As indicated above, the use of a sales test for estimating small business impacts for a rulemaking is consistent with guidance offered by the EPA on compliance with the RFA and is consistent with guidance published by the SBA's Office of Advocacy that suggests that cost as a percentage of total revenues is a metric for evaluating cost increases on small entities in relation to increases on large entities. EPA assessed the economic and financial impacts of the rule using the ratio of compliance costs to the value of revenues from electricity generation, focusing in particular on entities for which this measure is greater than 1 percent.
The projected impacts, including compliance costs, of the proposed rule on small entities are summarized in Table 5-25-2. All costs are presented in 2019 dollars. We projected the annual net compliance cost to small entities to be approximately $26 million in 2028. Relative to the baseline, the proposed rule is projected to generate compliance cost reductions greater than 1 percent of baseline revenue for two of the 26 small entities directly impacted, and compliance cost increases greater than 1 percent are projected for two. The remaining 22 entities are not projected to experience compliance cost changes of more than 1 percent. Of the 26 entities considered in this analysis, two are holding units projected to experience compliance cost increases greater than 1 percent of generation revenue at a facility level as well as at a parent holding company level.

Table 5-2	Projected Impacts of Proposal on Small Entities in 2028
                                     EGU
                                Ownership Type
                    Number of Potentially Affected Entities
               Total Net Compliance Cost (millions 2019 dollars)
 Number of Small Entities with Compliance Costs >1% of Generation Revenues
                                   Municipal
                                       0
                                       0
                                       0
                                    Private
                                      12
                                     -3.9
                                       1
                                     Co-op
                                      14
                                      30
                                       1
                                     Total
                                      26
                                      26
                                       2

A similar analysis of the projected impacts, including compliance costs, of the more stringent alternative on small entities is summarized in Table 5-3. We projected annual net compliance cost to small entities to be approximately -$6.0 million in 2028. Relative to the baseline, the more stringent alternative is projected to generate compliance cost reductions greater than 1 percent of baseline revenues for 15 of the 26 entities directly impacted, and compliance cost increases greater than 1 percent are projected for three. The remaining eight small entities are not projected to experience compliance cost changes of more than 1 percent.
Table 5-3	Projected Impacts of More Stringent Alternative on Small Entities in 2028
                                     EGU
                                Ownership Type
                    Number of Potentially Affected Entities
               Total Net Compliance Cost (millions 2019 dollars)
 Number of Small Entities with Compliance Costs >1% of Generation Revenues
                                   Municipal
                                       0
                                       0
                                       0
                                    Private
                                      12
                                      -62
                                       0
                                     Co-op
                                      14
                                      56
                                       3
                                     Total
                                      26
                                     -6.0
                                       3
Conclusion
Making a determination that there is not a significant economic impact on a substantial number of small entities (often referred to as a "SISNOSE") requires an assessment of whether an estimated economic impact is significant and whether that impact affects a substantial number of small entities. The analysis indicates that 8 small entities see a +/- 1 percent change in either emissions, fuel use, or generation, and 3 of these are projected to have a cost impact of greater than 1 percent of their revenues. EPA identified 107 potentially affected EGU entities in the projection year of 2028. Of these, EPA identified 26 small entities affected by the rule, and of these, three small entities may experience costs of greater than 1 percent of revenues. Based on this analysis, for this rule overall we conclude that the estimated costs for the proposed rule will not have a significant economic impact on a substantial number of small entities.
Labor Impacts
This section discusses potential employment impacts of this regulation. As economic activity shifts in response to a regulation, typically there will be a mix of declines and gains in employment in different parts of the economy over time and across regions. To present a complete picture, an employment impact analysis will describe the potential positive and negative changes in employment levels. There are significant challenges when trying to evaluate the employment effects of an environmental regulation due to a wide variety of other economic changes that can affect employment, including the impact of the coronavirus pandemic on labor markets and the state of the macroeconomy generally. Considering these challenges, we look to the economics literature to provide a constructive framework and empirical evidence. To simplify, we focus on impacts on labor demand related to compliance behavior. Environmental regulation may also affect labor supply through changes in worker health and productivity (Zivin and Neidell, 2018).
Economic theory of labor demand indicates that employers affected by environmental regulation may increase their demand for some types of labor, decrease demand for other types, or for still other types, not change their demand at all(Berman and Bui, 2001; Deschenes, 2018; Morgenstern et al., 2002). To study labor demand impacts empirically, a growing literature has compared employment levels at facilities subject to an environmental regulation to employment levels at similar facilities not subject to that environmental regulation; some studies find no employment effects, and others find significant differences. For example, see Berman and Bui (2001), Greenstone (2002), Ferris et al. (2014), and Curtis (2018, 2020). A variety of conditions can affect employment impacts of environmental regulation, including baseline labor market conditions and employer and worker characteristics such as occupation and industry. Changes in employment may also occur in different sectors related to the regulated industry, both upstream and downstream, or in sectors producing substitute or complimentary products. Employment impacts in related sectors are often difficult to measure. Consequently, we focus our labor impacts analysis primarily on the directly regulated facilities and other EGUs and related fuel markets.
This section discusses and projects potential employment impacts for the utility power, coal and natural gas production sectors that may result from the proposed rule. EPA has a long history of analyzing the potential impacts of air pollution regulations on changes in the amount of labor needed in the power generation sector and directly related sectors. The analysis conducted for this RIA builds upon the approaches used in the past and takes advantage of newly available data to improve the assumptions and methodology.
The results presented in this section are based on a methodology that estimates the impact on employment based on the differences in projections between two modeling scenarios: the baseline scenario, and a scenario that represents the implementation of the rule. The estimated employment difference between these scenarios can be interpreted as the incremental effect of the rule on employment in this sector. As discussed in Section 3, there is uncertainty related to the future baseline projections. Because the incremental employment estimates presented in this section are based on projections discussed in Section 3, it is important to highlight the relevance of the Section 3 uncertainty discussion to the analysis presented in this section. Note that there is also uncertainty related to the employment factors applied in this analysis, particularly factors informing job-years related to relatively new technologies, such as energy storage, on which there is limited data to base assumptions.
Like previous analyses, this analysis represents an evaluation of "first-order employment impacts" using a partial equilibrium modeling approach. It includes some of the potential ripple effects of these impacts on the broader economy. These ripple effects include the secondary job impacts in both upstream and downstream sectors. The analysis includes impacts on upstream sectors including coal, natural gas, and uranium. However, the approach does not analyze impacts on other fuel sectors, nor does it analyze potential impacts related to transmission or distribution. This approach excludes the economy-wide employment effects of changes to energy markets (such as higher or lower forecasted electricity prices). This approach also excludes labor impacts that are sometimes reflected in a benefits analysis for an environmental policy, such as increased productivity from a healthier workforce and reduced absenteeism due to fewer sick days of employees and dependent family members (e.g., children).
Overview of Methodology
The methodology includes the following two general approaches, based on the available data. The first approach utilizes the rich employment data that is available for several types of generation technologies in the 2020 U.S. Energy and Employment Report. For employment related to other electric power sector generating and pollution control technologies, the second approach utilizes information available in the U.S. Economic Census.
Detailed employment inventory data is available regarding recent employment related to coal, hydro, natural gas, geothermal, wind, and solar generation technologies. The data enables the creation of technology-specific factors that can be applied to model projections of capacity (reported in megawatts, or MW) and generation (reported in megawatt-hours, or MWh) in order to estimate impacts on employment. Since employment data is only available in aggregate by fuel type, it is necessary to disaggregate by labor type in order to differentiate between types of jobs or tasks for categories of workers. For example, some types of employment remain constant throughout the year and are largely a function of the size of a generator, e.g., fixed operation and maintenance activities, while others are variable and are related to the amount of electricity produced by the generator, e.g., variable operation and maintenance activities.
The approach can be summarized in three basic steps:
 Quantify the total number of employees by fuel type in a given year;
 Estimate total fixed operating & maintenance (FOM), variable operating & maintenance (VOM), and capital expenditures by fuel type in that year; and
 Disaggregate total employees into three expenditure-based groups and develop factors for each group (FTE/MWh, FTE/MW-year, FTE/MW new capacity).

Where detailed employment data is unavailable, it is possible to estimate labor impacts using labor intensity ratios. These factors provide a relationship between employment and economic output and are used to estimate employment impacts related to construction and operation of pollution control retrofits, as well as some types of electric generation technologies.
For a detailed overview of this methodology, including all underlying assumptions and the types of employment represented by this analysis, see the U.S. EPA Methodology for Power Sector-Specific Employment Analysis, available in the docket.
Overview of Power Sector Employment
In this section we focus on employment related to electric power generation, as well as coal and natural gas extraction because these are the segments of the power sector that are most relevant to the projected impacts of the rule. Other segments not discussed here include other fuels, energy efficiency, and transmission, distribution, and storage. The statistics presented here are based on the 2020 USEER, which reports data from 2019.
In 2019, the electric power generation sector employed nearly 900,000 people. Relative to 2018, this sector grew by over 2 percent, despite job losses related to nuclear and coal generation. These losses were offset by increases in employment related to other generating technologies, including natural gas, solar, and wind. The largest component of total 2019 employment in this sector is construction (33 percent). Other components of the electric power generation workforce include: utility workers (20 percent), professional and business service employees (20 percent), manufacturing (13 percent), wholesale trade (8 percent), and other (5 percent). In 2019, jobs related to solar and wind generation represent 31 percent and 14 percent of total jobs, respectively, and jobs related to coal generation represent 10 percent of total employment.
In addition to generation-related employment we also look at employment related to coal and natural gas use in the electric power sector. In 2019, the coal industry employed about 75,000 workers. Mining and extraction jobs represent the vast majority of total coal-related employment in 2019 (74 percent). The natural gas fuel sector employed about 276,000 employees in 2019. About 60 percent of those jobs were related to mining and extraction.
Projected Sectoral Employment Changes due to the Proposed Rule
Electric generating units subject to the mercury and filterable PM emission limits in this proposed rule will likely use various mercury and PM control strategies to comply. Under the modeling of the proposed rule, about 2 GW of coal capacity is estimated to install ESP upgrades, and about 3 GW of coal capacity is estimated to either upgrade existing fabric filters or construct new fabric filter controls by 2028. Additionally, the proposed rule is projected to result in an additional 500 MW of retired coal capacity (less than one percent) in 2028, and small increase in new natural gas and energy storage capacity (each significantly less than 1 GW and less than 1 percent) in that year.
Based on these power sector modeling projections, we estimate an increase in construction-related job-years related to the installation of new pollution controls under the rule, as well as the construction of new generating capacity. In 2028, we estimate an increase of approximately 800 construction-related job-years related to the construction of new pollution controls. We estimate an increase of over 20,00 job-years in 2028 related to the construction of new capacity in that year. In 2030 and 2035, we estimate decreases in construction-related job-years. This near-term increase followed by subsequent decreases results from the projected acceleration of a small amount of new capacity that is projected to be built in the baseline in 2030 and beyond. Construction-related job-year changes are one-time impacts, occurring during each year of the multi-year periods during which construction of new capacity is completed. Construction-related figures in Table 5-3 represent a point estimate of incremental changes in construction jobs for each year (for a three-year construction projection, this table presents one-third of the total jobs for that project).
Table 5-3	Changes in Labor Utilization: Construction-Related (Number of Job-Years of Employment in a Single Year)

