3 - 1 **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 United States for the entire power system. Documentatio n 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 Mercury and Air Toxics Standards (MATS) Rule ” (herea fter 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 recently 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 and 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/airmarkt/progsregs/epa - ipm/index.html). EPA also analyzed several multi - pollutant reduction scenarios in July 2009 at the request of Senator Tom Car per to illustrate the costs and benefits of multiple levels of SO 2 and NO X control in the power sector. 2 Additionally, 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 prea mble section III.A ). Note that this approach does not significantly affect SO 2 and NO X projections underlying the cost and benefit results presented in this RIA3 - 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 th e 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 the cumulative regulatory requirements for air emissions in tot ality, 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 u pdates 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 Information 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 emiss ions 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- specific rules . The IPM 4.10 S upplemental 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 units that combust coal or oil for the purpose of generating electricity for sale and distribution through the national electric grid to the public.

4 The NSR settlements include agreements between EPA and Southern Indiana Gas and Electric Com pany (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 Po wer 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, an d other emissions controls for the fleets of these major Eastern companies by specified dates. Many of the pollution 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 Virgini a, and Wisconsin the cover certain emissions from the power sector.3 - 3

Coal -fired electric utility steam generating units include electric utility steam generating units that burn coal, coal refuse, or a synt hetic 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 establishes standards for HAP emissio ns 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 fluoride (HF), hydrogen chlorid e (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) tha t 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 600 power plants potentially c overed 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 covered 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 .3 -4

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

**Utility Steam Generating Units Subcategory**  **Filterable Particulate Matter**  **Hydrogen C hloride**  **Mercury** Existing coal -fired unit not low Btu virgin coal 0.030 lb/MMBtu (0.30 lb/MWh) 0.0020 lb/MMBtu

(0.020 lb/MWh) 1.2 lb/TBtu (0.020 lb/GWh) Existing coal -fired unit low Btu virgin coal 0.030 lb/MMBtu (0.30 lb/MWh) 0.0020 lb/MMBtu

(0.020 lb/MWh) 11.0 lb/TBtu (0.20 lb/GWh) 4.0 lb/TBtu a (0.040 lb/GWh a ) Existing - IGCC 0.040 lb/MMBtu (0. 4 0 lb/MWh) 0.00050 lb/MMBtu

(0. 00 5 0 lb/MWh) 2. 5 lb/TBtu (0. 0 3 0 lb/GWh) Existing – Solid oil - derived 0.0080 lb/MMBtu (0.090 lb/MWh) 0.0050 lb/MMBtu

(0.080 lb/MWh) 0.20 lb/TBtu (0.0020 lb/GWh) New coal -fired unit not low Btu virgin coal 0.0070 lb/MWh 0.40 lb/GWh 0.00020 lb/GWh New coal -fired unit low Btu virgin coal 0.0070 lb/MWh 0.40 lb/GWh 0.040 lb/GWh
New – IGCC 0.070 lb/MWh b 0.090 lb/MWh c 0. 00 2 0 lb/MWh d 0.0030 lb/GWh e New – Solid oil - derived 0.020 lb/MWh 0.0004 0 lb/MWh 0.0020 lb/GWh 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 - hour electric output (gross) lb/GWh = pounds pollutant per gigawatt - hour electric output (gross) a Beyond -the -floor limit as discussed elsewhere b Duct burners on syngas; based on permit levels in comments received c Duct burners on natural gas; based on permit levels in comments received d Based on best - performing similar source e Based on permit levels in comments received3 - 5

**Table 3**  **- 2**  **. Emissions Limitations for Liquid**  **Oil -**  **Fired Electric Utility Steam Generating Units** Subcategory Filterable PM Hydrogen Chloride Hydrogen Fluoride Existing – Liquid oil continental 0.0 3 0 lb/MMBtu (0. 3 0 lb/MWh) 0.00 2 0 lb/MMBtu

(0.0 1 0 lb/MWh) 0.0004 0 lb/MMBtu (0.004 0 lb/MWh) Existing – Liquid oil non - continental 0.0 3 0 lb/MMBtu (0. 3 0 lb/MWh) 0.000 2 0 lb/MMBtu

(0.00 2 0 lb/MWh) 0.000060 lb/MMBtu ( 0.00050 lb/MWh) New – Liquid oil – continental 0.070 lb/MWh 0.0004 0 lb/MWh 0.0004 0 lb/MWh New – Liquid oil – non - continental 0.20 lb/MWh 0.0020 lb/MWh 0.00 05 0 lb/MWh 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 -fi red 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 pot entially 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. Mercury emissions are modeled as a function of mer cury content of the fuel type(s) consumed at each plant in concert with that plant’s pollutant control configuration. 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 mu st emit at or below the final mercury and HCl emission rate standards in order to operate from 2015 onwards. EGUs 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 th e 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 unit’s characteristics.

