Coal 105.0  105.4 110.4 0.3% 5.1% Natural Gas 6.2  6.2 6.2 -0.1% 0.2% Oil/Gas Steam 14.8  14.9 14.8 0.4% -0.1% Non-Hydro RE 3.3  3.3 3.3 0.0% 0.0% Hydro 0.0  0.0 0.0 0% 0% Nuclear 9.9  9.9 9.9 0.0% 0.0% Other 0.1  0.1 0.1 0.0% 0.0% Total 139.3  139.7 144.7 0.3% 3.9% Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind. Finally, Table 310 shows the projected U.S. new capacity builds by fuel type for the baseline run and the regulatory control alternatives in all run years. For the proposed rule, the incremental changes in projected new capacity for any given fuel type are one percent or less, and all under 1 GW. The more-stringent alternative is projected to result in an increase in incremental builds in the energy storage (6.0 GW), natural gas (3.9 GW), and renewables (1.7 GW) categories in 2028. Some of these incremental changes reflect a projected acceleration of new capacity that was projected to occur after 2028 in the baseline.  Table 310 2028, 2030, and 2035 Projected U.S. New Capacity Builds by Fuel Type for the Baseline Run and the Proposed Rule and More Stringent Alternative

New Capacity (GW) Percent Change from Baseline

Baseline Run Proposed Rule More Stringent Alternative Proposed Rule More Stringent Alternative 2028

Coal 0.0 0.0 0.0 0.0% 0.0% Natural Gas 31.6 32.0 35.5 1.4% 12.5% Energy Storage 32.5 32.8 38.5 0.8% 18.3% Non-Hydro RE 42.0 41.9 43.7 -0.3% 3.9% Hydro 0.0 0.0 0.0 0.0% 0.0% Nuclear 0.0 0.0 0.0 0.0% 0.0% Other 0.0 0.0 0.0 0.0% 0.0% Total 106.2 106.8 117.8 0.5% 10.9% 2030

    Coal 0.0 0.0 0.0 0.0% 0.0% Natural Gas 31.6 32.0 35.7 1.5% 13.0% Energy Storage 50.6 50.7 51.5 0.3% 1.9% Non-Hydro RE 130.8 130.7 132.5 -0.1% 1.3% Hydro 1.5 1.5 1.5 0.0% 0.0% Nuclear 0.0 0.0 0.0 0.0% 0.0% Other 0.0 0.0 0.0 0.0% 0.0% Total 214.5 215.0 221.2 0.2% 3.1% 2035

Coal 0.0 0.0 0.0 0.0% 0.0% Natural Gas 45.0 45.2 49.6 0.5% 10.3% Energy Storage 80.7 80.8 81.0 0.1% 0.3% Non-Hydro RE 395.0 395.9 395.0 0.2% 0.0% Hydro 5.8 5.8 5.8 0.0% 0.0% Nuclear 0.0 0.0 0.0 0.0% 0.0% Other 0.0 0.0 0.0 0.0% 0.0% Total 526.5 527.7 531.3 0.2% 0.9% Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind. Generation Mix In this section, we discuss the projected changes in generation mix for the 2028, 2030, and 2035 for the proposed rule and more stringent alternative. Table 311 presents the projected generation and percentage changes in national generation mix by fuel type for run years 2028, 2030, and 2035. These generation mix estimates reflect a very modest increase in natural gas and renewables and decrease in coal beginning in 2028 as a result of proposed rule and more stringent alternative. Estimated changes in coal and natural gas use as a result of each regulatory option are examined further in section 3.5.6

Table 311 2028, 2030, and 2035 Projected U.S. Generation by Fuel Type for the Baseline Run and the Proposed Rule and More Stringent Alternative

Generation Mix (TWh) Incremental Change from Baseline

Base Case Proposed Rule More Stringent Alternative Proposed Rule More Stringent Alternative

TWh     % TWh % 2028

Coal 484 484 454 -0.3 -0.1% -29.9 -6.2% Natural Gas 1,773 1,774 1,802 0.7 0.0% 28.5 1.6% Oil/Gas Steam 30 30 28 0.0 0.1% -1.6 -5.5% Non-Hydro RE 964 964 967 -0.3 0.0% 3.1 0.3% Hydro 294 294 292 -0.2 -0.1% -1.5 -0.5% Energy Storage 68 69 76 0.3 0.5% 7.7 11.3% Nuclear 765 765 765 0.0 0.0% 0.0 0.0% Other 30 30 30 0.0 0.0% 0.0 0.0% Total 4,409 4,409 4,415 0.2 0.0% 6.3 0.1% 2030

      Coal 309 307 292 -1.6 -0.5% -17.1 -5.5% Natural Gas 1,771 1,774 1,783 2.3 0.1% 12.1 0.7% Oil/Gas Steam 33 33 33 -0.1 -0.5% 0.4 1.1% Non-Hydro RE 1,269 1,268 1,274 -0.4 0.0% 5.1 0.4% Hydro 303 303 303 -0.1 0.0% 0.0 0.0% Energy Storage 98 98 99 0.1 0.1% 1.2 1.2% Nuclear 734 734 734 0.0 0.0% 0.0 0.0% Other 29 29 29 0.0 0.0% 0.0 0.0% Total 4,545 4,545 4,546 0.3 0.0% 1.6 0.0% 2035

        Coal 120 115 104 -4.1 -3.5% -15.3 -12.8% Natural Gas 1,402 1,402 1,418 0.2 0.0% 16.1 1.1% Oil/Gas Steam 16 16 16 -0.1 -0.4% -0.3 -1.8% Non-Hydro RE 2,180 2,183 2,178 2.4 0.1% -2.2 -0.1% Hydro 329 329 329 0.0 0.0% 0.0 0.0% Energy Storage 154 154 155 0.2 0.1% 0.4 0.2% Nuclear 660 660 660 0.0 0.0% 0.0 0.0% Other 29 29 29 0.0 0.0% 0.0 0.0% Total 4,891 4,889 4,889 -1.4 0.0% -1.3 0.0% Note: In this table, "Non-Hydro RE" includes biomass, geothermal, landfill gas, solar, and wind. Coal and Natural Gas Use for the Electric Power Sector In this section we discuss the estimated changes in coal use and natural gas use in 2028, 2030, and 2035.. Table 312 and Table 313 present percentage changes in national coal usage by EGUs by coal supply region and coal rank, respectively. These fuel use estimates reflect virtually no reduction in coal use in the proposed rule relative to the baseline in 2028, and very modest reductions in coal use in 2030 and 2035. All regulatory options reflect a continuing trend of decreasing coal use nationwide; between 2015 and 2021, annual coal consumption in the electric power sector fell between 8 and 19 percent annually. The proposed rule is projected to result in up to a 3 percent decrease in coal use in 2035 relative to the baseline. Additionally, the proposed rule is not projected to result in significant coal switching between supply regions or coal rank.  Table 3122028, 2030, and 2035 Projected U.S. Power Sector Coal Use by Coal Supply Region for the Baseline Run and the Proposed Rule and More Stringent Alternative     Million Tons Percent Change from Baseline

Year Baseline Run Proposed Rule More-Stringent Alt. Proposed Rule More-Stringent Alt. Appalachia 2028 48.4 48.3 45.3 -0.2% -6.3% Interior

50.6 50.5 47.8 0.0% -5.5% Waste Coal

4.3 4.3 4.3 0.0% 0.0% West

148.0 148.0 137.6 0.0% -7.0% Total

251.3 251.2 235.1 0.0% -6.4% Appalachia 2030 28.2 27.6 26.7 -2.1% -5.3% Interior

36.6 36.6 34.6 0.0% -5.4% Waste Coal

4.3 4.3 4.3 0.0% 0.0% West

106.8 106.7 99.3 -0.1% -7.0% Total

176.0 175.3 165.0 -0.4% -6.2% Appalachia 2035 10.9 10.9 10.0 0.0% -7.9% Interior

19.6 19.7 18.2 0.7% -7.3% Waste Coal

2.0 1.9 2.0 -3.4% -0.2% West

47.9 45.3 39.4 -5.3% -17.8% Total

80.4 77.9 69.6 -3.1% -13.4%

Table 3132028, 2030, and 2035 Projected U.S. Power Sector Coal Use by Rank for the Baseline Run and the Proposed Rule and More Stringent Alternative     Million Tons Percent Change from Baseline  Rank Year Baseline Run Proposed Rule More-Stringent Alt. Proposed Rule More-Stringent Alt. Bituminous 2028 94.0 93.9 88.2 -0.1% -6.2% Subbituminous

126.3 126.3 117.5 0.0% -6.9% Lignite

27.2 27.2 25.6 -0.2% -6.1% Total

247.5 247.4 231.3 0.0% -6.5% Bituminous 2030 59.5 58.9 56.1 -1.0% -5.8% Subbituminous

86.8 86.7 80.7 -0.1% -7.0% Lignite

25.4 25.4 23.9 0.0% -5.9% Total

171.6 171.0 160.6 -0.4% -6.4% Bituminous 2035 25.2 25.3 22.9 0.5% -8.9% Subbituminous

35.9 33.3 27.4 -7.1% -23.7% Lignite

17.4 17.4 17.3 0.0% -0.2% Total

78.4 76.0 67.6 -3.1% -13.8%

Table 314 presents the projected changes in national natural gas usage by EGUs in the 2028, 2030, and 2035 run years. These fuel use estimates reflect very modest changes to projected gas generation in 2028, 2030 and 2035. Table 3142028, 2030, and 2035 Projected U.S. Power Sector Natural Gas Use for the Baseline Run and the Proposed Rule and More Stringent Alternative   Trillion Cubic Feet Percent Change from Baseline Year Baseline Run  Proposed Rule More-Stringent Alternative Proposed Rule More-Stringent Alternative 2028 12.5 12.5 12.7 0.0% 1.3% 2030 12.6 12.7 12.7 0.1% 0.5% 2035 9.9 9.9 10.0 -0.1% 0.9%

Fuel Price, Market, and Infrastructure The projected impacts of the proposed rule and more stringent alternative on coal and natural gas prices are presented below in Table 315 and Table 316, respectively. As with the projected impact on fuel use, the projected impact of the proposed rule on minemouth and delivered coal prices is very small. The small increase in the national weighted average price of coal reflects the small projected decrease in the use of western subbituminous coal (see Table 312) which is characterized by a lower price on a MMBtu basis than bituminous coal. Table 3152028, 2030, and 2035 Projected Minemouth and Power Sector Delivered Coal Price (2019 dollars) for the Baseline Run and the Proposed Rule and More Stringent Alternative     $/MMBtu Percent Change from Baseline    Year Baseline Run Proposed Rule More-Stringent Alternative Proposed Rule More-Stringent Alternative Minemouth 2028 1.16 1.16 1.15 0.00% -0.50% Delivered

1.59 1.59 1.57 0.00% -1.50% Minemouth 2030 1.17 1.17 1.18 -0.10% 0.60% Delivered

1.47 1.47 1.47 -0.20% 0.00% Minemouth 2035 1.34 1.35 1.38 0.90% 2.90% Delivered

1.38 1.40 1.40 1.70% 2.00%

Consistent with the projected change in natural gas use under the proposed rule, Henry Hub and power sector delivered natural gas prices are not projected to significantly change under the proposed rule over the period analyzed. Under the more stringent alternative, the small projected increase in natural gas demand is projected to result in a similarly small impact on average natural gas prices. Table 316 summarizes the projected impacts on Henry Hub and delivered natural gas prices in 2028, 2030, and 2035. Table 3162028, 2030, and 2035 Projected Henry Hub and Power Sector Delivered Natural Gas Price (2019 dollars) for the Baseline Run and the Proposed Rule and More Stringent Alternative     $/MMBtu Percent Change from Baseline    Year Baseline Run  Proposed Rule More-Stringent Alternative Proposed Rule More-Stringent Alternative Henry Hub 2028 2.98 2.98 3.03 0.00% 1.80% Delivered

3.02 3.02 3.08 0.00% 2.00% Henry Hub 2030 2.41 2.41 2.45 0.00% 1.70% Delivered

2.53 2.53 2.57 0.00% 1.50% Henry Hub 2035 1.88 1.89 1.89 0.10% 0.20% Delivered

2.10 2.10 2.10 0.09% 0.10%

Retail Electricity Prices EPA estimated the change in the retail price of electricity (2019 dollars) using the Retail Price Model (RPM). The RPM was developed by ICF for EPA and uses the IPM estimates of changes in the cost of generating electricity to estimate the changes in average retail electricity prices. The prices are average prices over consumer classes (i.e., consumer, commercial, and industrial) and regions, weighted by the amount of electricity used by each class and in each region. The RPM combines the IPM annual cost estimates in each of the 64 IPM regions with EIA electricity market data for each of the 25 electricity supply regions (shown in Figure 31) in the electricity market module of the National Energy Modeling System (NEMS). Table 317, Table 318, and Table 319 present the projected percentage changes in the retail price of electricity for the regulatory control alternatives in 2028, 2030, and 2035, respectively. Consistent with other projected impacts presented above, average retail electricity prices at both the national and regional level are projected to be small in each year. In 2028, EPA estimates that this proposed rule will result in a one tenth of one percent increase in national average retail electricity price, or by less than one tenth of one mill/kWh.