                                                                           2028
                                                                           2030
                                                                           2035
New Pollution Controls
                                                                            800
                                                                        <100
                                                                        <100
New Capacity
                                                                         20,600
                                                                         -8,700
                                                                           -500
Notes: "<100" denotes an increase or decrease of less than 100 job-years; A large share of the construction-related job years is attributable to construction of energy storage, a relatively new technology on which there is limited data to base labor assumptions.

We also estimate changes in the number of job-years related to recurring non-construction employment. Recurring employment changes are job-years associated with annual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job-years, while retiring generating capacity, as well as avoided capacity builds, create a stream of negative job-years. The rule is projected to result, generally, in a replacement of relatively labor-intensive coal capacity with less labor-intensive capacity, which results in an overall decrease of non-construction jobs in 2028 and 2030. The total net estimated decrease in recurring employment is about 300 job-years in over 2028-2035, which is a very small percentage of total 2019 power sector employment reported in the 2020 USEER (approximately 900,000 generation-related jobs, 75,000 coal-related jobs, and 276,000 natural gas-related jobs). Table 5-4 provide detailed estimates of recurring non-construction employment changes.
Table 5-4	Changes in Labor Utilization: Recurring Non-Construction (Number of Job-Years of Employment in a Single Year)

                                     2028
                                     2030
                                     2035
                              Pollution Controls
                                    <100
                                    <100
                                    <100
                               Existing Capacity
                                     -200
                                     -200
                                     -200
                                 New Capacity
                                    <100
                                    <100
                                      300
                      Fuels (Coal, Natural Gas, Uranium)
                                    <100
                                    <100
                                    <100
                                     Coal
                                    <100
                                    <100
                                    <100
                                  Natural Gas
                                    <100
                                    <100
                                    <100
                                    Uranium
                                    <100
                                    <100
                                    <100
Note: "<100" denotes an increase or decrease of less than 100 job-years; Numbers may not sum due to rounding
Conclusions
	Generally, there are significant challenges when trying to evaluate the employment effects due to an environmental regulation from employment effects due to a wide variety of other economic changes, including the impact of the coronavirus pandemic on labor markets and the state of the macroeconomy generally. For EGUs, this proposed rule may result in a sizable near-term increase in construction-related jobs related to the installation of new pollution controls, as well as the acceleration of small amounts of new generating capacity construction. The rule is also projected to result, generally, in a replacement of relatively labor-intensive coal capacity with less labor-intensive capacity (primarily solar), which results in an overall decrease of non-construction jobs. Speaking generally, a variety of federal programs are available to invest in communities potentially affected by coal mine and coal power plant closures. An initial report by The Interagency Working Group on Coal and Power Plant Communities and Economic Revitalization (April 2021) identifies funding available to invest in such "energy communities" through existing programs from agencies including Department of Energy, Department of Treasury, Department of Labor, and others. The Inflation Reduction Act also provides incentives to encourage investment in communities affected by coal mine and coal power plant closures.
References
Berman, E., & Bui, L. T. M. (2001). Environmental regulation and labor demand: evidence from the South Coast Air Basin. Journal of Public Economics, 79(2), 265-295. doi:https://doi.org/10.1016/S0047-2727(99)00101-2
Curtis, E. M. (2018). Who Loses under Cap-and-Trade Programs? The Labor Market Effects of the NOx Budget Trading Program. The Review of Economics and Statistics, 100(1), 151-166. doi:10.1162/REST_a_00680
Curtis, E. M. (2020). Reevaluating the ozone nonattainment standards: Evidence from the 2004 expansion. Journal of Environmental Economics and Management, 99, 102261. doi:10.1016/j.jeem.2019.102261
Deschenes, O. (2018). Environmental regulations and labor markets. IZA World of Labor, 22. doi:10.15185/izawol.22.v2
Ferris, A. E., Shadbegian, R. J., & Wolverton, A. (2014). The Effect of Environmental Regulation on Power Sector Employment: Phase I of the Title IV SO2 Trading Program. Journal of the Association of Environmental and Resource Economists, 1(4), 521-553. doi:10.1086/679301
Greenstone, M. (2002). The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures. Journal of Political Economy, 110(6), 1175-1219. doi:10.1086/342808
Morgenstern, R. D., Pizer, W. A., & Shih, J.-S. (2002). Jobs Versus the Environment: An Industry-Level Perspective. Journal of Environmental Economics and Management, 43(3), 412-436. doi:https://doi.org/10.1006/jeem.2001.1191
Zivin, J. G., & Neidell, M. (2018). Air pollution's hidden impacts. Science, 359(6371), 39-40. doi:doi:10.1126/science.aap7711

Environmental Justice Impacts
Introduction
E.O. 12898 directs EPA to "achiev[e] environmental justice (EJ) by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects" (59 FR 7629, February 16, 1994), termed disproportionate impacts in this section. Additionally, E.O. 13985 was signed to advance racial equity and support underserved communities through Federal government actions (86 FR 7009, January 20, 2021). EPA defines EJ as the fair treatment and meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies. EPA further defines the term fair treatment to mean that "no group of people should bear a disproportionate burden of environmental harms and risks, including those resulting from the negative environmental consequences of industrial, governmental, and commercial operations or programs and policies." Meaningful involvement means that: (1) potentially affected populations have an appropriate opportunity to participate in decisions about a proposed activity that will affect their environment and/or health; (2) the public's contribution can influence the regulatory Agency's decision; (3) the concerns of all participants involved will be considered in the decision-making process; and (4) the rule-writers and decision-makers seek out and facilitate the involvement of those potentially affected.
The term "disproportionate impacts" refers to differences in impacts or risks that are extensive enough that they may merit Agency action. In general, the determination of whether a disproportionate impact exists is ultimately a policy judgment which, while informed by analysis, is the responsibility of the decision-maker. The terms "difference" or "differential" indicate an analytically discernible distinction in impacts or risks across population groups. It is the role of the analyst to assess and present differences in anticipated impacts across population groups of concern for both the baseline and proposed regulatory options, using the best available information (both quantitative and qualitative) to inform the decision-maker and the public.
A regulatory action may involve potential EJ concerns if it could: (1) create new disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples; (2) exacerbate existing disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples; or (3) present opportunities to address existing disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples through the action under development.
The Presidential Memorandum on Modernizing Regulatory Review (86 FR 7223; January 20, 2021) calls for procedures to "take into account the distributional consequences of regulations, including as part of a quantitative or qualitative analysis of the costs and benefits of regulations, to ensure that regulatory initiatives appropriately benefit, and do not inappropriately burden disadvantaged, vulnerable, or marginalized communities." Under E.O. 13563, federal agencies may consider equity, human dignity, fairness, and distributional considerations, where appropriate and permitted by law. For purposes of analyzing regulatory impacts, EPA relies upon its June 2016 "Technical Guidance for Assessing Environmental Justice in Regulatory Analysis," which provides recommendations that encourage analysts to conduct the highest quality analysis feasible, recognizing that data limitations, time, resource constraints, and analytical challenges will vary by media and circumstance.
A reasonable starting point for assessing the need for a more detailed EJ analysis is to review the available evidence from the published literature and from community input on what factors may make population groups of concern more vulnerable to adverse effects (e.g., underlying risk factors that may contribute to higher exposures and/or impacts). It is also important to evaluate the data and methods available for conducting an EJ analysis. EJ analyses can be grouped into two types, both of which are informative, but not always feasible for a given rulemaking:
          Baseline: Describes the current (pre-control) distribution of exposures and risk, identifying potential disparities.
          Policy: Describes the distribution of exposures and risk after the regulatory option(s) have been applied (post-control), identifying how potential disparities change in response to the rulemaking.

EPA's 2016 Technical Guidance does not prescribe or recommend a specific approach or methodology for conducting EJ analyses, though a key consideration is consistency with the assumptions underlying other parts of the regulatory analysis when evaluating the baseline and regulatory options.
Analyzing EJ Impacts in This Proposal
In addition to the benefits assessment (see Section 4), EPA considers potential EJ concerns associated with this proposed rulemaking. A potential EJ concern is defined as "the actual or potential lack of fair treatment or meaningful involvement of minority populations, low-income populations, tribes, and Indigenous peoples in the development, implementation and enforcement of environmental laws, regulations and policies." For analytical purposes, this concept refers more specifically to "disproportionate impacts on minority populations, low-income populations, and/or Indigenous peoples that may exist prior to or that may be created by the proposed regulatory action." Although EJ concerns for each rulemaking are unique and should be considered on a case-by-case basis, EPA's EJ Technical Guidance states that "[t]he analysis of potential EJ concerns for regulatory actions should address three questions:
 Are there potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern in the baseline?
 Are there potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern for the regulatory option(s) under consideration?
 For the regulatory option(s) under consideration, are potential EJ concerns created [, exacerbated,] or mitigated compared to the baseline?"

