Notes: Retail sales are not equal to net generation (Table 2-2) because net generation includes net imported electricity and loss of electricity that occurs through transmission and distribution, along with data collection frame differences and non-sampling error. Direct Use represents commercial and industrial facility use of onsite net electricity generation; electricity sales or transfers to adjacent or co-located facilities; and barter transactions.

2.3.1	Electricity Prices
Electricity prices vary substantially across the United States, differing both between the ultimate customer categories and by state and region of the country. Electricity prices are typically highest for residential and commercial customers because of the relatively high costs of distributing electricity to individual homes and commercial establishments. The higher prices for residential and commercial customers are the result both of the necessary extensive distribution network reaching to virtually every part of the country and every building, and also the fact that generating stations are increasingly located relatively far from population centers (which increases transmission costs). Industrial customers generally pay the lowest average prices, reflecting both their proximity to generating stations and the fact that industrial customers receive electricity at higher voltages (which makes transmission more efficient and less expensive). Industrial customers frequently pay variable prices for electricity, varying by the season and time of day, while residential and commercial prices historically have been less variable. Overall industrial customer prices are usually considerably closer to the wholesale marginal cost of generating electricity than residential and commercial prices.
On a state-by-state basis, all retail electricity prices vary considerably. In 2018, the national average retail electricity price (all sectors) was 10.53 cents/KWh, with a range from 7.71 cents (Louisiana) to 29.18 (Hawaii).
      Average national retail electricity prices decreased between 2014 and 2018 by 5 percent in real terms (2018$). The amount of decrease differed for the three major end use categories (residential, commercial and industrial). National average industrial prices decreased the most (9 percent), and residential prices decreased the least (4 percent). The real year prices for 2014 through 2018 are shown in Figure 2-5.

Figure 2-5. Real National Average Electricity Prices (including taxes) for Three Major End-Use Categories
Source: EIA Monthly Energy Review (May 2020), Table 9.8.

	Most of these electricity price decreases occurred between 2014 and 2015, when nominal residential electricity prices followed inflation trends, while nominal commercial and industrial electricity prices declined. The years 2016 and 2017 saw an increase in nominal commercial and industrial electricity prices, while 2018 saw flattening of this growth. The increase in nominal electricity prices for the major end use categories, as well as increases in the GDP price and CPI-U indices for comparison, are shown in Figure 2-6.

Figure 2-6. Relative Increases in Nominal National Average Electricity Prices for Major End-Use Categories (including taxes), With Inflation Indices
Source: EIA Monthly Energy Review (May 2020), Table 9.8.
	For a longer-term perspective, Figure 2-7 shows real (2018$) electricity prices for the three major customer categories since 1960, and Figure 2-8 shows the relative change in real electricity prices relative to the prices since 1960. As can be seen in the figures, the price for industrial customers has always been lower than for either residential or commercial customers, but the industrial price has been more volatile. While the industrial real price of electricity in 2018 was relatively unchanged from 1960 (5 percent lower), residential and commercial real prices are 25 percent and 33 percent lower respectively than in 1960.

Figure 2-7. Real National Average Electricity Prices for Three Major End-Use Categories (including taxes), 1960-2018 (2018$)
Source: EIA Monthly Energy Review, May 2020, Table 9.8

Figure 2-8. Relative Change in Real National Average Electricity Prices (2018$) for Three Major End-Use Categories (including taxes)
Source: EIA Monthly Energy Review, May 2020, Table 9.8.
2.3.2	Prices of Fossil Fuels Used for Generating Electricity
      Another important factor in the changes in electricity prices are the changes in delivered fuel prices for the three major fossil fuels used in electricity generation; coal, natural gas and petroleum products. Relative to real prices in 2014, the national average real price (in 2018$) of coal delivered to EGUs in 2018 had decreased by 18 percent, while the real price of natural gas decreased by 33 percent. The real price of delivered petroleum products also decreased by 22 percent, but with petroleum products declining as an EGU fuel (in 2018 petroleum products generated 1 percent of electricity) the higher delivered oil prices had little overall impact in the electricity market. The combined real delivered price of all fossil fuels in 2014 decreased by 20 percent over 2014 prices. Figure 2-9 shows the relative changes in real price of all 3 fossil fuels between 2014 and 2018.

Figure 2-9. Relative Real Prices of Fossil Fuels for Electricity Generation; Change in National Average Real Price per MMBtu Delivered to EGU
Source: EIA Monthly Energy Review, May 2020, Table 9.9.

2.3.3	Changes in Electricity Intensity of the U.S. Economy from 2014 to 2018
An important aspect of the changes in electricity generation (i.e., electricity demand) between 2014 and 2018 is that while total net generation increased by 2 percent over that period, the demand growth for generation was lower than both the population growth (3 percent) and real GDP growth (10 percent). Figure 2-10 shows the growth of electricity generation, population and real GDP during this period.

Figure 2-10. Relative Growth of Electricity Generation, Population and Real GDP Since 2014
Sources: Generation: U.S. EIA Monthly Energy Review, May 2020. Table 7.2a Electricity Net Generation: Total (All Sectors). Population: U.S. Census. Real GDP: 2019 Economic Report of the President, Table B-3.

	Because demand for electricity generation grew more slowly than both the population and GDP, the relative electric intensity of the U.S. economy improved (i.e., less electricity used per person and per real dollar of output) during 2014 to 2018. On a per capita basis, real GDP per capita grew by 7 percent between 2014 and 2018. At the same time electricity generation per capita decreased by 1 percent. The combined effect of these two changes improved the overall electricity generation efficiency in the U.S. market economy. Electricity generation per dollar of real GDP decreased 7 percent. These relative changes are shown in Figure 2-11.

Figure 2-11. Relative Change of Real GDP, Population and Electricity Generation Intensity Since 2014
Sources: Generation: EIA Monthly Energy Review, May 2020. Table 7.2a Electricity Net Generation: Total (All Sectors). Population: U.S. Census. Real GDP: 2019 Economic Report of the President, Table B-3.

2.4	Deregulation and Restructuring
The process of restructuring and deregulation of wholesale and retail electricity markets has changed the structure of the electric power industry. In addition to reorganizing asset management between companies, restructuring sought a functional unbundling of the generation, transmission, distribution, and ancillary services the power sector has historically provided, with the aim of enhancing competition in the generation segment of the industry.
Beginning in the 1970s, government policy shifted against traditional regulatory approaches and in favor of deregulation for many important industries, including transportation (notably commercial airlines), communications, and energy, which were all thought to be natural monopolies (prior to 1970) that warranted governmental control of pricing. However, deregulation efforts in the power sector were most active during the 1990s. Some of the primary drivers for deregulation of electric power included the desire for more efficient investment choices, the economic incentive to provide least-cost electric rates through market competition, reduced costs of combustion turbine technology that opened the door for more companies to sell power with smaller investments, and complexity of monitoring utilities' cost of service and establishing cost-based rates for various customer classes. Deregulation and market restructuring in the power sector involved the divestiture of generation from utilities, the formation of organized wholesale spot energy markets with economic mechanisms for the rationing of scarce transmission resources during periods of peak demand, the introduction of retail choice programs, and the establishment of new forms of market oversight and coordination.
The pace of restructuring in the electric power industry slowed significantly in response to market volatility in California and financial turmoil associated with bankruptcy filings of key energy companies. By the end of 2001, restructuring had either been delayed or suspended in eight states that previously enacted legislation or issued regulatory orders for its implementation (shown as "Suspended" in Figure 2-12). Eighteen other states that had seriously explored the possibility of deregulation in 2000 reported no legislative or regulatory activity in 2001 (EIA, 2003) ("Not Active" in Figure 2-12). Currently, there are 15 states plus the District of Columbia where price deregulation of generation (restructuring) has occurred ("Active" in Figure 2-12). Power sector restructuring is more or less at a standstill; by 2010 there were no active proposals under review by the Federal Energy Regulatory Commission (FERC) for actions aimed at wider restructuring, and no additional states have begun retail deregulation activity since that time.

Figure 2-12. Status of State Electricity Industry Restructuring Activities
Source:	EIA 2010. "Status of Electricity Restructuring by State." Available online at: <https://www.eia.gov/todayinenergy/detail.php?id=6250>.
	One major effect of the restructuring and deregulation of the power sector was a significant change in type of ownership of electricity generating units in the states that deregulated prices. Throughout most of the 20th century electricity was supplied by vertically integrated regulated utilities. The traditional integrated utilities provided generation, transmission and distribution in their designated areas, and prices were set by cost of service regulations set by state government agencies (e.g., Public Utility Commissions). Deregulation and restructuring resulted in unbundling of the vertical integration structure. Transmission and distribution continued to operate as monopolies with cost of service regulation, while generation shifted to a mix of ownership affiliates of traditional utility ownership and some generation owned and operated by competitive companies known as Independent Power Producers (IPPs). The resulting generating sector differed by state or region, as the power sector adapted to the restructuring and deregulation requirements in each state.
	By the year 2000, the major impacts of adapting to changes brought about by deregulation and restructuring during the 1990s were nearing completion. In 2014, traditional utilities owned 61 percent of U.S. generating capacity (MW) while IPPs owned 39 percent of U.S. generating capacity, respectively. The mix of electricity generated (MWh) was more heavily weighted towards the utilities, with a distribution in 2014 of 61 percent, and 39 percent for IPPs.
	In 2018, the share of capacity (59 percent utility, 41 percent IPPs) and generation (58 percent utility, 42 percent IPP) has remained relatively stable relative to 2014 levels.
	The mix of capacity and generation for each of the ownership types is shown in Figures 2-13 (capacity) and 2-14 (generation). The capacity and generation data for commercial and industrial owners are not shown on these figures due to the small magnitude of those ownership

 Figures 2-13. and 2-14. Capacity and Generation Mix by Ownership Type, 2014 & 2018
types. A portion of the shift of capacity and generation is due to sales and transfers of generation assets from traditional utilities to IPPs, rather than strictly the result of newly built units.

--------------------------------------------------------------------------------
CHAPTER 3:  EMISSIONS AND AIR QUALITY IMPACTS
Overview
This Chapter describes the methods for developing spatial fields of air quality concentrations for the baseline and regulatory control alternatives in 2021 and 2025. These spatial fields provide the air quality inputs to potentially calculate health benefits for the proposed Revised CSAPR Update. The spatial fields for this proposal were constructed using the method and air quality modeling developed to support the regulatory impact analysis (RIA) for the Repeal of the Clean Power Plan, and the Emission Guidelines for Greenhouse Gas Emissions from Existing Electric Utility Generating Units (U.S. EPA 2019), also referred to as the Affordable Clean Energy (ACE) rule.
	In Section 3.1 we describe the ACE air quality modeling platform; in Section 3.2 we describe the ACE approach for processing the air quality modeling outputs to create inputs for estimating benefits; in Section 3.3 we describe how the ACE approach was applied in the proposed Revised CSAPR Update, in Section 3.4 we present maps showing the impacts on ozone and PM2.5 concentrations of each of the three regulatory control alternatives compared to the corresponding baseline; and in Section 3.5 we identify uncertainties and limitations in the application of the ACE approach for generating spatial fields of pollutant concentrations.
3.1 ACE Air Quality Modeling Platform
      The air quality modeling for the ACE analysis utilized a 2011-based modeling platform which included meteorology and base year emissions from 2011 and projected emissions for 2023. The air quality modeling included annual photochemical model simulations for a 2011 base year and a 2023 future year to provide hourly concentrations of ozone and primary and secondarily formed PM2.5 component species (e.g., sulfate, nitrate, ammonium, elemental carbon (EC), organic aerosol (OA), and crustal material) for both years nationwide. In particular, source apportionment modeling was performed for 2023 to quantify the contributions to ozone and PM2.5 component species from coal-fired and non-coal EGUs on a state-by-state or multi-state basis. As described below, the modeling results for 2011 and 2023, in conjunction with emissions data for the baseline and regulatory control alternatives, were used to construct the air quality spatial fields that reflect the influence of emissions changes between the baseline and the regulatory control alternatives.
      The air quality model simulations (i.e., model runs) were performed using the Comprehensive Air Quality Model with Extensions (CAMx) (Ramboll Environ 2016). Our CAMx nationwide modeling domain (i.e., the geographic area included in the modeling) covers all lower 48 states plus adjacent portions of Canada and Mexico using a horizontal grid resolution of 12 x 12 km shown in Figure 3-1.