Table 317Average Retail Electricity Price by Region for the Baseline Run and the Proposed Rule and More Stringent Alternative, 2028 All Sector 2028 Average Retail Electricity Price Percent Change from Baseline

(2019 mills/kWh)

Region Baseline Run Proposed Rule More-Stringent Alternative Proposed Rule More-Stringent Alternative TRE 99.4 99.3 100.0 -0.1% 0.6% FRCC 99.7 99.7 100.3 0.0% 0.6% MISW 79.4 79.4 79.9 0.0% 0.6% MISC 101.7 101.7 103.2 0.0% 1.5% MISE 123.0 123.1 123.7 0.0% 0.6% MISS 105.1 105.0 105.7 0.0% 0.6% ISNE 142.3 142.1 144.0 -0.1% 1.2% NYCW 213.4 211.8 212.1 -0.7% -0.6% NYUP 142.1 141.1 141.7 -0.6% -0.2% PJME 121.5 121.7 123.9 0.1% 2.0% PJMW 105.5 106.3 109.7 0.7% 3.9% PJMC 92.3 92.4 93.4 0.0% 1.1% PJMD 82.8 83.6 86.9 1.0% 5.0% SRCA 109.8 109.9 110.1 0.0% 0.3% SRSE 112.1 112.1 112.2 0.0% 0.1% SRCE 74.2 74.2 74.1 0.0% -0.1% SPPS 85.4 85.5 85.3 0.1% -0.1% SPPC 84.1 84.0 83.2 0.0% -1.0% SPPN 77.3 77.3 77.4 0.0% 0.2% SRSG 92.8 92.8 93.2 0.0% 0.4% CANO 149.9 149.9 150.2 0.0% 0.2% CASO 198.7 198.7 198.9 0.0% 0.1% NWPP 78.3 78.5 78.7 0.3% 0.6% RMRG 87.3 87.3 88.4 0.0% 1.3% BASN 86.5 86.5 86.3 0.1% -0.2% National 107.0 107.0 107.9 0.1% 0.9%

Table 318Average Retail Electricity Price by Region for the Baseline Run and the Proposed Rule and More Stringent Alternative, 2030 All Sector 2030 Average Retail Electricity Price Percent Change from Baseline

(2019 mills/kWh)

Region Baseline Run Proposed Rule More-Stringent Alternative Proposed Rule More-Stringent Alternative TRE 78.4 78.4 78.6 0.0% 0.2% FRCC 88.7 88.7 89.2 0.0% 0.5% MISW 80.5 80.5 80.5 0.0% 0.0% MISC 88.9 88.9 88.9 0.0% 0.0% MISE 96.7 96.8 99.1 0.1% 2.5% MISS 89.4 89.4 89.6 0.0% 0.3% ISNE 146.9 146.9 147.1 0.0% 0.2% NYCW 202.3 202.9 202.9 0.3% 0.3% NYUP 121.6 121.9 121.9 0.3% 0.3% PJME 101.5 102.1 102.1 0.5% 0.5% PJMW 94.0 94.0 94.3 0.0% 0.3% PJMC 77.8 77.8 80.6 0.1% 3.6% PJMD 72.3 72.3 71.9 0.0% -0.6% SRCA 96.8 96.8 96.7 0.0% 0.0% SRSE 90.4 90.4 90.5 0.0% 0.1% SRCE 104.9 104.9 105.1 0.0% 0.2% SPPS 69.0 69.0 68.9 0.0% -0.1% SPPC 80.3 80.3 80.4 0.0% 0.2% SPPN 59.9 59.9 59.8 0.0% -0.2% SRSG 83.0 83.0 83.0 0.1% 0.1% CANO 154.8 154.8 154.7 0.0% -0.1% CASO 187.0 186.9 187.4 0.0% 0.2% NWPP 73.8 73.9 74.1 0.2% 0.4% RMRG 86.4 86.5 87.1 0.1% 0.9% BASN 88.4 88.5 89.3 0.1% 1.0% National 97.0 97.0 97.3 0.1% 0.3%

Table 319Average Retail Electricity Price by Region for the Baseline Run and the Proposed Rule and More Stringent Alternative, 2035 All Sector 2035 Average Retail Electricity Price Percent Change from Baseline

(2019 mills/kWh)

Region Baseline Run Proposed Rule More-Stringent Alternative Proposed Rule More-Stringent Alternative TRE 68.3 68.3 68.3 0.0% 0.0% FRCC 81.0 81.0 81.0 0.0% 0.1% MISW 80.2 80.2 80.3 0.0% 0.0% MISC 80.2 80.2 80.2 0.0% 0.1% MISE 88.9 88.8 88.9 0.0% 0.0% MISS 84.4 84.4 84.5 0.0% 0.0% ISNE 150.4 150.4 150.4 0.0% 0.0% NYCW 187.2 187.2 187.3 0.0% 0.0% NYUP 106.7 106.7 106.7 0.0% 0.0% PJME 105.3 105.2 105.2 0.0% 0.0% PJMW 82.4 82.3 82.3 0.0% -0.1% PJMC 82.4 82.4 82.5 0.0% 0.1% PJMD 73.3 73.3 73.2 0.0% -0.1% SRCA 92.9 92.9 93.0 0.0% 0.1% SRSE 113.5 113.5 113.5 0.0% 0.0% SRCE 69.1 69.1 69.1 0.0% 0.0% SPPS 70.3 70.3 70.4 0.0% 0.1% SPPC 67.9 67.9 67.9 0.0% 0.1% SPPN 62.8 62.8 62.9 0.0% 0.0% SRSG 93.5 93.5 93.5 0.0% 0.0% CANO 150.9 150.9 150.9 0.0% 0.0% CASO 177.8 177.8 177.8 0.0% 0.0% NWPP 79.6 79.6 79.6 0.0% 0.0% RMRG 91.5 91.5 91.6 0.0% 0.1% BASN 78.2 78.5 79.1 0.3% 1.1% National 92.7 92.8 92.8 0.0% 0.0%

Figure 31Electricity Market Module Regions  Source: EIA (http://www.eia.gov/forecasts/aeo/pdf/nerc_map.pdf)

Limitations of Analysis and Key Areas of Uncertainty      EPA's power sector modeling is based on expert judgment of various input assumptions for variables whose outcomes are uncertain. As a general matter, the Agency reviews the best available information from engineering studies of air pollution controls and new capacity construction costs to support a reasonable modeling framework for analyzing the cost, emission changes, and other impacts of regulatory actions for EGUs. The annualized cost of the proposed rule, as quantified here, is EPA's best assessment of the cost of implementing the proposed rule on the power sector.       The IPM-projected annualized cost estimates of private compliance costs provided in this analysis are meant to show the increase in production (generating) costs to the power sector in response to the proposed rule. To estimate these annualized costs, as discussed earlier, the EPA uses a conventional and widely accepted approach that applies a CRF multiplier to capital investments and adds that to the annual incremental operating expenses to calculate annual costs. The CRF is derived from estimates of the cost of capital (private discount rate), the amount of insurance coverage required, local property taxes, and the life of capital. The private compliance costs presented earlier are EPA's best estimate of the direct private compliance costs of the rule.      In addition, there are several key areas of uncertainty related to the electric power sector that are worth noting, including:  Electricity demand: The analysis includes an assumption for future electricity demand. To the extent electricity demand is higher and lower, it may increase/decrease the projected future composition of the fleet.  Natural gas supply and demand: To the extent natural gas supply and delivered prices are higher or lower, it would influence the use of natural gas for electricity generation and overall competitiveness of other EGUs (e.g., coal and nuclear units).  Longer-term planning by utilities: Many utilities have announced long-term clean energy and/or climate commitments, with a phasing out of large amounts of coal capacity by 2030 and continuing through 2050. These announcements, some of which are not legally binding, are not necessarily reflected in the baseline, and may alter the amount of coal capacity projected in the baseline that would be covered under this proposed rule or the more stringent alternative.  Filterable PM emissions and control: As discussed above, the baseline filterable PM emissions rates for each unit are based on the analysis documented in the memorandum titled: "2023 Technology Review for the Coal- and Oil-Fired EGU Source Category." For those EGUs with rates greater than the proposed limit or more stringent alternative, EPA assumes that control technology summarized in Section 3.4 would be necessary to remain operational. While the baseline emissions rate for each EGU and the cost and performance assumption for each PM control technology are the best available to EPA at this time, it is possible that some EGUs may be able to achieve the proposed or alternative filterable PM emissions limits with less costly control technology (e.g., an ESP upgrade instead of a fabric filter installation). It is also possible that EPA's cost assumptions reflect higher technology costs than might be incurred by EGUs.        These are key uncertainties that may affect the overall composition of electric power generation fleet and/or compliance with the proposed emissions limits and could thus have an effect on the estimated costs and impacts of this proposed action. While it is important to recognize these key areas of uncertainty, they do not change the EPA's overall confidence in the projected impacts of the proposed rule presented in this section. EPA continues to monitor industry developments and makes appropriate updates to the modeling platforms in order to reflect the best and most current data available.      The impacts of the Later Model Year Light-Duty Vehicle GHG Emissions Standards are not captured in the baseline. This rule is projected to increase the total demand for electricity by 0.5 percent in 2030 and 1 percent in 2040 relative to 2020 levels. This translates into a 0.4 percent increase in electricity demand in 2030 and a 0.8 percent increase in electricity demand in 2040 relative to the baseline electricity demand projections assumed in this analysis. The impacts of the Proposed Standards of Performance for New, Reconstructed, and Modified Sources and Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review are also not included in this analysis. Inclusion of these standards would likely increase the price of natural gas modestly as a result of limitations on the usage of reciprocating internal combustion engines in the pipeline transportation of natural gas. All else equal inclusion of these two programs would likely result in a modest increase in the total cost of compliance for this rule. References

