***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 1 EO12866MACT_NSPSUtilitiesMATS2060_AP52RIA20111104Ch3.docx **CHAPTER 3 COST, ECONOMIC, AND**  **ENERGY IMPACTS** This chapter reports the compliance cost, economic, and energy impact analysis performed for the Mercury and Air Toxics Standards (MATS). EPA used the Integrated Planning Model (IPM), developed by ICF Consulting, to conduct its analysis. IPM is a dynamic linear programming model that can be used to examine air pollution control policies for SO 2, NO x, Hg, HCl, and other air pollutants throughout the Unite d States for the entire power system. Documentation for IPM can be found at http://www.epa.gov/airmarkets/progsregs/epa - ipm , and updates specific to the MATS modeling are in the “ Documentation Supplement for EPA Base Case v.4.10_MATS – Updates for Final Me rcury and Air Toxics Standards (MATS) Rule ” (hereafter IPM 4.10 Supplemental Documentation for MATS). **3.1 Background**  Over the last decade, EPA has on several occasions used IPM to consider pollution control options for reducing power- sector emissions . 1 Most re cently EPA used IPM extensively in the development and analysis of the impacts of the Cross - State Air Pollution Rule (CSAPR). 2 As discussed in Chapter 2 , MATS coincides with a period when many new pollution controls are being installed. Many are needed for compliance with NSR settlements and state rules, while others may have been planned in expectation of CAIR and its replacement, the CSAPR . The emissions scenarios for the RIA reflects the Cross - State Air Pollution Rule (CSAPR) as finalized in July 2011 a nd the emissions reductions of SO X, NO X, directly emitted PM, and CO 2 are consistent with application of federal rules, state rules and statutes, and other binding, enforceable commitments in place by December 2010 for the analysis timeframe . 3

1 Many EPA analyses with IPM have focused on legislative proposals with national scope, such as EPA’s IPM analyses of the Clean Air Planning Act (S.843 in 108th Congress), the Clean Power Act (S .150 in 109th Congress), the Clear Skies Act of 2005 (S.131 in 109th Congress), the Clear Skies Act of 2003 (S.485 in 108th Congress), and the Clear Skies Manager's Mark (of S.131). These analyses are available at EPA’s website: (http://www.epa.gov/airmar kt/progsregs/epa - ipm/index.html). EPA also analyzed several multi - pollutant reduction scenarios in July 2009 at the request of Senator Tom Carper to illustrate the costs and benefits of multiple levels of SO 2 and NO X control in the power sector. 2 Additio nally, IPM has been used to develop the NO X Budget Trading Program, the Clean Air Interstate Rule programs, the Clean Air Visibility Programs, and other EPA regulatory programs for the last 15 years. 3 Consistent with the mercury risk deposition modeling for MATS, EPA did not model non -federally enforceable mercury- specific emissions reduction rules in the base case or MATS policy case (see preamble section III.A ).***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 2 EPA has made these base case assumptions recognizing that the power sector will install a significant amount of pollution controls in response to several requirements. The inclusion of CSAPR and other regulatory actions (including federal, state, and local actions) in the base case is necessary in order to reflect the level of controls that are likely to be in place in response to other requirements apart from MATS. This base case will provide meaningful projections of how the power sector will respond to all the regul atory requirements for air emissions in totality, while isolating the incremental impacts of MATS relative to a base case with other air emission reduction requirements separate from today’s action. The model ’ s base case features an updated Title IV SO 2 allowance bank assumption and incorporates updates related to the Energy Independence and Security Act of 2007. Some modeling assumptions, most notably the projected demand for electricity, are based on the 2010 Annual Energy Outlook from the Energy Infor mation Administration (EIA) . In addition, the model includes existing policies affecting emissions from the power sector: the Title IV of the Clean Air Act (the Acid Rain Program); the NO x SIP Call; various New Source Review (NSR) settlements 4; and several state rules 5 affecting emissions of SO 2, NO x, and CO 2 that were finalized through June of 2011 . IPM includes state rules that have been finalized and/or approved by a state ’ s legislature or environmental agency, with the exception of non -federal mercury- s pecific rules . The IPM 4.10 Supplemental Documentation for MATS contains details on all of these other legally binding and enforceable commitments for installation and operation of pollution controls . This chapter focuses on results of EPA’s analysis with IPM for the model’s 2015 run -year in connection with the compliance date for MATS. MATS establishes National Emissions Standards for Hazardous Air Pollutants (NESHAPS) for the “electric utility steam generating unit” source category, which includes those u nits that Note that this approach does not significantly affect SO 2 and NO X projections underlying th e cost and benefit results presented in this RIA 4 The NSR settlements include agreements between EPA and Southern Indiana Gas and Electric Company (Vectren), Public Service Enterprise Group, Tampa Electric Company, We Energies (WEPCO), Virginia Electric & Power Company (Dominion), Santee Cooper, Minnkota Power Coop, American Electric Power (AEP), East Kentucky Power Cooperative (EKPC), Nevada Power Company, Illinois Power, Mirant, Ohio Edison, Kentucky Utilities, Hoosier Energy, Salt River Project, Westar, Puerto Rico Power Authority, Duke Energy, American Municipal Power, and Dayton Power and Light. These agreements lay out specific NO x, SO 2, and other emissions controls for the fleets of these major Eastern companies by specified dates. Many of the pollut ion controls are required between 2010 and 2015 . 5 These include current and future state programs in Alabama, Arizona, California, Colorado, Connecticut, Delaware, Georgia, Illinois, Kansas, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Montana, New Hampshire, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Tennessee, Texas, Utah, Washington, West Virginia, and Wisconsin the cover certain emissions from the power sector.***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 3 combust coal or oil for the purpose of generating electricity for sale and distribution through the national electric grid to the public. Coal -fired electric utility steam generating units include electric utility steam generating units that burn coal, coal refuse, or a synthetic gas derived from coal either exclusively, in any combination together, or in any combination with other supplemental fuels. Examples of supplemental fuels include petroleum coke and tire - derived fuels. The NESHAP establis hes standards for HAP emissions from both coal - and oil -fired EGUs and will apply to any existing, new, or reconstructed units located at major or area sources of HAP. Although all HAP are pollutants of interest, those of particular concern are hydrogen fl uoride (HF), hydrogen chloride (HCl), dioxins/furans, and HAP metals, including antimony, arsenic, beryllium, cadmium, chromium, cobalt, mercury, manganese, nickel, lead, and selenium. This rule affects any fossil fuel fired combustion unit of more than 25 megawatts electric (MWe) that serves a generator that produces electricity for sale. A unit that cogenerates steam and electricity and supplies more than one -third of its potential electric output capacity and more than 25 MWe output to any utility power distribution system for sale is also considered an electric utility steam generating unit. The rule affect s roughly 1,400 EGUs: approximately 1,100 existing coal -fired generating units and 300 oil -fired steam units, should those units combust oil. Of the 6 00 power plants potentially covered by this rule, about 430 have coal -fired units only, 30 have both coal - and oil - or gas -fired steam units, and 130 have oil - or gas -fired steam units only. Note that only steam electric units combusting coal or oil are co vered by this rule. EPA analyzed for the RIA the input - based (lbs/MMBtu) MATS control requirements shown in Table 3 - 1. In this analysis, EPA does not model an alternative SO 2 standard. For further discussion about the scope and requirements of MATS, see the preamble or Chapter 1 of this RIA .***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 4 **Table 3**  **- 1**  **. Emissions Limitations for Coal**  **- Fired and Solid Oil**  **- Derived Fuel**  **- Fired Electric** 

