Advanced Resources International. 2011. Improving Domestic Energy Security and Lowering CO2 Emissions with "Next Generation" CO2-Enhanced Oil Recovery (CO2-EOR). Available at <http://www.netl.doe.gov/research/energy-analysis/publications/details?pub=df02ffba-6b4b-4721-a7b4-04a505a19185>. Accessed June 9, 2015.Han, Weon S., McPherson, B J., Lichtner, P C., and Wang, F P. 2010. Evaluation of CO2 trapping mechanisms at the SACROC northern platform, Permian basin, Texas, site of 35 years of CO2 injection. American Journal of Science 310: 282-324.Interagency Task Force on Carbon Capture and Storage. Report of the Interagency Task Force on Carbon Capture and Storage. August 2010. Available at http://www.epa.gov/climatechange/downloads/CCS-Task-Force-Report-2010.pdf>. Accessed June 9, 2015. Intergovernmental Panel on Climate Change. 2001. Climate Change 2001: The Scientific Basis. Available at <http://www.grida.no/publications/other/ipcc_tar/?src=/climate/ipcc_tar/wg1/218.htm>. Accessed June 9, 2015. International Energy Agency (IEA). 2013. Tracking Clean Energy Progress 2013. Input to the Clean Energy Ministerial. Available at <http://www.iea.org/etp/tracking/>. Accessed June 9, 2015.Koottungal, Leena. 2014. 2014 Worldwide EOR Survey, Oil & Gas Journal, Volume 112, Issue 4, April 7, 2014 (corrected tables appear in Volume 112, Issue 5, May 5, 2014).National Energy Technology Laboratory (NETL). 2008. Reducing CO2 Emissions by Improving the Efficiency of Existing Coal-fired Power Plant Fleet. Available at <http://www.netl.doe.gov/energy-analyses/pubs/CFPP%20Efficiency-FINAL.pdf>. Accessed June 9, 2015.National Energy Technology Laboratory (NETL). 2012. The United States 2012 Carbon Utilization and Storage Atlas, Fourth Edition. Available at <http://www.netl.doe.gov/technologies/carbon_seq/refshelf/atlasIV/>. Accessed June 9, 2015.National Energy Technology Laboratory (NETL). 2013. Energy Analyses: Cost and Performance Baselines for Fossil Energy Plants. Available at <http://www.netl.doe.gov/energy-analyses/baseline_studies.html>. Accessed June 9, 2015.Pacific Northwest National Laboratory (PNNL). 2009. An Assessment of the Commercial Availability of Carbon Dioxide Capture and Storage Technologies as of June 2009. Available at <http://www.pnl.gov/science/pdf/PNNL-18520_Status_of_CCS_062009.pdf>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2000. Carbon Dioxide Emissions from the Generation of Electric Power in the United States. Available at <ftp://ftp.eia.doe.gov/environment/co2emiss00.pdf>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2003. Electric Power Annual 2003. Available at <http://www.eia.gov/electricity/annual/archive/03482003.pdf>. Accessed June 9, 2015. U.S. Energy Information Administration (U.S. EIA). 2009. Electric Power Annual 2009. Available at <http://www.eia.gov/electricity/annual/archive/03482009.pdf>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2013. Electric Power Annual 2011. Available at <http://www.eia.gov/electricity/annual/>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2010a. "Status of Electricity Restructuring by State." Available at <http://www.eia.gov/cneaf/electricity/page/restructuring/restructure_elect.html>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2010b. AEO 2010 Retrospective Review. Available at <http://www.eia.gov/forecasts/aeo/retrospective/>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2010c. Annual Energy Outlook 2010. Available at <http://www.eia.gov/oiaf/archive/aeo10/index.html>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2010d. Annual Energy Review 2010. Available at <http://www.eia.gov/totalenergy/data/annual/pdf/aer.pdf>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2011. Annual Energy Outlook 2011. Available at <http://www.eia.gov/forecasts/archive/aeo11/>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2012. Annual Energy Outlook 2012 (Early Release). Available at <http://www.eia.gov/forecasts/aeo/>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). Today in Energy: Most states have Renewable Portfolio Standards. 2012a. Available at <http://www.eia.gov/todayinenergy/detail.cfm?id=4850>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2013. Annual Energy Outlook 2013. Available at <http://www.eia.gov/forecasts/aeo/>. Accessed June 9, 2015.U.S. Energy Information Administration (U.S. EIA). 2015. Monthly Energy Review, April 2015. Available at <http://www.eia.gov/totalenergy/data/monthly/>. Accessed June 9, 2015.U.S. Environmental Protection Agency (U.S. EPA). 2013. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 - 2011. Available at <http://www.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2013-Main-Text.pdf>. Accessed June 9, 2015.U.S. Geological Survey (USGS) Carbon Dioxide Storage Resources Assessment Team. 2013. National assessment of geologic carbon dioxide storage resources  -  Results: U.S. Geological Survey Circular 1386. Available at <http://pubs.usgs.gov/circ/1386/>. Accessed June 9, 2015.Chapter 3: Cost, Emissions, Economic, and Energy Impacts3.1	IntroductionThis chapter reports the compliance cost, emissions, economic, and energy impact analysis performed for the Clean Power Plan Final Rule. EPA used the Integrated Planning Model (IPM), developed by ICF International, to conduct most of the analysis discussed in this Chapter. IPM is a dynamic linear programming model that can be used to examine air pollution control policies for CO2, SO2, NOX, Hg, HCl, and other air pollutants throughout the contiguous United States for the entire power system. The IPM electricity demand projections are based on projections from the Energy Information Administration (EIA), adjusted for demand-side energy efficiency measures that can be reasonably anticipated to occur under the Clean Power Plan. To estimate compliance costs, emissions, and other impacts for states and territories with power markets not represented in IPM, EPA performed supplementary analysis to account for the potential benefits and costs of the Clean Power Plan Final Rule for Alaska, Hawaii, and U.S. territories with affected EGUs. This chapter combines IPM analysis of the continental U.S. with the supplementary analysis performed for Alaska, Hawaii, and U.S. Territories. Supplementary estimates of monitoring, recordkeeping, and reporting costs are reported in this chapter as well. The sum of emission reductions across these regions informs the benefits analyses presented elsewhere in this RIA.3.2	OverviewThis chapter of the RIA presents illustrative analyses of the final rule by making assumptions about the possible approaches that States might pursue as they develop their state plans. Over the last decade, EPA has conducted extensive analyses of regulatory actions affecting the power sector. These efforts support the Agency's understanding of key variables that influence the effects of a policy and provide the framework for how the Agency estimates the costs and benefits associated with its actions.3.3	Power Sector Modelling FrameworkThe Integrated Planning Model (IPM), developed by ICF Consulting, is a state-of-the-art, peer-reviewed, dynamic linear programming model that can be used to project power sector behavior under future business-as-usual conditions and examine prospective air pollution control policies throughout the contiguous United States for the entire electric power system. EPA used IPM to project likely future electricity market conditions with and without the rule. Additional demand-side energy efficiency measures that may be adopted in response to the regulation, and the resulting changes to future demand projections, are also accounted for in the illustrative plan scenarios. The level of energy efficiency-driven reductions in electricity demand, and their associated costs, are reported in section 3.6.IPM is a multi-regional, dynamic, deterministic linear programming model of the contiguous U.S. electric power sector. It provides forecasts of least cost capacity expansion, electricity dispatch, and emission control strategies while meeting energy demand and environmental, transmission, dispatch, and reliability constraints. EPA has used IPM for over two decades to better understand power sector behavior under future business-as-usual conditions and to evaluate the economic and emission impacts of prospective environmental policies. The model is designed to reflect electricity markets as accurately as possible. EPA uses the best available information from utilities, industry experts, gas and coal market experts, financial institutions, and government statistics as the basis for the detailed power sector modeling in IPM. The model documentation provides additional information on the assumptions discussed here as well as all other model assumptions and inputs. Although the Agency typically focuses on broad system effects when assessing the economic impacts of a particular policy, EPA's application of IPM includes a detailed and sophisticated regional representation of key variables affecting power sector behavior.The model incorporates a detailed representation of the fossil-fuel supply system that is used to forecast equilibrium fuel prices. The model includes an endogenous representation of the North American natural gas supply system through a natural gas module that reflects a partial supply/demand equilibrium of the North American gas market accounting for varying levels of potential power sector and non-power sector gas demand and corresponding gas production and price levels. This module consists of 118 supply, demand, and storage nodes and 15 liquefied natural gas re-gasification facility locations that are tied together by a series of linkages (i.e., pipelines) that represent the North American natural gas transmission and distribution network.IPM also endogenously models the partial equilibrium of coal supply and EGU coal demand levels throughout the contiguous U.S., taking into account assumed non-power sector demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal transport network, which consists of over four thousand linkages representing rail, barge, and truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough bottom-up, mine-by-mine approach that depicts the coal choices and associated supply costs that power plants would face if selecting that coal over the modeling time horizon. The IPM documentation outlines the methods and data used to quantify the economically recoverable coal reserves, characterize their cost, and build the 36 coal regions' supply curves. EPA has used IPM extensively over the past two decades to analyze options for reducing power sector emissions. Previously, the model has been used to forecast the costs, emission changes, and power sector impacts for the Clean Air Interstate Rule, Cross-State Air Pollution Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), and the proposed Carbon Pollution Standards for New Power Plants. Recently IPM has also been used to estimate the air pollution reductions and power sector impacts of water and waste regulations affecting EGUs, including Cooling Water Intakes (316(b)) Rule, Disposal of Coal Combustion Residuals from Electric Utilities (CCR) and Steam Electric Effluent Limitation Guidelines (ELG).The model and EPA's input assumptions undergo periodic formal peer review. The rulemaking process also provides opportunity for expert review and comment by a variety of stakeholders, including owners and operators of capacity in the electricity sector that is represented by the model, public interest groups, and other developers of U.S. electricity sector models. The feedback that the Agency receives provides a highly-detailed review of key input assumptions, model representation, and modeling results. IPM has received extensive review by energy and environmental modeling experts in a variety of contexts. For example, in the late 1990s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 prospective studies that are periodically conducted. The model has also undergone considerable interagency scrutiny when it was used to conduct over a dozen legislative analyses (performed at Congressional request) over the past decade. The Agency has also used the model in a number of comparative modeling exercises sponsored by Stanford University's Energy Modeling Forum over the past 15 years.IPM has also been employed by states (e.g., for RGGI, the Western Regional Air Partnership, Ozone Transport Assessment Group), other Federal and state agencies, environmental groups, and industry, all of whom subject the model to their own review procedures.3.3.1	Recent Updates to EPA's Base Case using IPM (v.5.15)The "Base Case" for this analysis is a business-as-usual scenario that would be expected under market and regulatory conditions in the absence of this rule. EPA frequently updates the IPM base case to reflect the latest available electricity demand forecasts, as well as, expected costs and availability of new and existing generating resources, fuels, and emissions control technologies. EPA's IPM modeling platform used to analyze this final rule (v.5.15) incorporates updates to the version of the model used to analyze the impacts of the proposed rule (v.5.13). These updates are primarily routine calibrations with the Energy Information Agency's (EIA) Annual Energy Outlook (AEO), including updating the electric demand forecast consistent with the AEO 2015 and an update to natural gas supply. Additional updates, based on the most up-to-date information and/or public comments received by EPA, include unit-level specifications (e.g., control configuration), planned new power plant construction and closures, and updated cost and performance for onshore wind and utility-scale solar technologies. This IPM modeling platform incorporates federal and most state laws and regulations whose provisions were either in effect or enacted and clearly delineated in March 2015. This update also includes two non-air federal rules affecting EGUs: Cooling Water Intakes (316(b)) Rule and Combustion Residuals from Electric Utilities (CCR). Additionally, all new capacity projected by the model is compliant with Clean Air Act 111(b) standards. For a detailed account of all updates made to the v.5.15 modeling platform, see the Incremental Documentation for EPA Base Case v.5.15 Using IPM. EPA also updated the National Electric Energy Data System (NEEDS). This database contains the unit-level data that is used to construct the "model" plants that represent existing and committed units in EPA modeling applications of IPM. NEEDS includes detailed information on each individual EGU, including geographic, operating, air emissions, and other data on every generating units in the contiguous U.S.To estimate compliance costs, emissions and other impacts for states and territories with power markets not represented in IPM, EPA performed supplementary analysis to account for the potential benefits and costs of the Clean Power Plan Final Rule for Alaska, Hawaii, and U.S. territories with affected EGUs.3.4	 State Goals in this Final RuleIn this final rule, the EPA is establishing CO2 emission performance rates for each of two source categories of fossil fuel-fired EGUs -- fossil fuel-fired electric steam generating units and stationary combustion turbines. The EPA has translated the source category-specific CO2 emission performance rates into equivalent state-level rate-based and mass-based CO2 goals to afford states a range of choices, including application of statewide measures, for developing their plans. For modeling purposes, the illustrative rule impacts shown in the RIA reflect an application of both the state-wide rate-based and mass-based goals in each state.Table 3-1.	