Benefits Analysis Introduction This proposed rule is projected to reduce emissions of mercury and non-mercury metal HAP, fine particulate matter (PM2.5), sulfur dioxide (SO2), nitrogen oxides (NOX), and carbon dioxide (CO2) nationwide. The projected reductions in mercury are expected to reduce the bioconcentration of MeHg in fish. Subsistence fishing is associated with vulnerable populations, including minorities and those of low socioeconomic status. Further reductions in mercury emissions from lignite-fired facilities could help address exposure inequities for the subsistence fisher sub-population. The projected reductions in non-mercury metal HAP are expected to help EPA maintain an ample margin of safety by reducing exposure to carcinogenic metal HAP. Regarding the potential benefits of the rule from HAP reduction, we note that these are discussed only qualitatively and not quantitatively. The analysis of the overall EGU sector completed for the Appropriate and Necessary determination (87 FR 7624) (2022 Proposal) did identify significant reductions in cardiovascular and neuro-developmental effects from exposure to MeHg. However, the amount of mercury reduction expected is only a fraction of the mercury estimates used in the Appropriate and Necessary determination and lignite facilities have a smaller geographic footprint. Overall, the uncertainty associated with modeling potential benefits of mercury reduction for fish consumers would be sufficiently large as to compromise the utility of those benefit estimates. Further, estimated risks from exposure to non-mercury metal HAP were not expected to exceed health thresholds for adverse effects, preventing quantitative estimate of non-mercury metal HAP benefits.  Reducing emissions of fine PM2.5 and SO2 emissions is expected to reduce ground-level PM2.5 concentrations. Reducing NOX emissions is expected to reduce both ground-level ozone and PM2.5 concentrations. Below we present the estimated number and economic value of these avoided PM2.5 and ozone-attributable premature deaths and illnesses. We also present the estimated monetized climate and health benefits associated with emission reductions for each of three regulatory options described in prior sections.  In addition to reporting results, this section details the methods used to estimate the benefits to human health of reducing concentrations of PM2.5 and ozone resulting from the projected emissions reductions from EGUs under this proposal. This analysis uses methods for determining air quality changes that has been used in the RIAs from multiple previous proposed and final rules (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2022b). The approach involves two major steps: (1) developing spatial fields of air quality across the U.S. for a baseline scenario and the proposed and more stringent regulatory options examined in this RIA for 2028, 2030 and 2035 using nationwide photochemical modeling and related analyses; and (2) using these spatial fields in BenMAP-CE to quantify the benefits under each regulatory control alternative and each year as compared to the baseline in that year. See Section 4.3.3 for more detail on BenMAP-CE. When estimating the value of improved air quality over a multi-year time horizon, the analysis applies population growth and income growth projections for each future year through 2037 and estimates of baseline mortality incidence rates at five-year increments.  Elevated concentrations of GHGs in the atmosphere have been warming the planet, leading to changes in the Earth's climate including changes in the frequency and intensity of heat waves, precipitation, and extreme weather events, rising seas, and retreating snow and ice. The well-documented atmospheric changes due to anthropogenic GHG emissions are changing the climate at a pace and in a way that threatens human health, society, and the natural environment. There will be important climate benefits associated with the CO2 emissions reductions expected from this proposed rule. Climate benefits from reducing emissions of CO2 can be monetized using estimates of the SC-CO2.  Though the proposed rule is likely to also yield positive benefits associated with reducing pollutants other than mercury, non-mercury metal HAP, PM2.5, ozone, and CO2, time, resource, and data limitations prevented us from quantifying and estimating the economic value of those reductions. Specifically, in this RIA EPA does not monetize health benefits of reducing direct exposure to NO2 and SO2 nor ecosystem effects and visibility impairment associated with changes in air quality. In addition, this RIA does not include monetized impacts from changes in pollutants in other media, such as water effluents. We qualitatively discuss these unquantified impacts in this section. Hazardous Air Pollutant Benefits This proposed rule is projected to reduce emissions of mercury and non-mercury metal HAP. The projected reductions in mercury are expected to help alleviate disproportionate levels of exposure to methylmercury tovulnerable sub-populations that rely on subsistence fishing. The projected reductions in emissions of non-mercury metal HAP are expected to reduce exposure to carcinogens, such as nickel, arsenic, and hexavalent chromium, in the surrounding areas.  Mercury Mercury is a persistent, bioaccumulative toxic metal that is emitted from power plants in three forms: gaseous elemental mercury (Hg0), oxidized mercury compounds (Hg+2), and particle-bound mercury (HgP). Elemental mercury does not quickly deposit or chemically react in the atmosphere, resulting in residence times that are long enough to contribute to global scale deposition. Oxidized mercury and HgP deposit quickly from the atmosphere impacting local and regional areas in proximity to sources. MeHg is formed by microbial action in the top layers of sediment and soils, after mercury has precipitated from the air and deposited into waterbodies or land. Once formed, MeHg is taken up by aquatic organisms and bioaccumulates up the aquatic food web. Larger predatory fish may have MeHg concentrations many times, typically on the order of one million times, that of the concentrations in the freshwater body in which they live. MeHg can adversely impact ecosystems and wildlife. Human exposure to MeHg is known to have several adverse neurodevelopmental impacts, such as IQ loss measured by performance on neurobehavioral tests, particularly on tests of attention, fine motor-function, language, and visual spatial ability. In addition, evidence in humans and animals suggests that MeHg can have adverse effects on both the developing and the adult cardiovascular system, including fatal and non-fatal ischemic heart disease (IHD). Further, nephrotoxicity, immunotoxicity, reproductive effects (impaired fertility), and developmental effects have been observed with MeHg exposure in animal studies (Agency for Toxic Substances and Disease Registry, 2022). MeHg has some genotoxic activity and is capable of causing chromosomal damage in a number of experimental systems. The EPA has classified MeHg as a "possible" human carcinogen.  The projected reductions in mercury under this proposed rule are expected to reduce the bioconcentration of MeHg from lignite-fired sources in fish. Risk from near-field deposition of mercury to subsistence fishers has previously been evaluated, using a site-specific assessment of a lake near three lignite-fired facilities (U.S. EPA, 2020d). The results suggest that MeHg exposure to subsistence fishers from lignite-fired units is well below the current reference dose (RfD) for MeHg neurodevelopmental toxicity or IQ loss, with an estimated hazard quotient (HQ) of 0.06. In general, the EPA believes that exposures at or below the RfD are unlikely to be associated with appreciable risk of deleterious effects. However, no RfD defines an exposure level corresponding to zero risk; moreover, the RfD does not represent a bright line above which individuals are at risk of adverse effects. In addition, there was no evidence of a threshold for MeHg-related neurotoxicity within the range of exposures in the Faroe Islands study which served as the primary basis for the RfD (U.S. EPA, 2001).  Regarding the potential magnitude of human health risk reductions and benefits associated with this proposed rule, we make the following observations. All of the exposure results generated as part of the 2020 Residual Risk analysis were significantly below the RfD. While these results suggest that the magnitude of risk for high self-caught fish consuming populations is anticipated to be extremely low, we do recognize that this proposed regulation could still address potential disparities between mercury exposures across at-risk populations such as subsistence fishers in the vicinity of these facilities. Regarding potential benefits of the rule to the general population of fish consumers, while we note that the analysis of the overall EGU sector completed for the Appropriate and Necessary determination (87 FR 7624) (2022 Proposal) did identify significant reductions in cardiovascular and neuro-developmental effects, given the substantially smaller mercury reduction associated with this proposed rule (approximately 200 pounds compared with approximately 29 tons) together with the fact that the lignite facilities have a smaller geographic footprint, overall uncertainty associated with modeling potential benefits for then broader population of fish consumers would be sufficiently large as to compromise the utility of those benefit estimates.  Despite the lack of quantifiable risks from mercury emissions from lignite-fired units, reductions would be expected to have some impact (reduction) on the overall MeHg burden in fish for waterbodies near lignite-fired facilities. In the Appropriate and Necessary determination, EPA illustrated that the burden of mercury exposure is not equally distributed across the population and that some subpopulations bore disproportionate risks associated with exposure to emissions from U.S. EGUs. High levels of fish consumption observed with subsistence fishing were associated with vulnerable populations, including minorities and those with low socioeconomic status (SES). Reductions in mercury emissions could reduce MeHg exposure and body burden for subsistence fishers thereby helping to address the disproportionate magnitude of exposure to vulnerable sub-populations that rely on subsistence fishing. Metal HAP U.S. EGUs are the largest source of selenium (Se) emissions and a major source of metallic HAP emissions including arsenic (As), chromium (Cr), nickel (Ni), and cobalt (Co). Additionally, U.S. EGUs emit cadmium (Cd), beryllium (Be), lead (Pb), and manganese (Mn). These emissions include metal HAPs that are persistent and bioaccumulative (Cd, As, and Pb) and others have the potential to cause cancer (Ni, Cr, Cd, Be, Co, and Pb). PM controls are expected to reduce metal HAP emissions and therefore reduce the potential for adverse effects from metal HAP exposure.  Exposure to these metal HAP, depending on exposure duration and levels of exposures, is associated with a variety of adverse health effects. These adverse health effects may include chronic health disorders (e.g., irritation of the lung, skin, and mucus membranes; decreased pulmonary function, pneumonia, or lung damage; detrimental effects on the central nervous system; damage to the kidneys; and alimentary effects such as nausea and vomiting). As of 2023, three of the key metal HAP or their compounds emitted by EGUs (As, Cr, and Ni) have been classified as human carcinogens, while two others (Cd and Se) are classified as probable human carcinogens. See 76 FR 25003 - 25005 for a fuller discussion of the health effects associated with these pollutants. The emission estimates for this source category were obtained in 2020 from two main sources: EPA's Air Markets Program Data and EPA's WebFIRE. PM impacted U.S. EGU source category emissions of non-mercury HAP are not expected to exceed 1 in a million for inhalation cancer risk. Further, cancer risk was determined to fall below 1 in a million from multi-pathway exposure to the persistent and bioaccumulative non-mercury metal HAPs, such as arsenic, cadmium, and lead. However, the proposed controls would be expected to reduce levels of exposure to carcinogenic HAP in communities near the impacted facilities.  EPA also evaluated the potential for noncancer risks from exposure to non-mercury metal HAPs in 2020. To address the risk from chronic inhalation exposure to multiple pollutants, we aggregated the health risks associated with pollutants that affect the same target organ. Further, we examined the potential for adverse health effects from acute inhalation exposure to individual pollutants. Lastly, we also examined the potential for health impacts stemming from multiple pathways exposure for arsenic, cadmium, and lead. The estimated risks were not expected to exceed current health thresholds for adverse effects (U.S. EPA, 2020d). Therefore, we are unable to identify or quantify noncancer benefits from the proposed non-mercury metal HAP emission reductions.  Additional HAP Benefits  U.S. EGU mercury emissions can lead to increased deposition of mercury to nearby waterbodies. Deposition of mercury to waterbodies can also have an impact on ecosystems and wildlife. Mercury contamination is present in all environmental media with aquatic systems being particularly impacted due to bioaccumulation. Bioaccumulation refers to the net uptake of a contaminant from all possible pathways and includes the accumulation that may occur by direct exposure to contaminated media as well as uptake from food. Atmospheric mercury enters freshwater ecosystems by direct deposition and through runoff from terrestrial watersheds. Once mercury deposits, it may be converted to organic methylmercury mediated primarily by sulfate-reducing bacteria. Methylation is enhanced in anaerobic and acidic environments, greatly increasing mercury toxicity and potential to bioaccumulate in aquatic foodwebs (Munthe et al. 2007). Observations of difficulty flying, and other grossly abnormal behavior were observed among methylmercury exposed birds in the early 1950s in Minamata, Japan. In domesticated animals, such as cats that consumed seafood, signs of neurological disease including convulsions, fits, highly erratic movements were observed. The highest levels of methylmercury accumulation are most often measured in fish eating (piscivorous) animals and those which prey on other fish eaters. In laboratory studies, adverse effects from exposure to methylmercury in wildlife have been observed in fish, mink, otters, and several avian species at exposure levels as low as 0.25 μg/g bw/day (U.S. EPA, 1997). The risk of mercury exposure may also extend to insectivorous terrestrial species such as songbirds, bats, spiders, and amphibians that receive mercury deposition or from aquatic systems near the forest areas they inhabit (Bergeron et al., 2010, b; Cristol et al., 2008; Rimmer et al., 2005; Wada et al., 2009 & 2010). The proposed emissions reductions of mercury are expected to lower deposition of mercury into ecosystems and reduce U.S. EGU attributable bioaccumulation of methylmercury in wildlife.    Criteria Pollutant Benefits  The benefits analysis presented in this section applies methods consistent with those applied most recently in the RIA for the proposed PM National Ambient Air Quality Standards (NAAQS). EPA's approach for selecting PM2.5 and ozone-related health endpoints to quantify and monetize is detailed in the interest of brevity, we summarize our approach below and refer readers to the referenced Health Benefits TSD (U.S. EPA, 2023). In the interest of brevity, we summarize our approach below and refer readers to the referenced the Health Benefits TSD for a full description of the methodology.  Estimating the health benefits of reductions in PM2.5 and ozone exposure begins with estimating the change in exposure for each individual and then estimating the change in each individual's risks for those health outcomes affected by exposure. The benefit of the reduction in each health risk is based on the exposed individual's willingness to pay (WTP) for the risk change, assuming that each outcome is independent of one another. The greater the magnitude of the risk reduction from a given change in concentration, the greater the individual's WTP, all else equal. The social benefit of the change in health risks equals the sum of the individual WTP estimates across all of the affected individuals residing in the U.S. We conduct this analysis by adapting primary research -- specifically, air pollution epidemiology studies and economic value studies -- from similar contexts. This approach is sometimes referred to as "benefits transfer." Below we describe the procedure we follow for: (1) developing spatial fields of air quality for baseline and three regulatory control alternatives (2) selecting air pollution health endpoints to quantify; (3) calculating counts of air pollution effects using a health impact function; (4) specifying the health impact function with concentration-response parameters drawn from the epidemiological literature to calculate the economic value of the health impacts. We estimate the quantity and economic value of air pollution-related effects using a "damage-function." This approach quantifies counts of air pollution-attributable cases of adverse health outcomes and assigns dollar values to those counts, while assuming that each outcome is independent of one another.  As structured, the proposed rule would affect the distribution of ozone and PM2.5 concentrations in much of the U.S. This RIA estimates avoided ozone- and PM2.5-related health impacts that are distinct from those reported in the RIAs for both ozone and PM NAAQS (U.S. EPA, 2015, 2022c) The ozone and PM NAAQS RIAs illustrate, but do not predict, the benefits and costs of strategies that states may choose to enact when implementing a revised NAAQS; these costs and benefits are illustrative and cannot be added to the costs and benefits of policies that prescribe specific emission control measures. This RIA estimates the benefits (and costs) of specific emissions control measures. The benefit estimates are based on these modeled changes in PM2.5 and summer season average ozone concentrations. Air Quality Modeling Methodology The proposed rule influences the level of pollutants emitted in the atmosphere that adversely affect human health, including directly emitted PM2.5, as well as SO2 and NOX, which are both precursors to ambient PM2.5. NOX emissions are also a precursor to ambient ground-level ozone. EPA used air quality modeling to estimate changes in ozone and PM2.5 concentrations that may occur as a result of the proposed regulatory option and the more stringent regulatory option in the proposed rule relative to the baseline As described in the Air Quality Modeling Appendix (Appendix A), gridded spatial fields of ozone and PM2.5 concentrations representing the baseline and two regulatory options were derived from CAMx source apportionment modeling in combination with NOX, SO2, and primary PM2.5 EGU emissions obtained from the outputs of the IPM runs described in Section 3 of this RIA. While the air quality modeling includes all inventoried pollution sources in the contiguous U.S., contributions from all sources other than EGUs are held constant at projected 2026 levels in this analysis, and the only changes quantified between the baseline and the regulatory options are those associated with the projected impacts of the proposed rule on EGU emissions. EPA prepared gridded spatial fields of air quality for the baseline and the regulatory options for two health-impact metrics: annual mean PM2.5 and April through September seasonal average eight-hour daily maximum (MDA8) ozone (AS-MO3). These ozone and PM2.5 gridded spatial fields cover all locations in the contiguous U.S. and were used as inputs to BenMAP-CE which, in turn, was used to quantify the benefits from this proposed rule.  The basic methodology for determining air quality changes is the same as that used in the RIAs from multiple previous rules (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2022b). The Air Quality Modeling Appendix (Appendix A) provides additional details on the air quality modeling and the methodologies EPA used to develop gridded spatial fields of summertime ozone and annual PM2.5 concentrations. The appendix also provides figures showing the geographical distribution of air quality changes.  Selecting Air Pollution Health Endpoints to Quantify The methods used in this RIA incorporate evidence reported in the most recent completed PM and Ozone ISAs and accounts for recommendations from the Science Advisory Board (U.S. EPA, 2022f). When updating each health endpoint EPA considered: (1) the extent to which there exists a causal relationship between that pollutant and the adverse effect; (2) whether suitable epidemiologic studies exist to support quantifying health impacts; (3) and whether robust economic approaches are available for estimating the value of the impact of reducing human exposure to the pollutant. Our approach for updating the endpoints and to identify suitable epidemiologic studies, baseline incidence rates, population demographics, and valuation estimates is summarized below. Detailed descriptions of these updates are available in the Health Benefits TSD, which is in the docket for this rulemaking. The Health Benefits TSD fully describes the Agency's approach for quantifying the number and value of estimated air pollution-related impacts. In this document the reader can find the rationale for selecting health endpoints to quantify; the demographic, health and economic data used; modeling assumptions; and our techniques for quantifying uncertainty.  Table 41Health Effects of Ambient Ozone and PM2.5 and Climate Effects Category Effect Effect Quantified Effect Monetized More Information Premature mortality from exposure to PM2.5 Adult premature mortality based on cohort study estimates and expert elicitation estimates (age 65-99 or age 30-99)   PM ISA