Figure 3-1. Air Quality Modeling Domain
      The impact of specific emissions sources on ozone and PM2.5 in the 2023 modeled case was tracked using a tool called "source apportionment." In general, source apportionment modeling quantifies the air quality concentrations formed from individual, user-defined groups of emissions sources or "tags". These source tags are tracked through the transport, dispersion, chemical transformation, and deposition processes within the model to obtain hourly gridded contributions from the emissions in each individual tag to hourly modeled concentrations of ozone and PM2.5. Thus, the source apportionment method provides an estimate of the effect of changes in emissions from each group of emissions sources (i.e., each tag) to changes in ozone and PM2.5 concentrations. For this analysis we applied outputs from source apportionment modeling for ozone and PM2.5 using the 2023 modeled case to obtain the contributions from EGU emissions as well as other sources to ozone and to PM2.5 component species concentrations. Ozone contributions were modeled using the Ozone Source Apportionment Technique/Anthropogenic Precursor Culpability Assessment (OSAT/APCA) tool and PM2.5 component species contributions were modeled using the Particulate Source Apportionment Technique (PSAT) tool. The source apportionment modeling, which was already available from analysis performed to support the ACE rule RIA (U.S. EPA, 2019) was used to quantify the contributions from EGU emissions on a state-by-state or, in some cases, on a multi-state basis. For ozone, we modeled the contributions from the 2023 EGU sector emissions of NOX and VOC to hourly ozone concentrations for the period April through October to provide data for developing spatial fields for two seasonal ozone benefits metrics identified above (i.e., for the May-September seasonal average of the maximum daily 8-hour average (MDA8) ozone and the April-October seasonal average of the maximum daily 1-hour average (MDA1) ozone). For PM2.5, we modeled the contributions from the 2023 EGU sector emissions of SO2, NOX, and directly emitted PM2.5 for the entire year to inform the development of spatial fields of annual mean PM2.5. For each state, or multi-state group, we separately tagged EGU emissions depending on whether the emissions were from coal-fired units or non-coal units. In addition to tagging coal-fired and non-coal EGU emissions we also tracked the ozone and PM2.5 contributions from all other sources.
3.2. Applying Modeling Outputs to Create Spatial Fields
     In this section we describe the ACE approach for creating spatial fields based on the 2011 and 2023 modeling performed for the ACE rule. The foundational data from ACE include the ozone contributions from EGU emissions in each state based on the 2023 ACE EGU state-sector sector contribution modeling and the 2023 emissions for coal and non-coal fired EGUs that were input to that modeling. These data are used to generate spatial fields based on ozone season EGU NOx emissions (tons) and annual total EGU emissions of NOx, SO2 and PM2.5.  The inputs for this method include emissions for each state with a breakout of emissions for coal-fired and non-coal EGUs. The ozone season NOx emissions are used to prepare spatial fields of the May-September seasonal average MDA8 ozone and the April-October seasonal average MDA1 ozone concentration and the annual emissions are used to prepare spatial fields of annual PM2.5 concentrations. This method calculates the scaling ratios, described below, that are used to prepare the air quality spatial fields.
     To create the spatial fields for each future emissions scenario the 2023 state-sector source apportionment modeling outputs from the ACE modeling described above are used in combination with the EGU SO2, NOx, and PM2.5 emissions for each scenario. Contributions from each state-sector contribution "tag" were scaled based on the ratio of emissions in the year/scenario being evaluated to the emissions in the modeled ACE 2023 scenario. In this approach, scaling ratios for PM2.5 components that are emitted directly from the source (OA, EC, crustal) are based on relative changes in annual primary PM2.5 emissions between the modeled ACE 2023 emissions scenario and the specific baseline or control scenario being analyzed. Also the scaling ratios for components that are formed through chemical reactions in the atmosphere were created as follows: scaling ratios for sulfate were based on relative changes in annual SO2 emissions; scaling ratios for nitrate were based on relative changes in annual NOX emissions; and scaling ratios for ozone formed in NOX-limited regimes ("O3N") were based on relative changes in ozone season (May-September) NOX emissions.  Tags representing sources other than EGUs are held constant at 2023 ACE baseline levels for emissions scenarios analyzed by the user.  For each control scenario analyzed, the scaled contributions from all sources were summed together to create a gridded surface of total modeled ozone or total modeled PM2.5. Finally, spatial fields of ozone and PM2.5 are created based on "fusing" modeled data with measured concentrations at air quality monitoring locations. The process is described in a step-by-step manner below.
 The EGU annual SO2, NOX, and directly emitted PM2.5 emissions for the control scenario of interest and the corresponding 2023 SO2, NOX, and directly emitted PM2.5 emissions used in the ACE modeling to calculate the ratio of control case emissions to the ACE emissions for each of these pollutants for each EGU tag.
 The tag-specific 2025 to 2023 EGU emissions-based scaling ratios from step (1) are multiplied by the corresponding 365 gridded daily 24-hour average PM2.5 component species contributions from the 2023 contribution modeling. The emissions ratios for SO2 are applied to sulfate contributions; ratios for annual NOX are applied to nitrate contributions; and ratios for directly emitted PM2.5 are applied to the EGU contributions to primary OA, EC and crustal material. This step results in 365 adjusted gridded daily PM2.5 component species contributions for each EGUs tag that reflects the emissions in the control scenario.
 For each individual PM2.5 component species, the adjusted gridded contributions for each EGU tag from step (2) are added together to produce a gridded daily EGU tag total.
 The daily total EGU contributions for each PM2.5 component species from step (3) are then combined with the species contributions from source tags representing all other sources of PM2.5. As part of this step we also add the total secondary organic aerosol concentrations from the 2023 ACE modeling to the net EGU contributions of primary OA. Note that the secondary organic aerosol concentration does not change between scenarios. This step results in 24-hour average PM2.5 component species concentrations for the control scenario in each model grid cell, nationwide for each day in the year.
 For each PM2.5 component species, the daily concentrations from step (4) are averaged for each quarter of the year.
 The quarterly average PM2.5 component species concentrations from step (5) are divided by the corresponding quarterly average species concentrations from the base period air quality model run. This step provides a Relative Response Factor (i.e., RRF) between the base period and the control scenario for each species in each model grid cell.
 The species-specific quarterly RRFs from step (6) are then multiplied by the corresponding species-specific quarterly average concentrations from the base period fused surfaces to produce quarterly average species concentrations for the control scenario.
 The quarterly average species concentrations from step (7) are summed over the species to produce total PM2.5 concentrations for each quarter. Finally, total PM2.5 concentrations for the four quarters of the year are averaged to produce the spatial field of annual average PM2.5 concentrations for the 2025 baseline.

      To generate the spatial fields for each of the two ozone concentration metrics (i.e., April-October MDA1 and May-September MDA8) we follow the steps similar to those above for PM2.5.
 The EGU May through September (i.e., Ozone Season - OS) NOX for the control scenario and the corresponding modeled 2023 OS NOX emissions are used to calculate the ratio of control scenario emissions to 2023 ACE emissions for each EGU tag (i.e. an ozone-season scaling factor for each tag).
 The source apportionment modeling provided separate ozone contributions for ozone formed in VOC-limited chemical regimes (O3V) and ozone formed in NOX-limited chemical regimes (O3N). The EGU OS NOx emissions for the control scenario and the 2023 ACE OS NOx baseline emissions are used to calculate the ratio of the control scenario emissions to the 2023 ACE emission to create the EGU NOx emissions scaling ratios. The emissions scaling ratios are multiplied by the corresponding O3N gridded daily contributions to MDA1 and MDA8 concentrations. This step results in adjusted gridded daily MDA1 and MDA8 contributions due to NOX changes for each EGUs tag that reflect the emissions in the 2025 baseline.
 For MDA1 and MDA8, the adjusted contributions for each EGU tag from step (2) are added together to produce a daily adjusted EGU tag total. Since IPM does not output VOC from EGUs, there are no predicted changes in VOC emissions in these scenarios so the O3V contributions remain unchanged. The contributions from the unaltered O3V tags from the 2023 ACE modeling are added to the summed adjusted O3N EGU tags.
 The daily total EGU contributions for MDA1 and MDA8 from step (3) are then combined with the contributions to MDA1 and MDA8 from all other sources. This step results in MDA1 and MDA8 concentrations for the control scenario in each model grid cell, nationwide for each day in the ozone season.
 For MDA1, we average the daily concentrations from step (4) across all the days in the period April 1 through October 31. For MDA8, we average the daily concentrations across all days in the period May 1 through September 30.
 The seasonal mean concentrations from step (5) are divided by the corresponding seasonal mean concentrations from the base period air quality model run. This step provides a Relative Response Factor (i.e., RRF) between the base period and control scenario for MDA1 and MDA8 in each model grid cell.
 Finally, the RRFs for the seasonal mean metrics from step (6) are then multiplied by the corresponding seasonal mean concentrations from the base period MDA1 and MDA8 fused surfaces to produce seasonal mean concentrations for MDA1 and MDA8 for the control scenario that are input to BenMAP-CE.
3.3 Application of ACE Approach for the Revised CSAPR Update
     In this section we describe how we applied the ACE approach to generate spatial fields of seasonal ozone and annual PM2.5 concentrations associated with the regulatory control alternatives (i.e., the proposal and the less stringent and more stringent alternatives) in this proposed rule RIA. The data for creating the Revised CSAPR Update spatial fields include EGU emissions for the 2021 and 2025 baseline and the regulatory control alternatives. The EGU emissions include OS NOx and annual NOx, SO2, and PM2.5 for coal-fired and non-coal units in each state in the continental U.S. These EGU emissions are taken from the electricity sector analysis described in Chapter 4. In the case of the Revised CSAPR Update proposal analysis, there are no impacts on SO2 -  or PM2.5 emissions in the regulatory control scenarios compared to the 2025 baseline.
     To potentially calculate ozone-related benefits in 2021 and 2025 we used the ozone season EGU NOx emissions (tons) for the 2021 and 2025 baseline along with emissions for the proposal, and each of the two other regulatory control alternatives. These emissions were applied using the ACE approach and source apportionment data to produce spatial fields of the May-September seasonal average MDA8 ozone and the April-October seasonal average MDA1 ozone concentrations as described in the previous section.
     In 2021, the only control measure expected to be adopted for compliance in each of the regulatory control alternatives is optimization of existing SCRs beginning in May of 2021, and this measure will operate only during the ozone season. This is relevant because NOx reductions in the ozone season provide minimal PM2.5 reductions since PM2.5 nitrate concentrations, which result from conversion of NOx emissions to nitrate, are minimal during the warmer temperatures during the ozone season. Conversely, the conversion of nitrates to PM2.5 is much greater in cooler (non-ozone season) months, and thus it would be considered worthwhile to estimate PM2.5 benefits from NOx reductions in those months (Hand et al., 2012).  In 2025, the presence of additional control measures that operate year-round and other changes in market conditions as a result of the proposed rule lead to notable NOx reductions in the winter months.
     To create spatial fields for PM2.5 we pre-processed the 2025 coal and non-coal fired EGU emissions in order to obtain annual emissions of NOx, SO2, and directly emissions PM2.5 in a manner that is appropriate for assessing the impacts on annual average PM2.5 concentrations. This additional pre-processing was needed because the vast majority of the emissions reductions are expected to occur during the ozone season but, as noted above, PM2.5 nitrate concentrations are lowest during that time of year. In this regard, simply treating the summer emissions reductions as if they were abated proportionately throughout the year would overstate the impacts of the emissions reductions on PM2.5 and therefore overstate benefits associated with reducing exposure to PM2.5. For those states in which there are NOx emissions reductions during the ozone season only, we reset the annual NOx emission in the regulatory alternative to be equivalent to the corresponding baseline emissions to avoid distributing the ozone season reductions across the entire year. That is, we assumed that there would be no impact on PM2.5 nitrate concentrations of NOx reductions in the ozone season. For those states in which there are NOx emissions changes between the baseline and regulatory control alternative outside of the ozone season, we accounted for those reductions by "annualizing" the EGU emissions for the period outside the ozone season in the regulatory alternative as well as the corresponding baseline. This method essentially applies the change in NOX tons outside the ozone season on a daily basis to changes in NOx emissions tons within the ozone season. With this adjustment the impact of the regulatory control alternative on annual average PM2.5 concentrations reflects the emissions reductions that will occur outside the ozone season when PM2.5 nitrate concentrations are highest. The emissions of SO2 and directly emitted PM2.5 in 2025 for each of the regulatory alternatives do not change from the 2025 baseline. That is, the regulatory control alternatives analyzed in this RIA reduce emissions of NOx, but do not impact emissions of SO2 and directly emitted PM2.5.
3.4 Spatial Distribution of Air Quality Impacts
Below we present the estimated impacts on May-September MDA8 ozone between the baseline and each of the regulatory control alternatives for 2021 and 2025 as well as the estimated impacts on annual mean PM2.5 concentrations between the baseline and the regulatory control alternatives in 2025 (Figure 3-2 through Figure 3-10). The data shown in these figures are calculated as the baseline minus the regulatory control alternative concentrations (i.e., positive values indicate reductions in pollutant concentrations). The spatial patterns of the impacts of emissions reductions are a result of (1) the spatial distribution of EGU sources that are predicted to have changes in emissions and (2) the physical or chemical processing that the model simulates in the atmosphere.