Rate-Based and Mass-Based State Goals Statewide Rate-Based CO2 Emission Performance Goals (Adjusted Output-Weighted-Average Pounds of CO2 Per Net MWh From All Affected Fossil Fuel-Fired EGUs) StateInterim GoalFinal GoalInterim Goal Final Goal Alaska Alabama1,1741,018Arkansas1,3191,130Arizona1,1901,031California925828Colorado1,3771,174Connecticut870786Delaware1,040916Florida1,044919Fort Mojave Georgia1,2141,049Hawaii Iowa1,5191,283Idaho851771Illinois1,4701,245Indiana1,4661,242Kansas1,5321,293Kentucky1,5231,286Louisiana1,3081,121Massachusetts920824Maryland1,5241,287Maine861778Michigan1,3701,169Minnesota1,4281,213Missouri1,5041,272Mississippi1,078945Montana1,5481,305Navajo North Carolina1,3271,135North Dakota1,5481,305Nebraska1,5361,296New Hampshire964858New Jersey904811New Mexico1,3411,146Nevada960854New York1,042918Ohio1,3981,190Oklahoma1,2391,068Oregon982871Pennsylvania1,2741,095Rhode Island851771South Carolina1,3531,156South Dakota1,3671,166Tennessee1,4251,211Texas1,2041,041Ute Utah1,3831,179Virginia1,064934Washington1,128983Wisconsin1,3791,176West Virginia1,5481,305Wyoming1,5411,300  3.5	Illustrative Plan Approaches AnalyzedTo estimate the costs, benefits, and impacts of implementing the CPP guidelines, the EPA modeled two illustrative plan scenarios, each at the state level, based on a rate-based approach and a mass-based approach. The rate-based scenario requires affected sources in each state to achieve a single average emissions rate in each period as represented by the state-wide goals. The mass-based scenario requires affected sources in each state to limit their aggregate emissions not to exceed the mass goal for that state. In each of these scenarios, affected EGUs include:Existing fossil steam boilers with nameplate capacity greater than 25 MWExisting NGCC units with nameplate capacity greater than 25 MW In the rate-based scenario, generation (or avoided generation) from these additional sources represented in the model is counted toward meeting state goals:All renewable capacity (hydro, solar PV, wind, geothermal) that comes online after 2012Under-construction nuclear Demand-side energy efficiency in addition to levels implicit in base case electricity demand. In the illustrative rate-based plan scenario analyzed in this RIA, the affected EGUs within each state are required to achieve an average emissions rate that is less than or equal to the state goals for each state. In order meet the goal for each state, the affected sources in this scenario have the ability to do one or both of the following:generate in amounts within that state such that the average emissions rate is achieved, and/orinclude in the average emissions rate calculation new renewable generation or demand-side energy efficiency located outside of the state but within each of the illustrative Interconnection-based regions shown in Figure 3-3 below. Figure 3-3.	Illustrative Regions for EE/RE Procurement Used in this AnalysisThis rate-based implementation approach enables some sources to emit at rates higher than their applicable state goal, as long as there is corresponding generation coming from affected sources in that state (or new renewables or demand-side EE located in any state within the illustrative region for EE/RE procurement) that emits at a lower rate, such that each state's goal (in lbs/MWh) is met across all affected sources located within the state including generation (or avoided generation) from EE/RE procurement across the illustrative regions. In this rate-based scenario, specific generation (or avoided generation) from EE/RE procurement may only be used once for compliance toward a state goal; in other words, while emitting sources in all states may avail themselves of qualifying EE/RE across the illustrative region, no particular EE/RE MWh can be claimed by more than one emitter as part of reaching a state goal. While the final rule enables states to achieve their mass goals with the flexibility of interstate trading, this RIA presents an illustrative scenario that conservatively assumes that each state achieves its goal independently. Cooperation between the states that allows for trading across states would provide EGUs with additional low cost abatement opportunities and would therefore lower the overall cost of compliance across the affected states. Each illustrative plan scenario assumes identical levels of DS-EE megawatt-hour (MWh) savings and associated costs, which are specified exogenously and consistent with the EE plan scenario performance levels described in section 3.6. EPA has specified and imposed EE-related costs and changes in future electricity demand when modeling the illustrative plan scenarios presented for this rule. Details of the implementation of the demand reduction are reported in the following section. The full array of estimates of the benefits, costs, and economic impacts of this proposed action are presented for both the illustrative rate- and mass-based scenarios. These illustrative plan scenarios are designed to reflect, to the extent possible, the scope and nature of the CPP guidelines. However, there is considerable uncertainty with regard to the regulatory form and precise measures that states will adopt to meet the requirements, since there are considerable flexibilities afforded to the states in developing state plans. Nonetheless, the analysis of the benefits, costs, and relevant impacts of the rule attempts to encapsulate some of those flexibilities in order to inform states and stakeholders of the potential overall impacts of the CPP. The relevant impacts, costs, and benefits are provided for 2020 (prior to implementation of the rule), 2025, and 2030.It is also important to note that the analysis does not specify any particular CO2 reduction measure to occur, with the exception of the level of demand-side energy efficiency (EE) assumed to be adopted in response to the CPP. In other words, aside from investments in EE, the analysis allows the power system the flexibility to respond to average emissions rate or mass constraints on affected sources in the illustrative scenarios to achieve the goals in the most cost-effective manner determined by IPM, as specified below. While IPM produces a cost-minimizing solution to achieve the state goals imposed in the illustrative scenarios, there may be yet lower-cost approaches that the states may adopt to achieve their state goals inasmuch as states and sources take advantage of emission reduction opportunities in practice, and flexibilities afforded under the final rule, that are not represented in this analysis and would yield different cost and emissions outcomes.3.6	Demand-Side Energy Efficiency3.6.1	Demand-Side Energy Efficiency Improvements (Electricity Demand Reductions)The final CPP provides states the flexibility to use demand reductions resulting from demand-side energy efficiency measures as a component of their compliance strategy, either directly recognized towards compliance with a rate-based goal or as a complementary approach for achieving a mass-based goal. The EPA has included in our illustrative plan scenarios (both rate- and mass-based) a level of demand reduction that could be achieved, and the associated costs incurred, through implementation of demand-side energy efficiency measures. This "demand-side energy efficiency plan scenario" represents a level of performance that has already been demonstrated or is required by policies (e.g., energy efficiency resource standards) of leading energy efficiency implementing states, and is consistent with a demonstrated or required annual pace of performance improvement over time. The resulting levels of demand reduction are consistent with recent studies of achievable demand reduction potential conducted throughout the U.S. For these reasons the demand-side energy efficiency plan scenario represents a reasonable assumption about the level of demand-side energy efficiency investments that may be encouraged in response to the final CPP. For the illustrative demand-side energy efficiency plan scenario, electricity demand reductions for each state for each year are developed by ramping up from an historical basis to a target annual incremental demand reduction rate of 1.0 percent of electricity demand over a period of years starting in 2020, and maintaining that rate throughout the modeling horizon. Twenty leading states have either achieved, or have established requirements that will lead them to achieve, this rate of incremental electricity demand reduction on an annual basis. Based on historic performance and existing state requirements, for each state the pace of improvement from the state's historical incremental demand reduction rate is set at 0.2 percent per year, beginning in 2020, until the target rate of 1.0 percent is achieved. States already at or above the 1.0 percent target rate are assumed to achieve a 1.0 percent rate beginning in 2020 and sustain that rate thereafter. The incremental demand reduction rate for each state, for each year, is used to derive cumulative annual electricity demand reductions based upon information about the average life of energy efficiency measures and the distribution of measure lives across energy efficiency programs. The cumulative annual electricity demand reduction derived using this methodology is used to adjust base case electricity demand levels in the power sector compliance modeling. To reflect the implementation of the illustrative energy efficiency plan scenario in modeling, the IPM base case electricity demand was adjusted exogenously to reflect the estimated future-year demand reductions calculated as described above. State-level demand reductions were scaled up to account for transmission losses and applied to base case generation demand in each model year to derive adjusted demand for each state, reflecting the energy efficiency plan scenario energy reductions. The demand adjustments were applied proportionally across all segments (peak and non-peak) of the load duration curve. To reflect the adjusted state-level demand within IPM model regions that cross state borders, energy reductions from a bisected state were distributed between the applicable IPM model regions using a distribution approach based on reported sales in 2013 as a proxy for the distribution of energy efficiency investment opportunities.Table 3-3 summarizes the results of the demand-side energy efficiency illustrative plan scenario at the national level.Table 3-3.	Demand-Side Energy Efficiency Plan Scenario: Net Cumulative Demand Reductions [Contiguous U.S.] (GWh and as Percent of BAU Sales)<PHONE_NUMBER>30Net Cumulative Demand Reduction (GWh)23,150194,126327,092Net Cumulative Demand Reduction as Percent of BAU Sales0.59%4.81%7.83%Source: Greenhouse Gas Abatement Measures TSD3.6.2	Demand-Side Energy Efficiency CostsTotal costs of achieving the demand-side energy efficiency scenario for each year were calculated exogenous to the power sector modeling. The power system cost impacts resulting from the illustrative plan scenario were captured within IPM and include the effects of reduced demand levels driven by the energy efficiency scenario discussed above. The integration of the exogenously calculated demand-side energy efficiency scenario costs with the power system cost impacts of the illustrative plan scenario are discussed in section 3.7.2. In addition to the demand reduction results, the demand-side energy efficiency costs were based upon an estimate of the total first-year cost of saved energy (i.e., reduced demand), the average life of the demand-side energy efficiency measures, the distribution of those measure lives, and cost factors as greater levels of demand reductions are achieved. The total first-year cost of saved energy accounts for both the costs of the demand-side energy efficiency programs, known as the program costs, and the additional cost to electricity consumers participating in the program (e.g., purchasing a more energy efficient technology), known as the participant costs. To calculate total annualized demand-side energy efficiency costs, first-year costs for each year for each state were levelized (at 3 percent and 7 percent discount rates) over the estimated distribution of measure lives and the results summed for each year for each state. For example, the 2025 estimate of annualized EE cost includes levelized value of first-year costs for energy efficiency investments made in 2020 through 2025. The annualized costs rise in each analysis year as additional first-year costs are incurred. The annualized cost results are summarized below in Table 3-4. The total levelized cost of saved energy was calculated based upon the same inputs and using a 3 percent discount rate resulted in average values of 9.2 cents per kWh in 2020, 8.6 cents per kWh in 2025, and 8.1 cents per kWh in 2030. Table 3-4.	