes that has been used in the RIAs from multiple previous proposed and final rules (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2022c). The approach involves two major steps: (1) developing spatial fields of air quality across the U.S. for a baseline scenario and the proposed and more stringent regulatory options examined in this RIA for 2028, 2030 and 2035 using nationwide photochemical modeling and related analyses; and (2) using these spatial fields in BenMAP-CE to quantify the benefits under each regulatory control alternative and each year as compared to the baseline in that year. See Section 4.3.3 for more detail on BenMAP-CE. When estimating the value of improved air quality over a multi-year time horizon, the analysis applies population growth and income growth projections for each future year through 2037 and estimates of baseline mortality incidence rates at five-year increments.
Elevated concentrations of GHGs in the atmosphere have been warming the planet, leading to changes in the Earth's climate including changes in the frequency and intensity of heat waves, precipitation, and extreme weather events, rising seas, and retreating snow and ice. The well-documented atmospheric changes due to anthropogenic GHG emissions are changing the climate at a pace and in a way that threatens human health, society, and the natural environment. There will be important climate benefits associated with the CO2 emissions reductions expected from this proposed rule. Climate benefits from reducing emissions of CO2 can be monetized using estimates of the SC-CO2.
Though the proposed rule is likely to also yield positive benefits associated with reducing pollutants other than mercury, non-mercury metal HAP, PM2.5, ozone, and CO2, time, resource, and data limitations prevented us from quantifying and estimating the economic value of those reductions. Specifically, in this RIA EPA does not monetize health benefits of reducing direct exposure to NO2 and SO2 nor ecosystem effects and visibility impairment associated with changes in air quality. In addition, this RIA does not include monetized impacts from changes in pollutants in other media, such as water effluents. We qualitatively discuss these unquantified impacts in this section.
Hazardous Air Pollutant Benefits
This proposed rule is projected to reduce emissions of mercury and non-mercury metal HAP. Specifically, projected reductions in mercury are expected to help reduce exposure to methylmercury for sub-populations that rely on subsistence fishing. In addition, projected emissions reductions should also reduce exposure to non-mercury metal HAP including carcinogens such as nickel, arsenic, and hexavalent chromium, for residents located in the vicinity of these facilities.
Mercury
Mercury is a persistent, bioaccumulative toxic metal that is emitted from power plants in three forms: gaseous elemental mercury (Hg0), oxidized mercury compounds (Hg+2), and particle-bound mercury (HgP). Elemental mercury does not quickly deposit or chemically react in the atmosphere, resulting in residence times that are long enough to contribute to global scale deposition. Oxidized mercury and HgP deposit quickly from the atmosphere impacting local and regional areas in proximity to sources. Methylmercury is formed by microbial action in the top layers of sediment and soils, after mercury has precipitated from the air and deposited into waterbodies or land. Once formed, methylmercury is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger predatory fish may have methylmercury concentrations many times, typically on the order of one million times, that of the concentrations in the freshwater body in which they live. Methylmercury can adversely impact ecosystems and wildlife.
Human exposure to methylmercury is known to have several adverse neurodevelopmental impacts, such as IQ loss measured by performance on neurobehavioral tests, particularly on tests of attention, fine motor-function, language, and visual spatial ability. In addition, evidence in humans and animals suggests that methylmercury can have adverse effects on both the developing and the adult cardiovascular system, including fatal and non-fatal ischemic heart disease (IHD). Further, nephrotoxicity, immunotoxicity, reproductive effects (impaired fertility), and developmental effects have been observed with methylmercury exposure in animal studies (Agency for Toxic Substances and Disease Registry, 2022). Methylmercury has some genotoxic activity and is capable of causing chromosomal damage in a number of experimental systems. The EPA has classified methylmercury as a "possible" human carcinogen.
The projected reductions in mercury under this proposed rule are expected to reduce the bioconcentration of methylmercury in fish due to mercury emissions from MATS-affected sources. Risk from near-field deposition of mercury to subsistence fishers has previously been evaluated, using a site-specific assessment of a lake near three lignite-fired facilities (U.S. EPA, 2020d). The results suggest that methylmercury exposure to subsistence fishers from lignite-fired units is below the current reference dose (RfD) for methylmercury neurodevelopmental toxicity or IQ loss, with an estimated hazard quotient (HQ) of 0.06. In general, the EPA believes that exposures at or below the RfD are unlikely to be associated with appreciable risk of deleterious effects. However, no RfD defines an exposure level corresponding to zero risk; moreover, the RfD does not represent a bright line above which individuals are at risk of adverse effects. In addition, there was no evidence of a threshold for methylmercury-related neurotoxicity within the range of exposures in the Faroe Islands study which served as the primary basis for the RfD (U.S. EPA, 2001).
Regarding the potential magnitude of human health risk reductions and benefits associated with this proposed rule, we make the following observations. All of the exposure results generated as part of the 2020 Residual Risk analysis were below the presumptive acceptable cancer risk threshold and noncancer health-based thresholds. While these results suggest that the residual risks from HAP exposure are low, we do recognize that this proposed regulation should still reduce exposure to HAP.
Regarding potential benefits of the rule to the general population of fish consumers, while we note that the analysis of the overall EGU sector completed for the 2023 Final A&N Review did identify significant reductions in cardiovascular and neuro-developmental effects, given the substantially smaller mercury reduction associated with this proposed rule (approximately 60 to 80 pounds per year under the proposal compared to the approximately 29 tons of mercury evaluated in the 2023 Final A&N Review), overall uncertainty associated with modeling potential benefits for the broader population of fish consumers would be sufficiently large as to compromise the utility of those benefit estimates.
Despite the lack of quantifiable risks from mercury emissions, reductions would be expected to have some impact (reduction) on the overall methylmercury burden in fish for waterbodies near covered facilities. In the Appropriate and Necessary determination, EPA illustrated that the burden of mercury exposure is not equally distributed across the population and that some subpopulations bore disproportionate risks associated with exposure to emissions from U.S. EGUs. High levels of fish consumption observed with subsistence fishing were associated with vulnerable populations, including minorities and those with low socioeconomic status (SES). Reductions in mercury emissions should reduce methylmercury exposure and body burden for subsistence fishers.
U.S. EGU mercury emissions can lead to increased deposition of mercury to nearby waterbodies. Deposition of mercury to waterbodies can also have an impact on ecosystems and wildlife. Mercury contamination is present in all environmental media with aquatic systems being particularly impacted due to bioaccumulation. Bioaccumulation refers to the net uptake of a contaminant from all possible pathways and includes the accumulation that may occur by direct exposure to contaminated media as well as uptake from food. Atmospheric mercury enters freshwater ecosystems by direct deposition and through runoff from terrestrial watersheds. Once mercury deposits, it may be converted to organic methylmercury mediated primarily by sulfate-reducing bacteria. Methylation is enhanced in anaerobic and acidic environments, greatly increasing mercury toxicity and potential to bioaccumulate in aquatic foodwebs (Munthe et al. 2007). The highest levels of methylmercury accumulation are most often measured in fish eating (piscivorous) animals and those which prey on other fish eaters. In laboratory studies, adverse effects from exposure to methylmercury in wildlife have been observed in fish, mink, otters, and several avian species at exposure levels as low as 0.25 μg/g bw/day (U.S. EPA, 1997). The risk of mercury exposure may also extend to insectivorous terrestrial species such as songbirds, bats, spiders, and amphibians that receive mercury deposition or from aquatic systems near the forest areas they inhabit (Bergeron et al., 2010, b; Cristol et al., 2008; Rimmer et al., 2005; Wada et al., 2009 & 2010). The proposed emissions reductions of mercury are expected to lower deposition of mercury into ecosystems and reduce U.S. EGU attributable bioaccumulation of methylmercury in wildlife, particularly for areas closer to the effected units subject to near-field deposition. Because mercury emissions from U.S. EGUs can both become deposited in or bioaccumulate in organisms living in foreign and international waters, reduction of mercury emissions from U.S. EGUs could lead to some benefits internationally as well. EPA is currently unable to quantify or monetize such effects.
Metal HAP
U.S. EGUs are the largest source of selenium (Se) emissions and a major source of metallic HAP emissions including arsenic (As), chromium (Cr), nickel (Ni), and cobalt (Co). Additionally, U.S. EGUs emit cadmium (Cd), beryllium (Be), lead (Pb), and manganese (Mn). These emissions include metal HAP that are persistent and bioaccumulative (Cd, As, and Pb) and others have the potential to cause cancer (Ni, Cr, Cd, Be, Co, and Pb). PM controls are expected to reduce metal HAP emissions and therefore reduce exposure to metal HAP for the general population including those living near these facilities.
Exposure to these metal HAP, depending on exposure duration and levels of exposures, is associated with a variety of adverse health effects. These adverse health effects may include chronic health disorders (e.g., irritation of the lung, skin, and mucus membranes; decreased pulmonary function, pneumonia, or lung damage; detrimental effects on the central nervous system; damage to the kidneys; and alimentary effects such as nausea and vomiting). As of 2023, three of the key metal HAP or their compounds emitted by EGUs (As, Cr, and Ni) have been classified as human carcinogens, while two others (Cd and Se) are classified as probable human carcinogens. See 76 FR 25003 - 25005 for a fuller discussion of the health effects associated with these pollutants.
The emission estimates for this source category were obtained in 2020 from two main sources: EPA's Air Markets Program Data and EPA's WebFIRE. U.S. EGU source category emissions of non-mercury HAP are not expected to exceed 1 in a million for inhalation cancer risk for those facilities impacted by the proposed controls.. Further, cancer risk was determined to fall within the acceptable range for multipathway exposure to the persistent and bioaccumulative non-mercury metal HAP, such as arsenic, cadmium, and lead. However, the proposed controls should reduce levels of exposure to carcinogenic HAP in communities near the impacted facilities.
EPA also evaluated the potential for noncancer risks from exposure to non-mercury metal HAP in 2020. To address the risk from chronic inhalation exposure to multiple pollutants, we aggregated the health risks associated with pollutants that affect the same target organ. Further, we examined the potential for adverse health effects from acute inhalation exposure to individual pollutants. Lastly, we also examined the potential for health impacts stemming from multiple pathways of exposure for arsenic, cadmium, and lead. The estimated risks were not expected to exceed current health thresholds for adverse effects (U.S. EPA, 2020d). Therefore, we are unable to identify or quantify noncancer benefits from the proposed non-mercury metal HAP emission reductions, although we do note that emissions reductions associated with this rule should further reduce exposure to these non-mercury metal HAP in communities near these facilities.
Additional HAP Benefits
As discussed in detail in the 2023 Final A&N Review, it is challenging to quantify the full range of benefits of HAP reductions. But that does not mean that these benefits are small, insignificant, or nonexistent. In the 2011 MATS RIA (U.S. EPA, 2011), EPA discussed the potential for non-monetizable benefits from effects on fish, birds, and mammals, in part represented through the commercial and recreational fishing economy. A report submitted to EPA in comment concluded that recreational and commercial fishing are substantial contributors to regional U.S. economies with dollar values in the tens of billions (IEc, 2019). At this scale of economic activity, even small shifts in consumer behavior prompted by further HAP reductions can result in substantial economic impacts.
As another example of the potential value of these emissions reductions, EPA received numerous comments in the public comment periods of past EGU HAP regulation highlighting that benefits of mercury reductions to tribal health, subsistence, fishing rights, and cultural identity, while not easily quantified or monetized, are nonetheless important to consider. Finally, EPA also qualitatively considers impacts on ecosystem services, which are generally defined as the economic benefits that individuals and organizations obtain from ecosystems. The monetization of endpoints like ecosystem services, tribal culture, and the activity related to fishing remains challenging. While EPA is not able to monetize the impacts of reduced HAP exposures resulting from this proposal, we note the importance of the contributions of further reductions of HAP emissions to the sustainability of these important economic and cultural values.
Criteria Pollutant Benefits
The benefits analysis presented in this section applies methods consistent with those applied most recently in the RIA for the proposed PM National Ambient Air Quality Standards (NAAQS). EPA's approach for selecting PM2.5 and ozone-related health endpoints to quantify and monetize is detailed in the interest of brevity, we summarize our approach below and refer readers to the referenced Health Benefits TSD (U.S. EPA, 2023). In the interest of brevity, we summarize our approach below and refer readers to the referenced the Health Benefits TSD for a full description of the methodology.
Estimating the health benefits of reductions in PM2.5 and ozone exposure begins with estimating the change in exposure for each individual and then estimating the change in each individual's risks for those health outcomes affected by exposure. The benefit of the reduction in each health risk is based on the exposed individual's willingness to pay (WTP) for the risk change, assuming that each outcome is independent of one another. The greater the magnitude of the risk reduction from a given change in concentration, the greater the individual's WTP, all else equal. The social benefit of the change in health risks equals the sum of the individual WTP estimates across all of the affected individuals residing in the U.S. We conduct this analysis by adapting primary research -- specifically, air pollution epidemiology studies and economic value studies -- from similar contexts. This approach is sometimes referred to as "benefits transfer." Below we describe the procedure we follow for: (1) developing spatial fields of air quality for baseline and three regulatory control alternatives (2) selecting air pollution health endpoints to quantify; (3) calculating counts of air pollution effects using a health impact function; (4) specifying the health impact function with concentration-response parameters drawn from the epidemiological literature to calculate the economic value of the health impacts. We estimate the quantity and economic value of air pollution-related effects using a "damage-function." This approach quantifies counts of air pollution-attributable cases of adverse health outcomes and assigns dollar values to those counts, while assuming that each outcome is independent of one another.
As structured, the proposed rule would affect the distribution of ozone and PM2.5 concentrations in much of the U.S. This RIA estimates avoided ozone- and PM2.5-related health impacts that are distinct from those reported in the RIAs for both ozone and PM NAAQS (U.S. EPA, 2015c, 2022d) The ozone and PM NAAQS RIAs illustrate, but do not predict, the benefits and costs of strategies that states may choose to enact when implementing a revised NAAQS; these costs and benefits are illustrative and cannot be added to the costs and benefits of policies that prescribe specific emission control measures. This RIA estimates the benefits (and costs) of specific emissions control measures. The benefit estimates are based on these modeled changes in PM2.5 and summer season average ozone concentrations.
Air Quality Modeling Methodology
The proposed rule influences the level of pollutants emitted in the atmosphere that adversely affect human health, including directly emitted PM2.5, as well as SO2 and NOX, which are both precursors to ambient PM2.5. NOX emissions are also a precursor to ambient ground-level ozone. EPA used air quality modeling to estimate changes in ozone and PM2.5 concentrations that may occur as a result of the proposed regulatory option and the more stringent regulatory option in the proposed rule relative to the baseline
As described in the Air Quality Modeling Appendix (Appendix A), gridded spatial fields of ozone and PM2.5 concentrations representing the baseline and two regulatory options were derived from CAMx source apportionment modeling in combination with NOX, SO2, and primary PM2.5 EGU emissions obtained from the outputs of the IPM runs described in Section 3 of this RIA. While the air quality modeling includes all inventoried pollution sources in the contiguous U.S., contributions from all sources other than EGUs are held constant at projected 2026 levels in this analysis, and the only changes quantified between the baseline and the regulatory options are those associated with the projected impacts of the proposed rule on EGU emissions. EPA prepared gridded spatial fields of air quality for the baseline and the regulatory options for two health-impact metrics: annual mean PM2.5 and April through September seasonal average eight-hour daily maximum (MDA8) ozone (AS-MO3). These ozone and PM2.5 gridded spatial fields cover all locations in the contiguous U.S. and were used as inputs to BenMAP-CE which, in turn, was used to quantify the benefits from this proposed rule.
The basic methodology for determining air quality changes is the same as that used in the RIAs from multiple previous rules (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2022c). The Air Quality Modeling Appendix (Appendix A) provides additional details on the air quality modeling and the methodologies EPA used to develop gridded spatial fields of summertime ozone and annual PM2.5 concentrations. The appendix also provides figures showing the geographical distribution of air quality changes.
Selecting Air Pollution Health Endpoints to Quantify
The methods used in this RIA incorporate evidence reported in the most recent completed PM and Ozone ISAs and accounts for recommendations from the Science Advisory Board (U.S. EPA, 2022f). When updating each health endpoint EPA considered: (1) the extent to which there exists a causal relationship between that pollutant and the adverse effect; (2) whether suitable epidemiologic studies exist to support quantifying health impacts; (3) and whether robust economic approaches are available for estimating the value of the impact of reducing human exposure to the pollutant. Our approach for updating the endpoints and to identify suitable epidemiologic studies, baseline incidence rates, population demographics, and valuation estimates is summarized below. Detailed descriptions of these updates are available in the Health Benefits TSD, which is in the docket for this rulemaking. The Health Benefits TSD fully describes the Agency's approach for quantifying the number and value of estimated air pollution-related impacts. In this document the reader can find the rationale for selecting health endpoints to quantify; the demographic, health and economic data used; modeling assumptions; and our techniques for quantifying uncertainty.
Table 4-1	Health Effects of Ambient Ozone and PM2.5 and Climate Effects
                                   Category
                                    Effect
                               Effect Quantified
                               Effect Monetized
                               More Information
Premature mortality from exposure to PM2.5
Adult premature mortality based on cohort study estimates and expert elicitation estimates (age 65-99 or age 30-99)
                                      
