Figure 5-1. 	Monetized Health Benefits of CSAPR update for 2017 *
*The PM2.5 graph shows the estimated PM2.5 co-benefits at discount rates of 3% and 7% using effect coefficients derived from the Krewski et al. (2009) study and the Lepeule et al. (2012) study, as well as 8 of the 12 effect coefficients derived from EPA's expert elicitation on PM mortality (Roman et al., 2008); four of the coefficients reported no mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response functions provided in those studies. Ozone benefits occur in the analysis year, so they are the same for all discount rates. These estimates do not include benefits from reductions in CO2. The monetized co-benefits do not include reduced health effects from direct exposure to NO2 as well as ecosystem effects or visibility impairment from reductions in NOX.

5.2.7 	Characterization of Uncertainty in the Estimated Health Benefits
In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty. This analysis is no exception. This analysis includes many data sources as inputs, including emission inventories, air quality data from models (with their associated parameters and inputs), population data, population estimates, health effect estimates from epidemiology studies, economic data for monetizing benefits, and assumptions regarding the future state of the world (i.e., regulations, technology, and human behavior). Each of these inputs may be uncertain and would affect the estimated benefits. When the uncertainties from each stage of the analysis are compounded, even small uncertainties can have large effects on the total quantified benefits. The use of the benefit-per-ton approach adds additional uncertainties beyond those for analyses based directly on air quality modeling. Therefore, the estimates of benefits should be viewed as illustrating the general magnitude of benefits of the CSAPR update and regulatory control alternatives for the 2017 analysis year, rather than the actual benefits anticipated from implementing the rule.
This RIA shares the same detailed uncertainty assessment found in the Ozone NAAQS RIA (U.S. EPA, 2015) or the PM NAAQS RIA (U.S. EPA, 2012a) because of the air quality modeling input data used to run the benefits model. The results of the quantitative and qualitative uncertainty analyses presented in the Ozone NAAQS RIA and PM NAAQS RIA provide some information regarding the uncertainty inherent in the estimated benefits results presented in this analysis. For example, sensitivity analyses conducted for the PM NAAQS RIA indicate that alternate cessation lag assumptions could change the estimated PM2.5-related mortality co-benefits discounted at 3 percent by between 10 percent and  - 27 percent and that alternative income growth adjustments could change the PM2.5-related mortality benefits by between 33 percent and −14 percent. Although we generally do not calculate confidence intervals for benefit-per-ton estimates as they can provide an incomplete picture about the overall uncertainty in the benefits estimates, the PM NAAQS RIA provides an indication of the random sampling error in the health impact and economic valuation functions using Monte Carlo methods. In general, the 95[th] percentile confidence interval for monetized PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). The 95[th] percentile confidence interval for the health impact function alone ranges from approximately +-30 percent for mortality incidence based on Krewski et al. (2009) and +-46 percent based on Lepeule et al. (2012).
After determining the health impact assessment using the air quality modeling data, we calculated and applied benefit-per-ton estimates, which reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions. For example, these estimates may not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over-estimate or under-estimate of the actual co-benefits of controlling PM and ozone precursors. As such, it is not feasible to estimate the proportion of co-benefits occurring in different locations. Use of these benefit-per-ton values to estimate benefits may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on a broad emission reduction scenario and therefore represent average benefits-per-ton over the entire region. The benefit-per-ton for emission reductions in specific locations may be very different than the estimates presented here. To the extent that the geographic distribution of the emissions reductions achieved by implementing the final rule relative to the baseline used to estimate costs and emission reductions is different than the emissions reductions in the air quality modeling of the illustrative budgets and the baseline described in Chapter 3, the benefits may be underestimated or overestimated.
The benefits reported here reflect the reduction in NOx emissions among the 22 CSAPR states alone. Excluding states outside of the 22-state region may under-estimates benefits because it does not reflect the improved air quality that could occur among states downwind of the 22-state region. However, for reasons noted above, the air quality modeling simulation for this analysis did not account for the size and distribution of reduced NOx emissions in this rule. The modeling simulated emission changes in certain states -- including North Carolina and Georgia -- that were subsequently omitted from the CSAPR region. To avoid incorrectly accounting for ozone-related benefits from reduced NOx emissions from such locations, we elected to calculate benefits only within the 22-state region. Finally, by estimating ozone health impacts from May to September only, we may have underestimated ozone related benefits in areas experiencing a longer ozone season.

Our estimate of the total monetized benefits is based on the EPA's interpretation of the best available scientific literature and methods and supported by the SAB-HES and the National Academies of Science (NRC, 20022.5-related premature mortality, which accounts for 98 percent of the monetized PM2.5 health co-benefits.
 We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM2.5 varies considerably in composition across sources, but the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The PM ISA concluded that "many constituents of PM2.5 can be linked with multiple health effects, and the evidence is not yet sufficient to allow differentiation of those constituents or sources that are more closely related to specific outcomes" (U.S. EPA, 2009b).
 We assume that the health impact function for fine particles is log-linear without a threshold. Thus, the estimates include health co-benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both areas that do not meet the fine particle standard and those areas that are in attainment, down to the lowest modeled concentrations.
 We assume that there is a "cessation" lag between the change in PM exposures and the total realization of changes in mortality effects. Specifically, we assume that some of the incidences of premature mortality related to PM - 2.5 exposures occur in a distributed fashion over the 20 years following exposure based on the advice of the SAB-HES (U.S. EPA-SAB, 2004c), which affects the valuation of mortality co-benefits at different discount rates.
In general, we are more confident in the magnitude of the risks we estimate from simulated PM2.5 concentrations that coincide with the bulk of the observed PM concentrations in the epidemiological studies that are used to estimate the benefits. Likewise, we are less confident in the risk we estimate from simulated PM2.5 concentrations that fall below the bulk of the observed data in these studies. Concentration benchmark analyses (e.g., lowest measured level [LML], one standard deviation below the mean of the air quality data in the study, etc.) allow readers to determine the portion of population exposed to annual mean PM2.5 levels at or above different concentrations, which provides some insight into the level of uncertainty in the estimated PM2.5 mortality benefits. In this analysis, we apply two concentration benchmark approaches (LML and one standard deviation below the mean) that have been incorporated into recent RIAs and the EPA's Policy Assessment for Particulate Matter (U.S. EPA, 2011d). There are uncertainties inherent in identifying any particular point at which our confidence in reported associations becomes appreciably less, and the scientific evidence provides no clear dividing line. However, the EPA does not view these concentration benchmarks as a concentration threshold below which we would not quantify health benefits of air quality improvements. Rather, the co-benefits estimates reported in this RIA are the best estimates because they reflect the full range of air quality concentrations associated with the regulatory control alternatives. The PM ISA concluded that the scientific evidence collectively is sufficient to conclude that the relationship between long-term PM2.5 exposures and mortality is causal and that, overall, the studies support the use of a no-threshold log-linear model to estimate PM-related long-term mortality (U.S. EPA, 2009b).
We report also the key assumptions associated with our analysis of ozone-related effects:
 Key assumption and uncertainties related to modeling of ozone-related premature mortality: Ozone-related short-term mortality represents a substantial proportion of total monetized benefits (over 94% of the ozone-related-benefits), and these estimates have the following key assumptions and uncertainties. We utilize a log-linear impact function without a threshold in modeling short-term ozone-related mortality. However, we acknowledge reduced confidence in specifying the nature of the C-R function in the range of <=20ppb and below (ozone ISA, section <IP_ADDRESS>). Thus, ozone-related premature deaths estimated at or below this level are subject to greater uncertainty, but we cannot judge whether (and in what direction) these impacts are biased.
 Avoided premature mortality according to baseline pollutant concentrations: We recognize that, in estimating short-term ozone-related mortality, we are less confident in specifying the shape of the C-R function at lower ambient ozone concentrations (at and below 20 ppb, ozone ISA, section <IP_ADDRESS>). Quantitative uncertainty analyses completed for the Ozone NAAQS RIA (U.S. EPA, 2015) found almost 100% of mortality reductions occurred above 20 ppb, where we are more confident in specifying the nature of the ozone-mortality effect (ozone ISA, section <IP_ADDRESS>). However, as discussed in section 6B.7 of that RIA, care must be taken in interpreting these results since the ambient air metric used in modeling this endpoint is the mean 8-hour  max value in each grid cell (and not the full distribution of 8-hour daily max values). Had the latter been used, then the distribution would have likely been wider.

For this analysis, policy-specific air quality data are not available, and the control  scenarios are illustrative of what utilities may choose to do within the trading program. However, we believe that it is still important to characterize the distribution of exposure to baseline concentrations. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM2.5 concentration in the baseline of the air quality modeling used to calculate the benefit-per-ton estimates for this RIA using 12 km grid cells across the contiguous U.S. It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population and change in air quality. In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction policy. The most important aspect, which we are unable to quantify without rule-specific air quality modeling, is the shift in exposure anticipated by implementing the CSAPR update. Therefore, caution is warranted when interpreting the LML assessment in this RIA because these results are not consistent with results from RIAs that had air quality modeling.
Figure 5-3 shows a bar chart of the percentage of the population exposed to various air quality levels, including the LML concentration benchmarks in the illustrative control case modeling, and Figure 5-4 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.

