§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

1 This analysis is limited to potentially affected facilities in primary SIC codes 26, 28, 29, and 33.

B3A5-1 

# Appendix 5 to Chapter B3: Estimating Capital Outlays for Section 316(b) Phase III Manufacturing Sectors Discounted Cash Flow Analyses

I NTRODUCTION The analysis of economic impacts to Phase III manufacturing facilities associated with the proposed Section 316(b) Regulation involves calculation of the business value of sample facilities on the basis of a discounted cash flow (DCF) analysis of operating cash flow as reported in the detailed industry questionnaires. 1 Business value is calculated on a pre- and post-compliance basis and the change in this value serves as an important factor in estimating regulatory impacts in terms of potential facility closures. To be accurate in concept, the business value calculation should recognize cash outlays for capital acquisition as a component of cash flow. Howeve r, the Section 316(b) Detailed Industry Questionnaire did not request information from surveyed facilities on thei r cash outlays for capital acquisition. Absent this data, EPA developed an estimate of cash outlays for capital acquisition. This appendix describes the methodology EPA used to derive, for each sample facility, an estimate of cash outlays for capital acquisition. EPA Office of Water (OW) previously identified that the omission of cash outlays for capital acquisition from DCF analyses may lead to overstatement of the bus iness value of sample facilities and, as a consequence, understatement of regulatory impacts in terms of estim ated facility closures (EPA, 2003). In response to this omission, the Office of Management and Budget suggest ed the adoption of depreciation expense as a surrogate for cash outlays for capital replacement and additions. Ho wever, for several reasons EPA believes depreciation is a poor surrogate. First, depreciation is meant to capture the consumption/use of previously acquired assets, not the cost of replacing, or adding to, the existing capital b ase. Therefore, depreciation is fundamentally the wrong concept to use as a surrogate for capital outlays for cap ital replacement and additions. Second, depreciation is estimated based on the historical asset cost, which may unders tate or overstate the real replacement cost of assets. Third, both book and tax depreciation schedules generally understate the assets’ useful life. Thus, reported depreciation will overstate real depreciation value for r ecently acquired assets that are still in the depreciable asset base, and conversely, understate the real depreciation va lue of assets that have expired from the depreciable asset base but still remain in valuable use. Finally, depreciation does not capture the important variations in capital outlays that result from differences in revenue growthand financial performance among firms. Businesses with real growth in revenue will need to expand both thei r fixed and working capital assets to support business growth, and all else being equal, growing businesses will have higher ongoing outlays for fixed and working capital assets. Similarly, the ability of businesses to renew and expand their asset base depends on the financial productivity of the deployed capital as indicated by m easures such as return on assets or return on invested capital. As a result, businesses with “strong” asset produc tivity will attract capital fo r renewal and expansion of their asset base, while businesses with “weak” asset productiv ity will have difficulty attracting the capital for renewal and expansion of the business’ asset base. A ll else being equal, businesses with strong asset productivity 

### A PPENDIX 

### C ONTENTS B3A5-1 Analytic Concepts Underlying Analysis of Capital Outlays . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A5-2 B3A5-2 Specifying Variables for the Analysis . . . B3A5-4 B3A5-3 Selecting the Regression Analysis Dataset B3A5-7 B3A5-4 Specification of Models to be Tested . . . . B3A5-9 B3A5-5 Model Validation . . . . . . . . . . . . . . . . . . B3A5-12 Attachment B3A5.A: Bibliography of Literature Reviewed for this Analysis . . . . . . . . . . . . . B3A5-17 Attachment B3A5.B: Historical Variables Contained in the Value Line Investment Survey Dataset . . . . . . . . . . . . . . B3A5-18§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

2 Since the estimated regression model for the Phase III facilitie s is based on an earlier model developed for the MP&M final regulation, much of the underlying research involved in the analytic development of the model had been previously completed and was not required to be redone. Nonetheless, in order to present a luci d discussion of the analytic concepts underlying the model and t he rationale behind specifying variables for the analysis and specification of the regression model, a complete discussion of how the regres sion model was developed is presented. During the c ourse of the discussion, instances where prior experience gained during estimating the regression model for the MP&M final regulation had a significant influence in the development of the curre nt model are clearly highlighte d.

B3A5-2 will have higher ongoing outlays for capital assets; bus inesses with weak asset productivity will have lower ongoing outlays for capital assets. As an approach to addressing the absence of capital acquisition cash outlay data to support the Phase III DCF analysis, EPA estimated a regression model of capital outlays using reported capital expenditures and relevant explanatory financial and business environment in formation for public-reporting firms in the Phase III manufacturing sectors. The resulting estimated model is u sed to estimate capital outlays for facilities in the Phase III sample dataset. The estimated capital outlay values werethen used in the DCF an alyses to calculate business value of sample facilities and estimate regulatory impacts in terms of facility closures. The approach and regression model described above ar e based largely on the approach and regression model developed in support of the analysis of economic im pacts for the Metal Products and Machinery Regulation (MP&M), which provides a recent example of the need to address the omission of capital acquisition cash outlay data from a DCF analysis. EPA notes that the facilities /industry sectors examined in the Section 316(b) Phase III analysis are similar to those anal yzed in the MP&M analysis: both an alyses estimate impacts to facilities in manufacturing industries only and facilities in SIC 33 are c overed under both regulations. In addition, the Section 316(b) Detailed Industry Questionnaire and the MP&M survey instruments are similar; therefore, similar data are available for Phase III and MP&M survey facilities. As such, EPA relied heavily on prior experience from the MP&M final regulation in estimating the regression model u sed to estimate of capital outlays for facilities in the Phase III sample dataset. This appendix reports the results of the effort to estimate capital outlays for Phase III manufacturing facilities, including: an overview of the analytic concepts underl ying the analysis of capital outlays; specific variables included in the regression analysis; summary of data selec tion and preparation; general specification of regression models to be tested; and the findings from the regression analyses. 2



### B3A5-1 A NALYTIC 

### C ONCEPTS 

### U NDERLYING 

### A NALYSIS OF 

### C APITAL 

### O UTLAYS On the basis of general economic and financial concepts of investment behavior, EPA began its analysis by outlining a framework relating the level of a firm’s capital outlays to explanatory factors that:

< can be observed for public-reporting firms – either as firm-specific information or general business environment information – and thus be included in a regression analysis; and

< for firm-specific information, are also av ailable from the Phase III sample facility dataset. To aid in identifying the explanatory concepts and variab les that might be used in the analysis and as well in specifying the models for analysis, EPA reviewed recent studies of the determinants of capital outlays. EPA’s review of this literature generally confirmed the overall approach in seeking to estimate capital outlays and helped to identify additional specific variables that other anal ysts found to contribute important information in the analysis of capital outlays (e.g., the decision to test capacity utilization as an explanatory variable, see below, resulted from the literature review). Articles reviewed are listed in Attachment B3A5A to this appendiB3A5 Table B3A5.1, beginning below and continuing on the sub sequent page, summarizes the conceptual relationships between a firm’s capital outlays and explanatory factors that EPA sought to capture in this analysis. In the table, EPA outlines the concept of influence on capital outlays, th e general explanatory variable(s) that EPA identified§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

B3A5-3 to capture the concept in a regression analysis, and the hypothesized mathematical relationship (sign of estimated coefficients) between the concept and capital outlays. Table B3A52 identifies the specific variables included in the analysis, including any needed manipulations and the correspondence of the variables to Phase III survey information.

Table B3A5.1: Summary of Fact ors Influencing Capital Outlays Explanatory Factor/Concept To Be Captured in Analysis Translation of Concept to Explanatory Variable(s) Expected Relationship Availability of attractive opportunities for additional capital investment . A firm’s owners, or management acting on behalf of owners, should expend cash for capital outlays only to the extent that the expected return on the capital outlays – whether for replacement of, or additions to, existing capital stock – are sufficient to compensate providers of capital for the expected return on alternative, competing investment opportunities, taking into account the risk of investment opportunities. Historical Return On Assetsof establishment as a indicator of investment opportunities and manageme nt effectiveness, and, hence, of desirability to expand capital stock and ability to attract capital investment. Use of a historical variable implicitly assumes past performance is indicative of future expectations. Positive

Business growth and outlook as a determinant of need for capital expansion and attractiveness of investment opportunities . All else equal, a firm is more likely to have attractive investment opportunities and need to expand its capital base if the business is growing and the outlook for business performance is favorable. Revenue Growth , from the prior time period(s) to the present, provides a historical measure of business gr owth and is a potential indicator of need for capital expansion. Use of a historical variable implicitly assumes past performance is indicative of future expectations. Positive

Clearly, the theoretical preference is for a forward-looking indicator of business growth and need for capital expansion. Options EPA identified include Index of Leading Indicatorsand current Capacity Utilization , by industry. Higher current Capacity Utilization may presage need for capital expansion. Positive

Importance of capital in business production . All else equal, the more capital intensive the production activities of a business, the greater will be the need for capital outlay to replenish, and add to, the existing capital stock. More capital intensive businesses will spend more in capital outlays to sustain a given level of revenue over time. The Capital Intensityof production as measured by the production capital required to produce a dollar of revenue provides an indicator of the level of capital outlay needed to sustain and grow production. As an alternative to a firm-specific concept such as Capital Intensity of production, differences in business characteristics might be captured by an Industry Classification variable. Positive

Life of capital equipment in the business

. All else equal, the shorter the useful life of the capital equipment in a business, the greater will be the need for capital outlay to replenish, and add to, the existing capital stock. No information is available on the actual useful life of capital equipment by business or industry classification. However, the Capital Turnover Rate , as calculated by the ratio of book depreciation to net capital assets, provides an indicator of the rate at which capital is depleted, according to book accounting principles: the higher the turnover rate, the shorter the life of the capital equipment. However, the measure is imperfect for reasons of both the inaccuracies of book reporting as a measure of useful life, and as well the confounding effects of growth in the asset base due to business expansion – which will tend to lower the indicated turnover rate, all else equal, without a real reduction in life of capital equipment. As above, an alternative to a firm -specific concept, differences in business characteristics might be captured by an Industry Classification variable. Positive, generally, but with recognition of the potentialfor countertrend effects§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3 Table B3A5.1: Summary of Fact ors Influencing Capital Outlays Explanatory Factor/Concept To Be Captured in Analysis Translation of Concept to Explanatory Variable(s) Expected Relationship

B3A5-4

The cost of financial capital . The cost at which capital – both debt and equity – is made available to a firm will determine which investment opportunities can be expected to generate sufficient return to warrant use of the financial capital for equipment purchases. All else equal, the higher the cost of financial capital, the fewer the investment/capital outlay opportunities that would be expected to be profitable and the lower the level of outlays for replacement of, or additions to, capital stock. Preferably, measures of cost -of-capital would be developed separately for debt and equity.
The Cost of Debt Capital, as measured by an appropriate benchmark interest rate, provides an indication of the terms of debt availability and how those terms are changing over time. Preferably, the debt cost/terms would reflect the credit condition of the firm, which could be based on a credit safetyrating (e.g., S&P Debt Rating). Negative

The cost of equity capital is more problematic than the cost of debt capital since it is not directly obs ervable for either public-reporting firms or, in particular, private firms in the Phase III dataset. However, a readily available surrogate such as Market-to-Book Ratio provides insight into the terms at which capital markets are providing equity capital to public-reporting firms : the higher the Market-to-Book Ratio, the more favorable the terms of equity availability. Negative

The price of capital equipment . The price of capital equipment – in particular, how capital equipment prices are changing over time – will influence the expected return from capital outlays. All else equal, when capital equipment prices are increasing, the expected return from incremental capital outlays will decline and vice versa. However, although the generally expected effect of higher capital equipment prices is to remove certain investment opportunities from consideration, the potential effect on total capital outlaymay be mixed. If expected returns are such that the demand to invest in capital projects is relatively inelastic, the effect of higher prices for capital equipment may be to raise, instead of lower, the total capital outlay for a firm. Index provides an indicator of the change in capital equipment prices. Negative, generally, but with recognition of the potentialfor countertrend effects

Source: U.S. EPA analysis, 2004.



### B3A5-2 S PECIFYING 

### V ARIABLES FOR THE 

### A NALYSIS Working from the general concepts of explanatory variab les outlined above, EPA defined the specific explanatory variables to be included in the analysis. A key requireme nt of the regression analysis is that the firm-specific explanatory variables included in the regression analysis late r be able to be used as the basis for estimating capital expenditures for facilities in the Phase III dataset. As a r esult, in defining the firm-specific variables, it was necessary to ensure that the definition of variables sel ected for the regression analysis using data on publicreporting firms be consistent with the data items available for facilities in the Phase III dataset. Also, EPA’s selection of firm-specific variables was further constrained by the decision to use the Value Line Investment Survey (VL) as the source of firm-specific information for the regression analysis. The decision to use VL as the source of firm-specific data for the analysis was driven by several considerations:

< Reasonable breadth of public -reporting firm coverage. The VL dataset includes 8,500 firms.

< Reasonable breadth of temporal coverage. VL provides data for the most recent 11 years – i.e., 1992-2002. Although ideally EPA woul d have preferred a longer time series to include more years§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

B3A5-5 not in the “boom” business investment period of the mid- to late-1990s.

< Reasonable coverage of concepts/data needed for analysis. The VL data includes a wide range of financial data that are applicable to the anal ysis (VL provides 37 data items over the 11 reporting years; see Attachment DB). However, because of th e pre-packaged nature of the VL data, it was not possible to customize any data items to support more precise definition of variables in the analysis. In particular, EPA found that certain balance sheet items were not reported to the level of specificity preferred for the analysis. Overall, though, EPA expects the consequence of using more aggregate, less-refined concepts should be minor. The decision to use VL data for the analysis constraine d, in some instances, EPA’s choice of variables for the analysis. Table B3A5.2 reports the specific definitions of variabl es included in the analysis (both the dependent variable and explanatory variables), including any needed manipul ations, the data source, the Phase III estimation analysis equivalent (either the corresponding variable(s) in th e Section 316(b) Phase III Detailed Industry Questionnaire or other source outside the questionnaire), and any issues in variable definition.

Table B3A5.2: Variables For Capi tal Expenditure Modeling Analysis Variables for Regression Analysis Phase III Analysis Equivalent Comment / Issue Variable Source Calculation Dependent Variable Gross expenditures on fixed assets: CAPEX , includes outlays to replace, and add to, existing capital stock Value Line Obtained from VL as Capital Spending per Share

. CAPEXcalculated by multiplying by Average Shares Outstanding

. None: to be estimated based on estimated coefficients. This value and all other dollar values in the regression analysis were deflated to 2002 using 2-digit SIC PPI values.

Explanatory Variables Firm-Specific Variables Return On Assets: ROA Value Line ROA = Operating Income / Total Assets . Both Operating Income , defined as Revenue less Operating Expenses (CoGS+SG&A), and Total Assets were obtained directly from VL. From Survey: Revenue less Total Operating Expenses ( Material & Product Costs + Production Labor + Cost of Contract Work + Fixed Overhead+ R&D + Other Costs & Expenses ) Would have preferred an after-tax concept in numerator and a deployed production capital concept in denominator. However, VL provides no tax value per seand would require calculation of tax using an estimated tax rate, which could introduce error. Also neither VL nor Phase III survey data provide sufficient information to get at deployed production capital.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3
Table B3A5.2: Variables For Capi tal Expenditure Modeling Analysis Variables for Regression Analysis Phase III Analysis Equivalent Comment / Issue Variable Source Calculation

B3A5-6

Revenue: REV Value Line REV = Revenues

. Revenues directly available from VL. From Survey: Revenue In the log-linear formulation this variable captures percent change/growth in revenues. However, the use of the log-linear formulation, eliminates the potential to set the growth term to zero in estimating baseline capital outlays for Phase III facilities. During the specification of the regression model for the MP&M final regulation, Total Assets was also tested as a scale variable. Since it provided a good, but not as strong, an explanation, as REVit was not included in the final specification. Based on this previous finding, Total Assets was not considered while specifying the Phase III regression model. Capital Turnover Rate: CAPT Value Line CAPT = Depreciation / Total Assets. Depreciation and Total Assets directly available from VL. From Survey: Depreciation / Total Assets Would have preferred denominator of net fixed assets instead of total assets

. However, VL provide s detailed balance sheet information for only the four most recent years. Not possible to separate current assets and intangibles from total assets. Capital Intensity: CAPI Value Line CAPI = Total Assets / Revenue. Total Assets and Revenue directly available from VL From Survey: Total Assets / Revenue As above, would have preferred net fixed assetsinstead of total assets , but needed data are not available from VL for the full analysis period.
Market-toBook Ratio: MV/B Value Line MV/B = average market price of common equity ( Price ) divided by book value of common equity ( Book Value per Share ). Price and Book Value per Share directly available from VL. N/A (see Comment/Issue) During specification of the MP&M regression model, MV/B was found to highly correlated with other, more important explanatory variables, which makes sense, given that equity terms would be derived from more fundamental factors, such as ROA

. Thus, MV/B was omitted from the MP&M regression model. As a result, MV/B was not considered during the specification of the Phase III regression model which eliminated the need to define an approach to use this variable with Phase III survey data.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3
Table B3A5.2: Variables For Capi tal Expenditure Modeling Analysis Variables for Regression Analysis Phase III Analysis Equivalent Comment / Issue Variable Source Calculation

B3A5-7 General Business Environment Variables Interest on10-year, Arated industrial debt: DEBTCST Moody’s Investor Services DEBTCST = annual average of rates for each data year Use average of DEBTCST rates at time of Phase III survey. 10-year maturity, industry debt selected as reasonable benchmark for industry debt costs. 10 years became “standard” maturity for industrial debt during 1990s.

Index of Leading Indicators: ILI Conference Board Monthly index series available from Conference Board. ILI =geometric mean of current year values. Use average of ILI values at time of Phase III survey. During specification of the MP&M regression model, EPA found that ILI and the CAPPRC (see below) are highly correlated

. Thus, ILI was omitted from the MP&M regression model. As a result, ILI was not considered during the specification of the Phase III regression model

. Capacity Utilization by Industry: CAPUTIL Federal Reserve Board (Dallas Federal Reserve) Monthly index series available from Federal Reserve. CAPUTIL = current year average value. Use average of CAPUTIL values at time of Phase III survey.

Producer Price Index series for capital equipment: CAPPRC Bureau of Labor Statistics (BLS) Annual average values available from BLS. CAPPRC = current year average value as reported by BLS. Use average of CAPPRC values at time of Phase III survey. BLS reports PPI series for capital equipment based on “consumption bundles” defined for manufacturing and non-manufacturing industries. For this analysis, EPA used the PPI series based on the manufacturing industry bundle. Source: U.S. EPA analysis, 2004.