                                      
PM ISA

Infant mortality (age <1)
                                      
                                      
PM ISA
Nonfatal morbidity from exposure to PM2.5
Heart attacks (age > 18)
                                      
                                    [1]
PM ISA

Hospital admissions -- cardiovascular (ages 65-99)
                                      
                                      
PM ISA

Emergency department visits --  cardiovascular (age 0-99)
                                      
                                      
PM ISA

Hospital admissions -- respiratory (ages 0-18 and 65-99)
                                      
                                      
PM ISA

Emergency room visits -- respiratory (all ages)
                                      
                                      
PM ISA

Cardiac arrest (ages 0-99; excludes initial hospital and/or emergency department visits)
                                      
                                    [1]
PM ISA

Stroke (ages 65-99)
                                      
                                    [1]
PM ISA

Asthma onset (ages 0-17)
                                      
                                      
PM ISA

Asthma symptoms/exacerbation (6-17)
                                      
                                      
PM ISA

Lung cancer (ages 30-99)
                                      
                                      
PM ISA

Allergic rhinitis (hay fever) symptoms (ages 3-17)
                                      
                                      
PM ISA

Lost work days (age 18-65)
                                      
                                      
PM ISA

Minor restricted-activity days (age 18-65)
                                      
                                      
PM ISA

Hospital admissions -- Alzheimer's disease (ages 65-99)
                                      
                                      
PM ISA

Hospital admissions -- Parkinson's disease (ages 65-99)
                                      
                                      
PM ISA

Other cardiovascular effects (e.g., other ages)
                                       --
                                       --
PM ISA[2]

Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations)
                                       --
                                       --
PM ISA[2]

Other nervous system effects (e.g., autism, cognitive decline, dementia)
                                       --
                                       --
PM ISA[2]

Metabolic effects (e.g., diabetes)
                                       --
                                       --
PM ISA[2]

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc.)
                                       --
                                       --
PM ISA[2]

Cancer, mutagenicity, and genotoxicity effects
                                       --
                                       --
PM ISA[2]
Mortality from exposure to ozone
Premature respiratory mortality based on short-term study estimates (0-99)
                                      
                                      
Ozone ISA

Premature respiratory mortality based on long-term study estimates (age 30 - 99)
                                      
                                      
Ozone ISA
Nonfatal morbidity from exposure to ozone
Hospital admissions -- respiratory (ages 0-99)
                                      
                                      
Ozone ISA

Emergency department visits -- respiratory (ages 0-99)
                                      
                                      
Ozone ISA

Asthma onset (0-17)
                                      
                                      
Ozone ISA

Asthma symptoms/exacerbation (asthmatics age 2-17)
                                      
                                      
Ozone ISA

Allergic rhinitis (hay fever) symptoms (ages 3-17)
                                      
                                      
Ozone ISA

Minor restricted-activity days (age 18 - 65)
                                      
                                      
Ozone ISA

School absence days (age 5 - 17)
                                      
                                      
Ozone ISA

Decreased outdoor worker productivity (age 18 - 65)
                                       --
                                       --
Ozone ISA[2]

Metabolic effects (e.g., diabetes)
                                       --
                                       --
Ozone ISA[2]

Other respiratory effects (e.g., premature aging of lungs)
                                       --
                                       --
Ozone ISA[2]

Cardiovascular and nervous system effects
                                       --
                                       --
Ozone ISA[2]

Reproductive and developmental effects
                                       --
                                       --
Ozone ISA[2]
Climate
effects
Climate impacts from carbon dioxide (CO2)
                                       --
                                      
Section 5.2

Other climate impacts (e.g., ozone, black carbon, aerosols, other impacts)
                                       --
                                       --
IPCC,
Ozone ISA,
PM ISA
[1] Valuation estimate excludes initial hospital and/or emergency department visits.
[2] Not quantified due to data availability limitations and/or because current evidence is only suggestive of causality.