Among the populations exposed to PM2.5 in the baseline:
      88% are exposed to PM2.5 levels at or above the LML of the Krewski et al. (2009) study
      47% are exposed to PM2.5 levels at or above the LML of the Lepeule et al. (2012) study

Figure 5-2. 	Percentage of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in the Baseline used for the Air Quality Analysis in Chapter 3

Figure 5-3. 	Cumulative Distribution of Adult Population (age 30+) by Annual Mean PM2.5 Exposure in the Baseline used for the Air Quality Analysis in Chapter 3
5.3 	Estimated Climate Co-Benefits from CO2
A co-benefit of this proposal is reducing emissions of CO2. In this section, we provide a brief overview of the 2009 Endangerment Finding and climate science assessments released since then. We also provide information regarding the economic valuation of CO2 using the Social Cost of Carbon (SC-CO2), a metric that estimates the monetary value of impacts associated with marginal changes in CO2 emissions in a given year.
There are several important considerations in assessing the climate-related benefits for an ozone air quality-focused rulemaking. First, the estimated health benefits do not account for any climate-related air quality changes (e.g., increased ambient ozone associated with higher temperatures). Excluding climate-related air quality changes may underestimate ozone-related health benefits. It is unclear how PM2.5-related health benefits would be affected by excluding climate-related air quality changes since the science is unclear as to how climate change may affect PM2.5 exposure. Second, the estimated health benefits also do not consider temperature modification of PM2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, the estimated climate co-benefits reported in this RIA reflect global benefits, while the estimated health benefits are calculated for the contiguous U.S. only. Excluding temperature modification of air pollution risks and international air quality-related health benefits likely leads to underestimation of quantified health benefits (Anenberg et al, 2009, Jhun et al, 2014). Fourth, we do not estimate the climate co-benefits associated with reductions in PM and ozone precursors.

5.3.1 	Climate Change Impacts
     Through the implementation of CAA regulations, the EPA addresses the negative externalities caused by air pollution. In 2009, the EPA Administrator found that elevated concentrations of greenhouse gases in the atmosphere may reasonably be anticipated both to endanger public health and to endanger public welfare. For health, these include the increased likelihood of heat waves, negative impacts on air quality, more intense hurricanes, more frequent and intense storms and heavy precipitation, and impacts on infectious and waterborne diseases. For welfare, these include reduced water supplies in some regions, increased water pollution, increased occurrences of floods and droughts, rising sea levels and damage to coastal infrastructure, increased peak electricity demand, changes in ecosystems, and impacts on indigenous communities.
     Major scientific assessments released since the 2009 Endangerment Finding have improved scientific understanding of the climate, and provide even more evidence that GHG emissions endanger public health and welfare for current and future generations. The National Climate Assessment (NCA), in particular, assessed the impacts of climate change on human health in the United States, finding that Americans will be affected by "increased extreme weather events, wildfire, decreased air quality, threats to mental health, and illnesses transmitted by food, water, and disease-carriers such as mosquitoes and ticks." These assessments also detail the risks to vulnerable groups such as children, the elderly and low income households. Furthermore, the assessments present an improved understanding of the impacts of climate change on public welfare, higher projections of future sea level rise than had been previously estimated, a better understanding of how the warmth in the next century may reach levels that would be unprecedented relative to the preceding millions of years of history, and new assessments of the impacts of climate change on permafrost and ocean acidification. The impacts of GHG emissions will be realized worldwide, independent of their location of origin, and impacts outside of the United States will produce consequences relevant to the United States.

5.3.2 	Social Cost of Carbon
We estimate the global social benefits of CO2 emission reductions expected from the final emission guidelines using the SC-CO2 estimates presented in the Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (May 2013, Revised July 2015) ("current TSD"). We refer to these estimates, which were developed by the U.S. government, as "SC-CO2 estimates." The SC-CO2 is a metric that estimates the monetary value of impacts associated with marginal changes in CO2 emissions in a given year. It includes a wide range of anticipated climate impacts, such as net changes in agricultural productivity and human health, property damage from increased flood risk, and changes in energy system costs, such as reduced costs for heating and increased costs for air conditioning. It is typically used to assess the avoided damages as a result of regulatory actions (i.e., benefits of rulemakings that lead to an incremental reduction in cumulative global CO2 emissions).
The SC-CO2 estimates used in this analysis were developed over many years, using the best science available, and with input from the public. Specifically, an interagency working group (IWG) that included the EPA and other executive branch agencies and offices used three integrated assessment models (IAMs) to develop the SC-CO2 estimates and recommended four global values for use in regulatory analyses. The SC-CO2 estimates were first released in February 2010 and updated in 2013 using new versions of each IAM. The 2013 update did not revisit the 2010 modeling decisions with regards to the discount rate, reference case socioeconomic and emission scenarios, and equilibrium climate sensitivity distribution.  Rather, improvements in the way damages are modeled are confined to those that have been incorporated into the latest versions of the models by the developers themselves and published in the peer-reviewed literature.  The 2010 SC-CO2 Technical Support Document (2010 SC-CO2 TSD) provides a complete discussion of the methods used to develop these estimates and the current SC-CO2 TSD presents and discusses the 2013 update (including recent minor technical corrections to the estimates). One key methodological aspect discussed in the SC-CO2 TSDs is the global scope of the estimates.  The SC-CO2 estimates represent global measures because of the distinctive nature of climate change, which is highly unusual in at least three respects. First, emissions of most GHGs contribute to damages around the world independent of the country in which they are emitted. Second, the U.S. operates in a global and highly interconnected economy, such that impacts on the other side of the world can affect our economy. This means that the true costs of climate change to the U.S. are much larger than the direct impacts that simply occur within the U.S. Third, climate change represents a classic public goods problem because each country's greenhouse gas emissions reductions benefit everyone else and no country can be excluded from enjoying the benefits of other countries' reductions, even if it provides no reductions itself. In this situation, the only way to achieve an economically efficient level of emissions reductions is for countries to cooperate in providing mutually beneficial reductions beyond the level that would be justified only by their own domestic benefits. In reference to the public good nature of mitigation and its role in foreign relations, thirteen prominent academics noted that these "are compelling reasons to focus on a global SCC" in a recent article on the SCC (Pizer et al., 2014). In addition, the IWG recently noted that there is no bright line between domestic and global damages. Adverse impacts on other countries can have spillover effects on the United States, particularly in the areas of national security, international trade, public health and humanitarian concerns.
The 2010 TSD noted a number of limitations to the SC-CO2 analysis, including the incomplete way in which the integrated assessment models capture catastrophic and non-catastrophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. Currently integrated assessment models do not assign value to all of the important physical, ecological, and economic impacts of climate change recognized in the climate change literature due to a lack of precise information on the nature of damages and because the science incorporated into these models understandably lags behind the most recent research. The  limited amount of research linking climate impacts to economic damages makes the modeling exercise even more difficult. These individual limitations do not all work in the same direction in terms of their influence on the SC-CO2 estimates, though taken together they suggest that the SC-CO2 estimates are likely conservative. In particular, the IPCC Fourth Assessment Report (2007), which was the most current IPCC assessment available at the time of the IWG's 2009-2010 review, concluded that "It is very likely that [SC-CO2 estimates] underestimate the damage costs because they cannot include many non-quantifiable impacts." Since then, the peer-reviewed literature has continued to support this conclusion.  For example, the IPCC Fifth Assessment report (2014) observed that SC-CO2 estimates continue to omit various impacts, such as "the effects of the loss of biodiversity among pollinators and wild crops on agriculture."  Nonetheless, these estimates and the discussion of their limitations represent the best available information about the social benefits of CO2 reductions to inform benefit-cost analysis.  The new versions of the models used to estimate the values presented below offer some improvements in these areas, although further work is warranted.
The EPA and other agencies have continued to consider feedback on the SC-CO2 estimates from stakeholders through a range of channels, including public comments on rulemakings that use the SC-CO2 in supporting analyses and through regular interactions with stakeholders and research analysts implementing the SC-CO2 methodology used by the interagency working group. In addition, OMB's Office of Information and Regulatory Affairs issued a separate request for public comment on the approach used to develop the estimates. After careful evaluation of the full range of comments submitted to OMB's Office of Information and Regulatory Affairs, the IWG continues to recommend the use of these SC-CO2 estimates in regulatory impact analysis. With the release of the response to comments, the IWG announced plans to obtain expert independent advice from the National Academies of Sciences, Engineering, and Medicine (Academies) to ensure that the SC-CO2 estimates continue to reflect the best available scientific and economic information on climate change. The Academies' process will be informed by the public comments received and focuses on the technical merits and challenges of potential approaches to improving the SC-CO2 estimates in future updates.
Accordingly, EPA and other agencies continue to engage in research on modeling and valuation of climate impacts with the goal to improve these estimates.  The EPA and other federal agencies also continue to consider feedback on the SC-CO2 estimates from stakeholders through a range of channels, including public comments on Agency rulemakings that use the SC-CO2 in supporting analyses and through regular interactions with stakeholders and research analysts implementing the SC-CO2 methodology used by the IWG.  In addition, OMB sought public comment on the approach used to develop the SC-CO2 estimates through a separate comment period and published a response to those comments in 2015.
After careful evaluation of the full range of comments submitted to OMB, the IWG continues to recommend the use of the SC-CO2 estimates in regulatory impact analysis.  With the July 2015 release of the response to comments, the IWG announced plans to obtain expert independent advice from the National Academies of Sciences, Engineering and Medicine to ensure that the SC-CO2 estimates continue to reflect the best available scientific and economic information on climate change.   The Academies then convened a committee, "Assessing Approaches to Updating the Social Cost of Carbon," (Committee) which is reviewing the state of the science on estimating the SC-CO2, and will provide expert, independent advice on the merits of different technical approaches for modeling and highlight research priorities going forward.  EPA will evaluate its approach based upon any feedback received from the Academies' panel.
To date, the Committee has released an interim report, which recommended against doing a near term update of the SC-CO2 estimates.  For future revisions, the Committee recommended the IWG move efforts towards a broader update of the climate system module consistent with the most recent, best available science, and also offered recommendations for how to enhance the discussion and presentation of uncertainty in the SC-CO2 estimates.  Specifically, the Committee recommended that "the IWG provide guidance in their technical support documents about how [SC-CO2] uncertainty should be represented and discussed in individual regulatory impact analyses that use the [SC-CO2]" and that the technical support document for each update of the estimates present a section discussing the uncertainty in the overall approach, in the models used, and uncertainty that may not be included in the estimates.  At the time of this writing, the IWG is reviewing the interim report and considering the recommendations.  EPA looks forward to working with the IWG to respond to the recommendations and will continue to follow IWG guidance on SC-CO2.
The four SC-CO2 estimates are as follows: $12, $41, $63, and $120 per metric ton of CO2 emissions in the year 2017 (2011$). The first three values are based on the average SC-CO2 from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. SC-CO2 estimates for several discount rates are included because the literature shows that the SC-CO2 is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate to use in an intergenerational context (where costs and benefits are incurred by different generations). The fourth value is the 95[th] percentile of the SC-CO2 from all three models at a 3 percent discount rate. It is included to represent lower probability but higher impact outcomes from climate change, which are captured further out in the tail of the SC-CO2 distribution, and while less likely than those reflected by the average SC-CO2 estimates, would be much more harmful to society and therefore, are relevant to policy makers.
Table 5-7 presents the global SC-CO2 estimates in metric tons for the years 2015 to 2050. In order to calculate the dollar value for emission reductions, the SC-CO2 estimate for each emissions year would be applied to changes in CO2 emissions for that year, and then discounted back to the analysis year using the same discount rate used to estimate the SC-CO2.[,]  The SC-CO2 increases over time because future emissions are expected to produce larger incremental damages as physical and economic systems become more stressed in response to greater climate change. Note that the interagency group estimated the growth rate of the SC-CO2 directly using the three integrated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. Table 5-8 reports the incremental climate co-benefits from CO2 emission impacts estimated for the final CSPAR update and more and less stringent alternatives for the 2017 analysis year.
Table 5-7. 	Social Cost of CO2, 2015-2050 (in 2011$ per metric ton)*