**Utility Steam Generating Units Subcategory**  **Total Particulate Matter**  **Hydrogen**

**C hloride**  **Mercury** Existing coal -fired unit designed for coal > 8,300 Btu/lb Existing coal -fired unit designed for coal < 8,300 Btu/lb Existing - IGCC Existing – Solid oil - derived New coal - fired unit designed for coal > 8,300 Btu/lb New coal - fired unit designed for coal < 8,300 Btu/lb New – IGCC New – Solid oil - derived Note: lb/MMBtu = pounds pollutant per million British thermal units fuel input lb/TBtu = pounds pollutant per trillion British thermal units fuel input lb/MWh = pounds pollutant per megawatt- electric output lb/GWh = pounds pollutant per gigawatt- electric output * Beyond-the - floor limit as discussed elsewhere. **Table 3**  **- 2**  **. Emissions Limitations for Liquid**  **Oil -**  **Fired Electric Utility Steam Generating Units** Subcategory Total HAP

Metals Hydrogen

Chloride Hydrogen

Fluoride Mercury Existing – Liquid oil New – Liquid oil EPA used the Integrated Planning Model (IPM) v.4.10 to assess the impacts of the MATS emission limitations for coal -fired electricity generating units (EGU) in the contiguous United States. IPM modeling did not subject oil -fired units to policy criteria. 6 Furthermore, IPM modeling did not include generation outside the contiguous U.S., where EPA is aware of only 2 facilities that would be subject to the coal -fired requirements of the final rule. Given the limited number of potentially impacted facilities, limited availability of input data to inform the modeling, and limited connection to the continental grid, EPA did not model the impacts of the rule beyond the contiguous U.S.

6 EPA did not model the impacts of MATS on oil - fired units using IPM. Rather, EPA performed an analysis of impacts on oil - fired units for the final rule. The results are provided in a TSD titled: “Assessment of MATS C ompliance C osts for O il - F ired U nits in the C ontinental U.S.”***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 5 Mercury emissions are modeled as a function of mercury content of the fuel type(s) consumed at each plant in concert with that plant’s pollutant control configura tion. HCl emissions are projected in a similar fashion using the chlorine content of the fuel(s). For both mercury and HCl, EGUs in the model must emit at or below the final mercury and HCl emission rate standards in order to operate from 2015 onwards. EGU s may change fuels and/or install additional control technology to meet the standard, or they may choose to retire if it is more economic for the power sector to meet electricity demand with other sources of generation. See IPM 4.10 documentation and IPM 4 .10 Supplemental Documentation for MATS for more details. Total PM emissions are calculated exogenously to IPM , using EPA’ s Source Classification Code (SCC) and control - based emissions factors. SCC is a classification system that describes a gen erating uni t’s characteristics. Instead of emission limitations for the organic HAP, EPA is proposing that if requested, owners or operators of EGUs submit to the delegated authority or EPA, as appropriate, documentation showing that an annual performance test meetin g the requirements of the rule was conducted. IPM modeling of the MATS policy assumes compliance with these work practice standards. Electricity demand is anticipated to grow by roughly 1 percent per year, and total electricity demand is projected to be 4, 10 3 billion kWh by 2015 . Table 3 - 3 shows current electricity generation alongside EPA’s base case projection for 2015 generation using IPM . EPA’s IPM modeling for this rule relies on EIA’ s Annual Energy Outlook for 2010’ s electric demand forecast for the US and employs a set of EPA assumptions regarding fuel supplies and the performance and cost of electric generation technologies as well as pollution controls. The base case includes CSAPR as well as other existing state a nd federal programs for air emissions control from electric generating units, with the exception of state mercury rules.***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or

**Table 3**  **- 3**  **. 200**  **9 U.S.**  **Electricity Net Generation and EPA Base Case Projections for**

**2030 (Billion kWh)** 

Coal Oil Natural Gas Nuclear Hydroelectric Non - hydro Renewables Other **Total** Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1; Projections from Integrated Planning Model run by EPA, 2011.

**Figure 3**  **- 1**  **. Geographic Distribution of** 

**2012** Source/Notes: National Electric Energy Data System (NEEDS 4.10) (EPA, December 2010). fossil facilities in the NEEDS 4.10 IPM frame this includes committed new builds and committed retirements. s ome facilities may be obscured. Do Not Cite, Quote, Or Release During Review ***

3 - 6 **Electricity Net Generation and EPA Base Case Projections for**

**Historical Base Case**  **2009 2015**  **2020** 1,756 1,982 2,002 39 0.11 0.13 921 710 847 799 828 837 273 286 286 144 252 289 18 45 45 **3,950 4,103**  **4,307** Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1; Projections from Integrated Planning Model run

**Geographic Distribution of Affected Units, by Facility, Size and Fuel Source**  Source/Notes: National Electric Energy Data System (NEEDS 4.10) (EPA, December 2010). This map displays all fossil facilities in the NEEDS 4.10 IPM framework . NEEDS reflects available capacity on - line by the end of 2011; this includes committed new builds and committed retirements. In areas with a dense concentration of facilities, Release During Review *** **Electricity Net Generation and EPA Base Case Projections for 2015**  **-**

**2030** 2,027 0.21 1,185 817 286 333 55 **4,702** Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1; Projections from Integrated Planning Model run