Calculating Counts of Air Pollution Effects Using the Health Impact Function
We use the environmental Benefits Mapping and Analysis Program -- Community Edition (BenMAP-CE) software program to quantify counts of premature deaths and illnesses attributable to photochemical modeled changes in annual mean PM2.5 and summer season average ozone concentrations for the years 2030, 2035, and 2040 using health impact functions (Sacks et al., 2020). A health impact function combines information regarding: the concentration-response relationship between air quality changes and the risk of a given adverse outcome; the population exposed to the air quality change; the baseline rate of death or disease in that population; and, the air pollution concentration to which the population is exposed.
BenMAP quantifies counts of attributable effects using health impact functions, which combine information regarding the: concentration-response relationship between air quality changes and the risk of a given adverse outcome; population exposed to the air quality change; baseline rate of death or disease in that population; and air pollution concentration to which the population is exposed.
The following provides an example of a health impact function, in this case for PM2.5 mortality risk. We estimate counts of PM2.5-related total deaths (yij) during each year i among adults aged 18 and older (a) in each county j in the contiguous U.S. (where j=1,...,J and J is the total number of counties) as:
                        yij= Σa yija

            yija = moija x(e[β∙∆C][ij]-1) x Pija,  	  	Eq[1]

where moija is the baseline total mortality rate for adults aged a = 18-99 in county j in year i stratified in 10-year age groups, β is the risk coefficient for total mortality for adults associated with annual average PM2.5 exposure, Cij is the annual mean PM2.5 concentration in county j in year i, and Pija is the number of county adult residents aged a = 18-99 in county j in year i stratified into 5-year age groups.
The BenMAP-CE tool is pre-loaded with projected population from the Woods & Poole company; cause-specific and age-stratified death rates from the Centers for Disease Control and Prevention, projected to future years; recent-year baseline rates of hospital admissions, emergency department visits and other morbidity outcomes from the Healthcare Cost and Utilization Program and other sources; concentration-response parameters from the published epidemiologic literature cited in the ISAs for fine particles and ground-level ozone; and cost of illness or willingness to pay economic unit values for each endpoint.
To assess economic value in a damage-function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. In some cases, the changes in environmental quality can be directly valued. In other cases, such as for changes in ozone and PM, a health and welfare impact analysis must first be conducted to convert air quality changes into effects that can be assigned dollar values.
We note at the outset that EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to work by Künzli et al. (2000) and co-authors and other, more recent health impact analyses, our estimates are based on the best available methods of benefits transfer. Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under analysis. Adjustments are made for the level of environmental quality change, the socio-demographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates.
Calculating the Economic Valuation of Health Impacts
After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante WTP for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to use these data in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a regulation reduces the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $1000, then the WTP for an avoided statistical premature mortality amounts to $10 million ($1000/0.0001 change in risk). Hence, this value is population-normalized, as it accounts for the size of the population and the percentage of that population experiencing the risk. The same type of calculation can produce values for statistical incidences of other health endpoints.
For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we instead use the cost of treating or mitigating the effect to economically value the health impact. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These cost-of-illness (COI) estimates generally (although not in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect.
Benefits Analysis Data Inputs
In Figure 4-1, we summarize the key data inputs to the health impact and economic valuation estimates, which were calculated using BenMAP-CE tool version 1.5.1. (Sacks et al., 2020). In the sections below we summarize the data sources for each of these inputs, including demographic projections, incidence and prevalence rates, effect coefficients, and economic valuation.