                                     Year
                          Discount Rate and Statistic

                                  5% Average
                                  3% Average
                                 2.5% Average
                             3% (95th percentile)
                                     2015
                                      $12
                                      $38
                                      $59
                                     $110
                                     2017
                                      $12
                                      $41
                                      $63
                                     $120
                                     2020
                                      $13
                                      $45
                                      $66
                                     $130
                                     2025
                                      $15
                                      $49
                                      $72
                                     $150
                                     2030
                                      $17
                                      $53
                                      $77
                                     $160
                                     2035
                                      $19
                                      $58
                                      $83
                                     $180
                                     2040
                                      $22
                                      $64
                                      $89
                                     $190
                                     2045
                                      $24
                                      $68
                                      $94
                                     $210
                                     2050
                                      $28
                                      $73
                                     $100
                                     $230
* These SC-CO2 values are stated in $/metric ton and rounded to two significant figures. The estimates vary depending on the year of CO2 emissions and are defined in real terms, i.e., adjusted for inflation using the GDP implicit price deflator.

Table 5-8. 	Estimated Global Climate Co-benefits of CO2 Reductions for the CSAPR Update and More and Less Stringent Alternatives for 2017 (millions of 2011$)*
Discount rate and statistic
                                 CSPAR Update
                          More Stringent Alternative
                          Less Stringent Alternative
Million metric tons of CO2 reduced
                                      1.6
                                      2.1

                                      1.3
                                 5% (average)
                                      $19
                                      $25
                                      $15
                                 3% (average)
                                      $66
                                      $87
                                      $54
                                2.5% (average)
                                     $100
                                     $130
                                      $81
                            3% (95[th] percentile)
                                     $190
                                     $250
                                     $150
* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting of climate impacts.

It is important to note that the climate co-benefits presented above are associated with changes in CO2 emissions only. Implementing the CSAPR update, however, will have an impact on the emissions of other pollutants that would affect the climate. Both predicting reductions in emissions and estimating the climate impacts of these other pollutants, however, is complex. The climate impacts of these other pollutants have not been calculated for the rule.
5.4 	Combined Health Benefits and Climate Co-Benefits Estimates
In this analysis, we were able to monetize the estimated benefits associated with the reduced exposure to ozone and PM2.5 and co-benefits of decreased emissions of CO2, but we were unable to monetize the co-benefits associated with reducing exposure to mercury, carbon monoxide, and NO2, as well as ecosystem effects and visibility impairment. In addition, there are expected to be unquantified health and welfare impacts associated with changes in hydrogen chloride. Specifically, we estimated combinations of health benefits at discount rates of 3 percent and 7 percent (as recommended by the EPA's Guidelines for Preparing Economic Analyses [U.S. EPA, 2014] and OMB's Circular A-4 [OMB, 2003]) and climate co-benefits at estimates of the SC-CO2 (average SC-CO2 at each of three discount rates -- 5 percent, 3 percent, 2.5 percent -- and the 95[th] percentile SC-CO2 at 3 percent) (as recommended by the IWG).
Different discount rates are applied to SC-CO2 than to the health benefit estimates because CO2 emissions are long-lived and subsequent damages occur over many years. Moreover, several rates are applied to SC-CO2 because the literature shows that it is sensitive to assumptions about discount rate and because no consensus exists on the appropriate rate to use in an intergenerational context. The SC-CO2 interagency group centered its attention on the 3 percent discount rate but emphasized the importance of considering all four SC-CO2 estimates. The EPA has evaluated the range of potential impacts by combining all SC-CO2 values with health benefits values at the 3 percent and 7 percent discount rates. Combining the 3 percent SC-CO2 values with the 3 percent health benefit values assumes that there is no difference in discount rates
Table 5-9 provides the combined health and climate benefits for the CSAPR update and more and less stringent alternatives for the 2017 analysis year.
Table 5-9. 	Combined Health Benefits and Climate Co-Benefits for the CSAPR update and More and Less Stringent Alternatives for 2017 (millions of 2011$)*
SC-CO2 Discount Rate**
  Health and Climate Benefits
(Discount Rate Applied to Health Co-Benefits)
                           Climate Co-Benefits Only

                                      3%
                                      7%

CSAPR Update

5%
                                 $480 to $830
                                 $470 to $810
                                      $19
3%
                                 $530 to $880
                                 $520 to $860
                                      $66
2.5%
                                 $560 to $910
                                 $550 to $890
                                     $100
3% (95[th] percentile)
                                $650 to $1,000
                                 $640 to $980
                                     $190
More Stringent Alternative

5%
                                 $520 to $900
                                 $510 to $870
                                      $25
3%
                                 $580 to $960
                                 $570 to $940
                                      $87
2.5%
                                $630 to $1,000
                                 $620 to $980
                                     $130
3% (95[th] percentile)
                                $750 to $1,100
                                $740 to $1,100
                                     $250
Less Stringent Alternative

5%
                                 $210 to $360
                                 $210 to $350
                                      $15
3%
                                 $250 to $400
                                 $250 to $390
                                      $54
2.5%
                                 $280 to $420
                                 $270 to $420
                                      $81
3% (95[th] percentile)
                                 $350 to $500
                                 $350 to $490
                                     $150
*All estimates are rounded to two significant figures. Climate benefits are based on reductions in CO2 emissions. Health benefits are based on benefit-per-ton estimates. Benefits for ozone are based on ozone season NOx emissions. Ozone benefits occur in analysis year, so they are the same for all discount rates. The health benefits reflect the sum of the ozone benefits and PM2.5 co-benefits and reflect the range based on adult mortality functions (e.g., from Krewski et al. (2009) with Smith et al. (2009) to Lepeule et al. (2012) with Zanobetti and Schwartz (2008)). The monetized health benefits do not include reduced health effects from direct exposure to NO2 as well as ecosystem effects and visibility impairment associated with reductions in NOX.
**As discussed in section 5.3, the SC-CO2 estimates are calculated with four different values of a one metric ton reduction.