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

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

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

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

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

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

Stroke (ages 65-99)  1 PM ISA

Asthma onset (ages 0-17)   PM ISA

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

Lung cancer (ages 30-99)   PM ISA

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

Lost work days (age 18-65)   PM ISA

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

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

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

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

Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations)  --   --  PM ISA2

Other nervous system effects (e.g., autism, cognitive decline, dementia)  --   --  PM ISA2

Metabolic effects (e.g., diabetes)  --   --  PM ISA2

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc.)  --   --  PM ISA2

Cancer, mutagenicity, and genotoxicity effects  --   --  PM ISA2 Mortality from exposure to ozone Premature respiratory mortality based on short-term study estimates (0-99)   Ozone ISA

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

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

Asthma onset (0-17)   Ozone ISA

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

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

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

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

Decreased outdoor worker productivity (age 18 - 65)  --   --  Ozone ISA2

Metabolic effects (e.g., diabetes)  --   --  Ozone ISA2

Other respiratory effects (e.g., premature aging of lungs)  --   --  Ozone ISA2

Cardiovascular and nervous system effects  --   --  Ozone ISA2

Reproductive and developmental effects  --   --  Ozone ISA2 Climate effects Climate impacts from carbon dioxide (CO2)  --   Section 5.2

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

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

yija = moija x(eβ∙∆Cij-1) x Pija,    Eq[1]

where moija is the baseline total mortality rate for adults aged a = 18-99 in county j in year i stratified in 10-year age groups, β is the risk coefficient for total mortality for adults associated with annual average PM2.5 exposure, Cij is the annual mean PM2.5 concentration in county j in year i, and Pija is the number of county adult residents aged a = 18-99 in county j in year i stratified into 5-year age groups.  The BenMAP-CE tool is pre-loaded with projected population from the Woods & Poole company; cause-specific and age-stratified death rates from the Centers for Disease Control and Prevention, projected to future years; recent-year baseline rates of hospital admissions, emergency department visits and other morbidity outcomes from the Healthcare Cost and Utilization Program and other sources; concentration-response parameters from the published epidemiologic literature cited in the ISAs for fine particles and ground-level ozone; and cost of illness or willingness to pay economic unit values for each endpoint. To assess economic value in a damage-function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. In some cases, the changes in environmental quality can be directly valued. In other cases, such as for changes in ozone and PM, a health and welfare impact analysis must first be conducted to convert air quality changes into effects that can be assigned dollar values.  We note at the outset that EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to work by Künzli et al. (2000) and co-authors and other, more recent health impact analyses, our estimates are based on the best available methods of benefits transfer. Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under analysis. Adjustments are made for the level of environmental quality change, the socio-demographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates. Calculating the Economic Valuation of Health Impacts After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante WTP for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to use these data in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a regulation reduces the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $1000, then the WTP for an avoided statistical premature mortality amounts to $10 million ($1000/0.0001 change in risk). Hence, this value is population-normalized, as it accounts for the size of the population and the percentage of that population experiencing the risk. The same type of calculation can produce values for statistical incidences of other health endpoints. For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we instead use the cost of treating or mitigating the effect to economically value the health impact. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These cost-of-illness (COI) estimates generally (although not in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect. Benefits Analysis Data Inputs In Figure 41, we summarize the key data inputs to the health impact and economic valuation estimates, which were calculated using BenMAP-CE tool version 1.5.1. (Sacks et al., 2020). In the sections below we summarize the data sources for each of these inputs, including demographic projections, incidence and prevalence rates, effect coefficients, and economic valuation.