To address these questions, EPA developed an analytical approach that considers the purpose and specifics of the rulemaking, as well as the nature of known and potential exposures across various demographic groups. While the proposal targets HAP emissions, other local air pollutants emissions may also be reduced, such as NOX and SO2. These emissions can lead to localized exposures that may be associated with health effects in nearby populations at sufficiently high concentrations and certain populations may be at increased risk of exposure-related health effects, such as people with asthma.
As HAP exposure results generated as part of the 2020 Residual Risk analysis were below both the presumptive acceptable cancer risk threshold and the noncancer health benchmarks, and this proposed regulation should further reduce exposure to HAP, there are no `disproportionate and adverse effects' of potential EJ concern. Therefore, we did not perform a quantitative EJ assessment of HAP risk. In addition, technical limitations prevented analysis of NOX and SO2 emission reductions. While HAP, NO2, and SO2 exposures and concentrations were not directly evaluated as part of this EJ assessment, due to the potential for reductions in these and other environmental stressors nearby affected sources, EPA qualitatively discussed EJ impacts of HAP (Section 6.3) and conducted a proximity analysis to evaluate the potential EJ implications of changes in localized exposures (Section 6.4).
As this proposed rule is also expected to reduce ambient PM2.5 and ozone concentrations, EPA conducted a quantitative analysis of modeled changes in PM2.5 and ozone concentrations across the continental U.S. resulting from the control strategies projected to occur under the rule, characterizing aggregated and distributional exposures both prior to and following implementation of the proposed regulatory and more stringent regulatory options in 2028, 2030, and 2035 (Section 6.5).
Unique limitations and uncertainties are specific to each type of analysis, which are described prior to presentation of analytic results in the subsections below.
Qualitative Assessment of HAP Impacts
As required by Section 112(n)(1)(A) of the Clean Air Act, the EPA has determined that it is appropriate and necessary to regulate HAP emissions from coal- and oil-fired EGUs. This determination is driven by the significant public health risks and harms posed by these emissions as evaluated against the availability and costs of emissions controls that could be employed to reduce this harmful pollution. As part of the appropriate and necessary determination, the Administrator specifically considered the impacts of EGU HAP emissions on different populations and concluded that certain parts of the U.S. population may be especially vulnerable to mercury emissions based on their characteristics or circumstances. In some cases, the enhanced vulnerability relates to life stage (e.g., fetuses, infants, young children). In other cases, the enhanced vulnerability can be ascribed to the communities in which the population lives. Higher cumulative levels of pollution are often associated with areas affected by past and present environmental injustice. In this second category, the greater sensitivity to HAP emissions can be attributed to poorer levels of overall health (e.g., higher rates of cardiovascular disease, nutritional deficiencies) or to dietary practices which are more common in low-income communities of color (e.g., subsistence fishers). The net effect is that certain sub-populations may be especially vulnerable to EGU HAP emissions and that these emissions are a potential EJ concern.
Of the HAP potentially impacted by this proposed rulemaking, mercury is a persistent and bioaccumulative toxic metal that can be readily transported and deposited to soil and aquatic environments where it is transformed by microbial action into methylmercury. Consumption of fish is the primary pathway for human exposure to methylmercury. Methylmercury bioaccumulates in the aquatic food web eventually resulting in highly concentrated levels of methylmercury within larger fish. A NAS Study reviewed the effects of methylmercury on human health and concluded that it is highly toxic to multiple human and animal organ systems. Of particular concern is chronic prenatal exposure via maternal consumption of foods containing methylmercury. Elevated exposure has been associated with developmental neurotoxicity and manifests as poor performance on neurobehavioral tests, particularly on tests of attention, fine motor function, language, verbal memory, and visual-spatial ability. Because the impacts of the neurodevelopmental effects of methylmercury are greatest during periods of rapid brain development, developing fetuses, infants, and young children are particularly vulnerable. In particular, children born to populations with high fish consumption (e.g., people consuming fish as a dietary staple) or impaired nutritional status may be especially susceptible to adverse neurodevelopmental outcomes. As part of the 2023 Final A&N Review, EPA evaluated how the neurodevelopmental and cardiovascular risks varied across populations. That analysis completed in support of the appropriate and necessary determination (addressing the EGU sector collectively) suggested that subsistence fisher populations that are racially, culturally, geographically, and/or income-differentiated could experience elevated exposures relative to not only the general population but also the population of subsistence fishers generally. As noted in Section 4 of this document, while previous EPA studies have shown that current modeled exposures are well below the RfD, we conclude that further reductions in mercury emissions from lignite-fired EGUs covered in this proposed action should further reduce exposures for the subsistence fisher sub-population. However, as we do not expect appreciable adverse health effects as a result of HAP emissions from this source category we have not conducted quantitative or qualitative analyses to assess specific mercury-related impacts of this action for EJ communities of potential concern or how those impacts differ from U.S. population-wide effects.
Demographic Proximity Analyses of Existing Facilities
Demographic proximity analyses allow one to assess the potentially vulnerable populations residing near affected facilities as a proxy for exposure and the potential for adverse health impacts that may occur at a local scale due to economic activity at a given location including noise, odors, traffic, and emissions such as NO2 and SO2 covered under this EPA action and not modeled elsewhere in this RIA.
Although baseline proximity analyses are presented here, several important caveats should be noted. Emissions are expected to both decrease and increase from the rulemaking in the three modeled future years, so communities near affected facilities could experience either improvements or worsening in air quality from directly emitted pollutants. It should also be noted that facilities may vary widely in terms of the impacts they already pose to nearby populations. In addition, proximity to affected facilities does not capture variation in baseline exposure across communities, nor does it indicate that any exposures or impacts will occur and should not be interpreted as a direct measure of exposure or impact. These points limit the usefulness of proximity analyses when attempting to answer questions from EPA's EJ Technical Guidance.
Demographic proximity analyses were performed for all plants with at least one coal- fired unit greater than 25 MW without retirement or gas conversion plans before 2029 affected by this proposed rulemaking. Due to the distinct regulatory requirements, the following subsets of affected facilities were separately evaluated:
 Lignite plants (12 facilities) with units potentially subject to the proposed mercury standard revision: Comparison of the percentage of various populations (race/ethnicity, age, education, poverty status, income, and linguistic isolation) living near the facilities to average national levels.
 Coal plants (12 facilities) with units potentially subject to the proposed filterable PM standard revision: Comparison of the percentage of various populations (race/ethnicity, age, education, poverty status, income, and linguistic isolation) living near the facilities to average national levels.
 Coal plants (48 facilities) with units potentially subject to the alternate filterable PM standard revision: Comparison of the percentage of various populations (race/ethnicity, age, education, poverty status, income, and linguistic isolation) living near the facilities to average national levels.

The current analysis identified all census blocks with centroids within a 10 km radius of the latitude/longitude location of each facility, and then linked each block with census-based demographic data. The total population within a specific radius around each facility is the sum of the population for every census block within that specified radius, based on each block's population provided by the 2020 decennial Census. Statistics on race, ethnicity, age, education level, poverty status and linguistic isolation were obtained from the Census' American Community Survey (ACS) 5-year averages for 2016-2020. These data are provided at the block group level. For the purposes of this analysis, the demographic characteristics of a given block group  -  that is, the percentage of people in different races/ethnicities, the percentage without a high school diploma, the percentage that are below the poverty level, the percentage that are below two times the poverty level, and the percentage that are linguistically isolated  -  are presumed to also describe each census block located within that block group.
In addition to facility-specific demographics, the demographic composition of the total population within the specified radius (e.g., 10 km) for all facilities was also computed (e.g., all EGUs potentially subject to the mercury standard revision). In calculating the total populations, to avoid double-counting, each census block population was only counted once. That is, if a census block was located within the selected radius (i.e., 10 km) for multiple facilities, the population of that census block was only counted once in the total population. Finally, this analysis compares the demographics at each specified radius (i.e., 10 km) to the demographic composition of the nationwide population.
Table 6-1 shows the results of the proximity analysis for the three sets of affected facilities investigated. The analysis indicates that, on average, the percentage of the population living within 10 km of these units that is African American, Hispanic/Latino, and Other/Multiracial is significantly lower than the national average. One exception is the percent of the population that is Native American within 10 km of the lignite plants (0.9 percent) that is above the national average (0.6 percent). This is driven by four facilities that have a percent Native American population living within 10 km ranging from 1.3 percent up to 5.9 percent. Also, on average, the populations living within 10 km of the units subject to the proposed or alternate filterable PM standards have a higher percentage of people living below two times the poverty level than the national average (30 to 33 percent versus 29 percent).