**Affected Units, by Facility, Size and Fuel Source in**  This map displays all line by the end of 2011; In areas with a dense concentration of facilities,***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 7 As noted above, IPM has been used for evaluating the economic and emission impacts of environmental policies for over two decade s . The economic modeling presented in this chapter has been developed for specific analyses of the power sector. Thus, the model has been designed to reflect the industry as accurately as possible. To that end , EPA use s a series of capital charge factors in IPM that embody financial terms for the various types of investments that the power sector considers for meeting future generation and environmental constraints . The model applies a discount rate of 6.15% for optimizing the sector’s decision - making over time. IPM’s discount rate , designed to represent a broad range of private - sector decisions for power generation, rates differs from discount rates used in other analyses in this RIA, such as the benefits analys i s which each assume alternative social discount rates of 3% and 7%. These discount rates represent social rates of time preference, whereas the discount rate in IPM represents an empirically- informed price of raising capital for the power sector. Like all other assumed price inputs in IPM, EPA uses the best available information from utilities, financial institutions, debt rating agencies, and government statistics as the basis for the capital charge rates and the discount rate used for power sector modeli ng in IPM . More detail on IPM can be found in the model documentation, which provides additional information on the assumptions discussed here as well as all other assumptions and inputs to the model (http://www.epa.gov/airmarkets/progsregs/epa - ipm). Updat es specific to MATS modeling are also in the IPM 4.10 Supplemental Documentation for MATS. **3.2 Projected Emissions**  MATS is anticipated to achieve substantial emissions reductions from the power sector . Since the technologies available to meet the emission redu ction requirements of the rule reduce multiple air pollutants, EPA expects the rule to yield a broad array of pollutant reductions from the power sector. The primary pollutants of concern under MATS from the power sector are mercury, acid gases such as hyd rogen chloride (HCl), and HAP metals, including antimony, arsenic, beryllium, cadmium, chromium, cobalt, mercury, manganese, nickel, lead, and selenium. EPA has extensively analyzed mercury emissions from the power sector, and IPM modeling assesses the mer cury contents in all coals and the removal efficiencies of relevant emission control technologies (e.g., ACI). EPA also models emissions and the pollution control technologies associated with HCl (as a surrogate for acid gas emissions). Like SO 2, HCl is re moved by both scrubbers and DSI (dry sorbent injection). Projected emissions are based on both control technology and detailed coal supply curves used in the model that reflect the chlorine content of coals, which corresponds with the supply region, coal g rade, and sulfur, mercury, and ash content of each coal type. This information is critical for accurately***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 8 projecting future HCl emissions, and for understanding how the power sector will respond to a policy requiring reductions of multiple HAPs. Generally, existing pollution control technologies reduce emissions across a range of pollutants. For example, both FGD and SCR can achieve notable reductions in mercury in addition to their primary targets of SO 2 and NO X reductions. DSI will reduce HCl emissions while also yielding substantial SO 2 emission reductions, but is not assumed in EPA modeling to result in mercury reductions. Since there are many avenues to reduce emissions, and because the power sector is a high ly complex and dynamic industry, EPA employs IPM in order to reflect the relevant components of the power sector accurately, while also providing a sophisticated view of how the industry could respond to particular policies to reduce emissions. For more de tail on how EPA models emissions from the power sector, including recent updates to include acid gases, see IPM 4.10 Supplemental Documentation for MATS. Under MATS, EPA projects annual HCl emissions reductions of 88 percent in 2015, Hg emissions reduction s of 68 percent in 2015, and PM 2.5 emissions reductions of 20 percent in 2015 from coal -fired EGUs greater than 25 MW. In addition, EPA projects SO 2 emission reductions of 4 1 percent, and annual CO 2 reductions of 1 percent from coal -fired EGUs greater than 25 MW by 2015, relative to the base case (see Table 3 - 4 ). 7 Mercury emission projections in EPA’s base case are affected by the incidental capture in other pollution control technologies (such as FGD and SCR) as described above. **Table 3**  **- 4**  **. Projected Emissions of SO**  **2 , NO**  **X , Mercury, Hydrogen Chloride, PM**  **, and CO 2**  **with**

**the Base Case and with MATS** **, 2015**

**Million Tons Mercury** 

**(Tons) Thousand Tons**  **CO 2**  **(Million Metric Tonnes)**  **SO 2**  **NO X**  **HCl PM**  **2.5** Base All EGUs 3.4 1.9 28.7 48.7 277 2,230 Coal > 25 MW 3.3 1.7 26.6 45.3 270 1,906 MATS All EGUs 2.1 1.9 10.6 9.0 224 2,215 Coal > 25 MW 1.9 1.7 8.4 5.5 215 1,881 Source: Integrated Planning Model run by EPA, 2011

7 The CO 2 emissions reported from IPM account for the direct CO 2 emissions from fuel combustion and CO 2 created from chemical reactions in pollution controls to reduced sulfur***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 9 **Figure 3**  **- 2**  **. SO**  **2 Emissions from the Power Sector in 2015 with and without**  **MATS** Source: 2015 emissions include coal steam (including IGCC and petroleum coke) units >25 MW from I PM v4.10 base case and control case projections (EPA, February 2011)

**Figure 3**  **- 3**  **. NO**  **X Emissions from the Power Sector in 2015 with and without**  **MATS** Source: 2015 emissions include coal steam (including IGCC and petroleum coke) units >25 MW from IPM v4.10_MATS base case and control case projections (EPA, 2011)***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 10 **Figure 3**  **- 4**  **. Mercury Emissions from the Power Sector in 2015 with and without**  **MATS** Source: 2015 emissions include coal steam (including IGCC and petroleum coke) units >25 MW from IPM v4.10_MATS base case and control case projections (EPA, 2011)

**Figure 3**  **- 5**  **. H**  **ydrogen Chloride Emissions from the Power Sector in 2015 with and without**

**MATS** Source: 2 015 emissions include coal steam (including IGCC and petroleum coke) units >25 MW from IPM v4.10_MATS base case and control case projections (EPA, 2011)***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 11 **3.3 Projected**  **Compliance Costs**  The power industry’s “compliance costs” are represented in this analysis as the change in electric power generation costs between the base case and policy case in which the sector pursues pollution control approaches to meet the final HAP emission standards. In simple terms, these costs are the resource costs of what the power ind ustry will directly expend to comply with EPA’s requirements. EPA projects that the annual incremental compliance cost of MATS is $ 9.2 billion in 2015 ($2007) . The annual incremental cost is the projected additional cost of complying with the final rule in the year analyzed, and includes the amortized cost of capital investment and the ongoing costs of operating additional pollution controls, needed new capacity, shifts between or amongst various fuels, and other actions associated with compliance. **Table 3**  **- 5**  **. Annualized Compliance Cost for MATS Requirements on Coal**  **- fired Generation**  **2015 2020**  **2030** Annualized Compliance Cost (billions of 2007$) $9.2 $8.5 $7.5 Source: Integrated Planning Model run by EPA, 2011 . EPA’ s projection of $ 9.2 billion in additional costs in 2015 should be put into context for power sector operations. As shown in section 2.7, the power sector is expected in the base case to expend over $320 billion in 2015 to generate, transmit, and distribute electricity to end - use consumers. Therefore, the projected costs of compliance with MATS amount to less than a 3 % increase in the cost to meet electricity demand, while securing public health benefits that are several times more valuable (as descri bed in Chapters 4 and 5). **3.4 Projected**  **Compliance Actions for Emissions Reductions** Fossil fuel -fired electric generating units are projected to achieve HAP e mission reductions through a combination of compliance options . These actions include improved opera tion of existing controls, additional pollution control installations, coal switching (including blending of coals), and generation shifts towards more efficient units and loweremitting generation technologies (e.g., some reduction of coal -fired generatio n with an increase of generation from natural gas). In addition, there will be some affected sources that find it uneconomic to invest in new pollution control equipment and will be removed from service. These facilities are generally amongst the oldest an d least efficient power plants, and typically run infrequently. In order to ensure that any retirements resulting from MATS do not adversely impact the ability of affected sources and electric utilities from meeting the demand***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 12 for electricity, EPA has cond ucted an analysis of the impacts of projected retirements on electric reliability. This analysis is discussed in TSD titled: “ Resource Adequacy and Reliability in the IPM Projections for the Toxics Rule ” which is available in the docket. The requirements u nder MATS are largely met through the installation of pollution controls (see Figure 3 - 6). To a lesser extent, there is a small degree of shifting within and across various ranks and types of coals, and a relatively small shift from coal -fired generation t o greater use of natural gas and non - emitting sources of electricity (e.g., hydro and nuclear) (see Table 3 - 6). The l argest share of emissions reductions occur from coal -fired units installing new pollution control devices, such as FGD, ACI, and fabric fil ters; a smaller share of emission reductions come from fuel shifts and unit retirements. Mercury emission reductions are largely driven by SCR/FGD combinations and ACI installations. HCl emission reductions are largely driven by FGD and DSI installations, which also incidentally provide substantial SO 2 reductions in the policy case. Mercury, PM 2.5 , and HCl emission reductions are also facilitated by the installation of fabric filters, which boost mercury and HCl removal efficiencies of ACI and DSI, respecti vely.