### B3A5-3 S ELECTING THE 

### REGRESSION 

### A NALYSIS 

### D ATASET In addition to specifying the variables to be used in the regression analysis, EPA also needed to select the public firm dataset on which the analysis would be performed. As noted above, EPA used the Value Line Investment Survey as the source for public firm data. VL includes over 8,500 publicly traded firms and identifies firms’ principal business both by a broad industry classification (e.g., Paper/Forest) and by an SIC code assignment. Va lue Line’s SIC code definitions do not match the U.S. Census Bureau’s SIC code definitions; however, in mo st instances a Value Line SIC code can be reasonably matched to one or several U.S. Census Bureau defined SIC codes. To build the public firm dataset corresponding to the Phase III sectors (SIC 26: Paper and allied pr oducts, SIC 28: Chemicals and allied products, SIC 29: Petroleum and coal products, and SIC 33 Primary metal i ndustries) , EPA initially selected all firms included in the Value Line SIC code families:

< 2600: Paper/forest products,

< 2640: Packaging and container,

< 2810: Chemical (basic),

< 2813: Chemical (diversified),

< 2820: Chemical (speciality),

< 2830: Biotechnology,§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

3 The profiles only focus on 4-digit SIC categories represented in the sample of facilities which received the Section 316(b) d etailed industry questionnaire.

4 When VL adds a firm to its dataset, it fills in the public-reported data history for the firm for the lesser of 11 years or t he length of time that the firm has been publicly listed and thus subject to SEC pub lic reporting requirements.

B3A5-8 < 2834: Drug,

< 2840: Household products,

< 2844: Toiletries/cosmetics,

< 2900: Petroleum (integrated),

< 3311: Steel (general), and

< 3312: Steel (integrated). In order to derive a dataset of firms whose business activ ities closely match the activities of firms included in the Phase III sample survey EPA made or attempted to make the following revisions to the initial dataset:

< EPA found that the VL SIC code definition does not include categories which match SIC 331 and SIC 335 (combined together to form the aluminum sector in the Phase III analysis). Since U.S. aluminum companies are generally vertically integrated (S&P, 2001), most aluminum companies own large bauxite reserves and mine bauxite ore. As such, these firms are classified in VL under SIC 1000: Metals and mining. EPA reviewed the bus iness activities of firms listed in SIC 1000: Metals and mining, and included only those firms described as aluminum companies in the regression analysis dataset.

< EPA reviewed the business activities of firms liste d in SIC 3400: Metal fabricating, however, no firms whose activities matched those described w ithin the profiles of the Phase III Manufacturing Sectors were found. 3

< EPA reviewed the business activities of firms lis ted in SIC 2840: Household products and SIC 2844: Toiletries/cosmetics, and retained only those fi rms in the dataset whose activities matched those described within the profiles of the Phase III Manufacturing Sectors (see footnote 4).

< EPA deleted firms within SIC 2600: Paper/forest products whose business activities are solely limited to timber/lumber production. These facilities are unlik ely to use cooling water intake structures and therefore fall outside the Phase III Manufacturing Sectors.

< EPA reviewed the business activities of firms lis ted in SIC 2830: Biotechnology and SIC 2834: Drug in order to exclude firms that are exclusively r esearch and development (R&D) firms and are unlikely to use cooling water intake structures. Howeve r, based on the information provided by Value Line EPA was unable to segregate R&D firms from the rest of the firms listed in these SIC codes.

< EPA only retained firms in the VL dataset if they are situated in the U.S. or Canada, and for whom financial information is available in U.S. dollars. On inspection, EPA found that a substantial number of firm s did not have data for the full 11 years of the analysis period. The general reason for the omission of some years of data is that the firms did not become publicly listed in their current operating structure – whether through an initial public offering, spin-off, divestiture of business assets, or other significant corporate restructuring that re nders earlier year data inconsistent with more recent data – until after the beginning of the 11-year data period. 4 As a result, the omission of observation years for a firm always starts at the beginning of the data analys is period. This systematic front-end truncation of firm observations in the dataset could be expected to bias th e analysis in favor of the capital expenditure behavior nearer the end of the 1990s decade. To avoid this probl em, EPA removed all firm observations that have fewer than 11 years of data. As a result, the dataset used in th e analysis has a total of 2,244 yearly data observations and represents 204 firms.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

5 All dollar values were deflated to 2002 using 2-digit SIC PPI values.

6 While specifying the MP&M regression model, EPA tested both linear and log-linear model specifications. The pattern of coefficient significance was found to be better in the log-linear m odel. In addition, the log-linear model offered advantages in terms of retention of early time period observations (b y eliminating the need to use percent change variables) and variable specificatio ns, and helped to reduce outlier effects in the model . As a result, EPA selected a log-linear specification as the final regression m odel for the MP&M final regulation. Based on these reas ons and the similarity of industry sectors analyzed for the two regulations, EPA dec ided to test only log-linear model specificati ons for the Phase III regression model.

B3A5-9 Table B3A5.3 presents the number of firms by industry classifications.

Table B3A5.3: Number of Firms by Industry Classifications SIC Industry Classification Number of Firms 26: Paper and allied products 24 28: Chemicals and allied products 136 29: Petroleum and coal products 20 33: Primary metal industries 24



### B3A5-4 S PECIFICATION OF 

### M ODELS TO BE 

### T ESTED On the basis of the variables listed above and their hypothesized relationship to capital outlays, EPA specified a time-series, cross sectional model to be tested in the regression analysis. EPA’s dataset consisted of 204 cross sections observed at 11 years (1992 through 2002). The general structure of this model was as follows:
CAPEX i,t = 

### f (ROA i,t , REV i,t , CAPT i,t , CAPI i,t , DEBTCST i,t , CAPPRC t , CAPUTIL j,t) Where: CAPEX i,t = capital expenditures of firm i , in time period t ; 5 t = year (year = 1992, . . . , 2002); i = firm i( i= 1, . . . , 204); j = industry classification j ROA i,t = return on total assets for firm iin year t ; REV i,t = revenue ($ millions) for firm iin year t ; CAPT i,t = capital turnover rate for firm iin year t ; CAPI i,t = capital intensity for firm iin year t ; DEBTCST t = financial cost of capital in year t ; CAPPRC t = price of capital goods in year t ; CAPUTIL j,t = the Federal Reserve Board’s Index of Capacity utilization for a given industry jin year t

. EPA only tested log-linear model specifications for this analysis. 6 The main advantage of the log-linear model is that it incorporates directly the concept of percent change in the explanatory variables. Specifying the key regression variables as logarithms permitted EPA to estimat e directly as the coefficients of the model, the elasticities of capital expenditures with respect to firm financial characteristics and general business environment factors. The following paragraphs briefly discuss testi ng of the log-linear forms of the model. Parameter estimates are presented for the final log-linear model only. EPA specified a log-linear model, as follows: ln(CAPEX i,t) = " + E [ $ xln(X i,t)] + E [( yln(Y t)] + ,§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

7 While specifying the MP&M regression model EPA conducted a se nsitivity analysis to examine th e degree to which the estimated model was affected by this data transformation. Results of this analysis showed that the data transformation produces results that are compatible with a model considering only positive ROA values and a model considering a ll ROA values. As a result, the Phase I II regression model utilized the same data transformation procedure.

8 While specifying the MP&M regression model, EPA also tested specifications that included the following structural modificatio ns: (1) testing contemporary vs. lagged specificati on of certain explanatory variables: e.g ., using prior, instead of current perio d revenue, REV, as an explanatory variable; (2) testing scale-normalized specifi cation of the dependent variable: e.g., using CAPEX/REV as the dependent variable instead of simple CAPEX; (3) testing flexible functi onal forms that included quadratic terms; and (4) testing addition al explanatory variables including the index of 10 leading economic indicators (ILI) and market-to-book ratio (MV/B). Because EPAfound

B3A5-10 Where: CAPEX i,t = capital expenditures of firm i , year t ; $ x = elasticity of capital expenditures with respect to firm characteristic X; X i,t , = a vector of financial characteristics of firm i , year t ; (

y = elasticity of capital expenditures with respect to economic indicator Y; Y t = a vector of economic indicators, year t ; for CAPUTIL, Y is also differentiated by industry classification , = an error term; and ln( x ) = natural log of x Based on this model, the elasticity of capital expenditures with respect to an explanatory variable, for example, return on assets is calculated as follows: 

## ( ) ROA ROA d CAPEX CAPEX d ROA d CAPEX d CAPEX E ) ( ) ( ) ln( ) ln(
= = Since logarithmic transformation is not feasible for negative and zero values, such values in the VL public firm dataset required linear transformation to be included in the analysis. The following variables in the sample required transformation:

< CAPEX: Eighteen firms in the sample reported zero capital expenditures at least in one time period. EPA set these expenditures to $1.

< REVENUE: Seven firms reported negative revenues in at least one time period. Because these are likely due to accounting adjustments from prior pe riod reporting, EPA set negative revenues for these firms to $1.

< ROA: the values for return on assets in the public fi rm sample range from -2.9 to 0.7. Approximately 34 percent of the firms in the dataset reported negative ROAs in at least one year. To address this issue while reducing potential effects of data tr ansformation on the modeling results, EPA used the following data transformation approach: 7



### R EPA excluded 27 firms with anyannual ROA values below the 95th percentile of the ROA distribution (i.e., ROA # - 0.51).



### R EPA used an additive data transformation to ensure that remaining negative ROA values were positive in the logarithm transformation. The additive transformation was performed by adding 0.51 to all ROA values. As a result of the data transformation procedures outlined above, the VL public firm dataset on which the regression model is based was reduced to 177 firms ( 204 - 27 firms) and 1,947 yearly data observations. The analysis tested several specifications of a log-lin ear model, including models with the intercept and slope dummies for different industrial sectors and models with the intercept suppressed. 8 Slope dummies were used to§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

that these structural modifications either did not improve the fit of the MP&M regression model or resulted in the introduction of multicollinearity among variables, these structural modifications were not tested while specifying the Phase III regression mod el.

B3A5-11 test the influence of industry classification on the elasticityof capital expenditures with respect to an explanatory variable: e.g., using the product of an industry classification dummy variable and CAPPRC to test whether certain industries responded differently to change in price of capital equipment over time. Following review of the different models tested, EPA concluded that the estimated coefficients did not vary, significantly, by industry and thus selected the simple log-linear model, with the intercept and no slope dummies as the basis for the 316(b) Phase III capital expenditures analysis. The resu lts for this model are summarized below. Cross-sectional, time-series datasets typically exhibit both autocorrelation and group-wise heteroscedasticity characteristics. Autocorrelation is frequently present in economic time series data as the data display a “memory” with the variation not being independe nt from one period to the next. Hete roscedasticity usually occurs in crosssectional data where the scale of the dependent variable and the explanatory power of the model vary across observations. Not surprisingly, the dataset used in th is analysis had both characteristics. Therefore, EPA estimated the specified model using the generalized l east squares procedure. This procedure involves the following two steps:

< First, EPA estimated the model using simple OLS, ignoring autocorrelation for the purpose of obtaining a consistent estimator of the autocorrelation coefficient ( D );

< Second, EPA used the generalized least squares procedure, where the analysis is applied to transformed data. The resulting autocorrelation adjustment is as follows:
Z i,t = Z i,t - D Z i,t-1 where Z it is either dependent or independent variables. EPA was unable to correct the estimated model fo r group-wise heteroscedasticity due to computational difficulties. The statistical software used in the analysis (LIMDEP) failed to correct the covariance matrix due to the very large number of groups (i.e., 177 firms) include d in the dataset. Application of other techniques to correct for group-wise heteroscedasticity was not feasible due to time constraints. The estimated coefficients remain unbiased; however, they are not minimum variance estimators. Regression results reveal strong systematic elements influencing capital expenditures: the analysis finds both statistically significant and intuitive patterns that influence firm's investment behavior. We find a strong systematic element of capital expenditures variation which allows forecasting of capital expenditures based on firm and business environment characteristics. Table B3A5.4 presents model results. The m odel has a fairly good fit, with adjusted R2 of 0.81. All coefficients have the expected sign and all but one variable (cost of debt capital) are significantly different from zero at the 95 thpercentile.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

B3A5-12 Table B3A5.4: Time Seri es, Cross-Sectional Model Results Variable Coefficient t-Statistics Constant 21.880 2.618 Ln(ROA) 0.526 3.964 Ln(REV) 1.129 58.450 Ln(CAPT) 0.687 11.085 Ln(CAPI) 1.078 18.491 Ln(DEBTCST) -0.789 -1.605 Ln(CAPPRC) -5.957 -4.369 Ln(CAPUTIL) 1.716 2.842 Autocorrelation Coefficient r 0.385 18.402
The empirical results show that among the firm-specifi c variables, the output variable (REV) is a dominant determinant of firms’ investment spending. A positive coe fficient on this variable means that larger firms invest more, all else equal, which is clearly a simple expected result. In addition, as expected, firms with higher financial performance and better investment opportunities (R OA) invest more, all else equal: for each one percent increase in ROA, a firm is expected to increase its cap ital outlays by 0.53 percent. Other firm-specific characteristics were also found important and will aid in differentiating the expected capital outlay for Phase III facilities according to firm-specific characteristics. Firm s that require more capital to produce a given level of business activity (i.e., firms that have high capital intens ity, CAPI) tend to invest more: a one percent increase in capital intensity leads to a 1.08 percent increase in capita l spending. Higher capital turnover/shorter capital life (CAPT) also has a positive effect on investment d ecisions: a one percent increase in capital turnover rate translates to a 0.69 percent increase in capital outlays. The model also shows that current business environment c onditions play an important role in firms’ decision to invest. Negative signs on the capital price (CAPPRC) and debt cost (DEBTCST) variables match expectations, indicating that falling (either relatively or absolutely) capital equipment prices and less costly credit are likely to have a positive effect on firms’ capital expenditures. The most influential factor is capital equipment prices for manufacturing facilities. A one percent increase in the capital price index (CAPPRC) leads to a 5.96 percent decrease in capital investment. Capacity utilization is al so an influential factor: a one percent increase in the Federal Reserve Index of Capacity Utilization for the rele vant industrial sector (CAPUTIL) leads to a 1.7 percent increase in capital investments. The f act that these systematic variables ar e significant in the regression analysis means that EPA will be able to control for economy- and industry-wide conditions in estimating capital outlays for Phase III facilities.



### B3A5-5 M ODEL 

### V ALIDATION To validate the results of the regression analysis, EPA u sed the estimated regression equation to calculate capital expenditures and then compared the resulting estimate of capital expenditures with actual data. EPA used two methods to validate its results:

< EPA used median values for explanatory variabl es from the Value Line data as inputs to estimate capital expenditures and then compared the es timated value to the median reported capital expenditures, and§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

B3A5-13 < EPA used Phase III survey data to estimate capita l expenditures and then compared the estimated values to depreciation reported in the survey. First, EPA estimated capital expenditures for a hypothe tical firm based on the median values of the four dependent variables from the Value Line data and the re levant values of the three economic indicators. The estimated capital expenditures for this hypothetical firm ar e $43 million. EPA then compared this estimate to the median value of capital expenditures from the Value Line data. The median capital expenditure value in the dataset is $36 million, which provides a close match to the estimated value. This is not surprising since the same dataset was used to estimate the regression model and to calculate the median values used in this analysis. EPA also used Phase III survey data to confirm that th e estimated capital expenditures seem reasonable. Because the Phase III survey does not provide information on capita l expenditures, EPA compared the capital expenditure estimates to the depreciation values reported in the survey. Depreciation had been proposed as a possible surrogate for cash outlays for capital replacements and additions. However, depreciation does not capture important variations in capital outlays that result from differences in firms’ financial performance. For this analysis, EPA chose a representative facilityfrom each of the four Phase III primary manufacturing sectors for model validation. The selected facility fo r each sector corresponds as closely as possible to the hypothetical median facility in the sector based on the distribution of facility revenues and facility return on assets. For each of the four facilities, EPA estimated capital expenditures using the estimated regression equation and facility financial data. Table B3 A55 shows the estimated regression coefficients, financial averages for the four Phase III sectors, estimated facility capital expenditur es, reported facility depreciation, and the comparison of capital expenditures and depreciation. As shown in Table B3A5.5, the estimated model pr ovides reasonable estimates of capital expenditures.

Table B3A5.5: Estimation of Capital Outlays for Phase III Sample Facilities: Median Facilities Selected by Revenue and ROA Percentiles

Sectors Pre-Tax Return

on Assets (ROA) Revenue ($2003, millions) Capital Turnover

Rate Capital Intensity Cost of Debt Price of Capital Goods Capacity Utilization Estimated Capital Expenditures ($2003, millions) Depreciation ($2003, millions) Difference between Depreciation and Capital Expenditures ($2003, millions) Coefficient Intercept (21.88) 0.53 1.13 0.69 1.08 -0.79 -5.96 1.72 Paper and allied products 0.16 $244 0.09 0.89 7.71 137. 60 86.24 $18.94 $16.16 ($2.78)

Chemicals and allied products 0.22 $237 0.06 1.14 7.71 137. 60 79.36 $15.25 $13.66 $1.59

Petroleum and coal products 0.15 $1,470 0.05 0.58 7.71 137.60 91.88 $45.58 $62.95 $17.37 Primary metals industries 0.11 $444 0.06 0.52 7.71 137. 60 88.77 $15.58 $18.55 $2.97 Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

4 For presentation purposes, two outlier facilities were excluded from the graph for SIC 28: Chemicals and allied products, and one outlier facility was excluded from the gra ph for SIC 26: Paper and allied products.

B3A5-14 One of the possible implications of the hypothesized relationships and estimated coefficient values from the prior analysis is that a facility’s predicted capital expenditures might be expected to increase relative to the facility’s actual depreciation as the facility’s ROA increases. An extens ion and somewhat version of this hypothesis is that, at lower ROA values, predicted capital expenditures would be less than the depreciation but, that at higher ROA values, predicted capital expenditures exceed depreciation. These hypotheses are consistent with the expectation that businesses with higher financial performance will have relatively more attractive investment opportunities and are more likely to attract the capital to undertak e those investments. EPA examined whether these relationships occur in the 316(b) sample facilities. Sp ecifically, EPA calculated the predicted capital expenditure for each facility and compared these values to the faciliti es’ reported depreciation values. To remove the scale effect of revenue, EPA normalized both the predicted capital expenditure and repor ted depreciation values by dividing by the three-year average of revenue for each f acility. EPA then estimated the simple linear relationship of the resulting revenue-normalized capital expenditure and deprecation values against facility ROA. The four graphs on the following pages present, for each of the f our two-digit SIC code sectors, the normalized capital expenditure and deprecation values, and the estimated trend lines for each sector’s depreciation and capital expenditures with respect to ROA. 4 The graphs indicate the following:

< The Paper and Allied Products (S IC 26) graph shows depreciation exceeding predicted capital expenditure at low ROA values but this relati onship reverses with predicted capital expenditure exceeding depreciation as ROA increases. Thus, the calculations for these facilities match the hypothesized relationship.