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 summarized in section 3 - XX.3 - 6

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 meeting 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 us ing 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. 7 The base case includes CSAPR as well as other existing state and federal programs for air emissions control from electric generating units, with the exception of state mercury rules.

7 Note that projected electricity demand in AEO 2010 is about 2% higher than the AEO 2011 projection in 2015. Since this RIA assumes higher electricity demand in 2015 than is shown in the latest AEO projection, it is possible that the model may be taking compliance actions to meet incremental electricity demand that may not actually occur, and projected compliance costs may therefore be somewhat overstated in this analysis.**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 Affected Units, by Facility, Size and Fuel Source** 

**2012** Source/Notes: National Electric Energy Data System (NEEDS 4.10) (EPA, December 2010). fo ssil facilities in the NEEDS 4.10 IPM frame this includes committed new builds and committed retirements. s ome facilities may be obscured.

3 - 7

**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 ssil 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,

**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

**Geographic Distribution of 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,3 - 8

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 mode l 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 gener ation 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 repres ents 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 b asis for the capital charge rates and the discount rate used for power sector modeling 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 assumpti ons and inputs to the model (http://www.epa.gov/airmarkets/progsregs/epa - ipm). Updates 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 reductio ns from the power sector . Since the technologies available to meet the emission reduction 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 pollut ants of concern under MATS from the power sector are mercury, acid gases such as hydrogen 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 mercury contents in all coals and the removal efficiencies of relevant emission control technologies (e.g., ACI). EPA also models emissions and the pollution control technolog ies associated with HCl (as a surrogate for acid gas emissions). Like SO 2, HCl is removed 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 ref lect the chlorine content of coals, which corresponds with the supply region, coal grade, and sulfur, mercury, and ash content of each coal type. This information is critical for accurately3 - 9

projecting future HCl emissions, and for understanding how the pow er 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 highly 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 po licies to reduce emissions. For more detail 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 reductions of 75 percent in 2015, and PM 2.5 emissions reductions of 19 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 ). 8 Mercury emission projections in EPA’s base case are affected by the incidental capture in other pollution control technologies (such as FG D 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 Covered EGUs 3.3 1.7 26.6 45.3 270 1,906 MATS All EGUs 2.1 1.9 8.8 9.0 22 7 2,215 Covered EGUs 1.9 1.7 6.6 5.5 218 1,88 2 Source: Integrated Planning Model run by EPA, 2011

8 The CO 2 emissions reported from IPM account for the direct CO 2 emissi ons from fuel combustion and CO 2 created from chemical reactions in pollution controls to reduced sulfur.3 - 10

**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)3 - 11

**Figure 3**  **- 4**  **. Mercury Emissions from the Power Sector in 2015 with and without**  **MATS** Source: 2015 emissions include c oal steam (including IGCC and petroleum coke) units >25 MW from IPM v4.10_MATS base case and control case projections (EPA, 2011)3 - 12

**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) **3.3 Projected**  **Com pliance**  **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 emi ssion standards. In simple terms, these costs are the resource costs of what the power industry will directly expend to comply with EPA’s requirements. EPA projects that the annual incremental compliance cost of MATS is $ 9.4 billion in 2015 ($2007) . The an nual 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, investments in new generat ing sources, shifts between or amongst various fuels, and other actions associated with compliance. This projected cost does not include the compliance calculated outside of IPM modeling, namely the compliance costs for oil -fired EGUs, and monitoring, reporting, and record - keeping costs. See section 3. 14 for further details on these costs. EPA believes that the3 - 13

cost assumptions used for the final rule reflect, as closely as possible, the best information available to the Agency today. **Table 3**  **- 5**  **. Annualized Compliance Cost for MATS Requirements on Coal**  **- fired Generation**  **2015 2020**  **2030** Annualized Compliance Cost (billions of 2007$) $ 9.4 $8. 6 $7. 4 Source: Integrated Planning Model run by EPA, 2011 . EPA’ s projection of $ 9.4 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 described 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 complianc e options . These actions include improved operation of existing controls, additional pollution control installations, coal switching (including blending of coals), and generation shifts towards more efficient units and loweremitting generation technologie s (e.g., some reduction of coal -fired generation 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 and 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 m eeting the demand for electricity, EPA has conducted 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 MATS Rule ” which i s available in the docket. The requirements under 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 relati vely small shift from coal -fired generation to 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 cont rol devices, such as FGD, ACI, and fabric filters; a smaller share of emission3 - 14

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, respectively.