Figure 41Data Inputs and Outputs for the BenMAP-CE Tool Demographic Data Quantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income. We use projections based on economic forecasting models developed by Woods & Poole, Inc. (2015). The Woods & Poole database contains county-level projections of population by age, sex, and race to 2060, relative to a baseline using the 2010 Census data. Projections in each county are determined simultaneously with every other county in the U.S. to consider patterns of economic growth and migration. The sum of growth in county-level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollmann et al., 2000). According to Woods & Poole, linking county-level growth projections together and constraining the projected population to a national-level total growth avoids potential errors introduced by forecasting each county independently (for example, the projected sum of county-level populations cannot exceed the national total). County projections are developed in a four-stage process: First, national-level variables such as income, employment, and populations are forecasted. Second, employment projections are made for 179 economic areas defined by the Bureau of Economic Analysis, using an "export-base" approach, which relies on linking industrial-sector production of non-locally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy. The export-based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector. Third, population is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort-component method based on fertility and mortality in each area. Fourth, employment and population projections are repeated for counties, using the economic region totals as bounds. The age, sex, and race distributions for each region or county are determined by aging the population by single year by sex and race for each year through 2060 based on historical rates of mortality, fertility, and migration. Baseline Incidence and Prevalence Estimates Epidemiological studies of the association between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk of a health effect, rather than estimating the absolute number of avoided cases. For example, a typical result might be that a 5 ug/m3 decrease in daily PM2.5 levels is associated with a decrease in hospital admissions of 3 percent. A baseline incidence rate, necessary to convert this relative change into a number of cases, is the estimate of the number of cases of the health effect per year in the assessment location, as it corresponds to baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per million people, that number must be multiplied by the millions of people in the total population. The Health Benefits TSD (see Table 12) summarizes the sources of baseline incidence rates and reports average incidence rates for the endpoints included in the analysis. For both baseline incidence and prevalence data, we used age-specific rates where available. We applied concentration-response functions to individual age groups and then summed over the relevant age range to provide an estimate of total population benefits. National-level incidence rates were used for most morbidity endpoints, whereas county-level data are available for premature mortality. Whenever possible, the national rates used are national averages, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence information comes from the studies themselves; in these cases, incidence in the study population is assumed to represent typical incidence at the national level.  We projected mortality rates such that future mortality rates are consistent with our projections of population growth. To perform this calculation, we began first with an average of 2007-2016 cause-specific mortality rates. Using Census Bureau projected national-level annual mortality rates stratified by age range, we projected these mortality rates to 2060 in 5-year increments (U.S. Census Bureau). Further information regarding this procedure may be found in the Health Benefits TSD and the appendices to the BenMAP user manual (U.S. EPA, 2022a). The baseline incidence rates for hospital admissions and emergency department visits reflect the revised rates first applied in the Revised Cross-State Air Pollution Rule Update cross-state (U.S. EPA, 2021). In addition, we revised the baseline incidence rates for acute myocardial infarction. These revised rates are more recent than the rates they replace and more accurately represent the rates at which populations of different ages, and in different locations, visit the hospital and emergency department for air pollution-related illnesses. Lastly, these rates reflect unscheduled hospital admissions only, which represents a conservative assumption that most air pollution-related visits are likely to be unscheduled. If air pollution-related hospital admissions are scheduled, this assumption would underestimate these benefits. Effect Coefficients Our approach for selecting and parametrizing effect coefficients for the benefits analysis is described fully in the Health Benefits TSD. Because of the substantial economic value associated with estimated counts of PM2.5-attributable deaths, we describe our rationale for selecting among long-term exposure epidemiologic studies below; a detailed description of all remaining endpoints may be found in the Health Benefits TSD.  A substantial body of published scientific literature documents the association between PM2.5 concentrations and the risk of premature death integrated (U.S. EPA, 2019a, 2022d). This body of literature reflects thousands of epidemiology, toxicology, and clinical studies. The PM ISA, completed as part of this review of the filterable PM standards and reviewed by the Clean Air Scientific Advisory Committee (CASAC) (U.S. EPA Science Advisory Board, 2022) concluded that there is a causal relationship between mortality and both long-term and short-term exposure to PM2.5 based on the full body of scientific evidence. The size of the mortality effect estimates from epidemiologic studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most significant health endpoint quantified in this analysis.  EPA selects Hazard Ratios from cohort studies to estimate counts of PM-related premature death, following a systematic approach detailed in the Health Benefits TSD accompanying this RIA that is generally consistent with previous RIAs (e.g. (U.S. EPA, 2019b, 2020a, 2020b, 2021, 2022b)). Briefly, clinically significant epidemiologic studies of health endpoints for which ISAs report strong evidence are evaluated using established minimum and preferred criteria for identifying studies and hazard ratios best characterizing risk. Following this systematic approach led to the identification of three studies best characterizing the risk of premature death associated with long-term exposure to PM2.5 in the U.S. (Pope et al., 2019; Turner et al., 2016; Wu et al., 2020). The PM ISA, Supplement to the ISA, and 2022 Policy Assessment also identified these three studies as providing key evidence of the association between long-term PM2.5 exposure and mortality. These studies used data from three U.S. cohorts: (1) an analysis of Medicare beneficiaries (Medicare); (2) the American Cancer Society (ACS); and (3) the National Health Interview Survey (NHIS). As premature mortality typically constitutes the vast majority of monetized benefits in a PM2.5 benefits assessment, quantifying effects using risk estimates reported from multiple long-term exposure studies using different cohorts helps account for uncertainty in the estimated number of PM-related premature deaths. Below we summarize the three identified studies and hazard ratios and then describe our rationale for quantifying premature PM-attributable deaths using two of these studies. Wu et al., 2020 evaluated the relationship between long-term PM2.5 exposure and all-cause mortality in more than 68.5 million Medicare enrollees (over the age of 64), using Medicare claims data from 2000-2016 representing over 573 million person-years of follow up and over 27 million deaths. This cohort included over 20 percent of the U.S. population and was, at the time of publishing, the largest air pollution study cohort to date. The authors modeled PM2.5 exposure at a 1 km2 grid resolution using a hybrid ensemble-based prediction model that combined three machine learning models and relied on satellite data, land-use information, weather variables, chemical transport model simulation outputs, and monitor data. Wu et al., 2020 fit five different statistical models: a Cox proportional hazards model, a Poisson regression model, and three causal inference approaches (GPS estimation, GPS matching, and GPS weighting). All five statistical approaches provided consistent results; we report the results of the Cox proportional hazards model here. The authors adjusted for numerous individual-level and community-level confounders, and sensitivity analyses suggest that the results are robust to unmeasured confounding bias. In a single-pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.066) and 95 percent confidence interval (1.058-1.074) associated with a change in annual mean PM2.5 exposure of 10.0 ug/m3 (Wu et al., 2020, Table S3, Main analysis, 2000-2016 Cohort, Cox PH). We use a risk estimate from this study in place of the risk estimate from (Di et al., 2017). These two epidemiologic studies share many attributes, including the Medicare cohort and statistical model used to characterize population exposure to PM2.5. As compared to Di et al., 2017, Wu et al., 2020 includes a longer follow-up period and reflects more recent PM2.5 concentrations.  Pope III et al., 2019 examined the relationship between long-term PM2.5 exposure and all-cause mortality in a cohort of 1,599,329 U.S. adults (aged 18-84 years) who were interviewed in the National Health Interview Surveys (NHIS) between 1986 and 2014 and linked to the National Death Index (NDI) through 2015. The authors also constructed a sub-cohort of 635,539 adults from the full cohort for whom body mass index (BMI) and smoking status data were available. The authors employed a hybrid modeling technique to estimate annual-average PM2.5 concentrations derived from regulatory monitoring data and constructed in a universal kriging framework using geographic variables including land use, population, and satellite estimates. Pope et al., 2019 assigned annual-average PM2.5 exposure from 1999-2015 to each individual by census tract and used complex (accounting for NHIS's sample design) and simple Cox proportional hazards models for the full cohort and the sub-cohort. We select the Hazard Ratio calculated using the complex model for the sub-cohort, which controls for individual-level covariates including age, sex, race-ethnicity, inflation-adjusted income, education level, marital status, rural versus urban, region, survey year, BMI, and smoking status. In a single-pollutant model, the coefficient and standard error for PM2.5 are estimated from the hazard ratio (1.12) and 95 percent confidence interval (1.08-1.15) associated with a change in annual mean PM2.5 exposure of 10.0 ug/m3 (Pope et al., 2019, Table 2, Subcohort). This study exhibits two key strengths that makes it particularly well suited for a benefits analysis: (1) it includes a long follow-up period with recent (and thus relatively low) PM2.5 concentrations; (2) the NHIS cohort is representative of the U.S. population, especially with respect to the distribution of individuals by race, ethnicity, income and education. EPA has historically used estimated Hazard Ratios from extended analyses of the ACS cohort (Krewski et al., 2009; Pope et al., 2002; Pope et al., 1995) to estimate PM-related risk of premature death. More recent ACS analyses (Turner et al. 2016): extended the follow-up period of the ACS CSP-II to 22 years (1982-2004),  evaluated 669,046 participants over 12,662,562 person-years of follow up and 237,201 observed deaths, and applied a more advanced exposure estimation approach than had previously been used when analyzing the ACS cohort, combining the geostatistical Bayesian Maximum Entropy framework with national-level land use regression models.  The total mortality hazard ratio best estimating risk from these ACS cohort studies was based on a random-effects Cox proportional hazard model incorporating multiple individual and ecological covariates (relative risk =1.06, 95 percent confidence intervals 1.04 - 1.08 per 10ug/m3 increase in PM2.5) from Turner et al., 2016. The relative risk estimate is identical to a risk estimate drawn from earlier ACS analysis of all-cause long-term exposure PM2.5-attributable mortality (Krewski et al., 2009). However, as the ACS hazard ratio is quite similar to the Medicare estimate of (1.066, 1.058-1.074), especially when considering the broader age range (greater than 29 versus greater than 64), only the Wu et al., 2020 and Pope et al., 2019 are included in the main benefits assessments, with Wu et al., 2020 representing results from both the Medicare and ACS cohorts. Quantifying Cases of Ozone-Attributable Premature Death Mortality risk reductions account for the majority of monetized ozone-related and PM2.5-related benefits. For this reason, this subsection and the following provide a brief background of the scientific assessments that underly the quantification of these mortality risks and identifies the risk studies used to quantify them in this RIA, for ozone and PM2.5 respectively. As noted above, (U.S. EPA, 2023) describes fully the Agency's approach for quantifying the number and value of ozone and PM2.5 air pollution-related impacts, including additional discussion of how the Agency selected the risk studies used to quantify them in this RIA. The Health Benefits TSD also includes additional discussion of the assessments that support quantification of these mortality risk than provide here.  In 2008, the National Academies of Science (NRC, 2008) issued a series of recommendations to EPA regarding the procedure for quantifying and valuing ozone-related mortality due to short-term exposures. Chief among these was that "...short-term exposure to ambient ozone is likely to contribute to premature deaths" and the committee recommended that "ozone-related mortality be included in future estimates of the health benefits of reducing ozone exposures..." The NAS also recommended that "...the greatest emphasis be placed on the multicity and [National Mortality and Morbidity Air Pollution Studies (NMMAPS)] ...studies without exclusion of the meta-analyses" (NRC, 2008). Prior to the 2015 Ozone NAAQS RIA, the Agency estimated ozone-attributable premature deaths using an NMMAPS-based analysis of total mortality (Bell et al., 2004), two multi-city studies of cardiopulmonary and total mortality (Huang et al., 2005; Schwartz, 2005) and effect estimates from three meta-analyses of non-accidental mortality (Bell et al., 2005; Ito et al., 2005; Levy et al., 2005). Beginning with the 2015 Ozone NAAQS RIA, the Agency began quantifying ozone-attributable premature deaths using two newer multi-city studies of non-accidental mortality smith (Smith et al., 2009; Zanobetti and Schwartz, 2008) and one long-term cohort study of respiratory mortality (Jerrett et al. 2009). The 2020 Ozone ISA included changes to the causality relationship determinations between short-term exposures and total mortality, as well as including more recent epidemiologic analyses of long-term exposure effects on respiratory mortality. We estimate counts of ozone-attributable respiratory death from short-term exposures a pooled risk estimate calculated using parameters from Zanobetti and Schwartz (2008) and Katsouyanni et al. (2009).Consistent with the RIA for the Final Revised Cross-State Air Pollution Rule (CSAPR) Update for the 2008 Ozone NAAQSRCU analysis (U.S. EPA, 2021), we use two estimates of ozone-attributable respiratory deaths from short-term exposures are estimated using the risk estimate parameters from Zanobetti and Schwartz (2008) and Katsouyanni et al. (2009). Ozone-attributable respiratory deaths from long-term exposures are estimated using Turner et al. (2016). Due to time and resource limitations, we were unable to reflect the warm season defined by Zanobetti and Schwartz (2008) as June-August. Instead, we apply this risk estimate to our standard warm season of May-September. Quantifying Cases of PM2.5-Attributable Premature Death When quantifying PM-attributable cases of adult mortality, we use the effect coefficients from two epidemiology studies examining two large population cohorts: the American Cancer Society cohort (Turner et al., 2016) and the Medicare cohort (Di et al., 2017). The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2019a) indicates that the ACS and Medicare cohorts provide strong evidence of an association between long-term PM2.5 exposure and premature mortality with support from additional cohort studies. There are distinct attributes of both the ACS and Medicare cohort studies that make them well-suited to being used in a PM benefits assessment and so here we present PM2.5 related effects derived using relative risk estimates from both cohorts. The PM ISA, which was reviewed by the Clean Air Scientific Advisory Committee of EPA's Science Advisory Board (U.S. EPA Science Advisory Board, 2022), concluded that there is a causal relationship between mortality and both long-term and short-term exposure to PM2.5 based on the entire body of scientific evidence. The PM ISA also concluded that the scientific literature supports the use of a no-threshold log-linear model to portray the PM-mortality concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response relationship. The 2019 PM ISA, which informed the setting of the 2020 PM NAAQS, reviewed available studies that examined the potential for a population-level threshold to exist in the concentration-response relationship. Based on such studies, the ISA concluded that the evidence supports the use of a "no-threshold" model and that "little evidence was observed to suggest that a threshold exists" (U.S. EPA, 2009a). Consistent with this evidence, the Agency historically has estimated health impacts above and below the prevailing NAAQS (U.S. EPA, 2019b, 2021, 2022b) Characterizing Uncertainty in the Estimated Benefits In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. The Health Benefits TSD details our approach to characterizing uncertainty in both quantitative and qualitative terms (U.S. EPA, 2023). The Health Benefits TSD describes the sources of uncertainty associated with key input parameters including emissions inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions regarding the future state of the country (i.e., regulations, technology, and human behavior). Each of these inputs is uncertain and affects the size and distribution of the estimated benefits. When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits. To characterize uncertainty and variability into this assessment, we incorporate three quantitative analyses described below and in greater detail within the Health Benefits TSD (Section 7.1):  A Monte Carlo assessment that accounts for random sampling error and between study variability in the epidemiological and economic valuation studies; The quantification of PM-related mortality using alternative PM2.5 mortality effect estimates drawn from two long-term cohort studies; and Presentation of 95th percentile confidence interval around each risk estimate.

Quantitative characterization of other sources of PM2.5 uncertainties are discussed only in Section 7.1 of the Health Benefits TSD: For adult all-cause mortality: The distributions of air quality concentrations experienced by the original cohort population (Health Benefits TSD Section <IP_ADDRESS>); Methods of estimating and assigning exposures in epidemiologic studies (Health Benefits TSD Section <IP_ADDRESS>); Confounding by ozone (Health Benefits TSD Section <IP_ADDRESS>); and The statistical technique used to generate hazard ratios in the epidemiologic study (Health Benefits TSD Section <IP_ADDRESS>). Plausible alternative risk estimates for asthma onset in children (TSD Section 7.1.3), cardiovascular hospital admissions (Health Benefits TSD Section 7.1.4,), and respiratory hospital admissions (Health Benefits TSD Section 7.1.5); Effect modification of PM2.5-attributable health effects in at-risk populations (Health Benefits TSD Section 7.1.6).           Quantitative consideration of baseline incidence rates and economic valuation estimates are provided in Section 7.3 and 7.4 of the Health Benefits TSD, respectively. Qualitative discussions of various sources of uncertainty can be found in Section 7.5 of the Health Benefits TSD. Monte Carlo Assessment Similar to other recent RIAs, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and random effects modeling to characterize both sampling error and variability across the economic valuation functions. The Monte Carlo simulation in the BenMAP-CE software randomly samples from a distribution of incidence and valuation estimates to characterize the effects of uncertainty on output variables. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and monetized benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates for endpoints estimated using a single study. For endpoints estimated using a pooled estimate of multiple studies, the confidence intervals reflect both the standard errors and the variance across studies. The confidence intervals around the monetized benefits incorporate the epidemiology standard errors as well as the distribution of the valuation function. These confidence intervals do not reflect other sources of uncertainty inherent within the estimates, such as baseline incidence rates, populations exposed, and transferability of the effect estimate to diverse locations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the benefits estimates. Sources of Uncertainty Treated Qualitatively Although we strive to incorporate as many quantitative assessments of uncertainty as possible, there are several aspects we are only able to address qualitatively. These attributes are summarized below and described more fully in the Health Benefits TSD.  Key assumptions underlying the estimates for premature mortality, which account for over 98 percent of the total monetized benefits in this analysis, include the following: We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA, which was reviewed by CASAC, concluded that "across exposure durations and health effects categories ... the evidence does not indicate that any one source or component is consistently more strongly related with health effects than PM2.5 mass" (U.S. EPA Science Advisory Board, 2022). We assume that the health impact function for fine particles is log-linear down to the lowest air quality levels modeled in this analysis. Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both regions that are in attainment with the fine particle standard and those that do not meet the standard down to the lowest modeled concentrations. The PM ISA concluded that "the majority of evidence continues to indicate a linear, no-threshold concentration-response relationship for long-term exposure to PM2.5 and total (nonaccidental) mortality" (U.S. EPA Science Advisory Board, 2022).  We assume that there is a "cessation" lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the board (U.S. EPA Science Advisory Board, 2004), which affects the valuation of mortality benefits at different discount rates. Similarly, we assume there is a cessation lag between the change in PM exposures and both the development and diagnosis of lung cancer. Estimated Number and Economic Value of Health Benefits To directly compare benefits estimates associated with a rulemaking to cost estimates, the number of instances of each air pollution-attributable health impact must be converted to a monetary value. This requires a valuation estimate for each unique health endpoint, and potentially also discounting if the benefits are expected to accrue over more than a single year, as recommended by the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014). Below we report the estimated number of reduced premature deaths and illnesses in each year relative to the baseline along with the 95 percent confidence interval (Table 42 and Table 43 for ozone-related health impacts and Table 44 and Table 45 for PM2.5-related impacts). The number of reduced estimated deaths and illnesses from the proposed regulatory option and more stringent regulatory alternative are calculated from the sum of individual reduced mortality and illness risk across the population.  To directly compare benefits estimates associated with a rulemaking to cost estimates, the number of instances of each air pollution-attributable health impact must be converted to a monetary value. This requires a valuation estimate for each unique health endpoint, and potentially also discounting if the benefits are expected to accrue over more than a single year, as recommended by the U.S. EPA (2014). Table 46 and Table 47 report the estimated economic value of avoided premature deaths and illness in each year relative to the baseline along with the 95 percent confidence interval. We also report the stream of benefits from 2028 through 2037 for the proposed regulatory option and the unselected more stringent regulatory alternative, using the monetized sums of long-term ozone and PM2.5 mortality and morbidity impacts (Table 48 and Table 49). Table 42Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the Proposed Regulatory Option for 2028, 2030, and 2035 (95 percent confidence interval) a

2028 2030 2035 Avoided premature respiratory mortalities

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

Allergic rhinitis symptomsf 110 (59 to 160) 210 (110 to 310) 560 (300 to 820) Short-term exposure Hospital admissions -- respiratoryc 0.33 (-0.087 to 0.74) 0.71 (-0.18 to 1.6) 1.9 (-0.49 to 4.2)

ED visits -- respiratorye 6.3 (1.7 to 13) 13 (3.5 to 27) 32 (8.9 to 68)