< The Chemicals and Allied Products (SIC 28) gra ph also shows depreciation exceeding predicted capital expenditure at low ROA values, but again the relationship reverses with predicted capital expenditure exceeding depreciation as ROA increases. This predicted relationship is observed more strongly for facilities in the Chemicals and Allied Products industry than in the Paper and Allied Products industry.

< The Petroleum and Coal Products (SIC 29) gra ph shows predicted capital expenditures exceeding depreciation over the ROA range analyzed. Howeve r, the extent of difference does not materially change as ROA increases.

< The Primary Metal Industries (SIC 33) graph al so shows predicted capital expenditures exceeding depreciation over the ROA range analyzed. Howe ver, unlike for the Petroleum and Coal Products facilities, the amount by which predicted capita l expenditures exceeds depreciation increases as ROA increases, thus matching the hypothesized relationship. In summary, with the exception of facilities in the Petr oleum and Coal Products industry, the estimated model produces capital expenditure values that increase relative to reported depreciation with increasing ROA, which matches the hypothesized relationship.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

B3A5-15 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Return on AssetsCapital Outlays & Depreciation Normalized by Facility Revenues

DEPR CAPEX Linear (DEP R) Linear (CAP EX)

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14

-0.20 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 Return on AssetsCapital Outlays & Depreciation Normalized by Facility Revenues

DEPR CAPEX Linear (DEP R) Linear (CAP EX) Figure B3A5.1: Comparison of Estimated Capita l Outlays to Reported Depreciation for Phase III Survey Facilities in the Paper and Allied Products Sector

Source: U.S. EPA analysis, 2004. Figure B3A5.2: Comparison of Estimated Capita l Outlays to Reported Depreciation for Phase III Survey Facilities in the Chemicals and Allied Products Sector

Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

B3A5-16 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16

-0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 Return on AssetsCapital Outlays & Depreciation Normalized by Facility Revenues

DEPR CAPEX Linear (DEP R) Linear (CAP EX)

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

-0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 Return on Asse tsCapital Outlays & Depreciation Normalized by Facility Revenues

DEPR CAPEX L in ear (D E P R) Linear (CAP EX) Figure B3A5.3: Comparison of Estimated Capita l Outlays to Reported Depreciation for Phase III Survey Facilities in the Petroleum and Coal Products Sector

Source: U.S. EPA analysis, 2004. Figure B3A5.4: Comparison of Estimated Capita l Outlays to Reported Depreciation for Phase III Survey Facilities in the Primary Metal Industries Sector

Source: U.S. EPA analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

B3A5-17 

### A TTACHMENT 

### B3A5.A: B IBLIOGRAPHY OF 

### L ITERATURE 

### REVIEWED FOR THIS 

### A NALYSIS As noted above, EPA relied on previous studies of investme nt behavior to select critical determinants of firms’ capital expenditures. Empirical results from these studies suggest that investment is most sensitive to quantity variables (output or sales), return-over-cost, and capital utilization (R. Chirinko). Empirical results from more recent studies further found that increasing depreciation rates and capital equipment prices were of first-order importance in the equipment investment behavior in the 1990 (T. Tevlin, K. Whelan). Specifically, declining prices of micro-processor based equipment played a crucial role in the investment boom in the 1990. Chirinko, Robert S. 1993. “Business Fixed Investment Spending: A Critical Survey of Modeling Strategies, Empirical Results and Policy Implications.” Journal of Economic Literature31, no. 4: 1875-1911. Goolsbee, Austan. 1997. “The Business Cycle, Financ ial Performance, and the Retirement of Capital Goods.” University of Chicago, Graduate School of Business Working Paper. Greenspan, Alan. 2001. “Economic Developments.” Remarks before the Economic Club of New York, New York, May 24. Kiyotaki, Nobuhiro and Kenneth D. West. 1996. “Business Fixed Investment And The Recent Business Cycle In Japan.” National Bureau of Economic Research Working Paper 5546. McCarthy, Jonathan. 2001. “Equipment Expenditures since 1995: The Boom and the Bust.” Current Issues In Economics And Finance7, no. 9: 1-6. Opler, Tim and Lee Pinkowitz, Rene Stulz and Rohan Williamson. 1997. “The Determinants and Implications of Corporate Cash Holdings.” Working paper, Ohio State University College of Business. Tevlin, Stacey and Karl Whelan. 2000. “Explaining the Investment Boom of the 1990s.” Board of Governors of the Federal Reserve System Finance and Economics Discussion Paper no. 2000-11 Uchitelle, Louis. 2001. “Wary Spending by Companies Cools Economy.” New York Times , May 14, p. A1.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 5 to Chapter B3

B3A5-18 

### A TTACHMENT 

### B3A5.B: H ISTORICAL 

### V ARIABLES 

### C ONTAINED IN THE 

### V ALUE 

### L INE 

### I NVESTMENT 

### S URVEY 

### D ATASET All variables are provided for 10 years (except where a firm has been publicly listed for less than 10 years):

< Price of Common Stock

< Revenues
< Operating Income

< Operating Margin

< Net Profit Margin

< Depreciation

< Working Capital

< Cash Flow per share

< Dividends Declared per share

< Capital Spending per share

< Revenues per share

< Average Annual Price-Earnings Ratio

< Relative Price-Earnings Ratio

< Average Annual Dividend

< Return Total Capital

< Return Shareholders Equity

< Retained To Common Equity

< All Dividends To Net Worth

< Employees

< Net Profit

< Income Tax Rate
< Earnings Before Extras

< Earnings per share

< Long Term Debt

< Total Loans

< Total Assets

< Preferred Dividends

< Common Dividends

< Book Value

< Book Value per share

< Shareholder Equity

< Preferred Equity

< Common Shares Outstanding

< Average Shares Outstanding

< Beta
< Alpha

< Standard Deviation§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 6 to Chapter B3

B3A6-1 

# Appendix 6 to Chapter B3: Summary of Moderate Impact Threshold Values by Industry

I NTRODUCTION Facilities subject to moderate impacts from the proposed regulation are expected to experience financial stress short of closure. This analysis uses two financial indicators: (1) Pre-Tax Return on Assets (PTRA) and (2) Interest Coverage Ratio (ICR). These threshold values were calculated atthe industry-level and compared to pre- and postcompliance PTRA and ICR values for sample facilities to de termine if facilities choosing to remain in business after promulgation of effluent guidelines would experience moderate impacts on their ability to attract and finance new capital. The six industries considered in this anal ysis are: Paper, Chemicals, Petroleum, Steel, Aluminum (the “Primary Manufacturing Industries”), and Other Indus tries. The remainder of this appendix describes the sources and methodology used to derive industr y-specific moderate impact threshold values. EPA calculated the thresholds using in come and financial structure information by 4-digit SIC code from the Risk Management Association (RMA) Annual Statement Studies for eight years 1994-2001 (RMA, 2001; RMA 1998). This source provides quartile values derived from statemen ts of commercial bank borrowers and loan applicants for firms having less than $250 million in total assets. These criteria may introduce bias, since firms with particularly poor financial statements might be less likely to apply to banks for loans, and some types of firms may be more likely to use bank financing than others. However, the RMA data offers the advantage of being available by 4-digit SIC codes and for quartile ranges. RMA did not provide data for all 4-digit SIC codes associat ed with an in-scope Section 316(b) facility. Out of 26 SIC codes associated with facilities in the Primary Manufacturing Industries and 14 SIC codes associated with facilities in Other Industries, 10 SIC codes associated w ith facilities in the Primary Manufacturing Industries (38 percent) and 7 SIC codes associated with facilities in Other Industries (50 percent), had no years of data available. In addition, no data were available for the Aluminum industry, so EPA applied a combined Steel/Aluminum industry value to facilities in those industries. The 4-digit SIC code data were consolidated into weighted industry averages, weighted by 1997 value of shipments from the Economic Censuses (U.S. DOC, 1997). For each industry and impact measure, a separate threshold was calculated. The use of the RMA data for calculating the threshold values for pre-tax return on assets and interest coverage ratio is outlined below. 

### A PPENDIX 

### C ONTENTS B3A6-1 Developing Threshold Values for Pre-Tax Return on Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A6-2 B3A6-2 Developing Threshold Values for Interest Coverage Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A6-2 B3A6-3 Summary of Results . . . . . . . . . . . . . . . . . . . B3A6-4 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A6-5§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 6 to Chapter B3

B3A6-2 

### B3A6.1 D EVELOPING 

### T HRESHOLD 

### V ALUES FOR 

### P RE 

### -T AX 

### RETURN ON 

### A SSETS 

### (PTRA)
Pre-tax return on total assets measures management’s effectiveness in employing the capital resources of the business to produce income. A low ratio may indicate thata borrower would have difficulty financing treatment investments and continuing to attract investment. The following data from Risk Management Association Annual Statement Studies were used to calculate PTRA:

< % Profit Before Taxes / Total Assets 25th Ratio of profit before taxes divided by total assets and multiplied by 100 for the lowest quartile of values in each 4-digit SIC code.

< Operating Profit Gross profit minus operating expenses.

< Profit Before Taxes Operating profit minus all other expenses (net). RMA provides a measure of pre-tax return on assets that approximates the measure that EPA defined for the moderate impact analysis. As defined by RMA, this measure is the ratio of pre-tax incometo assets, designated ROA RMA : ROA RMA= Pre-Tax Income (EBT) / ASSETS 25th However, as defined by EPA for its analysis, the numerator of the PTRA measure requires the use of earnings before interest and taxes (EBIT) instead of pre-tax income (EBT). Defined as EBIT, the PTRA numerator will capture all return from assets, whether going to debt or e quity. To derive a pre-tax, total return value, EPA adjusted RMA’s measure of PTRA using the median per centage values of EBIT and EBT available from RMA. This adjustment yields the PTRA measure that EPA u sed in the moderate impact analysis, designated ROA 316(b) : ROA 316(b) = ROA RMA* EBIT / EBT Negative values are included in the weighted-industry PTRA av erages but a different method is used to adjust the ROA values reported in RMA to the value used in the m oderate impact analysis. Specifically, using only those observations (i.e., 4-digit SIC code and year combinati ons) with positive values for % Profit Before Taxes / Total Assets, Operating Profit, and Profit Before Taxes, EPA calculated an adjustment factor by subtracting the difference between ROA 316(b) and ROA RMAas follows:
ROA 316(b) -ROA RMA= adjustment factor. Those values were consolidated into industry-specific ad justment factors, weighted by 1997 value of shipments from the Economic Censuses (U.S. DOC, 1997). Each negative PTRA observation from RMA was adjusted by its industry specific adjustment factor to approximate the measure used in the moderate impact analysis:
ROA RMA+ industry specific adjustment factor = ROA 316(b) The industry specific adjustment factors average 0.40 and range from 0.12 for Paper to 0.55 for the combined Steel/Aluminum industry.



### B3A6-2 D EVELOPING 

### T HRESHOLD 

### V ALUES FOR 

### I NTEREST 

### C OVERAGE 

### R ATIO 

### (ICR) Interest coverage ratio measures a business’ ability to meet current interest payments and, on a pro-forma basis, to meet the additional interest payments under a new lo an. A high ratio may indicate that a borrower would have little difficulty in meeting the interest obligations of a loan . This ratio serves as an indicator of a firm's capacity to take on additional debt.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 6 to Chapter B3

1 Numerator (% Depr., Dep., Amort./Sales) is available for quartile values; denominator (Operating Profit) only for median values. B3A6-3 The following data from Risk Management Association Annual Statement Studies were used to calculate ICR:

< EBIT/Interest 25th Ratio of earnings (profit) befo re annual interest expense and taxes (EBIT) divided by annual interest expense for the lowest quartile of values in each 4-digit SIC code.

< % Depr., Dep., Amort./Sales med Median ratio of annual depreciation, amortization and depletion expenses divided by net sales and multiplied by 100.

< Operating Profit Gross profit minus operating expenses. RMA provides a measure of interest coverage that approximates the measure that EPA defined for the moderate impact analysis. As defined by RMA, this measure is th e ratio of earnings before interest and taxes to interest, designated ICR RMA : ICRRMA= EBIT / INTEREST 25th However, as defined by EPA for its analysis, the numerator of the ICR measure requires the use of earnings before interest, taxes, depreciation, and amortization (E BITDA) instead of earnings before interest and taxes (EBIT). Defined this way, the ICR numerator will include all operating cash flow that could be used for interest payments. To derive the desired ICR value (designated ICR316(b) ), EPA adjusted the RMA value as outlined below:
ICR 316(b) = EBITDA / INTEREST Therefore, ICR 316(b) = ICRRMA* (EBIT + DA) / EBIT or ICR 316(b) = ICRRMA* {1+ [(DA / SALES) / (EBIT / SALES)]} For consistency of calculation, EPA used the medianvalues available from RMA for the adjusting both the numerator (DA / SALES) and denominator (EBIT / SALES) terms. 1 EPA used the same method as described above to adjust the negative ICR values reported in RMA to the value used in the moderate impact analysis. Including only those observations with positive values for EBIT/Interest, % Depr., Dep., Amort./Sales, and Operating Profit, an adjustment factor was calculated by subtracting the difference between ICR 316(b) and ICR RMA as follows:
ICR 316(b) -ICRRMA= adjustment factor. An industry specific adjustment factor was calculated fo r ICR values similar to the PTRA. Each negative ICR observation from RMA was adjusted by its industry specific adjustment factor to approximate the measure used in the moderate impact analysis:
ICRRMA+ industry specific adjustment factor = ICR 316(b) The industry specific adjustment factors average 0.65 and range from 0.55 for Petroleum to 0.70 for Paper and the combined Steel/Aluminum industry.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 6 to Chapter B3

B3A6-4 

### B3A6-3 S UMMARY OF 

### RESULTS Table B3A6.1 reports the resulting threshold values for PTRA and ICR by industry. The PTRA values range from 1.8 percent for Other Industries to 2.9 percent fo r Chemicals. The ICR values range from 2.0 for Other Industries to 2.4 for Chemicals.

Table B3A6.1: Summary of Moderate Impact Thresholds by Industry based on 25th percentile value of firms reporting data to RMA
Industry Pre-Tax Return on Assets (PTRA) Interest Coverage Ratio (ICR) Paper 2.1% 2.2 Chemicals 2.9% 2.4 Petroleum 2.1% 2.2 Steel/Aluminum 2.0% 2.1 Other Industries 1.8% 2 Source: RMA, 2001; RMA, 1998; U.S. Economics Census, 1997; U.S. EPA Analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 6 to Chapter B3

B3A6-5 

### REFERENCES U.S. Department of Commerce. 1997. Bureau of the Census. Census of Manufacturers, Census of Transportation, Census of Wholesa le Trade, Census of Retail Trade , Census of Service Industries. Risk Management Association (RMA). 1997-1998. Annual Statement Studies

. Risk Management Association (RMA). 2000-2001. Annual Statement Studies

.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 6 to Chapter B3

B3A6-6 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 7 to Chapter B3

B3A7-1 

# Appendix 7 to Chapter B3: Analysis of Baseline Closure Rates

I NTRODUCTION This appendix presents information on the annual entry and closure of establishments in the Primary Manufacturing Industries.



### B3A7-1 A NNUAL 

### E STABLISHMENT 

### C LOSURES EPA used the dynamic data from the Statistics of U.S. Businesses (SUSB) to estimate the rate at which facilities in these industries leave the industry each year. The SU SB data report numbers of establishments starting up, closing, expanding employment and contracting empl oyment each year from 1989 through 2001 (the latest year currently available). EPA compared the percent of facilities predicted to close inthe baseline closure analysis to typical closure rates in the five primary industries. The SUSB data are organized by 3-digit SIC code for years 1990 through 1998, and 4-digit NAICS code for years 1999 through 2001. As a result, it is not possible to compile a series of data consistently aligned with the industries profiled. Ne vertheless, EPA believes the SUSB data can provide a general measure of establishment closures for comparison for the broad industry segments. Table B3A7.1 shows the percentage of facilities assessed as closures in the baseline analysis, and the range and average of closure rates for each of the five Primary Manufacturing Industries. As reported in the table, between 1.4 percent and 12.5 percent of all facilities in these i ndustries close annually. The estimated baseline closure rates for facilities in the Steel and Aluminum industri es are higher than the observed closure rates in these industries, as reported in SUSB data. However, EPA’s baseline closure rates are estimated from sample survey data and are thus subject to the statistical uncertaintyof the sample survey. EPA believes the individual sample facility analyses accurately represent the baseline financia l condition of the facilities, based on the data provided in the facility questionnaires.

Table B3A7.1: Predicted Baseline Clos ures and Annual Percentage of Closures for Primary Manufacturing Industries (1990-2001)

Sector Percent of 316(b) Facilities Assessed as

Baseline Closures Percent of Establishments Closing Range Average Paper 13.6% 1.4% - 9.8% 5.0% Chemicals 2.2% 2.3% - 9.2% 6.4% Petroleum 13.9% 3.3% - 10.6% 6.6% Steel 36.8% 4.6% - 10.0% 6.5% Aluminum 33.3% 2.3% - 12.5% 6.2% Total 13.6% 1.4% - 12.5% 6.1% Source: Small Business Administrati on, Statistics of U.S. Businesses. 

### A PPENDIX 

### C ONTENTS B3A7-1 Annual Establishment Closures . . . . . B3A7-1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B3A7-2§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analysis for Existing Facilities Appendix 7 to Chapter B3

B3A7-2 

### REFERENCES Small Business Administration. Statistics of U.S. Businesses

. Available at: http://www.sba.gov/ADVO/stats/data.html. U.S. Department of Commerce (U.S. DOC). 1997. Bureau of the Census. 1997 Economic Census Bridge Between NAICS and SIC.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-1 

# Chapter B4: Profile of the Electric Power Industry

I NTRODUCTION This profile compiles and analyzes economic and operational data for the electric power generating industry. It provides information on the structure and overall performance of the industry and explains important trends that may influence the nature and magnitude of economic impacts that could result from regulation of facilities in Phase III. Based on the proposed design intake flow threshold-based options in today’s proposed rule, Electric Generators would not be subject to national categorical requirements under the proposed Phase III rule. However, in developing the proposed rule, EPA analyzed other flow threshold options that would have subjected Electric Generators to national requirements. This chapter provides a profile of this industry, while Chapter B5 provides the economic impact analysis for this industry, based on the other threshold options considered – but not proposed – by EPA. The electric power industry is one of the most extens ively studied industries. The Energy Information Administration (EIA), among others, publishes a multitude of reports, documents, and studies on an annual basis. This profile is not intended to duplicate those efforts. Rather, this profile compiles, summarizes, and presents those industry data that are important in the context of the proposed rule for Phase III existing facilities. For more information on general concepts, trends , and developments in the electric power industry, the last section of this profile, “References,” presents a select list of other publications on the industry. The remainder of this profile is organized as follows:

< Section B4-1 provides a brief overview of the industry, including descriptions of major industry sectors, types of generating facilities, and the entities that own generating facilities.