Figure 3-2. Map of change in May-September MDA8 ozone (ppb):
2021 baseline  -  less stringent regulatory alternative (scale: + 0.10 ppb)

Figure 3-3. Map of change in May-September MDA8 ozone (ppb):
2021 baseline  -  proposal (scale: + 0.50 ppb)

Figure 3-4. Map of change in May-September MDA8 ozone (ppb):
2021 baseline  -  more stringent regulatory alternative (scale: + 0.50 ppb)

Figure 3-5. Map of change in May-September MDA8 ozone (ppb):
2025 baseline  -  less stringent regulatory alternative (scale: + 0.10 ppb)

Figure 3-6. Map of change in May-September MDA8 ozone (ppb):
2025 baseline  -  proposal (scale: + 0.50 ppb)

Figure 3-7. Map of change in May-September MDA8 ozone (ppb):
2025 baseline  -  more stringent regulatory alternative (scale: + 0.50 ppb)

Figure 3-8. Map of change in annual mean PM2.5 (ug/m[3]):
2025 baseline  -  less stringent regulatory alternative (scale: + 0.01 ug/m[3])

Figure 3-9. Map of change in annual mean PM2.5 (ug/m[3]):
2025 baseline  -  proposal (scale: + 0.01 ug/m[3])

Figure 3-10. Map of change in annual mean PM2.5 (ug/m[3]):
2025 baseline  -  more stringent regulatory alternative (scale: + 0.01 ug/m[3])

3.5 Uncertainties and Limitations of ACE Approach
      One limitation of the scaling methodology for creating PM2.5 surfaces associated with the baseline and regulatory alternatives described above is that it treats air quality changes from the tagged sources as linear and additive. It therefore does not account for nonlinear atmospheric chemistry and does not account for interactions between emissions of different pollutants and between emissions from different tagged sources. This is consistent with how air quality estimations have been treated in past regulatory analyses (U.S. EPA 2012; 2019; 2020b). We note that air quality is calculated in the same manner for the baseline and the regulatory alternatives, so any uncertainty associated with these assumptions is carried through both sets of scenarios in the same manner and is thus not expected to impact the air quality differences between scenarios. In addition, emissions changes between baseline and the regulatory alternatives are relatively small compared to modeled 2023 emissions that form the basis of the ACE source apportionment approach. Previous studies have shown that air pollutant concentrations generally respond linearly to small emissions changes of up to 30 percent (Dunker et al., 2002; Cohan et al., 2005; Napelenok et al., 2006; Koo et al., 2007; Zavala et al., 2009; Cohan and Napelenok, 2011) and that linear scaling from source apportionment can do a reasonable job of representing impacts of 100 percent of emissions from individual sources (Baker and Kelly 2014). Therefore, while simplistic, it is reasonable to expect that the emissions concentration differences between the baseline and regulatory control alternatives can be adequately represented using this methodology and any uncertainty should be weighed against the speed in which this method may be used to account for spatial differences in the effect of EGU emissions on ozone and PM2.5 concentrations.
      A second limitation is that the source apportionment PM2.5 contributions represent the spatial and temporal distribution of the emissions from each source tag as they occur in the 2023 modeled case. Thus, the contribution modeling results do not allow us to represent any changes to "within tag" spatial distributions. As a result, the method does not account for any changes of spatial patterns that would result from changes in the relative magnitude of sources within a source tag in the scenarios investigated here. As described above, the EGU tags are generally by state and by two EGU types; one for coal-fired units and one for non-coal units.
      In addition, the 2023 CAMx-modeled concentrations themselves have some uncertainty. While all models have some level of inherent uncertainty in their formulation and inputs, the base-year 2011 model outputs have been evaluated elsewhere against ambient measurements (U.S. EPA 2017; 2019) and have been shown to adequately reproduce spatially and temporally varying ozone and PM2.5 concentrations.
      The regulatory alternatives lead to decreased concentrations of ozone and PM2.5, the extent to which varies by location, relative to the baseline. However, the analysis does not account for how interaction with NAAQS compliance would affect the benefits and costs of the regulatory alternatives, which introduces uncertainty in the benefits and costs of the alternatives. To the extent the Revised CSAPR Update proposal will decrease NOX and consequentially ozone and PM2.5, these changes may affect compliance with existing NAAQS standards and subsequently affect the actual benefits and costs of the proposed rule. In areas not projected to attain the 2015 ozone NAAQS without further emissions reductions from the baseline, states may be able avoid applying some emissions control measures to reduce emissions from local sources as a result of this proposed rule. If compliance behavior with the 2015 ozone NAAQS were accounted for in the baseline in this RIA there may be additional benefits from reduced compliance costs, while the level and spatial pattern of changes in ozone and PM2.5 concentrations, and their associated health and ecological benefits, would differ.
      Similarly, the regulatory alternatives may project decreases in ozone and PM2.5 concentrations in areas attaining the NAAQS in the baseline. In practice, these potential changes in concentrations may influence NAAQS compliance plans in these areas, which in turn would further influence concentrations and the cost of complying with the NAAQS. However, such behavior will be mitigated by NAAQS requirements such as Prevention of Significant Deterioration (PSD) requirements. This RIA does not account for how interaction with NAAQS compliance would affect the benefits and costs of the regulatory alternatives.

3.6 References
Baker, Kirk R., and James T. Kelly. 2014. "Single Source Impacts Estimated with Photochemical Model Source Sensitivity and Apportionment Approaches." Atmospheric Environment 96 (October): 266 - 74. https://doi.org/10.1016/j.atmosenv.2014.07.042.
Cohan Daniel S., Amir Hakami, Yongtao Hu, Armistead G. Russell. 2005. "Nonlinear response of ozone to emissions: Source apportionment and sensitivity analysis." Environmental Science & Technology 39:6739 - 6748
Cohan, Daniel, and Sergey Napelenok. 2011. "Air Quality Response Modeling for Decision Support." Atmosphere 2 (December): 407 - 25. https://doi.org/10.3390/atmos2030407.
Ding, Dian, Yun Zhu, Carey Jang, Che-Jen Lin, Shuxiao Wang, Joshua Fu, Jian Gao, Shuang Deng, Junping Xie, and Xuezhen Qiu. 2016. "Evaluation of Health Benefit Using BenMAP-CE with an Integrated Scheme of Model and Monitor Data during Guangzhou Asian Games." Journal of Environmental Sciences 42 (April): 9 - 18. https://doi.org/10.1016/j.jes.2015.06.003.
Dunker, Alan M., Greg Yarwood, Jerome P. Ortmann, and Gary M. Wilson. 2002. "The Decoupled Direct Method for Sensitivity Analysis in a Three-Dimensional Air Quality Model Implementation, Accuracy, and Efficiency." Environmental Science & Technology 36 (13): 2965 - 76. https://doi.org/10.1021/es0112691.
Hand, J. L., B.A. Schichtel, M. Pitchford, W.C. Malm, and N.H. Frank. 2012. "Seasonal Composition of Remote and Urban Fine Particulate Matter in the United States. Journal of Geophysical Research, 117, D05209, doi: 10.1029/2011JD017122.
Koo, Bonyoung, Alan M. Dunker, and Greg Yarwood. 2007. "Implementing the Decoupled Direct Method for Sensitivity Analysis in a Particulate Matter Air Quality Model." Environmental Science & Technology 41 (8): 2847 - 54. https://doi.org/10.1021/es0619962.

Napelenok, Sergey L., Daniel S. Cohan, Yongtao Hu, and Armistead G. Russell. 2006. "Decoupled Direct 3D Sensitivity Analysis for Particulate Matter (DDM-3D/PM)." Atmospheric Environment 40 (32): 6112 - 21. https://doi.org/10.1016/j.atmosenv.2006.05.039.
Ramboll Environ. 2016. "Comprehensive Air Quality Model with Extensions Version 6.40." User's Guide. Novato, CA: Ramboll Environ International Corporation. http://www.camx.com/files/camxusersguide_v6-40.pdf.
US EPA, 2012. "Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter." EPA-452/R-12-005. Research Triangle Park, NC: U.S. Environmental Protection Agency. https://www3.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf.
US EPA, 2017. Documentation for the EPA's Preliminary 2028 Regional Haze Modeling. Research Triangle Park, NC (https://www3.epa.gov/ttn/scram/reports/2028_Regional_Haze_Modeling-TSD.pdf).
US EPA, 2019. "Regulatory Impact Analysis for the Repeal of the Clean Power Plan, and the Emission Guidelines for Greenhouse Gas Emissions from Existing Electric Utility Generating Units." EPA-452/R-19-003. Research Triangle Park, NC: U.S. Environmental Protection Agency. https://www.epa.gov/sites/production/files/2019-06/documents/utilities_ria_final_cpp_repeal_and_ace_2019-06.pdf.
US EPA, 2020a. "Regulatory Impact Analysis for Revisions to the Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source Category".  EPA-821-R-20-004. Washington, DC: U.S. Environmental Protection Agency. https://www.epa.gov/sites/production/files/2020-08/documents/steam_electric_elg_2020_final_reconsideration_rule_regulatory_impact_analysis.pdf.
US EPA, 2020b. "Analysis of Potential Costs and Benefits for the "National Emission Standards for Hazardous Air Pollutants: Coal- and Oil-Fired Electric Utility Steam Generating Units  -  Subcategory of Certain Existing Electric Utility Steam Generating Units Firing Easter". Memo to Docket for rulemaking: "National Emission Standards for Hazardous Air Pollutants: Coal- and Oil-Fired Electric Utility Steam Generating Units  -  Subcategory of Certain Existing Electric Utility Steam Generating Units Firing Eastern Bituminous Coal Refuse for Emissions of Acid Gas Hazardous Air Pollutants" (EPA-HQ-OAR-2018-0794), April 8, 2020.  Available at: https://www.epa.gov/sites/production/files/2020-04/documents/mats_coal_refuse_cost-benefit_memo.pdf
Zavala, M., Lei, W., Molina, M.J., Molina, L.T., 2009. Modeled and observed ozone sensitivity to mobile-source emissions in Mexico City. Atmos. Chem. Phys. 9, 39 - 55.

--------------------------------------------------------------------------------
APPENDIX 3A:  Methodology for developing air quality Surfaces
     In this appendix we describe the air quality modeling platform and methodology that was leveraged to prepare the air quality surfaces that could inform the calculation of health benefits of the proposed Revised CSAPR Update. The modeling and methodology described here were developed to support the Regulatory Impact Analysis for the Repeal of the Clean Power Plan, and the Emission Guidelines for Greenhouse Gas Emissions from Existing Electric Utility Generating Units (U.S. EPA 2019), also referred to the Affordable Clean Energy (ACE) rule. The foundational data in the ACE approach include the 2023 ACE baseline EGU emissions and the 2023 ACE EGU air quality contribution data described below. To generate spatial fields for alternative EGU scenarios, such as the scenarios analyzed for the Revised CSPR Update proposal, the user provides as input EGU emissions for coal-fired and non-coal units for each state, separately. Ozone season EGU NOx emissions (tons) are used to prepare spatial fields of the May-September seasonal average MDA8 ozone and the April-October seasonal average MDA1 ozone concentrations and annual total EGU emissions of NOx, SO2 and PM2.5 are used to prepare spatial fields of annual PM2.5 concentrations. Emissions scaling ratios, described below, that are used to prepare the air quality spatial fields.