5.5 	Unquantified Benefits and Co-benefits
The monetized co-benefits estimated in this RIA reflect a subset of benefits and co-benefits attributable to the health effect reductions associated with ambient ozone and fine particles. Data, time, and resource limitations prevented the EPA from quantifying the impacts to, or monetizing the co-benefits from several important benefit categories as well as ecosystem effects and visibility impairment associated with reductions in NOx due to the absence of air quality modeling data for these pollutants in this analysis. This does not imply that there are no co-benefits associated reductions in exposures to NO2. In this section, we provide a qualitative description of these benefits, which are listed in Table 5-10.
Table 5-10. 	Unquantified Health and Welfare Benefit and Co-benefit Categories
                                   Category
                                Specific Effect
                          Effect Has Been Quantified
                           Effect Has Been Monetized
                               More Information
Improved Human Health

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

Chronic lung disease hospital admissions (age > 65)
                                       --
                                       --
NO2 ISA[1]

Respiratory emergency department visits (all ages)
                                       --
                                       --
NO2 ISA[1]

Asthma exacerbation (asthmatics age 4 - 18)
                                       --
                                       --
NO2 ISA[1]

Acute respiratory symptoms (age 7 - 14)
                                       --
                                       --
NO2 ISA[1]

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

Other respiratory effects (e.g., airway hyperresponsiveness and inflammation, lung function, other ages and populations)
                                       --
                                       --
NO2 ISA[2,3]
Improved Environment

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

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

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

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

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

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

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

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

Other non-use effects

Ozone ISA[2]

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

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

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

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

Other non-use effects

NOx SOx ISA[2]

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

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

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

Other non-use effects

NOx SOx ISA[2]

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

Injury to vegetation from NOx exposure
                                       --
                                       --
NOx SOx ISA[2]
1 We assess these co-benefits qualitatively due to data and resource limitations for this RIA.
[2]We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.
3 We assess these co-benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.

5.5.2 	Additional NO2 Health Co-Benefits
NO and NO2 are often grouped together into their own group or family, which the atmospheric sciences community refers to as NOx (U.S. EPA, 2016). In addition to being a precursor to PM2.5 and ozone, NOx/NO2 emissions -- which emanate from a variety of sources including EGU's -- are also linked to a variety of adverse health effects associated with direct exposure. We were unable to estimate the health co-benefits associated with reduced NO2 exposure in this analysis for two reasons. First, we lacked a reliable reduced-form approach for quantifying NO2-attributable benefits. A second, and related reason, is that it is generally necessary to perform air quality modeling that characterizes well the near-field gradient associated with NO2 concentrations -- particularly from the mobile sector (U.S. EPA, 2016); such an analysis was not performed for this rule. Therefore, this analysis only quantified and monetized the ozone benefits and PM2.5 co-benefits associated with the reductions in NO2 emissions.
Following a comprehensive review of health evidence from epidemiologic and laboratory studies, the Integrated Science Assessment for Oxides of Nitrogen  -- Health Criteria (NOx ISA) (U.S. EPA, 2016) concluded that there is a causal relationship between respiratory health effects and short-term exposure to NO2. These epidemiologic and experimental studies encompass a number of endpoints including emergency department visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway inflammation, and lung function. The NOx ISA also concluded that the relationship between short-term NO2 exposure and premature mortality was "suggestive but not sufficient to infer a causal relationship," because it is difficult to attribute the mortality risk effects to NO2 alone. Although the NOx ISA stated that studies consistently reported a relationship between NO2 exposure and mortality, the effect was generally smaller than that for other pollutants such as PM.
5.5.4 	Additional NO2 Welfare Co-Benefits
As described in the Integrated Science Assessment for Oxides of Nitrogen and Sulfur  -- Ecological Criteria (NOx/SOx ISA) (U.S. EPA, 2008d), NOx emissions also contribute to a variety of adverse welfare effects, including those associated with acidic deposition, visibility impairment, and nutrient enrichment. Deposition of nitrogen causes acidification, which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern U.S., the surface waters affected by acidification are a source of food for some recreational and subsistence fishermen and for other consumers and support several cultural services, including aesthetic and educational services and recreational fishing. Biological effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root growth, restricting the ability of the plant to take up water and nutrients. These direct effects can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy trees.
Deposition of nitrogen is also associated with aquatic and terrestrial nutrient enrichment. In estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of estuaries can disrupt an important source of food production, particularly fish and shellfish production, and a variety of cultural ecosystem services, including water-based recreational and aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets the balance between native and nonnative plants, changing the ability of an area to support biodiversity. When the composition of species changes, then fire frequency and intensity can also change, as nonnative grasses fuel more frequent and more intense wildfires (U.S. EPA, 2008d).
Reductions in emissions of NO2 will improve the level of visibility throughout the United States because these gases (and the particles of nitrate formed from this gas as discussed below) impair visibility by scattering and absorbing light (U.S. EPA, 2009). Visibility is also referred to as visual air quality (VAQ), and it directly affects people's enjoyment of a variety of daily activities (U.S. EPA, 2009). Good visibility increases quality of life where individuals live and work, and where they travel for recreational activities, including sites of unique public value, such as the Great Smoky Mountains National Park (U. S. EPA, 2009).
5.5.5 	Ozone Welfare Benefits
Exposure to ozone has been associated with a wide array of vegetation and ecosystem effects in the published literature (U.S. EPA, 2013b). Sensitivity to ozone is highly variable across species, with over 65 plant species identified as "ozone-sensitive", many of which occur in state and national parks and forests. These effects include those that damage or impair the intended use of the plant or ecosystem. Such effects can include reduced growth and/or biomass production in sensitive plant species, including forest trees, reduced yield and quality of crops, visible foliar injury, species composition shift, and changes in ecosystems and associated ecosystem services.
5.5.6 	PM2.5 Visibility Impairment Co-Benefits
Reducing secondary formation of PM2.5 would improve levels of visibility in the U.S. because suspended particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009b). Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). Visibility has direct significance to people's enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they engage in recreational activities. Particulate sulfate is the dominant source of regional haze in the eastern U.S. and particulate nitrate is an important contributor to light extinction in California and the upper Midwestern U.S., particularly during winter (U.S. EPA, 2009b). Previous analyses (U.S. EPA, 2011a) show that visibility co-benefits can be a significant welfare benefit category. Without air quality modeling, we are unable to estimate visibility-related benefits, and we are also unable to determine whether the emission reductions associated with the final CSAPR Update would be likely to have a significant impact on visibility in urban areas or Class I areas.
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U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2002. Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants: Developing Best Estimates of Dose-Response Functions An SAB Workshop Report of an EPA/SAB Workshop (Final Report). EPA-SAB-EC-WKSHP-02-001. January. Available at: <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/34355712EC011A358525719A005BF6F6/$File/ecwkshp02001%2Bappa-g.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2008. Characterizing Uncertainty in Particulate Matter Benefits Using Expert Elicitation. EPA-COUNCIL-08-002. July. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/0/43B91173651AED9E85257487004EA6CB/$File/EPA-COUNCIL-08-002-unsigned.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2000. An SAB Report on EPA's White Paper Valuing the Benefits of Fatal Cancer Risk Reduction. EPA-SAB-EEAC-00-013. July. Available at: <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/41334524148BCCD6852571A700516498/$File/eeacf013.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2004c. Advisory Council on Clean Air Compliance Analysis Response to Agency Request on Cessation Lag. EPA-COUNCIL-LTR-05-001. December. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/0/39F44B098DB49F3C85257170005293E0/$File/council_ltr_05_001.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2008. Benefits of Reducing Benzene Emissions in Houston, 1990 - 2020. EPA-COUNCIL-08-001. July. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9DAEDA8A548525748600728A83/$File/EPA-COUNCIL-08-001-unsigned.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2009b. Review of EPA's Integrated Science Assessment for Particulate Matter (First External Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2009c. Review of Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009). EPA-CASAC-10-001. November. Available at: <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/151B1F83B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2010a. Review of EPA's DRAFT Health Benefits of the Second Section 812 Prospective Study of the Clean Air Act. EPA-COUNCIL-10-001. June. Available at: < http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/72D4EFA39E48CDB28525774500738776/$File/EPA-COUNCIL-10-001-unsigned.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency -- Science Advisory Board (U.S. EPA-SAB). 2011. Review of Valuing Mortality Risk Reductions for Environmental Policy: A White Paper (December 10, 2010). EPA-SAB-11-011 July. Available at: <http://yosemite.epa.gov/sab/sabproduct.nsf/298E1F50F844BC23852578DC0059A616/$File/EPA-SAB-11-011-unsigned.pdf>. Accessed June 4, 2015.
Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. "The Relationship between Selected of postneonatal infant mortality and particulate air pollution in the United States." Environmental Health Perspectives. 105(6): 608-612.