Figure 4-1	Data Inputs and Outputs for the BenMAP-CE Tool
Demographic Data
Quantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income. We use projections based on economic forecasting models developed by Woods & Poole, Inc. (2015). The Woods & Poole database contains county-level projections of population by age, sex, and race to 2060, relative to a baseline using the 2010 Census data. Projections in each county are determined simultaneously with every other county in the U.S. to consider patterns of economic growth and migration. The sum of growth in county-level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollmann et al., 2000). According to Woods & Poole, linking county-level growth projections together and constraining the projected population to a national-level total growth avoids potential errors introduced by forecasting each county independently (for example, the projected sum of county-level populations cannot exceed the national total). County projections are developed in a four-stage process:
First, national-level variables such as income, employment, and populations are forecasted.
Second, employment projections are made for 179 economic areas defined by the Bureau of Economic Analysis, using an "export-base" approach, which relies on linking industrial-sector production of non-locally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export-based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.
Third, population is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort-component method based on fertility and mortality in each area.
Fourth, employment and population projections are repeated for counties, using the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by aging the population by single year by sex and race for each year through 2060 based on historical rates of mortality, fertility, and migration.
Baseline Incidence and Prevalence Estimates
Epidemiological studies of the association between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a typical result might be that a 5 ug/m[3] decrease in daily PM2.5 levels is associated with a decrease in hospital admissions of 3 percent. A baseline incidence rate, necessary to convert this relative change into a number of cases, is the estimate of the number of cases of the health effect per year in the assessment location, as it corresponds to baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per million people, that number must be multiplied by the millions of people in the total population.
The Health Benefits TSD (see Table 12) summarizes the sources of baseline incidence rates and reports average incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age-specific rates where available. We applied concentration-response functions to individual age groups and then summed over the relevant age range to provide an estimate of total population benefits. National-level incidence rates were used for most morbidity endpoints, whereas county-level data are available for premature mortality. Whenever possible, the national rates used are national averages, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level.
We projected mortality rates such that future mortality rates are consistent with our projections of population growth. To perform this calculation, we began first with an average of 2007-2016 cause-specific mortality rates. Using Census Bureau projected national-level annual mortality rates stratified by age range, we projected these mortality rates to 2060 in 5-year increments (U.S. Census Bureau). Further information regarding this procedure may be found in the Health Benefits TSD and the appendices to the BenMAP user manual (U.S. EPA, 2022a).
The baseline incidence rates for hospital admissions and emergency department visits reflect the revised rates first applied in the Revised Cross-State Air Pollution Rule Update cross-state (U.S. EPA, 2021). In addition, we revised the baseline incidence rates for acute myocardial infarction. These revised rates are more recent than the rates they replace and more accurately represent the rates at which populations of different ages, and in different locations, visit the hospital and emergency department for air pollution-related illnesses. Lastly, these rates reflect unscheduled hospital admissions only, which represents a conservative assumption that most air pollution-related visits are likely to be unscheduled. If air pollution-related hospital admissions are scheduled, this assumption would underestimate these benefits.
Effect Coefficients
Our approach for selecting and parametrizing effect coefficients for the benefits analysis is described fully in the Health Benefits TSD. Because of the substantial economic value associated with estimated counts of PM2.5-attributable deaths, we describe our rationale for selecting among long-term exposure epidemiologic studies below; a detailed description of all remaining endpoints may be found in the Health Benefits TSD.
A substantial body of published scientific literature documents the association between PM2.5 concentrations and the risk of premature death integrated (U.S. EPA, 2019a, 2022f). This body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA, completed as part of this review of the filterable PM standards and reviewed by the Clean Air Scientific Advisory Committee (CASAC) (U.S. EPA Science Advisory Board, 2022) concluded that there is a causal relationship between mortality and both long-term and short-term exposure to PM2.5 based on the full body of scientific evidence. The size of the mortality effect estimates from epidemiologic 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.
EPA selects Hazard Ratios from cohort studies to estimate counts of PM-related premature death, following a systematic approach detailed in the Health Benefits TSD accompanying this RIA that is generally consistent with previous RIAs (e.g. (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2022c)). Briefly, clinically significant epidemiologic studies of health endpoints for which ISAs report strong evidence are evaluated using established minimum and preferred criteria for identifying studies and hazard ratios best characterizing risk. Following this systematic approach led to the identification of three studies best characterizing the risk of premature death associated with long-term exposure to PM2.5 in the U.S. (Pope et al., 2019; Turner et al., 2016; Wu et al., 2020). The PM ISA, Supplement to the ISA, and 2022 Policy Assessment also identified these three studies as providing key evidence of the association between long-term PM2.5 exposure and mortality. These studies used data from three U.S. cohorts: (1) an analysis of Medicare beneficiaries (Medicare); (2) the American Cancer Society (ACS); and (3) the National Health Interview Survey (NHIS). As premature mortality typically constitutes the vast majority of monetized benefits in a PM2.5 benefits assessment, quantifying effects using risk estimates reported from multiple long-term exposure studies using different cohorts helps account for uncertainty in the estimated number of PM-related premature deaths. Below we summarize the three identified studies and hazard ratios and then describe our rationale for quantifying premature PM-attributable deaths using two of these studies.
Wu et al., 2020 evaluated the relationship between long-term PM2.5 exposure and all-cause mortality in more than 68.5 million Medicare enrollees (over the age of 64), using Medicare claims data from 2000-2016 representing over 573 million person-years of follow up and over 27 million deaths. This cohort included over 20 percent of the U.S. population and was, at the time of publishing, the largest air pollution study cohort to date. The authors modeled PM2.5 exposure at a 1 km[2] grid resolution using a hybrid ensemble-based prediction model that combined three machine learning models and relied on satellite data, land-use information, weather variables, chemical transport model simulation outputs, and monitor data. Wu et al., 2020 fit five different statistical models: a Cox proportional hazards model, a Poisson regression model, and three causal inference approaches (GPS estimation, GPS matching, and GPS weighting). All five statistical approaches provided consistent results; we report the results of the Cox proportional hazards model here. The authors adjusted for numerous individual-level and community-level confounders, and sensitivity analyses suggest that the results are robust to unmeasured confounding bias. In a single-pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.066) and 95 percent confidence interval (1.058-1.074) associated with a change in annual mean PM2.5 exposure of 10.0 ug/m[3] (Wu et al., 2020, Table S3, Main analysis, 2000-2016 Cohort, Cox PH). We use a risk estimate from this study in place of the risk estimate from (Di et al., 2017). These two epidemiologic studies share many attributes, including the Medicare cohort and statistical model used to characterize population exposure to PM2.5. As compared to Di et al., 2017, Wu et al., 2020 includes a longer follow-up period and reflects more recent PM2.5 concentrations.
Pope III et al., 2019 examined the relationship between long-term PM2.5 exposure and all-cause mortality in a cohort of 1,599,329 U.S. adults (aged 18-84 years) who were interviewed in the National Health Interview Surveys (NHIS) between 1986 and 2014 and linked to the National Death Index (NDI) through 2015. The authors also constructed a sub-cohort of 635,539 adults from the full cohort for whom body mass index (BMI) and smoking status data were available. The authors employed a hybrid modeling technique to estimate annual-average PM2.5 concentrations derived from regulatory monitoring data and constructed in a universal kriging framework using geographic variables including land use, population, and satellite estimates. Pope et al., 2019 assigned annual-average PM2.5 exposure from 1999-2015 to each individual by census tract and used complex (accounting for NHIS's sample design) and simple Cox proportional hazards models for the full cohort and the sub-cohort. We select the Hazard Ratio calculated using the complex model for the sub-cohort, which controls for individual-level covariates including age, sex, race-ethnicity, inflation-adjusted income, education level, marital status, rural versus urban, region, survey year, BMI, and smoking status. In a single-pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.12) and 95 percent confidence interval (1.08-1.15) associated with a change in annual mean PM2.5 exposure of 10.0 ug/m[3] (Pope et al., 2019, Table 2, Subcohort). This study exhibits two key strengths that makes it particularly well suited for a benefits analysis: (1) it includes a long follow-up period with recent (and thus relatively low) PM2.5 concentrations; (2) the NHIS cohort is representative of the U.S. population, especially with respect to the distribution of individuals by race, ethnicity, income and education.
EPA has historically used estimated Hazard Ratios from extended analyses of the ACS cohort (Krewski et al., 2009; Pope et al., 2002; Pope et al., 1995) to estimate PM-related risk of premature death. More recent ACS analyses (Turner et al. 2016):
extended the follow-up period of the ACS CSP-II to 22 years (1982-2004),
evaluated 669,046 participants over 12,662,562 person-years of follow up and 237,201 observed deaths, and
applied a more advanced exposure estimation approach than had previously been used when analyzing the ACS cohort, combining the geostatistical Bayesian Maximum Entropy framework with national-level land use regression models.
The total mortality hazard ratio best estimating risk from these ACS cohort studies was based on a random-effects Cox proportional hazard model incorporating multiple individual and ecological covariates (relative risk =1.06, 95 percent confidence intervals 1.04 - 1.08 per 10ug/m[3] increase in PM2.5) from Turner et al., 2016. The relative risk estimate is identical to a risk estimate drawn from earlier ACS analysis of all-cause long-term exposure PM2.5-attributable mortality (Krewski et al., 2009). However, as the ACS hazard ratio is quite similar to the Medicare estimate of (1.066, 1.058-1.074), especially when considering the broader age range (greater than 29 versus greater than 64), only the Wu et al., 2020 and Pope et al., 2019 are included in the main benefits assessments, with Wu et al., 2020 representing results from both the Medicare and ACS cohorts.
Quantifying Cases of Ozone-Attributable Premature Death
Mortality risk reductions account for the majority of monetized ozone-related and PM2.5-related benefits. For this reason, this subsection and the following provide a brief background of the scientific assessments that underly the quantification of these mortality risks and identifies the risk studies used to quantify them in this RIA, for ozone and PM2.5 respectively. As noted above, (U.S. EPA, 2023) describes fully the Agency's approach for quantifying the number and value of ozone and PM2.5 air pollution-related impacts, including additional discussion of how the Agency selected the risk studies used to quantify them in this RIA. The Health Benefits TSD also includes additional discussion of the assessments that support quantification of these mortality risk than provide here.
In 2008, the National Academies of Science (NRC, 2008) issued a series of recommendations to EPA regarding the procedure for quantifying and valuing ozone-related mortality due to short-term exposures. Chief among these was 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 multicity and [National Mortality and Morbidity Air Pollution Studies (NMMAPS)] ...studies without exclusion of the meta-analyses" (NRC, 2008). Prior to the 2015 Ozone NAAQS RIA, the Agency estimated ozone-attributable premature deaths using an NMMAPS-based analysis of total mortality (Bell et al., 2004), two multi-city studies of cardiopulmonary and total mortality (Huang et al., 2005; Schwartz, 2005) and effect estimates from three meta-analyses of non-accidental mortality (Bell et al., 2005; Ito et al., 2005; Levy et al., 2005). Beginning with the 2015 Ozone NAAQS RIA, the Agency began quantifying ozone-attributable premature deaths using two newer multi-city studies of non-accidental mortality smith (Smith et al., 2009; Zanobetti and Schwartz, 2008) and one long-term cohort study of respiratory mortality (Jerrett et al. 2009). The 2020 Ozone ISA included changes to the causality relationship determinations between short-term exposures and total mortality, as well as including more recent epidemiologic analyses of long-term exposure effects on respiratory mortality. We estimate counts of ozone-attributable respiratory death from short-term exposures a pooled risk estimate calculated using parameters from Zanobetti and Schwartz (2008) and Katsouyanni et al. (2009).Consistent with the RIA for the Final Revised Cross-State Air Pollution Rule (CSAPR) Update for the 2008 Ozone NAAQSRCU analysis (U.S. EPA, 2021), we use two estimates of ozone-attributable respiratory deaths from short-term exposures are estimated using the risk estimate parameters from Zanobetti and Schwartz (2008) and Katsouyanni et al. (2009). Ozone-attributable respiratory deaths from long-term exposures are estimated using Turner et al. (2016). Due to time and resource limitations, we were unable to reflect the warm season defined by Zanobetti and Schwartz (2008) as June-August. Instead, we apply this risk estimate to our standard warm season of May-September.
Quantifying Cases of PM2.5-Attributable Premature Death
When quantifying PM-attributable cases of adult mortality, we use the effect coefficients from two epidemiology studies examining two large population cohorts: the American Cancer Society cohort (Turner et al., 2016) and the Medicare cohort (Di et al., 2017). The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2019a) indicates that the ACS and Medicare cohorts provide strong evidence of an association between long-term PM2.5 exposure and premature mortality with support from additional cohort studies. There are distinct attributes of both the ACS and Medicare cohort studies that make them well-suited to being used in a PM benefits assessment and so here we present PM2.5 related effects derived using relative risk estimates from both cohorts.
The PM ISA, which was reviewed by the Clean Air Scientific Advisory Committee of EPA's Science Advisory Board (U.S. EPA Science Advisory Board, 2022), 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. The PM ISA also concluded that the scientific literature supports the use of a no-threshold log-linear model to portray the PM-mortality concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response relationship. The 2019 PM ISA, which informed the setting of the 2020 PM NAAQS, reviewed available studies that examined the potential for a population-level threshold to exist in the concentration-response relationship. Based on such studies, the ISA concluded that the evidence supports the use of a "no-threshold" model and that "little evidence was observed to suggest that a threshold exists" (U.S. EPA, 2009a). Consistent with this evidence, the Agency historically has estimated health impacts above and below the prevailing NAAQS (U.S. EPA, 2019b, 2021, 2022c)
Characterizing Uncertainty in the Estimated Benefits
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. The Health Benefits TSD details our approach to characterizing uncertainty in both quantitative and qualitative terms (U.S. EPA, 2023). The Health Benefits TSD describes the sources of uncertainty associated with key input parameters including emissions 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 country (i.e., regulations, technology, and human behavior). Each of these inputs is uncertain and affects the size and distribution of the estimated benefits. When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits.
To characterize uncertainty and variability into this assessment, we incorporate three quantitative analyses described below and in greater detail within the Health Benefits TSD (Section 7.1):
 A Monte Carlo assessment that accounts for random sampling error and between study variability in the epidemiological and economic valuation studies;
 The quantification of PM-related mortality using alternative PM2.5 mortality effect estimates drawn from two long-term cohort studies; and
 Presentation of 95th percentile confidence interval around each risk estimate.

Quantitative characterization of other sources of PM2.5 uncertainties are discussed only in Section 7.1 of the Health Benefits TSD:
 For adult all-cause mortality:
 The distributions of air quality concentrations experienced by the original cohort population (Health Benefits TSD Section <IP_ADDRESS>);
 Methods of estimating and assigning exposures in epidemiologic studies (Health Benefits TSD Section <IP_ADDRESS>);
 Confounding by ozone (Health Benefits TSD Section <IP_ADDRESS>); and
 The statistical technique used to generate hazard ratios in the epidemiologic study (Health Benefits TSD Section <IP_ADDRESS>).
 Plausible alternative risk estimates for asthma onset in children (TSD Section 7.1.3), cardiovascular hospital admissions (Health Benefits TSD Section 7.1.4,), and respiratory hospital admissions (Health Benefits TSD Section 7.1.5);
 Effect modification of PM2.5-attributable health effects in at-risk populations (Health Benefits TSD Section 7.1.6).