Annualized Cost of Demand-Side Energy Efficiency Plan Scenario [Contiguous U.S.] (at discount rates of 3 percent and 7 percent, billions 2011$) Discount Rate2018202020252030at 3 percent<IP_ADDRESS>6.3at 7 percent0.02.620.632.5Source: Greenhouse Gas Abatement Measures TSDThe funding for demand-side energy efficiency programs (to cover program costs) is typically collected through a standard per kWh surcharge to the ratepayer; the regional retail price impacts analyzed from this RIA's illustrative plan scenarios assumes the recovery of these program costs through the following procedure. For each state, the first-year energy efficiency program costs are calculated for each year. These costs were distributed between the applicable IPM regions using an approach based on reported sales in 2013 as a proxy for the distribution of energy efficiency investment opportunities. These regionalized energy efficiency program costs were then incorporated into the regional retail price calculation as discussed in section 3.7.9.  Chapter 5 of the GHG Abatement Measures TSD provides complete details on the calculations of annualized costs and first-year costs as well as comprehensive results (by state, by year) for the illustrative demand-side energy efficiency plan scenario.3.7	Projected Power Sector ImpactsThe following sections present projected impacts from the two illustrative scenarios described above. The tables present impacts from 2020 (prior to the initial compliance year), 2025 (representative of the interim compliance period), and 2030 (representative of the final compliance period). The narrative focuses on results during the initial and final compliance periods.3.7.1	Projected Emissions (in the Contiguous U.S.)Under the rate-based scenario, EPA projects annual CO2 reductions of 3 percent below the base case in 2020, 11 percent below the base case in 2025, and 19 percent below base case projections in 2030 (reaching 28 percent to 32 percent below 2005 emissions in 2025 and 2030, respectively). For the mass-based scenario, EPA projects annual CO2 reductions of [X] percent below the base case in 2020, [X] percent below the base case in 2025 and [X] percent below base case projections in 2030 (reaching [X] percent to [X] percent below 2005 emissions in 2025 and 2030, respectively). Table 3-5.	Projected CO2 Emission Impacts, Relative to Base Case CO2 Emissions (MM Tonnes)CO2 Emissions Reductions from Base Case (MM Tonnes)CO2 Emissions Reductions: Percent Change from Base Case<PHONE_NUMBER>30202020252030202020252030Base Case1,9551,9642,021      Rate1,8921,7541,644632103773%11%19%Mass[X][X][X][X][X][X][X][X][X]Source: Integrated Planning Model run by EPA, 2015Table 3-6.	Projected CO2 Emission Impacts, Relative to 2005 CO2 Emissions (MM Tonnes)CO2 Emissions Reductions from 2005 (MM Tonnes)CO2 Emissions Reductions: Percent Change from 2005<PHONE_NUMBER>30202020252030202020252030Base Case1,9551,9642,021      Rate1,8921,7541,64454268079022%28%32%Mass[X][X][X][X][X][X][X][X][X]Source: Integrated Planning Model run by EPA, 2015Under the rate-based illustrative scenario, EPA projects a 14 percent reduction of SO2, 13 percent reduction of NOX, and a 11 percent reduction of mercury in 2025, and a 24 percent reduction of SO2, 22 percent reduction of NOX, and a 27 percent reduction of mercury in 2030. Under the mass-based illustrative scenario, EPA projects a [X] percent reduction of SO2, [X] percent reduction of NOX, and a [X] percent reduction of mercury in 2025, and a [X] percent reduction of SO2, [X] percent reduction of NOX, and a [X] percent reduction of mercury in 2030. The projected non-CO2 reductions are summarized below in Table 3-7.Table 3-7. Projected Non-CO2 Emission Impacts, 2020-2030Base CaseRateMassRateMass2020     SO2(thousand tons)1,3111,298[X]-1.0%[X]NOX(thousand tons)1,3331,282[X]-3.8%[X]Hg(tons)6.66.4[X]-2.8%[X]PM2.5(thousand tons)[X][X][X][X][X]2025SO2(thousand tons)1,2751,097[X]-13.9%[X]NOX(thousand tons)1,3021,138[X]-12.6%[X]Hg(tons)6.65.9[X]-10.8%[X]PM2.5(thousand tons)[X][X][X][X][X]2030SO2(thousand tons)1,314996[X]-24.2%[X]NOX(thousand tons)1,2931,010[X]-21.9%[X]Hg(tons)6.85.6[X]-17.3%[X]PM2.5(thousand tons)[X][X][X][X][X]Source: Integrated Planning Model run by EPA, 2015While the EPA has not quantified the climate impacts of non-CO2 emissions changes or CO2 emissions changes outside the electricity sector for the final emissions guidelines, the Agency has analyzed the potential changes in upstream methane emissions from the natural gas and coal production sectors that may result from the illustrative approaches examined in this RIA. The EPA assessed whether the net change in upstream methane emissions from natural gas and coal production is likely to be positive or negative and also assessed the potential magnitude of changes relative to CO2 emissions reductions anticipated at power plants. This assessment included CO2 emissions from the flaring of methane, but did not evaluate potential changes in other combustion-related CO2 emissions, such as emissions associated with drilling, mining, processing, and transportation in the natural gas and coal production sectors. This analysis found that the net upstream methane emissions from natural gas systems and coal mines and CO2 emissions from flaring of methane will likely decrease under the final emissions guidelines. Furthermore, the changes in upstream methane emissions are small relative to the changes in direct CO2 emissions from power plants. The projections include voluntary and regulatory activities to reduce emissions from coal mining and natural gas and oil systems, including the 2012 Oil and Natural Gas NSPS. In addition, the EPA plans to issue a proposed rule later this summer that would build on its 2012 Oil and Gas NSPS. When these standards are finalized and implemented, they would further reduce projected emissions from natural gas and oil systems. The technical details supporting this analysis can be found in the Appendix to this chapter. 3.7.2	Projected Compliance Costs (in the Contiguous U.S.)The power industry's "compliance costs" are represented in this analysis as the change in electric power generation costs between the base case and illustrative CPP scenarios, inclusive of the cost of demand-side EE measures. The system costs reflect the least cost power system outcome in which the sector employs all the flexibilities assumed in the modeling, as discussed above, and pursues the most cost-effective emission reduction opportunities in order to meet the rate- and mass-based goals, as represented in the illustrative plan scenarios. In simple terms, these costs are an estimate of the increased power industry expenditures required to meet demand projections while complying with state goals, including the total demand-side energy efficiency costs.  The compliance costs for the final emissions guidelines for EGUs in the contiguous U.S. states is forecast using IPM. The cost of demand-side EE programs assumed in the IPM analysis are reported in section 3.6.2. The compliance costs for non-contiguous states and territories are reported in section 3.X. EPA projects that the annual compliance cost of the rate-based illustrative plan scenario are $2.4 billion in 2020, $1.1 billion in 2025, and $8.4 billion in 2030. The annual compliance cost of the mass-based illustrative plan scenario are estimated to be [X] billion in 2020, $[X] billion in 2025, and $[X] billion in 2030. The annual compliance cost is the projected additional cost of complying with the rule in the year analyzed and includes the net change in the annualized cost of capital investment in new generating sources and heat rate improvements at coal steam facilities, the change in the ongoing costs of operating pollution controls, the change in expenditures on various fuels (inclusive of changes in the price of these fuels), demand-side energy efficiency measures, and other actions associated with compliance. Table 3-8.	Annualized Compliance Costs (billions of 2011$)<PHONE_NUMBER>30Rate$2.4$1.1$8.4Mass[X][X][X]* These costs do not include monitoring, reporting, and recordkeeping (MM&R) costs. For more information on MM&R costs, see section 3.11.Source: Integrated Planning Model run by EPA, 2015 and with post-processing to account for exogenous demand side management energy efficiency costs.In order to contextualize EPA's projection of the additional costs in 2030 across the two illustrative plan scenarios evaluated in this RIA, it is useful to compare these incremental cost estimates to total projected power sector expenditures. The power sector is expected in the base case to expend over $201 billion in 2030 to generate, transmit, and distribute electricity to end-use consumers. In 2014, according to EIA, the power sector generated $389 billion in revenue from retail sales of electricity. For context, the projected costs of compliance with the proposed rule amount to a 4 percent increase in the cost of meeting electricity demand, while securing public health and welfare benefits that are several times greater (as described in Chapters 4 and 8).The following example uses projected results for the year 2030 to illustrate how different components of estimated expenditures are combined to form the full compliance costs presented in Table 3-8. In Table 3-9 we present the IPM modeling results for the two illustrative plan scenarios in 2030. The results show that annualized expenditures required to supply enough electricity to meet demand decline by $15.7 billion (rate) and $[X] billion (mass) from the base case in 2030. This incremental decline is a net outcome of two simultaneous effects that move in opposite directions. First, imposing the CO2 constraints represented by each illustrative plan scenario on electric generators would, other things equal, result in an incremental increase in expenditures to supply any given level of electricity. However, once electricity demand is reduced to reflect demand-side energy efficiency improvements, there is a substantial reduction in the expenditures needed to supply a correspondingly lower amount of electricity demand. Table 3-9.	Total Power Sector Generating Costs (IPM) (billions 2011$)<PHONE_NUMBER>30Base Case$166.5$178.3$201.3Rate$166.8$162.7$183.4MassSource: Integrated Planning Model run by EPA, 2015In order to reflect the full compliance cost attributable to the CPP scenarios, it is necessary to include the annualized expenditures needed to secure the demand-side energy efficiency improvements. As described in section 3.6.2, EPA has estimated these energy efficiency-related expenditures to be $26.3 billion in 2030 (using a 3 percent discount rate). The EE-related expenditures include costs incurred by parties administering EE programs and costs incurred by participants in those programs. As a result, this analysis finds the cost of the rate-based and mass-based illustrative plan scenarios in 2030 to be $8.4 billion and $[X] billion, respectively.3.7.3	Projected Compliance Actions for Emissions ReductionsHeat Rate Improvements (HRI) EPA analysis assumes that the existing coal steam electric generating fleet has, on average, the ability to improve operating efficiency (i.e., reduce the average net heat rate, or the Btu of fuel energy needed to produce one kWh of net electricity output). All else held constant, an HRI allows the EGU to generate the same amount of electricity using less fuel. The decrease in required fossil fuel results in a lower output-based CO2 emissions rate (lbs/MWh), as well as a lower variable cost of electricity generation. In the modeling conducted for these illustrative plan scenarios, coal boilers have the choice to improve heat rates by 4.3 percent in the eastern illustrative compliance region, 2.1 percent in the western illustrative compliance region, and 2.3 percent in Texas, all at a capital cost of $100 per kW. The option for heat rate improvement is only made available in the illustrative plan scenarios, in response to the final rule.The vast majority of existing coal boilers are projected to adopt the aforementioned heat rate improvements. EPA projects that 99 GW of existing coal steam capacity (greater than 25 MW) will improve operating efficiency (i.e., reduce the average net heat rate) under the rate-based scenario by 2030. Under the mass-based scenario, EPA projects that [X] GW of existing coal steam capacity with improve operating efficiency by 2030.Generation ShiftingAnother approach for reducing the average emission rate from existing units is to shift some generation from more CO2-intensive generation to less CO2-intensive generation. Compared to the base case, existing coal steam capacity is, on average, projected to operate at a lower capacity factor for both illustrative plan scenarios. Under the illustrative rate-based plan scenario, the average 2030 capacity factor is 69 percent, and under the mass-based scenario, the average capacity factor for existing coal steam is [X] percent. Existing natural gas combined cycle units, which are less carbon-intensive than coal steam capacity on an output basis, operate at noticeably higher capacity factor under both illustrative plan scenarios, on average. See Table 3-10. The utilization of existing natural gas combined cycle capacity is lower than the BSER level of 75 percent on an annual average basis in these illustrative plan scenarios, reflecting the fact that, in practice, the most cost-effective CO2 reduction strategies to meet each state's goal may not require that each building block be achieved in entirety. For example, building block 2 (generation shifting) is not deployed to the maximum extent in the illustrative plan scenarios. Table 3-10.	Projected Capacity Factor of Existing Coal Steam and Natural Gas Combined Cycle Capacity Existing Coal SteamExisting Natural Gas Combined Cycle202020252030202020252030Base Case77%76%79%54%56%51%Rate78%75%69%56%59%61%Mass[X][X][X][X][X][X]Source: Integrated Planning Model run by EPA, 2015Demand-Side Energy EfficiencyAnother approach for reducing emissions from affected EGUs is to consider reductions in demand attributable to demand-side energy efficiency measures as discussed in section 3.6. In the illustrative plan scenarios presented in this RIA, each state is credited for total demand-side energy efficiency consistent with the state-by-state demand reductions that are represented by the demand-side energy efficiency scenario discussed in section 3.6.1. Deployment of Cleaner Generating TechnologiesAnother key opportunity to reduce emissions from existing sources is to build more lower- or zero-emitting generating resources, in particular renewable energy. These sources of electricity, including wind and solar, can displace higher emitting existing sources and are also directly credited toward the state goals in the rate-based illustrative scenario. Increased deployment results in CO2 reductions in both rate-based and mass-based scenarios. 