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

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

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

Table 43Estimated Avoided Ozone-Related Premature Respiratory Mortalities and Illnesses for the More Stringent Regulatory Option for 2028, 2030, and 2035 (95 percent confidence interval) a

2028 2030 2035 Avoided premature respiratory mortalities

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

Allergic rhinitis symptomsf 2,100 (1,100 to 3,100) 1,600 (840 to 2,300) 1,500 (790 to 2,200) Short-term exposure Hospital admissions -- respiratoryc 6.3 (-1.6 to 14) 5.0 (-1.3 to 11) 5.2 (-1.3 to 11)

ED visits -- respiratorye 120 (33 to 250) 87 (24 to 180) 89 (24 to 190)

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

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

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

Table 44Estimated Avoided PM2.5-Related Premature Respiratory Mortalities and Illnesses for the Proposed Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval) Avoided Mortality 2028 2030 2035 (Pope et al., 2019) (adult mortality ages 18-99 years) 11 (7.7 to 14) 8.2 (5.8 to 10) 15 (11 to 19) (Wu et al., 2020) (adult mortality ages 65-99 years) 5.1 (4.5 to 5.7) 4.0 (3.5 to 4.4) 7.4 (6.5 to 8.2) (Woodruff et al., 2008) (infant mortality) 0.013 (-0.0079 to 0.032) 0.0079 (-0.0049 to 0.020) 0.014 (-0.0087 to 0.036) Avoided Morbidity  2028 2030 2035 Hospital admissions -- cardiovascular (age > 18) 0.76 (0.55 to 0.96) 0.57 (0.41 to 0.72) 1.1 (0.78 to 1.4) Hospital admissions -- respiratory 0.42 (0.19 to 0.65) 0.27 (0.12 to 0.41) 0.48 (0.21 to 0.74) ED visits--cardiovascular 1.6 (-0.61 to 3.7) 1.1 (-0.44 to 2.7) 2.2 (-0.84 to 5.1) ED visits -- respiratory 3.1 (0.61 to 6.4) 2.2 (0.44 to 4.7) 4.2 (0.82 to 8.7) Acute Myocardial Infarction 0.17 (0.10 to 0.25) 0.13 (0.073 to 0.18) 0.24 (0.14 to 0.33) Cardiac arrest 0.082 (-0.033 to 0.19) 0.059 (-0.024 to 0.13) 0.11 (-0.044 to 0.24) Hospital admissions-- Alzheimer's Disease 2.6 (1.9 to 3.2) 1.7 (1.3 to 2.1) 3.8 (2.9 to 4.8) Hospital admissions-- Parkinson's Disease 0.35 (0.18 to 0.51) 0.28 (0.14 to 0.41) 0.49 (0.25 to 0.72) Stroke 0.32 (0.084 to 0.55) 0.24 (0.062 to 0.41) 0.44 (0.11 to 0.76) Lung cancer 0.37 (0.11 to 0.61) 0.27 (0.082 to 0.45) 0.52 (0.16 to 0.87) Hay Fever/Rhinitis 82 (20 to 140) 55 (13 to 95) 100 (24 to 170) Asthma Onset 13 (12 to 13) 8.4 (8.1 to 8.8) 15 (15 to 16) Asthma symptoms  -  Albuterol use 2,400 (-1,200 to 5,800) 1,600 (-780 to 3,900) 2,900 (-1,400 to 7,200) Lost work days 630 (530 to 720) 420 (360 to 490) 770 (650 to 880) Minor restricted-activity days 3,700 (3,000 to 4,400) 2,500 (2,000 to 2,900) 4,500 (3,700 to 5,300) Note: Values rounded to two significant figures.

Table 45Estimated Avoided PM2.5-Related Premature Respiratory Mortalities and Illnesses for the More Stringent Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval)a,b Avoided Mortality 2028 2030 2035 (Pope et al., 2019) (adult mortality ages 18-99 years) 240 (170 to 300) 38 (27 to 48) 96 (69 to 120) (Wu et al., 2020) (adult mortality ages 65-99 years) 110 (100 to 130) 19 (16 to 21) 47 (41 to 52) (Woodruff et al., 2008) (infant mortality) 0.24 (-0.15 to 0.63) 0.031 (-0.019 to 0.080) 0.10 (-0.064 to 0.26) Avoided Morbidity  2028 2030 2035 Hospital admissions -- cardiovascular (age > 18) 18 (13 to 22) 2.7 (2.0 to 3.5) 6.9 (5.0 to 8.7) Hospital admissions -- respiratory 8.0 (3.5 to 12) 0.83 (0.36 to 1.3) 3.5 (1.5 to 5.4) ED visits--cardiovascular 35 (-14 to 83) 5.8 (-2.2 to 13) 14 (-5.5 to 34) ED visits -- respiratory 68 (13 to 140) 10 (2.0 to 22) 27 (5.4 to 57) Acute Myocardial Infarction 3.9 (2.3 to 5.5) 0.55 (0.32 to 0.77) 1.6 (0.93 to 2.2) Cardiac arrest 1.8 (-0.72 to 4.0) 0.27 (-0.11 to 0.62) 0.69 (-0.28 to 1.6) Hospital admissions-- Alzheimer's Disease 56 (42 to 70) 2.0 (1.5 to 2.5) 26 (19 to 32) Hospital admissions-- Parkinson's Disease 7.7 (3.9 to 11) 1.2 (0.61 to 1.8) 3.0 (1.5 to 4.4) Stroke 7.5 (1.9 to 13) 1.2 (0.30 to 2.0) 2.8 (0.73 to 4.8) Lung cancer 8.2 (2.5 to 14) 1.3 (0.40 to 2.2) 3.3 (1.0 to 5.5) Hay Fever/Rhinitis 1,500 (360 to 2,600) 220 (54 to 390) 670 (160 to 1,200) Asthma Onset 230 (220 to 240) 35 (33 to 36) 100 (98 to 110) Asthma symptoms  -  Albuterol use 43,000 (-21,000 to 100,000) 6,600 (-3,200 to 16,000) 20,000 (-9,600 to 48,000) Lost work days 12,000 (10,000 to 14,000) 1,800 (1,600 to 2,100) 5,000 (4,200 to 5,800) Minor restricted-activity daysd,f 70,000 (57,000 to 83,000) 11,000 (8,800 to 13,000) 30,000 (24,000 to 35,000) a Values rounded to two significant figures.  b We estimated ozone benefits for changes in NOx for the ozone season and changes in PM2.5 and PM2.5 precursors for EGUs in 2026.  c Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September warm season. d Converted ozone risk estimate metric from MDA1 to MDA8. e Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm season. f Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm season. g Converted ozone risk estimate metric from DA24 to MDA8.

Table 46Estimated Discounted Economic Value of Avoided Ozone and PM2.5-Attributable Premature Mortality and Illness for the Proposed Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval; millions of 2019 dollars)a,b Disc. Rate Pollutant 2028 2030 2035 3% Ozone Benefits  $4 ($1 to $8) and $30 ($3 to $78)  $7 ($2 to $16) and $64 ($7 to $170) $19 ($5 to $41) and $170 ($18 to $450)

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

Ozone plus PM2.5  Benefits  $59 ($7 to $150)c and $140 ($14 to $380)d $50 ($6 to $130)c and $150 ($15 to $400)d $10 ($13 to $250)c and $330 ($33 to $880)d 7% Ozone Benefits $3 ($1 to $7)  and $27 ($3 to $70) $7 ($1 to $15) and $58 ($6 to $150) $17 ($3 to $39) and $150 ($15 to $400)

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

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

Table 47Estimated Discounted Economic Value of Avoided Ozone and PM2.5-Attributable Premature Mortality and Illness for the More Stringent Regulatory Option in 2028, 2030, and 2035 (95 percent confidence interval; millions of 2019 dollars)a,b Disc. Rate Pollutant 2028 2030 2035 3% Ozone Benefits  $69 ($17 to $150) and $570 ($62 to $1,500) $53 ($13 to $110) and $460 ($48 to $1,200) $51 ($12 to $110) and $460 ($48 to $1,200)

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

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

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

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

Table 48Stream of Estimated Human Health Benefits from 2028 through 2037: Monetized Benefits Quantified as Sum of Long-Term Ozone Mortality and Long-Term PM2.5 Mortality (discounted at 3 percent; millions of 2019 dollars)a Year Proposed  Regulatory Option More Stringent  Regulatory Option 2028*  $140  $3,100  2029  $150  $840  2030* $150  $860  2031 $160  $890  2032 $310  $1,400  2033 $320  $1,400  2034 $320  $1,500  2035* $330  $1,500  2036 $340  $1,600  2037 $350  $1,600  Present Value $1,900  $11,000  Equivalent Annualized Value $220  $1,300  *Year in which air quality models were run. Benefits for all other years were extrapolated from years with model-based air quality estimates. Benefits calculated as value of avoided: PM2.5-attributable deaths (quantified using a concentration-response relationship from the Di et al. 2017 study); Ozone-attributable deaths (quantified using a concentration-response relationship from the Turner et al. 2017 study); and PM2.5 and ozone-related morbidity effects.  a For the years 2023 to 2027, benefits associated with emissions reductions are not included as implementation of standards will not be complete until 2028.

Table 49Stream of Estimated Human Health Benefits from 2028 through 2037: Monetized Benefits Quantified as Sum of Long-Term Ozone Mortality and Long-Term PM2.5 Mortality (discounted at 7 percent; millions of 2019 dollars)a Year Proposed Regulatory Option More Stringent Regulatory Option 2028*  $130  $2,800  2029 $130  $750  2030* $140  $770  2031 $140  $800  2032 $270  $1,200  2033 $280  $1,300  2034 $290  $1,300  2035* $300  $1,400  2036 $310  $1,400  2037 $310  $1,400  Present Value $1,200  $7,100  Equivalent Annualized Value $170  $1,000  *Year in which air quality models were run. Benefits for all other years were extrapolated from years with model-based air quality estimates. Benefits calculated as value of avoided: PM2.5-attributable deaths (quantified using a concentration-response relationship from the Di et al. 2017 study); Ozone-attributable deaths (quantified using a concentration-response relationship from the Turner et al. 2017 study); and PM2.5 and ozone-related morbidity effects.  a For the years 2023 to 2027, benefits associated with emissions reductions are not included as implementation of standards will not be complete until 2028.