< Section B4-2 provides data on industry produc tion, capacity, and geographic distribution.

< Section B4-3 focuses on electric generating facilities potentially subject to Phase III regulation. This section provides information on the physical, geographi c, and ownership characteristics of the potential Phase III generators.

< Section B4-4 provides a brief discussion of factors affecting the future of the electric power industry, including the status of restructuring, and summarizes forecasts of market conditions through the year 2025. 

### C HAPTER 

### C ONTENTS B4-1 Industry Overview . . . . . . . . . . . . . . . . . . . . . . . . B4-2 B4-1.1 Industry Sectors . . . . . . . . . . . . . . . . . . . B4-2 B4-1.2 Prime Movers . . . . . . . . . . . . . . . . . . . . . B4-2 B4-1.3 Ownership . . . . . . . . . . . . . . . . . . . . . . . . B4-5 B4-2 Domestic Production . . . . . . . . . . . . . . . . . . . . . . B4-8 B4-2.1 Generating Capacity . . . . . . . . . . . . . . . . B4-8 B4-2.2 Electricity Generation . . . . . . . . . . . . . . . B4-9 B4-2.3 Geographic Distribution . . . . . . . . . . . . B4-10 B4-3 Power Plants Potentially Subject to Phase III Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B4-13 B4-3.1 Ownership Type . . . . . . . . . . . . . . . . . . B4-14 B4-3.2 Ownership Size . . . . . . . . . . . . . . . . . . . B4-15 B4-3.3 Plant Size . . . . . . . . . . . . . . . . . . . . . . . B4-17 B4-3.4 Geographic Distribution . . . . . . . . . . . . B4-18 B4-3.5 Cooling Water Characteristics . . . . . . . B4-19 B4-4 Industry Outlook . . . . . . . . . . . . . . . . . . . . . . . . B4-21 B4-4.1 Current Status of Industry Deregulation B4-21 B4-4.2 Energy Market Model Forecasts . . . . . . B4-22 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B4-24 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B4-27§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

1 Terms highlighted in bold and italic font are defi ned in the glossary at the end of this chapter.

2 The terms “plant” and “facility” are used interchangeably throughout this profile.

B4-2 

### B4-1 I NDUSTRY 

### O VERVIEW This section provides a brief overview of the electric pow er industry, including descriptions of major industry sectors, types of generating facilities, and the entities that own generating facilities.
B4-1.1 Industry Sectors The electricity business is made up of three major functional service components or sectors: generation , transmission , and distribution . These terms are defined as follows (Beamon, 1998; Joskow, 1997; U.S. DOE, 2004): 1

< The generation sector includes the power plants that produce, or “generate,” electricity. 2Electric power is usually produced by a mechanically driven rotary generator called a turbine. Generator drivers, also called prime movers, include gas or diesel internal combustion machines, as well as streams of moving fluid such as wind, water from a hydroelectric dam, or steam from a boiler. Most boilers are heated by direct combustion of fossil or biomass-derived fuels or waste heat from the exhaust of a gas turbine or diesel engine, but heat from nuclear, solar, and geothermal sources is also used. Electric power may also be produced without a generator by using electrochemical, thermoelectric, or photovoltaic (solar) technologies.

< The transmission sector can be thought of as the interstate highway system of the business – the large, high-voltage power lines that deliver electricity from pow er plants to local areas. Electricity transmission involves the “transportation” of electricity from power plants to distribution centers using a complex system. Transmission requires: interconnecting and integrating a number of generating facilities into a stable, synchronized, alternating current (AC) network; scheduling and dispatching all connected plants to balance the demand and supply of electricity in real time; and managing the system for equipment failures, network constraints, and inter action with other transmission networks.

< The distribution sector can be thought of as the local delivery system – the relatively low-voltage power lines that bring power to homes and businesses. Electricity distribution relies on a system of wires and transformers along streets and underground to provideelectricity to residential, commercial, and industrial consumers. The distribution system involves both the provision of the hardware (e.g., lines, poles, transformers) and a set of retailing functi ons, such as metering, billing, and various demand management services. Of the three industry sectors, only electricity generation uses cooling water and is subject to section 316(b) regulation. The remainder of this profile w ill focus on the generation sector of the industry.

B4-1.2 Prime Movers Electric power plants use a variety of prime movers to generate electricity. The type of prime mover used at a given plant is determined based on the type of load the plant is designed to serve, the availability of fuels, and energy requirements. Most prime movers use fossil fuel s (coal, oil, and natural gas) as an energy source and employ some type of turbine to produce electricity. A ccording to the Department of Energy, the most common prime movers are (U.S. DOE, 2004):§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-3 < Steam Turbine: “Most of the electricity in the United Stat es is produced in steam turbines. In a fossil-fueled steam turbine, the fuel is burned in a boiler to produce steam. The resulting steam then turns the turbine blades that turn the sh aft of the generator to produce electricity. In a nuclear-powered steam turbine, the boiler is replaced by a reactor containing a core of nuclear fuel (primarily enriched uranium). Heat produced in the reactor by fission of the uraniu m is used to make steam. The steam is then passed through the turbine generator to produce electricity, as in the fossil-fueled steam turbine. Steam-turbine generating units are used primarily to serve the base load of electric utilities. Fossil-fueled steam-turbine generating units range in size ( nameplate capacity ) from 1 megawatt (MW) to more than 1,000 megawatts. The size of nuclear-powered steam-turbine generating units in operation today ranges from 75 megawatts to more than 1,400 megawatts.”

< Gas Turbine: “In a gas turbine (combustion-turbine) un it, hot gases produced from the combustion of natural gas and distillate oil in a high-pressure combustion chamber are passed directly through the turbine, which spins the generator to produce electri city. Gas turbines are commonly used to serve the peak loads of the electric utility. Gas-turbine units can be installed at a variety of site locations, because their size is generally less than 100 megawatts. Gas-turbine units also have a quick startup time, compared with steam-turbine units. As a result, g as-turbine units are suitabl e for peakload, emergency, and reserve-power requirements. The gas turbine, as is typical with peaking units, has a lower efficiency than the steam turbine used for baseload power.”

< Combined-Cycle Unit: “The efficiency of the gas turbine is increased when coupled with a steam turbine in a combined-cycle operation. In this operation, hot gases (which have already been used to spin one turbine generator) are moved to a waste-heat r ecovery steam boiler where the water is heated to produce steam that, in turn, produces electricity by running a second steam-turbine generator. In this way, two generators produce electricity from one initial fuel input. All or part of the heat required to produce steam may come from the exhaust of the gas tu rbine. Thus, the steam-turbine generator may be supplementarily fired in addition to the waste heat . Combined-cycle generating units generally serve intermediate loads .”

< Internal Combustion Engine: “These prime movers have one or more cylinders in which the combustion of fuel takes place. The engine, which is connected to the shaft of the generator, provides the mechanical energy to drive the generator to produce electricity. Internal-combustion (or diesel) generators can be easily transported, can be installed upon short notice, and can begin producing electricity nearly at the moment they start. Thus, like gas turbines, they are usually operated during periods of high demand for electricity. They are generally about 5 megawatts in size.”

< Hydroelectric Generating Unit : “Hydroelectric power is the result of a process in which flowing water is used to spin a turbine conn ected to a generator. The two basic types of hydroelectric systems are those based on falling water and natural river current. In the first system, water accumulates in reservoirs created by the use of dams. This water then falls through conduits (penstocks) and applies pressure against the turbine blades to driv e the generator to produce electricity. In the second system, called a run-of-the-river system, the force of the river current(rather than falling water) applies pressure to the turbine blades to produce electricity. Since run-of-the-river systems do not usually have reservoirs and cannot store substantial quantities of water, power production from this type of system depends on seasonal changes and stream flow. These conventional hydroelectric generating units range in size from less than 1 megawatt to 700 megawatts. Because of thei r ability to start quickly and make rapid changes in power output, hydroelectric generating units are suitable for serving peak loads and providing spinning reserve power, as well as serving baseload requirements. Another kind of hydroelectric power generation is the pumped storage hydroelectric system. Pumped storage hydroelectric plants use the same principle for generation of power as the conventional hydroe lectric operations based on falling water and river current. However, in a pumped storage operation, low- cost off-peak energy is used to pump water to an upper reservoir where it is stored as potential energy. The water is then released to flow back down through the turbine generator to produce electricity during periods of high demand for electricity.” In addition, there are a number of other prime movers:§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

3 Form EIA-860 (Annual Electric Generator Report) collects data used to create an annual inventory of all units, plants, and utilities. The data collected includes: type of prime mover; nameplate rating; energy source; year of initial commercial operation; operating status; cooling water source, and NERC region.

B4-4 < Other Prime Movers: “Other methods of electric power generation, which presently contribute only small amounts to total power production, have potentia l for expansion. These include geothermal, solar, wind, and biomass (wood, municipal solid waste, agricultural waste, etc.). Geothermal power comes from heat energy buried beneath the surface of the earth . Although most of this heat is at depths beyond current drilling methods, in some areas of the country, magma – the molten matter under the earth's crust from which igneous rock is formed by cooling – fl ows close enough to the surface of the earth to produce steam. That steam can then be harnessed for use inconventional steam-turbine plants. Solar power is derived from the energy (both light and heat) of th e sun. Photovoltaic conversion generates electric power directly from the light of the sun; whereas, so lar-thermal electric generators use the heat from the sun to produce steam to drive turbines. Wind pow er is derived from the conversion of the energy contained in wind into electricity. A wind turbine is similar to a typical wind mill. However, because of the intermittent nature of sunlight and wind, high capacity utilization factors cannot be achieved for these plants. Several electric utilities have incorporated wood and waste (for example, municipal waste, corn cobs, and oats) as energy sources for producing electricity at their power plants. These sources replace fossil fuels in the boiler. The combustion of wood and waste creates steam that is typically used in conventional steam-electric plants.” Section 316(b) regulation is only relevant for electric generators that use cooling water. However, not all prime movers require cooling water. Only prime movers with a steam electric generating cycle use large enough amounts of cooling water to fall under the scope of the opti ons evaluated for this proposed rule. This profile will, therefore, differentiate between steam electric and other prime movers. EPA identified steam electric prime movers using data collected by the EIA (U.S. DOE, 2001a). 3 For this profile, the following prime movers, including both steam turbines and combined-cycletechnologies, are classified as steam electric:

< Steam Turbine, including nuclear, geothermal, and solar steam (not including combined cycle),

< Combined Cycle Steam Part,

< Combined Cycle Combustion Turbine Part,

< Combined Cycle Single Shaft (combustion turbine and steam turbine share a single generator), and

< Combined Cycle Total Unit (used only for plants /generators that are in the planning stage). Table B4-1 provides data on the number of existing power plants, by prime mover and regulatory status. This table includes all plants that have at least one non-re tired unit and that submitted Form EIA-860 (Annual Electric Generator Report) in 2001. For the purpose of this analysis, plants were classified as “steam turbine” or “combined-cycle” if they have at least one generating unit of that type. Plants that do not have any steam electric units were classified under the prime mover type that account s for the largest share of the plant’s total generating capacity.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-5 Table B4-1: Number of Existing Utility and Nonutility Plants by Prime Mover in 2001
Prime Mover Number of Plants Utility a Nonutility a Total Steam Turbine 635 903 1,538 Combined-Cycle 59 239 298 Gas Turbine 308 426 734 Internal Combustion 557 346 903 Hydroelectric 900 490 1,390 Other 22 134 156 Total 2,481 2,538 5,019

a See definition of utility and nonutility in Section B4-1.3. Source: U.S. DOE, 2001a.
B4-1.3 Ownership The U.S. electric power industry consists of two broa d categories of firms that own and operate electric generating plants: traditional electric utilities and nontraditi onal participants. Generally, they can be defined as follows (adapted from U.S. DOE, 2003a): 

###  ̃ Traditional electric utilities Traditional electric utilities are regulated and traditionally vertically integrated entities. They all have distribution facilities for delivery of electric energy for use primarily by the public, but they may or may not generate electricity. “Transmission utility” refers to the regulated owner/operator of the transmission system only. “Distribution utility” refers to the regulated owner/operator of the distribution system serving retail customers. Electric utilities can be further divided into four major ownership categories: investor-owned utilities, publiclyowned utilities, rural electric cooperatives, and Federal utilities. Each category is discussed below (U.S. DOE, 2004).

< Investor-owned utilities (IOUs) are privately owned entities. Like all private businesses, investorowned electric utilities have the fundamental objective of producing a return for their investors. These utilities either distribute profits to stockholders as di vidends or reinvest the profits. Investor-owned electric utilities are granted service monopolies in certain geographic areas and are obliged to serve all consumers. As franchised monopolies, these utilities are regulated and required to charge reasonable prices, to charge comparable prices to similar classi fications of consumers, and to give consumers access to services under similar conditions. Most investo r-owned electric utilities are operating companies that provide basic services for the generation, transmission, and distribution of electricity. The majority of investor-owned utilities perform all three functions . In 2001, IOUs operated 1,148 facilities, which accounted for approximately 44% of all U.S. electric generation capacity (U.S. DOE, 2001a).

< Publicly-owned utilities are nonprofit local government agencies established to provide service to their communities and nearby consumers at cost. Publicly owned electric utilities include municipalities, State authorities, and political subdivisions (e.g., public power districts, irrigation projects, and other State agencies established to serve their local municipalities or nearby communities). Excess funds or “profits” from the operation of these utilities are put toward re ducing rates, increasing facility efficiency and capacity, and funding community programs and local government budgets. Most municipal utilities are nongenerators engaging solely in the purchase of whol esale electricity for resale and distribution. The larger municipal utilities, however, generate and tran smit electricity as well. In general, publicly-owned utilities have access to tax-free financing and do not pay certain taxes or dividends, giving them some cost advantages over IOUs. In 2001, municipalities ope rated 785 facilities (4.9% of U.S. capacity), States§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-6 operated 85 facilities (2.1% of U.S. capacity), and political subdivisions operated 103 facilities (2.0% of U.S. capacity) (U.S. DOE, 2001a).

< Cooperative utilities (or “coops”) are member-owned entities cr eated to provide electricity to those members. These utilities, established under the Rural Electrification Act of 1936, operate in rural areas with low concentrations of consumers because these areas historically have been viewed as uneconomical operations for IOUs. The National Rural Utilities Cooperative Finance Corporation, the Federal Financing Bank, and the Bank of Cooperatives are important sources of financing for these utilities. Cooperatives operate in 47 States and are incorpor ated under State laws. In 2001, rural electric cooperatives operated 166 generating f acilities and accounted for approximately 3.2% of all U.S. electric generation capacity (U.S. DOE, 2001a).

< Federal electric utilities are part of several agencies in th e U.S. Government: the Army Corps of Engineers (Department of Defense), the Bureau of Indian Affairs and the Bureau of Reclamation (Department of the Interior), the International Boundary and Water Commission (Department of State), the Power Marketing Administrations (Departmen t of Energy), and the Tennessee Valley Authority (TVA). Three Federal agencies operate generating f acilities: TVA, the largest Federal producer; the U.S. Army Corps of Engineers; and the U.S. Bureau of Reclamation. In 2001, the ten Federal electric utilities operated 194 facilities, accounting for 7.6% of total U.S. electric generation capacity (U.S. DOE, 2001a).
Traditional electric utilities are hereafter referred to as “ utilities”. 

###  ̃ Nontraditional participants Nontraditional participants are unregulated entities and include energy service providers, power marketers, independent power producers (IPPs), and combined heat and power plants (CHPs, formerly referred to as cogenerators). IPPs own or operatefacilities whose primary business is to produce electricity for use by the public; they are not aligned with distribution facilities. CHPs are plants designed to produce both heat and electricity from a single heat source. CHPs can be i ndependent power producers, or industrial or commercial establishments. In 2001, nontraditional participants operated 2,538 facilities, accounting for 36.1% of total U.S. electric generation capacity (U.S. DOE, 2001a). Nontraditional participants in the electric power industry are hereafter referred to as “ nonutilities”.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-7 19469,402 MW

2,538 329,550 MW 166 29,010 MW 103 18,012 MW 85 19,098 MW 785 44,895 MW 1,148 404,158 MW

0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% Nonutility Federal Cooperative Political Subdivision State Municipality Investor-Owned

Capacity (MW)

N

umber of Plants Figure B4-1 presents the number of generating facilities and their capacity in 2001, by type of ownership. The horizontal axis also presents the percentage of the U.S. total that each type represents. This figure is based on data for all plants that have at least one non-retired unit and that submitted Form EIA-860 in 2001. The graphic shows that nonutilities account for the largest percentage of facilities (2,538, or approximately 51%), but only represent 36% of total U.S. generating capacity. Inv estor-owned utilities operate the second largest number of facilities, 1,147, and account for 44.1% of total U.S. capacity.

Figure B4-1: Distribution of Facilities a nd Capacity by Ownership Type in 2001

Source: U.S. DOE, 2001a.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-8 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Nonutility

Utility 

### B4-2 D OMESTIC 

### P RODUCTION This section presents an overview of U.S. generating capacity and electricity generation. Section B4-2.1 provides data on capacity, and Section B4-2.2 provides data on gene ration. Section B4-2.3 presents an overview of the geographic distribution of generation plants and capacity.
B4-2.1 Generating Capacity Utilities own and operate the majority of the generating capacity (65%) in the United States. Nonutilities owned only 35% of total capacity in 2001. Nonutility capacity has increased substantially in the past few years, as a result of both new plant construction by independent power producers and plant divestitures by investor-owned utilities. Nonutility capacity has increased 537% between 1991 and 2001, compared with a decrease in utility capacity of 21% over the same time period (U.S. DOE, 2003a). Figure B4-2 shows the growth in utility and nonutility capacity from 1991 to 2001. The growth in nonutility capacity, combined with a decrease in utility capacity, has resulted in a modest growth in total generating capacity. The significant increase in nonutility capacity and decrease in utility capacity since 1997 is mainly attributable to utility plants being sold to nonutilities. Figure B4-2: Net Summer Capacity, 1991 to 2001 (MW)

Source: U.S. DOE, 2003a. 