3.7.4	Projected Generation MixTable 3-3 and Figure 3-4 show the generation mix in the base case and under the two illustrative plan scenarios. In both scenarios, total generation declines relative to the base case as a result of the reduction in total demand attributable to the demand-side energy efficiency applied in the illustrative scenarios, by 5 percent in 2025 and 8 percent in 2030.Under the rate-based scenario, coal-fired generation is projected to decline 12 percent in 2025, and natural-gas-fired generation from existing combined cycle capacity is projected to increase 5 percent relative to the base case. The coal-fired fleet in 2030 generates 23 percent less than in the base case, while natural-gas-fired generation from existing combined cycles increases 18 percent relative to the base case. Gas-fired generation from new combined cycle capacity decreases in 2025 and 2030, consistent with the decrease in new capacity (see section 3.7.6). Relative to the base case, generation from non-hydro renewables decreases 1 percent in 2025 and increases 9 percent in 2030.Similarly, under the mass-based scenario, coal-fired generation is projected to decline [X] percent in 2025, and natural-gas-fired generation from existing combined cycle capacity is projected to increase [X] percent relative to the base case. The coal-fired fleet in 2030 generates [X] percent less than in the base case, while natural-gas-fired generation from existing combined cycles increases [X] percent relative to the base case. Gas-fired generation from new combined cycle capacity changes [X] percent relative to the base case. Relative to the base case, generation from non-hydro renewables increases [X] percent in 2025 and [X] percent in 2030. Table 3-11.	Generation Mix (thousand GWh)  Base CaseRateMassRateMass2020     Coal1,4621,391-5%NG Combined Cycle (existing)1,1111,1251%NG Combined Cycle (new)335463%Combustion Turbine152039%Oil/Gas Steam51500%Non-Hydro Renewables3933992%Hydro3103110%Nuclear798792-1%Other18180%Total4,1904,160-1%2025   Coal1,4281,256-12%NG Combined Cycle (existing)1,1521,2065%NG Combined Cycle (new)11354-53%Combustion Turbine233031%Oil/Gas Steam3921-46%Non-Hydro Renewables417414-1%Hydro3403400%Nuclear799791-1%Other17170%Total4,3284,129-5%2030   Coal1,4661,131-23%NG Combined Cycle (existing)1,0421,23118%NG Combined Cycle (new)324101-69%Combustion Turbine222621%Oil/Gas Steam2211-52%Non-Hydro Renewables4504899%Hydro3403410%Nuclear783777-1%Other17170%Total4,4674,124-8%Note: "Other" mostly includes generation from MSW and fuel cells. Source: Integrated Planning Model run by EPA, 2015Figure 3-4.	Generation Mix with the Base Case and 111(d) Options, 2020-2030 (thousand GWh)Source: Integrated Planning Model run by EPA, 20153.7.5	Projected Incremental RetirementsRelative to the base case, about 23 GW of additional coal-fired capacity is projected to be uneconomic to maintain (about 11 percent of all coal-fired capacity projected to be in service in the base case) by 2025 under the rate-based illustrative scenario. Under the mass-based scenario, about [X] GW of additional coal-fired capacity is projected to be uneconomic to maintain (about [X] percent of all coal-fired capacity projected to be in service in the base case) by 2025. Capacity changes from the base case are shown in Table 3-12.Table 3-12.	Total Generation Capacity by 2020-2030 (GW) Base CaseRateMassRateMass2020     Coal208195-6%NG Combined Cycle (existing)233231-1%NG Combined Cycle (new)4764%Combustion Turbine141137-3%Oil/Gas Steam8881-8%Non-Hydro Renewables1301321%Hydro1061060%Nuclear100100-1%Other550%Total1,016994-2%2025   Coal208187-10%NG Combined Cycle (existing)233231-1%NG Combined Cycle (new)157-52%Combustion Turbine143138-4%Oil/Gas Steam8271-14%Non-Hydro Renewables139137-1%Hydro1121120%Nuclear10099-1%Other550%Total1,037988-5%2030    Coal207183-11%NG Combined Cycle (existing)233231-1%NG Combined Cycle (new)4414-68%Combustion Turbine147138-6%Oil/Gas Steam8270-16%Non-Hydro Renewables15417413%Hydro1121120%Nuclear9998-1%Other550%Total1,0821,025-5%Source: Integrated Planning Model run by EPA, 20153.7.6	Projected Capacity AdditionsDue largely to the electricity demand reduction attributable to the demand-side energy efficiency improvements applied in the illustrative scenarios, the EPA projects less new natural gas combined cycle capacity built under the rate-based scenario than is built in the base case over the period covered by the rule. While this new NGCC capacity cannot be directly counted towards the average emissions rate used for compliance in this scenario, it can displace some generation from covered sources and thus indirectly lower the average emissions rate from covered sources. Conversely, the EPA projects an overall increase in new renewable capacity. New non-hydro renewables are able to contribute their generation to the average emissions rate in each state or region.Under the rate-based illustrative scenario, new natural gas combined cycle capacity is projected to decrease by 8 GW in 2025 and 30 GW in 2030 (52 percent and 68 percent decrease relative to the base case). New renewable capacity is projected to decrease by about 2 GW (3 percent decrease) below the base case in 2025, and increase by 21 GW (28 percent increase) by 2030.Under the mass-based illustrative scenario, new natural gas combined cycle capacity is projected to [X] in 2025 and [X] in 2030 ([X] percent and [X] percent relative to the base case). New renewable capacity is projected to [X] (X percent) relative to the base case in 2025, and [X] ([X] percent increase) by 2030.Table 3-13.	Projected Capacity Additions, Gas (GW) Cumulative Capacity Additions: Gas Combined CycleIncremental Cumulative Capacity Additions: Gas Combined Cycle<PHONE_NUMBER>30202020252030Base Case4.414.944.0   Rate<IP_ADDRESS>.8-7.7-30.0MassSource: Integrated Planning Model run by EPA, 2015Table 3-14.	Projected Capacity Additions, Renewable (GW) Cumulative Capacity Additions: RenewablesIncremental Cumulative Capacity Additions: Renewables<PHONE_NUMBER>30202020252030Base Case<IP_ADDRESS>   Rate40.557.494.61.4-1.820.5MassSource: Integrated Planning Model run by EPA, 20153.7.7	Projected Coal Production and Natural Gas Use for the Electric Power SectorCoal production is projected to decrease in 2025 and beyond in the illustrative scenarios due to (1) improved heat rates (generating efficiency) at existing coal units, (2) electricity demand reduction attributable to demand-side energy efficiency improvements, and (3) a shift in generation from coal to less-carbon intensive generation. As shown in Table 3-15, the largest decrease in coal production is projected to occur in the western region.Table 3-15.	Coal Production for the Electric Power Sector, 2025 Coal Production (MM Tons)Percent Change from Base Case Base CaseRateMassRateMassAppalachia9271-23%Interior250242-3%West379306-19%Waste Coal660%Imports11-37%Total729626-14%Source: Integrated Planning Model run by EPA, 2015Power sector natural gas use is projected to decrease by about 1 percent in 2025 and 2030 under the rate-based illustrative plan scenario. In the mass-based scenario, [X]. These trends are consistent with the change in generation mix described above in Section 3.7.4. Table 3-16.	Power Sector Gas Use Power Sector Gas Use (TCF)Percent Change in Power Sector Gas Use<PHONE_NUMBER>30202020252030Base Case8.629.389.72   Rate8.919.289.603.4%-1.0%-1.2%MassSource: Integrated Planning Model run by EPA, 20153.7.8	Projected Fuel Price, Market, and Infrastructure ImpactsThe impacts of the two illustrative plan scenarios on coal and natural gas prices before shipment are shown below in Table 3-17, Table 3-18, Table 3-19, and Table 3-20, and are attributable to the changes in overall power sector demand for each fuel due to the CPP. Coal demand decreases by 2030, resulting in a decrease in the price of coal delivered to the electric power sector. In 2030, gas demand and price decrease below the base case projections, due to the cumulative impact of demand-side energy efficiency improvements and the consequent reduced overall electricity demand.IPM modeling of natural gas prices uses both short- and long-term price signals to balance supply and demand for the fuel across the modeled time horizon. As such, it should be understood that the pattern of IPM natural gas price projections over time is not a forecast of natural gas prices incurred by end-use consumers at any particular point in time. The natural gas market in the United States has historically experienced some degree of price volatility from year to year, between seasons within a year, and during short-lived weather events (such as cold snaps leading to short-run spikes in heating demand). These short-term price signals are fundamental for allowing the market to successfully align immediate supply and demand needs. However, end-use consumers are typically shielded from experiencing these rapid fluctuations in natural gas prices by retail rate regulation and by hedging through longer-term fuel supply contracts by the power sector. IPM assumes these longer-term price arrangements take place "outside of the model" and on top of the "real-time" shorter-term price variation necessary to align supply and demand. Therefore, the model's natural gas price projections should not be mistaken for traditionally experienced consumer price impacts related to natural gas, but a reflection of expected average price changes over the period of time represented by the modeling horizon.There are very small changes to natural gas pipeline infrastructure needs over time, in response to the illustrative plan scenarios. These changes, compared to historical deployment of new infrastructure, are very modest. In the rate-based scenario, pipeline capacity construction through 2020 is projected to increase 1 percent beyond base case projections. By 2025 and 2030, however, the total cumulative pipeline capacity construction built is projected to decrease by up to 5 percent compared to the base case, consistent with the projected decrease in total demand and natural gas use. The projected increase in pipeline capacity in the near term is largely the result of building pipeline capacity a few years earlier than projected in the base case. Table 3-17.	Projected Average Minemouth and Delivered Coal Prices (2011$/MMBtu) MinemouthDelivered - Electric Power Sector<PHONE_NUMBER>30202020252030Base Case1.551.671.792.382.502.68Rate1.541.581.732.342.352.46MassRate-0.8%-5.0%-3.8%-1.7%-6.2%-8.0%MassSource: Integrated Planning Model run by EPA, 2015Table 3-19.	Projected Average Henry Hub (spot) and Delivered Natural Gas Prices (2011$/MMBtu)Source: Integrated Planning Model run by EPA, 20153.8	Projected Primary PM Emissions from Power PlantsIPM is not configured to endogenously model primary PM emissions from power plants. These emissions are calculated as a function of IPM outputs, emission factors and control configuration. IPM-projected fuel use (heat input) is multiplied by PM emission factors (based in part on the presence of PM-relevant pollution control devices) to determine PM emissions. Primary PM emissions are calculated by adding the filterable PM and condensable PM emissions.Filterable PM emissions for each unit are based on historical information regarding existing emissions controls and types of fuel burned and ash content of the fuel burned, as well as the projected emission controls (e.g., scrubbers and fabric filters).Condensable PM emissions are based on plant type, sulfur content of the fuel, and SO2/HCl and PM control configurations. Although EPA's analysis is based on the best available emission factors, these emission factors do not account for the potential changes in condensable PM emissions due to the installation and operation of SCRs. The formation of additional condensable PM (in the form of SO3 and H2SO4) in units with SCRs depends on a number of factors, including coal sulfur content, combustion conditions and characteristics of the catalyst used in the SCR, and is likely to vary widely from unit to unit. SCRs are generally designed and operated to minimize increases in condensable PM. This limitation means that IPM post-processing is potentially underestimating condensable PM emissions for units with SCRs. In contrast, it is possible that IPM post-processing overestimates condensable PM emissions in a case where the unit is combusting a low-sulfur coal in the presence of a scrubber.For a more complete description of the methodologies used to post-process PM emissions from IPM, see "IPM ORL File Generation Methodology" (March, 2011), available in the docket.3.9	Limitations of AnalysisEPA's modeling is based on expert judgment of various input assumptions for variables whose outcomes are in fact uncertain. As a general matter, the Agency reviews the best available information from engineering studies of air pollution controls, the ability to improve operating efficiency, and new capacity construction costs to support a reasonable modeling framework for analyzing the cost, emission changes, and other impacts of regulatory actions.The costs presented in this RIA include both the IPM-projected annualized estimates of private compliance costs as well as the estimated costs incurred by utilities and program participants to achieve demand-side energy efficiency improvements. The IPM-projected annualized estimates of private compliance costs provided in this analysis are meant to show the increase in production (generating) costs to the power sector in response to the final rule. To estimate these annualized costs, the EPA uses a conventional and widely-accepted approach that applies a capital recovery factor (CRF) multiplier to capital investments and adds that to the annual incremental operating expenses. The CRF is derived from estimates of the cost of capital (private discount rate), the amount of insurance coverage required, local property taxes, and the life of capital. The demand-side energy efficiency costs are developed based on a review of energy efficiency data and studies, and expert judgment. The EPA recognizes that significant variation exists in these analyses reflecting data and methodological limitations. The method used for estimating the demand-side energy efficiency costs is discussed in more detail in the Greenhouse Gas Abatement Measures TSD. The evaluation, measurement and verification (EM&V) of demand-side energy efficiency is addressed in the section VIII, State Plans, of the preamble for the final rule.The base case electricity demand in IPM v.5.15 is calibrated to reference case demand in AEO 2015. AEO 2015 demand may reflect, to some extent, a continuation of the impacts of state demand-side energy efficiency policies but does not explicitly represent the most significant existing state policies in this area (e.g., energy efficiency resource standards). To some degree the implicit representation of state policies in the EPA's base case alters the impacts assessment, but the direction and magnitude of change is not known with certainty. This issue is discussed in the Greenhouse Gas Abatement Measures TSD.The modeling of these policy scenarios does not account for an additional compliance flexibility allowed in the final rule whereby "overcompliance" with state goals (more reductions than required) during the interim period can be "banked" for compliance with state goals in the final period.Cost estimates for the proposed rule are based on rigorous power sector modeling using ICF's Integrated Planning Model. IPM assumes "perfect foresight" of market conditions over the time horizon modeled; to the extent that utilities and/or energy regulators misjudge future conditions affecting the economics of pollution control, costs may be understated as well. Furthermore, IPM does not represent electricity markets in Alaska, Hawaii, and U.S. territories outside the contiguous U.S. and therefore the costs (and benefits) that may be expected from the proposed rule in this states and territories are not accounted for in the compliance cost modeling. 