**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 below may not reflect the increments displayed in t his figure. 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 20 GW of dry FGD ( dry scrubbers), 44 GW of DSI, 99 GW of additional ACI, 102 GW of additional fabric filters, 63 GW of scrubber upgrades, and 34 GW of ESP upgrades. Furthermore, the final rul e results in a 3 GW decrease in retrofit wet FGD capacity relati ve to the base. 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 are projected to operate under MATS but not under the base case. With respect to the increase in operating ACI, some3 - 15

of this increase represents existing ACI capacity on units built before 2008. EPA’s modeling does not reflect the presence o f 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 controls. 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 p revious paragraph). **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 fir ed 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 3.6 % Natural Gas 921 710 73 1 22 3.1% Nuclear 799 828 831 3 0.4% Hydroelectric 273 286 288 2 0. 8 % Non - hydro Renewables 144 252 250 - 1 - 0. 6 % 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 .3 - 16

**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 4.7 GW of coal -fired capacity is projecte d 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., efficiency and age) . Uneconomic units, for t he 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**  **Covered 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 5 2 1 29 54 % Operational 43 3 22 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 important limitation to recognize in these results,

1.98 1.96 2.00 1.98 2.03 2.02 0.828 0.831 0.837 0.840 0.817 0.819 0.71 0.73 0.85 0.87 1.18 1.19 0.286 0.288 0.286 0.286 0.286 0.286 0.252 0.250 0.289 0.289 0.333 0.333



### 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Bas e MA TS Bas e MA TS Bas e MA TS**M i** **l l** **i o** **n G** **W h**  

## Gener**at ion**  **Mix** Ot he r

Oil Non - Hyd ro Re ne wa bl es Hydr o Na tur al Gas Nuc lear Coal
2015 2020 20303 - 17

which should be used to consider “potential” closures using these numbers. To the extent that plants operate in response to local factors unaccounted for in EPA’s modeling, these results could be overstating the capacity rem oved 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 generation 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 addresses reliabl e 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 i n, 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 f requency 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 the IPM Projections for the MATS Rule . 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 Capacity by 2015**  **(GW) 2010**  **Base Case MATS**  Pulverized Coal 317 310 30 5 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 1,02 1 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 2015.3 - 18

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 withdrawals 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 suggests that projected economic withdrawals from service unde r 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 characterized by substantial excess capacity. T he weighted a verage 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 capac ity 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 potential 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 region. 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 concerns. **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 ne w 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 electricity demand expectations have trended downwards in r ecent forecasts, reducing the need for new capacity in the 2015 timeframe ( see Chapter 2 for more discussion on future electricity demand).3 - 19

**Table 3**  **- 9**  **. Total Generation Capacity by 2030 (GW)**  **2010 Base Case**  **MATS Change**  Pulverized Coal 317 308 304 - 3.9 Natural Gas Combined Cycle 201 275 27 8 2.9 Other Oil/Gas 253 235 235 0. 6 Non - Hydro Renewables 31 79 79 0. 1 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 - 0.1 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 Secto**  **r** Coal production for electricity generation under MATS is expected to increase from 2009 levels and decline modestly relative to the base case without the rule. The reductions in emissions from the power sector will be met through the installation and ope ration 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 bituminous 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 in 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 po llution 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, which can achieve low emissions when combined with post - combustion emissions controls. This explains the increase from the base case in coal supplied from the Interior region, which is located in relatively close proximity to many coal -fired generators subject to MATS . This continues a t rend of increased Interior supply (due to abundant Illinois Basin reserves that are relatively inexpensive to mine) and decrease d Central Appalachian supply which is forecasted to occur in the ba se case from historic levels . The decline in Appalachia is a result of an increase in the relative cost of Central Appalachian3 - 20

extraction due both to rising mining cost (e.g., in 2010 major producers reported mining cost increases up to 15% with this trend continuing into 2011) and shrinking economically recoverabl e capacity . Growing international demand for Appalachian thermal coal is also contributing to its rising cost. The increase in lignite use occurs at units blending subbituminous and lignite coals, and reflects a small shift in blended balance towards a g reater use of lignite . **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 246 184 172 - 6 % Interior 129 216 236 9% West 553 554 537 - 3% Waste Coal 14 14 13 - 5% Imports 30 30 0% Total 942 998 98 9 - 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 (p osted 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 11,24 8 - 0.6% Subbituminous 7,736 7,55 4 - 2% Lignite 849 895 5% Total 19,900 19,69 8 - 1% Source: Integrated Planning Model run by EPA, 2011 .3 - 21

**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 p rice of 3 .1 % 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 - 12 (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).3 - 22