Quantitative consideration of baseline incidence rates and economic valuation estimates are provided in Section 7.3 and 7.4 of the Health Benefits TSD, respectively. Qualitative discussions of various sources of uncertainty can be found in Section 7.5 of the Health Benefits TSD.
Monte Carlo Assessment
Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and random effects modeling to characterize both sampling error and variability across the economic valuation functions. The Monte Carlo simulation in the BenMAP-CE software randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study. For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies. The confidence intervals around the monetized benefits incorporate the epidemiology standard errors as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed, and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates.
Sources of Uncertainty Treated Qualitatively
Although we strive to incorporate as many quantitative assessments of uncertainty as possible, there are several aspects we are only able to address qualitatively. These attributes are summarized below and described more fully in the Health Benefits TSD.
Key assumptions underlying the estimates for premature mortality, which account for over 98 percent of the total monetized benefits in this analysis, include the following:
 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, which was reviewed by CASAC, concluded that "across exposure durations and health effects categories ... the evidence does not indicate that any one source or component is consistently more strongly related with health effects than PM2.5 mass" (U.S. EPA Science Advisory Board, 2022).
 We assume that the health impact function for fine particles is log-linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both regions that are in attainment with the fine particle standard and those that do not meet the standard down to the lowest modeled concentrations. The PM ISA concluded that "the majority of evidence continues to indicate a linear, no-threshold concentration-response relationship for long-term exposure to PM2.5 and total (nonaccidental) mortality" (U.S. EPA Science Advisory Board, 2022).
 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 board (U.S. EPA Science Advisory Board, 2004), which affects the valuation of mortality benefits at different discount rates. Similarly, we assume there is a cessation lag between the change in PM exposures and both the development and diagnosis of lung cancer.
Estimated Number and Economic Value of Health Benefits
To directly compare benefits estimates associated with a rulemaking to cost estimates, the number of instances of each air pollution-attributable health impact must be converted to a monetary value. This requires a valuation estimate for each unique health endpoint, and potentially also discounting if the benefits are expected to accrue over more than a single year, as recommended by the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014). Below we report the estimated number of reduced premature deaths and illnesses in each year relative to the baseline along with the 95 percent confidence interval (Table 4-2 and Table 4-3 for ozone-related health impacts and Table 4-4 and Table 4-5 for PM2.5-related impacts). The number of reduced estimated deaths and illnesses from the proposed regulatory option and more stringent regulatory alternative are calculated from the sum of individual reduced mortality and illness risk across the population.
To directly compare benefits estimates associated with a rulemaking to cost estimates, the number of instances of each air pollution-attributable health impact must be converted to a monetary value. This requires a valuation estimate for each unique health endpoint, and potentially also discounting if the benefits are expected to accrue over more than a single year, as recommended by the U.S. EPA (2014). Table 4-6 and Table 4-7 report the estimated economic value of avoided premature deaths and illness in each year relative to the baseline along with the 95 percent confidence interval. We also report the stream of benefits from 2028 through 2037 for the proposed regulatory option and the unselected more stringent regulatory alternative, using the monetized sums of long-term ozone and PM2.5 mortality and morbidity impacts (Table 4-8 and Table 4-9). Note the less stringent regulatory alternative has no quantified emissions reductions associated with the proposed requirements for PM CEMS and the removal of startup definition number two. As a result, there are no quantified benefits associated with this regulatory option.
Table 4-2	Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the Proposed Regulatory Option for 2028, 2030, and 2035 (95 percent confidence interval) a

                                     2028
                                     2030
                                     2035
Avoided premature respiratory mortalities

Long-term exposure
Turner et al. (2016)[b]
                                      2.6
                                 (1.8 to 3.4)
                                      5.7
                                 (3.9 to 7.4)
                                      15
                                  (10 to 19)
Short-term exposure
Katsouyanni et al. (2009)[b][,c] and Zanobetti et al. (2008)c pooled
                                     0.12
                                (0.048 to 0.19)
                                     0.26
                                (0.10 to 0.40)
                                     0.66
                                 (0.27 to 1.0)
Morbidity effects
Long-term exposure
Asthma onset[d]
                                      19
                                  (16 to 22)
                                      37
                                  (31 to 42)
                                      95
                                  (82 to 110)

Allergic rhinitis symptoms[f]
                                      110
                                  (59 to 160)
                                      210
                                 (110 to 310)
                                      560
                                 (300 to 820)
Short-term exposure
Hospital admissions -- respiratory[c]
                                     0.33
                               (-0.087 to 0.74)
                                     0.71
                                (-0.18 to 1.6)
                                      1.9
                                (-0.49 to 4.2)

ED visits -- respiratory[e]
                                      6.3
                                  (1.7 to 13)
                                      13
                                  (3.5 to 27)
                                      32
                                  (8.9 to 68)

Asthma symptoms
                                     3,600
                                (-440 to 7,500)
                                     6,900
                               (-850 to 14,000)
                                    18,000
                              (-2,200 to 37,000)

Minor restricted-activity days[c,e]
                                     1,700
                                (680 to 2,700)
                                     3,300
                               (1,300 to 5,100)
                                     8,100
                               (3,300 to 13,000)

School absence days
                                     1,300
                                (-180 to 2,700)
                                     2,500
                                (-350 to 5,200)
                                     6,500
                               (-910 to 14,000)
a Values rounded to two significant figures.
[b] Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September warm season.
c Converted ozone risk estimate metric from MDA1 to MDA8.
d Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm season.
[e] Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm season.
[f] Converted ozone risk estimate metric from DA24 to MDA8.

Table 4-3	Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the More Stringent Regulatory Option for 2028, 2030, and 2035 (95 percent confidence interval) a

                                     2028
                                     2030
                                     2035
Avoided premature respiratory mortalities

Long-term exposure
Turner et al. (2016)[b]
                                      51
                                  (35 to 66)
                                      40
                                  (28 to 52)
                                      39
                                  (27 to 51)
Short-term exposure
Katsouyanni et al. (2009)[b,c] and Zanobetti et al. (2008)c pooled
                                      2.3
                                 (0.92 to 3.6)
                                      1.8
                                 (0.73 to 2.9)
                                      1.8
                                 (0.72 to 2.8)
Morbidity effects
Long-term exposure
Asthma onset[d]
                                      370
                                 (320 to 420)
                                      270
                                 (230 to 310)
                                      250
                                 (220 to 290)

Allergic rhinitis symptoms[f]
                                     2,100
                               (1,100 to 3,100)
                                     1,600
                                (840 to 2,300)
                                     1,500
                                (790 to 2,200)
Short-term exposure
Hospital admissions -- respiratory[c]
                                      6.3
                                 (-1.6 to 14)
                                      5.0
                                 (-1.3 to 11)
                                      5.2
                                 (-1.3 to 11)

ED visits -- respiratory[e]
                                      120
                                  (33 to 250)
                                      87
                                  (24 to 180)
                                      89
                                  (24 to 190)

Asthma symptoms
                                    69,000
                              (-8,500 to 140,000)
                                    51,000
                              (-6,300 to 110,000)
                                    48,000
                              (-5,900 to 99,000)

Minor restricted-activity days[c,e]
                                    32,000
                              (13,000 to 51,000)
                                    23,000
                               (9,400 to 37,000)
                                    22,000
                               (8,800 to 35,000)

School absence days
                                    24,000
                              (-3,400 to 51,000)
                                    18,000
                              (-2,600 to 38,000)
                                    17,000
                              (-2,400 to 36,000)
a Values rounded to two significant figures.
[b] Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September warm season.
c Converted ozone risk estimate metric from MDA1 to MDA8.
d Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm season.
[e] Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm season.
[f] Converted ozone risk estimate metric from DA24 to MDA8.

Table 4-4	Estimated Avoided PM2.5-Related Premature Respiratory Mortalities and Illnesses for the Proposed Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval)
Avoided Mortality
                                     2028
                                     2030
                                     2035
(Pope et al., 2019) (adult mortality ages 18-99 years)
                                      11
                                  (7.7 to 14)
                                      8.2
                                  (5.8 to 10)
                                      15
                                  (11 to 19)
(Wu et al., 2020) (adult mortality ages 65-99 years)
                                      5.1
                                 (4.5 to 5.7)
                                      4.0
                                 (3.5 to 4.4)
                                      7.4
                                 (6.5 to 8.2)
(Woodruff et al., 2008) (infant mortality)
                                     0.013
                              (-0.0079 to 0.032)
                                    0.0079
                              (-0.0049 to 0.020)
                                     0.014
                              (-0.0087 to 0.036)
Avoided Morbidity
                                     2028
                                     2030
                                     2035
Hospital admissions -- cardiovascular (age > 18)
                                     0.76
                                (0.55 to 0.96)
                                     0.57
                                (0.41 to 0.72)
                                      1.1
                                 (0.78 to 1.4)
Hospital admissions -- respiratory
                                     0.42
                                (0.19 to 0.65)
                                     0.27
                                (0.12 to 0.41)
                                     0.48
                                (0.21 to 0.74)
ED visits--cardiovascular
                                      1.6
                                (-0.61 to 3.7)
                                      1.1
                                (-0.44 to 2.7)
                                      2.2
                                (-0.84 to 5.1)
ED visits -- respiratory
                                      3.1
                                 (0.61 to 6.4)
                                      2.2
                                 (0.44 to 4.7)
                                      4.2
                                 (0.82 to 8.7)
Acute Myocardial Infarction
                                     0.17
                                (0.10 to 0.25)
                                     0.13
                                (0.073 to 0.18)
                                     0.24
                                (0.14 to 0.33)
Cardiac arrest
                                     0.082
                               (-0.033 to 0.19)
                                     0.059
                               (-0.024 to 0.13)
                                     0.11
                               (-0.044 to 0.24)
Hospital admissions-- Alzheimer's Disease
                                      2.6
                                 (1.9 to 3.2)
                                      1.7
                                 (1.3 to 2.1)
                                      3.8
                                 (2.9 to 4.8)
Hospital admissions-- Parkinson's Disease
                                     0.35
                                (0.18 to 0.51)
                                     0.28
                                (0.14 to 0.41)
                                     0.49
                                (0.25 to 0.72)
Stroke
                                     0.32
                                (0.084 to 0.55)
                                     0.24
                                (0.062 to 0.41)
                                     0.44
                                (0.11 to 0.76)
Lung cancer
                                     0.37
                                (0.11 to 0.61)
                                     0.27
                                (0.082 to 0.45)
                                     0.52
                                (0.16 to 0.87)
Hay Fever/Rhinitis
                                      82
                                  (20 to 140)
                                      55
                                  (13 to 95)
                                      100
                                  (24 to 170)
Asthma Onset
                                      13
                                  (12 to 13)
                                      8.4
                                 (8.1 to 8.8)
                                      15
                                  (15 to 16)
Asthma symptoms  -  Albuterol use
                                     2,400
                               (-1,200 to 5,800)
                                     1,600
                                (-780 to 3,900)
                                     2,900
                               (-1,400 to 7,200)
Lost work days
                                      630
                                 (530 to 720)
                                      420
                                 (360 to 490)
                                      770
                                 (650 to 880)
Minor restricted-activity days
                                     3,700
                               (3,000 to 4,400)
                                     2,500
                               (2,000 to 2,900)
                                     4,500
                               (3,700 to 5,300)
Note: Values rounded to two significant figures.