Table 6-1	Proximity Demographic Assessment Results Within 10 km of Coal-Fired Units Greater than 25 MW Without Retirement or Gas Conversion Plans Before 2029 Affected by this Proposed Rulemaking [a,b]

                            Population within 10 km
                               Demographic Group
                       Nationwide Average for Comparison
        Lignite plants potentially subject to proposed mercury standard
      Coal plants potentially subject to proposed filterable PM standard
      Coal plants potentially subject to alternate filterable PM standard
                               Total Population
                                  329,824,950
                                    17,790
                                    233,575
                                    854,120
Number of Facilities
                                       -
                                      12
                                      12
                                      48
                         Race and Ethnicity by Percent
White
                                      60%
                                      79%
                                      80%
                                      74%
African American
                                      12%
                                      12%
                                      4%
                                      6%
Native American
                                     0.60%
                                     0.9%
                                     0.40%
                                     0.40%
Hispanic or Latino[2]
                                      19%
                                      5%
                                      12%
                                      15%
Other and Multiracial
                                      9%
                                      2%
                                      3%
                                      4%
                               Income by Percent
Below Poverty Level
                                      13%
                                      12%
                                      14%
                                      13%

Below 2x Poverty Level
                                      29%
                                      28%
                                      33%
                                      30%
                             Education by Percent
>25 and w/o a HS Diploma
                                      12%
                                      13%
                                      13%
                                      11%
                      Linguistically Isolated by Percent
Linguistically Isolated
                                      5%
                                      2%
                                      3%
                                      2%
[a] The nationwide population count and all demographic percentages are based on the Census' 2016-2020 American Community Survey five-year block group averages and include Puerto Rico. Demographic percentages based on different averages may differ. The total population counts are based on the 2020 Decennial Census block populations.
[b] To avoid double counting, the "Hispanic or Latino" category is treated as a distinct demographic category for these analyses. A person is identified as one of five racial/ethnic categories above: White, African American, Native American, Other and Multiracial, or Hispanic/Latino. A person who identifies as Hispanic or Latino is counted as Hispanic/Latino for this analysis, regardless of what race this person may have also identified as in the Census. Includes white and nonwhite.
EJ PM2.5 and Ozone Exposure Impacts
This EJ air pollutant exposure analysis aims to evaluate the potential for EJ concerns related to PM2.5 and ozone exposures among potentially vulnerable populations. To assess EJ ozone and PM2.5 exposure impacts, we focus on the first and third of the three EJ questions from the EPA's 2016 EJ Technical Guidance, which ask if there are potential EJ concerns associated with stressors affected by the regulatory action for population groups of concern in the baseline and if those potential EJ concerns in the baseline are exacerbated, unchanged, or mitigated under the regulatory options being considered.
To address these questions with respect to the PM2.5 and ozone exposures, EPA developed an analytical approach that considers the purpose and specifics of this proposed rulemaking, as well as the nature of known and potential exposures and impacts. Specifically, as 1) this proposed rule affects EGUs across the U.S., which typically have tall stacks that result in emissions from these sources being dispersed over large distances, and 2) both ozone and PM2.5 can undergo long-range transport, it is appropriate to conduct an EJ assessment of the contiguous U.S. Given the availability of modeled PM2.5 and ozone air quality surfaces under the baseline and proposed regulatory options, we conduct an analysis of changes in PM2.5 and ozone concentrations resulting from the emission changes projected by IPM to occur under the proposed rule as compared to the baseline scenario, characterizing average and distributional exposures following implementation of the proposed regulatory options in the implementation year (2028), 2030, and 2035. However, several important caveats of this analysis are as follows:
 The baseline scenarios for 2028, 2030 and 2035 represent EGU emissions expected in 2028, 2030 and 2035 respectively, but emissions from all other sources are projected to the year 2026. The 2028, 2030 and 2035 baselines therefore do not capture any anticipated changes in ambient ozone and PM2.5 between 2026 and 2028, 2030 or 2035 that would occur due to emissions changes from sources other than EGUs.
 Modeling of post-policy air quality concentration changes are based on state-level emission data paired with facility-level baseline 2026 emissions that were available in the summer 2021 version of IPM. While the baseline spatial patterns represent ozone and PM2.5 concentrations associated with the facility level emissions described above, the post-policy air quality surfaces will capture expected ozone and PM2.5 changes that result from state-to-state emissions changes but will not capture heterogenous changes in emissions from multiple facilities within a single state.
 Air quality simulation input information are at a 12 km[2] grid resolution and population information is either at the Census tract- or county-level, potentially masking impacts at geographic scales more highly resolved than the input information.
 The two specific air pollutant metrics evaluated in this assessment, warm season maximum daily eight-hour ozone average concentrations and average annual PM2.5 concentrations, are focused on longer-term exposures that have been linked to adverse health effects. This assessment does not evaluate disparities in other potentially health-relevant metrics, such as shorter-term exposures to ozone and PM2.5.
 PM2.5 EJ impacts were limited to exposures, and do not extend to health effects, given additional uncertainties associated with estimating health effects stratified by demographic population and the ability to predict differential PM2.5-attributable EJ health impacts.
Population variables considered in this EJ exposure assessment include race, ethnicity, educational attainment, employment status, health insurance status, linguistic isolation, poverty status, age, and sex (Table 6-).
Table 6-2	Demographic Populations Included in the PM2.5 and Ozone EJ Exposure Analyses
                                  Demographic
                                    Groups
                                     Ages
                       Spatial Scale of Population Data
                                     Race
                     Asian; American Indian; Black; White
                                     0-99
                                 Census tract
                                   Ethnicity
                            Hispanic; Non-Hispanic
                                     0-99
                                 Census tract
                            Educational Attainment
               High school degree or more; No high school degree
                                     25-99
                                 Census tract
                               Employment Status
                 Employed; Unemployed; Not in the labor force
                                     0-99
                                    County
                               Health Insurance
                              Insured; Uninsured
                                     0-64
                                    County
                             Linguistic Isolation
Speaks English "very well" or better; Speaks English less than "very well" OR
   Speaks English "well" or better; Speaks English less than "well"
                                     0-99
                                 Census tract
                                Poverty Status
               Above the poverty line; Below the poverty line OR
             Above 2x the poverty line; Below 2x the poverty line
                                     0-99
                                 Census tract
                                      Age
                                   Children
                                    Adults
                                 Older Adults
                                     0-17
                                     18-64
                                     65-99
                                 Census tract
                                      Sex
                                 Female; Male
                                     0-99
                                 Census tract
Populations Predicted to Experience PM2.5 and Ozone Air Quality Changes
As IPM predicts the proposed rule will lead to both decreases and increases in emissions, the contiguous U.S. was grouped into areas where air quality 1) improves or does not change, or 2) worsens as a result of the proposed rulemaking. Figure 6-1 shows the average PM2.5 and ozone concentration in the two above categories for both the proposed and more stringent regulatory options in each of the three future years for which air quality modeling is available. In general, the more stringent regulatory option leads to large portions of the population experiencing greater average PM2.5 and ozone concentration reductions than the proposed policy option, but also results in portions of the population experiencing greater average PM2.5 and ozone concentration increases. However, the magnitude of the air pollution exposure changes from both proposed regulatory options is quite small and somewhat variable across the three future years analyzed.

Figure 6-1	Number of People Residing in the Contiguous U.S., Areas Improving or Not Changing (Teal) or Worsening (Red) in 2028, 2030, and 2035 for PM2.5 and Ozone and the National Average Magnitude of Pollutant Concentration Changes (ug/m[3] and ppb) for the Proposed and More Stringent Regulatory Options
PM2.5 EJ Exposure Analysis
We evaluated the potential for EJ concerns among potentially vulnerable populations resulting from exposure to PM2.5 under the baseline and proposed regulatory options in this rule. This was done by characterizing the distribution of PM2.5 exposures both prior to and following implementation of the proposed regulatory option, as well as under the more stringent regulatory option, in 2028, 2030, and 2035.
As this analysis is based on the same PM2.5 spatial fields as the benefits assessment (see Appendix A for a discussion of the spatial fields), it is subject to similar types of uncertainty (see Section 4.3.8for a discussion of the uncertainty). A particularly germane limitation for this analysis is that the expected concentration changes are quite small, likely making uncertainties associated with the various input data more relevant.
National Aggregated Results
National average baseline PM2.5 concentrations in micrograms per cubic meter (ug/m[3]) in 2028, 2030, and 2035 are shown in the colored column labeled "baseline" in the Figure 6-2 heat map. Concentrations in the "baseline" columns represent the total estimated PM2.5 exposure burden averaged over the 12-month calendar year and are colored to visualize differences more easily in average concentrations (lighter blue coloring representing smaller average concentrations and darker blue coloring representing larger average concentrations). Average national disparities observed in the baseline of this rule are similar to those described by recent rules (e.g., the PM NAAQS Proposal), that is, populations with national average PM2.5 concentrations higher than the reference population ordered from most to least difference were: those linguistically isolated, Hispanics, Asians, Blacks, the less educated, and children. Average national disparities observed in the baseline of this rule are generally consistent across the three future years and similar to those described by recent rules (e.g., the PM NAAQS Proposal).
Columns labeled "Proposal" and "More Stringent" provide information regarding how the proposed regulatory and more stringent options will impact PM2.5 concentrations across various populations, respectively. For all three future years evaluated, there were no discernable PM2.5 changes under the proposed regulatory option for any population analyzed when showing concentrations out to the thousandths digit, reiterating the small magnitude of national average PM2.5 changes. Going to the thousandths digit showed small national-level PM2.5 concentration reductions for the more stringent regulatory option in all three future years. While the national-level PM2.5 concentration reductions were identical for all population groups evaluated in 2030 and 2035, there were some differences observed in 2028. For example, on average, the Black population, which has higher average baseline exposures, is predicted to experience a slightly greater PM2.5 concentration reduction than the overall reference population. In contrast, the Asian population, which also has higher average baseline exposures, is estimated to experience a smaller PM2.5 concentration reduction than the overall reference population.
The national-level assessment of PM2.5 before and after implementation of this proposed rulemaking suggests that while EJ exposure disparities are present in the pre-policy scenario, meaningful EJ exposure concerns are not likely created or exacerbated by the rule for the population groups evaluated, due to the small magnitude of the PM2.5 concentration reductions.

Figure 6-2	Heat Map of the National Average PM2.5 Concentrations in the Baseline and Reductions in Concentrations Due to the Proposed and More Stringent Regulatory Options Across Demographic Groups in 2028, 2030, and 2035 (ug/m[3])
State Aggregated Results
We also provide PM2.5 concentration reductions by state and demographic population in 2028, 2030, and 2035 for the 48 states in the contiguous U.S, for the proposed and more stringent regulatory options (Figure 6-3). In this heat map, darker blue again indicates larger PM2.5 reductions and red indicates PM2.5 concentration increases with states shown as columns and demographic groups as rows. In order to show all the information in a single heat map, only colors are used to show relative PM2.5 concentrations and only the overall reference group (i.e., everyone ages 0-99) is included.
Compared to the magnitude of state-level PM2.5 concentration changes under the more stringent regulatory option, the magnitude of state-level PM2.5 concentration changes under the proposed regulatory scenario is very small. State-level average populations are projected to experience reductions in PM2.5 concentrations by up to 0.05 ug/m[3] in Florida (FL) in 2028 under the more stringent regulatory option and increases of up to 0.02 ug/m[3] in Missouri (MO) in 2030, also under the more stringent regulatory option. However, under both regulatory options, populations potentially of concern are projected to experience similar PM2.5 concentration changes as the state-level reference population. Therefore, whereas PM2.5 exposure impacts vary considerably across states, the small magnitude of differential impacts expected by the proposed rule is not likely to meaningfully exacerbate or mitigate EJ concerns within individual states.