**Table 3**  **- 12**  **. Projected**  **Contiguous U.S. and Regional Retail Electricity 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 4.5% 2.8% 1.0% ERCOT 8.9 8.8 11.3 9.2 8.8 11.3 3.3% 0.6% - 0.2% MAAC 9.5 10.4 12.7 9.8 10.4 12.7 2.8% 0.4% - 0.2% MAIN 8.1 8.4 9.7 8.3 8.6 9.7 2.8% 2.2% 0.2% MAPP 8.0 7.9 8.5 8.5 8.3 8.8 5.3% 5.6% 3.4% NY 13.8 13.4 16.6 14.1 13.5 16.6 2.2% 0.7% - 0.1% NE 12.3 11.8 13.8 12.6 11.9 13.8 2.0% 0.8% 0.0% FRCC 10.2 9.7 11.0 10.4 9.8 11.0 2.2% 0.9% 0.4% STV 7.9 7.8 8.4 8.2 8.0 8.6 3.1% 2.4% 1.6% SPP 7.7 7.4 8.1 8.1 7.8 8.4 6.3% 6.1% 4.6% 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 9.7 11.1 2.3% 1.9% 1.1% CALI 13.0 12.5 12.7 13.2 12.6 12.7 1.3% 0.7% 0.0% Contiguous U.S. Average 9.0 9.0 10.2 9.3 9.2 10.3 3.1% 2.0% 0.9%
Source: EPA’ s Retail Electricity Price Model , 2011 .3 - 23

**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 decre ases 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 though 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 transportation costs) after installing additional pollution co ntrol 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 increase in regional costs, resulting over time in a small pric e increase.3 - 24

**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 3.3% 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** 

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

**Base Case MATS**  **Percent Change from Base** Henry Hub 5.29 5.3 2 0. 6 % Delivered - Electric Power 5.56 5.60 0. 6 % Delivered - Residential 10.94 10.9 7 0. 3 % 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 markets 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 significant price volatility from year to year, between seasons within a year, and even sees major price swings during short - lived weather events (su ch 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 experien cing these rapid fluctuations in natural 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 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 time period 2015 to 2030.3 - 25

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. 3 % to 0. 6 % 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. 9 The length of time required to conduct emissions and photoch emical modeling precluded 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 scen ario 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 st ate rules and consent decrees 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 decide 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 capit al cost for new nuclear capacity 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 Washin gton and Colorado; and numerous 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).

9 http://www.epa.gov/airmarkt/progsregs/epa - ipm/index.html3 - 26

**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 emiss ion 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 o n 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, s ulfur content of the 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 installati on and operation of 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. I n 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 calc ulation methodologies. 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 the response to comments document in the docket ) and believes that EPA’s modeling assumptions regarding DSI cost and performance a re reasonable.3 - 27

However, to examine the potential 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 18 GW compared to the primary MATS scenar io, an additional 14 GW of coal capacity chooses to install scrubbers, and an additional 1.3 GW of capacity is projected to withdraw from service. 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 additional 62,000 tons (a 4.7% increase in SO 2 reductions and 4.5% increase in health benefits). **3.14 Additional Compliance Costs Analyzed for Covered Units**  **3.14.1 Compliance Cost for Oil**  **- Fired Units.**  As discussed in section 3.1, EPA used IPM to assess impacts of the MATS emission limitations for coal -fired EGUs but did not use IPM to assess the impacts for oil -fired units. IPM , with its power system and fuel cost assumptions, predicts many dual fuel un its switch to natural gas and oil -fired units will not operate because IPM focuses on least cost operation of the power system. However, despite their apparent economic disadvantages, many of these units have run during many of the past five years (2006 - 20 10). Therefore, EPA conducted a separate analysis to assess the impacts of the MATS emission limitations for oil -fired units. 10 EPA limited this analysis to oil -fired units in the contiguous U.S. Although there are several oil fired units in states and terr itories outside the contiguous U.S., the final MATS emission limitations (shown in Table 3 - 2) for non - continental units will likely allow these units to continue firing residual fuel oil without additional air pollution controls. For the base case, EPA cat egorized units by modeled fuels as listed in NEEDS 4.10 (EPA, December 2010) and assigned each unit the least - cost fuel among its available fuels. For units with natural gas curtailment provisions that might require the firing of residual fuel oil, EPA ass igned a mixed fuel ratio based on each unit’s 2008 - 2010 weighted average natural gas -to fuel oil ratio. For the policy case, EPA assessed three compliance options: (1) switching to natural gas where available, (2) switching to distillate fuel oil, and (3) installing an electrostatic precipitator (ESP) capable of 90% particulate removal efficiency. These compliance options address particulate emissions only. However, there might be additional emission reductions that result from changes to oil -fired units’ generation due to changes in relative generating costs.