Table 4-5	Estimated Avoided PM2.5-Related Premature Respiratory Mortalities and Illnesses for the More Stringent Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval)[a,b]
Avoided Mortality
                                     2028
                                     2030
                                     2035
(Pope et al., 2019) (adult mortality ages 18-99 years)
                                      240
                                 (170 to 300)
                                      38
                                  (27 to 48)
                                      96
                                  (69 to 120)
(Wu et al., 2020) (adult mortality ages 65-99 years)
                                      110
                                 (100 to 130)
                                      19
                                  (16 to 21)
                                      47
                                  (41 to 52)
(Woodruff et al., 2008) (infant mortality)
                                     0.24
                                (-0.15 to 0.63)
                                     0.031
                               (-0.019 to 0.080)
                                     0.10
                               (-0.064 to 0.26)
Avoided Morbidity
                                     2028
                                     2030
                                     2035
Hospital admissions -- cardiovascular (age > 18)
                                      18
                                  (13 to 22)
                                      2.7
                                 (2.0 to 3.5)
                                      6.9
                                 (5.0 to 8.7)
Hospital admissions -- respiratory
                                      8.0
                                  (3.5 to 12)
                                     0.83
                                 (0.36 to 1.3)
                                      3.5
                                 (1.5 to 5.4)
ED visits--cardiovascular
                                      35
                                  (-14 to 83)
                                      5.8
                                 (-2.2 to 13)
                                      14
                                 (-5.5 to 34)
ED visits -- respiratory
                                      68
                                  (13 to 140)
                                      10
                                  (2.0 to 22)
                                      27
                                  (5.4 to 57)
Acute Myocardial Infarction
                                      3.9
                                 (2.3 to 5.5)
                                     0.55
                                (0.32 to 0.77)
                                      1.6
                                 (0.93 to 2.2)
Cardiac arrest
                                      1.8
                                (-0.72 to 4.0)
                                     0.27
                                (-0.11 to 0.62)
                                     0.69
                                (-0.28 to 1.6)
Hospital admissions-- Alzheimer's Disease
                                      56
                                  (42 to 70)
                                      2.0
                                 (1.5 to 2.5)
                                      26
                                  (19 to 32)
Hospital admissions-- Parkinson's Disease
                                      7.7
                                  (3.9 to 11)
                                      1.2
                                 (0.61 to 1.8)
                                      3.0
                                 (1.5 to 4.4)
Stroke
                                      7.5
                                  (1.9 to 13)
                                      1.2
                                 (0.30 to 2.0)
                                      2.8
                                 (0.73 to 4.8)
Lung cancer
                                      8.2
                                  (2.5 to 14)
                                      1.3
                                 (0.40 to 2.2)
                                      3.3
                                 (1.0 to 5.5)
Hay Fever/Rhinitis
                                     1,500
                                (360 to 2,600)
                                      220
                                  (54 to 390)
                                      670
                                (160 to 1,200)
Asthma Onset
                                      230
                                 (220 to 240)
                                      35
                                  (33 to 36)
                                      100
                                  (98 to 110)
Asthma symptoms  -  Albuterol use
                                    43,000
                             (-21,000 to 100,000)
                                     6,600
                              (-3,200 to 16,000)
                                    20,000
                              (-9,600 to 48,000)
Lost work days
                                    12,000
                              (10,000 to 14,000)
                                     1,800
                               (1,600 to 2,100)
                                     5,000
                               (4,200 to 5,800)
Minor restricted-activity days[d,f]
                                    70,000
                              (57,000 to 83,000)
                                    11,000
                               (8,800 to 13,000)
                                    30,000
                              (24,000 to 35,000)
a Values rounded to two significant figures.
[b] We estimated ozone benefits for changes in NOx for the ozone season and changes in PM2.5 and PM2.5 precursors for EGUs in 2026.
c Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September warm season.
d Converted ozone risk estimate metric from MDA1 to MDA8.
e Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm season.
[f] Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm season.
[g] Converted ozone risk estimate metric from DA24 to MDA8.

Table 4-6	Estimated Discounted Economic Value of Avoided Ozone and PM2.5-Attributable Premature Mortality and Illness for the Proposed Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval; millions of 2019 dollars)[a,b]
                                  Disc. Rate
                                   Pollutant
                                     2028
                                     2030
                                     2035
3%
Ozone Benefits
                                      $4
                                  ($1 to $8)
                                      and
                                      $30
                                 ($3 to $78)
                                      $7
                                  ($2 to $16)
                                      and
                                      $64
                                 ($7 to $170)
                                      $19
                                  ($5 to $41)
                                      and
                                     $170
                                 ($18 to $450)

PM2.5 Benefits
                                      $55
                                 ($6 to $140)
                                     and
                                     $110
                                 ($11 to $300)
                                      $43
                                 ($4 to $110)
                                     and
                                      $87
                                 ($8 to $230)
                                      $81
                                 ($8 to $210)
                                     and
                                     $160
                                 ($15 to $430)

Ozone plus PM2.5  Benefits
                                      $59
                                ($7 to $150)[c]
                                      and
                                     $140
                               ($14 to $380)[d]
                                      $50
                                ($6 to $130)[c]
                                      and
                                     $150
                               ($15 to $400)[d]
                                     $100
                               ($13 to $250)[c]
                                      and
                                     $330
                               ($33 to $880)[d]
7%
Ozone Benefits
                                      $3
                                  ($1 to $7)
                                      and
                                      $27
                                  ($3 to $70)
                                      $7
                                  ($1 to $15)
                                      and
                                      $58
                                 ($6 to $150)
                                      $17
                                  ($3 to $39)
                                      and
                                     $150
                                 ($15 to $400)

PM2.5 Benefits
                                      $49
                                 ($5 to $130)
                                     and
                                     $100
                                 ($10 to $270)
                                      $39
                                 ($4 to $100)
                                     and
                                      $79
                                 ($7 to $210)
                                      $73
                                 ($7 to $190)
                                     and
                                     $150
                                 ($14 to $390)

Ozone plus PM2.5  Benefits
                                      $52
                                ($6 to $140)[c]
                                      and
                                     $130
                               ($12 to $340)[d]
                                      $46
                                ($5 to $120)[c]
                                      and
                                     $140
                               ($13 to $360)[d]
                                      $90
                               ($10 to $230)[c]
                                      and
                                     $300
                               ($29 to $790)[d]
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should not be summed.
[b] We estimated changes in NOX for the ozone season and changes in PM2.5 and PM2.5 precursors in 2028, 2030, and 2035.
[c] Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al. (2020) long-term PM2.5 exposure mortality risk estimate.
[d] Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2019) long-term PM2.5 exposure mortality risk estimate.

Table 4-7	Estimated Discounted Economic Value of Avoided Ozone and PM2.5-Attributable Premature Mortality and Illness for the More Stringent Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval; millions of 2019 dollars)[a,b]
                                  Disc. Rate
                                   Pollutant
                                     2028
                                     2030
                                     2035
3%
Ozone Benefits
                                      $69
                                 ($17 to $150)
                                      and
                                     $570
                                ($62 to $1,500)
                                      $53
                                 ($13 to $110)
                                      and
                                     $460
                                ($48 to $1,200)
                                      $51
                                 ($12 to $110)
                                      and
                                     $460
                                ($48 to $1,200)

PM2.5 Benefits
                                    $1,200
                               ($120 to $3,200)
                                     and
                                    $2,500
                               ($240 to $6,700)
                                     $200
                                 ($20 to $520)
                                     and
                                     $410
                                ($38 to $1,100)
                                     $520
                                ($53 to $1,300)
                                     and
                                    $1,100
                                ($99 to $2,800)

Ozone plus PM2.5  Benefits
                                    $1,300
                              ($140 to $3,400)[c]
                                      and
                                    $3,100
                              ($300 to $8,200)[d]
                                     $250
                               ($33 to $630)[c]
                                      and
                                     $870
                              ($86 to $2,300)[d]
                                     $570
                              ($65 to $1,400)[c]
                                      and
                                    $1,600
                              ($150 to $4,000)[d]
7%
Ozone Benefits
                                      $62
                                 ($11 to $140)
                                      and
                                     $510
                                ($51 to $1,300)
                                      $48
                                 ($8 to $110)
                                      and
                                     $410
                                ($40 to $1,100)
                                      $46
                                 ($8 to $110)
                                      and
                                     $410
                                ($40 to $1,100)

PM2.5 Benefits
                                    $1,100
                               ($110 to $2,900)
                                     and
                                    $2,300
                               ($210 to $6,000)
                                     $180
                                 ($18 to $470)
                                     and
                                     $370
                                 ($34 to $970)
                                     $470
                                ($46 to $1,200)
                                     and
                                     $950
                                ($88 to $2,500)

Ozone plus PM2.5  Benefits
                                    $1,200
                              ($120 to $3,000)[c]
                                      and
                                    $2,800
                              ($260 to $7,300)[d]
                                     $230
                               ($26 to $580)[c]
                                      and
                                     $780
                              ($74 to $2,100)[d]
                                     $520
                              ($54 to $1,300)[c]
                                      and
                                    $1,400
                              ($130 to $3,600)[d]
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify that they are two separate estimates. The estimates do not represent lower- and upper-bound estimates and should not be summed.
[b] We estimated changes in NOX for the ozone season and changes in PM2.5 and PM2.5 precursors in 2028, 2030, and 2035.
[c] Sum of ozone mortality estimated using the pooled short-term ozone exposure risk estimate and the Wu et al. (2020) long-term PM2.5 exposure mortality risk estimate.
[d] Sum of the Turner et al. (2016) long-term ozone exposure risk estimate and the Pope et al. (2019) long-term PM2.5 exposure mortality risk estimate.