3A.1 Air Quality Modeling Platform for the ACE Rule
       As part of the ACE assessment we used existing air quality modeling for 2011 and 2023 to estimate PM2.5 and ozone concentrations in the future years analyzed for the ACE final rule. The modeling platform consists of several components including the air quality model, meteorology, estimates of international transport, and base year and future year emissions from anthropogenic and natural sources. An overview of each of these platform comments is provided in the subsections below.
3A.1.1 Air Quality Model, Meteorology and Boundary Conditions
      We used the Comprehensive Air Quality Model with Extensions (CAMx version 6.40) with the Carbon Bond chemical mechanism CB6r4 for modeling base year and future year ozone and PM2.5 concentrations (Ramboll, 2016). CAMx is a three-dimensional grid-based photochemical air quality model designed to simulate the formation and fate of oxidant precursors, primary and secondary particulate matter concentrations, and deposition over national, regional and urban spatial scales. Consideration of the different processes (e.g., transport and deposition) that affect primary (directly emitted) and secondary (formed by atmospheric processes) pollutants in different locations is fundamental to understanding and assessing the effects of emissions on air quality concentrations.
      The geographic extent of the modeling domain covers the 48 contiguous states along with the southern portions of Canada and the northern portions of Mexico as shown in Figure1. This modeling domain contains 25 vertical layers with a top at about 17,550 meters and horizontal grid resolution of 12 km x 12 km. The model simulations produce hourly air quality concentrations for each 12-km grid cell across the modeling domain.

Figure 3A-1. Air Quality Modeling Domain

      The 2011 meteorological data for air quality modeling were derived from running Version 3.4 of the Weather Research Forecasting Model (WRF) (Skamarock, et al., 2008). The meteorological outputs from WRF include hourly-varying horizontal wind components (i.e., speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each vertical layer in each grid cell. The 2011 meteorology was used for both the 2011 base year and 2023 future year air quality modeling. Details of the annual 2011 meteorological model simulation and evaluation are provided in a separate technical support document (US EPA, 2014a) which can be obtained at: http://www.epa.gov/ttn/scram/reports/MET_TSD_2011_final_11-26-14.pdf
	The lateral boundary and initial species condition concentrations are provided by a three-dimensional global atmospheric chemistry model, GEOS-Chem (Yantosca, 2004) standard version 8-03-02 with 8-02-01 chemistry. The global GEOS-Chem model simulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA's Goddard Earth Observing System (GEOS-5). GEOS-Chem was run for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude-longitude). The predictions were used to provide one-way dynamic boundary condition concentrations at three-hour intervals and an initial concentration field for the CAMx simulations. The 2011 boundary concentrations from GEOS-Chem were used for both the 2011 and 2023 model simulations. The procedures for translating GEOS-Chem predictions to initial and boundary concentrations are described elsewhere (Henderson, 2014). More information about the GEOS-Chem model and other applications using this tool is available at: http://www-as.harvard.edu/chemistry/trop/geos.
3A.1.2 2011 and 2023 Emissions
      The purpose of the 2011 base year modeling is to represent the year 2011 in a manner consistent with the methods used in the 2023 future year base case. The emissions data in this platform are primarily based on the 2011 National Emissions Inventory (NEI) v2 for point sources, nonpoint sources, commercial marine vessels, nonroad mobile sources and fires. The onroad mobile source emissions are similar to those in the 2011 NEIv2, but were generated using the 2014a version of the Motor Vehicle Emissions Simulator (MOVES2014a) (http://www.epa.gov/otaq/models/moves/). The 2011 and 2023 emission inventories incorporate revisions implemented based on comments received on the Notice of Data Availability (NODA) issued in January 2017 "Preliminary Interstate Ozone Transport Modeling Data for the 2015 Ozone National Ambient Air Quality Standard" (82 FR 1733), along with revisions made from prior notices and rulemakings on earlier versions of the 2011 platform. The preparation of the emission inventories for air quality modeling is described in the Technical Support Document (TSD) Additional Updates to Emissions Inventories for the Version 6.3, 2011 Emissions Modeling Platform for the Year 2023 (US EPA, 2017a). Electronic copies of the emission inventories and ancillary data used to produce the emissions inputs to the air quality model are available from the 2011en and 2023 en section of the EPA Air Emissions Modeling website for the 2011v6.3 emissions modeling platform: https://www.epa.gov/air-emissions-modeling/2011-version-63-platform.
      The emission inventories for the 2023 ACE future year were developed using projection methods that are specific to the type of emission source. Future emissions are projected from the 2011 current year either by running models to estimate future year emissions from specific types of emission sources (e.g., EGUs, and onroad and nonroad mobile sources), or for other types of sources by adjusting the base year emissions according to the best estimate of changes expected to occur in the intervening years. For sectors which depend strongly on meteorology (such as biogenic and fires), the same emissions are used in the base and future years to be consistent with the 2011 meteorology used when modeling 2023. For the remaining sectors, rules and specific legal obligations that go into effect in the intervening years, along with changes in activity for the sector, are considered when possible. Emissions inventories for neighboring countries used in our modeling are included in this platform, specifically 2011 and 2023 emissions inventories for Mexico, and 2013 and 2025 emissions inventories for Canada. The meteorological data used to create and temporalize emissions for the future year cases is held constant and represents the year 2011. The same ancillary data files are used to prepare the future year emissions inventories for air quality modeling as were used to prepare the 2011 base year inventories with the exception of chemical speciation profiles for mobile sources and temporal profiles for EGUs.
The projected EGU emissions reflect the emissions reductions expected due to the Final Mercury and Air Toxics (MATS) rule announced on December 21, 2011, the Cross-State Air Pollution Rule (CSAPR) issued July 6, 2011, and the CSAPR Update issued October 26, 2016. The 2023 EGU projected inventory was developed using an engineering analysis approach. EPA started with 2016 reported, seasonal, historical emissions for each unit. The emissions data for NOX and SO2 for units that report data under either the Acid Rain Program (ARP) and/or the CSAPR were aggregated to the summer/ozone season period (May-September) and winter/non-ozone period (January-April and October-December). Adjustments to 2016 levels were made to account for retirements, coal to gas conversion, retrofits, state-of-the-art combustion controls, along with other unit-specific adjustments. Details and these adjustments, and information about handling for units not reporting under Part 75 and pollutants other than NOX and SO2 are described in the emissions modeling TSD (US EPA, 2017a).
The 2023 non-EGU stationary source emissions inventory includes impacts from enforceable national rules and programs including the Reciprocating Internal Combustion Engines (RICE) and cement manufacturing National Emissions Standards for Hazardous Air Pollutants (NESHAPs) and Boiler Maximum Achievable Control Technology (MACT) reconsideration reductions. Projection factors and percent reductions for non-EGU point sources reflect comments received by EPA in response to the January 2017 NODA, along with emissions reductions due to national and local rules, control programs, plant closures, consent decrees and settlements. Growth and control factors provided by states and by regional organizations on behalf of states were applied. Reductions to criteria air pollutant (CAP) emissions from stationary engines resulting as co-benefits to the Reciprocating Internal Combustion Engines (RICE) National Emission Standard for Hazardous Air Pollutants (NESHAP) are included. Reductions due to the New Source Performance Standards (NSPS) VOC controls for oil and gas sources, and the NSPS for process heaters, internal combustion engines, and natural gas turbines were also included.
For point and nonpoint oil and gas sources, state projection factors were generated using state-specific historical oil and gas production data available from EIA for 2011 to 2015 and information from regional factors based AEO 2017 to project the emission to the year 2023. Co-benefits of stationary engines CAP reductions (RICE NESHAP) and controls from New Source Performance Standards (NSPS) are reflected for select source categories. Mid-Atlantic Regional Air Management Association (MARAMA) factors for the year 2023 were used where applicable. Projection factors for other nonpoint sources such as stationary source fuel combustion, industrial processes, solvent utilization, and waste disposal, reflect emissions reductions due to control programs along with comments on the growth and control of these sources as a result of the January 2017 NODA and information gathered from prior rulemakings and outreach to states on emission inventories.
The MOVES2014a-based 2023 onroad mobile source emissions account for changes in activity data and the impact of on-the-books national rules including: the Tier 3 Vehicle Emission and Fuel Standards Program, the 2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards (LD GHG), the Renewable Fuel Standard (RFS2), the Mobile Source Air Toxics Rule, the Light Duty Green House Gas/Corporate Average Fuel Efficiency (CAFE) standards for 2012-2016, the Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles, the Light-Duty Vehicle Tier 2 Rule, and the Heavy-Duty Diesel Rule. The MOVES-based emissions also include state rules related to the adoption of LEV standards, inspection and maintenance programs, Stage II refueling controls, and local fuel restrictions.
The nonroad mobile 2023 emissions, including railroads and commercial marine vessel emissions also include all national control programs. These control programs include the Clean Air Nonroad Diesel Rule  -  Tier 4, the Nonroad Spark Ignition rules, and the Locomotive-Marine Engine rule. For ocean-going vessels (Class 3 marine), the emissions data reflect the 2005 voluntary Vessel Speed Reduction (VSR) within 20 nautical miles, the 2007 and 2008 auxiliary engine rules, the 40 nautical mile VSR program, the 2009 Low Sulfur Fuel regulation, the 2009-2018 cold ironing regulation, the use of 1 percent sulfur fuel in the Emissions Control Area (ECA) zone, the 2012-2015 Tier 2 NOX controls, the 2016 0.1 percent sulfur fuel regulation in ECA zone, and the 2016 International Marine Organization (IMO) Tier 3 NOX controls. Non-U.S. and U.S. category 3 commercial marine emissions were projected to 2025 using consistent methods that incorporated controls based on ECA and IMO global NOX and SO2 controls.
3A.1.3 2011 Model Evaluation for Ozone and PM2.5
An operational model performance evaluation was conducted to examine the ability of the 2011 base year model run to simulate the corresponding 2011 measured ozone and PM2.5 concentrations. This evaluation focused on four statistical metrics comparing model predictions to the corresponding observations. The performance statistics include mean bias, mean error, normalized mean bias, and normalized mean error. Mean bias (MB) is the sum of the difference (predicted  -  observed) divided by the total number of replicates (n). Mean bias is given in units of ppb and is defined as:
MB =  1n1nP-O 									(Eq-1)
Where:
 P is the model-predicted concentration;
 O is the observed concentrations; and
 n is the total number of observations

Mean error (ME) calculates the sum of the absolute value of the difference (predicted - observed) divided by the total number of replicates (n). Mean error is given in units of ppb and is defined as:
ME = 1n1n|P-O|									(Eq-2)
Normalized mean bias (NMB) is the sum of the difference (predicted - observed) over the sum of observed values. NMB is a useful model performance indicator because it avoids over inflating the observed range of values, especially at low concentrations. Normalized mean bias is given in percentage units and is defined as:
NMB =  1n(P-O)1n(O)*100								(Eq-3)
Normalized mean error (NME) is the sum of the absolute value of the difference (predicted - observed) divided by the sum of observed values. Normalized mean error is given in percentage units and is defined as:
NME = 1n|P-O|1n(O)*100									(Eq-4)
     For PM2.5, performance statistics were calculated for modeled and observed 24-hour average concentrations paired by day and location for the entire year. Performance statistics were calculated for monitoring data in the Chemical Speciation Network (CSN) and, separately, for monitoring data in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network. For ozone, performance statistics were calculated for modeled concentrations with observed 8-hour daily maximum (MDA8) ozone concentrations at or above 60 ppb over the period May through September for monitoring sites in the Air Quality System (AQS)[,] network. For both PM2.5 and ozone, the modeled and predicted pairs of data were aggregated by 9 regions across the U.S. for the calculation of model performance statistics. These 9 regions are shown in Figure 3A-2.

Figure 3A-2. NOAA Climate Regions
      Model performance statistics for PM2.5 for each region are provided in Table 3A.1. These data indicate that over the year as a whole, PM2.5 is over predicted in the Northeast, Ohio Valley, Upper Midwest, Southeast, and Northwest regions and under predicted in the South and Southwest regions. Normalized mean bias is within +-30 percent in all regions except the Northwest which has somewhat larger model over-predictions. Model performance for PM2.5 for the 2011 modeling platform is similar to the model performance results for other contemporary, state of the science photochemical model applications (Simon et al., 2012). Additional details on PM2.5 model performance for the 2011 base year model run can be found in the Technical Support Document for EPA's preliminary regional haze modeling (US EPA, 2017b).