3.10	Significant Energy ImpactsThe CPP, under both illustrative plan scenarios analyzed, would have a significant impact according to E.O. 13211: Actions that Significantly Affect Energy Supply, Distribution, or Use. Under the rate-based illustrative scenario, EPA projects that approximately 23 GW of additional coal-fired generation (about 11 percent of all coal-fired capacity and 2.2 percent of total generation capacity in 2025) may be removed from operation by 2025.  EPA also projects the average delivered coal price decreases by 6.2 percent with decreased production of 103 million tons (14 percent of US production) in 2025 and that electric power sector delivered natural gas prices will decrease by about 7.7 percent with decreased power sector consumption of 95 billion cubic feet (BCF) in 2025. Average retail electricity prices are projected to increase in the contiguous U.S. by [X] percent in 2025.3.11	Monitoring, Reporting, and Recordkeeping CostsEPA projected monitoring, reporting and recordkeeping costs for both state entities and affected EGUs for the compliance years 2020, 2025 and 2030. In calculating the costs for state entities, EPA estimated personnel costs to oversee compliance, and review and report annually to EPA on program progress relative to meeting the state's reduction goal. To calculate the national costs, EPA estimated that 49 states, 2 territories and 1,052 facilities would be affected.The EPA estimated that the majority of the cost to EGUs would be in calculating net energy output, which is needed whether the state plan utilizes a rate-based or a mass-based limit. Since the majority of EGUs do have some energy usage meters or other equipment available to them, EPA believes a new system for calculating net energy output is not needed. Under the CPP, states are required to use monitoring and reporting requirements for their affected EGUs to ensure that the sources are meeting the appropriate CO2 emission performance rates or emission goals. In general, the EPA has made it a priority to streamline reporting and monitoring requirements. In this rule, the EPA is making implementation as efficient as possible for both the states and the affected EGUs by allowing state plans to utilize the current monitoring and recordkeeping requirements and pathways that have already been well established in other EPA rulemakings. For example, under the Acid Rain Program's continuous emissions monitoring, 40 CFR Part 75, the EPA has established requirements for the majority of the EGUs that would be affected by a 111(d) state plan to monitor CO2 emissions and report that data using the Emissions Collection and Monitoring Plan System (ECMPS). Additionally since the CO2 hourly data is already reported to the EPA's ECMPS there is no additional burden associated with the reporting of that data. Since the ECMPS pathway is already in place, the EPA will allow for states to utilize the ECMPS system to facilitate the data reporting of the additional net energy output data required under the emission guidelines. However, because the Acid Rain Program does not require net energy output to be reported, there is some additional burden (Shown in Table 3-25) in updating an affected EGUs monitoring system to be able to report the associated net energy output of an affected EGU.The EPA estimates that it would take 3 working months for a technician to retrofit any existing energy meters to meet the requirements set in the state plan. Additionally EPA believes that 50 hours will be needed for each EGU operator to read the rule and understand how the facility will comply with the rule, based on an average reading rate of 100 words per minute and a projected rule word count of 300,000 words.  Also, after all modifications are made at a facility to measure net energy output, each EGU's Data Acquisition System (DAS) would need to be upgraded to supply the rate-based emissions value to either the state or EPA's Emissions Collection and Monitoring Plan System (ECMPS). Note the costs to develop net energy output monitoring and to upgrade each facility's DAS system are one-time costs incurred in 2020. Recordkeeping and reporting costs substantially decrease for the period 2021-2030. The projected costs for 2020, 2025, and 2030 are summarized below.In calculating the cost for states to comply, EPA estimates that each state will rely on the equivalent of 2 full time staff (1 each from the energy and air offices) to oversee program implementation, assess progress, develop possible contingency measures, perform state plan revisions and host the subsequent public meetings if revisions are indeed needed, download data from the ECMPS for their annual reporting and develop their annual EPA report. The burden estimate was based on an analysis of similar tasks performed under the Regional Haze Program, whereby states were required to develop their list of eligible sources, draft implementation plans, revise initial drafts, identify baseline controls, identify data gaps, identify initial strategies, conduct various reviews, and manage their programs. A total estimate of 78,000 hours of labor performed by 7 states over a 3 year period resulted in 3,714 hours per year, per entity. Due to the nature of this final rule whereby we believe the air office and the energy office will both be involved in performing the above-mentioned tasks, we rounded up to the equivalent of 2, full time staff, which totaled 4,160 hours per year. In addition, in the absence of specific information, EPA assumed that 27 states with strong, state-driven EE programs as documented in the Energy and Environment Guide to Action would submit a plan under the state measures approach, which adds to the state respondent burden. Table 3-25 shows the annual state respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030. Table 3-26 shows the annual industry respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030. Table 3-27 shows the annual territories respondent burden and costs of reporting and recordkeeping for 2020, 2025 and 2030.Table 3-25.	Years 2020, 2025 and 2030: Summary of State Annual Respondent Burden and Cost of Reporting and Recordkeeping Requirements (2011$)Nationwide TotalsTotal Annual Labor Burden (Hours)TotalAnnualLabor CostsTotal AnnualizedCapital CostsTotalAnnualO&M CostsTotal Annualized CostsTotal AnnualRespondent CostsYear 2020203,84014,427,298036,01536,01511       14,463,313Year 2025225,92015,990,066024,50024,50016,014,566Year 2030225,92015,990,066024,50024,50016,014,566Table 3-26.	Years 2020, 2025 and 2030: Summary of Industry Annual Respondent Burden and Cost of Reporting and Recordkeeping Requirements (2011$)Nationwide TotalsTotal Annual Labor Burden (Hours)TotalAnnualLabor CostsTotal AnnualizedCapital CostsTotalAnnualO&M CostsTotal Annualized CostsTotal AnnualRespondent CostsYear 2020595,43251,125,81501,556,5001,556,500  52,682,315Year<PHONE_NUMBER>Year<PHONE_NUMBER>Table 3-27.	Years 2020, 2025 and 2030: Summary of Territories Annual Respondent Burden and Cost of Reporting and Recordkeeping Requirements (2011$)Nationwide TotalsTotal Annual Labor Burden (Hours)TotalAnnualLabor CostsTotal AnnualizedCapital CostsTotalAnnualO&M CostsTotal Annualized CostsTotal AnnualRespondentCostsYear 20208,320588,86901,4701,470590,339Year 20258,960634,16701,0001,000635,167Year 20308,960634,16701,0001,000635,167The annual costs of the rate-based and mass-based illustrative plan scenarios, including monitoring reporting and recordkeeping costs, are shown in Table 3-28 below.Table 3-28.	Annualized Compliance Costs Including Monitoring, Reporting and Recordkeeping Costs Requirements (billions of 2011$)<PHONE_NUMBER>30Rate<IP_ADDRESS>Mass[X][X][X]Source: Integrated Planning Model run by EPA, 2015 and GHG Abatement Measures TSD. Monitoring, reporting and recordkeeping costs calculated outside IPM.3.12	Social CostsAs discussed in the EPA Guidelines for Preparing Economic Analyses, social costs are the total economic burden of a regulatory action. This burden is the sum of all opportunity costs incurred due to the regulatory action, where an opportunity cost is the value lost to society of any goods and services that will not be produced and consumed as a result of reallocating some resources towards pollution mitigation. Estimates of social costs may be compared to the social benefits expected as a result of a regulation to assess its net impact on society. The social costs of a regulatory action will not necessarily be equivalent to the expenditures associated with compliance. Nonetheless, here we use compliance costs as a proxy for social costs. This section provides a qualitative discussion of the relationship between social costs and compliance cost estimates presented in this chapter. The cost estimates for the illustrative plan scenarios presented in this chapter are the sum of expenditures on demand-side energy efficiency and the change in expenditures required by the electricity sector to comply with the proposed guidelines. These two components are estimated separately. The expenditures required to achieve the assumed demand reductions through demand-side energy efficiency programs are estimated using historical data, analysis, and expert judgment. The change in the expenditures required by the electricity sector to meet demand and maintain compliance are estimated by IPM and reflect both the reduction in electricity production costs due to the reduction in demand caused by the demand-side energy efficiency measures and the increase in electricity production costs required to achieve the additional emission reductions necessary to comply with the state goals. As described in section 3.6.1, the illustrative plan scenarios assume that, in achieving their goals, demand-side energy efficiency measures are adopted which lead to demand reductions in each year represented by the illustrative energy efficiency plan scenario. The estimated expenditures required to achieve those demand reductions through demand-side energy efficiency are presented in this chapter and detailed in Chapter 5 of the GHG Abatement Measures TSD. The social cost of achieving these energy savings comes in the form of increased expenditures on technologies and/or services that are required to lower electricity consumption beyond the business as usual. Under the assumption of complete and well-functioning markets the expenditures required to reduce electricity consumption on the margin will represent society's opportunity cost of the resources required to produce the energy savings. Due to the flexibility held by states in implementing their compliance with the proposed standards these energy efficiency expenditures may be borne by end-users through direct participant expenditures or electricity rate increases, or by producers through reductions in their profits. While the allocation of these expenditures between consumers and producers is important for understanding the distributional impact of potential compliance strategies, it does not necessarily affect the opportunity cost required for the production of the energy savings from a social perspective. However, specific design elements of demand-side energy efficiency measures included to address distributional outcomes may have an effect on the economic efficiency of the programs and therefore the social cost.Another reason the expenditures associated with demand-side energy efficiency may differ from social costs is due to differences in the services provided by more energy efficient technologies and services adopted under the program relative to the baseline. For example, if under the program end-users adopted more energy efficient products which were associated with quality or service attributes deemed less desirable, then there would be an additional welfare loss that should be accounted for in social costs but is not necessarily captured in the measure of expenditures. However, there is an analogous possibility that in some cases the quality of services, outside of the energy savings, provided by the more energy efficient products and practices are deemed more desirable by some end-users. For example, weatherization of buildings to reduced electricity demand associated with cooling will likely have a significant impact on natural gas use associated with heating. In either case these real welfare impacts are not fully captured by end-use energy efficiency expenditure estimates.The fact that such quality and service differences may exist in reality but may not be reflected in the price difference between more and less energy efficient products is one potential hypothesis for the energy paradox. The energy paradox is the observation that end-users do not always purchase products that are more energy efficient when the additional cost is less than the reduction in the net present value of expected electricity