### C APACITY

### /C APABILITY The rating of a generating unit is a measure of its ability to produce electricity. Generator ratings are expressed in megawatts (MW). Capacity and capability are the two common measures: Nameplate capacity is the full-load continuous output rating of the generating unit under specified conditions, as designated by the manufacturer. Net capability is the steady hourly output that the generating unit is expected to supply to the system load, as demonstrated by test procedures. The capability of the generating unit in the summer is generally less than in the winter due to high ambient-air and cooling-water temperatures, which cause generating units to be less efficient. The nameplate capacity of a generating unit is generally greater than its net capability. U.S. DOE, 2004§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-9 B4-2.2 Electricity Generation In 2001, total net electricity generation in the U.S. was 3,737 million MWh. Utility-owned plants accounted for 70% of this amount. Total net generation has increased by 22% over the 11 year period from 1991 to 2001. During this period, nonutilities increased their electricity generation by 345%. In comparison, generation by utilities decreased by 7% (U.S. DOE, 2003a). This trend is expected to continue with deregulation in the coming years, as more facilities are purchased and built by nonutility power producers. Table B4-2 shows the change in net generation between 1991 and 2001 by energy source and ownership type.

Table B4-2: Net Generation by Energy Source and Ownership Type, 1991 to 2001 (million MWh) Energy Source Utilities Nonutilities Total 1991 2001 % Change 1991 2001 % Change 1991 2001 % Change Coal 1,551 1,560 0.6% 39 344 771.4% 1,591 1,904 19.7% Nuclear 613 534 -12.8% - 235 n/a 613 769 25.5% Natural Gas 264 264 0.1% 117 375 219.2% 382 639 67.5% Hydropower 276 190 -31.0% 9 18 101.9% 284 208 -26.8% Oil 111 79 -29.2% 8 46 454.7% 120 125 4.3% Renewables a 10 2 -78.8% 59 76 29.3% 69 78 13.4% Other Gases - - - 11 9 -20.3% 11 9 -20.3% Otherb - - - 5 5 -1.1% 5 5 -1.0% Total 2,825 2,630 -6.9% 249 1,107 344.9% 3,074 3,737 21.6%

a Renewables include solar, wind, wood, bi omass, and geothermal energy sources. b Other includes batteries, chemicals, hydrogen, pitch, purchased steam, su lfur, and miscellaneous technologies Source: U.S. DOE, 2003a.
As shown in Table B4-2, natural gas generation grew the fastest among the fuel source categories, increasing by 68% between 1991 and 2001. Nuclear generation increasedby 26%, while coal generation increased by 20%. Generation from renewable energy sources increased 13%. Hydropower, however, experienced a decline of 27%. For utilities, generation using natural gas and coal as fuelsources was relatively constant. Generation using other sources fell, mostly because of sales to nonutilities. Nonutility generation grew quickly between 1991 and 2001 with the passage of legislation aimed at increasing compe tition in the industry. Coal generation was the fastest growing nonutility energy source, increasing 771% between1991 and 2001. Generation from oil-fired facilities also increased substantially, by 455%. 

### M EASURES OF 

### G ENERATION The production of electricity is referred to as generation and is measured in kilowatthours (kWh) . Generation can be measured as: Gross generation: The total amount of power produced by an electric power plant. Net generation: Power available to the transmission system beyond that needed to operate plant equipment. For example, around 7% of electricity generated by steam electric units is used to operate equipment. Electricity available to consumers: Power available for sale to customers. Approximately 8% to 9% of net generation is lost during the transmission and distribution process. U.S. DOE, 2004§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-10 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% 50.0%

C oa l Nuc l e a r Natur a l Ga

s Hyd ro powe r Petro l e u m R e newab l e s Othe r Gas

e s Nonutility

Utility Figure B4-3 shows total net generation for the U.S. by primary fuel source, for utilities and nonutilities. Electricity generation from coal-fired plants accountedfor 51% of total 2001 generation. Electric utilities generated 82% (1,560 billion kWh) of the 1,904 billion kWh of electricity generated by coal-fired plants. This represents approximately 59% of total utility generation. The remaining 18% (344 billion kWh) of coal-fired generation were provided by nonutilities, accounting for 31% of total nonutility generation. The second largest source of electricity generation was nuclear power plan ts, accounting for 20% total utility generation and 21% of nonutility generation. Another significant source of electri city generation were gas -fired power plants, which accounted for 34% of nonutility generation and 17% of total generation.

Figure B4-3: Percentage of Electricity Generation by Primary Fuel Source in 2001

Source: U.S. DOE, 2003a.
The options evaluated for this proposed rule would affect facilities differently based on the fuel sources and prime movers used to generate electricity. As described in Section B4-1.2 above, only prime movers with a steam electric generating cycle use substantial amounts of cooling water and are potentially subject to Phase III regulation.
B4-2.3 Geographic Distribution Electricity is a commodity that cannot be stored or easily transported over long distances. As a result, the geographic distribution of power plants is of primary importance to ensure a reliable supply of electricity to all customers. The U.S. bulk power system is composed of three major networks, or power grids:

< the Eastern Interconnected System , consisting of one third of the U.S., from the East Coast to East of the Missouri River;

< the Western Interconnected System , West of the Missouri River, including the Southwest and areas West of the Rocky Mountains; and

< the Texas Interconnected System , the smallest of the three, consisting of the majority of Texas.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-11 The Texas system is not connected with the other two systems, but the other two have limited interconnection to each other. The Eastern and Western systems are integrated with or have links to the Canadian grid system. The Western and Texas systems have links with Mexico. These major networks contain extra-high voltage connecti ons that allow for power transactions from one part of the network to another. Wholesale transactions can take place within these networks to reduce power costs, increase supply options, and ensure system reliability. Reliability refers to the ability of power systems to meet the demands of consumers at any given time. Efforts to enhance reliability reduce the chances of power outages. The North American Electric Reliability Council (NERC) is responsible for the overall reliability, planning, and coordination of the power grids. This voluntary organi zation was formed in 1968 by electric utilities, following a 1965 blackout in the Northeast. NERC is organized into ten regional councils that c over the 48 contiguous States, and affiliated councils that cover Hawaii, part of Alaska, and portions of Canada and Mexico. These regional councils are responsible for the overall coordination of bulk power policies that affect their regions’ reliability and quality of service. Each NERC region deals with electric ity reliability issues in its region, based on available capacity and transmission constraints. The councils al so aid in the exchange of information among member utilities in each region and among regions. Service areas of the member utilities determine the boundaries of the NERC regions. Though limited by the larger bulk power grids described above, NERC regions do not necessarily follow any State boundaries. Historically, almost all wholesale trade was within the NERC regions, but utilities are expanding wholesale trade beyond those traditional boundaries (U.S. DOE, 2004). Figure B4-4 below provides a map of the NERC regions, which include:

< ECAR – East Central Area Reliability Coordination Agreement

< ERCOT – Electric Reliability Council of Texas, Inc.

< FRCC – Florida Reliability Coordinating Council

< MAAC – Mid-Atlantic Area Council

< MAIN – Mid-America Interconnected Network, Inc.

< MAPP – Mid-Continent Area Power Pool

< NPCC – Northeast Power Coordinating Council

< SERC – Southeastern Electric Reliability Council

< SPP – Southwest Power Pool, Inc.

< WECC – Western Electricity Coordinating Council (formerly the Western Systems Coordinating Council) Alaska and Hawaii are not shown in Figure B4-4. Part of Alaska is covered by the Alaska Systems Coordinating Council (ASCC), an affiliate NERC member. The State of Hawaii also has its own reliability authority (HICC).§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-12 Figure B4-4: North American Electric Reliability Council (NERC) Regions

Source: NERC, 2004.
The options evaluated for Phase III existing facilities may affect plants located in different NERC regions differently. Economic characteristics of existing facilities affected by the analyzed options are likely to vary across regions by fuel mix, and the costs of fuel, tran sportation, labor, and construc tion. Baseline differences in economic characteristics across regions may influence the impact of an option on profitability, electricity prices, and other impact measures. However, as discussed in the appendix to Chapter B5: Economic Impact Analysis for Electric Generators , the three proposed options are estimated to have no impact on electricity prices in each region since none of the three options requires any pow er plants to comply with the national categorical requirements of the proposed rule. Table B4-3 shows the distribution of all existing plants and capacity by NERC region. The table shows that 1,306 plants, equal to 26% of all facilities in the U.S., are located in the Western Electric Coordinating Council (WECC). However, these plants account for only 17% of total national capacity. Conversely, only 13% of generating plants are located in the Southeastern Elect ric Reliability Council (SERC), yet these plants account for 22% of total national capacity.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

4 Existing manufacturing facilities as well as new offshore oil and gas extraction facilities are also potentially subject to Phase III regulation. See chapters A1, B2, and C2 for more information on these industries.

B4-13 Table B4-3: Distribution of Existing Plan ts and Capacity by NERC Region in 2001
NERC Region Plants Capacity Number % of Total Total MW % of Total ASCC 124 2.5% 2,261 0.2% ECAR 448 8.9% 128,301 14.0% ERCOT 215 4.3% 80,523 8.8% FRCC 128 2.6% 45,505 5.0%
HI CC 34 0.7% 2,452 0.3% MAAC 246 4.9% 63,676 7.0% MAIN 412 8.2% 70,568 7.7% MAPP 445 8.9% 37,410 4.1% NPCC 718 14.3% 69,861 7.6% SERC 661 13.2% 204,538 22.4% SPP 282 5.6% 51,743 5.7% WECC 1,306 26.0% 157,287 17.2% Total 5,019 100% 914,124 100% Source: U.S. DOE, 2001a.



### B4-3 P OWER 

### P LANTS 

### P OTENTIALLY 

### S UBJECT TO 

### P HASE 

### III R EGULATION Section 316(b) of the Clean Water Act applies to point source facilities which use or propose to use a cooling water intake structure that withdraws cooling water directly from a surface waterbody of the United States. Among power plants, only those facilities employing a steam electric generating technology require cooling water and are therefore of interest to this analysis. The following sections describe power plants that are potentially subject to Phase III regulation. These are existing, steam electric power generating facilities that meet all of the following conditions: 4

< They use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or arrangement with an independent supp lier who has a cooling water intake structure; or their cooling water intake structure(s) withdraw(s) cooling water from waters of the U.S., and at least twenty-five (25) percent of the water withdrawn is used for contact or non-contact cooling purposes;

< they have a National Pollutant Discharge Eliminati on System (NPDES) permit or are required to obtain one; and

< they have a design intake flow (DIF) of 2 million gallons per day (MGD) or greater but were not covered by the final Phase II rule (i.e., their DIF is at least 2 MGD but less than 50 MGD).§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

5 EPA applied sample weights to the 113 facilities to account for non-sampled facilities and facilities that did not respond to the survey. For more information on EPA’s 2000 Section 316(b) Industry Survey, please refer to the Information Collection Request (U.S. EPA, 2000).

B4-14 Phase III regulation also covers substantial additions or modifications to operations undertaken at such facilities. While all electric generators that meet these criteria are potentially subject to Phase III regulation, this Economic Analysis (EA) focuses on 113 steam electric power generating facilities identified in EPA’s 2000 Section 316(b) Industry Survey. These 113 facilities represent 117 facilities nation-wide. 5 The remainder of this chapter will refer to these potentially regulated facilities as “potential Phase III Electric Generators.” The following sections present a variety of physical, geographic, and ownershi p information about the potential Phase III Electric Generators. Topics discussed include:

< Ownership type: Section B4-3.1 discusses potential Phase III Electric Generators with respect to the electric utility entities that own them (referred to as “owner-utilities”).

< Ownership size: Section B4-3.2 presents information on the size of the ultimate parent entities of potential Phase III Electric Generators.

< Plant size: Section B4-3.3 discusses the size distribution of potential Phase III Electric Generators by generation capacity.

< Geographic distribution: Section B4-3.4 discusses the distribution of potential Phase III Electric Generators by NERC region.

< Cooling Water Characteristics: Section B4-3.5 presents information on the type of waterbody from which potential Phase III Electric Generators draw their cooling water, the type of cooling system they operate, and the design intake flow of their cooling water intake structures.
B4-3.1 Ownership Type The owners and operators of power pl ants can be divided into two broad ownership categories: traditional utilities and nonutilities. Utilities can further be classified as investor-owned utilities, publicly-owned utilities (municipalities, State authorities, and political subdivisi ons), cooperatives, and Federal electric utilities (see also Section B4-1.3 above). This classification is important because EPA has separately considered impacts on governments in its regulatory development (see Chapter D2: UMRA Analysis for the analysis of government impacts of the proposed rule). 

### W ATER 

### U SE BY 

### S TEAM 

### E LECTRIC 

### P OWER 

### P LANTS Steam electric generating plants are the single largest industrial users of water in the United States. In 2000:

< steam electric plants withdrew an estimated 195 billion gallons per day, accounting for 48% of total water withdrawals and 60% of total surface water withdrawals in the U.S.;

< steam electric plants accounted for 96% of all saline withdrawals in the U.S.;

< steam electric water withdrawals have increased by 3% between 1995 and 2000;

< surface water accounted for more than 99% of steam electric water withdrawals;

< approximately 69% of water intake by the electric power industry was from freshwater sources, 31% was from saline sources;

< 91% of water withdrawal by power plants was used in once-through cooling systems; 9% was used in closed-loop cooling systems;

< Illinois, Texas, and Tennessee combined accounted for 22% of steam electric freshwater withdrawals; California and Florida combined accounted for 41% of steam electric saline withdrawals;

< the average amount of water used to produce one kilowatthour (kWh) decreased from 63 gallons in 1950 to 21 gallons in 2000. USGS, 2004§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

6 Owner-utilities are the direct owners of generating plan ts. They are not necessarily the ultimate parents of the plants.

B4-15 Table B4-4 shows the number of owner-utilities, 6plants, and capacity by ownership type. Numbers are presented for the industry as a whole and the portion of the industry potentially subject to Phase III regulation. Overall, 2.9% of all owner-utilities, 2.3% of all plants, and 7.5% of all capacity is potentially subject to Phase III regulation. The table further shows that most potential Phase III Electric Generators (55) are owned by investorowned utilities. An additional 34 potential Phase III Generators are owned by nonutilities. For all ownership types, less than 6% of all power plants are potentially s ubject to Phase III regulation. However, the percentage of capacity potentially subject to Phase III regulation is hi gher for State-owned power plants and cooperatives (26% and 20%, respectively) compared to the other ownership types.

Table B4-4: Utilities, Plants, and Capacity by Ownership Type in 2001 a

Ownership Type Owner-Utilities b Plants Capacity (MW)

Total c With Potential Phase III

Plants % With Potential Phase III

Plants Total c Potential Phase

III d % Potential Phase III Total c Potential Phase

III d % Potential Phase III
Investor-Owned 467 37 7.9% 1,148 55 4.8% 404,158 41,681 10.3% Federal 8 1 12.5% 194 1 0.5% 69,402 2,409 3.5% State 20 3 15.0% 85 4 4.7% 19,098 4,946 25.9% Municipal 532 12 2.3% 785 13 1.7% 44,895 688 1.5% Political Subdivision 46 0 0.0% 103 0 0.0% 18,012 0 0.0% Cooperative 85 8 9.4% 166 9 5.4% 29,010 5,812 20.0% Total Utility 858 61 7.1% 2,481 83 3.3% 584,574 55,537 9.5% Nonutility e 2,127 26 1.2% 2,538 34 1.4% 329,550 12,961 3.9% Total 2,985 87 2.9% 5,019 117 2.3% 914,124 68,498 7.5%

a Numbers may not add up to totals due to independent rounding. b Owner-utilities are the direct owners of gene rating plants. They are not necessarily the ultimate parents of the plants. Numb ers exclude utilities that engage solely in transmission and distribution.

c Information on the total number of owner-utilities is based on data from Form EIA-861 (U.S. DOE, 2001b). Information on plants and capacity is based on data from Form EIA-860 (U.S. DOE, 2001a). These two data sources report information for noncorresponding sets of power producers. Th erefore, the total number of owner-utilities is not directly comparable to the information on total plants or total capacity. d The number of potential Phase III Electric Generators and capacity was sample weighted to account for survey non-respondents.

e Total nonutilities from Form EIA-860; Form EIA-861 does not provide information for nonutilities. Source: U.S. DOE, 2001a; U.S. DOE, 2001b; U. S. EPA, 2000; U.S. EPA Analysis, 2004.
B4-3.2 Ownership Size In developing the proposed rule, EPA conducted an analysis of small entity impacts. The small entity analysis is conducted at the ultimate parent firm level which, fo r investor-owned utilities and nonutilities, is often different from the owner-utility level. EPA estimates that the 87 owner-utilities with plants potentially subject to Phase III regulation, presented in Table B4-4 above, are owned by 73 ultimate parent firms. Of these 73 entities, EPA§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

7 Small entities are defined as: (1) a small business accord ing to the Small Business Administration (SBA) size standards; (2) a small governmental jurisdiction that is a government of a city, county, town, school district, or special district with a population of less than 50,000; or (3) a small organization that is a not-for-profit enterprise that is independently owned and operated and is not dominant in its field.

B4-16 estimates that 13, or 17.8%, are small. 7 The size distribution varies considerably by ownership type: none of the potential Phase III investor-owned entiti es are small, compared to 75% of potential Phase III municipalities, 25% of potential Phase III cooperatives, and 9.5% of potential Phase III nonutilities. By definition, States and the Federal government are considered large parent entities. In general, traditional utility entities that own potential Phase III Electric Generators are larger than other entiti es in the industry. Of the 817 traditional utility parent entities in the industry, 412 entities, or 50.4%, are small. In contrast, only 21.2% of potential Phase III traditional utility entities are small. Overall, EPA estimates that 2.7% of all small utility parent entities own plants that are potentially subject to Phase III regulation. For nonutiliti es, the industry-wide number of small entities is not available.
For a detailed discussion of the identification and size determination of parent entities, see Chapter D1: Regulatory Flexibility Analysis. The chapter also documents how EPA considered the economic impacts on small entities when developing this regulation.