Table 3A.1. Model Performance Statistics by Region for PM2.5
Region
                                    Network
                                  No. of Obs
                                 MB
(ug/m[3])
                                 ME
(ug/m[3])
                                    NMB
(%)
                                    NME
(%)
Northeast
IMPROVE
1577
0.87
2.21
17.70
44.90

CSN
2788
0.97
4.04
9.70
40.40
Ohio Valley
IMPROVE
680
0.10
2.96
1.20
35.50

CSN
2475
0.13
3.85
1.10
32.80
Upper Midwest
IMPROVE
700
0.83
2.37
14.20
40.40

CSN
1343
1.37
3.66
13.60
36.30
Southeast
IMPROVE
1172
0.52
3.54
6.30
43.20

CSN
1813
0.19
3.92
1.70
34.20
South
IMPROVE
933
-0.47
2.69
-6.50
37.40

CSN
962
-0.08
4.48
-0.75
39.50
Southwest
IMPROVE
3695
-1.12
1.86
-28.00
46.30

CSN
746
-0.08
3.93
-1.00
47.10
N. Rockies/
Plains
IMPROVE
1952
0.07
1.39
2.40
44.90

CSN
275
-2.07
4.18
-21.80
43.90
Northwest
IMPROVE
1901
1.19
2.28
43.20
82.90

CSN
668
5.77
7.25
69.90
87.90
West
IMPROVE
1782
-1.08
2.08
-25.30
48.50

CSN
936
-2.92
5.08
-23.10
40.30

     Model performance statistics for May through September MDA8 ozone concentrations for each region are provided in Table 3A.2. Overall, measured ozone is under predicted in most regions, except for the Northeast and Southeast where over prediction is found. Normalized mean bias is within +-15 percent in all regions. Model performance for ozone for the 2011 modeling platform is similar to the model performance results for other contemporary, state of the science photochemical model applications (Simon et al., 2012). Additional details on ozone model performance for the 2011 base year model run can be found in the Air Quality Technical Support Document for EPA's preliminary interstate ozone transport modeling for the 2015 ozone National Ambient Air Quality Standard (US EPA, 2017c).

Table 3A.2. Model Performance Statistics by Region for Ozone on Days Above 60 ppb (May-Sep)
Region
                                  No. of Obs
                                   MB
(ppb)
                                   ME
(ppb)
                                    NMB
(%)
                                    NME
(%)
Northeast
                                                                           4085
                                     1.20
                                     7.30
                                     1.80
                                     10.70
Ohio Valley
                                                                           6325
                                     -0.60
                                     7.50
                                     -0.90
                                     11.10
Upper Midwest
                                                                           1162
                                     -4.00
                                     7.60
                                     -5.90
                                     11.10
Southeast
                                                                           4840
                                     2.30
                                     6.80
                                     3.40
                                     10.20
South
                                                                           5694
                                     -5.30
                                     8.40
                                     -7.60
                                     12.20
Southwest
                                                                           6033
                                     -6.20
                                     8.50
                                     -9.40
                                     12.90
N. Rockies/Plains
                                                                            380
                                     -7.20
                                     8.40
                                    -11.40
                                     13.40
Northwest
                                                                             79
                                     -5.60
                                     9.00
                                     -8.70
                                     14.00
West
                                                                           8655
                                     -8.60
                                     10.30
                                    -12.20
                                     14.50

      Thus, the model performance results demonstrate the scientific credibility of our 2011 modeling platform for predicting PM2.5 and ozone concentrations. These results provide confidence in the ability of the modeling platform to provide a reasonable projection of expected future year ozone concentrations and contributions.
3A.2 Source Apportionment Tags
      CAMx source apportionment modeling was used to track ozone and PM2.5 component species impacts from pre-defined groups of emissions sources (source tags). Separate tags were created for state-level EGUs split by fuel type (coal units versus non-coal units). For some states with low EGU emissions, EGUs are grouped with nearby states that also have low EGU emissions. In addition, there are no coal EGUs operating in the 2023 emissions case for the following states: Idaho, Oregon, and Washington. Therefore, there is no coal EGU tag for those states. Similarly, there were no EGUs (coal or non-coal) in Washington D.C. in the 2023 emissions scenario, so there were no EGU tags for Washington D.C. There were also several domain-wide tags for sources other than EGUs. Table 3A.3 provides a full list of the emissions group tags that were tracked in the source apportionment modeling.
Table 3A.3. Source Apportionment Tags
Coal-fired EGU tags
Non-coal EGU tags
Domain-wide tags
 Alabama
 Arizona
 Arkansas
 California
 Colorado
 Connecticut + Rhode Island
 Delaware + New Jersey
 Florida
 Georgia
 Illinois
 Indiana
 Iowa
 Kansas
 Kentucky
 Louisiana
 Maine + Mass. + New Hamp. + Vermont
 Maryland
 Michigan
 Minnesota
 Mississippi
 Missouri
 Montana
 Nebraska
 Nevada
 New Mexico
 New York
 North Carolina
 North Dakota + South Dakota
 Ohio
 Oklahoma
 Pennsylvania
 South Carolina
 Tennessee
 Texas
 Utah
 Virginia
 West Virginia
 Wisconsin
 Wyoming
 Tribal Data*
 Alabama
 Arizona
 Arkansas
 California
 Colorado
 Connecticut + Rhode Island
 Delaware + New Jersey
 Florida
 Georgia
 Idaho + Oregon + Washington
 Illinois
 Indiana
 Iowa
 Kansas
 Kentucky
 Louisiana
 Maine + Mass. + New Hamp. + Vermont
 Maryland
 Michigan
 Minnesota
 Mississippi
 Missouri
 Montana
 Nebraska
 Nevada
 New Mexico
 New York
 North Carolina
 North Dakota + South Dakota
 Ohio
 Oklahoma
 Pennsylvania
 South Carolina
 Tennessee
 Texas
 Utah
 Virginia
 West Virginia
 Wisconsin
 Wyoming
 Tribal Data
 EGU retirements through 2025
 EGU retirements 2026-2030
 All U.S. anthropogenic emissions from source sectors other than EGUs
 International within-domain emissions (sources occurring in Canada, Mexico, and from offshore marine vessels and drilling platforms)
 Fires (wildfires and prescribed fires)
 Biogenic sources
 Boundary conditions

      The contributions represent the spatial and temporal distribution of the emissions within each source tag. Thus, the contribution modeling results do not allow us to represent any changes to any "within tag" spatial distributions. For example, the location of coal-fired EGUs in Michigan are held in place based on locations in the 2023 emissions. Additionally, the relative magnitude of sources within a source tag do not change from what was modeled with the 2023 emissions inventory.
3A.3 Applying Source Apportionment Contributions to Create Air Quality Fields
      We created air quality surfaces for the ACE future year baseline and illustrative policy scenarios by scaling the EGU sector tagged contributions from the 2023 modeling based on relative changes in EGU emissions associated with each tagged category between the 2023 emissions case and the ACE scenarios. Below, we provide equations used to apply these scaling ratios along with tables of the ratios.
3A.3.2 Scaling Ratio Applied to Source Apportionment Tags
      Scaling ratios for PM2.5 components that are emitted directly from the source (OA, EC, crustal) were based on relative changes in annual primary PM2.5 emissions between the 2023 emissions case and the ACE baseline and the illustrative policy scenario. Scaling ratios for components that are formed through chemical reactions in the atmosphere were created as follows: scaling ratios for sulfate were based on relative changes in annual SO2 emissions; scaling ratios for nitrate were based on relative changes annual NOX emissions; and scaling ratios for ozone formed in NOX-limited regimes ("O3N") were based on relative changes in ozone season (May-September) NOX emissions. The scaling ratios that were determined based on emissions provided for each scenario.
      Scaling ratios were applied to create air quality surfaces for ozone using equation (9):
Ozonem,g,d,i,y=Cm,g,d,BC+Cm,g,d,int+ Cm,g,d,bio+Cm,g,d,fires+Cm,g,d,USanthro + Cm,g,d,y,EGUret+ t=1TCVOC,m,g,d,t+ t=1TCNOx,m,g,d,tSt,i,y
                                                                         (Eq-9)
      where:
 Ozonem,g,d,i,y is the estimated ozone for metric, "m" (MDA8 or MDA1), grid-cell, "g", day, "d", scenario, "i", and year, "y";
 Cm,g,d,BC is the total ozone contribution from the modeled boundary inflow; Cm,g,d,int is the total ozone contribution from international emissions within the model domain;
 Cm,g,d,bio is the total ozone contribution from biogenic emissions;
 Cm,g,d,fires is the total ozone contribution from fires;
  Cm,g,d,USanthro is the total ozone contribution from U.S. anthropogenic sources other than EGUs;
  Cm,g,d,y,EGUret is the total ozone contribution from retiring EGUs after year, "y" (this term is equal to 0 in 2030 and 2035);
 CVOC,m,g,d,t is the ozone contribution from EGU emissions of VOCs from tag, "t";
 CNOx,m,g,d,t is the ozone contribution from EGU emissions of NOX from tag, "t"; and
 St,i,y is the ozone scaling ratio for tag, "t", scenario, "i", and year, "y".
	Scaling ratios were applied to create air quality surfaces for PM2.5 species using equation (10) (for sulfate, nitrate, EC or crustal material) or using equation (11) (for OA):
PMs,g,d,i,y=Cs,g,d,BC+Cs,g,d,int+ Cs,g,d,bio+Cs,g,d,fires+Cs,g,d,USanthro + Cs,g,d,y,EGUret+ t=1TCs,g,d,tSs,t,i,y

                                                                        (Eq-10)
OAg,d,i,y=CPOA,g,d,BC+CPOA,g,d,int+ CPOA,g,d,bio+CPOA,g,d,fires+CPOA,g,d,USanthro + CPOA,g,d,y,EGUret+SOAg,d+ t=1TCPOA,g,d,tSpri,t,i,y