10 Additional details and methodology for the analysis are presented in appendix 3A.3 - 28

Between the base case and policy case, 12 units convert from residual fuel oil to distillate fuel oil at a cost of approximately $12 million annually (2007$) to meet the MATS emission limitations for oil -fired units. An additional 11 units, eight of which are subject to natural gas curtailment, that do not have existing ESP particulate pollution controls install an ESP at a cost of approximately $44 million annually (2007$) to achieve the MATS emission limitations for oil -fired units (see Table 3 - 15). EPA believes the emission impacts from these potential actions will be relatively small when compared to the full impacts of the MATS emission limitations because particulate emissions from oil -fired units are a small fraction of the total particulate emissions from EGUs. **Table 3 -**  **15. Cost Impacts of Compliance Actions for Oil**  **- Fired Units**  **Compliance option Number of units affected**  **Capacity of units affected Annual cost (2007$)**  Switch to distillate fuel oil 12 2,675 MW $12 million Install ESP for residual fuel oil 11 4,015 MW $44 million **Total 23**  **6,690 MW $56 million**  **3.14.2 Monitoring, Reporting and Record** **- keeping Costs**  The annual monitoring, reporting, and record - keeping burden for this collection (averaged over the first 3 years after the effective date of the standards) is estimated to be $ 15 8 million. This includes 698,907 labor hours per year at a total labor cost of $4 9 million per year, and total non - labor capital costs of $10 8 million per ye ar. This estimate includes initial and annual performance tests, semiannual excess emission reports, developing a monitoring plan, notifications, and record - keeping. Initial capital expenses to purchase monitoring equipment for affected units are estimated at a cost of $ 231 million. This includes 504,629 labor hours at a total labor cost of $ 35 million for planning, selection, purchase, installation, configuration, and certification of the new systems and total non - labor capital costs of $196 million. All b urden estimates are in 2007 dollars and represent the most cost effective monitoring approach for affected facilities. S ee Secti on 7.3, Paperwork Reduction Act. **3.14.3 Total Costs Projected for Covered Units under MATS**  EPA used IPM to analyze the compliance cost, and economic and energy impacts of the MATS rule. IPM estimated the costs for coal -fired electric utility steam generating units that burn coal, coal refuse, or solid - oil derived fuel. EPA did not use IPM , howe ver, estimate compliance costs3 - 29

for most oil/gas steam boilers because IPM projection shows least - cost dispatch in an environment where oil/gas -fired units are primarily selecting natural gas on an economic basis. In the separate analysis summarized above, EPA estimates compliance costs for oil -fired EGUs in a s cenario in which these units continue to burn oil as historically observed and thus take compl iance measures to remain on oil. This is a reasonable estimate of compliance costs for these units, but does not represent a re - balancing of electricity dispatch where these units combust oil rather than natural gas. Therefore, the summation of IPM - projected compliance costs for least - cost dispatch with the oil -fired compliance costs and the monitoring, rep orting, and record - keeping costs is a reasonable approximation of total compliance costs, but does not represent projected compliance costs under an economically efficient dispatch (see Table 3 - 1 6 ). **Table 3**  **- 1**  **6 .**  **Total Costs Projected for Covered Units under MATS, 2015 (billions of**  **200 7**  **$ )**  2015 IPM Projection $ 9.4 Monitoring/Reporting/Record - keeping $0.1 58 Oil - Fired Fleet $0.0 56 Total $9. 6 **3.15 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 b est available information from engineering studies of air pollution controls to support a reasonable modeling framework for analyzing the cost, emission changes, and other impacts of regulatory actions . The IPM - projected annualized cost estimates of privat e 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, EPA uses a conventional and widely- accepted approach that ap plie 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 earlier are EPA’ s best estimate of the direct private compliance costs of MATS .3 - 30

The annualized cost of the final rule , as quantified here, is EPA’ s best assessment of th e cost of implementing the r ule. These costs are generated from rigorous economic modeling 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. Cost estimates for MATS are based on results from ICF ’ s Integrated Planning Model. The model minimizes the costs of producing electricity (includ ing 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 assumes “ perfect foresight” of mar ket 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 case modeling, EPA exogenously dete rmines 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 assigning these controls to EGUs in poli cy case modeling is based on historic PM emission rates and reported control efficiencies, and is explained in the IPM 4.10 Supplemental Documentation for MATS . 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 sector may innovate to achieve the required emission reductions under MATS, which would potentially reduce overall compliance costs. Where possible, EPA des igns 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 gene rally lead to lower costs associated with the rule than modeled in this analysis. For example, unit operators may find opportunities to improve or upgrade existing pollution control equipment without requiring as many new retrofit devices (i.e., meeting th e PM standard with an existing ESP without requiring installation of a new fabric filter). With that in 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 compliance, the power sector is free to adopt alternative technologies and behaviors to achieve3 - 31

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 ro ughly $2.7 to $6.2 billion 11 in 1989 were an overestimate of the costs of the program in part because of 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 program 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. 12 To date, and including this analysis, EPA has not incorporated a similar approach into IPM modeling of EGU compliance with environmental constraints. As a result, this anal ysis may overstate costs where such cost savings from as -yet untapped improvements to pollution control technologies may occur in the future. Considering the broad and complex suite of generating technologies, fuels, and pollution control strategies availa ble 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 addit ional methodologies in the future which may inform the amount by which projected compliance costs could 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 electr icity and encourage them to use substitutes. 13 Th ose response s would lessen the demand for electricity, resulting in electricity price increases slightly lower than IPM predicts, which would also reduce generation and emissions. D emand response