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CHAPTER 6:  ECONOMIC IMPACTS
Overview
This chapter addresses economic impacts on small entities, other government entities, and employment.
6.1 	Impacts on Small Entities
      The EPA certifies that this action will not have a significant economic impact on a substantial number of small entities under the Regulatory Flexibility Act (RFA). The small entities subject to the requirements of this action are small businesses, small organizations, and small governmental jurisdictions. The EPA has determined that 1 entity (of 11 small entities identified as potentially affected) may experience an impact of greater than 3 percent of annual revenues. Details of this analysis are presented below.
      The Regulatory Flexibility Act (5 U.S.C. 601 et seq.), as amended by the Small Business Regulatory Enforcement Fairness Act (Public Law No. 104 121), provides that whenever an agency is required to publish a general notice of final rulemaking, it must prepare and make available an final regulatory flexibility analysis, unless it certifies that the final rule will not have a significant economic impact on a substantial number of small entities (5 U.S.C.  605[b]). Small entities include small businesses, small organizations, and small governmental jurisdictions.
      The EPA conducted regulatory flexibility analysis at the ultimate (i.e., highest) level of ownership, evaluating parent entities with the largest share of ownership in at least one potentially-affected EGU included in EPA's base case using the IPM v.5.15, used in this RIA. This analysis draws on the "parsed" unit-level estimates using IPM results for 2018, as well as ownership, employment, and financial information for the potentially affected small entities drawn from other resources described in more detail below.
      The EPA identified the size of ultimate parent entities by using the Small Business Administration (SBA) size threshold guidelines. The criteria for size determination vary by the organization/operation category of the ultimate parent entity, as follows:
 Privately-owned (non-government) entities (see Table 6-1)
 Privately-owned entities include investor-owned utilities, non-utility entities, and entities with a primary business other than electric power generation.
 For entities with electric power generation as a primary business, small entities are those with less than the threshold number of employees specified by SBA for each of the relevant North American Industry Classification System (NAICS) sectors (NAICS 2211).
 For entities with a primary business other than electric power generation, the relevant size criteria are based on revenue, assets, or number of employees by NAICS sector.
 Publicly-owned entities
 Publicly-owned entities include federal, state, municipal, and other political subdivision entities.
 The federal and state governments were considered to be large. Municipalities and other political units with population fewer than 50,000 were considered to be small.
 Rural Electric Cooperatives
 Small entities are those with fewer than the threshold level of employees or revenue specified by SBA for each of the relevant NAICS sectors.
6.1.1 	Identification of Small Entities
      In this analysis, the EPA considered EGUs which meet the following five criteria: 1) EGU is represented in NEEDS v5.15; 2) EGU is fossil fuel-fired; 3) EGU is located in a state covered by this rule; 4) EGU is neither a cogeneration unit nor solid waste incineration unit; 5) EGU capacity is 25MW or larger. The EPA next refined this list of EGUs, narrowing it to those that exhibit at least one of the following changes under the CSAPR Update scenario, in comparison to the baseline.
 Summer fuel use (BTUs) changes by +/- 1% or more
 Summer generation (GWh) changes by +/- 1% or more
 NOx summer emissions (tons) changes by +/- 1% or more
	Based on these criteria, the EPA identified a total of 365 potentially affected EGUs warranting examination in this RFA analysis. Next, we determined power plant ownership information, including the name of associated owning entities, ownership shares, and each entity's type of ownership. We primarily used data from SNL and Ventyx, supplemented by limited research using publicly available data. Majority owners of power plants with affected EGUs were categorized as one of the seven ownership types. These ownership types are:
 Investor-Owned Utility (IOU): Investor-owned assets (e.g., a marketer, independent power producer, financial entity) and electric companies owned by stockholders, etc.
 Cooperative (Co-Op): Non-profit, customer-owned electric companies that generate and/or distribute electric power.
 Municipal: A municipal utility, responsible for power supply and distribution in a small region, such as a city.
 Sub-division: Political subdivision utility is a county, municipality, school district, hospital district, or any other political subdivision that is not classified as a municipality under state law.
 Private: Similar to an investor-owned utility, however, ownership shares are not openly traded on the stock markets.
 State: Utility owned by the state.
 Federal: Utility owned by the federal government.
      Next, the EPA used both the Hoover's online database and the SNL database to identify the ultimate owners of power plant owners identified in the SNL and Ventyx databases. This was necessary, as many majority owners of power plants (listed in SNL or Ventyx) are themselves owned by other ultimate parent entities (listed in Hoover's or SNL). In these cases, the ultimate parent entity was identified via Hoover's or SNL, whether domestically or internationally owned.
      The EPA followed SBA size standards to determine which non-government ultimate parent entities should be considered small entities in this analysis. These SBA size standards are specific to each industry, each having a threshold level of either employees, revenue, or assets below which an entity is considered small. SBA guidelines list all industries, along with their associated NAICS code and SBA size standard. Therefore, it was necessary to identify the specific NAICS code associated with each ultimate parent entity in order to understand the appropriate size standard to apply. Data from Hoover's was used to identify the NAICS codes for most of the ultimate parent entities. In many cases, an entity that is a majority owner of a power plant is itself owned by an ultimate parent entity with a primary business other than electric power generation. Therefore, it was necessary to consider SBA entity size guidelines for the range of NAICS codes listed in Table 6-1. This table represents the range of NAICS codes and areas of primary business of ultimate parent entities which are majority owners of potentially affected EGUs in the EPA's IPM base case.

Table 6-1. SBA Size Standards by NAICS Code
NAICS Code
NAICS Description
SBA Size Standard
221112
Fossil Fuel Electric Power Generation
750 employees
221118
Other Electric Power Generation
250 employees
221122
Electric Power Distribution
1,000 employees
221210
Natural Gas Distribution
1,000 employees
238210
Electrical Contractors and Other Wiring Installation Contractors
$15 million in revenue
324110
Petroleum Refineries
1,500 employees
325180
Other Basic Inorganic Chemical Manufacturing
1,000 employees
325320
Pesticide and Other Agricultural Chemical Manufacturing
1,000 employees
331313
Alumina Refining and Primary Aluminum Production
1,000 employees
333613
Mechanical Power Transmission Equipment Manufacturing
750 employees
424720
Petroleum and Petroleum Products Merchant Wholesalers (except Bulk Stations and Terminals)
200 employees
486210
Pipeline Transportation of Natural Gas
$27.5 million in revenue
522110
Commercial Banking
$550 million in assets
522220
Sales Financing
$38.5 million in revenue
523120
Securities Brokerage
$38.5 million in revenue
523910
Miscellaneous Intermediation
$38.5 million in revenue
523930
Investment Advice
$38.5 million in revenue
524126
Direct Property and Casualty Insurance Carriers
1,500 employees
525120
Health and Welfare Funds
$32.5 million in revenue
525990
Other Financial Vehicles
$32.5 million in revenue
541611
Administrative Management and General Management Consulting Services
$15 million in revenue
551112
Offices of Other Holding Companies
$20.5 million in revenue
  Note: Based on size standards effective at the time the EPA conducted this analysis (SBA size standards, effective February 26, 2016)
  Source: SBA, 2016