**Figure 3**  **- 6**  **. Operating**  **Pollution Control Capacity**  **on Coal -** **fired Capacity (by Technology) with**

**the Base Case and with MATS** **, 2015 (GW)** Note: The difference between controlled capacity in the base case and under the MATS may not necessarily equal new retrofit construction, since controlled capacity above reflects incremental operation of dispatchable controls in 2015 . Additionally, existing ACI installed on those units online before 2008 are not included in the base case to reflect removal of state mercury rules from IPM modeling. For these reasons, and due to rounding, numbers in the text above may not reflect the increments displayed in this fi gure. See IPM Documentation for more information on dispatchable controls . Source: Integrated Planning Model run by EPA, 2011 . As shown in Figure 3 - 6, this analysis projects that by 2015, the final rule will drive the installation of an additional 17 GW of FGD (scrubbers) , 45 GW of DSI, 92 GW of additional ACI,***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 13 106 GW of additional fabric filters, and 52 GW of ESP upgrades. For more information, see section 8.14. The difference between operating controlled capacity in the base case and under MATS in Figure 3 - 6 may not necessarily equal new retrofit construction, since total controlled capacity in the figure reflects incremental operation of existing controls that a re projected to operate under MATS but not under the base case. With respect to the increase in operating ACI, some of this increase represents existing ACI capacity on units built before 2008. EPA’s modeling does not reflect the presence of state mercury rules, and EPA assumes that ACI controls on units built before 2008 do not operate in the absence of these rules. In the policy case, these controls are projected to operate and the projected compliance cost thus reflects the operating cost of these contro ls. Since these controls are in existence, EPA does not count their capacity toward new retrofit construction, nor does EPA’s compliance costs projection reflect the capital cost of these controls (new retrofit capacity is reported in the previous paragrap h). **3.5 Projected Generation Mix**  Table 3 - 6 and Figure 3 - 7 show the generation mix in the base case and in MATS . In 2015, c oal -fired generation is projected to decline slightly and natural - gas -fired generation is projected to increase slightly relative to the base case. Coal -fired generation is projected to increase above 2009 actual levels. The vast majority ( over 98%) of base case coal capacity is projected to remain in service under MATS. In addition, the operating costs of complying coal fired units are not so affected as to result in major changes in the electricity generation mix . **Table 3**  **- 6**  **. Generation Mix with the Base Case and the**  **MATS , 2015 (Thousand GWh)**  **2009 2015**  **Historical Base Case**  **Policy Case Change from**  **Base Percent Change**  Coal 1,756 1,982 1,957 − 25 − 1.3% Oil 39 0.11 0.11 0.00 2.7% Natural Gas 921 710 732 22 3.1% Nuclear 799 828 831 3 0.4% Hydroelectric 273 286 288 2 0.7% Non - hydro Renewables 144 252 250 − 2 − 0.7% Other 18 45 45 0.0 0.0% **Total 3,950**  **4,103 4,104**  **1 0.0%**  Note: Numbers may not add due to rounding. Source: 200 9 data from EIA Electric Power Annual 2009, Table 2.1; 2015 projections are from the Integrated Planning Model run by EPA, 2011 .***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 14 **Figure 3**  **- 7**  **. Generation Mix with the Base Case and with**  **MATS , 2015**  **- 2030**  Source: Integrated Planning Model run by EPA, 2011 . **3.6 Projected**  **Withdrawals from Service** Relative to the base case, about 5.5 GW of coal -fired capacity is projected to be uneconomic to maintain ( less than 2 percent of all coal -fired capacity) by 2015 after consideration of various regional factors (e.g., other available capacity and fuel prices) and unit attributes (e.g., effici ency and age) . Uneconomic units, for the most part, are older, smaller, and less frequently used generating units that are dispersed throughout the country (see Table 3 - 7) . **Table 3**  **- 7**  **. Characteristics of**  **Cover ed**  **Operational Coal Units and Additional Coal Units** 