Figure 6-3	Heat Map of the State Average PM2.5 Concentration Reductions (Blue) and Increases (Red) Due to the Proposed and More Stringent Regulatory Options Across Demographic Groups in 2028, 2030, and 2035 (ug/m[3])
 Distributional Results
We also present cumulative proportion of each population exposed to ascending levels of PM2.5 concentration changes across the contiguous U.S. Results allow evaluation of what percentage of each subpopulation (e.g., Hispanics) in the contiguous U.S. experience what change in PM2.5 concentrations compared to what percentage of the overall reference group (i.e., the total population of contiguous U.S.) experiences similar concentration changes from EGU emission changes under the two regulatory options in 2028, 2030, and 2035 (Figure 6-4).
This distributional EJ analysis is also subject to additional uncertainties related to more highly resolved input parameters and additional assumptions. For example, this analysis does not account for potential difference in underlying susceptibility, vulnerability, or risk factors across populations to PM2.5 exposure. Nor could we include information about differences in other factors that could affect the likelihood of adverse impacts (e.g., exercise patterns) across groups. Therefore, this analysis should not be used to assert that there are meaningful differences in PM2.5 exposure impacts associated with either the baseline or the rule across population groups.
As the baseline scenario is similar to that described by other RIAs, we focus on the PM2.5 changes due to this proposed rulemaking. Distributions of 12 km[2] gridded PM2.5 concentration changes from EGU control strategies of affected facilities under the two regulatory options analyzed in this proposed rulemaking in 2028, 2030, and 2035 are shown in Figure 6-4. For clarity, only above/below the poverty line and those who speak English "well or better"/"less than well" are shown and sex and the overall reference group are excluded from the cumulative distribution figures.
The vast majority of PM2.5 concentration changes for each population distribution are less than 0.02 ug/m[3] under either regulatory option for all three future years analyzed. Therefore, the distributions of PM2.5 concentration changes across population demographics are all reasonably similar and the very small difference in impacts shown in the distributional analyses of PM2.5 concentration changes under the various regulatory options provides additional evidence that the proposed rule is not likely to meaningfully exacerbate or mitigate EJ PM2.5 exposure concerns for population groups evaluated.

Figure 6-4	Distributions of PM2.5 Concentration Changes Across Populations, Future Years, and Regulatory Options

Ozone EJ Exposure Analysis
To evaluate the potential for EJ concerns among potentially vulnerable populations resulting from exposure to ozone under the baseline and regulatory options proposed in this rule, we characterize the distribution of ozone exposures both prior to and following implementation of the proposed rule, as well as under the more stringent regulatory option, in 2028, 2030, and 2035.
As this analysis is based on the same ozone spatial fields as the benefits assessment (see Appendix A for a discussion of the spatial fields), it is subject to similar types of uncertainty (see Section 4.3.8 for a discussion of the uncertainty). In addition to the small magnitude of differential ozone concentration changes associated with this proposed rulemaking when comparing across demographic populations, a particularly germane limitation is that ozone, being a secondary pollutant, is the byproduct of complex atmospheric chemistry such that direct linkages cannot be made between specific affected facilities and downwind ozone concentration changes based on available air quality modeling.
Ozone concentration and exposure metrics can take many forms, although only a small number are commonly used. The analysis presented here is based on the average April-September warm season maximum daily eight-hour average ozone concentrations (AS-MO3), consistent with the health impact functions used in the benefits assessment (Section 4). As developing spatial fields is time and resource intensive, the same spatial fields used for the benefits analysis were also used for the ozone exposure analysis performed here to assess EJ impacts.
The construct of the AS-MO3 ozone metric used for this analysis should be kept in mind when attempting to relate the results presented here to the ozone NAAQS and when interpreting the confidence in the association between exposures and health effects. Specifically, the seasonal average ozone metric used in this analysis is not constructed in a way that directly relates to NAAQS design values, which are based on daily maximum eight-hour concentrations. Thus, AS-MO3 values reflecting seasonal average concentrations well below the level of the NAAQS at a particular location do not necessarily indicate that the location does not experience any daily (eight-hour) exceedances of the ozone NAAQS. Relatedly, EPA is confident that reducing the highest ambient ozone concentrations will result in substantial improvements in public health, including reducing the risk of ozone-associated mortality. However, the Agency is less certain about the public health implications of changes in relatively low ambient ozone concentrations. Most health studies rely on a metric such as the warm-season average ozone concentration; as a result, EPA typically utilizes air quality inputs such as the AS-MO3 spatial fields in the benefits assessment, and we judge them also to be the best available air quality inputs for this EJ ozone exposure assessment.
National Aggregated Results
National average baseline ozone concentrations in ppb in 2028, 2030, and 2035 are shown in the colored column labeled "baseline" in the heat map (Figure 6-5). Concentrations in the "baseline" columns represent the total estimated daily eight-hour maximum ozone exposure burden averaged over the 6-month April-September ozone season and are colored to visualize differences more easily in average concentrations, with lighter green coloring representing smaller average concentrations and darker green coloring representing larger average concentrations. Populations with national average ozone concentrations higher than the reference population ordered from most to least difference were: American Indians, Hispanics, linguistically isolated, Asians, the less educated, and children. Average national disparities observed in the baseline of this rule are fairly consistent across the three future years and similar to those described by recent rules (e.g., the proposed GNP rule).
Columns labeled "Proposal" and "More Stringent" provide information regarding how the proposed regulatory and more stringent options will impact ozone concentrations across various populations, respectively. For all three future years evaluated, there were no discernable ozone changes under the proposed regulatory option for any population analyzed when showing concentrations out to the hundredths digit, reiterating the small magnitude of national average ozone changes. Going to the hundredths digit did show small national-level ozone concentration reductions for the more stringent regulatory option in all three future years, that were very similar across all population groups evaluated.
The national-level assessment of ozone burden concentrations in the baseline and ozone exposure changes due to the regulatory options suggests that while EJ exposure disparities are present in the pre-policy scenario, meaningful EJ exposure concerns are not likely created or exacerbated by the rule for the population groups evaluated, due to the small magnitude of the ozone concentration changes.

Figure 6-5	Heat Map of the National Average Ozone Concentrations in the Baseline and Reductions in Concentrations Due to the Proposed and More Stringent Regulatory Options Across Demographic Groups in 2028, 2030, and 2035 (ppb)

State Aggregated Results
We also provide ozone concentration reductions by state and demographic population in 2028, 2030, and 2035 for the 48 states in the contiguous U.S, for the policy and more stringent regulatory alternatives (Figure 6-6). In this heat map, darker green again indicates larger ozone reductions, with demographic groups shown as rows and each state as a column. On average, the state-specific reference populations are projected to experience reductions in ozone concentrations by up to 0.10 ppb for American Indian populations in Montana (MT) under the proposed and more stringent regulatory options. Ozone increases are only observed in Utah (UT) and Nevada (NV) in both policy options in 2035, with the maximum ozone increases of 0.02 ppb being predicted for several populations in Utah (UT).
Outside of MT, South Dakota (SD), and Wyoming (WY), population averages within individual states do not vary by more than 0.02 ppb. Elsewhere, populations potentially of concern are projected to experience similar ozone concentration reductions as the state-level reference population. Please note that population counts vary greatly by state and that as of 2022, MT, SD, and WY were the 43[rd], 46[th], and 50[th] least populated states.
Therefore, ozone exposure impacts vary considerably across states. In addition, although American Indians in MT, SD, and WY may experience slightly greater reductions due to this proposed rulemaking, the small magnitude of differential impacts expected by the proposed rule is not likely to meaningfully exacerbate or mitigate EJ concerns within individual states.

Figure 6-6	Heat Map of the State Average Ozone Concentrations Reductions (Green) and Increases (Red) Due to the Proposed and More Stringent Regulatory Options Across Demographic Groups in 2028, 2030, and 2035 (ppb)
Distributional Results
We also present cumulative proportion of each population exposed to ascending levels of ozone concentration changes across the contiguous U.S. Results allow evaluation of what percentage of each subpopulation (e.g., Hispanics) in the contiguous U.S. experience what change in ozone concentrations compared to what percentage of the overall reference group (i.e., the total population of contiguous U.S.) experiences similar concentration changes from EGU emission changes under the two regulatory options in 2028, 2030, and 2035.
This distributional EJ analysis is also subject to additional uncertainties related to more highly resolved input parameters and additional assumptions. For example, this analysis does not account for potential difference in underlying susceptibility, vulnerability, or risk factors across populations expected to experience post-policy ozone exposure changes. Nor could we include information about differences in other factors that could affect the likelihood of adverse impacts (e.g., exercise patterns) across groups. Therefore, this analysis should not be used to assert that there are meaningful differences in ozone exposures impacts in either the baseline or the rule across population groups.
As the baseline scenario is similar to that described by other RIAs, we focus on the ozone changes due to this proposed rulemaking. Distributions of 12 km[2] gridded ozone concentration changes from EGU control strategies of affected facilities under the regulatory options analyzed in this proposed rulemaking are shown in Figure 6-7. For clarity, only above/below the poverty line and those who speak English "well or better"/"less than well" are shown and sex and the overall reference group are excluded from the cumulative distribution figures.
The vast majority of ozone concentration changes are less than 0.05 ppb under either regulatory option for all three future years analyzed. Therefore, the distributions of ozone concentration changes across population demographics are all reasonably similar and the very small difference shown in the distributional analyses of ozone concentration changes under the two regulatory options provides additional evidence that the proposed rule is not likely to meaningfully exacerbate or mitigate EJ ozone exposure concerns for population groups evaluated.