11 2010 Phase II cost estimate in $1995. 12 See regulatory impact analysis for the Tier 2 Regulations for passenger vehicles (1999) and Heavy - Duty Diesel Vehicle Rules (2000). 13 The degree of substitution/curtailment depends on the costs and performance of t he goods that substitute for more energy consuming goods, which is reflected in the demand elasticity.3 - 32

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 14 are not insignificant. EIA analysis examining multi - pollutant legislation in 2003 indicate d that the annualized costs of MATS may be overstated su bstantially by not considering demand response, depending on the magnitude and coverage of the price increases. 15 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 - effecti vely 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. Thi s averaging flexibility has the potential to offer further cost savings beyond this analysis if particular units find ways to achieve superior pollution control beyond EPA’ s assumptions of retrofit technology performance at the modeled costs (which could t hen reduce the need to control other units at the same facility). Additionally, EPA has chosen to express 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 technologies that they adopt to meet the requirements. Historically, such an approach encourages industry to engineer cheaper solutions over time to achie ve 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 supply chains in response to MATS . To the extent that such conditions may develop during the

14 Investor - owned utilities alone accounted for nearly $300 billion in revenue in 2008 (EIA). 15 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.3 - 33

sector’ s install ation of pollution control technologies under the final rule , this analysis may understate the cost of compliance. **3.16 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 4.7 GW of coal -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 woul d create a greater incentive to keep these units operational. EPA also projects fuel price increases resulting from MATS . Average retail electricity price are shown to increase in the contiguous U.S. by 3 .1 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 average 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. 6 % percent 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 10 million tons in 2015 from base case levels, which is about 1 percent of total coal produced for the 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 significan ce criteria. **3.17 References**  EIA Annual Coal Report 2008. DOE/EIA- 0584 (2008). Available at: http://www.eia.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/ind ex.html EIA Electric Power Annual 2008. DOE/EIA- 0348 (2008). Available at: http://www.eia.doe.gov/cneaf/electricity/epa/epa_sum.htm 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.html3 - 34

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 Regul atory 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.3A- 1 **APPENDIX 3A COMPLIANCE COSTS FOR**  **OIL -**  **FIRED ELECTRIC GENER ATING UNITS**  This appendix highlights the supplemental oil -fired electric generating unit (EGU) compliance cost analysis performed for the Mercury and Air Toxics Standards (MATS). EPA used the Integrated Planning Model (IPM) to assess the cost, economic, and energy imp acts of the MATS emission limitations on coal -fired EGUs in the contiguous U.S., but did not use IPM to assess the compliance costs for oil -fired EGUs because IPM focuses on the least cost operation of the power system and, therefore, predicts the oil -fire d units will not operate. These oil -fired units, however, do not operate on a purely economic basis. Some oil -fired units may operate as “must run”, “black start”, or “spinning reserve”. In addition, some dual fuel fired units which IPM predicts will fire natural gas may be required to fire fuel oil when subject to mandatory curtailment of natural gas supplies. When practicable, this supplemental analysis for oil -fired EGUs was based on the data and assumptions used in IPM. Documentation for IPM can be fou nd at http://www.epa.gov/airmarkets/progsregs/epa - ipm. **3A.1 Methodology and Assumptions**  **3A.1.1 Base Case**  EPA developed the base case for oil -fired units listed in the National Electric Energy Data System (hereafter, NEEDS) (EPA, 2010a). NEEDS lists 302 “oil/gas steam” units greater than 25 MW for which distillate fuel oil and/or residual fuel oil are among the modeled fuels (see Table 3A- 1). 16 For each of these units, EPA projected 2015 heat input and apportioned the heat input among the NEEDS modeled fue ls. EPA used each unit’s average annual heat input from 2006 - 2010 17 as a proxy for 2015 heat input. For units not subject to mandatory natural gas curtailment, EPA assumed the unit fired the least cost fuel available based on regional IPM fuel cost projecti ons for 2015. For units that may be required to fire fuel oil due to mandatory natural gas curtailment, EPA apportioned the heat input based on the unit’s weighted average natural gas and fuel oil apportionment from 2008 - 2010. 18 EPA used the three most rece nt years because, as a percentage of total heat input, fuel oil heat input has fallen steadily since 2007 (see Figure 3A- 1). With increased availability of natural gas in the New York region from new

16 One unit, Charles Poletti unit 001 (ORIS 2491), was removed because the unit retired in 2010 (EPA, 2011). 17 Designated representatives for each of the oil - fired units included in this analysis certify and report hourly heat input and emission data to EPA under 40CFR Part 75. 18 The units subject to mandatory natural gas curtailment report fuel - apportioned heat input to EPA under 40CFR Part 75 (Appendix D). EPA categorized “diesel” as distillate fuel oil and “oil” and “other oil” as residual fuel oil.3A- 2

gas supplies and new gas pipelines (FERC, 2011), it is l ikely this trend will continue even in the absence of the MATS. Therefore, using a longer historical period might significantly overestimate the proportion of heat input derived from fuel oil for these units. **Table 3A -**  **1. Oil**  **- fired EGUs by Fuel Type** 