      The EPA compared the relevant entity size criterion for each ultimate parent entity to the SBA threshold value noted in Table 6-1. We used the following data sources and methodology to estimate the relevant size criterion values for each ultimate parent entity:
 Employment, Revenue, and Assets: The EPA used the Hoover's database as the primary source for information on ultimate parent entity employee numbers, revenue, and assets. In parallel, the EPA also considered estimated revenues from affected EGUs based on analysis of parsed-file estimates for the final rule. The EPA assumed that the ultimate parent entity revenue was the larger of the two revenue estimates. In limited instances, supplemental research was also conducted to estimate an ultimate parent entity's number of employees, revenue, or assets.
 Population: Municipal entities are defined as small if they serve populations of less than 50,000. The EPA primarily relied on data from the Ventyx database and the U.S. Census Bureau to inform this determination. Supplemental research of individual municipalities was also conducted in some instances.
      Ultimate parent entities for which the relevant measure is less than the SBA size criterion were identified as small entities and carried forward in this analysis. In the case of one entity, data limitations prevented the comparison of the entity against its appropriate SBA size standard. For the purposes of this analysis, the EPA assumed that this entity is a small entity. Overall, the EPA identified 30 potentially affected EGUs owned by 11 small entities included in the EPA's Base Case.
6.1.2 	Overview of Analysis and Results
	This section presents the methodology and results for estimating the impact of the CSAPR Update to small entities in 2017 based on the following endpoints:
 annual economic impacts of the CSAPR Update on small entities, and
 ratio of small entity impacts to revenues from electricity generation.
<IP_ADDRESS> 	Methodology for Estimating Impacts of the CSAPR Update on Small Entities
	An entity can comply with the CSAPR Update through some combination of the following: optimizing existing SCR, turning on idled SCR or SNCR controls, upgrading to state of the art combustion controls, using allocated allowances, purchasing allowances, or reducing emissions through a reduction in generation or improved efficiency. Additionally, units with more allowances than needed can sell these allowances in the market. The chosen compliance strategy will be primarily a function of the unit's marginal control costs and its position relative to the marginal control costs of other units.
      To attempt to account for each potential control strategy, the EPA estimates compliance costs as follows:
	CCompliance = Δ COperating+Retrofit + Δ CFuel + Δ CAllowances + Δ CTransaction + Δ R
where C represents a component of cost as labeled, and Δ R represents the value of foregone electricity generation, calculated as the difference in revenues between the base case and the CSAPR Update in 2017. This analysis is based on the NOX budgets and modeling results presented in Chapter 4.
      In reality, compliance choices and market conditions can combine such that an entity may actually experience a savings in any of the individual components of cost. Under the CSAPR Update, some units will forgo some level of electricity generation (and thus revenues) to comply and this impact will be lessened on these entities by the projected increase in electricity prices under the CSAPR Update. On the other hand, those increasing generation levels will see an increase in electricity revenues and as a result, lower net compliance costs. If entities are able to increase revenue more than an increase in fuel cost and other operating costs, ultimately they will have negative net compliance costs (or savings). Overall, small entities are not projected to install relatively costly emissions control retrofits, but may choose to do so in some instances. Because this analysis evaluates the total costs along each of the compliance strategies laid out above for each entity, it inevitably captures savings or gains such as those described. As a result, what we describe as cost is really more of a measure of the net economic impact of the rule on small entities.
      For this analysis, the EPA used IPM-parsed output to estimate costs based on the parameters above, at the unit level. These impacts were then summed for each small entity, adjusting for ownership share. Net impact estimates were based on the following: operating and retrofit costs, sale or purchase of allowances, and the change in fuel costs or electricity generation revenues under the CSAPR Update relative to the base case. These individual components of compliance cost were estimated as follows:
      (1) 	Operating and retrofit costs: Using the IPM-parsed output for the base case and the CSAPR Update, the EPA identified units that install control technology under the policy, and what technology was installed. The equations for calculating retrofit costs were adopted from the EPA's version of IPM. The model calculates the capital cost (in $/MW); the fixed operation and maintenance (O&M) cost (in $/MW-year); the variable O&M cost (in $/MWh); and the total annual cost for units projected to turn on existing idled SCR, fully operate existing SCR, or turn on existing idled SNCR.
      (2) 	Sale or purchase of allowances: To estimate the value of allowances holdings, allocated allowances were subtracted from projected emissions, and the difference was then multiplied by $1,400 per ton. $1,400 per ton is the marginal cost of NOX reductions used to set the budgets in the final rule. While this is a reasonable approximation, it is possible that the actual allowance price could be lower. Units were assumed to purchase or sell allowances to exactly cover their projected emissions under the policy.
      (3) 	Fuel costs: The change in fuel expenditures under the policy was estimated by taking the difference in projected fuel expenditures between the IPM estimates for the CSAPR Update and the base case.
      (4) 	Value of electricity generated: To estimate the value of electricity generated, the projected level of electricity generation is multiplied by the regional-adjusted retail electricity price ($/MWh) estimate, for all entities except those categorized as Private in Ventyx. For private entities, the EPA used segmental wholesale electricity price instead retail electricity price because most of the private entities are independent power producers (IPP). IPPs sell their electricity to wholesale purchasers and do not own transmission facilities and thus their revenue was estimated with wholesale electricity prices.
      (5) 	Administrative costs: Because most affected units are already monitored as a result of other regulatory requirements, the EPA considered the primary administrative cost to be transaction costs related to purchasing or selling allowances. The EPA assumed that transaction costs were equal to 1.5 percent of the total absolute value of the difference between a unit's allocation and projected NOX emissions. This assumption is based on market research by ICF International.
<IP_ADDRESS> 	Results
      The potential impacts of the CSAPR Update on small entities are summarized in Table 6-2. All costs are presented in $2011. The EPA estimated the annual net compliance cost to small entities to be approximately $23.9 million in 2017. At a plant level, the net compliance costs for all entities includes net savings at a number of plants in this analysis.  These net savings are driven by entities that are able to increase their revenues by increasing generation.
Table 6-2.  Projected Impact of the CSAPR Update on Small Entities in 2017

                              EGU Ownership Type

                    Number of Potentially Affected Entities
                  Total Net Compliance Cost ($2011 millions)

 Number of Small Entities with Compliance Costs >1% of Generation Revenues

 Number of Small Entities with Compliance Costs >3% of Generation Revenues
Cooperative
                                       3
                                     24.1
                                       1
                                       1
Municipal
                                       3
                                      0.0
                                       0
                                       0
Private
                                       5
                                     -0.2
                                       0
                                       0
Total
                                      11
                                     23.9
                                       1
                                       1
Note: 	The total number of entities with costs greater than 1 percent or 3 percent of revenues includes only entities experiencing positive costs. A negative cost value implies that the group of entities experiences a net savings under the CSAPR Update.
Source: 	IPM analysis
      The EPA assessed the economic and financial impacts of the rule using the ratio of compliance costs to the value of revenues from electricity generation, focusing in particular on entities for which this measure is greater than 1 percent. Although this metric is commonly used in the EPA impact analyses, it makes the most sense when as a general matter an analysis is looking at small businesses that operate in competitive environments. However, small businesses in the electric power industry often operate in a price -regulated environment where they are able to recover expenses through rate increases. Given this, the EPA considers the 1 percent measure in this case a crude measure of the price increases these small entities will be asking of rate commissions or making at publicly owned companies.
      Of the 11 small entities considered in this analysis, 1 entity may experience compliance costs greater than 1 percent of generation revenues in 2017, and only 2 entities may experience net positive compliance costs. The other 9 entities may experience negative net costs under the CSAPR Update. The EPA has concluded that there is no significant economic impact on a substantial number of small entities (No SISNOSE) for this rule. The number of entities with compliance costs exceeding 3 percent of generation revenues is also included in Table 6-2.
      The distribution across entities of economic impacts as a share of base case revenue is summarized in Table 6-3. Since there are few potentially-impacted small entities included in this analysis, the distributions of economic impacts on each ownership type are in general fairly tight.
Table 6-3.  Summary of Distribution of Economic Impacts of the CSAPR Update on Small Entities in 2017
                              EGU Ownership Type
   Capacity-Weighted Average Economic Impacts as a % of Generation Revenues
                                      Min
                                      Max
Cooperative
                                     9.0%
                                     -7.8%
                                     9.0%
Municipal
                                     -0.8%
                                    -11.9%
                                     0.2%
Private
                                     -1.9%
                                    -11.7%
                                     -0.1%
Source:  IPM analysis