**Projected to Withdraw as Uneconomic under MATS, 2015**  **Average Age (Years)**  **Average Capacity**

**MW Factor in Base**  Withdrawn as Uneconomic 52 117 53% Operational 43 327 71% Source: Integrated Planning Model run by EPA, 2011 . There could be a variety of local factors that would make plant owners decide to keep various units in service to meet local reliability and other local requirements that are beyond EPA’s ability to assess in modeling. This is an im portant limitation to recognize in these results, which should be used to consider “potential” closures using these numbers. To the extent that***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 15 plants operate in response to local factors unaccounted for in EPA’s modeling, these results could be overstatin g the capacity removed from service as a result of this rule. For the final rule, EPA has examine d whether the IPM - projected closures may adversely impact reserve margins and reliability planning . The IPM model is specifically designed to ensure that gener ation resource availability is maintained in the projected results subject to reserve margins in 32 modeling regions for the contiguous US , which must be preserved either by using existing resources or through the construction of new resources. IPM also ad dresses reliable delivery of generation resources by limiting the ability to transfer power between regions using the bulk power transmission system. Within each model region, IPM assumes that adequate transmission capacity is available to deliver any resources located in, or transferred to, the region. The IPM model projects available capacity given certain constraints such as reserve margins and transmission capability but does not constitute a detailed reliability analysis. For example, the IPM model does not examine frequency response. For more detail on IPM’s electric load modeling and power system operation, please see IPM documentation ( http://www.epa.gov/airmarkt/progsregs/epa - ipm/index.html ) and the TSD on Resource Adequacy and Reliability in th e IPM Projections for MATS . Total operational capacity is lower in the policy scenario, primarily as a result of additional coal projected to be uneconomic to maintain. Since most regions are projected to have excess capacity above their target reserve margins, most of these withdrawals from service are absorbed by a reduction in excess reserves. Operational capacity changes from the base case in 2015 are shown in Table 3 - 8. **Table 3**  **- 8**  **. Total Generation Capacit** **y by 2015 (GW) 2010**  **Base Case MATS**  Pulverized Coal 317 310 304 Natural Gas Combined Cycle 201 206 206 Other Oil/Gas 253 233 233 Non - Hydro Renewables 31 70 70 Hydro 99 99 99 Nuclear 102 104 105 Other 5 4 4 Total 1,009 1,026 1,020 Source: 20 10 data from EPA’ s NEEDS v .4.10_PTox. Projections from Integrated Planning Model run by EPA. Note: “Non - Hydro Renewables” include biomass, geothermal, solar, and wind electric generation capacity. 2015 capacity reflects plant closures planned to occur prior to 2015.***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 16 The policy case analyzed maintain s resource adequacy in each region projected to decrease in coal capacity by using excess reserve capacity within the region , reversing base case withdr awals of non - coal capacity, building new capacity, or by importing excess reserve capacity from other regions . Although any closure of a large generation facility will need to be studied to determine potential local reliability concerns, EPA analysis sugge sts that projected economic withdrawals from service under the final rule could have little to no overall impact on electric reliability. Not only are projected withdrawals under MATS limited in scope, but the existing state of the power sector is also cha racterized by substantial excess capacity. T he weighted average reserve margin at the national level is projected to be approximately 25% in the base case, while the North American Electric Reliability Corporation (NERC) recommends a margin of 15% . EPA projects that MATS would only reduce total operational capacity by less than one percent in 2015 . Moreover, coal units projected to withdraw as uneconomic are distributed throughout the power grid with limited effect at the regional level, such that any poten tial impacts should not adversely affect reserve margins and should be manageable through the normal industry processes. For example, in the RFC NERC reliability Region, containing coal -fired generating area in Pennsylvania, West Virginia and the Midwest, there is a decrease of less than 2% in the reserve margin in the policy case and a remaining overall reserve margin of over 20%. Furthermore, subregions may share each other’ s excess reserves to ensure adequate reserve margins within a larger reliability r egion. EPA’ s IPM modeling accommodates such transfers of reserves within the assumed limits of reliability of the inter- regional bulk power system. For these reasons, the projected closures of coal plants are not expected to raise broad reliability concern s. **3.7 Projected Capacity Additions**  Due in part to a low growth rate anticipated for future electricity demand levels in the latest EIA forecast, EPA analysis indicates that there is sufficient excess capacity through 2015 to compensate for capacity that is retired from service under MATS. In the short -term, most new capacity is projected as a mix of wind and natural gas in response to low fuel prices and other energy policies (such as tax credits and state renewable portfolio standards). In addition, future el ectricity demand expectations have trended downwards in recent forecasts, reducing the need for new capacity in the 2015 timeframe ( see Chapter 2 for more discussion on future electricity demand).***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 17 **Table 3**  **- 9**  **. Total Generation Capacity by 2030 (GW)**  **2010 Base Case**  **MATS Change**  Pulverized Coal 317 308 303 −4.7 Natural Gas Combined Cycle 201 275 279 3.6 Other Oil/Gas 253 235 235 0.4 Non - Hydro Renewables 31 79 80 0.2 Hydro 99 99 99 0.0 Nuclear 102 103 103 0.3 Other 5 4 4 0.0 Total 1,009 1,103 1,102 − 0.2 Note: “Non - Hydro Renewables” include biomass, geothermal, solar, and wind electric generation capacity. Source: 20 10 data from EPA’ s NEEDS v .4.10_PTox. Projections from Integrated Planning Model run by EPA. **3.8 Projected Coal Production for the Electric Power Sector**  Coal production for electricity generation under MATS is expected to decline modestly relative to the base case without the rule. The reductions in emissions from the power sector will be met th rough the installation and operation of pollution controls for HAP removal. Many available pollution controls achieve emissions removal rates of up to 99 percent (e.g., HCl removal by new scrubbers), which allows industry to rely more heavily on local bitu minous coal in the eastern and central parts of the country that has higher contents of HCl and sulfur, and it is less expensive to transport than western subbituminous coal. Overall demand for coal is reduced as a result of MATS, with a slight reduction i n bituminous coal, and more of a reduction in subbituminous coal ( see Tables 3 - 10 and 3 - 11). The trend reflects the reduced demand for lower- sulfur coal under MATS, where nearly all units are operating with a post - combustion emissions control. In this case , because of the additional pollution controls, many of these units no longer find it economic to pay a transportation premium to purchase lower - sulfur subbituminous coals. Instead, EGUs are generally shifting consumption towards nearby bituminous coal, wh ich can achieve low emissions when combined with post - combustion emissions controls. This explains the increase in coal supplied from the Interior region, which is located in relatively close proximity to many coal -fired generators subject to MATS . The inc rease in lignite use occurs at units blending subbituminous and lignite coals, and reflects a small shift in blended balance towards a greater use of lignite .***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 18 **Table 3**  **- 10**  **. 2015**  **Coal Production for the Electric Power Sector with the Base Case and**  **MATS**

**(Million Tons) Supply Area**  **2009 2015 Base**  **2015 MATS Change in 2015**  Appalachia 246 184 173 − 6% Interior 129 216 236 9% West 553 554 536 − 3% Waste Coal 14 14 13 − 5% Imports 30 30 0% Total 942 998 988 − 1% Source: Production: U.S. Energy Information Administration (EIA), Coal Distribution — Annual (Final) , web site http://www.eia.doe.gov/cneaf/coal/page/coaldistrib/a_distributions.html (posted February 18, 2011); Waste Coal: U.S. EIA, Monthly Energy Review, January 2011 Edition, Table 6.1 Coal Overview, web site http://www.eia.doe.gov/emeu/mer/coal.html (posted Ja nuary 31, 2011). All projections from Integrated Planning Model run by EPA, 2011 . **Table 3**  **- 11**  **. 2015**  **Power Sector Coal Use with the Base Case and the MATS** **, by Coal Rank**

**(TBtu) Coal Rank**  **Base MATS**  **Change** Bituminous 11,314 11,268 − 0.4% Subbituminous 7,736 7,532 − 3% Lignite 849 895 5% Total 19,900 19,695 − 1% Source: Integrated Planning Model run by EPA, 2011 .***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 19 **Figure 3**  **- 8**  **. Total**  **Coal Production by Coal -**  **Producing Region, 2007 (Million Short Tons )**  Note: Regional totals do not include refuse recovery Source: EIA Annual Coal Report, 2007 **3.9 Projected Retail**  **Electricity Prices** EPA’s analysis projects a near-term increase in the average r etail electricity price of 3 % in 2015 falling to 2 % by 2020 under the final rule in the contiguous U.S. The projected price impacts vary by region and are provided in Table 3 - 1 2 (see Figure 3 - 9 for regional classifications) . Regional retail electricity prices are projected to range from 1 to 6 percent higher with MATS in 2015 . The extent of regional retail electricity increases correlates with states that have considerable coal -fired generation in total generation capacity and that coal -fired generation is less well - controlled (such as in the ECAR and SPP regions).***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 20 **Table 3**  **- 12**  **. Projected**  **Contiguous U.S. and Regional Retail Ele**  **ctricity Prices with the Base**