                                                                        (Eq-11)
where:
 PMs,g,d,i,y is the estimated concentration for species, "s" (sulfate, nitrate, EC, or crustal material), grid-cell, "g", day, "d", scenario, "i", and year, "y";
 Cs,g,d,BC is the species contribution from the modeled boundary inflow;
 Cs,g,d,int is the species contribution from international emissions within the model domain;
  Cs,g,d,bio is the species contribution from biogenic emissions;
 Cs,g,d,fires is the species contribution from fires;
 Cs,g,d,USanthro is the species contribution from U.S. anthropogenic sources other than EGUs;
 Cs,g,d,y,EGUret is the species contribution from retiring EGUs after year, "y" (this term is equal to 0 in 2030 and 2035);
 Cs,g,d,t is the species contribution from EGU emissions from tag, "t"; and
 Ss,t,i,y is the scaling ratio for species, "s", tag, "t", scenario, "i", and year, "y".
   Similarly, for Equation (11):
 OAg,d,i,y is the estimated OA concentration for grid-cell, "g", day, "d", scenario, "i", and year, "y";
 Each of the contribution terms refers to the contribution to primary OA (POA); and
  SOAg,d represents the modeled secondary organic aerosol concentration for gird-cell, "g", and day, "d", which does not change among scenarios
3A.4 Creating Fused Fields Based on Observations and Model Surfaces
      In this section we describe steps taken to estimate PM2.5 and ozone gridded surfaces associated with the baseline and the illustrative policy scenario for every year. For PM2.5, (daily gridded PM2.5 species were processed into annual average surfaces which combine observed values with model predictions using the enhanced Veronoi Neighbor Average (eVNA) method (Gold et al., 1997; US EPA, 2007; Ding et al., 2015). These steps were performed using EPA's software package, Software for the Modeled Attainment Test  -  Community Edition (SMAT-CE) and have been previously documented both in the user's guide for the predecessor software (Abt, 2014) and in EPA's modeling guidance document (U.S. EPA, 2014b). First, we create a 2011 eVNA surface for each PM component species. To create the 2011 eVNA surface, SMAT-CE first calculates quarterly average values (January-March; April-June; July-September; October-December) for each PM2.5 component species at each monitoring site with available measured data. For this calculation we used 3 years of monitoring data (2010-2012). SMAT-CE then creates an interpolated field of the quarterly-average observed data for each PM2.5 component species using inverse distance squared weighting resulting in a separate 3-year average interpolated observed field for each PM2.5 species and each quarter. The interpolated observed fields are then adjusted to match the spatial gradients from the modeled data. These two steps can be calculated using Equation (12):
eVNAg,s,q,2011=WeightxMonitorx,s,q,2010-2012Modelg,s,q,2011Modelx,s,q,2011	(Eq-12)
Where:
 eVNAg,s,q,current is the gradient adjusted quarterly-average eVNA value at grid-cell, g, for PM component species, s, during quarter, q for the year 2011;
 Weightx is the inverse distance weight for monitor x at the location of grid-cell, g;
 Monitorx,s,q,2010-2012 is the 3-year (2010-2012) average of the quarterly monitored concentration for species, s, at monitor, x, during quarter, q;
 Modelg,s,q,2011 is the 2011 modeled quarterly-average concentrations of species, s, at grid cell, g, during quarter, q; and
 Modelx,s,q,2011 is the 2011 modeled quarterly-average concentration of species, s, at the location of monitor, x, during quarter q.
      The 2011 eVNA field serves as the starting point for future-year projections. To create a gridded future-year eVNA surfaces for the baseline and ACE illustrative policy, we take the ratio of the modeled future year quarterly average concentration to the modeled 2011 concentration in each grid cell and multiply that by the corresponding 2011 eVNA quarterly PM2.5 component species value in that grid cell (Equation 13).
eVNAg,s,q,future=eVNAg,s,q,2011xModelg,s,q,futureModelg,s,q,2011	(Eq-13)
      This results in a gridded future-year projection which accounts for adjustments to match observations in the 2011 modeled data.
      Finally, particulate ammonium concentrations are impacted both by emissions of precursor ammonia gas as well as ambient concentrations of particulate sulfate and nitrate. Because of uncertainties in ammonium speciation measurements combined with sparse ammonium measurements in rural areas, the SMAT-CE default is to calculate ammonium values using the degree of sulfate neutralization (i.e., the relative molar mass of ammonium to sulfate with the assumption that all nitrate is fully neutralized). Degree of neutralization values are mainly available in urban areas while sulfate measurements are available in both urban and rural areas. Ammonium is thus calculated by multiplying the interpolated degree of neutralization value by the interpolated sulfate value at each grid-cell location which allows the ammonium fields to be informed by rural sulfate measurements in locations where no rural ammonium measurements are available. The degree of neutralization is not permitted to exceed the maximum theoretical molar ratio of 2:1 for ammonium:sulfate. When creating the future year surface for particulate ammonium, we use the default SMAT-CE assumption that the degree of neutralization for the aerosol remains at 2011 levels.
      A similar method for creating future-year eVNA surfaces is followed for the two ozone metrics with a few key differences. First, while PM2.5 is split into quarterly averages and then averaged up to an annual value, we look at ozone as a summer-season average using definitions that match metrics from epidemiology studies (May-Sep for MDA8 and Apr-Oct for MDA1). The other main difference in the SMAT-CE calculation for ozone is that the spatial interpolation of observations uses an inverse distance weighting rather than an inverse distance squared weighting. This results in interpolated observational fields that better replicate the more gradual spatial gradients observed in ozone compared to PM2.5.
3A.5 References
82 FR 1733. Notice of Availability of the Environmental Protection Agency's Preliminary Interstate Ozone Transport Modeling Data for the 2015 Ozone National Ambient Air Quality Standard (NAAQS), (January 6, 2017).
Abt Associates, 2014. User's Guide: Modeled Attainment Test Software. http://www.epa.gov/scram001/modelingapps_mats.htm
Cohan, D.S., Hakami, A., Hu, Y.T., Russell, A.G., 2005. Nonlinear response of ozone to emissions: Source apportionment and sensitivity analysis. Environ. Sci. Technol. 39, 6739 - 6748.
Cohan, D.S., Napelenok, S.L., 2011. Air Quality Response Modeling for Decision Support. Atmosphere. 2, 407-425.
Ding, D., Zhu, Y., Jang, C., Lin, C., Wang, S., Fu, J., Gao, J., Deng, S., Xie, J., Qui, X., 2015. Evaluation of heath benefit using BenMAP-CE with an integrated scheme of model and monitor data during Guangzhou Asian Games. Journal of Environmental Science. 29, 178-188.
Dunker, A.M., Yarwood, G., Ortmann, J.P., Wilson, G.M., 2002. The decoupled direct method for sensitivity analysis in a three-dimensional air quality model -- Implementation, accuracy, and efficiency. Environ. Sci. Technol. 36, 2965 - 2976.
Gold C, Remmele P.R., Roos T., 1997. In: Algorithmic Foundation of Geographic Information Systems. In: Lecture Notes in Computer Science, Vol. 1340 (van Kereveld M, Nievergelt J, Roos T, Widmayer P, eds) Berlin, Germany: Springer-Verlag. Voronoi methods in GIS. pp. 21 - 35.
Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.T., Hutzell, W.T., 2014. A Database and Tool for Boundary Conditions for Regional Air Quality Modeling: Description and Evaluations, Geoscientific Model Development. 7, 339-360.
Koo, B., Dunker, A.M., Yarwood, G., 2007. Implementing the decoupled direct method for sensitivity analysis in a particulate matter air quality model. Environ. Sci. Technol. 41, 2847 - 2854.
Napelenok, S.L., Cohan, D.S., Hu, Y., Russell, A.G., 2006. Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmospheric Environment. 40, 6112-6121.
Ramboll Environ, 2016. User's Guide Comprehensive Air Quality Model with Extensions version 6.40. Ramboll Environ International Corporation, Novato, CA.
Simon, H., Baker, K. R., Phillips, S., 2012. Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012, Atmos. Environ. 61, 124-139.
Skamarock, W.C., Klemp, J.B., Dudhia,J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., Wang, W., Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3. NCAR Tech. Note NCAR/TN-475+STR. (http://wwww.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf).
US EPA, 2007, Technical Report on Ozone Exposure, Risk, and Impact Assessments for Vegetation. EPA 452/R-07-002. Prepared by Abt Associates Inc. for U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. Research Triangle Park, NC. (https://www3.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_environmental_tsd.pdf).
US EPA, 2014a. Meteorological Model Performance for Annual 2011 Simulation WRF v3.4, Research Triangle Park, NC. (http://www.epa.gov/scram001/).
US EPA, 2014b, Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze- December 2014 DRAFT, Research Triangle Park, NC. (https://www3.epa.gov/ttn/scram/guidance/guide/Draft_O3-PM-RH_Modeling_Guidance-2014.pdf).
US EPA, 2015, Regulatory Impact Analysis of the Final Revisions to the National Ambient Air Quality Standards for Ground-Level Ozone, EPA-452/R-15-07, Research Triangle Park, NC. (https://www.epa.gov/sites/production/files/2016-02/documents/20151001ria.pdf).
US EPA, 2017a, Technical Support Document (TSD) Additional Updates to Emissions Inventories for the Version 6.3, 2011 Emissions Modeling Platform for the Year 2023, Research Triangle Park, NC. (https://www.epa.gov/sites/production/files/2017-11/documents/ 2011v6.3_2023en_update_emismod_tsd_oct2017.pdf).
US EPA, 2017b. Documentation for the EPA's Preliminary 2028 Regional Haze Modeling. Research Triangle Park, NC (https://www3.epa.gov/ttn/scram/reports/2028_Regional_Haze_Modeling-TSD.pdf).
US EPA, 2017c. Air Quality Modeling Technical Support Document for the 2015 Ozone NAAQS Preliminary Interstate Transport Assessment. Research Triangle Park, NC (https://www.epa.gov/airmarkets/notice-data-availability-preliminary-interstate-ozone-transport-modeling-data-2015-ozone).
Yantosca, B. 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA.
Zavala, M., Lei, W., Molina, M.J., Molina, L.T., 2009. Modeled and observed ozone sensitivity to mobile-source emissions in Mexico City. Atmos. Chem. Phys. 9, 39 - 55.

--------------------------------------------------------------------------------
CHAPTER 4: COST, EMISSIONS, AND ENERGY IMPACTS
Overview
This chapter reports the compliance costs, emissions, and energy analyses performed for the Revised CSAPR Update proposed rule. EPA used the Integrated Planning Model (IPM) to conduct most of the analysis discussed in this chapter. As explained in detail below, this chapter presents analysis for three regulatory control alternatives that differ in the level of electric generating units (EGU) nitrogen oxides (NOX) ozone season emissions budgets in 12 states subject to this action. These regulatory control alternatives impose different budget levels based on alternative assumptions about the possible actions that EGUs may be able to pursue to reduce their NOX emissions.
 The chapter is organized as follows: following a summary of the regulatory control alternatives analyzed and a summary of EPA's methodology, we present estimates of compliance costs, as well as estimated impacts on emissions, generation, capacity, fuel use, fuel price, and retail electricity price.
4.1	Regulatory Control Alternatives
Of the 22 states currently covered by the Cross-State Air Pollution Rule (CSAPR) NOX Ozone Season Group 2 trading program, EPA is proposing to establish revised budgets for 12 states. Therefore, EPA is proposing the creation of an additional geographic group and ozone season trading program comprised of these 12 upwind states with remaining linkages to downwind air quality problems in 2021. This new group, Group 3, will be covered by a new CSAPR NOX Ozone Season Group 3 trading program and will no longer be subject to Group 2 budgets. Aside from the removal of the 12 covered states from the current Group 2 program, this proposal leaves unchanged the budget stringency and geography of the existing CSAPR NOX Ozone Season Group 1 and Group 2 trading programs. The EGUs covered by the FIPs and subject to the budget are all fossil-fired EGUs with >25 megawatt (MW) capacity.
This RIA evaluates the benefits, costs and certain impacts of compliance with three regulatory control alternatives: the Revised CSAPR Update proposed rule, a less-stringent alternative, and a more-stringent alternative. For details on the derivation of these budgets, please see Section VII of the preamble. Aside from the difference in emission budgets, other key regulatory features of the allowance trading program, such as the ability to bank allowances for future use, are the same across all the three different sets of NOX emissions budgets analyzed.
4.1.2	Regulatory Control Alternatives Analyzed
     In accordance with Executive Orders 12866 and 13563, the guidelines of OMB Circular A-4, and EPA's Guidelines for Preparing Economic Analyses, this RIA analyzes the benefits and costs associated with complying with the Revised CSAPR Update proposed rule. The Revised CSAPR Update proposed emission budgets in this RIA represent EGU NOX ozone season emission budgets for each state that were developed using uniform control stringency represented by $1,600 per ton of NOX (2016$). This RIA analyzes the Revised CSAPR Update proposed emission budgets, as well as a more and a less stringent alternative to the Revised CSAPR Update proposal. The more and less stringent alternatives differ from the Revised CSAPR Update proposal in that they set different NOX ozone season emission budgets for the affected EGUs. The less-stringent scenario uses emission budgets that were developed using uniform control stringency represented by $500 per ton of NOX (2016$). The more-stringent scenario uses emission budgets that were developed using uniform control stringency represented by $9,600 per ton of NOX (2016$). For details, please see EGU NOX Mitigation Strategies Proposed Rule TSD, in the docket for this proposed rule.
Table 4-1 reports the EGU NOX ozone season emission budgets that are evaluated in this RIA. As described above, starting in 2021, emissions from affected EGUs in the 12 states cannot exceed the sum of emissions budgets but for the ability to use banked allowances from previous years for compliance. No further reductions in budgets occur after 2024, and budgets remain in place for future years. Furthermore, emissions from affected EGUs in a particular state are subject to the CSAPR assurance provisions, which require additional allowance surrender penalties (a total of 3 allowances per ton of emissions) on emissions that exceed a state's CSAPR NOX ozone season assurance level, or 121 percent of the emissions budget. Similar to the approach taken in the CSAPR Update, EPA is proposing a one-time conversion of banked Group 2 allowances according to a formula. The size of the initial bank would be set at a level that would ensure that the use of these converted allowances, in addition to the allowances provided in the states' emissions budgets under the Group 3 trading program, would not authorize emissions in the trading program region in the first year of the program to exceed the sum of the states' budgets by more than the sum of the states' variability limits. The CSAPR NOX ozone season allowance trading program is described in further detail in Section VIII of the preamble.
Table 4-1. NOX Ozone Season Emission Budgets (Tons) Evaluated

Revised CSAPR Update Proposal
 State
                                     2021
                                     2022
                                     2023
                                     2024
                                     2025
Illinois
                                     9,444
                                     9,415
                                     8,397
                                     8,397
                                     8,397
Indiana
                                    12,500
                                    11,998
                                    11,998
                                     9,447
                                     9,447
Kentucky
                                    14,384
                                    11,936
                                    11,936
                                    11,936
                                    11,936
Louisiana
                                    15,402
                                    14,871
                                    14,871
                                    14,871
                                    14,871
Maryland
                                     1,522
                                     1,498
                                     1,498
                                     1,498
                                     1,498
Michigan
                                    12,727
                                    11,767
                                     9,803
                                     9,614
                                     9,614
New Jersey
                                     1,253
                                     1,253
                                     1,253
                                     1,253
                                     1,253
New York
                                     3,137
                                     3,137
                                     3,137
                                     3,119
                                     3,119
Ohio
                                     9,605
                                     9,676
                                     9,676
                                     9,676
                                     9,676
Pennsylvania
                                     8,076
                                     8,076
                                     8,076
                                     8,076
                                     8,076
Virginia
                                     4,544
                                     3,656
                                     3,656
                                     3,395
                                     3,395
West Virginia
                                    13,686
                                    12,813
                                    11,810
                                    11,810
                                    11,810
Total
                                    106,280
                                    100,096
                                    96,111
                                    93,092
                                    93,092