Figure 6-7	Distributions of Ozone Concentration Changes Across Populations, Future Years, and Regulatory Options
Qualitative Assessment of Climate Impacts
In 2009, under the Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act ("Endangerment Finding"), the Administrator considered how climate change threatens the health and welfare of the U.S. population. As part of that consideration, she also considered risks to minority and low-income individuals and communities, finding that certain parts of the U.S. population may be especially vulnerable based on their characteristics or circumstances. These groups include economically and socially disadvantaged communities; individuals at vulnerable lifestages, such as the elderly, the very young, and pregnant or nursing women; those already in poor health or with comorbidities; the disabled; those experiencing homelessness, mental illness, or substance abuse; and/or Indigenous or minority populations dependent on one or limited resources for subsistence due to factors including but not limited to geography, access, and mobility.
Scientific assessment reports produced over the past decade by the U.S. Global Change Research Program (USGCRP),[,] the IPCC,[,][,][,] and the National Academies of Science, Engineering, and Medicine[,] add more evidence that the impacts of climate change raise potential EJ concerns. These reports conclude that poorer or predominantly non-White communities can be especially vulnerable to climate change impacts because they tend to have limited adaptive capacities and are more dependent on climate-sensitive resources such as local water and food supplies or have less access to social and information resources. Some communities of color, specifically populations defined jointly by ethnic/racial characteristics and geographic location, may be uniquely vulnerable to climate change health impacts in the U.S. In particular, the 2016 scientific assessment on the Impacts of Climate Change on Human Health found with high confidence that vulnerabilities are place- and time-specific, lifestages and ages are linked to immediate and future health impacts, and social determinants of health are linked to greater extent and severity of climate change-related health impacts.
      In a 2021 report, EPA considered the degree to which four socially vulnerable populations -- defined based on income, educational attainment, race and ethnicity, and age -- may be more exposed to the highest impacts of climate change. The report found that Blacks and African American populations are approximately 40 percent more likely to live in areas of the U.S. projected to experience the highest increases in mortality rates due to changes in extreme temperatures. Additionally, Hispanic and Latino individuals in weather-exposed industries were found to be 43 percent more likely to currently live in areas with the highest projected labor hour losses due to extreme temperatures. American Indian and Alaska Native individuals are projected to be 48 percent more likely to currently live in areas where the highest percentage of land may be inundated by sea level rise. Overall, the report confirmed findings of broader climate science assessments that Americans identifying as people of color, those with low-income, and those without a high school diploma face disproportionate risks of experiencing the most damaging impacts of climate change.
These findings suggest that CO2 reductions may benefit disproportionately impacted populations. However, as we have not conducted the wide-ranging analyses that would be needed to assess the specific impacts of this rule on the multiple climate-EJ interactions described above, we cannot analyze the potential impacts of the proposed rule quantitatively.
Summary
As with all EJ analyses, data limitations make it quite possible that disparities may exist that our analysis did not identify. This is especially relevant for potential EJ characteristics, environmental impacts, and more granular spatial resolutions that were not evaluated. For example, here we provide qualitative EJ assessment of ozone and PM2.5 concentration changes from this rule but can only qualitatively discuss EJ impacts of CO2 emission reductions. Therefore, this analysis is only a partial representation of the distributions of potential impacts. Additionally, EJ concerns for each rulemaking are unique and should be considered on a case-by-case basis, so results similar to those presented here should not be assumed for other rulemakings.
For the rule, we quantitatively evaluate the proximity of affected facilities populations of potential EJ concern (Section 6.4) and the potential for disproportionate pre- and policy-policy PM2.5 and ozone exposures across different demographic groups (Section 6.5). As exposure results generated as part of the 2020 Residual Risk analysis were below both the presumptive acceptable cancer risk threshold and the noncancer health benchmarks, and this proposed regulation should still reduce exposure to HAP, there are no `disproportionate and adverse effects' of potential EJ concern. Therefore, we did not perform a quantitative EJ assessment of HAP risk. Each of these analyses presented depend on mutually exclusive assumptions, was performed to answer separate questions, and is associated with unique limitations and uncertainties.
Baseline demographic proximity analyses provide information as to whether there may be potential EJ concerns associated with local environmental stressors such as local NO2 and SO2 emitted from sources affected by the regulatory action, traffic, or noise for certain population groups of concern in the baseline (Section 6.4). The baseline demographic proximity analyses examined the demographics of populations living within 10 km of the following sources: lignite plants with units potentially subject to the proposed mercury standard revision, coal plants with units potentially subject to the proposed filterable PM standard revision, and coal plants with units potentially subject to the alternate filterable PM standard revision. The proximity demographic analysis indicates that on average the percentage of the population living within 10 km of coal plants potentially subject to the proposed or alternate filterable PM standards have a higher percentage of people living below two times the poverty level than the national average. In addition, on average the percentage of the Native American population living within 10 km of lignite plants potentially subject to proposed mercury standard is higher than the national average. Relating these results to question 1 from Section 6.3, we conclude that there may be potential EJ concerns associated with directly emitted pollutants that are affected by the regulatory action (e.g., local NOX or SO2) for certain population groups of concern in the baseline (question 1). However, as proximity to affected facilities does not capture variation in baseline exposure across communities, nor does it indicate that any exposures or impacts will occur, these results should not be interpreted as a direct measure of exposure or impact.
While the demographic proximity analyses may appear to parallel the baseline analysis of nationwide ozone and PM2.5 exposures in certain ways, the two should not be directly compared. The baseline ozone and PM2.5 exposure assessments are in effect an analysis of total burden in the contiguous U.S., and include various assumptions, such as the implementation of promulgated regulations. It serves as a starting point for both the estimated ozone and PM2.5 changes due to this proposal as well as a snapshot of air pollution concentrations in the near future.
As HAP exposure results generated as part of the 2020 Residual Risk analysis were below both the presumptive acceptable cancer risk threshold and the noncancer health benchmarks, and this proposed regulation should further reduce exposure to HAP, there are no `disproportionate and adverse effects' of potential EJ concern. Therefore, we did not perform a quantitative EJ assessment of HAP risk.
This proposed rule is also expected to reduce emissions of direct PM2.5, NOX, and SO2 nationally throughout the year. Because NOX and SO2 are also precursors to secondary formation of ambient PM2.5 and NOX is a precursor to ozone formation, reducing these emissions would impact human exposure. Quantitative ozone and PM2.5 exposure analyses can provide insight into all three EJ questions, so they are performed to evaluate potential disproportionate impacts of this rulemaking.
The baseline ozone and PM2.5 exposure analyses respond to question 1 from EPA's EJ Technical Guidance document more directly than the proximity analyses, as they evaluate a form of the environmental stressor primarily affected by the regulatory action (Section 6.5). Baseline PM2.5 and ozone exposure analyses show that certain populations, such as Hispanic, Asian, those linguistically isolated, those less educated, and children may experience disproportionately higher ozone and PM2.5 exposures as compared to the national average. American Indian populations may also experience disproportionately higher ozone concentrations than the reference group. Therefore, there likely are potential EJ concerns associated with environmental stressors affected by the regulatory action for population groups of concern in the baseline.
Finally, we evaluate how the post-policy options of this proposed rulemaking are expected to differentially impact demographic populations, informing questions 2 and 3 from EPA's EJ Technical Guidance with regard to ozone and PM2.5 exposure changes. Due to the small magnitude of the exposure changes across population demographics associated with the rulemaking relative to the magnitude of the baseline disparities, we infer that baseline disparities in ozone and PM2.5 concentration burdens are likely to remain after implementation of the regulatory or more stringent option under consideration (question 2). Also due to the very small differences in the magnitude of post-policy ozone and PM2.5 exposure impacts across demographic populations, we do not find evidence that potential EJ concerns related to ozone or PM2.5 exposures will be meaningfully exacerbated or mitigated in the regulatory alternatives under consideration, compared to the baseline (question 3). Importantly, the action described in this rule is expected to lower ozone and PM2.5 in many areas, including those areas that struggle to attain or maintain the NAAQS, and thus mitigate some pre-existing health risks across all populations evaluated.
This EJ air quality analysis concludes that there are PM2.5 and ozone exposure disparities across various populations in the pre-policy baseline scenario (EJ question 1) and infer that these disparities are likely to persist after promulgation of this proposed rulemaking (EJ question 2). This EJ assessment also suggests that this action will neither mitigate nor exacerbate PM2.5 and ozone exposure disparities across populations of EJ concern analyzed (EJ question 3) at the national scale in a meaningful way.