**Case and with the MATS**  **(200 7**  **cents /kWh)** 

**Base Case MATS**  **Percent Change 2015**  **2020 2030**  **2015 2020**  **2030 2015**  **2020 2030**  ECAR 8.2 8.2 9.8 8.5 8.5 9.9 4.3% 2.9% 1.3% ERCOT 8.9 8.8 11.3 9.2 8.9 11.3 3.1% 0.9% − 0.1% MAAC 9.5 10.4 12.7 9.8 10.4 12.7 2.7% 0.4% − 0.1% MAIN 8.1 8.4 9.7 8.3 8.6 9.7 2.7% 2.2% 0.1% MAPP 8.0 7.9 8.5 8.4 8.3 8.8 4.4% 4.7% 2.7% NY 13.8 13.4 16.6 14.0 13.5 16.6 2.0% 0.7% 0.1% NE 12.3 11.8 13.8 12.6 11.9 13.8 1.9% 0.8% 0.1% FRCC 10.2 9.7 11.0 10.4 9.8 11.0 2.1% 1.0% 0.7% STV 7.9 7.8 8.4 8.2 8.0 8.6 3.2% 2.4% 1.8% SPP 7.7 7.4 8.1 8.1 7.8 8.4 5.9% 5.8% 4.4% PNW 7.1 6.8 7.6 7.3 7.0 7.6 2.7% 2.6% 1.1% RM 9.2 9.5 11.0 9.4 9.6 11.1 2.1% 1.8% 1.1% CALI 13.0 12.5 12.7 13.2 12.6 12.7 1.3% 0.8% 0.0% Contiguous U.S. Average 9.0 9.0 10.2 9.3 9.2 10.3 3.0% 2.0% 0.9% Source: EPA’ s Retail Electricity Price Model , 2011 .***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 2 1 **Figure 3**  **- 9**  **. Retail Price Model**  **Regions 3.10**  **Projected Fuel Price Impacts** The impacts of the final Rule on coal and natural gas prices before shipment are shown below in Tables 3 - 13 and 3 - 14 . Overall, average coal price changes are related to changes in demand for a wide variety of coals based upon a number of parameters (e.g., chlorine or mercury content, heat content, proximity to the power plant, etc.). Generally, total demand for coal decreases slightly under MATS, most notably subbituminous coal. This is reflected in the projected average minemouth price of coal , which goes up by about 3 percent even th ough total demand for coal is reduced slightly ( 1 percent reduction). Notwithstanding the projected “mine - mouth” coal price changes, many units may in fact be realizing overall fuel cost savings by switching to more local coal supplies (which reduces trans portation costs) after installing additional pollution control equipment. Gas price changes are directly related the projected increase in natural gas consumption under MATS. This increase in demand is met by producing additional natural gas at some increa se in regional costs, resulting over time in a small price increase.***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 22 **Table 3**  **- 13**  **. Average Minemouth and Delivered Coal Prices with the Base Case and with** 

**MATS (2007$/MMBtu)**

**2007 2015**  **2030**

**Base Case MATS**  **Percent Change**  **from Base Base Case**  **MATS Percent**  **Change from Base**  Minemouth 1.27 1.35 1.39 2.8 1.51 1.56 3.2 Delivered 1.76 2.11 2.15 1.9 2.29 2.33 1.7 Source: Historical data from EIA AEO 2010 Reference Case Table 15 (Coal Supply, Distribution, and Prices); projection s from the Integrated Planning Model run by EPA, 2011 . **Table 3**  **- 1**  **4 .**  **2015 -**  **2030 Weighted Average Henry Hub (spot) and Delivered Natural Gas Prices** 

**wit h the Base Case and with**  **MATS (200**  **7 $/MMBtu)** 

**Base Case MATS**  **Percent Change from Base** Henry Hub 5.29 5.33 0.8 Delivered - Electric Power 5.56 5.60 0.7 Delivered - Residential 10.94 10.98 0.4 Source: P rojections from the Integrated Planning Model run by EPA (2011) adjusted to Henry Hub prices using historical data from EIA AEO 2011 reference case to derive residential prices. IPM modeling of natural gas prices uses both short - and long-term price signals to balance supply of and demand in competitive mar kets 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 n atural gas market in the United States has historically experienced significant price volatility from year to year, between seasons within a year, and even sees major price swings during short - lived weather events (such as cold snaps leading to short - run s pikes 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 natura l gas prices by retail rate regulation and by hedging through longer -term fuel supply contracts. 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 alig n 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 time period 2015 to 20 30.***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 23 For this analysis, in order to represent a natural gas price evolution that end - use consumers can anticipate under retail rate regulation and/or typical hedging behavior, EPA is displaying the weighted average of IPM’s natural gas price projections for the 2015 - 2030 time horizon (see Table 3 - 1 4) . In that framework, consumer natural gas price impacts are anticipated to range from 0. 4 % to 0.7 % based on consumer class in response to MATS. **3.11 Key Differences in EPA Model Runs for**  **MATS Modeling**  In this analysis, we use the Integrated Planning Model (IPM) , which is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. 8 The length of time required to conduct emissions and photochemical modeling preclud ed the use of IPM version 4.10_MATS . Thus the air quality modeling for MATS relied on EGU emission projections from an interim IPM platform that was subsequently updated during the rulemaking process for the base case and policy scenario summarized in this chapter. The 2015 base case EGU emissions projections of mercury, hydrogen chloride, SO 2, and PM used in air quality modeling were obtained from a n earlier version of IPM, 4.10_FTransport. IPM v ersion 4.10_FTransport reflects all state rules and consent d ecrees adopted through December 2010. Units with SO 2 or NO X advanced controls (e.g., scrubber, SCR) that were not required to run for compliance with Title IV, New Source Review (NSR), state settlements, or state - specific rules were allowed in IPM to decid e on the basis of economic efficiency whether to operate those controls. Note that this base case includes CSAPR, which was finalized in July 2011 . Further details on the EGU emissions inventory used for this proposal can be found in the IPM Documentation. The results presented in this chapter, from IPM version 4.10_MATS, reflect updates made to the 4.10_FTransport base case. These revisions are fully documented in the IPM 4.10 Supplemental Documentation for MATS and include : updated assumptions regarding the removal of HCl by alkaline fly ash in subbituminous and lignite coals; an update to the fuel based mercury emission factor for petroleum coke, which was corrected based on re examination of the 1999 ICR data; updated capital cost for new nuclear capaci ty and nuclear life extension costs; corrected variable operating and maintenance cost (VOM) for ACI retrofits; adjusted coal rank availability for some units, consistent with EIA From 923 (2008); updated state rules in Washington and Colorado; and numerou s unit - level revisions based on comments received through the notice and comment process. Additionally, IPM v.4.10_MATS does not reflect mercury- specific state regulations (see section 1 above).