      The separate components of annual costs to small entities under the CSAPR Update are summarized in Table 6-4. The most significant components of incremental cost to these entities under the CSAPR Update are due to lower electricity revenues. The vast majority of the decreased electricity revenue component is attributable to the single entity that may experience compliance costs greater than 1 percent of generation revenues in 2017.  Since this one entity represents a large share of generation in this category, the projected reduction in generation at this single entity relative to the base case leads to higher net costs for the entire category.  The fuel costs decreases are largely attributable to a few entities that are projected to decrease generation relative to the base case, which translates to lower fuel costs for the whole group. However, many of these entities are projected to increase generation relative to the base case and thus counterbalance this overall impact.  Additionally, increases in electricity generation revenue, shown as cost savings or negative costs are experienced by cooperative, municipal, and private entities. This is due largely to the projected increase in generation at these entities under the CSAPR Update.
Table 6-4.  Incremental Annual Costs under the CSAPR Update Summarized by Ownership Group and Cost Category in 2017 (2011$ millions)
                              EGU Ownership Type
                                Operating Cost
                          Net Purchase of Allowances
                                   Fuel Cost
                           Lost Electricity Revenue
                              Administrative Cost
Cooperative
                                     -$1.8
                                     $1.5
                                     -$6.6
                                     $31.0
                                     $0.02
Municipal
                                     $0.5
                                     $0.3
                                     $0.3
                                     -$1.1
                                     $0.00
Private
                                     $0.4
                                     -$0.6
                                     -$0.1
                                     $0.0
                                     $0.01
Source:  IPM analysis
6.1.3 	Summary of Small Entity Impacts
      The EPA examined the potential economic impacts to small entities associated with this rulemaking based on assumptions of how the affected states will implement control measures to meet their emissions. To summarize, of the 11 small entities potentially affected, 1 may experience compliance costs in excess of 1 percent of revenues in 2017, based on assumptions of how the affected states implement control measures to meet their emissions budgets as set forth in this rulemaking. Potentially affected small entities experiencing compliance costs in excess of 1 percent of revenues have some potential for significant impact resulting from implementation of the CSAPR Update.
      The EPA has lessened the impacts for small entities by excluding all units smaller than 25 MW. This exclusion, in addition to the exemptions for cogeneration units and solid waste incineration units, eliminates the burden of higher costs for a substantial number of small entities located in the 22 states for which the EPA is finalzing FIPs.  Additionally, the CSAPR Update allows for the flexibility of trading, which greatly reduces compliance burden.  For further information, see the evaluation completed for the original CSAPR, available at 76 FR 48272- 48273 (August 8, 2011).
6.2 	Unfunded Mandates Reform Act
	Title II of the UMRA of 1995 (Public Law 104-4)(UMRA) establishes requirements for federal agencies to assess the effects of their regulatory actions on state, local, and Tribal governments and the private sector. Under Section 202 of the UMRA, 2 U.S.C. 1532, the EPA generally must prepare a written statement, including a cost-benefit analysis, for any proposed or final rule that includes any Federal mandate that may result in the expenditure by State, local, and Tribal governments, in the aggregate, or by the private sector, of $100,000,000 or more in any one year. A Federal mandate is defined under Section 421(6), 2 U.S.C. 658(6), to include a Federal intergovernmental mandate and a Federal private sector mandate. A Federal intergovernmental mandate, in turn, is defined to include a regulation that would impose an enforceable duty upon State, Local, or Tribal governments, Section 421(5)(A)(i), 2 U.S.C. 658(5)(A)(i), except for, among other things, a duty that is a condition of Federal assistance, Section 421(5)(A)(i)(I). A Federal private sector mandate includes a regulation that would impose an enforceable duty upon the private sector, with certain exceptions, Section 421(7)(A), 2 U.S.C. 658(7)(A).
      Before promulgating an EPA rule for which a written statement is needed under Section 202 of the UMRA, Section 205, 2 U.S.C. 1535, of the UMRA generally requires the EPA to identify and consider a reasonable number of regulatory alternatives and adopt the least costly, most cost-effective, or least burdensome alternative that achieves the objectives of the rule. Moreover, section 205 allows the EPA to adopt an alternative other than the least costly, most cost-effective or least burdensome alternative if the Administrator publishes with the final rule an explanation why that alternative was not adopted.
      Furthermore, as the EPA stated in the preamble, the EPA is not directly establishing any regulatory requirements that may significantly or uniquely affect small governments, including Tribal governments. Thus, under the CSAPR Update, the EPA is not obligated to develop under Section 203 of the UMRA a small government agency plan.
      The EPA did analyze the economic impacts of the CSAPR Update on government entities, however. This analysis does not examine potential indirect economic impacts associated with the CSAPR Update, such as employment effects in industries providing fuel and pollution control equipment, or the potential effects of electricity price increases on industries and households.
6.2.1 	Identification of Government-Owned Entities
      In this analysis, the EPA considered EGUs which meet the following five criteria: 1) EGU is represented in NEEDS v5.15; 2) EGU is fossil-fuel fired; 3) EGU is located in a state covered by this rule; 4) EGU is neither a cogeneration unit nor solid waste incineration unit; and 5) EGU capacity is 25 MW or larger.
      The EPA next refined this list of EGUs, narrowing it to those that exhibit at least one of the following changes under the final rule, in comparison to the base case.
 Summer fuel use (BTUs) changes by +/- 1% or more
 Summer generation (GWh) changes by +/- 1% or more
 NOx summer emissions (tons) changes by +/- 1% or more
	From the inventory of units meeting the criteria above, the EPA used Ventyx data to identify state and municipality-owned utilities and subdivisions in the CSAPR Update region. The EPA then used IPM-parsed output to associate these plants with individual generating units. The EPA identified 12 municipality-owned utilities that are potentially affected by the CSAPR Update.
6.2.2 	Overview of Analysis and Results
	After identifying potentially affected government entities, the EPA estimated the impact of the CSAPR Update in 2017 based on the following:
 total impacts of compliance on government entities; and
 ratio of government entity impacts to revenues from electricity generation.
      The financial burden to owners of EGUs under the CSAPR Update is composed of compliance and administrative costs. This section outlines the compliance and administrative costs for the 12 potentially affected government-owned units in the CSAPR Update region.
<IP_ADDRESS> 	Methodology for Estimating Impacts of the CSAPR Update on Government Entities
	An entity can comply with the CSAPR Update through any combination of the following: optimizing existing SCR, turning on idled SCR or SNCR controls, upgrading to state of the art combustion controls, using allocated allowances, purchasing allowances, or reducing emissions through a reduction in generation or improved efficiency. Additionally, units with more allowances than needed can sell these allowances on the market. The chosen compliance strategy will be primarily a function of the unit's marginal control costs and its position relative to the marginal control costs of other units.
	To attempt to account for each potential control strategy, the EPA estimates compliance costs as follows:
CCompliance = Δ COperating+Retrofit + Δ CFuel + Δ CAllowances + Δ CTransaction + Δ R
where C represents a component of cost as labeled, and Δ R represents the retail value of foregone electricity generation.
	In reality, compliance choices and market conditions can combine such that an entity may actually experience a savings in any of the individual components of cost. Under the CSAPR Update, for example, some units will forgo some level of electricity generation (and thus revenues) to comply, this impact will be lessened on these entities by the projected increase in electricity prices under the policy, while those not reducing generation levels will see an increase in electricity revenues. Because this analysis evaluates the total costs along each of the compliance strategies laid out above for each entity, it inevitably captures savings or gains such as those described. As a result, what we describe as cost is really more of a measure of the net economic impact of the rule on small entities.
	In this analysis, the EPA used IPM-parsed output for the base case and the CSAPR Update to estimate compliance cost at the unit level. These costs were then summed for each entity, adjusting for ownership share. Compliance cost estimates were based on the following: operating and retrofit costs, sale or purchase of allowances, and the change in fuel costs or electricity generation revenues under the CSAPR Update relative to the base case. These components of compliance cost were estimated as follows:
      (1) 	Operating and retrofit costs: Using the IPM-parsed output for the base case and the CSAPR Update, the EPA identified units that install control technology under the policy and the technology installed. The equations for calculating retrofit costs were adopted from the EPA's version of IPM. The model calculates the capital cost (in $/MW); the fixed operation and maintenance (O&M) cost (in $/MW-year); the variable O&M cost (in $/MWh); and the total annual cost for units projected to turn on existing idled SCR, fully operate existing SCR, or turn on existing idled SNCR.
      (2)	Sale or purchase of allowances: To estimate the value of allowances holdings, allocated allowances were subtracted from projected emissions, and the difference was then multiplied by $1,400 per ton. $1,400 per ton is the marginal annualized cost of NOX reductions used to set the budgets. While this is a reasonable approximation, it is possible that the actual allowance price could be lower. Units were assumed to purchase or sell allowances to exactly cover their projected emissions under the CSAPR Update.
      (3)	Fuel costs: The change in fuel expenditures under the policy was estimated by taking the difference in projected fuel expenditures between the illustrative CSAPR Update and the base case.
       Value of electricity generated: To estimate the value of electricity generated, the projected level of electricity generation is multiplied by the regional-adjusted retail electricity price ($/MWh) estimate, for all entities except those categorized as Private in Ventyx. For private entities, the EPA used wholesale electricity price instead retail electricity price because most of the private entities are independent power producers (IPP). IPPs sell their electricity to wholesale purchasers and do not own transmission facilities and thus their revenue was estimated with wholesale electricity prices.
       Administrative costs: Because most affected units are already monitored as a result of other regulatory requirements, the EPA considered the primary administrative cost to be transaction costs related to purchasing or selling allowances. The EPA assumed that transaction costs were equal to 1.5 percent of the total absolute value of the difference between a unit's allocation and projected NOX emissions. This assumption is based on market research by ICF International.
<IP_ADDRESS> 	Results
A summary of economic impacts on government owned entities is presented in Table 6-5. According to the EPA's analysis, the total net economic impact on government-owned entities (state- and municipality-owned utilities and subdivisions) is expected to be $20.5 million in 2017.
Table 6-5.  Summary of Potential Impacts on Government Entities under the CSAPR Update in 2017
                              EGU Ownership Type
                         Potentially Affected Entities
                  Projected Annualized Costs ($2011 millions)
Number of Government Entities with Compliance Costs >1% of Generation Revenues
Number of Government Entities with Compliance Costs >3% of Generation Revenues
Municipal
                                      11
                                     $14.7
                                       2
                                       2
State
                                       1
                                     $5.8
                                       1
                                       1
Total
                                      12
                                     $20.5
                                       3
                                       3
Note:	The total number of entities with costs greater than 1 percent or 3 percent of revenues includes only entities experiencing positive costs