Climate Pollutant Benefits We estimate the climate benefits from this proposed rule using estimates of the social cost of greenhouse gases (SC-GHG), specifically the SC-CO2. The SC-CO2 is the monetary value of the net harm to society associated with a marginal increase in CO2 emissions in a given year, or the benefit of avoiding that increase. In principle, SC-CO2 includes the value of all climate change impacts (both negative and positive), including (but not limited to) changes in net agricultural productivity, human health effects, property damage from increased flood risk natural disasters, disruption of energy systems, risk of conflict, environmental migration, and the value of ecosystem services. The SC-CO2, therefore, reflects the societal value of reducing emissions of the gas in question by one metric ton and is the theoretically appropriate value to use in conducting benefit-cost analyses of policies that affect CO2 emissions. In practice, data and modeling limitations naturally restrain the ability of SC-CO2 estimates to include all the important physical, ecological, and economic impacts of climate change, such that the estimates are a partial accounting of climate change impacts and will therefore, tend to be underestimates of the marginal benefits of abatement. The EPA and other Federal agencies began regularly incorporating SC-CO2 estimates in their benefit-cost analyses conducted under E.O. 12866 since 2008, following a Ninth Circuit Court of Appeals remand of a rule for failing to monetize the benefits of reducing CO2 emissions in that rulemaking process. In 2017, the National Academies of Sciences, Engineering, and Medicine published a report that provides a roadmap for how to update SC-GHG estimates used in Federal analyses going forward to ensure that they reflect advances in the scientific literature (National Academies, 2017). The National Academies' report recommended specific criteria for future SC-GHG updates, a modeling framework to satisfy the specified criteria, and both near-term updates and longer-term research needs pertaining to various components of the estimation process. The research community has made considerable progress in developing new data and methods that help to advance various components of the SC-GHG estimation process in response to the National Academies' recommendations.  In a first-day executive order (E.O. 13990), Protecting Public Health and the Environment and Restoring Science to Tackle the Climate Crisis, President Biden called for a renewed focus on updating estimates of the SC-GHG to reflect the latest science, noting that "it is essential that agencies capture the full benefits of reducing greenhouse gas emissions as accurately as possible." Important steps have been taken to begin to fulfill this directive of E.O. 13990. In February 2021, the Interagency Working Group on the SC-GHG (IWG) released a technical support document (hereinafter the "February 2021 SC-GHG TSD") that provided a set of IWG recommended SC-GHG estimates while work on a more comprehensive update is underway to reflect recent scientific advances relevant to SC-GHG estimation (IWG 2021). In addition, as discussed further below, EPA has developed a draft updated SC-GHG methodology within a sensitivity analysis in the regulatory impact analysis of EPA's November 2022 supplemental proposal for oil and gas standards that is currently undergoing external peer review and a public comment process. The EPA has applied the IWG's recommended interim SC-GHG estimates in the Agency's regulatory benefit-cost analyses published since the release of the February 2021 TSD and is likewise using them in this RIA. We have evaluated the SC-GHG estimates in the February 2021 TSD and have determined that these estimates are appropriate for use in estimating the social benefits of GHG reductions expected to occur as a result of the proposed and alternative standards. These SC-GHG estimates are interim values developed for use in benefit-cost analyses until updated estimates of the impacts of climate change can be developed based on the best available science and economics. After considering the TSD, and the issues and studies discussed therein, the EPA concludes that these estimates, while likely an underestimate, are the best currently available SC-GHG estimates until revised estimates have been developed reflecting the latest, peer-reviewed science. The SC-GHG estimates presented in the February 2021 SC-GHG TSD and used in this RIA were developed over many years, using a transparent process, peer-reviewed methodologies, the best science available at the time of that process, and with input from the public. Specifically, in 2009, an interagency working group (IWG) that included the EPA and other executive branch agencies and offices was established to develop estimates relying on the best available science for agencies to use. The IWG published SC-CO2 estimates in 2010 that were developed from an ensemble of three widely cited integrated assessment models (IAMs) that estimate global climate damages using highly aggregated representations of climate processes and the global economy combined into a single modeling framework. The three IAMs were run using a common set of input assumptions in each model for future population, economic, and CO2 emissions growth, as well as equilibrium climate sensitivity (ECS)  -  a measure of the globally averaged temperature response to increased atmospheric CO2 concentrations. These estimates were updated in 2013 based on new versions of each IAM. In August 2016 the IWG published estimates of the social cost of methane (SC-CH4) and nitrous oxide (SC-N2O) using methodologies that are consistent with the methodology underlying the SC-CO2 estimates. The modeling approach that extends the IWG SC-CO2 methodology to non-CO2 GHGs has undergone multiple stages of peer review. The SC-CH4 and SC-N2O estimates were developed by Marten et al. (2015) and underwent a standard double-blind peer review process prior to journal publication. These estimates were applied in RIAs of EPA proposed rulemakings with CH4 and N2O emissions impacts. The EPA also sought additional external peer review of technical issues associated with its application to regulatory analysis. Following the completion of the independent external peer review of the application of the Marten et al. (2015) estimates, the EPA began using the estimates in the primary benefit-cost analysis calculations and tables for a number of proposed rulemakings (U.S. EPA, 2015b, 2015d). The EPA considered and responded to public comments received for the proposed rulemakings before using the estimates in final regulatory analyses in 2016. In 2015, as part of the response to public comments received to a 2013 solicitation for comments on the SC-CO2 estimates, the IWG announced a National Academies of Sciences, Engineering, and Medicine review of the SC-CO2 estimates to offer advice on how to approach future updates to ensure that the estimates continue to reflect the best available science and methodologies. In January 2017, the National Academies released their final report, Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide (National Academies, 2017), and recommended specific criteria for future updates to the SC-CO2 estimates, a modeling framework to satisfy the specified criteria, and both near-term updates and longer-term research needs pertaining to various components of the estimation process (National Academies 2017). Shortly thereafter, in March 2017, President Trump issued E.O. 13783, which disbanded the IWG, withdrew the previous SC-GHG TSDs, and directed agencies to ensure SC-GHG estimates used in regulatory analyses are consistent with the guidance contained in OMB's Circular A-4, "including with respect to the consideration of domestic versus international impacts and the consideration of appropriate discount rates" (E.O. 13783, Section 5(c)). Benefit-cost analyses following E.O. 13783 used SC-CO2 estimates that attempted to focus on the specific share of climate change damages in the U.S. as captured by the models (which did not reflect many pathways by which climate impacts affect the welfare of U.S. citizens and residents) and were calculated using two default discount rates recommended by Circular A-4, 3 percent and 7 percent. All other methodological decisions and model versions used in SC- CO2 calculations remained the same as those used by the IWG in 2010 and 2013, respectively.  On January 20, 2021, President Biden issued E.O. 13990, which re-established an IWG and directed it to develop an update of the SC-CO2 estimates that reflect the best available science and the recommendations of the National Academies. In February 2021, the IWG recommended the interim use of the most recent SC- CO2 estimates developed by the IWG prior to the group being disbanded in 2017, adjusted for inflation (IWG, 2021) (IWG, 2021). As discussed in the February 2021 SC-GHG TSD, the IWG's selection of these interim estimates reflected the immediate need to have SC- CO2 estimates available for agencies to use in regulatory benefit-cost analyses and other applications that were developed using a transparent process, peer reviewed methodologies, and the science available at the time of that process. As noted above, the EPA participated in the IWG but has also independently evaluated the interim SC-CO2 estimates published in the February 2021 SC-GHG TSD and determined they are appropriate to use to estimate climate benefits for this action. The EPA and other agencies intend to undertake a fuller update of the SC- CO2 estimates that takes into consideration the advice of the National Academies (2017) and other recent scientific literature. The EPA has also evaluated the supporting rationale of the February 2021 SC-GHG TSD, including the studies and methodological issues discussed therein, and concludes that it agrees with the rationale for these estimates presented in the SC-GHG TSD and summarized below. In particular, the IWG found that the SC-CO2 estimates used under E.O. 13783 fail to reflect the full impact of GHG emissions in multiple ways. First, the IWG concluded that those estimates fail to capture many climate impacts that can affect the welfare of U.S. citizens and residents. Examples of affected interests include direct effects on U.S. citizens and assets located abroad, international trade, and tourism, and spillover pathways such as economic and political destabilization and global migration that can lead to adverse impacts on U.S. national security, public health, and humanitarian concerns. Those impacts are better captured within global measures of the SC-GHGs. In addition, assessing the benefits of U.S. GHG mitigation activities requires consideration of how those actions may affect mitigation activities by other countries, as those international mitigation actions will provide a benefit to U.S. citizens and residents by mitigating climate impacts that affect U.S. citizens and residents. A wide range of scientific and economic experts have emphasized the issue of reciprocity as support for considering global damages of GHG emissions. Using a global estimate of damages in U.S. analyses of regulatory actions allows the U.S. to continue to actively encourage other nations, including emerging major economies, to take significant steps to reduce emissions. The only way to achieve an efficient allocation of resources for emissions reduction on a global basis -- and so benefit the U.S. and its citizens -- is for all countries to base their policies on global estimates of damages. As a member of the IWG involved in the development of the February 2021 SC-GHG TSD, the EPA agrees with this assessment and, therefore, in this proposed rule the EPA centers attention on a global measure of SC-CO2. This approach is the same as that taken in EPA regulatory analyses over 2009 through 2016. A robust estimate of climate damages only to U.S. citizens and residents that accounts for the myriad of ways that global climate change reduces the net welfare of U.S. populations does not currently exist in the literature. As explained in the February 2021 SC-GHG TSD, existing estimates are both incomplete and an underestimate of total damages that accrue to the citizens and residents of the U.S. because they do not fully capture the regional interactions and spillovers discussed above, nor do they include all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature, as discussed further below. The EPA, as a member of the IWG, will continue to review developments in the literature, including more robust methodologies for estimating the magnitude of the various damages to U.S. populations from climate impacts and reciprocal international mitigation activities, and explore ways to better inform the public of the full range of carbon impacts. Second, the IWG concluded that the use of the social rate of return on capital (7 percent under current OMB Circular A-4 guidance) to discount the future benefits of reducing GHG emissions inappropriately underestimates the impacts of climate change for the purposes of estimating the SC-CO2. Consistent with the findings of the National Academies (2017) and the economic literature, the IWG continued to conclude that the consumption rate of interest is the theoretically appropriate discount rate in an intergenerational context (IWG, 2016b) (IWG, 2010, 2013, 2016a) and recommended that discount rate uncertainty and relevant aspects of intergenerational ethical considerations be accounted for in selecting future discount rates. Furthermore, the damage estimates developed for use in the SC-GHG are estimated in consumption-equivalent terms, and so an application of OMB Circular A-4's guidance for regulatory analysis would then use the consumption discount rate to calculate the SC-GHG. The EPA agrees with this assessment and will continue to follow developments in the literature pertaining to this issue. The EPA also notes that while OMB Circular A-4, as published in 2003, recommends using 3 percent and 7 percent discount rates as "default" values, Circular A-4 also reminds agencies that "different regulations may call for different emphases in the analysis, depending on the nature and complexity of the regulatory issues and the sensitivity of the benefit and cost estimates to the key assumptions." On discounting, Circular A-4 recognizes that "special ethical considerations arise when comparing benefits and costs across generations," and Circular A-4 acknowledges that analyses may appropriately "discount future costs and consumption benefits...at a lower rate than for intragenerational analysis." In the 2015 Response to Comments on the Social Cost of Carbon for Regulatory Impact Analysis, OMB, EPA, and the other IWG members recognized that "Circular A-4 is a living document" and "the use of 7 percent is not considered appropriate for intergenerational discounting. There is wide support for this view in the academic literature, and it is recognized in Circular A-4 itself." Thus, the EPA concludes that a 7 percent discount rate is not appropriate to apply to value the SC-GHGs in the analysis presented in this RIA. In this analysis, to calculate the present and annualized values of climate benefits, the EPA uses the same discount rate as the rate used to discount the value of damages from future GHG emissions, for internal consistency. That approach to discounting follows the same approach that the February 2021 SC-GHG TSD recommends "to ensure internal consistency -- i.e., future damages from climate change using the SC-GHG at 2.5 percent should be discounted to the base year of the analysis using the same 2.5 percent rate." EPA has also consulted the National Academies' 2017 recommendations on how SC-GHG estimates can "be combined in RIAs with other cost and benefits estimates that may use different discount rates." The National Academies reviewed "several options," including "presenting all discount rate combinations of other costs and benefits with [SC-GHG] estimates." While the IWG works to assess how best to incorporate the latest, peer reviewed science to develop an updated set of SC-GHG estimates, it recommended the interim estimates to be the most recent estimates developed by the IWG prior to the group being disbanded in 2017. The estimates rely on the same models and harmonized inputs and are calculated using a range of discount rates. As explained in the February 2021 SC-GHG TSD, the IWG has concluded that it is appropriate for agencies to revert to the same set of four values drawn from the SC-GHG distributions based on three discount rates as were used in regulatory analyses between 2010 and 2016 and subject to public comment. For each discount rate, the IWG combined the distributions across models and socioeconomic emissions scenarios (applying equal weight to each) and then selected a set of four values for use in agency analyses: an average value resulting from the model runs for each of three discount rates (2.5 percent, 3 percent, and 5 percent), plus a fourth value, selected as the 95th percentile of estimates based on a 3 percent discount rate. The fourth value was included to provide information on potentially higher-than-expected economic impacts from climate change, conditional on the 3 percent estimate of the discount rate. As explained in the February 2021 SC-GHG TSD, this update reflects the immediate need to have an operational SC-GHG that was developed using a transparent process, peer-reviewed methodologies, and the science available at the time of that process. Those estimates were subject to public comment in the context of dozens of proposed rulemakings as well as in a dedicated public comment period in 2013.  Table 410 summarizes the interim SC-CO2 estimates for the years 2025 to 2040. These estimates are reported in 2019 dollars but are otherwise identical to those presented in the IWG's 2016 SC-GHG TSD (IWG, 2016b). For purposes of capturing uncertainty around the SC-CO2 estimates in analyses, the 2021 SC-GHG TSD emphasizes the importance of considering all four of the SC-CO2 values. The SC-CO2 increases over time within the models  -  i.e., the societal harm from one metric ton emitted in 2030 is higher than the harm caused by one metric ton emitted in 2025  -  because future emissions produce larger incremental damages as physical and economic systems become more stressed in response to greater climatic change, and because GDP is growing over time and many damage categories are modeled as proportional to GDP. Table 410Interim Social Cost of Carbon Values, 2025-2040 (2019 dollars per Metric Tonne CO2)   Discount Rate and Statistic Emissions Year 5% 3% 2.50% 3%

Average Average Average 95th Percentile 2025 $17  $56  $82  $167  2026 $17  $57  $83  $171  2027 $18  $58  $85  $174  2028 $18  $59  $86  $178  2029 $19  $60  $87  $181  2030 $19  $61  $88  $184  2031 $20  $62  $90  $188  2032 $20  $63  $91  $192  2033 $21  $64  $92  $196  2034 $21  $66  $94  $200  2035 $22  $67  $95  $203  2036 $23  $68  $96  $207  2037 $23  $69  $98  $211  2038 $24  $70  $99  $215  2039 $24  $71  $101  $218  2040 $25  $72  $102  $222  Note: These SC-CO2 values are identical to those reported in the 2016 SC-GHG TSD (IWG 2016a) adjusted for inflation to 2019 dollars using the annual GDP Implicit Price Deflator values in the U.S. Bureau of Economic Analysis' (BEA) NIPA Table 1.1.9 (U.S. BEA 2021). The values are stated in $/metric tonne CO2 (1 metric tonne equals 1.102 short tons) and vary depending on the year of CO2 emissions. This table displays the values rounded to the nearest dollar; the annual unrounded values used in the calculations in this RIA are available on OMB's website: https://www.whitehouse.gov/omb/information-regulatory-affairs/regulatory-matters/#scghgs. Source: Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under E.O. 13990 (IWG 2021)