Less-Stringent Alternative
 State
                                     2021
                                     2022
                                     2023
                                     2024
                                     2025
Illinois
                                     9,667
                                     9,632
                                     8,579
                                     8,599
                                     8,579
Indiana
                                    15,677
                                    15,206
                                    15,206
                                    12,755
                                    12,603
Kentucky
                                    15,606
                                    15,606
                                    15,606
                                    15,588
                                    15,606
Louisiana
                                    15,442
                                    15,442
                                    15,442
                                    15,488
                                    15,442
Maryland
                                     1,565
                                     1,565
                                     1,565
                                     1,565
                                     1,565
Michigan
                                    13,120
                                    13,120
                                    10,313
                                    10,841
                                    10,116
New Jersey
                                     1,346
                                     1,346
                                     1,346
                                     1,346
                                     1,346
New York
                                     3,182
                                     3,182
                                     3,182
                                     3,169
                                     3,163
Ohio
                                    15,490
                                    15,560
                                    15,560
                                    15,917
                                    15,560
Pennsylvania
                                    11,487
                                    11,487
                                    11,487
                                    11,570
                                    11,487
Virginia
                                     4,588
                                     4,172
                                     4,172
                                     3,912
                                     3,908
West Virginia
                                    15,017
                                    15,017
                                    13,272
                                    13,407
                                    13,272
Total
                                    122,187
                                    121,334
                                    115,730
                                    114,156
                                    112,647

More-Stringent Alternative
 State
                                     2021
                                     2022
                                     2023
                                     2024
                                     2025
Illinois
                                     9,444
                                     9,415
                                     8,397
                                     7,142
                                     7,142
Indiana
                                    12,500
                                    11,998
                                    11,998
                                     8,264
                                     8,264
Kentucky
                                    14,384
                                    11,936
                                    11,936
                                     8,852
                                     8,852
Louisiana
                                    15,402
                                    14,871
                                    14,871
                                    12,636
                                    12,636
Maryland
                                     1,522
                                     1,498
                                     1,498
                                     1,239
                                     1,239
Michigan
                                    12,727
                                    11,767
                                     9,803
                                     7,315
                                     7,315
New Jersey
                                     1,253
                                     1,253
                                     1,253
                                     1,257
                                     1,257
New York
                                     3,137
                                     3,137
                                     3,137
                                     3,020
                                     3,020
Ohio
                                     9,605
                                     9,676
                                     9,676
                                     9,126
                                     9,126
Pennsylvania
                                     8,076
                                     8,076
                                     8,076
                                     7,578
                                     7,578
Virginia
                                     4,544
                                     3,656
                                     3,656
                                     3,022
                                     3,022
West Virginia
                                    13,686
                                    12,813
                                    11,810
                                     9,569
                                     9,569
Total
                                    106,280
                                    100,096
                                    96,111
                                    79,020
                                    79,020

Note that EGUs have flexibility in determining how they will comply with the allowance trading program. As discussed below, the way that they comply may differ from the methods forecast in the modeling for this RIA. See Section 4.3 for further discussion of the modeling approach used in the analysis presented below.
4.2	Power Sector Modeling Framework
IPM is a state-of-the-art, peer-reviewed, dynamic linear programming model that can be used to project power sector behavior under future business-as-usual conditions and to examine prospective air pollution control policies throughout the contiguous United States for the entire electric power system. EPA used IPM to project likely future electricity market conditions with and without the Revised CSAPR Update proposal.
IPM, developed by ICF, is a multi-regional, dynamic, deterministic linear programming model of the contiguous U.S. electric power sector. It provides estimates of least cost capacity expansion, electricity dispatch, and emissions control strategies while meeting energy demand and environmental, transmission, dispatch, and reliability constraints. EPA has used IPM for almost three decades to better understand power sector behavior under future business-as-usual conditions and to evaluate the economic and emissions impacts of prospective environmental policies. The model is designed to reflect electricity markets as accurately as possible. EPA uses the best available information from utilities, industry experts, gas and coal market experts, financial institutions, and government statistics as the basis for the detailed power sector modeling in IPM. The model documentation provides additional information on the assumptions discussed here as well as all other model assumptions and inputs.
The model incorporates a detailed representation of the fossil-fuel supply system that is used to estimate equilibrium fuel prices. The model uses natural gas fuel supply curves and regional gas delivery costs (basis differentials) to simulate the fuel price associated with a given level of gas consumption within the system. These inputs are derived using ICF's Gas Market Model (GMM), a supply/demand equilibrium model of the North American gas market.
      IPM also endogenously models the partial equilibrium of coal supply and EGU coal demand levels throughout the contiguous U.S., taking into account assumed non-power sector demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal transport network, which consists of over four thousand linkages representing rail, barge, and truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough bottom-up, mine-by-mine approach that depicts the coal choices and associated supply costs that power plants would face if selecting that coal over the modeling time horizon. The IPM documentation outlines the methods and data used to quantify the economically recoverable coal reserves, characterize their cost, and build the 36 coal regions' supply curves.
To estimate the annualized costs of additional capital investments in the power sector, EPA uses a conventional and widely accepted approach that applies a capital recovery factor (CRF) multiplier to capital investments and adds that to the annual incremental operating expenses. The CRF is derived from estimates of the power sector's cost of capital (i.e., private discount rate), the amount of insurance coverage required, local property taxes, and the life of capital. It is important to note that there is no single CRF factor applied in the model; rather, the CRF varies across technologies, book life of the capital investments, and regions in the model in order to better simulate power sector decision-making.
EPA has used IPM extensively over the past three decades to analyze options for reducing power sector emissions. Previously, the model has been used to estimate the costs, emission changes, and power sector impacts for the Clean Air Interstate Rule (U.S. EPA, 2005), the original Cross-State Air Pollution Rule (U.S. EPA, 2011), the Mercury and Air Toxics Standards (MATS) (U.S. EPA, 2011a), the Clean Power Plan (CPP) for Existing Power Plants (U.S. EPA, 2015), the Carbon Pollution Standards for New Power Plants (U.S. EPA, 2015), the Affordable Clean Energy Rule (U.S. EPA, 2019), and the Clean Power Plan Repeal (U.S. EPA, 2019). EPA has also used IPM to estimate the air pollution reductions and power sector impacts of water and waste regulations affecting EGUs, including Cooling Water Intakes (316(b)) Rule (U.S. EPA, 2014), Disposal of Coal Combustion Residuals from Electric Utilities (CCR) (U.S. EPA, 2015b) and Steam Electric Effluent Limitation Guidelines (ELG) (U.S. EPA, 2015c).
The model and EPA's input assumptions undergo periodic formal peer review. The rulemaking process also provides opportunity for expert review and comment by a variety of stakeholders, including owners and operators of capacity in the electricity sector that is represented by the model, public interest groups, and other developers of U.S. electricity sector models. The feedback that the Agency receives provides a highly detailed review of key input assumptions, model representation, and modeling results. IPM has received extensive review by energy and environmental modeling experts in a variety of contexts. For example, in October 2014 U.S. EPA commissioned a peer review of EPA Base Case version 5.13 using the Integrated Planning Model. Additionally, and in the late 1990s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 prospective studies that are periodically conducted. The Agency has also used the model in a number of comparative modeling exercises sponsored by Stanford University's Energy Modeling Forum over the past 15 years. IPM has also been employed by states (e.g., for the Regional Greenhouse Gas Initiative, the Western Regional Air Partnership, Ozone Transport Assessment Group), other Federal and state agencies, environmental groups, and industry.
4.3	EPA's Power Sector Modeling of the Base Case and Three Regulatory Control Alternatives
The IPM "base case" for any regulatory impact analysis is a business-as-usual scenario that represents expected behavior in the electricity sector under market and regulatory conditions in the absence of the proposed rule. As such, an IPM base case represents an element of the baseline for this RIA. EPA frequently updates the IPM base case to reflect the latest available electricity demand forecasts from the U.S. Energy Information Administration (EIA) as well as expected costs and availability of new and existing generating resources, fuels, emission control technologies, and regulatory requirements.
4.3.1	EPA's IPM Base Case v.6
For our analysis of the Revised CSAPR Update proposed rule, EPA used the January 2020 release of IPM version 6 to provide power sector emissions data for air quality modeling, as well as a companion updated database of EGU units (the National Electricity Energy Data System, or NEEDS v.6 rev: 1-8-2020) that is used in EPA's modeling applications of IPM. The IPM Base Case includes the Affordable Clean Energy (ACE) Rule consistent with the RIA for the final rule and includes both the CSAPR rule and CSAPR Update rule. The Base Case includes the 2015 Effluent Limitation Guidelines (ELG) and the 2015 Coal Combustion Residuals (CCR), but does not include the recently finalized 2020 ELG and CCR rules. The analysis of cost and impacts presented in this chapter is based on a single IPM base case, and represents incremental impacts projected solely as a result of compliance with the emissions budgets presented in Table 4-1 above.
4.3.2.	Methodology for Evaluating the Regulatory Control Alternatives
To estimate the costs, benefits, and economic and energy market impacts of the Revised CSAPR Update proposal, EPA conducted quantitative analysis of the three regulatory control alternatives: the Revised CSAPR Update proposed emission budgets and more and less stringent alternatives. Details about these regulatory control alternatives, including state-specific EGU NOX ozone-season emissions budgets for each alternative as analyzed in this RIA, are provided above in Section 4.1.
Before undertaking power sector analysis to evaluate compliance with the regulatory control alternatives, EPA first considered available EGU NOX mitigation strategies that could be implemented for the upcoming ozone season (i.e., the 2021 ozone season). EPA considered all widely-used EGU NOX control strategies: optimizing NOX removal by existing, operational selective catalytic reduction (SCRs) and turning on and optimizing existing idled SCRs; turning on existing idled selective non-catalytic reduction (SNCRs); installation of (or upgrading to) state-of-the-art NOX combustion controls; shifting generation to units with lower NOX emission rates; and installing new SCRs and SNCRs. EPA determined that affected EGUs within the 12 states could implement all of these NOX mitigation strategies, except installation of new SCRs or SNCRs and state of the art combustion controls for the 2021 ozone season. After assessing the available NOx mitigation methods for complying with the annual budgets, this RIA projects that the system-wide least-cost strategies for compliance with the proposed Revised CSAPR Update and the more and less stringent regulatory alternatives lead to the application of the same controls at the same sources as in the analysis used to calculate the budgets for these alternatives. As a consequence, the sectoral analyses used to establish the budgets are the same analyses used to estimate the compliance cost, benefits, and impacts of the proposed Revised CSAPR Update and the more and less stringent alternatives. In the analysis of the proposed rule presented in this RIA, in each year of the analysis period (2021-2025) and in each of the 12 states subject to tighter seasonal NOx budgets, seasonal NOx emissions from the sources subject to the proposed rule equal the seasonal NOx budget. For more details on these assessments, including the assessment of EGU NOX mitigation costs and feasibility, please refer to the EGU NOX Mitigation Strategies Proposed Rule TSD, in the docket for this proposed rule.
These mitigation strategies are primarily captured within the model. However, due to limitations on model size, IPM v.6 does not have the ability to endogenously determine whether or not to operate existing EGU post-combustion NOX controls (i.e., SCR or SNCR) in response to a regulatory emissions requirement. The operating status of existing post-combustion NOX controls at a particular EGU in a model scenario is determined by the model user. In order to evaluate compliance with the regulatory alternatives, EPA determined outside of IPM whether or not operation of existing controls that are idle in the baseline would be reasonably expected for compliance with each of the evaluated regulatory alternatives and for which model years they can feasibly be applied. IPM includes optimization and perfect foresight in solving for least cost dispatch. Given that the final rule will likely become effective either immediately prior to or slightly after the start of the 2021 ozone season, to avoid overstating optimization and dispatch decisions that are not possible in the short time frame, EPA complemented the projected IPM EGU outlook with historical (e.g., engineering analytics) perspective based on historical data that only factors in known changes to the fleet. This analysis forms the basis for the climate benefits calculations presented in this RIA.
EPA considers a unit to have optimized use of an SCR if emissions rates are equal to (or below) the "widely achievable" rate of 0.08 lbs/MMBtu. Within IPM, units with extant SCRs are defined as SCR-equipped units with ozone season NOX emission rates less than 0.20 lbs/ MMBtu in the Base Case. These units had their emission rates lowered to the lower of their mode 4 NOX rate in NEEDS and the "widely achievable" optimized emissions rate of 0.08 lbs/ MMBtu in the Revised CSAPR Update proposal. Units equipped with SCRs with an emissions rate exceeding 0.20 lbs/ MMBtu were considered to have idled SCRs. These units had their emission rates lowered to the lower of their mode 4 NOX rate in NEEDS and the "widely achievable" optimized emissions rate of 0.08 lbs/ MMBtu in the Revised CSAPR Update proposal. These control options are achievable in 2021 and were associated with a uniform control cost of $800 per ton and $1,600 per ton respectively. No further adjustments were made to the variable and fixed operating cost of these units, and their heat rates were also not adjusted to reflect energy requirements from increasing SCR removal efficiency within IPM. Under the proposed rule, 60 units are projected to fully run existing SCR controls, while 4 units are projected to turn on idled SCR controls.
Finally, unit combustion control configurations listed in NEEDS were compared against Table 3-11 in the Documentation for EPA Base Case v.5.13 Using the Integrated Planning Model IPM v.6, which lists state-of-the-art combustion control configurations based on unit firing type. This allowed EPA to identify units that would receive state-of-the-art combustion control upgrades in IPM. EPA then followed the procedure in the EGU NOx Mitigation Strategies Proposed Rule TSD to calculate each of these unit's new NOX emission rate. These upgrades were assumed to occur in 2022 and were assigned a uniform control cost of $1,600 per ton. No further adjustments were made to the variable and fixed operating cost of these units, and their heat rates were also not adjusted to reflect increased energy input requirements at a given load from the use of additional combustion controls, within IPM. Under the proposed rule, 27 units are projected to install state-of-the-art combustion controls.
     The EGU NOX mitigation strategies that are assumed to operate or are available to reduce NOX in order to comply with each of the regulatory control alternatives are shown in Table 4-2; more information about the estimated costs of these controls can be found in the EGU NOX Mitigation Strategies Proposed Rule TSD.
Table 4-2.	NOX Mitigation Strategies Implemented for Compliance with the Regulatory Control Alternatives
                        Regulatory Control Alternative
NOX Controls Implemented
Less Stringent Alternative
 Shift generation to minimize costs (costs estimated within IPM)
Revised CSAPR Update Proposed Rule
      (All controls above)
 Fully operating existing SCRs to achieve 0.08 lb/MMBtu NOx emission rate (costs estimated outside IPM)
 Turn on idled SCRs (costs estimated outside IPM) and fully operate akin to (2)
  Install state of the art combustion controls.
More Stringent Alternative
      (All controls above)
  In 2025, turn on idled SNCRs (costs estimated outside IPM)
  In 2025, install new SCRs (costs estimated outside IPM)