expenditures achieved by those products. Such circumstances are present in the analysis presented in this chapter, whereby in some regions the base case and illustrative scenarios suggest that cost of reducing demand through energy efficiency programs is less than the retail electricity price. In addition to heterogeneity in product services and consumer preferences, there are other explanations for the energy paradox, falling both within and outside the neoclassical rational expectations paradigm that is used in benefit/cost analysis. Chapter 5 of the GHG Abatement Measures TSD discusses the energy paradox and provides additional hypothesis for why consumers may not make energy efficiency investments that ostensibly seem to be in their own interest. The TSD discussion also provides details on how the presence of additional market failures can lead to levels of energy efficiency investment that may be too low from society's perspective even if that is not the case for the end-user. In such cases there is the potential for properly designed energy efficiency programs to address the source of under-investment, such as principal-agent problems where there is a disconnect between those making the purchase decision regarding energy efficient investments and energy use and those that would receive the benefits associated with reduced energy use through lower electricity bills.The other component of compliance cost reported in this chapter is the change in resource cost (i.e., expenditures) required by the electricity sector to fulfill the remaining demand while making additional CO2 emissions reductions necessary to comply with the state goals. Included in the estimate of these compliance costs, developed using IPM, are the cost savings associated with the reduction in required electricity generation due to the demand reductions from demand-side energy efficiency measures and improvements in heat rate. By shifting the demand curve for electricity, demand-side energy efficiency reduces the production cost in the sector. The resource cost estimates from IPM therefore account for the increased cost of providing electricity while EGUs comply with their obligations net of the reduction in production costs due to lower demand resulting from demand-side energy efficiency measures.3.13	ReferencesJust, R.J., D.L. Hueth and A. Schmitz, (2004) The Welfare Economics of Public Policy: A Practical Guide to Policy and Project Evaluation, Edwin Elgar Press, Cheltenham UK.US EPA (2010) EPA Guidelines for Preparing Economic Analyses, Chapters 8 and Appendix A. http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.htmlChapter 4: Estimated Climate Benefits and Human Health Co-benefits4.1	IntroductionImplementing the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units (hereafter referred to as the "final emission guidelines" or "Clean Power Plan Final Rule") is expected to reduce emissions of carbon dioxide (CO2) and have ancillary human health benefits (i.e., co-benefits) associated with lower ambient concentrations of criteria air pollutants. This chapter describes the methods used to estimate the monetized climate benefits and the monetized air quality health co-benefits associated with reducing exposure to ambient fine particulate matter (PM2.5) and ozone by reducing emissions of precursor pollutants (i.e., sulfur dioxide (SO2), nitrogen dioxide (NO2), and directly emitted PM2.5). Data, resource, and methodological limitations prevent the EPA from monetizing the benefits from several important co-benefit categories, including reducing direct exposure to SO2, NO2, and hazardous air pollutants (HAP), as well as ecosystem effects and visibility impairment. We qualitatively discuss these unquantified benefits in this chapter.This chapter provides estimates of the monetized climate benefits and air quality health co-benefits associated with emission reductions for the illustrative rate-based and mass-based scenarios across several analysis years and discount rates. The estimated benefits associated with these emission reductions are beyond those achieved by previous EPA rulemakings, including the Mercury and Air Toxics Standards (MATS).4.2	Estimated Climate Benefits from CO2The primary goal of the final emission guidelines is to reduce 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. Table 4-1 summarizes the quantified and unquantified climate benefits in this analysis. Table 4-1.	Climate EffectsBenefits CategorySpecific EffectEffect Has Been QuantifiedEffect Has Been MonetizedMore InformationImproved EnvironmentReduced climate effectsGlobal climate impacts from CO2 -- [1]SCC TSDClimate impacts from ozone and black carbon (directly emitted PM) --  -- Ozone ISA, PM ISA[2]Other climate impacts (e.g., other GHGs such as methane, aerosols, other impacts) --  -- IPCC[2]1 The global climate and related impacts of CO2 emissions changes, such as sea level rise, are estimated within each integrated assessment model as part of the calculation of the SC-CO2. The resulting monetized damages, which are relevant for conducting the benefit-cost analysis, are used in this RIA to estimate the welfare effects of quantified changes in CO2 emissions.[2] We assess these co-benefits qualitatively because we do not have sufficient confidence in available data or methods.4.2.1	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. It is these adverse impacts that make it necessary for the EPA to regulate GHGs from EGU sources. The preamble summarizes the public health and public welfare impacts that were detailed in the 2009 Endangerment Finding. 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. The preamble also summarizes new scientific assessments and recent climatic observations. 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 (NCA3), in particular, assessed the impacts of climate change on human health in the United States, finding that, Americans will be impacted 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 upon their location of origin, and impacts outside of the United States will produce consequences relevant to the United States.4.2.2	Social Cost of CarbonWe 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 June 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. As discussed further below, the IWG published two minor corrections to the SC-CO2 estimates in June 2015. The SC-CO2 estimates were developed using an ensemble of the three most widely cited integrated assessment models in the economics literature with the ability to estimate the SC-CO2. A key objective of the IWG was to draw from the insights of the three models while respecting the different approaches to linking GHG emissions and monetized damages taken by modelers in the published literature. After conducting an extensive literature review, the interagency group selected three sets of input parameters (climate sensitivity, socioeconomic and emissions trajectories, and discount rates) to use consistently in each model. All other model features were left unchanged, relying on the model developers' best estimates and judgments, as informed by the literature. Specifically, a common probability distribution for the equilibrium climate sensitivity parameter, which informs the strength of climate's response to atmospheric GHG concentrations, was used across all three models. In addition, a common range of scenarios for the socioeconomic parameters and emissions forecasts were used in all three models. Finally, the marginal damage estimates from the three models were estimated using a consistent range of discount rates, 2.5, 3.0, and 5.0 percent. See the 2010 TSD for a complete discussion of the methods used to develop the estimates and the key uncertainties, and the current TSD for the latest estimates. The SC-CO2 estimates represent global measures because of the distinctive nature of the 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. The SC-CO2 must therefore incorporate the full (global) damages caused by GHG emissions to address the global nature of the problem. 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 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 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, as noted in OMB's Response to Comments on the SCC, 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 observed that SC-CO2 estimates continue to omit various impacts that would likely increase damages. The 95th percentile estimate was included in the recommended range for regulatory impact analysis to address these concerns.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 this rulemaking and others 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. The SC-CO2 comments received on this rulemaking covered a wide range of topics including the technical details of the modeling conducted to develop the SC-CO2 estimates, the aggregation and presentation of the SC-CO2 estimates, and the process by which the SC-CO2 estimates were derived. Many but not all commenters were supportive of the SC-CO2 and its application to this rulemaking. Commenters also provided constructive recommendations for potential opportunities to improve the SC-CO2 estimates in future updates. The EPA Response to Comments document provides a summary and response to the SC-CO2 comments submitted to this rulemaking.Many of the comments EPA received were similar to those that OMB's Office of Information and Regulatory Affairs received in response to 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 Academy of Sciences' National Research Council (NRC) to ensure that the SC-CO2 estimates continue to reflect the best available scientific and economic information on climate change. The NRC process will be informed by the public comments received and focus on the technical merits and challenges of potential approaches to improving the SC-CO2 estimates in future updates. Concurrent with OMB's publication of the response to comments on SC-CO2 and announcement of the NRC process, OMB posted a revised TSD that includes two minor technical corrections to the current estimates. One technical correction addressed an inadvertent omission of climate change damages in the last year of analysis (2300) in one model and the second addressed a minor indexing error in another model. On average the revised SC-CO2 estimates are one dollar less than the mean SC-CO2 estimates reported in the November 2013 revision to the May 2013 TSD. The change in the estimates associated with the 95th percentile estimates when using a 3% discount rate is slightly larger, as those estimates are heavily influenced by the results from the model that was affected by the indexing error.   The four SC-CO2 estimates are as follows: $13, $45, $66, and $130 per metric ton of CO2 emissions in the year 2020 (2011$). 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 higher-than-expected impacts from temperature change further out in the tails of the SC-CO2 distribution (representing less likely, but potentially catastrophic, outcomes).Table 4-2 presents the global SC-CO2 estimates 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. Tables 4-3 through 4-5 report the incremental climate benefits estimated in three analysis years (2020, 2025, and 2030) for the rate-based and mass-based scenarios.  Table 4-2.	Social Cost of CO2, 2015-2050 (in 2011$)*YearDiscount Rate and Statistic5% Average3% Average2.5% Average3% (95th percentile)2015$12$38$59$1102020$13$45$66$1302025$15$49$72$1502030$17$53$77$1602035$19$58$83$1802040$22$64$89$1902045$24$68$94$2102050$28$73$100$230* The SC-CO2 values 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. These SC-CO2 values are stated in $/metric ton and rounded to two significant figures. Table 4-3.	Estimated Global Climate Benefits of CO2 Reductions for the Final Emission Guidelines in 2020 (billions of 2011$)*Discount rate and statisticRate-Based ScenarioMass-Based ScenarioMillion metric tonnes of CO2 reduced635% (average)$0.803% (average)$2.82.5% (average)$4.13% (95[th] percentile)$8.2* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting of climate impacts.Table 4-4.	Estimated Global Climate Benefits of CO2 Reductions for the Final Emission Guidelines in 2025 (billions of 2011$)*Discount rate and statisticRate-Based ScenarioMass-Based ScenarioMillion metric tonnes of CO2 reduced2105% (average)$3.13% (average)$102.5% (average)$153% (95[th] percentile)$31* The SC-CO2 values are dollar-year and emissions-year specific. SC-CO2 values represent only a partial accounting of climate impacts.Table 4-5.	Estimated Global Climate Benefits of CO2 Reductions for the Final Emission Guidelines in 2030 (billions of 2011$)*Discount rate and statisticRate-Based ScenarioMass-Based ScenarioMillion metric tonnes of CO2 reduced3775% (average)$6.43% (average)$202.5% (average)$293% (95[th] percentile)$61* 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 benefits presented above are associated with changes in CO2 emissions only. Implementing these final emission guidelines, 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 final emission guidelines. The other emissions potentially reduced as a result of the final emission guidelines include other greenhouse gases (such as methane), aerosols and aerosol precursors such as black carbon, organic carbon, sulfur dioxide and nitrogen oxides, and ozone precursors such as nitrogen oxides and volatile organic carbon compounds. Changes in emissions of these pollutants (both increases and decreases) could directly result from changes in electricity generation, upstream fossil fuel extraction and transport, and/or downstream secondary market impacts. Reductions in black carbon or ozone precursors are projected to lead to further cooling, but reductions in the other aerosol species and precursors are projected to lead to warming. Therefore, changes in non-CO2 pollutants could potentially augment or offset the climate benefits calculated here. These pollutants can act in different ways and on different timescales than carbon dioxide. For example, aerosols reflect (and in the case of black carbon, absorb) incoming radiation, whereas greenhouse gases absorb outgoing infrared radiation. In addition, these aerosols are thought to affect climate indirectly by altering properties of clouds. Black carbon can also deposit on snow and ice, darkening these surfaces and accelerating melting. In terms of lifetime, while carbon dioxide emissions can increase concentrations in the atmosphere for hundreds or thousands of years, many of these other pollutants are short lived and remain in the atmosphere for short periods of time ranging from days to weeks and can therefore exhibit large spatial and temporal variability. While the EPA has not quantified the climate impacts of these other pollutants for the final emission guidelines, the Agency has analyzed the potential changes in upstream methane emissions from the natural gas and coal production sectors that may result from the illustrative plan scenarios examined in this RIA in the appendix to Chapter 3. The EPA assessed whether the net change in upstream methane emissions from natural gas and coal production is likely to be positive or negative and also assessed the potential magnitude of changes relative to CO2 emissions reductions anticipated at power plants. This assessment included CO2 emissions from the flaring of methane, but did not evaluate potential changes in other combustion-related CO2 emissions, such as emissions associated with drilling, mining, processing, and transportation in the natural gas and coal production sectors. This analysis found that the net upstream CH4 emissions from natural gas systems and coal mines and CO2 emissions from flaring of methane will likely decrease under the final emission guidelines. Furthermore, the analysis suggests that the changes in upstream methane emissions are small relative to the changes in direct emissions from power plants. 