**NEEDS m odeled fuel**  **Number of units Capacity (MW)**  Distillate fuel oil 10 814 Distillate fuel oil, natural gas 99 19,822 Residual fuel oil 17 5,867 Residual fuel oil, distillate fuel oil 15 1,187 Residual fuel oil, natural gas 149 39,913 Residual fuel oil, distillate fuel oil, natural gas 12 3,706 Source: EPA. 2010. National Electricity Energy Data System (NEEDS 4.10). Available at: http://www.epa.gov/airmarkets/progsregs/epa - ipm/toxics.html.

**Figure 0** **A -**  **1 .**  **2006 -**  **2010 Heat Input Apportioned by Fuel for Oil -**  **Fired Units Subject to**

**Mandatory Natural Gas Curtailment** Source/Notes: EPA. 2011. Data and Maps. Available at: http://epa.gov/camddataandmaps/ Power companies are responding to fuel prices, natural gas suppl ies, and other market factors by replacing some oil - gas steam units with new combined cycle plants (Neville, J. 2011). EPA did not, however, factor in the effect of expanded availability of natural gas on the Oil -fired heat input (% of total heat input)

0% 5% 10% 15% 20% 25% 30% 35%

0 50 100 150 200 250

2006 2007 2008 2009 2010TrillionBtu Diesel Oil Gas3A- 3

utilization of these oil -fired units. As a resu lt, this analysis likely overestimates the impact of the MATS emission limitations on oil -fired units. In the base case, natural gas is the least cost fuel for the majority of units (see Table 3A 2). However, 41 units are expected to continue burning some amount of residual fuel oil because the units are subject to mandatory natural gas curtailment or may not have access to natural gas supplies. 19 Of these 41 units, 14 have existing electrostatic precipitator (ESP) particulate pollution controls. **Table 3A -**  **2. Least Cost NEEDS Modeled Fuels for Oil**  **- fired EGUs** 

**NEEDS modeled fuel Number of units**  **Capacity (MW)** Distillate fuel oil 19 1,228 Residual fuel oil 23 6,640 Natural gas 242 57,232 Natural gas with mandatory curtailment 18 6,208 **3A.1.2 Policy Case**  For the policy case, EPA considered three actions to comply with the MATS emission limitations: (1) switching to natural gas where available, (2) switching to distillate fuel oil, and (3) ESP particulate pollution control capable of 90% particulate removal efficiency . EPA modeled the cost of actions 2 and 3 for each unit in the base case. EPA did not model the cost of converting to natural gas because, for units with natural gas as a NEEDS modeled fuel, it was the least cost fuel and therefore the base case fuel for the unit. The cost of switching a unit’s heat input to distillate fuel oil was based on the cost of converting operations, including tank, line, and pump cleaning and burner atomizer assembly replacement, and the unit’s 2015 projected heat input from residual fuel oil multiplied by the cost difference between residual fuel oil and distillate fuel oil in the region where the unit is located. Conversion costs were annualized using the methodology described in the IPM documentation (EPA, 2010b). The cost of installing a flat plate -type ESP on oil -fired model units of various sizes was calculated using the methodology outlined in EPA’s Cost Manual (EPA, 2002) and adjusted to 2010 values using the Chemical Engineering Plant Cost Index (CEPCI). EPA devel oped non - linear
19 To ensure the analysis was not likely to underestimate compliance costs, EPA assumed units that do not include natural gas as a NEEDS modeled fuel do not have access to a natural gas pipeline. The cost of obtaining pipeline access for these units was assumed to be uneconomical and was not modeled in the analysis.3A-4