For the NOX controls identified in Table 4-2, under the proposed rule and the more stringent alternative, 60 units are projected to fully operate existing SCRs and 4 units are projected to turn on idled SCRs. Under the less stringent alternative, no units are projected to either fully operate existing SCRs or turn on idled SCRs. Under the proposed rule and the more stringent alternative, 27 units are projected to install state-of-the-art combustion controls, and under the less stringent alternative no units are projected to install state-of-the-art combustion controls. The book-life of the controls is assumed to be 15 years. Under the proposed rule and the less stringent alternative, no units are projected to install new SCRs, and under the more stringent alternative, 48 units are projected to install new SCRs. The book-life of the new SCRs is assumed to be 15 years.
      In addition to the limitation on ozone season NOX emissions required by the EGU emissions budgets for the 12 states, there are four important features of the allowance trading program represented in the model that may influence the level and location of NOX emissions from affected EGUs, including: the ability of affected EGUs to buy and sell NOX ozone season allowances from one another for compliance purposes; the ability of affected EGUs to bank NOX ozone season allowances for future use; the effect of limits on the total ozone season NOX emissions from affected EGUs in each state required by the assurance provisions; and the treatment of banked pre-2021 vintage NOX ozone season allowances issued under the CSAPR Update program now being revised under this proposal. Each of these features of the ozone season allowance trading program is described below.
     Affected EGUs are expected to choose the least-cost method of complying with the requirements of the allowance trading program, and the distribution of ozone season NOX emissions across affected EGUs is generally governed by this cost-minimizing behavior in the analysis. The total ozone season NOX emissions from affected EGUs in this analysis are limited to the amount allowed by the sum of the NOX budgets across the 12 states. Furthermore, allowances may be banked for future use. The number of banked allowances is influenced by the determination, outside the model, of whether (i) existing controls that are idle in the base case are turned on and (ii) it is less costly to abate ozone season NOX emissions in a current ozone season than to abate emissions in a later ozone season. Affected EGUs are expected to bank NOX ozone season allowances in the 2021 ozone season for use in a later ozone season. The model starts with an assumed bank level in 2021 and endogenously determines the bank in each subsequent year. Based on observation, EPA believes that this is a reasonable compliance path for EGUs, even though there may be other non-economic reasons, such as being prepared for future variability in power sector operations, that can potentially influence this decision.
     While there are no explicit limits on the exchange of allowances between affected EGUs and on the banking of 2021 and future vintage NOX ozone season allowances, the assurance provisions limit the amount of seasonal NOX emissions by affected EGUs in each of the 12 states. The assurance level limits affected EGU emissions over an ozone season to the state's NOX ozone season emissions budget plus an increment equal to 21 percent of each state's emissions budget. This increment is called the variability limit. See Section VIII.C.4 of the preamble for a discussion of the purpose of the assurance provision and further detail about how the variability limits and assurance levels are determined. If a state exceeds its assurance level in a given year, sources within that state are assessed a total of 3-to-1 allowance surrender on the excess tons. Section VIII.C.4 of the preamble also explains how EPA then determines which EGUs are subject to this surrender requirement. In the modeling, the assurance provisions are represented by a limit on the total ozone season NOX emissions that may be emitted by affected EGUs in each state, and thus the modeling does not permit affected EGUs to emit beyond the assurance levels and thus incur penalties.
      As described in Section VIII.D.4 of the preamble, the rule allows pre-2021 vintage NOX ozone season allowances (that had been issued under the CSAPR Update program now being revised under this proposal) to be used for compliance with this proposed rule, following a one-time conversion that reduces the overall quantity of banked allowances from that time period. Based on EPA's expectation of the size of the NOX allowance bank after the one-time conversion carried out pursuant to the terms of this proposed rule, the treatment of these banked allowances is represented in the modeling as an additional 21,020 tons of NOX allowances, the equivalent of one year of the variability limit associated with the emission budgets, that may be used by affected EGUs during the 2021 ozone season or in later ozone seasons under the Revised CSAPR Update rule. Under the more stringent and less stringent alternatives an additional 21,020 tons and 25,480 tons respectively may be used by affected EGUs during the 2021 ozone season or in later ozone seasons.
4.3.3	Methodology for Estimating Compliance Costs
     This section describes EPA's approach to quantify estimated compliance costs associated with the three regulatory control alternatives. These compliance costs include estimates projected directly by the model as well as calculations performed outside of the model that use IPM model inputs and methods. The model projections capture the costs associated with shifting generation to lower-NOX emitting EGUs. The costs of increasing the use and optimizing the performance of existing and operating SCRs, and for installing or upgrading NOX combustion controls, were estimated outside of the model. The costs for these two NOX mitigation strategies are calculated based on engineering analytics emissions projections and use the same NOX control cost equations used in IPM. Therefore, this estimate is consistent with modeled projections and provides the best available quantification of the costs of these NOX mitigation strategies.
     The following steps summarize EPA's methodology for estimating the component of compliance costs that are calculated outside of the model for the Revised CSAPR Update proposal alternative:
 In the model projections, identify all EGUs in the 12 states that can adopt the following NOX mitigation strategies:
 Fully operating existing SCRs
 Installing state of the art combustion controls
 Estimate the total NOX reductions that are attributable to each of these strategies:
 Fully operating existing SCRs (SCRs operating in base case): 9,154 tons
 Fully operating existing SCRs (SCRs not operating in base case): 5,870 tons
 Installing state-of-the-art combustion controls (not available in 2021): 0 tons

 Estimate the average cost associated with each of these strategies:
 Fully operating existing SCRs (SCRs operating in base case): $800/ton
 Fully operating existing SCRs (SCRs not operating in base case): $1,600/ton
 Installing state-of-the-art combustion controls: $1,600/ton

 Multiply (2) by (3) to estimate the total cost associated with each of these strategies.
     Table 4-3 summarizes the results of this methodology for the Revised CSAPR Update proposal alternative in 2021.
Table 4-3.	Summary of Methodology for Calculating Compliance Costs Estimated Outside of IPM for Revised CSAPR Update Proposal, 2021 (2016$)
NOX Mitigation Strategy
                                                     NOX Ozone Season Emissions
                                                                         (Tons)
                                                           Average Cost ($/ton)
                                                                     Total Cost
                                                                          ($MM)
Optimize existing SCRs
                                                                          9,154
                                                                            800
                                                                              7
Operate existing SCRs
                                                                          5,870
                                                                          1,600
                                                                              9

     EPA exogenously updated the emissions rates for the identified EGUs within the 12 states consistent with the set of controls determined for 2021-2025 within IPM. The model was updated to incorporate the emissions budgets identified for each case, and the first-year bank adjustment as outlined in Section 4.3.2. The Group 2 regional trading program was updated to exclude the 12-state Group 3 regional trading program, and budgets for the remaining Group 2 states were left otherwise unchanged. The change in the reported power system production cost between this model run and the base run was used to capture the cost of generation shifting. The total costs of compliance with the regulatory control alternatives are estimated as the sum of the costs that are modeled within IPM and the costs that are calculated outside the model.
4.4	Estimated Impacts of the Regulatory Control Alternatives
4.4.1	Emission Reduction Assessment
     As discussed in Chapter 1, EPA determined that NOX emissions in 12 eastern states affect the ability of downwind states to attain and maintain the 2008 ozone NAAQS. For these 12 eastern states, EPA is issuing Federal Implementation Plans (FIPs) that generally update the existing CSAPR Update NOX ozone-season emission budgets for EGUs and implement these budgets via the CSAPR NOX ozone-season allowance trading program.
     As indicated in Chapter 1, the NOX emissions reductions are presented in this RIA for two time periods: 2021 and 2025. The 2021 emissions estimates are based on IPM projections for 2021, and adjustments to account for historical data. For more information on these and other adjustments, see the Ozone Transport Policy Analysis TSD.
     Table 4-4 presents the estimated reduction in power sector NOX emissions resulting from compliance with the evaluated regulatory control alternatives (i.e., emissions budgets) in the 12 states, as well as the impact on other states. The emission reductions follow an expected pattern: the less stringent alternative produces substantially smaller emissions reductions than EPA's proposed emissions budgets, and the more stringent alternative results in slightly more NOX emissions reductions.
Table 4-4.	EGU Ozone Season NOX Emissions and Emissions Changes (thousand tons) for the Base Case and the Regulatory Control Alternatives
                               Ozone Season NOX
                                (thousand tons)
                                Total Emissions
                             Change from Base Case

                                   Base Case
                         Revised CSAPR Update Proposal
                          Less-Stringent Alternative
                          More-Stringent Alternative
                         Revised CSAPR Update Proposal
                          Less-Stringent Alternative
                          More-Stringent Alternative
                                     2021
12 States
                                      124
                                      106
                                      122
                                      106
                                      -17
                                      -2
                                      -17

Other States
                                      236
                                      236
                                      236
                                      236
                                       0
                                       0
                                       0

Total
                                      359
                                      342
                                      358
                                      342
                                      -17
                                      -2
                                      -17

12 States
                                      123
                                      100
                                      121
                                      100
                                      -23
                                      -2
                                      -23
                                     2022
Other States
                                      235
                                      235
                                      235
                                      235
                                       0
                                       0
                                       0

Total
                                      358
                                      335
                                      356
                                      335
                                      -23
                                      -2
                                      -23

12 States
                                      117
                                      96
                                      116
                                      96
                                      -21
                                      -2
                                      -21
                                     2023
Other States
                                      227
                                      227
                                      227
                                      227
                                       0
                                       0
                                       0

Total
                                      345
                                      324
                                      343
                                      324
                                      -21
                                      -2
                                      -21

12 States
                                      114
                                      93
                                      113
                                      79
                                      -21
                                      -2
                                      -35
                                     2024
Other States
                                      225
                                      225
                                      225
                                      225
                                       0
                                       0
                                       0

Total
                                      340
                                      319
                                      338
                                      304
                                      -21
                                      -2
                                      -35
                                     2025
12 States
                                      114
                                      93
                                      113
                                      79
                                      -21
                                      -2
                                      -35

Other States
                                      225
                                      225
                                      225
                                      225
                                       0
                                       0
                                       0

Total
                                      340
                                      319
                                      338
                                      304
                                      -21
                                      -2
                                      -35