4.3	Estimated Human Health Co-BenefitsIn addition to reducing emissions of CO2, implementing these final emission guidelines is expected to reduce emissions of SO2 and NOX, which are precursors to formation of ambient PM2.5, as well as directly emitted fine particles. Therefore, reducing these emissions would also reduce human exposure to ambient PM2.5 and the incidence of PM2.5-related health effects. In addition, in the presence of sunlight, NOX and VOCs can undergo a chemical reaction in the atmosphere to form ozone. Depending on localized concentrations of volatile organic compounds (VOCs), reducing NOX emissions would also reduce human exposure to ozone and the incidence of ozone-related health effects. Although we do not have sufficient data to quantify these impacts in this analysis, reducing emissions of SO2 and NOx would also reduce ambient exposure to SO2 and NO2 and their associated health effects, respectively. In this section, we provide an overview of the monetized PM2.5 and ozone-related co-benefits estimated for the final emission guidelines. A full description of the underlying data, studies, and assumptions is provided in the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d). The estimated co-benefits associated with these emission reductions are beyond those achieved by previous EPA rulemakings, including MATS. There are several important considerations in assessing the air quality-related health co-benefits for a climate-focused rulemaking. First, these estimated health co-benefits do not account for any climate-related air quality changes (e.g., increased ambient ozone associated with higher temperatures) but rather changes in precursor emissions affected by this rulemaking. Excluding climate-related air quality changes may underestimate ozone-related health co-benefits. It is unclear how PM2.5-related health co-benefits would be impacted 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 co-benefits also do not consider temperature modification of PM2.5 and ozone risks (Roberts 2004; Ren 2006a, 2006b, 2008a, 2008b). Third, the estimated climate benefits reported in this RIA reflect global benefits, while the estimated health co-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 co-benefits. Fourth, as noted earlier, we do not estimate the climate benefits associated with reductions in PM and O3 precursors.Implementing the final emission guidelines may lead to reductions in ambient PM2.5 concentrations below the National Ambient Air Quality Standards (NAAQS) for PM and ozone in some areas and assist other areas with attaining these NAAQS. Because the NAAQS RIAs (U.S. EPA, 2012a, 2008b, 2010d) also calculated PM and ozone benefits, there are important differences worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a revised air quality standard nationwide based on an array of emission reduction strategies for different sources reflecting the application of known and unknown controls, incremental to implementation of existing regulations and controls needed to attain the current standards. In short, NAAQS RIAs hypothesize, but do not predict, the reduction strategies that States may choose to enact when implementing a revised NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, the EPA's NAAQS RIAs are merely illustrative and the estimated costs and benefits are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emission reductions. Some of the emissions reductions estimated to result from implementation of the final emission guidelines may achieve some of the air quality improvements that resulted from the hypothesized attainment strategies presented in the illustrative NAAQS RIAs. The emissions reductions from implementing the final emission guidelines will decrease the remaining amount of emissions reductions needed in non-attainment areas and reduce the costs and benefits attributable to meeting the NAAQS.Similar to NAAQS RIAs, the emission reduction scenarios estimated for the final emission guidelines are also illustrative. In contrast to NAAQS RIAs, all of the emission reductions for the illustrative plan scenarios would occur in one well-characterized sector (i.e., the EGU sector). In general, the EPA is more confident in the magnitude and location of the emission reductions for implementation rules, which typically require specific emission reductions in a specific sector. As such, emission reductions achieved under promulgated implementation rules will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the incremental costs and benefits associated with attaining revised future NAAQS. The EPA does not re-issue illustrative RIAs outside of the rulemaking process that retroactively update the baseline to account for implementation rules promulgated after an RIA was completed. For more information on the relationship between illustrative analyses, such as for the NAAQS and this final emission guidelines, and implementation rules, please see section 1.3 of the PM NAAQS RIA (U.S. EPA, 2012a).4.3.1	Health Impact Assessment for PM2.5 and OzoneThe Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b) identified the human health effects associated with ambient PM2.5 exposure, which include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. Similarly, the Integrated Science Assessment for Ozone and Related Photochemical Oxidants (Ozone ISA) (U.S. EPA, 2013b) identified the human health effects associated with ambient ozone exposure, which include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. Table 4-6 identifies the quantified and unquantified co-benefit categories captured in the EPA's health co-benefits estimates for reduced exposure to ambient PM2.5 and ozone. Although the table below does not list unquantified health effects such as those associated with exposure to SO2, NO2, and mercury nor welfare effects such as acidification and nutrient enrichment, these effects are described in detail in Chapters 5 and 6 of the PM NAAQS RIA (U.S. EPA, 2012a) and summarized later in this chapter. It is important to emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification of each effect complete.Table 4-6.	Human Health Effects of Ambient PM2.5 and OzoneCategorySpecific EffectEffect Has Been QuantifiedEffect Has Been MonetizedMore InformationImproved Human HealthReduced incidence of premature mortality from exposure to PM2.5Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)PM ISAInfant mortality (age <1)PM ISAReduced incidence of morbidity from exposure to PM2.5Non-fatal heart attacks (age > 18)PM ISAHospital admissions -- respiratory (all ages)PM ISAHospital admissions -- cardiovascular (age >20)PM ISAEmergency room visits for asthma (all ages)PM ISAAcute bronchitis (age 8-12)PM ISALower respiratory symptoms (age 7-14)PM ISAUpper respiratory symptoms (asthmatics age 9-11)PM ISAAsthma exacerbation (asthmatics age 6-18)PM ISALost work days (age 18-65)PM ISAMinor restricted-activity days (age 18-65)PM ISAChronic Bronchitis (age >26) --  -- PM ISA[1]Emergency room visits for cardiovascular effects (all ages) --  -- PM ISA[1]Strokes and cerebrovascular disease (age 50-79) --  -- PM ISA[1]Other cardiovascular effects (e.g., other ages) --  -- PM ISA[2]Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations) --  -- PM ISA[2]Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc.) --  -- PM ISA[2,3]Cancer, mutagenicity, and genotoxicity effects --  -- PM ISA[2,3]Reduced incidence of mortality from exposure to ozonePremature mortality based on short-term study estimates (all ages)Ozone ISAPremature mortality based on long-term study estimates (age 30 - 99) --  -- Ozone ISA[1]Reduced incidence of morbidity from exposure to ozoneHospital admissions -- respiratory causes (age > 65)Ozone ISAHospital admissions -- respiratory causes (age <2)Ozone ISAEmergency department visits for asthma (all ages)Ozone ISAMinor restricted-activity days (age 18 - 65)Ozone ISASchool absence days (age 5 - 17)Ozone ISADecreased outdoor worker productivity (age 18 - 65) --  -- Ozone ISA[1]Other respiratory effects (e.g., premature aging of lungs) --  -- Ozone ISA[2]Cardiovascular and nervous system effects --  -- Ozone ISA[2]Reproductive and developmental effects --  -- Ozone ISA[2,3][1] We assess these co-benefits qualitatively due to data and resource limitations for this analysis, but we have quantified them in sensitivity analyses for other analyses.[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.We follow a "damage-function" approach in calculating benefits, which estimates changes in individual health endpoints (specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the values for those individual endpoints. Because the EPA rarely has the time or resources to perform new research to measure directly, either health outcomes or their values for regulatory analyses, our estimates are based on the best available methods of benefits transfer, which is the science and art of adapting primary research from similar contexts to estimate benefits for the environmental quality change under analysis. We use a "benefit-per-ton" approach to estimate the PM2.5 and ozone co-benefits in this RIA. Benefit-per-ton approaches apply an average benefit per ton derived from modeling of benefits of specific air quality scenarios to estimates of emissions reductions for scenarios where no air quality modeling is available. This section describes the underlying basis for the health and economic valuation estimates that inform the benefit-per-ton estimates, and the subsequent section provides an overview of the benefit-per-ton estimates, which are described in detail in the appendix to this chapter.The benefit-per-ton approach we use in this RIA relies on estimates of human health responses to exposure to PM and ozone obtained from the peer-reviewed scientific literature. These estimates are used in conjunction with population data, baseline health information, air quality data and economic valuation information to conduct health impact and economic benefits assessments. These assessments form the key inputs to calculating benefit-per-ton estimates. The next sections provide an overview of the health impact assessment (HIA) methodology and additional details on several key elements.The HIA quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM2.5 and ozone. We use the environmental Benefits Mapping and Analysis Program  -  Community Edition (BenMAP-CE) (version 1.1) to systematize health impact analyses by applying a database of key input parameters, including population projections, health impact functions, and valuation functions (Abt Associates, 2012). For this assessment, the HIA is limited to those health effects that are directly linked to ambient PM2.5 and ozone concentrations. There may be other indirect health impacts associated with reducing emissions, such as occupational health exposures. Epidemiological studies generally provide estimates of the relative risks of a particular health effect for a given increment of air pollution (often per 10 ug/m[3] for PM2.5 or ppb for ozone). These relative risks can be used to develop risk coefficients that relate a unit reduction in PM2.5 to changes in the incidence of a health effect. We refer the reader to the PM NAAQS RIA (U.S. EPA, 2012a) and Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d) for more information regarding the epidemiology studies and risk coefficients applied in this analysis, and we briefly elaborate on adult premature mortality below. The size of the mortality effect estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most significant health endpoint quantified in this analysis.