Table 4-8	Stream of Estimated Human Health Benefits from 2028 through 2037: Monetized Benefits Quantified as Sum of Long-Term Ozone Mortality and Long-Term PM2.5 Mortality (discounted at 3 percent; millions of 2019 dollars)[a]
                                     Year
                                   Proposed
                               Regulatory Option
                                More Stringent
                               Regulatory Option
                                    2028*
                                     $140
                                    $3,100
                                     2029
                                     $150
                                     $840
                                     2030*
                                     $150
                                     $860
                                     2031
                                     $160
                                     $890
                                     2032
                                     $310
                                    $1,400
                                     2033
                                     $320
                                    $1,400
                                     2034
                                     $320
                                    $1,500
                                     2035*
                                     $330
                                    $1,500
                                     2036
                                     $340
                                    $1,600
                                     2037
                                     $350
                                    $1,600
                                 Present Value
                                    $1,900
                                   $11,000
                          Equivalent Annualized Value
                                     $220
                                    $1,300
*Year in which air quality models were run. Benefits for all other years were extrapolated from years with model-based air quality estimates. Benefits calculated as value of avoided: PM2.5-attributable deaths (quantified using a concentration-response relationship from the Wu et al. 2020 study and the Pope et al. 2019 study); Ozone-attributable deaths (quantified using a concentration-response relationship from the Turner et al. 2017 study); and PM2.5 and ozone-related morbidity effects.
a For the years 2023 to 2027, benefits associated with emissions reductions are not included as implementation of standards will not be complete until 2028.

Table 4-9	Stream of Estimated Human Health Benefits from 2028 through 2037: Monetized Benefits Quantified as Sum of Long-Term Ozone Mortality and Long-Term PM2.5 Mortality (discounted at 7 percent; millions of 2019 dollars)[a]
                                     Year
                          Proposed Regulatory Option
                       More Stringent Regulatory Option
                                    2028*
                                     $130
                                    $2,800
                                     2029
                                     $130
                                     $750
                                     2030*
                                     $140
                                     $770
                                     2031
                                     $140
                                     $800
                                     2032
                                     $270
                                    $1,200
                                     2033
                                     $280
                                    $1,300
                                     2034
                                     $290
                                    $1,300
                                     2035*
                                     $300
                                    $1,400
                                     2036
                                     $310
                                    $1,400
                                     2037
                                     $310
                                    $1,400
                                 Present Value
                                    $1,200
                                    $7,100
                          Equivalent Annualized Value
                                     $170
                                    $1,000
*Year in which air quality models were run. Benefits for all other years were extrapolated from years with model-based air quality estimates. Benefits calculated as value of avoided: PM2.5-attributable deaths (quantified using a concentration-response relationship from the Wu et al. 2020 study and the Pope et al. 2019 study); Ozone-attributable deaths (quantified using a concentration-response relationship from the Turner et al. 2017 study); and PM2.5 and ozone-related morbidity effects.
a For the years 2023 to 2027, benefits associated with emissions reductions are not included as implementation of standards will not be complete until 2028.

Additional Unquantified Criteria Pollutant Benefits
Data, time, and resource limitations prevented EPA from quantifying the estimated health impacts or monetizing estimated benefits associated with direct exposure to NO2 and SO2 (independent of the role NO2 and SO2 play as precursors to PM2.5 and ozone), as well as ecosystem effects, and visibility impairment due to the absence of air quality modeling data for these pollutants in this analysis. While all health benefits and welfare benefits were not able to be quantified, it does not imply that there are not additional benefits associated with reductions in exposures to ozone, PM2.5, NO2 or SO2. In this section, we provide a qualitative description of these and water quality benefits, which are listed in Table 4-10. Criteria pollutants from U.S. EGUs can also be transported downwind into foreign countries, in particular Canada and Mexico. Therefore, reduced criteria pollutants from U.S. EGUs can lead to public health and welfare benefits in foreign countries. EPA is currently unable to quantify or monetize these effects.

Table 4-10	Additional Unquantified Benefit Categories
                                   Category
                                    Effect
                               Effect Quantified
                               Effect Monetized
                               More Information
Improved Human Health

Reduced incidence of morbidity from exposure to NO2
Asthma hospital admissions
                                       --
                                       --
NO2 ISA[1]

Chronic lung disease hospital admissions
                                       --
                                       --
NO2 ISA[1]

Respiratory emergency department visits
                                       --
                                       --
NO2 ISA[1]

Asthma exacerbation
                                       --
                                       --
NO2 ISA[1]

Acute respiratory symptoms
                                       --
                                       --
NO2 ISA[1]

Premature mortality
                                       --
                                       --
NO2 ISA[1,2,3]

Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations)
                                       --
                                       --
NO2 ISA[2,3]
Reduced incidence of mortality and morbidity through drinking water from reduced effluent discharges.
Bladder, colon, and rectal cancer from halogenated disinfection byproducts exposure.
                                       --
                                       --
SE ELG BCA[4]

Reproductive and developmental effects from halogenated disinfection byproducts exposure.
                                       --
                                       --
SE ELG BCA[4]
Reduced incidence of morbidity and mortality from toxics through fish consumption from reduced effluent discharges.
Neurological and cognitive effects to children from lead exposure from fish consumption (including need for specialized education).
                                       --
                                       --
SE ELG BCA[4]

Possible cardiovascular disease from lead exposure
                                       --
                                       --
SE ELG BCA[4]

Neurological and cognitive effects from in in-utero mercury exposure from maternal fish consumption
                                       --
                                       --
SE ELG BCA[4]

Skin and gastrointestinal cancer incidence from arsenic exposure
                                       --
                                       --
SE ELG BCA[4]

Cancer and non-cancer incidence from exposure to toxic pollutants (lead, cadmium, thallium, hexavalent chromium etc.
                                       --
                                       --
SE ELG BCA[4]

Neurological, alopecia, gastrointestinal effects, reproductive and developmental damage from short-term thallium exposure.

Reduced incidence of morbidity and mortality from recreational water exposure from reduced effluent discharges.
 Cancer and Non-Cancer incidence from exposure to toxic pollutants (methylmercury, selenium, and thallium.)
                                       --
                                       --
SE ELG BCA[4]
Improved Environment

Reduced visibility impairment
Visibility in Class 1 areas
                                       --
                                       --
PM ISA[1]

Visibility in residential areas
                                       --
                                       --
PM ISA[1]
Reduced effects on materials
Household soiling
                                       --
                                       --
PM ISA[1,2]

Materials damage (e.g., corrosion, increased wear)
                                       --
                                       --
PM ISA[2]
Reduced effects from PM deposition (metals and organics)
Effects on individual organisms and ecosystems
                                       --
                                       --
PM ISA[2]
Reduced vegetation and ecosystem effects from exposure to ozone
Visible foliar injury on vegetation
                                       --
                                       --
Ozone ISA[1]

Reduced vegetation growth and reproduction
                                       --
                                       --
Ozone ISA[1]

Yield and quality of commercial forest products and crops
                                       --
                                       --
Ozone ISA[1]

Damage to urban ornamental plants
                                       --
                                       --
Ozone ISA[2]

Carbon sequestration in terrestrial ecosystems
                                       --
                                       --
Ozone ISA[1]

Recreational demand associated with forest aesthetics
                                       --
                                       --
Ozone ISA[2]

Other non-use effects

Ozone ISA[2]

Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf-gas exchange, community composition)
                                       --
                                       --
Ozone ISA[2]
Reduced effects from acid deposition
Recreational fishing
                                       --
                                       --
NOx SOx ISA[1]

Tree mortality and decline
                                       --
                                       --
NOx SOx ISA[2]

Commercial fishing and forestry effects
                                       --
                                       --
NOx SOx ISA[2]

Recreational demand in terrestrial and aquatic ecosystems
                                       --
                                       --
NOx SOx ISA[2]

Other non-use effects

NOx SOx ISA[2]

Ecosystem functions (e.g., biogeochemical cycles)
                                       --
                                       --
NOx SOx ISA[2]
Reduced effects from nutrient enrichment from deposition.
Species composition and biodiversity in terrestrial and estuarine ecosystems
                                       --
                                       --
NOx SOx ISA[2]

Coastal eutrophication
                                       --
                                       --
NOx SOx ISA[2]

Recreational demand in terrestrial and estuarine ecosystems
                                       --
                                       --
NOx SOx ISA[2]

Other non-use effects

NOx SOx ISA[2]

Ecosystem functions (e.g., biogeochemical cycles, fire regulation)
                                       --
                                       --
NOx SOx ISA[2]
Reduced vegetation effects from ambient exposure to SO2 and NOx
Injury to vegetation from SO2 exposure
                                       --
                                       --
NOx SOx ISA[2]

Injury to vegetation from NOx exposure
                                       --
                                       --
NOx SOx ISA[2]
Improved water aesthetics from reduced effluent discharges.
Improvements in water clarity, color, odor in residential, commercial and recreational settings.
                                       --
                                       --
SE ELG BCA[4]
Effects on aquatic organisms and other wildlife from reduced effluent discharges
Protection of Threatened and Endangered (T&E) species from changes in habitat and potential population effects.
                                       --
                                       --
SE ELG BCA[4]