Table B4-5: Existing Parent Entities by Ownership Type and Size in 2001 a

Ownership Type Total Number of Parent Entities b Total Number of Parent Entities That Own Potential Phase III ElectricGenerators % of Small Entities That Own Potential Phase III Electric Generators Small Large Total % Small Small Large Total % Small

Investor-Owned 6 120 126 4.8% - 28 28 0.0% 0.0% Federal - 8 8 0.0% - 1 1 0.0% 0.0% State - 20 20 0.0% - 3 3 0.0% 0.0% Municipal 302 230 532 56.8% 9 34 12 75.0% 3.0% Political Subdivision 37 9 46 80.4% - - - 0.0% 0.0% Cooperative 67 18 85 78.8% 2 6 8 25.0% 3.0% Total Utility 412 405 817 50.4% 11 41 52 21.2% 2.7% Nonutility c n/a n/a 1,718 n/a 2 19 21 9.5% n/a Total n/a n/a 2,535 n/a 13 60 73 17.8% n/a

a Numbers may not add up to totals due to independent rounding. b The total number of parent entities that own generation utilities is based on data from Form EIA-861 (U.S. DOE, 2001b). Most o f the other industry-wide information in this profile is based on data from Form EI A-860 (U.S. DOE, 2001a). Since these two form s report data for differing sets of facilities, the information in this table is not directly compar able to the other informationpresented in this profile.

c Total nonutilities from Form EIA-860; Form EIA-861 does not provide data on nonutilities. Source: U.S. DOE, 2001a; U.S. DOE, 2001b; U.S. EPA Analysis, 2004.
Table B4-6 presents the sample-weighted number of pot ential Phase III Electric Generators that are owned by small entities. The table shows that 14 of the 117 poten tial Phase III Electric Generators, or 12.2%, are owned by§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-17 small entities. Ten of the 14 potential Phase III Generato rs owned by small entities are municipalities, two are nonutilities, and two are rural electric cooperatives. Th ere are no potential Phase III i nvestor-owned utilities that are owned by a small entity. By definition, States and the Federal government are considered large parent entities.

Table B4-6: Potential Phase III Power Plants by Ownership Type and Size in 2001
Ownership Type Number of Potential Phase III Facilties a Small Large Total % Small Investor-Owned 0 55 55 0.0% Federal 0 1 1 0.0% State 0 4 4 0.0% Municipal 10 3 13 76.9% Cooperative 2 7 9 21.2% Total Utility 12 71 83 14.5% Nonutility 2 32 34 6.4% Total 14 103 117 12.2%

a The number of potential Phase III Electric Generators was sample weighted to account for survey non-respondents. Numbers may not add up to totals due to independent rounding. Source: U.S. EPA analysis, 2004.

B4-3.3 Plant Size EPA also analyzed the potential Phase III Electric Generators with respect to their generating capacity. The size of a Generator is important because it partly determines its need for cooling water and its importance in meeting electricity demand and reliability needs. Figure B4-5 s hows that most potential Phase III Electric Generators have small generating capacities. Of the 117 potential Phase III facilities, 69 facilities (59%) have a capacity of less than 500 MW; 23 facilities (20%) have a capacity between500 MW and 1,000 MW. Only five facilities have a capacity of greater than 2,000 MW, one of which has capacity of 2,500MW or greater. Of the 69 facilities with capacities less than 500 MW, 37 have a capacity of l ess than 100 MW, 17 have a capacity between 100 and 250 MW, and 15 have a capacity between 250 and 500 MW.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-18 69

23

11 9 4 1
0 10 20 30 40 50 60 70 80

< 500 500 - 1,000 1,000 - 1,500 1,500 - 2,000 2,000 - 2,500 >=2,500 Figure B4-5: Number of Potential Phase III El ectric Generators by Plant Size in 2001 a(MW)

a The number of plants was sample weighted to account for survey non-respondents. Source: U.S. DOE, 2001a; U.S. EPA, 2000.
B4-3.4 Geographic Distribution The geographic distribution of facilities is important b ecause a high concentration of facilities with regulatory compliance costs could lead to impacts on a regional leve l. Everything else being equal, the higher the share of plants with costs in any one region, the higher the like lihood that there may be economic and/or system reliability impacts as a result of the regulation. Table B4-7 shows the distribution of potential Phase III El ectric Generators by NERC region. The table shows that there are only moderate differences between the re gions both in terms of the number of potential Phase III Electric Generators and the percentage of all plants that they represent. Excluding Alaska and Hawaii, which have no generators potentially subject to Phase III regul ation, the percentage of potential Phase III Electric Generators in each region ranges from 1% in the West ern Electric Coordinating Council (WECC) and Northeast Power Coordinating Council (NPCC) to 5% in the E ast Central Area Reliability Coordination Agreement (ECAR). ECAR also has the highest absolute number of potential Phase III power plants with 22 facilities, followed by WECC with 18 facilities.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

8 Once-through cooling systems withdraw water from th e water body, run the water through condensers, and discharge the water after a single use. Recirculating sy stems, on the other hand, reuse water withdrawn from the source. These systems take new water into the syst em only to replenish losses from evaporation or other processes. Recirculating systems use cooling towers or ponds to cool water beforepassing it through condensers again.

B4-19 Table B4-7: Existing Plants by NERC Region in 2001
NERC Region Total Number of Facilities Potential Phase III Electric Generators a Number % of Total in Region ASCC 124 - 0% ECAR 448 22 5% ERCOT 215 8 4% FRCC 128 4 3% HICC 34 - 0% MAAC 246 10 4% MAIN 412 6 2% MAPP 445 10 2% NPCC 718 11 1% SERC 661 17 3% SPP 282 11 4% WECC 1,306 18 1% Total 5,019 117 2%

a The number of potential Phase III facilities was sample wei ghted to account for survey non-respondents. Numbers may not add up to totals due to independent rounding. Source: U.S. DOE, 2001a; U.S. EPA, 2000.
B4-3.5 Cooling Water Characteristics The main determinants of the compliance actions potentia lly required of Phase III Electric Generators include (1) the waterbody type from which they withdraw cooling water, (2) the type of cooling system they have in place in the baseline, and (3) the design intake flow of their cooli ng water intake structure. Table B4-8 shows that most of the potential Phase III Electric Generators draw water from a freshwater river or stream (87 plants or 75%). The next most frequent waterbody types are lakes or reservoirs (19 plants or 16%) and the Great Lakes (seven plants or 5%). The table also shows that most of the potentia l Phase III Electric Generators (86 plants or 74%) employ a recirculating cooling system. 8 Of the four plants that withdraw from an estuary, the most sensitive type of waterbody, two use a recirculating system. Plants w ith once-through cooling water systems withdraw between 70% and 98% more water than those with recirculating systems.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-20 Table B4-8: Number of Potential Phase III Electric Generators by Water Body Type and Cooling System Type a

Waterbody Type Cooling System Type Total Recirculating Once-Through Combination/Other No. % of Total No. % of Total No. % of Total Estuary/ Tidal River 2 1.8% 2 1.7% - 0.0% 4 Ocean - 0.0% - 0.0% - 0.0%
- Lake/ Reservoir 14 12.4% 4 3.7% - 0.0% 19 Freshwater River/Stream 69 58.9% 14 11.6% 5 4.3% 87 Great Lake 1 0.9% 6 4.8% - 0.0% 7 Total 86 73.9% 26 21.8% 5 4.3% 117

a The number of potential Phase III facilities was sample weighted to account for survey non-respondents. Numbers may not add up to totals due to independent rounding. Source: U.S. DOE, 2001a; U.S. EPA, 2000.
Table B4-9 presents the distribution of Electric Generators potentially subject to Phase III regulation by water body type and design intake flow (DIF) category. Many of the options evaluated by EPA differentiate compliance requirements based on the facility’s DIF. Table B4-9 shows that more than half of the potential Phase III Electric Generators (66) have a DIF of less than 20 million gallons per day (MGD). Fifty-one, or 44%, of the facilities have a design intake flow of between 20 and 50 million MGD. None of the potential Phase III Electric Generators have a flow of 50 MGD or greater because those plants were regulated under the final Phase II rule (promulgated in July of 2004).

Table B4-9: Number of Potential Phase III Electric Generators by Water Body Type and Design Intake Flow Category a

Waterbody Type Design Intake Flow b Total < 20 MGD 20 - 50 MGD 50+ MGD No. % of Total No. % of Total No. % of Total Estuary/ Tidal River 4 3.5% - 0.0% - 0.0% 4 Ocean - 0.0% - 0.0% - 0.0%
- Lake/ Reservoir 12 10.7% 6 5.4% - 0.0% 19 Freshwater River/Stream 47 39.9% 41 34.9% - 0.0% 87 Great Lake 2 2.1% 4 3.5% - 0.0% 7 Total 66 56.2% 51 43.8% - 0.0% 117

a The number of potential Phase III Electric Generators was sample weighted to account for survey non-respondents. Numbers may not add up to totals due to independent rounding. b The three design intake flow (DIF) categories are defined as fo llows: “< 20 MGD” includes facilities that with a DIF of at leas t 2 MGD but less than 20 MGD; “20 - 50 MGD” includes facilities w ith a DIF of at least 20 MGD but less than 50 MGD; “50+ MGD” includes facilities with a DIF of at least 50 MGD. Source: U.S. DOE, 2001a; U.S. EPA, 2000.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

9 Several key pieces of Federal legislation have made the changes in the industry’s structure possible. The Public Utility Regulatory Policies Act (PURPA) of 1978 opened up competition in the generation market by creating a class of nonutility electricity-generating companies referred to as “qualifying facilities.” The Energy Policy Act (EPACT) of 1992 removed constraints on ownershi p of electric generation facilities, and encouraged increased competition in the w holesale electric power business (Beamon, 1998).

B4-21 

### B4-4 I NDUSTRY 

### O UTLOOK This section discusses industry trends that are currently affecting the structure of the electric power industry and may therefore affect the magnitude of impacts from the Proposed Section 316(b) Rule for Phase III Facilities. The most important change in the electric power industry is deregulation – the transition from a highly regulated monopolistic industry to a less regulated, more competitive industry. Section B4-4.1 discusses the current status of deregulation. Section B4-4.2 presents a summary of forecasts from the Annual Energy Outlook 2003.
B4-4.1 Current Status of Industry Deregulation The electric power industry is evolving from a highl y regulated, monopolistic industry with traditionallystructured electric utilities to a less regulated, more competitive industry. 9 The industry has traditionally been regulated based on the premise that the supply of electri city is a natural monopoly, where a single supplier could provide electric services at a lower total cost than could be provided by several competing suppliers. Today, the relationship between electricity cons umers and suppliers is undergoing subs tantial change. Some States have implemented plans that will change the procurement and pricing of electricity significantly, and many more plan to do so during the first few years of the 21st century (Beamon, 1998). a. Key changes in the industry’s structure Industry deregulation already has changed and continues to fundamentally change the structure of the electric power industry. Some of the key changes include:

< Provision of services: Under the traditional regulatory system, the generation, transmission, and distribution of electric power were handled by vertically-integrated utilities. Since the mid-1990s, Federal and State policies have led to increased co mpetition in the generation sector of the industry. Increased competition has resulted in a separation of power generation, transmission, and retail distribution services. Utilities that provide transmission and distribution services will continue to be regulated and will be required to divest of their generation assets. Entities that generate electricity will no longer be subject to geogr aphic or rate regulation.

< Relationship between electricity providers and consumers: Under traditional regulation, utilities were granted a geographic franchise area and provided electric service to all customers in that area at a rate approved by the regulatory commission. A consumer ’s electric supply choice was limited to the utility franchised to serve their area. Similarly, electricity suppliers were not free to pursue customers outside their designated service territories. Although most cons umers will continue to receive power through their local distribution company (LDC), retail competition w ill allow them to select the company that generates the electricity they purchase.

< Electricity prices: Under the traditional system, State and Federal authorities regulated all aspects of utilities’ business operations, including their prices. El ectricity prices were determined administratively for each utility, based on the average cost of pr oducing and delivering power to customers and a reasonable rate of return. As a result of deregulati on, competitive market forces will set generation prices. Buyers and sellers of power w ill negotiate through power pools or one-on-one to set the price of electricity. As in all competitive markets, prices will reflect the interaction of supply and demand for electricity. During most time periods, the price of electricity will be set by the generating unit with the§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-22 highest operating costs needed to meet spot market generation demand (i.e., the “marginal cost” of production) (Beamon, 1998). b. New industry participants The Energy Policy Act of 1992 (EPACT) provides for open access to transmission systems, to allow nonutility generators to enter the wholesale market more easily. In response to these requirements, utilities are proposing to form Independent System Operators (ISOs) to operate the transmission grid, regional transmission groups, and open access same-time information systems (OASIS) to in form competitors of available capacity on their transmission systems. The advent of open transmission access has fostered the development of power marketers and power brokers as new participants in the electric power industry. Power marketers buy and sell wholesale electricity and fall under the jurisdiction of the Federal Energy Regulatory Commission (FERC), since they take ownership of electricity and are engage d in interstate trade. Power marketers generally do not own generation or transmission facilities or sell power to retail customers. A growing number of power marketers have filed with the FERC and have ha d rates approved. Power brokers, on the other hand, arrange the sale and purchase of electric energy, transmission, and other services between buyers and sellers, but do not take title to any of the power sold. c. State activities Many States have taken steps to promote competition in th eir electricity markets. The status of these efforts varies across States. Some States are just beginning to study what a competitive electricity market might mean; others are beginning pilot programs; still others havedesigned restructured electricity markets and passed enabling legislation. However, the difficult transiti on to a competitive electricity market in California, characterized by price spikes and rolling black-outs in 2000, has affected restructuring in that State and several others. Since those difficulties, five States (Arkan sas, Montana, Nevada, New Mexico, and Oklahoma) have delayed the restructuring process pending further review of the issues while California has suspended direct retail access. As of February 2003, eighteen States had operating competitive retail electricity markets. Oregon did not have customers participating in the retail program, but nonresidential customers were allowed access (U.S. DOE, 2003b). Even in States where consumer choice is available, im portant aspects of implementation may still be undecided. Key aspects of implementing restructuring include treatment of stranded costs , pricing of transmission and distribution services, and the design market structures require d to ensure that the benefits of competition flow to all consumers (Beamon, 1998).
B4-4.2 Energy Market Model Forecasts This section discusses forecasts of electric energy supply, demand, and prices based on data and modeling by the Energy Information Administration (EIA) and presented in the Annual Energy Outlook 2003 (U.S. DOE, 2003c). The EIA models future market conditions through the year 2025, based on a range of assumptions regarding overall economic growth, global fuel prices, and legislation and regulations affecting energy markets. The projections are based on the results from EIA’s National Energy Modeling System (NEMS) using assumptions reflecting economic conditions as of November 2002. EPAused ICF Consulting’s Integrated Planning Model (IPM ® ), an integrated energy market model, to conduct the economic analyses supporting the Phase III rulemaking effort (see appendix to Chapter B5: Economic Impact Analysis for Electric Generators ). The IPM generates baseline and post compliance estimates of eachof the measures discussed below. For purposes of comparison, this section presents a discussion of EIA’s reference case results. a. Electricity demand The AEO2003 projects electricity demand to grow by approximately 1.8% annually between 2000 and 2025. This growth is driven by an estimated 2.2% annual increase in the demand for electricity from the commercial sector associated with a projected annual growth in co mmercial floor space of 1.6%. EIA expects electricity demand from the industrial sector to increase by 1.7% annually, largely in response to an increase in industrial§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-23 output of 2.6% per year. Residential demand is exp ected to increase by 1.6% annually over the same forecast period, due mostly to an increase in the number of U.S. households of 1.0% per year between 2000 and 2025. b. Capacity retirements The AEO2003 projects total fossil fuel-fired generation capac ity to decline due to retirements. EIA forecasts that total fossil-steam capacity will decrease by an estimated 12% (or 78 gigawatts) between 2000 and 2025, including 56 gigawatts of oil and natural gas fired steam capacity. EIA estimates total nuclear capacity to decline by an estimated 3% (or 3 gigawatts) between2000 and 2025 due to nuclear power plant retirement. These closures are primarily assumed to be the result of the high costs of maintaining the performance of nuclear units compared with the cost of constructing the least cost alternative. c. Capacity additions Additional generation capacity will be needed to meet the estimated growth in electricity demand and offset the retirement of existing capacity. EIA expects utilities to employ other options such as life extensions and repowering, power imports from Cana da and Mexico, and purchases from c ogenerators before building new capacity. EIA forecasts that utilities will choose technologi es for new generation capacity that seek to minimize cost while meeting environmental and emission constrai nts. Of the new capacity forecasted to come on-line between 2000 and 2025, approximately 80% is projected to be combined-cycle technology or combustion turbine technology, including distributed generation capacity. This a dditional capacity is expected to be fueled by natural gas and to supply primarily peak and intermediate capacity. Approximately 17% of the additional capacity forecasted to come on line between 2000 and 2025 is expectedto be provided by new coal-fired plants, while the remaining 3% is forecasted to come from renewable technologies. d. Electricity generation The AEO2003 projects increased electricity generation fro m both natural gas and coal-fired plants to meet growing demand and to offset lost capacity due to plant retirements. The forecast projects that coal-fired plants will remain the largest source of generation throughout the forecast period. Although coal-fired generation is predicted to increase steadily between 2000 and 2025, its sh are of total generation is expected to decrease from 53% to an estimated 50%. This decrease in the share of coal generation is in favor of less capital-intensive and more efficient natural gas generation technologies. Th e share of total generation associated with gas-fired technologies is projected to increase from approximately 14% in 2000 to an estimated 27% in 2025, replacing nuclear power as the second largest source of electricity ge neration. Generation from oil-fired plants is expected to remain fairly small throughout the forecast period. e. Electricity prices EIA expects the average real price of electricity, as well as the price paid by customers in each sector (residential, commercial, and industrial), to decrease between 2000 and 2008 as a result of competition among electricity suppliers, excess generating capacity, and a decline in coalprices. However, by 2025, EIA predicts that the average real price of electricity will return to 2000 leve ls as a result of rising natural gas costs and electricity demand growth.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-24 

### G LOSSARY Definitions are adapted from the following sources: U.S. Department of Energy’s Electric Power Industry Overview

. At: http://www.eia.doe.gov/cneaf/electricity/page/prim2/toc2.html U.S. Department of Energy’s International Energy Annual 2002 - Glossary

. At: http://www.eia.doe.gov/emeu/iea/glossary.html#W U.S. Department of Energy’s Electric Power Annual Volume I - Glossary of Electricity Terms