There are a number of limitations and uncertainties associated with the SC-CO2 estimates presented in Table 410. Some uncertainties are captured within the analysis, while other areas of uncertainty have not yet been quantified in a way that can be modeled. Figure 42 presents the quantified sources of uncertainty in the form of frequency distributions for the SC-CO2 estimates for emissions in 2030. The distributions of SC-CO2 estimates reflect uncertainty in key model parameters such as the equilibrium climate sensitivity, as well as uncertainty in other parameters set by the original model developers. To highlight the difference between the impact of the discount rate and other quantified sources of uncertainty, the bars below the frequency distributions provide a symmetric representation of quantified variability in the SC-CO2 estimates for each discount rate. As illustrated by the figure, the assumed discount rate plays a critical role in the ultimate estimate of the SC-CO2. This is because CO2 emissions today continue to impact society far out into the future, so with a higher discount rate, costs that accrue to future generations are weighted less, resulting in a lower estimate. As discussed in the 2021 SC-GHG TSD, there are other sources of uncertainty that have not yet been quantified and are thus not reflected in these estimates. Figure 42Frequency Distribution of SC-CO2 Estimates for 2030

The interim SC-CO2 estimates presented in Table 5-8 have a number of limitations. First, the current scientific and economic understanding of discounting approaches suggests discount rates appropriate for intergenerational analysis in the context of climate change are likely to be less than 3 percent, near 2 percent or lower (IWG, 2021). Second, the IAMs used to produce these interim estimates do not include all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature and the science underlying their "damage functions"  -  i.e., the core parts of the IAMs that map global mean temperature changes and other physical impacts of climate change into economic (both market and nonmarket) damages  -  lags behind the most recent research. For example, limitations include the incomplete treatment of catastrophic and non-catastrophic impacts in the integrated assessment models, their incomplete treatment of adaptation and technological change, the incomplete way in which inter-regional and intersectoral linkages are modeled, uncertainty in the extrapolation of damages to high temperatures, and inadequate representation of the relationship between the discount rate and uncertainty in economic growth over long time horizons. Likewise, the socioeconomic and emissions scenarios used as inputs to the models do not reflect new information from the last decade of scenario generation or the full range of projections.  The modeling limitations do not all work in the same direction in terms of their influence on the SC-CO2 estimates. However, as discussed in the February 2021 SC-GHG TSD, the IWG has recommended that, taken together, the limitations suggest that the SC-CO2 estimates used in this RIA likely underestimate the damages from CO2 emissions. EPA concurs that the values used in this RIA conservatively underestimate the rule's climate benefits. In particular, the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (IPCC, 2007), which was the most current IPCC assessment available at the time when the IWG decision over the ECS input was made, concluded that SC-CO2 estimates "very likely...underestimate the damage costs" due to omitted impacts. Since then, the peer-reviewed literature has continued to support this conclusion, as noted in the IPCC's Fifth Assessment report and other recent scientific assessments (IPCC, 2014, 2018, 2019a, 2019b; National Academies of Sciences and Medicine, 2016; USGCRP, 2016, 2018) These assessments confirm and strengthen the science, updating projections of future climate change and documenting and attributing ongoing changes. For example, sea level rise projections from the IPCC's Fourth Assessment report ranged from 18 to 59 centimeters by the 2090s relative to 1980-1999, while excluding any dynamic changes in ice sheets due to the limited understanding of those processes at the time (IPCC 2007). A decade later, the Fourth National Climate Assessment projected a substantially larger sea level rise of 30 to 130 centimeters by the end of the century relative to 2000, while not ruling out even more extreme outcomes (USGCRP, 2018). EPA has reviewed and considered the limitations of the models used to estimate the interim SC-GHG estimates and concurs with the February 2021 SC-GHG TSD's assessment that, taken together, the limitations suggest that the interim SC-GHG estimates likely underestimate the damages from GHG emissions.  The February 2021 SC-GHG TSD briefly previews some of the recent advances in the scientific and economic literature that the IWG is actively following and that could provide guidance on, or methodologies for, addressing some of the limitations with the interim SC-GHG estimates. The IWG is currently working on a comprehensive update of the SC-GHG estimates taking into consideration recommendations from the National Academies of Sciences, Engineering and Medicine, recent scientific literature, public comments received on the February 2021 SC-GHG TSD and other input from experts and diverse stakeholder groups (National Academies 2017). While that process continues, the EPA is continuously reviewing developments in the scientific literature on the SC-GHG, including more robust methodologies for estimating damages from emissions, and looking for opportunities to further improve SC-GHG estimation going forward. Most recently, the EPA presented a draft set of updated SC-GHG estimates within a sensitivity analysis in the regulatory impact analysis of the EPA's November 2022 supplemental proposal for oil and gas standards that that aims to incorporate recent advances in the climate science and economics literature (U.S. EPA, 2022b, 2022e). Specifically, the draft updated methodology incorporates new literature and research consistent with the National Academies near-term recommendations on socioeconomic and emissions inputs, climate modeling components, discounting approaches, and treatment of uncertainty, and an enhanced representation of how physical impacts of climate change translate to economic damages in the modeling framework based on the best and readily adaptable damage functions available in the peer reviewed literature. The EPA solicited public comment on the sensitivity analysis and the accompanying draft technical report, which explains the methodology underlying the new set of estimates, in the docket for the proposed Oil and Gas rule. The EPA is also embarking on an external peer review of this technical report. More information about this process and public comment opportunities is available on EPA's website. EPA's draft technical report will be among the many technical inputs available to the IWG as it continues its work.

Table 411 shows the estimated monetized value of the estimated changes in CO2 emissions the proposed option and the more-stringent alternative. EPA estimated the dollar value of the CO2-related effects for each analysis year between 2028 and 2037 by applying the SC-CO2 estimates, shown in Table 411, to the estimated changes in CO2 emissions in the corresponding year under the regulatory options.  Table 411Estimated Climate Benefits from Changes in CO2 Emissions for 2028, 2030, and 2035 (millions of 2019 dollars)a Regulatory Alternative Year 5% 3% 2.5% 3%

Average Average Average 95th Percentile Proposed Option 2028 $4  $13  $19  $40

2030 $16  $50  $72  $150

2035 $102  $308  $439  $939  More-Stringent Alternative 2028 $398  $1,292  $1,882  $3,893

2030 $166  $528  $765  $1,597

2035 $64  $193  $275  $588  a Climate benefits are based on changes (reductions) in CO2 emissions and are calculated using four different estimates of the SC-CO2 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; 95th percentile at 3 percent discount rate). We emphasize the importance and value of considering the benefits calculated using all four SC-CO2 estimates. As discussed in the Technical Support Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under E.O. 13990 (IWG 2021), a consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also warranted when discounting intergenerational impacts.

Table 412Stream of Projected Climate Benefits under Proposed Rule from 2028 through 2037 (millions of 2019 dollars)

SC-CO2 Discount Rate and Statistic Emissions Year 5% 3% 2.50% 3%

Average Average Average 95th Percentile 2028* $4 $13 $19 $40 2029 $15 $49 $71 $150 2030* $16 $50 $72 $150 2031 $16 $51 $73 $150 2032 $94 $290 $420 $890 2033 $96 $300 $430 $900 2034 $99 $300 $430 $920 2035* $100 $310 $440 $940 2036 $100 $310 $450 $960 2037 $110 $320 $450 $970 3% Discount Rate for PV and EAV Calculations Present Value $470 $1,400 $2,100 $4,400 Equivalent Annualized Value $55 $170 $240 $510 * IPM analysis years.

Table 413Stream of Projected Climate Benefits under More Stringent Regulatory Option from 2028 through 2037 (millions of 2019 dollars)

SC-CO2 Discount Rate and Statistic Emissions Year 5% 3% 2.50% 3%

Average Average Average 95th Percentile 2028* $400 $1,300 $1,900 $3,900 2029 $160 $520 $750 $1,600 2030* $170 $530 $770 $1,600 2031 $170 $540 $780 $1,600 2032 $59 $180 $260 $560 2033 $60 $190 $270 $570 2034 $62 $190 $270 $580 2035* $64 $190 $280 $590 2036 $65 $200 $280 $600 2037 $67 $200 $280 $610 3% Discount Rate for PV and EAV Calculations Present Value $1,000 $3,200 $4,700 $9,700 Equivalent Annualized Value $120 $380 $550 $1,100 * IPM analysis years.  Additional Unquantified Benefits Data, time, and resource limitations prevented EPA from quantifying the estimated health impacts or monetizing estimated benefits associated with direct exposure to NO2 and SO2 (independent of the role NO2 and SO2 play as precursors to PM2.5 and ozone), as well as ecosystem effects, and visibility impairment due to the absence of air quality modeling data for these pollutants in this analysis. While all health benefits and welfare benefits were not able to be quantified, it does not imply that there are not additional benefits associated with reductions in exposures to ozone, PM2.5, NO2 or SO2. In this section, we provide a qualitative description of these and water quality benefits, which are listed in Table 414.

Table 414Additional Unquantified Benefit Categories Category Effect Effect Quantified Effect Monetized More Information Improved Human Health       Reduced incidence of morbidity from exposure to NO2 Asthma hospital admissions   --   --  NO2 ISA1

Chronic lung disease hospital admissions   --   --  NO2 ISA1

Respiratory emergency department visits   --   --  NO2 ISA1

Asthma exacerbation   --   --  NO2 ISA1

Acute respiratory symptoms  --   --  NO2 ISA1

Premature mortality  --   --  NO2 ISA1,2,3

Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations)  --   --  NO2 ISA2,3 Reduced incidence of mortality and morbidity through drinking water from reduced effluent discharges. Bladder, colon, and rectal cancer from halogenated disinfection byproducts exposure.  --   --  SE ELG BCA4

Reproductive and developmental effects from halogenated disinfection byproducts exposure.  --   --  SE ELG BCA4 Reduced incidence of morbidity and mortality from toxics through fish consumption from reduced effluent discharges. Neurological and cognitive effects to children from lead exposure from fish consumption (including need for specialized education).  --   --  SE ELG BCA4

Possible cardiovascular disease from lead exposure   --   --  SE ELG BCA4

Neurological and cognitive effects from in in-utero mercury exposure from maternal fish consumption   --   --  SE ELG BCA4

Skin and gastrointestinal cancer incidence from arsenic exposure  --   --  SE ELG BCA4

Cancer and non-cancer incidence from exposure to toxic pollutants (lead, cadmium, thallium, hexavalent chromium etc.   --   --  SE ELG BCA4

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

Reduced incidence of morbidity and mortality from recreational water exposure from reduced effluent discharges.  Cancer and Non-Cancer incidence from exposure to toxic pollutants (methylmercury, selenium, and thallium.)  --   --  SE ELG BCA4 Improved Environment       Reduced visibility impairment Visibility in Class 1 areas  --   --  PM ISA1

Visibility in residential areas  --   --  PM ISA1 Reduced effects on materials Household soiling  --   --  PM ISA1,2

Materials damage (e.g., corrosion, increased wear)  --   --  PM ISA2 Reduced effects from PM deposition (metals and organics) Effects on individual organisms and ecosystems  --   --  PM ISA2 Reduced vegetation and ecosystem effects from exposure to ozone Visible foliar injury on vegetation  --   --  Ozone ISA1

Reduced vegetation growth and reproduction  --   --  Ozone ISA1

Yield and quality of commercial forest products and crops  --   --  Ozone ISA1

Damage to urban ornamental plants  --   --  Ozone ISA2

Carbon sequestration in terrestrial ecosystems  --   --  Ozone ISA1

Recreational demand associated with forest aesthetics  --   --  Ozone ISA2

Other non-use effects     Ozone ISA2

Ecosystem functions (e.g., water cycling, biogeochemical cycles, net primary productivity, leaf-gas exchange, community composition)  --   --  Ozone ISA2 Reduced effects from acid deposition Recreational fishing  --   --  NOx SOx ISA1

Tree mortality and decline  --   --  NOx SOx ISA2

Commercial fishing and forestry effects  --   --  NOx SOx ISA2

Recreational demand in terrestrial and aquatic ecosystems  --   --  NOx SOx ISA2

Other non-use effects     NOx SOx ISA2

Ecosystem functions (e.g., biogeochemical cycles)  --   --  NOx SOx ISA2 Reduced effects from nutrient enrichment from deposition. Species composition and biodiversity in terrestrial and estuarine ecosystems  --   --  NOx SOx ISA2

Coastal eutrophication  --   --  NOx SOx ISA2

Recreational demand in terrestrial and estuarine ecosystems  --   --  NOx SOx ISA2

Other non-use effects     NOx SOx ISA2