    Comparison of Benefits and Costs
Introduction
This section presents the estimates of the health benefits, compliance costs, and net benefits associated with the proposed MATS review relative to baseline MATS requirements. All analysis begins in the year 2028, the compliance year for the proposed standards. In this RIA, the regulatory impacts are evaluated for the specific years of 2028, 2030, and 2035. We also evaluate the potential regulatory impacts of the regulatory options using the present value (PV) and equivalent annualized value (EAV) of costs, benefits, and net benefits, calculated for the years 2028 to 2037 from the perspective of 2023, using both a three percent and seven percent end-of-period discount rate.
There are potential benefits and costs that may result from this proposed rule that have not been quantified or monetized. Due to current data and modeling limitations, quantified and monetized benefits from the proposed requirements from reducing mercury and non-mercury metal HAP emissions are not included in the monetized benefits presented here.
The compliance costs reported in this RIA are not social costs, although in this analysis we use compliance costs as a proxy for social costs. We do not account for changes in costs and benefits due to changes in economic welfare of suppliers to the electricity market or to non-electricity consumers from those suppliers. Furthermore, costs due to interactions with pre-existing market distortions outside the electricity sector are omitted.
Methods
EPA calculated the PV of costs, benefits, and net benefits for the years 2028 through 2037, using both a three percent and seven percent end-of-period discount rate from the perspective of 2023. All dollars are in 2019 dollars. In order to implement the OMB Circular A-4 requirement for fulfilling E.O. 12866, we assess one less stringent and one more stringent alternative to the proposed requirements.
This calculation of a PV requires an annual stream of values for each year of the 2028 to 2037 timeframe. EPA used IPM to estimate cost and emission changes for the projection years 2028, 2030, and 2035. The year 2028 is an approximation of the compliance year for the proposed requirements. In the IPM modeling for this RIA, the 2028 projection year is representative of 2028 alone, the 2030 projection year is representative of 2029 through 2031, and the 2035 projection year is representative of 2032 to 2037. Estimates of costs and emission changes in other years are determined from the mapping of projection years to the calendar years that they represent. Consequently, the cost and emission estimates from IPM in each projection year are applied to the years which it represents.
Health benefits are based on projection year emission estimates and also account for year-specific variables that influence the size and distribution of the benefits. These variables include population growth, income growth, and the baseline rate of death. Climate benefits estimates are based on these projection year emission estimates, and also account for year-specific interim SC-CO2 values.
EPA calculated the PV and EAV of costs, benefits, and net benefits over the 2028 through 2037 timeframe for the three regulatory options examined in this RIA. The EAV represents a flow of constant annual values that, had they occurred in each year from 2028 to 2037, would yield an equivalent present value. The EAV represents the value of a typical cost or benefit for each year of the analysis, in contrast to the year-specific estimates presented elsewhere for the snapshot years of 2028, 2030, and 2035.
Results
We first present net benefit analysis for the three years of detailed analysis, 2028, 2030, and 2035. Table 7-1, Table 7-2, and Table 7-3 present the estimates of the projected compliance costs, health benefits, climate benefits, and net benefits across the regulatory options examined in this proposal, respectively. The comparison of benefits and costs in PV and EAV terms for the proposed rule can be found in Table 7-4 for the proposed regulatory option. Table 7-5 presents the results for the less stringent regulatory option, and Table 7-6 presents results for the more stringent regulatory option. Estimates in the tables are presented as rounded values. Note the less stringent regulatory option 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 7-1	Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less and More Stringent Alternatives for 2028 for the U.S. (millions of 2019 dollars) [a,b]

                                   Proposed
                                     Rule

                          Less Stringent Alternative

                          More Stringent Alternative
                   PM2.5 and O3-related Health Benefits [c]
                                      58
                                      and
                                      140

                                      0.0
                                      and
                                      0.0

                                     1,300
                                      and
                                     3,100
                              Climate Benefits[d]

                                      13

                                      0.0

                                     1,300

                               Total Benefits[e]
                                      71
                                      and
                                      160

                                      0.0
                                      and
                                      0.0

                                     2,600
                                      and
                                     4,400
                               Compliance Costs

                                      56

                                     -5.9

                                      920

                                 Net Benefits
                                      16
                                      and
                                      100

                                      5.9
                                      and
                                      5.9

                                     1,700
                                      and
                                     3,500
[a] We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
[b] Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
[c] Monetized benefits include those related to public health associated with reductions in PM2.5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount rate of 3 percent.
[d] Climate benefits are based on reductions in CO2 emissions and are calculated using four different estimates of the social cost of carbon dioxide (SC-CO2): model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent discount rate. The 95th percentile estimate is included to provide information on potentially higher-than-expected economic impacts from climate change, conditional on the 3 percent estimate of the discount rate. For the presentational purposes of this table, we show the climate benefits associated with the average SC-CO2 at a 3 percent discount rate, but the Agency does not have a single central SC-CO2 point estimate. Climate benefits in this table are discounted using a 3 percent discount rate to obtain the PV and EAV estimates in the table. We emphasize the importance and value of considering the benefits calculated using all four SC-CO2 estimates. Section 4.4 of the RIA presents estimates of the projected climate benefits of this proposal using all four rates. We note that consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, is warranted when discounting intergenerational impacts.
[e] Several categories of benefits remain unmonetized and are thus not directly reflected in the quantified benefit estimates in the table. Non-monetized benefits include benefits from reductions in mercury and non-mercury metal HAP emissions and from the increased transparency and accelerated identification of anomalous emission anticipated from requiring CEMS.

Table 7-2	Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less and More Stringent Alternatives for 2030 for the U.S. (millions of 2019 dollars) [a,b]

                                   Proposed
                                    Rule
                                Less Stringent
                                  Alternative

                          More Stringent Alternative
                   PM2.5 and O3-related Health Benefits [c]
                                      50
                                      and
                                      150

                                      0.0
                                      and
                                      0.0

                                      250
                                      and
                                      860
                              Climate Benefits[d]

                                      50

                                       0

                                      530

                               Total Benefits[e]
                                      100
                                      and
                                      200

                                      0.0
                                      and
                                      0.0

                                      780
                                      and
                                     1,400
                               Compliance Costs

                                      46

                                     -5.9

                                     1,100

                                 Net Benefits
                                      54
                                      and
                                      160

                                      5.9
                                      and
                                      5.9

                                     -270
                                      and
                                      340
[a] We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
[b] Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
[c] Monetized benefits include those related to public health associated with reductions in PM2.5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount rate of 3 percent.
[d] Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
[e] Several categories of benefits remain unmonetized and are thus not directly reflected in the quantified benefit estimates in the table. Non-monetized benefits include benefits from reductions in mercury and non-mercury metal HAP emissions and from the increased transparency and accelerated identification of anomalous emission anticipated from requiring CEMS.
Table 7-3	Monetized Benefits, Costs, and Net Benefits of the Proposed Rule and Less and More Stringent Alternatives for 2035 for the U.S. (millions of 2019 dollars) [a,b]

                                   Proposed
                                     Rule
                                Less Stringent
                                  Alternative

                          More Stringent Alternative
                   PM2.5 and O3-related Health Benefits [c]
                                      100
                                      and
                                      330

                                      0.0
                                      and
                                      0.0

                                      570
                                      and
                                     1,500
                              Climate Benefits[d]

                                      310

                                      0.0

                                      190

                               Total Benefits[e]
                                      410
                                      and
                                      640

                                      0.0
                                      and
                                      0.0

                                      760
                                      and
                                     1,700
                               Compliance Costs

                                      39

                                     -5.9

                                      280

                                 Net Benefits
                                      370
                                      and
                                      600

                                      5.9
                                      and
                                      5.9

                                      480
                                      and
                                     1,400
[a] We focus results to provide a snapshot of costs and benefits in 2028, using the best available information to
approximate social costs and social benefits recognizing uncertainties and limitations in those estimates.
[b] Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
[c] Monetized benefits include those related to public health associated with reductions in PM2.5 and ozone
concentrations. The health benefits are associated with several point estimates and are presented at a real discount rate of 3 percent.
[d] Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
[e] Several categories of benefits remain unmonetized and are thus not directly reflected in the quantified benefit estimates in the table. Non-monetized benefits include benefits from reductions in mercury and non-mercury metal HAP emissions and from the increased transparency and accelerated identification of anomalous emission anticipated from requiring CEMS.
Table 7-4	Proposed Rule: Present Values and Equivalent Annualized Values of Projected Monetized Compliance Costs, Benefits, and Net Benefits for 2028 to 2037 (millions of 2019 dollars, discounted to 2023) [a]

                     PM2.5 and O3-related Health Benefits
                               Climate Benefits
                                  Compliance
                                     Costs
                                      Net
                                   Benefits

                                      3%
                                      7%
                                      3%

                                      3%
                                      7%
                                     2028
                                      140
                                      130
                                      13
                                      56
                                      100
                                      87
                                     2029
                                      150
                                      130
                                      49
                                      46
                                      150
                                      140
                                     2030
                                      150
                                      140
                                      50
                                      46
                                      160
                                      140
                                     2031
                                      160
                                      140
                                      51
                                      46
                                      160
                                      140
                                     2032
                                      310
                                      270
                                      290
                                      39
                                      560
                                      530
                                     2033
                                      320
                                      280
                                      300
                                      39
                                      570
                                      540
                                     2034
                                      320
                                      290
                                      300
                                      39
                                      590
                                      550
                                     2035
                                      330
                                      300
                                      310
                                      39
                                      600
                                      570
                                     2036
                                      340
                                      310
                                      310
                                      39
                                      620
                                      580
                                     2037
                                      350
                                      310
                                      320
                                      39
                                      630
                                      590

                     PM2.5 and O3-related Health Benefits
                               Climate Benefits
                                  Compliance
                                     Costs
                                      Net
                                   Benefits

                                 Discount Rate

                                      3%
                                      7%
                                      3%
                                      3%
                                      7%
                                      3%
                                      7%
                                 Present Value
                                     1,900
                                     1,200
                                     1,400
                                      330
                                      230
                                     3,000
                                     2,400
                          Equivalent Annualized Value
                                      220
                                      170
                                      170
                                      38
                                      33
                                      350
                                      300
[a] Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
[b] The health benefits estimates use the larger of the two benefits estimates presented in Table 7-1	. Monetized benefits include those related to public health associated with reductions in PM2.5 and ozone concentrations. The health benefits are associated with several point estimates.
[c] Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
[d] Several categories of benefits remain unmonetized and are thus not reflected in the table. Nonmonetized benefits include important benefits from reductions in mercury and non-mercury metal HAP.

Table 7-5	Less Stringent Regulatory Option: Present Values and Equivalent Annualized Values for the 2028 to 2037 Timeframe for Estimated Monetized Compliance Costs, Benefits, and Net Benefits (millions of 2019 dollars, discounted to 2023) [a]

                     PM2.5 and O3-related Health Benefits
                               Climate Benefits
                                  Compliance
                                     Costs
                                      Net
                                   Benefits

                                      3%
                                      7%
                                      3%

                                      3%
                                      7%
                                     2028
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2029
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2030
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2031
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2032
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2033
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2034
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2035
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2036
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9
                                     2037
                                      0.0
                                      0.0
                                      0.0
                                     -5.9
                                      5.9
                                      5.9

                     PM2.5 and O3-related Health Benefits
                               Climate Benefits
                                  Compliance
                                     Costs
                                      Net
                                   Benefits

                                 Discount Rate

                                      3%
                                      7%
                                      3%
                                      3%
                                      7%
                                      3%
                                      7%
                                 Present Value
                                      0.0
                                      0.0
                                      0.0
                                      -45
                                      -31
                                      45
                                      31
                          Equivalent Annualized Value
                                      0.0
                                      0.0
                                      0.0
                                     -5.2
                                     -4.5
                                      5.2
                                      4.5
[a] Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
[b] The health benefits estimates use the larger of the two benefits estimates presented in Table 7-. Monetized benefits include those related to public health associated with reductions in PM2.5 and ozone concentrations. The health benefits are associated with several point estimates.
[c] Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate
[d] Several categories of benefits remain unmonetized and are thus not reflected in the table. Nonmonetized benefits include important benefits from reductions in mercury and non-mercury metal HAP.