. At: http://www.eia.doe.gov/cneaf/electricity/epav1/glossary.html Base Load: A baseload generating unit is normally used to satisfy all or part of the minimum or base load of the system and, as a consequence, produces electricity at an essentially constant rate and runs continuously. Baseload units are generally the newest, largest, and most efficient of the three types of units (i.e., base load, intermediate load, and peak load units). Combined-Cycle Unit: An electric generating technology in which electricity is produced from otherwise lost waste heat exiting from one or more gas (combustion) turb ines. The exiting heat is routed to a conventional boiler or to heat recovery steam generator for utilization by a steam turbine in the production of electricity. This process increases the efficiency of the electric generating unit. Distribution: The delivery of electricity to retail customers (including homes, businesses, etc.). Electricity Available to Consumers: Power available for sale to customers. Approximately 8% to 9% of net generation is lost during the transmission and distribution process. Energy Policy Act (EPACT): In 1992 the EPACT removed constraints on ownership of electric generation facilities and encouraged increased competiti on in the wholesale electric power business. Gas Turbine: A gas turbine typically consisting of an axial -flow air compressor and one or more combustion chambers, where liquid or gaseous fuel is burned and the hot gases are passed to th e turbine. The hot gases expand to drive the generator and ar e then used to run the compressor. Generation: The process of producing electric energy by transforming other forms of energy. Generation is also the amount of electric energy produced, expressed in watthours (Wh)

. Gross Generation: The total amount of electric energy produced by the generating units at a generating station or stations, measured at the generator terminals. Hydroelectric Generating Unit: A unit in which the turbine generator is driven by falling water or natural river current. Intermediate load: Intermediate-load generating units meet system requirements that are greater than base load but less than peak load. Intermediate-load units are u sed during the transition between base load and peak load requirements. Internal Combustion Engine: An internal combustion engine has one or more cylinders in which the process of combustion takes place, converting energy released from the rapid burning of a fuel-air mixture into mechanical energy. Diesel or gas-fired engines are the principal fuel types used in these generators. Kilowatthours (kWh): One thousand watthours (Wh)

.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-25 Megawatt (MW): One million watts

. Nameplate Capacity: The amount of electric power delivered or required for which a generator, turbine, transformer, transmission circuit, station, or system is rated by the manufacturer. Nameplate capacity is expressed in watts or megawatts (MW)

. Net Capability: The steady hourly output that the generating unit is expected to supply to the system load, as demonstrated by test procedures. The capability of the ge nerating unit in the summer is generally less than in the winter due to high ambient-air and cooling water temperatures, which cause generating units to be less efficient. The nameplate capacity of a generating unit is generally greater than its net capability. Net Generation: Gross generation minus plant use from all plants owned by the same utility. Nonutility: A corporation, person, agency, authority, or other legal entity or instrumentality that owns electric generating capacity and is not an electric utility. N onutility power producers includequalifying cogenerators, qualifying small power producers, and other nonutility ge nerators (including independent power producers) without a designated franchised service area that do not fileforms listed in the Code of Federal Regulations, Title 18, Part 141. Other Prime Movers: Methods of power generation other than steam turbines , combined-cycle units , gas combustion turbines , internal combustion engines , and hydroelectric generating units . Other prime movers include: geothermal, solar, wind, and biomass. Peak load: A peakload generating unit, normally the least e fficient of the three unit types (i.e., base load, intermediate load, and peak load units ), is used to meet requirements during the periods of greatest, or peak, load on the system. Power Marketers: Business entities engaged in buying, selling, and marketing electricity. Power marketers do not usually own generating or transmission facilities. Po wer marketers, as opposed to brokers, take ownership of the electricity and are involved in interstate trade. These entities file with the Federal Energy Regulatory Commission for status as a power marketer. Power Brokers: An entity that arranges the sale and purchase of electric energy, transmission, and other services between buyers and sellers, but does not take title to any of the power sold. Prime Movers: The engine, turbine, water wheel, or similar machin e that drives an electric generator. Also, for reporting purposes, a device that directly converts energy to electricity, e.g., photovoltaic, solar, and fuel cell(s). Public Utility Regulatory Policies Act (PURPA): In 1978 PURPA opened up competition in the electricity generation market by creating a class of nonutility electri city-generating companies referred to as “qualifying facilities.” Reliability: Electric system reliability has two components: ad equacy and security. Adequacy is the ability of the electric system to supply customers at all times, taking into account scheduled and unscheduled outages of system facilities. Security is the ability of the electric system to withstand sudden disturbances, such as electric short circuits or unanticipated loss of system facilities. Steam Turbine: A generating unit in which the prime mover is a steam turbine. The turbines convert thermal energy (steam or hot water) produced by generators or boilers to mechanical energy or shaft torque. This mechanical energy is used to power electric generato rs, including combined-cycle electric generating units, that convert the mechanical energy to electricity. Stranded Costs: Prudent costs incurred by a utility that may not be recoverable under market based retail competition. Examples are undepreciated generating fac ilities, deferred costs, and long-term contract costs.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-26 Transmission: The movement or transfer of electric energy over an interconnected group of lines and associated equipment between points of supply and points at which it is transformed for delivery to consumers, or is delivered to other electric systems. Transmission is considered to end when the energy is transformed for distribution to the consumer. Utility: A corporation, person, agency, authority, or other legal entity or instrumentality that owns and/or operates facilities within the United States, its territori es, or Puerto Rico for the generation, transmission, distribution, or sale of electric energy primarily for use by the public and files forms listed in the Code of Federal Regulations, Title 18, Part 141. Facilities that qualify as cogenerators or small power producers under the Public Utility Regulatory Policies Act (PURPA) are not considered electric utilities. Watt: The electrical unit of power. The rate of energy tr ansfer equivalent to one ampere flowing under the pressure of one volt at unity power factor. Watthour (Wh): An electrical energy unit of measure equal to one watt of power supplied to, or taken from, an electric circuit steadily for one hour.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-27 

### R EFERENCES Beamon, J. Alan. 1998. Competitive Electricity Prices: An Update. At: http://www.eia.doe.gov/oiaf/archive/issues98/cep.html. Joskow, Paul L. 1997. “Restructuring, Competition and Regulatory Reform in the U.S. Electricity Sector,” Journal of Economic Perspectives , Volume 11, Number 3 - Summer 1997 - Pages 119-138. North American Electric Reliability Council (NERC). At: http://www.nerc.com/regional/. Accessed May 20, 2004. U.S. Department of Energy (U.S. DOE). 2004. Energy Information Administration (EIA). Electric Power Industry Overview. At: http://www.eia.doe.gov/cneaf/electricity /page/prim2/toc2.html. Accessed March 30, 2004. U.S. Department of Energy (U.S. DOE). 2003a. Energy Information Administration (EIA). Electric Power Annual 2002. At: http://www.eia.doe.gov/cneaf/electricity/epa/epa.pdf. U.S. Department of Energy (U.S. DOE). 2003b. Energy Information Administration (EIA). Status of State Electric Industry Restructuring Activity as of February 2003. At: http://www.eia.doe.gov/cneaf/electricity/chg_str/regmap.html. U.S. Department of Energy (U.S. DOE). 2003c. Energy Information Administration (EIA). Annual Energy Outlook 2003. At: http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2003).pdf. U.S. Department of Energy (U.S. DOE). 2001a. Energy Information Administration (EIA). Form EIA-860 (2001). Annual Electric Generator Report. U.S. Department of Energy (U.S. DOE). 2001b. Energy Information Administration (EIA). Form EIA-861 (2001). Annual Electric Utility Data

. U.S. Environmental Protection Agency (U.S. EPA ). 2000. Section 316(b) Industry Survey. Detailed Industry Questionnaire: Phase II Cooling Water Intake Structures and Industry Short Technical Questionnaire: Phase II Cooling Water Intake Structures, January, 2000 (OMB Control Number 2040-0213). Industry Screener Questionnaire: Phase I Cooling Water Intake Structures , January, 1999 (OMB Control Number 2040-0203). U.S. Geological Survey (USGS). 2004. Estimated Use of Water in the United States in 2000. At: http://water.usgs.gov/watuse/. Accessed March 31, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Phase III Existing Facilities B4: Electric Generator Profi le

B4-28 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-1 

# Chapter B5: Economic Impact Analysis for Electric Generators

I NTRODUCTION The design intake flow (DIF) applicability thresholds for national categorical requirements for the three proposed options for existing facilities are 50 MGD, 100 MGD, and 200 MGD, respectively. Since Electric Generators with a DIF of 50 MGD or greater were covered by the final Phase II rule, no Electric Generator would be subject to the national categorical requirements under any of the three proposed options; therefore there would be no compliance costs and no direct impacts on any Electric Generators, nor any indirect impacts on the Electric Generating Industry as a result of the proposed rule. However, Electric Generators would be regul ated and incur compliance costs under several other options that were analyzed but ultimately not proposed by EPA. This chapter assesses the expected economic effect on Electric Generators of these other options. This chapter (1) describes the methodology used to estimate the private cost to Electric Generators potentially subject to Phase III regulation and presents summary cost statistics; (2) summarizes EPA ’s electricity market model analysis for Electric Generators potentially subject to Phase III regulation and the electric power industry as a whole; and (3) presents an additional assessment of the magnitude of compliance co sts to Electric Generators, including a cost-to-revenue analysis at the facility and firm levels, an analysis of compliance costs per household at the North American Electric Reliability Council (NERC) level, and an analys is of compliance costs relative to electricity price projections, also at the NERC level. The appendix to this chapter presents the detailed methodology and results of EPA’s electricity market model analysis.



### B5-1 E STIMATION OF 

### P RIVATE 

### C OMPLIANCE COSTS This section summarizes EPA’s analysis of private co mpliance costs that would be incurred by Electric Generators under various regulatory opti ons that were considered but not pr oposed by EPA. The first subsection presents methodological components of estimating private costs that are unique to Electric Generators. For information on cost categories and cost methodologies that are common to all industry segments analyzed in developing the proposed requirements for Phase III existing facilities, please see Chapter B1: Summary of Cost Categories and Key Analysis Elements for Existing Facilities . The second subsection presents summary cost statistics for each analyzed option, including fac ility counts and compliance costs by cost category. 

### B5-1.1 Methodology a. Development of Present Value and Annualized Costs The estimation of compliance costs incurred by Electri c Generators potentially subject to Phase III regulation starts with facility-level compliance cost estimates fo r each model facility developed in EPA’s engineering analysis. EPA included the following compliance cost cat egories in this analysis: capital cost, annual operating and maintenance cost, administrative cost, and the lo ss of business income from potential shutdown of facilities during installation of compliance equipment. Of these co st categories, only operating and maintenance costs and certain administrative costs recur annually. The remain ing costs occur only once at the beginning of compliance or in multi-year intervals over the period of the comp liance analysis. Some of the impact analyses require 

### C HAPTER 

### C ONTENTS B5-1 Estimation of Private Compliance Costs . . . . . . B5-1 B5-1.1 Methodology . . . . . . . . . . . . . . . . . . . . . B5-1 B5-1.2 Summary Cost Statistics . . . . . . . . . . . . B5-4 B5-2 Summary of Electricity Market Model Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . B5-7 B5-3 Additional Impact Analyses . . . . . . . . . . . . . . . B5-7 B5-3.1 Cost-to-Revenue Analysis . . . . . . . . . . B5-8 B5-3.2 Cost per Household Analysis . . . . . . . . B5-9 B5-3.3 Electricity Price Analysis . . . . . . . . . . B5-11 B5-4 Uncertainties and Limitations . . . . . . . . . . . . . B5-13 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B5-15 Appendix 1 to Chapter B5 . . . . . . . . . . . . . . . . . . . B5A-1§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

1 This calculation is a conservative approxi mation of the actual tax effect of the compliance costs. For capital costs, it ass umes that the total annualized cost, which includes imputed interest and prin cipal charge components, is subj ect to a tax benefit. In ef fect, the schedule of principal charges over time in the annualized cost value is treated, for tax purposes, as though it were the depreciation schedule over time . In fact, the actual tax depreciation schedule that would be av ailable to a company would be accelerated in comparison to the principal charge schedule embedded in the annualized cost calcu lation. As a result, explicit accounting for the deprecation sc hedule would yield a slightly higher present value of tax benefits than is reflected in the analysis presented here.

B5-2 combining the annually recurring and non-recurring costs into a single, annual equivalent value. For combining the annually recurring and non-recurring costs in this analys is, EPA calculated the annual equivalent cost of the non-recurring cost categories and added these annualized costs to the annually recurring operating and maintenance cost.
To derive the constant annual value of the non-annual costs, EPA calculated the present value as of the first year of compliance of each facility (for this analysis, assumed to be 2010 to 2014) and then annualized it, using a 7.0% pre-tax discount rate in both steps. The costs of co mpliance equipment were annualized over 10 years; initial permitting cost and the income loss from installation s hutdown were annualized over 30 years; and repermitting costs were annualized over 5 years. EPA then added these annualized costs to annual O&M and administrative costs to derive each facility’s total annual pre-tax cost of complying with each evaluated option. For more information on the compliance cost components developed for this analys is and EPA’s methodology of discounting, see Chapter B1 and the Technical Development Document for th e Proposed Section 316(b) Rule for Phase III Facilities (TDD; U.S. EPA, 2004b). b. Consideration of taxes For understanding the economic impact of a regulation on f acilities, the costs incurred by complying facilities are adjusted for taxes and calculated on anafter-tax basis. The tax treatment of compliance outlays and income effects shifts part of these costs to the tax-paying pub lic and reduces the actual cost to private, tax-paying businesses. For this reason, the after-tax costs of comp liance are a more meaningful measure of the financial burden on complying facilities than the pre-tax costs. Inanalyzing and reporting the impact of compliance costs on private facilities, annualized costs are th erefore calculated on an after-tax basis. EPA used combined Federal and State tax rates, specific to the State of each facility, to estimate the annual aftertax cost of compliance. The total eff ective tax rate was calculated as follows:

The amount by which a facility’s annual tax liability would be reduced is the annualized compliance cost of the rule multiplied by the total tax rate. 1 A reduction in tax liability was only applied to privately-owned facilities subject to income taxes, i.e., costs incurred by govern ment-owned facilities and cooperatives are not adjusted for taxes, since these facilities are not subject to income taxes. c. Monetary valuation of installation downtime Installation of some of the compliance technologies cons idered for potential Phase III Electric Generators would require a one-time, temporary downtime of the facility’s cooling water intake system. During the downtime period, the facility’s cooling-water dependent operations would most likel y be halted, with a potential loss of revenue and income from those operations. Accordingly, a key element of the cost to facilities in complying with the proposed standards for Phase III existing facilities is th e loss in income from installation downtime. In the facility impact analyses for Electric Generators, this loss in income is accounted for as a loss in revenue offset by a reduction in variable costs in the affected business operations. For the Electric Generating industry, EPA estimated facility-specific baseline revenue losses using 2008 revenue projections from the Integrated Planning Model (IPM ® ; U.S. EPA, 2002; U.S. EPA, 2003). IPM ® revenues consist of energy revenues and capacity revenues (see discussion of the IPM ® in the appendix to this chapter). One-time losses due to installation downtime were cal culated by dividing each facility’s annual revenue§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-3 projections by 52 weeks and multiplying this value by th e estimated average downtime (in weeks) of the facility’s compliance technology.
EPA also used IPM ® estimates to calculate avoided variable production costs during the downtime, again using facility-specific 2008 projections from the IPM ® . Variable production costs include both fuel and other variable operating and maintenance costs. Similar to revenu es, each facility’s annual variable production costs were divided by 52 weeks and multiplied by the facility’s estimated average downtime (in weeks). The average cost of the technology installation down time is the revenue loss during the downtime less the variable expenses that would normally be incurred duri ng that period. The following formulas were used to calculate the net loss due to downtime for electric generators:

where

This approach may overstate the cost of the installation downtime because it is based on average annual revenues and average variable production costs. If downtime is sch eduled during off-peak times, the loss in revenues could be smaller as a result of lower electricity sales and electricity prices. d. Converting monetary values to current year dollar values The various economic information used in the cost and impact analyses for potential Phase III Electric Generators were initially estimated in dollars of different years. To ensure consistent analyses and to present the estimated cost of regulatory compliance in approximately current values, EPA adjusted all dollar values to constant dollars of the year 2003 (average or mid-year , depending on availability) using an appr opriate inflation adjustment index. For adjusting compliance costs, EPA used the Construction Cost Index (CCI) published by the Engineering News-Record (ENR, 2004; see Chapter B1 for index values used in this analysis). The economic analysis for Electric Generators also uses revenue, cost, and electricity price data from the IPM ® and electricity price data from the Annual Energy Outlook 2003 (U.S. DOE, 2003) and the Energy Information Administration’s Form EIA-861 (U.S. DOE, 2001). These values were adjusted to year 2003 values using the Commodity Producer Price Index (PPI) for Industrial Electric Power (U.S. DOL, 2004). Table B5-1 below presents the PPI values used in this analysis.

Table B5-1: PPI Series for Industrial Electric Power Year Value % Change 1997 130.8 1998 130.0 -0.6% 1999 128.9 -0.8% 2000 131.5 2.0% 2001 141.1 7.3% 2002 139.9 -0.9% 2003 145.8 4.2% Source: U.S. DOL, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-4 

### B5-1.2 Summary Cost Statistics a. Number of facilities with regulatory requirements In conducting the economic impact analyses for Electric Generators, EPA first eliminated from the analysis those facilities estimated to be in severe financial distress independent of Phase III regulation. EPA judges these facilities, which are referred to as “baseline closures,” to be at substantial risk of financ ial failure regardless of any additional financial burden that might result from the propo sed rule or any of the other evaluated options. EPA identified three of the 117 potentially regulated Electric Generators as baseline closures. The identification of baseline closures is based on EPA’s IPM ® analyses. The IPM ® considers a generator as a closure if the net present value of future operation is negative (see the appendix to this chapter). After setting aside baseline closures, EPA determined whic h facilities would be subject to the national categorical requirements under each evaluated option. Facilities that do not meet the design intake flow (DIF)/source waterbody threshold for an option would be subject to permitting based on best professional judgment (BPJ). These facilities do not incur incremental costs under this rule and are therefore excluded from EPA’s cost and economic impact analyses. Table B5-2 below presents, for each evaluated option, th e DIF applicability threshold, the number of Electric Generators potentially subject to Phase III regulation, th e number of baseline closures, the number of Electric Generators subject to best professional judgment, and, by DIF Category, the number of Electric Generators subject to the national requirements.
Table B5-2: Phase III Electric Genera tor Counts for Evaluated Options

DIF Applicability

Threshold Potentially Subject to Regulation Baseline Closures Subject to Best Professional

Judgment Subject to National Requirements

Total DIF Category 2-20 MGD 20-50 MGD 50+ MGD 50 MGD All (proposed) 50 MGD 117 3 114 - - - -
200 MGD All (proposed) 200 MGD 117 3 114 - - - -
100 MGD Cert. (proposed) a 100 MGD (C) BPJ (O) 117 3 114 - - - - Option 3 20 MGD 117 3 63 51 - 51 - Option 4 b 20 MGD (C) 50 MGD (O) 117 3 110 4 - 4 - Option 2 20 MGD 117 3 63 51 - 51 - Option 1 20 MGD 117 3 63 51 - 51 - Option 6 2 MGD 117 3 - 114 63 51 -

a The applicability threshold for the “100 MGD for Certain Wate rbodies” option is 100 MGD for facilities withdrawing from certain waterbodies (estuaries/tidal rivers and oceans) and the Great Lakes. Facilities withdrawing fro m other waterbodies (freshwater rivers, and lakes/reservoirs) are s ubject to best professional judgment. b The applicability threshold for Option 4 is 20 MGD for facilities withdrawing from certain water bodies (estuaries/tidal rivers and oceans) and the Great Lakes and 50 MGD for facilities withdrawing from other waterbodies (freshwater rivers, and lakes/reservoirs). Source: U.S. EPA, 2000.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

2For a detailed discussion of the NERC regions, see the appendix to this chapter. For a description of how EPA determined compliance years, see Chapter B1, Section B1-2.1 (Compliance Schedule).