8 http://www.epa.gov/airmarkt/progsregs/epa - ipm/index.html***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 24 **3.12 Projected Primary PM Emissions from Power Plants**  IPM does not 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 f uel 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 infor mation 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 t he fuel, and SO 2/ HCl and PM control configurations. A lthough 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 o f SCRs. The formation of additional condensable PM (in the form of SO 3 and H 2 SO 4 ) 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. EPA plans to continue improving and updating the PM emission factors and calculation methodolog ies. For a more complete description of the methodologies used to post - process PM emissions from IPM, see “ IPM ORL File Generation Methodology” (March, 2011). **3.13 Illustrative**  **Dry Sorbent Injection Sensitivity** Several commenters believe that EPA’ s IPM modeling assumptions regarding the efficacy and cost of DSI are based on too little data and are too optimistic. Some commenters believe that in practice there will be a need for many more FGD scrubbers for MATS compliance than projected by EPA for effective acid gas control, and at a corresponding higher cost. EPA disagrees with the se opinions for several reasons (see Response to Comments ) and believes that EPA’s modeling assumptions regarding DSI cost and performance are reasonable.***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 25 However, to examine the potent ial impacts of limited DSI availability, EPA analyzed a scenario that limited total DSI capacity to 35 GW in 2015. In this scenario, which reduces the capacity of DSI by 19 GW compared to the primary MATS scenario, an additional 15 GW of coal capacity choo ses to install scrubbers, and an additional 1 GW of capacity is projected to withdraw from service. Additionally, about 3 GW of capacity that installed DSI in the primary policy scenario switches to a very low chlorine subbituminous coal, which allows for compliance with the HCl standard absent additional acid gas control technology. Limiting total DSI capacity to 35 GW results in a $1.2 billion (2007$) increase in annualized compliance costs in 2015. Additionally, SO 2 is further reduced in 2015 by an addit ional 55,000 tons (a 4% increase in SO 2 reductions). **3.14 Limitations of Analysis**  EPA’ s modeling is based on expert judgment of various input assumptions for variables whose outcomes are in fact uncertain. Assumptions for future fuel supplies and electricity demand growth deserve particular attention because of the importance of these two key model inputs to the power sector. As a general matter, the Agency reviews the best available information from engineering studies of air pollution contro ls to support a reasonable modeling framework for analyzing the cost, emission changes, and other impacts of regulatory actions . The annualized cost estimates of private compliance costs provided in this analysis are meant to show the increase in productio n ( generating ) costs to the power sector in response to the final rule . To estimate these annualized costs, EPA uses a conventional and widely - accepted approach that applie s a capital recovery factor (CRF) multiplier to capital investments and add s 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 private compliance costs presented e arlier are EPA’ s best estimate of the direct private compliance costs of MATS . The annualized cost of the final rule , as quantified here, is EPA’ s best assessment of the cost of implementing the r ule. These costs are generated from rigorous economic modeli ng of changes in the power sector due to implementation of MATS . This type of analysis using IPM has undergone peer review, and federal courts have upheld regulations covering the power sector that have relied on IPM ’ s cost analysis. The direct private com pliance cost includes, but is not limited to, capital investments in pollution controls, operating expenses of the pollution controls, investments in new generating***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 26 sources, and additional fuel expenditures. EPA believes that the cost assumptions used for the final rule reflect, as closely as possible, the best information available to the Agency today. The cost associated with monitoring emissions, reporting, and record keeping for affected sources is not included in these annualized cost estimates, but EP A has done a separate analysis and estimated the cost to be approxim ately $ 128.6 million annually (see Section 7.3, Paperwork Reduction Act). Cost estimates for MATS are based on results from ICF ’ s Integrated Planning Model. The model minimizes the costs of producing electricity (including abatement costs) while meeting load demand and other constraints (full documentation for IPM can be found at http://www.epa.gov/airmarkets/progsregs/epa - ipm and in the IPM 4.10 Supplemental Documentation for MATS. IPM as sumes “ perfect foresight” of market conditions over the time horizon modeled; t o the extent that utilities and/or energy regulators misjudge future conditions affecting the economics of pollution control, costs may be understated as well. In the policy cas e modeling, EPA exogenously determines that a subset of covered units might require a retrofit fabric filter (also known as a baghouse) retrofit, or might need to upgrade existing ESP control in order to meet the PM standard. EPA’s methodology for assignin g these controls to EGUs in policy case modeling is based on historic PM emission rates and reported control efficiencies, and is explained in the TSD titled: “XXX,” which is available in the docket. Additionally, this modeling analysis does not take into account the potential for advancements in the capabilities of pollution control technologies as well as reductions in their costs over time. In addition, EPA modeling cannot anticipate in advance the full spectrum of compliance strategies that the power se ctor may innovate to achieve the required emission reductions under MATS, which would potentially reduce overall compliance costs. Where possible, EPA designs regulations to assure environmental performance while preserving flexibility for affected sources to design their own solutions for compliance. Industry will employ an array of responses, some of which regulators may not fully anticipate and will generally lead to lower costs associated with the rule than modeled in this analysis. For example, unit op erators may find opportunities to improve or upgrade existing pollution control equipment without requiring as many new retrofit devices (i.e., meeting the PM standard with an existing ESP without requiring installation of a new fabric filter). With that i n mind, MATS establishes emission rates on key HAPs, and although this analysis projects a specific set of technologies and behaviors as EPA’s judgment of least - cost***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 27 compliance, the power sector is free to adopt alternative technologies and behaviors to ac hieve the same environmental outcome EPA has deemed in the public interest as laid out in the Clean Air Act. Such regulation serves to promote innovation and the development of new and cheaper technologies. As an example, cost estimates of the Acid Rain SO 2 trading program by Resources for the Future (RFF) and MIT’ s Center for Energy and Environmental Policy Research (CEEPR) have been as much as 83 percent lower than originally projected by the EPA (see Carlson et al., 2000; Ellerman, 2003). It is important to note that the original analysis for the Acid Rain Program done by EPA also relied on an optimization model like IPM. Ex ante, EPA cost estimates of roughly $2.7 to $6.2 billion 9 in 1989 were an overestimate of the costs of the program in part because o f the limitation of economic modeling to perfectly anticipate technological improvement of pollution controls and economic improvement of other compliance options such as fuel switching. Ex post estimates of the annual cost of the Acid Rain SO 2 trading pro gram range from $1.0 to $1.4 billion. In recognition of th is historic pattern of overestimated regulatory cost , EPA’ s mobile source program uses adjusted engineering cost estimates of pollution control equipment and installation costs. 10 To date, and inclu ding this analysis, EPA has not incorporated a similar approach into IPM modeling of EGU compliance with environmental constraints. As a result, this analysis may overstate costs where such cost savings from as -yet untapped improvements to pollution contro l technologies may occur in the future. Considering the broad and complex suite of generating technologies, fuels, and pollution control strategies available to the power sector, as well as the fundamental role of operating cost in electricity dispatch, it is not possible to apply a single technology- improving “discount” transformation to the cost projections in this analysis. The Agency will consider additional methodologies in the future which may inform the amount by which projected compliance costs coul d be overstated regarding further technological development in analys e s of power sector regulations . As configured in this application, IPM does not take into account demand response (i.e., consumer reaction to electricity prices). The increased retail electricity prices shown in Table 3 - 13 would prompt end users to increase investment in energy efficiency and/or curtail (to some extent) their use of electricity and encourage them to use substitutes. 11 Th ose response s would lessen the demand for electrici ty, resulting in electricity price increases slightly