<IP_ADDRESS>	Mortality Concentration-Response Functions for PM2.5Considering a substantial body of published scientific literature and reflecting thousands of epidemiology, toxicology, and clinical studies, the PM ISA documents the association between elevated PM2.5 concentrations and adverse health effects, including increased premature mortality (U.S. EPA, 2009b). The PM ISA, which was twice reviewed by the Clean Air Scientific Advisory Committee of the EPA's Science Advisory Board (SAB-CASAC) (U.S. EPA-SAB, 2009b, 2009c), concluded that there is a causal relationship between mortality and both long-term and short-term exposure to PM2.5 based on the entire body of scientific evidence. The PM ISA also concluded that the scientific literature supports the use of a no-threshold log-linear model to portray the PM-mortality concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response function. In addition to adult mortality discussed in more detail below, we use effect coefficients from Woodruff et al. (1997) to estimate PM-related infant mortality.For adult PM-related mortality, we use the effect coefficients from the most recent epidemiology studies examining two large population cohorts: the American Cancer Society cohort (Krewski et al., 2009) and the Harvard Six Cities cohort (Lepeule et al, 2012). The PM ISA (U.S. EPA, 2009b) concluded that the ACS and Six Cities cohorts produce the strongest evidence of the association between long-term PM2.5 exposure and premature mortality with support from a number of additional cohort studies. The SAB's Health Effects Subcommittee (SAB-HES) also supported using these two cohorts for analyses of the benefits of PM reductions (U.S. EPA-SAB, 2010a). As both the ACS and Six Cities cohort studies have inherent strengths and weaknesses, we present PM2.5 co-benefits estimates based on benefits-per-ton derived using relative risk estimates from both these cohorts.As a characterization of uncertainty regarding the adult PM2.5-mortality relationship, the EPA graphically presents the PM2.5 co-benefits based on benefits-per-ton estimated using C-R functions derived from EPA's expert elicitation study (Roman et al., 2008; IEc, 2006). The primary goal of the 2006 study was to elicit from a sample of health experts probabilistic distributions describing uncertainty in estimates of the reduction in mortality among the adult U.S. population resulting from reductions in ambient annual average PM2.5 concentrations. In that study, twelve experts provided independent opinions regarding the PM2.5-mortality concentration-response function. Because the experts relied upon the ACS and Six Cities cohort studies to inform their concentration-response functions, the benefits estimates based on the expert responses generally fall between benefits estimates based on these studies (see Figure 4-1). We do not combine the expert results in order to preserve the breadth and diversity of opinion on the expert panel. This presentation of the expert-derived results is generally consistent with SAB advice (U.S. EPA-SAB, 2008), which recommended that the EPA emphasize that "scientific differences existed only with respect to the magnitude of the effect of PM - 2.5 on mortality, not whether such an effect existed" and that the expert elicitation "supports the conclusion that the benefits of PM2.5 control are very likely to be substantial". Although it is possible that newer scientific literature could revise the experts' quantitative responses if elicited again, we believe that these general conclusions are unlikely to change.<IP_ADDRESS>	Mortality Concentration-Response Functions for OzoneIn 2008, the National Academies of Science (NRC, 2008) issued a series of recommendations to the EPA regarding the quantification and valuation of ozone-related short-term mortality. Chief among these was that "...short-term exposure to ambient ozone is likely to contribute to premature deaths" and the committee recommended that "ozone-related mortality be included in future estimates of the health benefits of reducing ozone exposures..." The NAS also recommended that "...the greatest emphasis be placed on the multicity and NMMAPS [National Morbidity, Mortality, and Air Pollution Study] studies without exclusion of the meta-analyses" (NRC, 2008). In view of the findings of the National Academies panel, we estimate the co-benefits of avoiding short-term ozone mortality using the Bell et al. (2004) NMMAPS analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as effect estimates from the three meta-analyses (Bell et al. (2005), Levy et al. (2005), and Ito et al. (2005)). These studies are consistent with the studies used in the Ozone NAAQS RIA (U.S. EPA, 2008b, 2010d). For simplicity, we report the ozone mortality estimates in this RIA as a range reflecting application of dollar-per-ton estimates based on Bell et al. (2004) and Levy et al. (2005) to represent the lowest and the highest co-benefits estimates based on these six ozone mortality studies. In addition, we graphically present in Figure 4-1 the estimated co-benefits based on dollar-per-ton estimates derived from all six studies mentioned above as a characterization of uncertainty regarding the ozone -mortality relationship.4.3.2	Economic Valuation for Health Co-benefitsAfter quantifying the change in adverse health impacts, we estimate the economic value of these avoided impacts. Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. Therefore, the appropriate economic measure is willingness to pay (WTP) for changes in risk of a health effect. For some health effects, such as hospital admissions, WTP estimates are generally not available, so we use the cost of treating or mitigating the effect. These cost-of-illness (COI) estimates generally (although not necessarily in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect. The unit values applied in this analysis are provided in Table 5-9 of the PM NAAQS RIA for each health endpoint (U.S. EPA, 2012a).Avoided premature deaths account for 98 percent of monetized PM-related co-benefits and over 90 percent of monetized ozone-related co-benefits. The economics literature concerning the appropriate method for valuing reductions in premature mortality risk is still developing. The adoption of a value for the projected reduction in the risk of premature mortality is the subject of continuing discussion within the economics and public policy analysis community. Following the advice of the SAB's Environmental Economics Advisory Committee (SAB-EEAC), the EPA currently uses the value of statistical life (VSL) approach in calculating estimates of mortality benefits, because we believe this calculation provides the most reasonable single estimate of an individual's willingness to trade off money for reductions in mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary measure for the value of small changes in mortality risk experienced by a large number of people.The EPA continues work to update its guidance on valuing mortality risk reductions, and the Agency consulted several times with the SAB-EEAC on this issue. Until updated guidance is available, the Agency determined that a single, peer-reviewed estimate applied consistently, best reflects the SAB-EEAC advice it has received. Therefore, the EPA has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2014) while the Agency continues its efforts to update its guidance on this issue. This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). We then adjust this VSL to account for the currency year and to account for income growth from 1990 to the analysis year. Specifically, the VSLs applied in this analysis in 2011$ after adjusting for income growth are $9.9 million for 2020 and $10.1 million for 2025 and 2030. The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB-EEAC's specific recommendations. In the process, the Agency has identified a number of important issues to be considered in updating its mortality risk valuation estimates. These are detailed in a white paper, "Valuing Mortality Risk Reductions in Environmental Policy" (U.S. EPA, 2010c), which recently underwent review by the SAB-EEAC. A meeting with the SAB on this paper was held on March 14, 2011 and formal recommendations were transmitted on July 29, 2011 (U.S. EPA-SAB, 2011). The EPA is taking SAB's recommendations under advisement.In valuing PM2.5-related premature mortality, we discount the value of premature mortality occurring in future years using rates of 3 percent and 7 percent (OMB, 2003). We assume that there is a "cessation" lag between changes in PM exposures and the total realization of changes in health effects. Although the structure of the lag is uncertain, the EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 30 percent of mortality reductions in the first year, 50 percent over years 2 to 5, and 20 percent over the years 6 to 20 after the reduction in PM2.5 (U.S. EPA-SAB, 2004c). Changes in the cessation lag assumptions do not change the total number of estimated deaths but rather the timing of those deaths. Because short-term ozone-related premature mortality occurs within the analysis year, the estimated ozone-related co-benefits are identical for all discount rates.4.3.3	Benefit-per-ton Estimates for PM2.5We used a "benefit-per-ton" approach to estimate the PM2.5 co-benefits in this RIA. The EPA has applied this approach in several previous RIAs (e.g., U.S. EPA, 2011b, 2011c, 2012b, 2014a). These benefit-per-ton estimates provide the total monetized human health co-benefits (the sum of premature mortality and premature morbidity), of reducing one ton of PM2.5 (or PM2.5 precursor such as NOX or SO2) from a specified source. Specifically, in this analysis, we multiplied the benefit-per-ton estimates by the corresponding emission reductions that were generated from air quality modeling of the proposed Clean Power Plan. The method used to calculate the regional benefit-per-ton estimates is similar to the average EGU sector estimates used for the proposal (U.S. EPA, 2013a), but relies on air quality modeling of the proposed Clean Power Plan. Similar to the proposal, we generated regional benefit-per-ton estimates by aggregating the impacts in BenMAP to the region (i.e., East, West, and California) rather than aggregating to the nation. The appendix to this chapter provides additional detail regarding these calculations.As noted below in the characterization of uncertainty, all benefit-per-ton estimates have inherent limitations. Specifically, all benefit-per-ton estimates reflect the geographic distribution of the modeled proposal, which may not match the emission reductions anticipated by the final emission guidelines, and they may not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors for any specific location. The regional benefit-per-ton estimates, although less subject to these types of uncertainties than national estimates, still should be interpreted with caution. Even though we assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary between precursors depending on the location and magnitude of their impact on PM2.5 levels, which drive population exposure. 4.3.4	Benefit-per-ton Estimates for OzoneSimilar to PM2.5, we used a "benefit-per-ton" approach in this RIA to estimate the ozone co-benefits, which represent the total monetized human health co-benefits (the sum of premature mortality and premature morbidity) of reducing one ton of NOx (an ozone precursor). Also consistent with the PM2.5 estimates, we generated regional benefit-per-ton estimates for ozone based on air quality modeling for the proposed Clean Power Plan. In contrast to the PM2.5 estimates, the ozone estimates are not based on changes to annual emissions. Instead, the regional estimates (i.e., East, West, and California) correspond to NOX emissions from U.S. EGUs during the ozone-season (May to September). Because we estimate ozone health impacts from May to September only, this approach underestimates ozone co-benefits in areas with a longer ozone season such as southern California and Texas. These estimates assume that EGU-attributable ozone formation at the regional-level is due to NOx alone. Because EGUs emit little VOC relative to NOX emissions, it is unlikely that VOCs emitted by EGUs would contribute substantially to regional ozone formation. As noted above, all benefit-per-ton estimates have inherent limitations and should be interpreted with caution. We provide more detailed information regarding the generation of these estimates in the appendix to this chapter.4.3.5	Estimated Health Co-Benefits ResultsTables 4-7 through 4-9 provide the regional benefit-per-ton estimates for three analysis years: 2020, 2025, and 2030. Tables 4-10 through 4-12 and 4-13 through 4-15 provide the emission reductions estimated to occur in each analysis year for the rate-based and mass-based illustrative plan scenarios, respectively, by region (i.e., East, West, and California). Tables 4-16 through 4-18 and 4-19 through 4-21 summarize the national monetized PM and ozone-related health co-benefits estimated to occur in each analysis year for the illustrative rate-based and mass-based plan scenarios, respectively, by precursor pollutant using discount rates of 3 percent and 7 percent. Tables 4-22 through 4-24 and 4-25 through 4-27- provide national summaries of the reductions in estimated health incidences associated with the illustrative rate-based and mass-based plan scenarios, respectively, in each analysis year. Figure 4-1 provides a visual representation of the range of estimated PM2.5 and ozone-related co-benefits using benefit-per-ton estimates based on concentration-response functions from different studies and expert opinion for the illustrative rate-based and mass-based plan scenarios evaluated in 2025 as an illustrative analysis year. Figure 4-2 provides a breakdown of the monetized health co-benefits for the rate-based and mass-based plan scenarios evaluated in 2025 as an illustrative analysis year by precursor pollutant. Table 4-7.	Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)*PollutantDiscount RateRegionalEastWestCaliforniaSO23%$33,000 to $75,000$6,200 to $14,000$95,000 to $210,0007%$30,000 to $68,000$5,600 to $13,000$85,000 to $190,000Directly emitted PM2.5 (EC+OC)3%$140,000 to $320,000$27,000 to $60,000$370,000 to $830,0007%$130,000 to $290,000$24,000 to $54,000$330,000 to $740,000Directly emitted PM2.5 (crustal)3%$23,000 to $52,000$11,000 to $25,000$73,000 to $160,0007%$21,000 to $47,000$9,900 to $22,000$66,000 to $150,000NOX (as PM2.5)3%$3,100 to $7,000$0,670 to $1,500$22,000 to $49,0007%$2,800 to $6,300$0,610 to $1,400$19,000 to $44,000NOX (as Ozone)N/A$6,500 to $28,000$2,000 to $8,900$14,000 to $59,000* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for PM2.5 and ozone. All estimates are rounded to two significant figures. The monetized co-benefits do not include reduced health effects from direct exposure to NO2, SO2, ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the benefit-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 concentrations, which drive population exposure. The monetized co-benefits incorporate the conversion from precursor emissions to ambient fine particles and ozone. Benefit-per-ton estimates for ozone are based on ozone season NOX emissions. Ozone co-benefits occur in analysis year, so they are the same for all discount rates. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. In general, the 95[th] percentile confidence interval for monetized PM2.5 benefits ranges from approximately -90 percent to +180 percent of the central estimates based on Krewski et al. (2009) and Lepeule et al. (2012). Table 4-8.	Summary of Regional PM2.5 Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)*PollutantDiscount RateRegionalEastWestCaliforniaSO23%$37,000 to $83,000$7,100 to $16,000$110,000 to $240,0007%$33,000 to $75,00