      As was done for the small entities analysis, the EPA further assessed the economic and financial impacts of the rule using the ratio of compliance costs to the value of revenues from electricity generation in the base case, also focusing specifically on entities for which this measure is greater than 1 percent. The EPA projects that 3 government entities may have compliance costs greater than 1 percent of revenues from electricity generation in 2017. The majority of the units that have higher costs are not expected to make operational changes as a result of this rule (e.g., turn on controls). Their increased costs are largely due to a change in generation level, which results in a decrease in electricity revenue.  This approach is more indicative of a significant impact when an analysis is looking at entities operating in a competitive market environment. Government-owned entities do not operate in a competitive market environment and therefore will be able to recover expenses under the CSAPR Update through rate increases. Given this, the EPA considers the 1 percent measure in this case a crude measure of the extent to which rate increases will be made at publicly owned companies.
      For municipality- and state-owned entities, the capacity-weighted average economic impact as a share of base case revenue is slightly less than zero percent. This average reflects the fact that 6 of the 12 entities are projected to experience a negative economic impact as a share of base case revenue, which implies that this group of 5 entities experiences a net savings under the CSAPR Update.
      The various components of annual incremental cost under the CSAPR Update to government entities are summarized in Table 6-6. In 2017, state and municipal entities are a net purchaser of allowances, and experience both a decrease in fuel expenditures and a decrease in electricity revenue under the CSAPR Update. Incremental fuel costs are negative because most of these entities are projected to decrease generation
Table 6-6.  Incremental Annual Costs under the CSAPR Update Summarized by Ownership Group and Cost Category (2011$ millions) in 2017
                              EGU Ownership Type
                          Retrofit +  Operating Cost
                          Net Purchase of Allowances
                                   Fuel Cost
                           Lost Electricity Revenue
                              Administrative Cost
Municipal
                                     -$0.8
                                     $1.0
                                     -$5.8
                                    -$20.2
                                     $0.1
State
                                     -$1.4
                                     $0.9
                                     -$5.2
                                    -$11.6
                                     $0.0
Source: IPM analysis
6.2.3 	Summary of Government Entity Impacts
      The EPA examined the potential economic impacts on government-owned entities associated with this rulemaking based on assumptions of how the affected states will implement control measures to meet their emissions. According to the EPA's analysis, the total net economic impact on government-owned entities is expected to be $20.5 million in 2017. This does not mean that each government entity will experience net cost as the overall net savings is driven by some entities garnering savings. Of the 12 government entities considered in this analysis, three may experience compliance costs in excess of 1 percent of revenues in 2017, based on our assumptions of how the affected states implement control measures to meet their emissions budgets as set forth in this rulemaking.
      Government entities projected to experience compliance costs in excess of 1 percent of revenues have some potential for significant impact resulting from implementation of the CSAPR Update. However, as noted above, it is the EPA's position that because these government entities can pass on their costs of compliance to rate-payers, they will not be significantly affected.
6.3 	Employment
      Executive Order 13563 directs federal agencies to consider regulatory impacts on job creation and employment. According to the Executive Order, "our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and job creation. It must be based on the best available science" (Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically included a separate analysis of regulation-induced employment impacts, we typically conduct employment analyses for economically significant rules. This section discusses and projects potential employment impacts related to today's final rule.
      Section 6.3.1 describes the theoretical framework used to analyze regulation-induced employment impacts, discussing how economic theory alone cannot predict whether such impacts are positive or negative. Section 6.3.2 presents an overview of the peer-reviewed literature relevant to evaluating the effect of environmental regulation on employment. Section 6.3.3 provides background regarding recent employment trends in the electricity generation, coal and natural gas extraction sectors. Section 6.3.4 discusses the potential direct employment impacts in these sectors.
6.3.1	Economic Theory and Employment
     Regulatory employment impacts are difficult to disentangle from other economic changes affecting employment decisions over time and across regions and industries. Labor market responses to regulation are complex. They depend on labor demand and supply elasticities and possible labor market imperfections (e.g., wage stickiness, long-term unemployment, etc.). The unit of measurement (e.g., number of jobs, types of jobs, hours worked, and earnings) may affect observability of those responses. Net employment impacts are composed of a mix of potential declines and gains in different areas of the economy (e.g., the directly regulated sector, the environmental protection sector, upstream and downstream sectors, etc.) over time. In light of these difficulties, economic theory provides a constructive framework for analysis.
     Microeconomic theory describes how firms adjust their use of inputs in response to changes in economic conditions. Labor is one of many inputs to production, along with capital, energy, and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a function of market prices and technological constraints.[,] Berman and Bui (2001) adapt this model to analyze how environmental regulations affect labor demand. They model environmental regulation as effectively requiring certain factors of production, such as pollution abatement capital, at levels that firms would not otherwise choose. Berman and Bui (2001) model two components that drive changes in firm-level labor demand: output effects and substitution effects. Regulation affects the profit-maximizing quantity of output by changing the marginal cost of production. If a regulation causes marginal production cost to increase, it will place upward pressure on output prices, leading to a decrease in quantity demanded, and resulting in a decrease in production. The output effect describes how, holding labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by Berman and Bui, although many assume that regulations must increase marginal cost, in some cases they may decrease it. A regulation could induce a firm to upgrade to less polluting and more efficient equipment that lowers the marginal cost of production. In such a case, output could increase after firms comply with the regulation. An unregulated profit-maximizing firm may not have chosen to install such an efficiency-improving technology if the return on investment were too low, but once the technology is in place it lowers marginal production costs.
     The substitution effect describes how, holding output constant, regulation affects the labor-intensity of production. Although increased environmental regulation may increase use of pollution control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. For example, equipment inspection requirements, specialized waste handling, completing required paperwork, or pollution technologies that alter the production process may affect the number of workers necessary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regulation on pollution control equipment and expenditures required by the regulation and the corresponding change in the labor-intensity of production.
     In summary, as output and substitution effects may be positive or negative, economic theory alone cannot predict the direction of the net effect of regulation on labor demand. In addition, the empirical literature illustrates difficulties with estimation of net employment impacts. The most commonly used empirical methods, for example, Greenstone (2002), likely overstate employment impacts because they rely on relative comparisons between more regulated and less regulated counties, which can lead to "double counting" of impacts when production and employment shift from more regulated towards less regulated areas. Thus these empirical methods cannot be used to estimate net employment effects.
     The conceptual framework described thus far focused on regulatory effects on plant-level decisions within a regulated industry, but employment impacts at an individual plant do not necessarily represent impacts for the sector as a whole. At the industry-level, labor demand is more responsive if: (1) the price elasticity of demand for the product is high, (2) other factors of production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total production costs. For example, if all firms in an industry are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may not change much, and output of individual firms may change slightly.
     In addition to changes to labor demand in the regulated industry, net employment impacts encompass changes in other related sectors such as the environmental protection sector. This final rule may increase demand for the nitrogenous reagent (typically ammonia or urea) used in SCRs and SNCRs to reduce NOx, which may increase revenue and employment in the firms providing these chemicals.
     If the U.S. economy is at full employment, even a large-scale environmental regulation is unlikely to have a noticeable impact on aggregate net employment. Instead, labor in affected sectors would primarily be reallocated from one productive use to another (e.g., from producing electricity to manufacturing, installing, or operating and maintaining pollution-abatement equipment), and net national employment effects from environmental regulation would be small and transitory (e.g., as workers move from one job to another). Some workers may retrain or relocate in anticipation of new requirements or require time to search for new jobs, while shortages in some sectors or regions could bid up wages to attract workers. These adjustment costs can lead to local labor disruptions.
     If, on the other hand, the economy is operating at less than full employment, economic theory does not clearly indicate the direction or magnitude of the net impact of environmental regulation on employment; it could cause either a short-run net increase or short-run net decrease (Schmalansee and Stavins, 2011). For example, the Congressional Budget Office considered EPA's MATS and regulations for industrial boilers and process heaters as potentially leading to short-run net increases in economic growth and employment, driven by capital investments for compliance with the regulations (Congressional Budget Office, 2011). Environmental regulation may also affect labor supply and productivity. In particular, reducing pollution and other environmental risks may improve labor productivity or employees' ability to work. While the theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it is more difficult to study empirically. There is a small emerging literature that uses detailed labor and environmental data to assess these impacts.
To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The net employment effect incorporates expected employment changes (both positive and negative) in the regulated sector and other related sectors including the environmental protection sector. Labor demand impacts for regulated firms, and also for the regulated industry, can be decomposed into output and substitution effects which may be either negative or positive. Estimation of net employment effects for regulated sectors is possible when data of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possible. In the next section, we discuss the empirical literature.
<IP_ADDRESS>	Current State of Knowledge Based on the Peer-Reviewed Literature
	The peer-reviewed empirical literature specifically estimating employment effects of environmental regulations is limited but growing. We summarize it briefly in this section.
<IP_ADDRESS>	Regulated Sector
     Several empirical studies, including Berman and Bui (2001) and Ferris, Shadbegian, and Wolverton (2014), suggest that regulation-induced net employment impacts may be zero or slightly positive, but small in the regulated sector. Gray et al (2014) find that pulp mills that had to comply with both the air and water regulations in EPA's 1998 "Cluster Rule" experienced relatively small, and not always statistically significant, decreases in employment. Other research on regulated sectors suggests that employment growth may be lower in more regulated areas (Greenstone 2002, Walker 2011, 2013). However, since these latter studies compare more regulated to less regulated counties, this methodological approach likely overstates employment impacts to the extent that regulation causes plants to locate in one area of the country rather than another, which would lead to "double counting" of the employment impacts. List et al. (2003) find some evidence that this type of geographic relocation may be occurring.
<IP_ADDRESS>	Economy-Wide
     Given the difficulty with estimating national impacts of regulations, EPA has not generally estimated economy-wide employment impacts of its regulations in its benefit-cost analyses. However, in its continuing effort to advance the evaluation of costs, benefits, and economic impacts associated with environmental regulation, EPA has formed a panel of experts as part of EPA's Science Advisory Board (SAB) to advise EPA on the technical merits and challenges of using economy-wide economic models to evaluate the impacts of its regulations, including the impact on net national employment. Once EPA receives guidance from this panel, it will carefully consider this input and then decide if and how to proceed on economy-wide modeling of employment impacts of its regulations.
6.1.4	Labor Supply Impacts