9 2010 Phase II cost estimat e in $1995. 10 See regulatory impact analysis for the Tier 2 Regulations for passenger vehicles (1999) and Heavy - Duty Diesel Vehicle Rules (2000). 11 The degree of substitution/curtailment depends on the costs and performance of the goods that substitute for more energy consuming goods, which is reflected in the demand elasticity.***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 28 lower than IPM predicts , which would also reduce generation and emissions. D emand response would yield certain unquantified cost savings from requiring less electricity to meet the quantity demanded. To some degree, these saved resource costs will offset the additional costs of pollution controls and fuel switching that EPA anticipate s from th e final rule . Although the reduction in electricity use is likely to be small, the cost sa vings from such a large industry 12 are not insignificant. EIA analysis examining multi - pollutant legislation in 2003 indicate d that the annualized costs of MATS may be overstated substantially by not considering demand response, depending on the magnitude and coverage of the pric e increases. 13 EPA’ s IPM modeling of MATS reflects the Agency’ s authority to allow facility- level compliance with the HAP emission standards rather than require each affected unit at a given facility to meet the standards separately. This flexibility would offer important cost savings to facility owners in situations where a subset of affected units at a given facility could be controlled more cost - effectively such that their “overperformance ” would compensate for any “ underperformance ” of the rest of the affected units. EPA’ s modeling in this analysis required the average emission rate across all affected units at a given facility to meet the standard. This averaging flexibility has the potential to offer further cost savings beyond this analysis if particul ar units find ways to achieve superior pollution control beyond EPA’ s assumptions of retrofit technology performance at the modeled costs (which could then reduce the need to control other units at the same facility). Additionally, EPA has chosen to expres s most of the control requirements here as engineering performance standards (e.g., lbs/MMBtu of heat input), which provide power plant operators goals to meet as they see fit in choosing coals with various pollutant concentrations and pollutant control te chnologies that they adopt to meet the requirements. Historically, such an approach encourages industry to engineer cheaper solutions over time to achieve the pollution controls requirements. EPA’ s IPM modeling is based on retrofit technology cost assumptions which reflect the best available information on current and foreseeable market conditions for pollution control deployment. In the current economic environment, EPA does not anticipate (and thus this analysis does not reflect) significant near-term price increases in retrofit pollution control

12 Investor - owned utilities alone accounted for nearly $300 billion in revenue in 2008 (EIA). 13 See “Analysis of S. 485, the Clear Skies Act of 2003, and S. 843, the Clean Air Planning Act of 2003.” Energy Information Administration. September, 2003. EIA modeling indicated that the Clear Skies Act of 2003 (a nationwide cap and trade program for SO 2, NO X, and mercury), demand response could lower present value costs by as much as 47% below what it would have been without an emission constraint similar to the Transport Rule.***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 29 supply chains in response to MATS . To the extent that such conditions may develop during the sector’ s installation of pollution control technologies under the final rule , this analysis may understate the c ost of compliance. **3.15 Significant Energy Impact**  MATS would ha ve a significant impact according to E.O. 13211: Actions that Significantly Affect Energy Supply, Distribution, or Use . Under the provisions of this rule, EPA projects that approximately 5.5 GW of c oal -fired generation (less than 2 percent of all coal -fired capacity and 0.5% of total generation capacity in 2015) may be removed from operation by 2015 . These units are predominantly small er and less frequently- used generating units dispersed throughout the area affected by the rule. If current forecasts of either natural gas prices or electricity demand were revised in the future to be higher, that would create a greater incentive to keep these units operational. EPA also projects fuel price increases re sulting from MATS . Average retail electricity price are shown to increase in the contiguous U.S. by 3 percent i n 2015 . This is generally less of an increase than often occurs with fluctuating fuel prices and other market factors. Related to this, the avera ge delivered coal price increase s by less than 2 percent in 2015 as a result of shifts within and across coal types . As discussed above in section 8.10, EPA also projects that electric power sector- delivered natural gas prices will increase by about 0.7 % p ercent over the 2015 - 2030 timeframe and that natural gas use for electricity generation will increase by less than 2 00 billion cubic feet ( BCF ) in 2015 . The se impacts are well within the range of price variability that is regularly experienced in natural gas markets . Finally, the EPA projects coal production for use by the power sector, a large component of total coal production, will decrease by 11 million tons in 2015 from base case levels , which is about 1 percent of total coal produced for t he electric power sector in that year. The EPA does not believe that this rule will have any other impacts (e.g., on oil markets) that exceed the significance criteria. **3.16 References**  EIA Annual Coal Report 2008. DOE/EIA- 0584 (2008). Available at: http://www.e ia.doe.gov/cneaf/coal/page/acr/acr_sum.html EIA Annual Energy Outlook 2003. DOE/EIA- 0383 (2003). Available at: http://www.eia.doe.gov/oiaf/archive/aeo03/index.html EIA Electric Power Annual 2008. DOE/EIA- 0348 (2008). Available at: http://www.eia.doe.gov/cn eaf/electricity/epa/epa_sum.htm***E.O. 12866 Review– Draft– Do Not Cite, Quote, Or Release During Review ***

3 - 30 EIA Electric Power Monthly March 2010 with Data for December 2009. DOE/EIA- 0226 (2010/03). Available at: http://www.eia.doe.gov/cneaf/electricity/epm/epm_sum.html Freme, Fred. 2009. U.S. Coal Supply and Demand: 2008 Review. EIA. Available at: http://www.eia.doe.gov/cneaf/coal/page/special/tbl1.html Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “ On the Accuracy of Regulatory Cost Estimates. ” Journal of Policy Analysis and Management 19(2):297 - 322. Manson, Nelson, and Neumann. 2002. “Assessing the Impact of Progress and Learning Curves on Clean Air Act Compliance Costs. ” Industrial Economics Incorporated.