Table 7-6	More Stringent Regulatory Option: Present Values and Equivalent Annualized Values for the 2028 to 2037 Timeframe for Estimated Monetized Compliance Costs, Benefits, and Net Benefits (millions of 2019 dollars, discounted to 2023) [a]

                     PM2.5 and O3-related Health Benefits
                               Climate Benefits
                                  Compliance
                                     Costs
                                      Net
                                   Benefits

                                      3%
                                      7%
                                      3%

                                      3%
                                      7%
                                     2028
                                     3,100
                                     2,800
                                     1,300
                                      920
                                     3,500
                                     3,200
                                     2029
                                      840
                                      750
                                      520
                                     1,100
                                      300
                                      210
                                     2030
                                      860
                                      770
                                      530
                                     1,100
                                      330
                                      250
                                     2031
                                      890
                                      800
                                      540
                                     1,100
                                      370
                                      280
                                     2032
                                     1,400
                                     1,200
                                      180
                                      280
                                     1,300
                                     1,100
                                     2033
                                     1,400
                                     1,300
                                      190
                                      280
                                     1,300
                                     1,200
                                     2034
                                     1,500
                                     1,300
                                      190
                                      280
                                     1,400
                                     1,200
                                     2035
                                     1,500
                                     1,400
                                      190
                                      280
                                     1,400
                                     1,300
                                     2036
                                     1,600
                                     1,400
                                      200
                                      280
                                     1,500
                                     1,300
                                     2037
                                     1,600
                                     1,400
                                      200
                                      280
                                     1,500
                                     1,300

                     PM2.5 and O3-related Health Benefits
                               Climate Benefits
                                  Compliance
                                     Costs
                                      Net
                                   Benefits

                                 Discount Rate

                                      3%
                                      7%
                                      3%
                                      3%
                                      7%
                                      3%
                                      7%
                                 Present Value
                                    11,000
                                     7,100
                                     3,200
                                     4,600
                                     3,400
                                     9,800
                                     6,900
                          Equivalent Annualized Value
                                     1,300
                                     1,000
                                      380
                                      540
                                      490
                                     1,100
                                      900
[a] Values have been rounded to two significant figures. Rows may not appear to add correctly due to rounding.
[b] The health benefits estimates use the larger of the two benefits estimates presented in Table 7-. Monetized benefits include those related to public health associated with reductions in PM2.5 and ozone concentrations. The health benefits are associated with several point estimates.
[c] Climate benefits in this table are based on estimates of the SC-CO2 at a 3 percent discount rate.
[d] Several categories of benefits remain unmonetized and are thus not reflected in the table. Nonmonetized benefits include important benefits from reductions in mercury and non-mercury metal HAP.

The results presented in this section provide an incomplete overview of the effects of the proposal, because important categories of benefits, including benefits from reducing mercury and non-mercury metal HAP emissions, were not monetized and are therefore not directly reflected in the quantified benefit-cost comparisons. We anticipate that taking non-monetized effects into account would show the proposal to be more net beneficial than the tables in this section reflect.

    Appendix A: Air Quality Modeling
Introduction
As noted in Section 4, EPA used photochemical modeling to create air quality surfaces that were then used in air pollution health benefits calculations of the three regulatory control alternatives of the proposed rule. The modeling-based surfaces captured air pollution impacts resulting from changes in NOX, SO2, and direct PM2.5 emissions from EGUs. This appendix describes the source apportionment modeling and associated methods used to create air quality surfaces for the baseline scenario and two regulatory options (the proposed regulatory options and the more stringent regulatory option) in three analytic years: 2028, 2030 and 2035. EPA created air quality surfaces for the following pollutants and metrics: annual average PM2.5; April-September average of 8-hr daily maximum (MDA8) ozone (AS-MO3).
The ozone source apportionment modeling outputs are the same as those created for the Regulatory Impact Analysis for the proposed Federal Implementation Plan Addressing Regional Ozone Transport for the 2015 Ozone National Ambient Air Quality Standard (U.S. EPA, 2022c). New PM source apportionment modeling outputs were created using the same inputs and modeling configuration as were used for the available ozone source apportionment modeling. The basic methodology for determining air quality changes is the same as that used in the RIAs from multiple previous rules (U.S. EPA, 2019, 2020a, 2020b, 2021b, 2022c). EPA calculated baseline and regulatory option EGU emissions estimates of NOX and SO2 for all three analysis years using IPM (Section 3 of this RIA). EPA also used IPM outputs to estimate EGU emissions of PM2.5 based on emission factors described in flat (U.S. EPA, 2021a). This appendix provides additional details on the source apportionment modeling simulations and the associated analysis used to create ozone and PM2.5 air quality surfaces.
Air Quality Modeling Simulations
The air quality modeling utilized a 2016-based modeling platform which included meteorology and base year emissions from 2016 and projected future-year emissions for 2026.[,] The air quality modeling included photochemical model simulations for a 2016 base year and 2026 future year to provide hourly concentrations of ozone and PM2.5 component species nationwide. In addition, source apportionment modeling was performed for 2026 to quantify the contributions to ozone from NOX emissions and to PM2.5 from NOX, SO2 and directly emitted PM2.5 emissions from EGUs on a state-by-state basis. As described below, the modeling results for 2016 and 2026, in conjunction with EGU emissions data for the baseline and three regulatory options in 2028, 2030 and 2035 were used to construct the air quality surfaces that reflect the influence of emissions changes between the baseline and two regulatory options in each year.
The air quality model simulations (i.e., model runs) were performed using the Comprehensive Air Quality Model with Extensions (CAMx) version 7.10 (Ramboll Environ, 2021). The nationwide modeling domain (i.e., the geographic area included in the modeling) covers all lower 48 states plus adjacent portions of Canada and Mexico using a horizontal grid resolution of 12 km[2] shown in Figure 8-1. Model predictions of ozone and PM2.5 concentrations were compared against ambient measurements (U.S. EPA, 2022a, 2022b). Ozone and PM2.5 model evaluations showed model performance that was adequate for applying these model simulations for the purpose of creating air quality surfaces to estimate ozone and PM2.5 benefits.

Figure 8-1	Air Quality Modeling Domain
The contributions to ozone and PM2.5 component species (e.g., sulfate, nitrate, ammonium, elemental carbon (EC), organic aerosol (OA), and crustal material) from EGU emissions in individual states were modeled using the "source apportionment" tool approach. In general, source apportionment modeling quantifies the air quality concentrations formed from individual, user-defined groups of emissions sources or "tags." These source tags are tracked through the transport, dispersion, chemical transformation, and deposition processes within the model to obtain hourly gridded contributions from the emissions in each individual tag to hourly gridded modeled concentrations. For this RIA we used the source apportionment contribution data to provide a means to estimate of the effect of changes in emissions from each group of emissions sources (i.e., each tag) to changes in ozone and PM2.5 concentrations. Specifically, we applied outputs from source apportionment modeling for ozone and PM2.5 component species using the 2026 modeled case to obtain the contributions from EGUs emissions in each state to ozone and PM2.5 component species concentrations in each 12 km[2] model grid cell nationwide. Ozone contributions were modeled using the Anthropogenic Precursor Culpability Assessment (APCA) tool and PM2.5 contributions were modeled using the Particulate Matter Source Apportionment Technology (PSAT) tool (Ramboll Environ, 2021). The ozone source apportionment modeling was performed for the period April through September to provide data for developing spatial fields for the April through September maximum daily eight-hour (MDA8) (i.e., AS-MO3) average ozone concentration exposure metric. The PM2.5 source apportionment modeling was performed for a full-year to provide data for developing annual average PM2.5 spatial fields. Table 8-1 provides state-level 2026 EGU emissions that were tracked for each source apportionment tag.
Table 8-1	2026 Emissions Allocated to Each Modeled State-EGU Source Apportionment Tag
                                   State Tag
                       Ozone Season NOX Emissions (tons)
                          Annual NOX emissions (tons)
                          Annual SO2 emissions (tons)
                         Annual PM2.5 emissions (tons)
                                      AL
                                     6,205
                                     9,319
                                     1,344
                                     2,557
                                      AR
                                     5,594
                                     9,258
                                    22,306
                                     1,075
                                      AZ
                                     1,341
                                     3,416
                                     2,420
                                      814
                                      CA
                                     6,627
                                    16,286
                                      249
                                     4,810
                                      CO
                                     5,881
                                    12,725
                                     7,311
                                     1,556
                                      CT
                                     1,673
                                     3,740
                                      845
                                      467
                                      DC
                                      37
                                      39
                                       0
                                      53
                                      DE
                                      203
                                      320
                                      126
                                      119
                                      FL
                                    11,590
                                    22,451
                                     8,784
                                     6,555
                                      GA
                                     3,199
                                     5,937
                                     1,177
                                     2,452
                                      IA
                                     8,008
                                    17,946
                                     9,042
                                     1,182
                                      ID
                                      375
                                      705
                                       1
                                      185
                                      IL
                                     8,244
                                    16,777
                                    31,322
                                     3,018
                                      IN
                                    11,052
                                    36,007
                                    34,990
                                     6,281
                                      KS
                                     3,166
                                     4,351
                                      854
                                      709
                                      KY
                                    11,894
                                    25,207
                                    22,940
                                    10,476
                                      LA
                                    10,895
                                    16,949
                                    11,273
                                     3,119
                                      MA
                                     2,115
                                     4,566
                                      839
                                      384
                                      MD
                                     1,484
                                     3,008
                                      273
                                      783
                                      ME
                                     1,233
                                     3,063
                                     1,147
                                      414
                                      MI
                                    11,689
                                    22,378
                                    31,387
                                     3,216
                                      MN