Other non-use effects
                                       --
                                       --
SE ELG BCA[4]

Changes in sediment contamination on benthic communities and potential for re-entrainment.
                                       --
                                       --
SE ELG BCA[4]

Quality of recreational fishing and other recreational use values.
                                       --
                                       --
SE ELG BCA[4]

Commercial fishing yields and harvest quality.
                                       --
                                       --
SE ELG BCA[4]
Reduced water treatment costs from reduced effluent discharges
Reduced drinking, irrigation, and other agricultural use water treatment costs.
                                       --
                                       --
SE ELG BCA[4]
Reduced sedimentation from effluent discharges
Increased storage availability in reservoirs
                                       --
                                       --
SE ELG BCA[4]

Improved functionality of navigable waterways
                                       --
                                       --
SE ELG BCA[4]

Decreased cost of dredging
                                       --
                                       --
SE ELG BCA[4]
Benefits of reduced water withdrawal
Benefits from effects aquatic and riparian species from additional water availability.
                                       --
                                       --
SE ELG BCA[4]

Increased water availability in reservoirs increasing hydropower supply, recreation, and other services.
                                       --
                                       --
SE ELG BCA[4]
[1] We assess these benefits qualitatively due to data and resource limitations for this RIA.
2 We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.
[3] We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.
[4] Benefit and Cost Analysis (BCA) for Revisions to the Effluent Limitations Guidelines (ELG) and Standards for the Steam Electric (SE) Power Generating Point Source Category.
NO2 Health 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 benefits associated with reduced NO2 exposure in this analysis. 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, 2016) 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.
SO2 Health 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 benefits associated with reduced SO2 in this analysis. Therefore, this analysis only quantifies and monetizes the PM2.5 benefits 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 sulfur (U.S. EPA, 2017). 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 concentration-response relationship has been demonstrated in laboratory studies following exposures to SO2 at concentrations between 20 and 100 parts per billion (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.
Ozone Welfare Benefits
Exposure to ozone has been associated with a wide array of vegetation and ecosystem effects in the published literature ecological (U.S. EPA, 2020c). 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. See Section F of the Ozone Transport Policy Analysis Proposed Rule TSD (U.S. EPA, 2022g) for a summary of an assessment of risk of ozone-related growth impacts on selected forest tree species.
NO2 and SO2 Welfare Benefits
As described in the Integrated Science Assessment (ISA) for Oxides of Nitrogen, Oxides of Sulfur and Particulate Matter Ecological Criteria (U.S. EPA, 2020c), NOX and SO2 emissions also contribute to a variety of adverse welfare effects, including those associated with acidic deposition, visibility impairment, and nutrient enrichment. Deposition of nitrogen and sulfur 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 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).
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.
Visibility Impairment 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 2009). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil. Visibility has direct significance to people's enjoyment of daily activities and their overall sense of wellbeing. Good 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, 2012) show that visibility 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 this proposed rule would be likely to have a significant impact on visibility in urban areas or Class I areas.
Reductions in emissions of NO2 will improve the level of visibility throughout the U.S. because these gases (and the particles of nitrate and sulfate formed from these gases) impair visibility by scattering and absorbing light (U.S. EPA, 2009b). 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, 2009b). 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, 2009b).
Climate Pollutant Benefits
We estimate the climate benefits from this proposed rule using estimates of the social cost of greenhouse gases (SC-GHG), specifically the SC-CO2. The SC-CO2 is the monetary value of the net harm to society associated with a marginal increase in CO2 emissions in a given year, or the benefit of avoiding that increase. In principle, SC-CO2 includes the value of all climate change impacts (both negative and positive), including (but not limited to) changes in net agricultural productivity, human health effects, property damage from increased flood risk natural disasters, disruption of energy systems, risk of conflict, environmental migration, and the value of ecosystem services. The SC-CO2, therefore, reflects the societal value of reducing emissions of the gas in question by one metric ton and is the theoretically appropriate value to use in conducting benefit-cost analyses of policies that affect CO2 emissions. In practice, data and modeling limitations naturally restrain the ability of SC-CO2 estimates to include all the important physical, ecological, and economic impacts of climate change, such that the estimates are a partial accounting of climate change impacts and will therefore, tend to be underestimates of the marginal benefits of abatement. The EPA and other Federal agencies began regularly incorporating SC-CO2 estimates in their benefit-cost analyses conducted under E.O. 12866 since 2008, following a Ninth Circuit Court of Appeals remand of a rule for failing to monetize the benefits of reducing CO2 emissions in that rulemaking process.
In 2017, the National Academies of Sciences, Engineering, and Medicine published a report that provides a roadmap for how to update SC-GHG estimates used in Federal analyses going forward to ensure that they reflect advances in the scientific literature (National Academies, 2017). The National Academies' report recommended specific criteria for future SC-GHG updates, a modeling framework to satisfy the specified criteria, and both near-term updates and longer-term research needs pertaining to various components of the estimation process. The research community has made considerable progress in developing new data and methods that help to advance various components of the SC-GHG estimation process in response to the National Academies' recommendations.
In a first-day executive order (E.O. 13990), Protecting Public Health and the Environment and Restoring Science to Tackle the Climate Crisis, President Biden called for a renewed focus on updating estimates of the SC-GHG to reflect the latest science, noting that "it is essential that agencies capture the full benefits of reducing greenhouse gas emissions as accurately as possible." Important steps have been taken to begin to fulfill this directive of E.O. 13990. In February 2021, the Interagency Working Group on the SC-GHG (IWG) released a technical support document (hereinafter the "February 2021 SC-GHG TSD") that provided a set of IWG recommended SC-GHG estimates while work on a more comprehensive update is underway to reflect recent scientific advances relevant to SC-GHG estimation (IWG 2021). In addition, as discussed further below, EPA has developed a draft updated SC-GHG methodology within a sensitivity analysis in the regulatory impact analysis of EPA's November 2022 supplemental proposal for oil and gas standards that is currently undergoing external peer review and a public comment process.
The EPA has applied the IWG's recommended interim SC-GHG estimates in the Agency's regulatory benefit-cost analyses published since the release of the February 2021 TSD and is likewise using them in this RIA. We have evaluated the SC-GHG estimates in the February 2021 TSD and have determined that these estimates are appropriate for use in estimating the social benefits of GHG reductions expected to occur as a result of the proposed and alternative standards. These SC-GHG estimates are interim values developed for use in benefit-cost analyses until updated estimates of the impacts of climate change can be developed based on the best available science and economics. After considering the TSD, and the issues and studies discussed therein, the EPA concludes that these estimates, while likely an underestimate, are the best currently available SC-GHG estimates until revised estimates have been developed reflecting the latest, peer-reviewed science.
The SC-GHG estimates presented in the February 2021 SC-GHG TSD and used in this RIA were developed over many years, using a transparent process, peer-reviewed methodologies, the best science available at the time of that process, and with input from the public. Specifically, in 2009, an interagency working group (IWG) that included the EPA and other executive branch agencies and offices was established to develop estimates relying on the best available science for agencies to use. The IWG published SC-CO2 estimates in 2010 that were developed from an ensemble of three widely cited integrated assessment models (IAMs) that estimate global climate damages using highly aggregated representations of climate processes and the global economy combined into a single modeling framework. The three IAMs were run using a common set of input assumptions in each model for future population, economic, and CO2 emissions growth, as well as equilibrium climate sensitivity (ECS)  -  a measure of the globally averaged temperature response to increased atmospheric CO2 concentrations. These estimates were updated in 2013 based on new versions of each IAM. In August 2016 the IWG published estimates of the social cost of methane (SC-CH4) and nitrous oxide (SC-N2O) using methodologies that are consistent with the methodology underlying the SC-CO2 estimates. The modeling approach that extends the IWG SC-CO2 methodology to non-CO2 GHGs has undergone multiple stages of peer review. The SC-CH4 and SC-N2O estimates were developed by Marten et al. (2015) and underwent a standard double-blind peer review process prior to journal publication. These estimates were applied in RIAs of EPA proposed rulemakings with CH4 and N2O emissions impacts. The EPA also sought additional external peer review of technical issues associated with its application to regulatory analysis. Following the completion of the independent external peer review of the application of the Marten et al. (2015) estimates, the EPA began using the estimates in the primary benefit-cost analysis calculations and tables for a number of proposed rulemakings (U.S. EPA, 2015b, 2015d). The EPA considered and responded to public comments received for the proposed rulemakings before using the estimates in final regulatory analyses in 2016. In 2015, as part of the response to public comments received to a 2013 solicitation for comments on the SC-CO2 estimates, the IWG announced a National Academies of Sciences, Engineering, and Medicine review of the SC-CO2 estimates to offer advice on how to approach future updates to ensure that the estimates continue to reflect the best available science and methodologies. In January 2017, the National Academies released their final report, Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide (National Academies, 2017), and recommended specific criteria for future updates to the SC-CO2 estimates, a modeling framework to satisfy the specified criteria, and both near-term updates and longer-term research needs pertaining to various components of the estimation process (National Academies 2017). Shortly thereafter, in March 2017, President Trump issued E.O. 13783, which disbanded the IWG, withdrew the previous SC-GHG TSDs, and directed agencies to ensure SC-GHG estimates used in regulatory analyses are consistent with the guidance contained in OMB's Circular A-4, "including with respect to the consideration of domestic versus international impacts and the consideration of appropriate discount rates" (E.O. 13783, Section 5(c)). Benefit-cost analyses following E.O. 13783 used SC-CO2 estimates that attempted to focus on the specific share of climate change damages in the U.S. as captured by the models (which did not reflect many pathways by which climate impacts affect the welfare of U.S. citizens and residents) and were calculated using two default discount rates recommended by Circular A-4, 3 percent and 7 percent. All other methodological decisions and model versions used in SC- CO2 calculations remained the same as those used by the IWG in 2010 and 2013, respectively.