regression power functions similar to those used for costing air pollution controls in IPM. The cost functions are shown in equations (3A.1) - (3A.3). Capital costs = 243,494.4 X (MW capacity) 0.7800 (3A.1) Annual fixed costs = 13,883.4 X (M W capacity) 0.7294 (3A.2) Annual variable costs = 8,108.6 X (MWh generation) 0.8632 (3A.3) Capital costs were annualized using the capital cost recovery factor used in the IPM documentation (EPA, 2010b). Annual variable costs were calculated using the predi cted 2015 generation from residual fuel oil based on the unit’s base case 2015 residual fuel oil heat input and the unit’s heat rate listed in NEEDS (EPA, 2010a). **3A.1. 3**  **Cost Sensitivities Related to Mandatory Natural Gas Curtailment** There are 18 dual fue l fired units (i.e., units capable of firing both gas and oil) that are subject to mandatory natural gas curtailment. Of these units, six have existing ESP particulate pollution controls installed. For the remaining 12 units, nine fired natural gas for mor e than 90 percent of their total heat input (see Table 3A- 3). Because the MATS emission limits do not apply to units that fire coal or oil for less than 10 percent of total heat input averaged over three years or 15 percent in a single year, EPA analyzed h istorical oil -fired heat input between 2006 and 2010 at these units and found that four dual fuel fired units subject to mandatory natural gas curtailment did not exceed 15 percent in any single year and averaged less than 10 percent across all three year periods between 2006 and 2010. EPA did not include the cost of control on these units in the summary results. If these four units were to install ESPs, however, the annual compliance cost of the MATS emission limits would increase $13 million (2007$). As n oted in 3A.1.1, natural gas supplies to the region are increasing and operating data for dual fuel fired units subject to mandatory natural gas curtailment indicate that their proportion of heat input from residual oil is declining. There are four units in addition to those described in the paragraph above that exceeded 15 percent oil -fired heat input in 2006 and/or 2007, but between 2008 and 2010 did not exceed 15 percent oil -fired heat input in a single year and averaged below 10 percent across all three years. These units were assigned ESP particulate pollution controls in this analysis. However, if these four dual fuel fired units do not install ESPs, the annual compliance cost of the MATS emission limits would decline $16 million (2007$).3A- 5

**Table 3A -**  **3. P**  **ercentage of Total Heat Input Derived from Oil for Oil -**  **Fired Units Subject to**

**Mandatory Natural Gas Curtailment (2008 -**  **2010) Percentage**  **Number of units** < 1.0% 4 1.0% to 4.9% 1 5.0% to 9.9% 4 10.0% to 15.0% 3 **3A.2 Results** For the purpose of estimating the impacts of the MATS emission limitations for oil -fired units, EPA had to make assumptions about the compliance actions oil -fired units will take. Table 3A-4 lists those assumptions based on differences between the base and policy cases. EPA assumed that the least cost compliance option for 12 residual fuel oil -fired units would be converting to distillate fuel oil at an annual cost of approximately $12 million (2007$). An additional 11 units would likely continue to burn re sidual fuel oil following the installation of an ESP at a cost of approximately $44 million annually (2007$).3A- 6

**Table 3A -** **4. Costs to Achieve the MATS Emission Limitations for Oil**  **- Fired Units** 

**Unit Compliance action**  **Annual cost (2007$)** Cleary Flood, Unit 8 Distillate fuel oil $ 308,000 Jefferies, Unit 1 Distillate fuel oil $ 642 ,000 Jefferies, Unit 2 Distillate fuel oil $ 673 ,000 McManus, Unit 1 Distillate fuel oil $ 39 1,000 McManus, Unit 2 Distillate fuel oil $ 51 2,000 Montville Station, Unit 6 Distillate fuel oil $ 3 , 968, 0 00 Possum Point, Unit 5 Distillate fuel oil $ 119 ,000 Schuylkill Generating Station, Unit 1 Distillate fuel oil $ 2 , 11 3,000 Vienna Operations, Unit 8 Distillate fuel oil $ 1 , 741 ,000 William F Wyman, Unit 1 Distillate fuel oil $ 78 3,000 William F Wyman, Unit 2 Distillate fuel oil $ 646,000 Yorktown, Unit 3 Distillate fuel oil $ 119 ,000 Astoria Generating Station, Unit 30 ESP $ 4,214,000 Astoria Generating Station, Unit 40 ESP $ 4,132,000 Astoria Generating Station, Unit 50 ESP $ 4,202,000 B L England, Unit 3 ESP $ 2,155,000 East River, Unit 60 ESP $ 1,844,000 East River, Unit 70 ESP $ 2,336,000 Herbert A Wagner, Unit 4 ESP $ 4,352,000 Middletown, Unit 4 ESP $ 4,391,000 Ravenswood, Unit 10 ESP $ 3,904,000 Ravenswood, Unit 20 ESP $ 3,898,000 Ravenswood, Unit 30 ESP $ 8,322,0003A- 7

**3A.3 References**  EPA. 2002. EPA Air Pollution Control Cost Manual. Sixth Edition. EPA/452/B - 02 - 001. Available at: http://www.epa.gov/ttn/catc/dir1/c_allchs.pdf EPA. 2010a. National Electric Energy Data System (NEEDS 4.10). Available at: http://www.epa.gov/airmarkets/progsregs/epa - ipm/toxics.html EPA. 2010b. Documentation for EPA Base Case v. 4.10. Chapter 8: Financial Assumptions. Available at: http://www.epa.gov/airmarkets/progsregs/epa ipm/docs/v410/Chapter8.pdf EPA. 2011. Data and Maps. Available at: http://epa.gov/camddataandmaps/ FERC. 2011. Major Pipeline Projects Pending (Onshore). Available at: http://www.ferc.gov/industries/gas/indus - act/pipelines/pending - projects.asp Neville, J. 2011. “Top Plant: Astoria II Combined Cycle Plant, Queens, New York,” Power Magazine . September.