B5-5 b. Distribution of Electric Generato rs by NERC region and compliance year Table B5-3 presents the distribution of the existing Electric Generators potentially subject to Phase III regulation (excluding baseline closures) by North American El ectric Reliability Council (NERC) region and compliance year. 2The NERC regions presented in the table are:

< ASCC – Alaska

< ECAR – East Central Area Reliability Coordination Agreement

< ERCOT – Electric Reliability Council of Texas

< FRCC – Florida Reliability Coordinating Council

< HI – Hawaii

< MAAC – Mid-Atlantic Area Council

< MAIN – Mid-America Interconnect Network

< MAPP – Mid-Continent Area Power Pool

< NPCC – Northeast Power Coordinating Council

< SERC – Southeastern Electric Reliability Council

< SPP - Southwest Power Pool

< WECC – Western Electricity Coordinating Council Table B5-3: Weighted Number of Pha se III Electric Generating Facilities by NERC Region and Compliance Year a NERC Region 2010 2011 2012 2013 2014 Total ASCC - - - - - - ECAR 4 2 4 4 7 22 ERCOT 2 - 2 3 1 8 FRCC 4 - - - - 4 HI - - - - - - MAAC 3 4 1 - 1 10 MAIN - 1 1 3 1 6 MAPP 1 1 4 3 1 10 NPCC 3 1 1 2 3 11 SERC 10 2 - - 3 16 SPP 1 2 4 3 1 11 WECC 6 2 4 3 1 16 Total 35 15 22 21 20 114

aNote that compliance years were estimated for this analysis. Actual compliance years might be different than stated in this table. Numbers only include facilities estimated to operate in the baseline. Source: U.S. EPA Analysis, 2004.
c. Summary of compliance requirements Table B5-4 shows estimated compliance requirements for each evaluated option, based on the performance standard each Electric Generator would need to meet (depending on each Generator’s waterbody type, design intake flow, capacity utilization, and annual intake flow as a percent of s ource waterbody mean annual flow) and its baseline technologies in-place.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-6 Table B5-4: Number of Electric Ge nerators by Compliance Requirement Facility Compliance Requirement Proposed Options Option 3 Option 4 Option 2 Option 1 Option 6 Total Generators Potentially Subject to Regulation (excluding baseline closures) 114 114 114 114 114 114 Facilities Subject to Best Professional Judgment 114 63 110 63 63 - Facilities Subject to National Categorical Requirements - 51 4 51 51 114 No compliance requirement a
- 39 2 38 36 94 Impingement controls only
- 12 - 11 10 14 Impingement and entrainment controls
- -2256

aThese facilities meet compliance requirements in the baseline and thus would require no action to comply with the regulation. Source: U.S. EPA Analysis, 2004.
d. Summary of estimated private compliance costs Table B5-5 below presents, for each evaluated option, the annualized pre-tax and after-tax compliance costs estimated to be incurred by Electric Generators subject to the national categorical requirements. Table B5-5: Private Compliance Costs for Electric Generators by Cost (annualized, 2003$)

Number of Facilities Subject to NationalRequirements One-Time Costs Recurring Costs Total Annualized Costs Capital Technology Downtime Initial Permit Application Pilot Study O&M Monitoring, Record Keeping & Reporting Permit Renewal
Pre-Tax Compliance Costs Proposed Options - $0 $0 $0 $0 $0 $0 $0 $0 Option 3 51 $503,000 $0 $479,000 $0 $318,000 $328,000 $397,000 $2,025,000 Option 4 4 $168,000 $72,000 $250,000 $0 $70,000 $229,000 $183,000 $972,000 Option 2 51 $552,000 $72,000 $609,000 $0 $354,000 $492,000 $483,000 $2,562,000 Option 1 51 $608,000 $134,000 $625,000 $0 $419,000 $625,000 $490,000 $2,901,000 Option 6 114 $687,000 $151,000 $994,000 $0 $459,000 $872,000 $801,000 $3,963,000 After-Tax Compliance Costs Proposed Options - $0 $0 $0 $0 $0 $0 $0 $0 Option 3 51 $393,000 $0 $411,000 $0 $247,000 $283,000 $338,000 $1,673,000 Option 4 4 $168,000 $72,000 $219,000 $0 $70,000 $203,000 $156,000 $888,000 Option 2 51 $443,000 $72,000 $510,000 $0 $282,000 $420,000 $399,000 $2,127,000 Option 1 51 $497,000 $120,000 $521,000 $0 $328,000 $519,000 $403,000 $2,388,000 Option 6 114 $558,000 $136,000 $791,000 $0 $358,000 $696,000 $630,000 $3,169,000 Source: U.S. EPA Analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

3 The analysis of the final Phase II rule used a predecessor version, V.2.1.

B5-7 

### B5-2 S UMMARY OF 

### E LECTRICITY 

### M ARKET 

### M ODEL 

### A NALYSIS EPA used an electricity market model, the IPM ® , to assess potential economic and operational impacts of this proposal. As noted above, the three proposed options w ould not apply national requirements to any facilities in the Electric Generators segment; thus, the three proposed options have no effects to be considered in an IPM ® analysis. Since conducting electricity market model anal yses is time- and resource-intensive, EPA only analyzed one of the other options evaluated for th is proposal. EPA chose to conduct an IPM ® analysis of the most inclusive and most costly option, Option 6, to identify the upper bound of potential effects under any of the evaluated options. EPA conducted impact analyses at the market level (by NERC region) and for facilities subject to the national requirements under Option 6. Analyzed characteristics incl ude changes in electricity prices, capacity, generation, revenue, cost of generation, and income. These chang es were identified by comparing outcomes in the postcompliance scenario (“Policy Case”) with outcomes in th e base case. Because of the interrelationships between the final Phase II rule (promulgated in July 2004) and Phase III regulation, EPA developed two base cases for this analysis:

< The first base case (referred to as “Base Case 1”) models operational characteristics of the electricity market in the absence of any section 316(b) regulation (i.e., pre-Phase II regulation);

< The second base case (referred to as “Base Case 2”) models operational characteristics of the electricity market including compliance costs of the final Phase II rule (but pre-Phase III regulation). For the market-level analysis, EPA compared the Policy Case (after the implementation of Phase III compliance requirements) with Base Case 2 (including Phase II complian ce costs). This comparison allows EPA to identify the incremental market-level effects of Phase III regulation, beyond the effects of Phase II regulation. In contrast, for the analysis of facilities subject to Phase III regul ation, EPA compared the Policy Case with Base Case 1 (excluding Phase II compliance costs). This comparison was done to determine the “true” effect of Phase III regulation, net of any temporary effects that might beintroduced as the result of the staggering of Phases II and III. Because Phase II facilities have to comply before Ph ase III facilities are projected to comply (on average by two years), Phase III facilities may experience a short-te rm competitive advantage during the time when Phase II facilities incur the new incremental section 316(b) comp liance costs while Phase III facilities do not. The postcompliance economic performance of Phase III facilities shoul d not be compared to this potential short-term improvement in operating characteristics but to thei r steady-state, pre-section 316(b) regulation economic condition.
EPA used the most current version of the IPM ® , V.2.1.6 released in 2003, for the analysis in developing this proposal. 3 The 2003 version of the IPM ® has been updated to include, among other things, compliance costs of the State Multi-Pollutant regulations and the New Sour ce Review settlements, and updated costs for existing facilities, such as life extension costs.
A detailed discussion of the IPM ® , the methodology used in this analysis, and the analysis results for Option 6 is presented in the appendix to this chapter.



### B5-3 A DDITIONAL 

### I MPACT 

### A NALYSES This section presents an additional assessment of the magn itude of Electric Generator compliance costs associated with the options evaluated for Phase III existing facilities. The analyses presented in this section include a costto-revenue analysis at the facility and firm levels, an analysis of compliance costs per household at the North American Electric Reliability Council (NERC) level, and ananalysis of compliance costs relative to electricity price projections, also at the NERC level.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-8 

### B5-3.1 Cost-to-Revenue Analysis The cost-to-revenue ratio is used to assess the magnitude of compliance costs relative to revenues. The cost-torevenue ratio is a useful test because it compares the co st of reducing adverse environmental impact from the operation of the facility’s cooling wate r intake structure (CWIS) with the economic value (i.e., revenue) of the facility’s economic activities. EPA conducted this test at the facility and firm levels. This analysis uses impact thresholds of 0.5%, 1% and 3%. a. Facility-level analysis EPA received survey data for 113 Electric Generators potentially subject to Phase III regulation. EPA estimates that three of these 113 Electric Generators are baseline closures; these facilities are excluded from this analysis. For the remaining 110 facilities, EPA compared each f acility’s annualized after-tax compliance costs under each evaluated option to the facility’s annual revenues. EPA used facility-specific baseline revenue projections from the IPM ® for 2008 for this analysis. The IPM ® did not provide revenues for two facilities because they are not included in the model. In addition, the IPM ® projects that nine facilities will have zero revenues in the baseline. For the 11 facilities without IPM ® revenues, EPA researched facility-specific electricity generation and firmspecific wholesale prices, as reported to the Energy Inform ation Administration (EIA), to calculate the cost-torevenue ratio. This research yielded information for nineof the 11 facilities; for the remaining two facilities, EIA revenues are either zero or negative. EPA then app lied sample weights to the 110 facilities to account for nonsampled facilities and facilities that did not respond to the survey. The sample-weighted facility count, excluding baseline closures, is 114. Table B5-6 below presents the results of the facility-level cost-to-revenue analysis for each evaluated option. The table presents (1) the total number of facilities subject to the national categorical requirements; (2) the number of facilities with a cost-to-revenue ratio of less than 0.5%, at least 0.5% but less than 1%, at least 1% but less than 3%, and at least 3%; and (3) the minimum and maximum ratios. As previously noted, no Electric Generators are subject to the national requirements nor incur compliance costs under the three proposed options. Under the other eval uated options, between four and 114 Electric Generators are subject to the national requirements; the remaining facilities are subject to best professional judgment requirements and are excluded from this analysis. Tabl e B5-6 shows that under most options, the majority of facilities would have a cost-to-revenue ratio of less than 0.5%. Under Option 6, the most inclusive and costly of the evaluated options, 10 facilities are estimated to havea ratio of between 1% and 3%, and 13 facilities are estimated to have a ratio of greater than 3%. Th e maximum ratio under Option 6 is 430%; the maximum ratios under the other evaluated options are 8.7% for Option 4 and 75.6% for Options 1, 2, and 3. Table B5-6: Facility-Level Cost-to-R evenue Measure By Ownership Type
Option Total Number of Facilities a Number of Facilities with a Ratio of Minimum Ratio Maximum Ratio < 0.5% 0.5 to <1% 1 to <3% >= 3% No Rev. Proposed Options - - - - - - 0.0% 0.0% Option 3 51 35 3 3 9 1 0.0% 75.6% Option 4 4 1 - 1 2 - 0.0% 8.7% Option 2 51 34 3 4 9 1 0.0% 75.6% Option 1 51 33 1 7 9 1 0.0% 75.6% Option 6 114 88 1 10 13 2 0.0% 430.4%

aIndividual numbers may not a dd up due to independent rounding. Source: IPM ® analysis, V.2.1.6: model run for Section 316(b) base case, 2008, AEO electricity demand assumptions; U.S. EPA Analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-9 b. Firm-level analysis The facility-level analysis presented above showed that compliance costs are generally low compared to facilitylevel revenues. However, impacts experienced at the firm-level may be more significant for firms that own multiple facilities subject to Phase III regulation. EPA th erefore also analyzed the firm-level cost-to-revenue ratios of the evaluated options. EPA first identified the domestic parent entity of each of the 110 surveyed, non-baseline closure Electric Generators potentially subject to Phase III regulati on (for a detailed description of this analysis, see Chapter D1: Regulatory Flexibility Analysis ). EPA determined that 72 unique domestic parent entities own these 110 facilities. EPA identified 18 entities that own more thanone Electric Generator potentially subject to Phase III regulation. EPA obtained the sales revenues for each of the domestic parent entities from publicly available data sources (the 1999, 2000, and 2001 Forms EIA-861; the Dun a nd Bradstreet database; company 10-K filings; and entities’ websites). The firm-level analysis is based on the ratio of each parent entity’s aggregated after-tax compliance costs (summed over each facility owned by the parent entity and subject to the national requirements) to its total sales revenue. Table B5-7 below presents the results of the firm-level cost-to-revenue measure. The table presents (1) thesample-weighted number of facilities owned; (2) the total number of firms; (3) the number of firms with a cost-torevenue ratio of less than 0.5%, at least 0.5% but less than1%, at least 1% but less than 3%, and at least 3%; and (4) the minimum and maximum ratios. No Electric Generators are subject to the national requi rements under the three proposed options. Under the other evaluated options, between four and 72 entities own Electric Generators subject to the national requirements; the remaining entities own facilities subject to best professional judgment requirements and are excluded from this analysis. EPA estimates that Phase III compliance costs woul d comprise a low percentage of firm-level revenues. Under all of the evaluated options, no more than one en tity would experience a cost-to-revenue ratio of greater than 3%. Depending on the option, between one and fiveentities would have a ratio between 1% and 3%. The highest estimated cost-to-revenue ratio under any of the evaluated options is 3.39%.

Table B5-7: Firm-Level Cost-to-R evenue Measure by Entity Type

Option Total Number of Facilities Total Number of Entities Number of Entities with a Ratio of Minimum Ratio Maximum Ratio <0.5% 0.5 to <1% 1 to <3% >= 3% Proposed Options - - - - - - 0.00% 0.00% Option 3 51 42 38 1 3 - 0.00% 2.65% Option 4 4 4 2 - 1 1 0.00% 3.39% Option 2 51 42 38 - 3 1 0.00% 3.39% Option 1 51 42 37 - 4 1 0.00% 3.39% Option 6 114 72 66 - 5 1 0.00% 3.39% Source: U.S. EPA Analysis, 2004.


### B5.3-2 Cost Per Household Analysis EPA also conducted an analysis that evaluates the poten tial cost per household, if Phase III facilities were able to pass compliance costs on to their customers. This analysis estimates the average compliance cost per household§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

4The number of residential consumers reported in Form EIA-861 is based on the number of utility meters. This is a proxy for t he number of households but can differ slightly dueto bulk metering in some multi-family housing.

B5-10 for each North American Electric Reliability Council (NE RC) region, using data on residential consumers from the 2001 Form EIA-861. 4 EPA calculated the average annual cost per household fo r each evaluated option by dividing the total pre-tax compliance cost of all regulated facilities in a NERC regi on by the total number of households in that region. This analysis assumes that Electric Ge nerators pass costs on to consumers, on a dollar-to-dollar basis, and that there will be no reduction in electricity consumption by the consumers in response to price increases. EPA also used the conservative assumption that residential consumers bear the full burden of compliance costs; no other customer groups (e.g., commercial or industrial consumer s) are assumed to bear any of the compliance costs. Table B5-8 presents the annualized pre-tax compliance cost s, by NERC region, for each evaluated option. Table B5-9 shows the number of households in each NERC re gion, and the estimated annual compliance cost per household. No Electric Generators would incur compliance costs under the three proposed options. The highest estimated annual cost per household, under any option and in any region, is $0.12 in the Mid-Continent Area Power Pool (MAPP) under Options 1 and 6. Under all other options and in all other regions, the estimated annual cost per household is lower. Table B5-8: Annualized Pre-Tax Compliance Cost by NERC Region (2003$)
NERC Region a Proposed Options Option 3 Option 4 Option 2 Option 1 Option 6
ASCC $0 $0 $0 $0 $0 $0 ECAR $0 $256,000 $387,000 $504,000 $642,000 $917,000 ERCOT $0 $7,000 $0 $7,000 $7,000 $18,000 FRCC $0 $2,000 $0 $2,000 $2,000 $10,000
HI $0 $0 $0 $0 $0 $0 MAAC $0 $8,000 $0 $8,000 $8,000 $22,000 MAIN $0 $400,000 $376,000 $482,000 $482,000 $687,000 MAPP $0 $540,000 $0 $540,000 $603,000 $612,000 NPCC $0 $417,000 $209,000 $623,000 $623,000 $1,109,000 SERC $0 $14,000 $0 $14,000 $14,000 $36,000 SPP $0 $275,000 $0 $275,000 $275,000 $289,000 WECC $0 $107,000 $0 $107,000 $246,000 $262,000 U.S. $0 $2,025,000 $972,000 $2, 562,000 $2,901,000 $3,963,000

a Key to NERC regions: ASCC – Alaska Systems Coordinating Council; ECAR– East Central Area Reliability Coordination Agreement; ERCOT – Electric Reliability Council of Texas; FRCC – Florida Reliability Coordinating Council; HI – Hawaii; MAAC – Mid-Atlantic Area Council; MAIN – Mid-America Interconnect Network; MAPP – Mid-Continent Area Power Pool; NPCC – Northeast Power Coordinating Council; SERC – Southeaste rn Electric Reliability Council; SPP – Southwest Power Pool; WECC – Western Electricity Coordinating Council. Source: U.S. DOE, 2001; U.S. EPA Analysis, 2004.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-11 Table B5-9: Annual Compliance Cost per R esidential Consumer by NERC Region (2001)

NERC Region a Number of Households (2001) Annual Compliance Cost/ Residential Consumer (2003 $) Proposed Options Option 3 Option 4 Option 2 Option 1 Option 6
ASCC 234,646 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 ECAR 15,698,205 $0.00 $0.02 $0.02 $0.03 $0.04 $0.06 ERCOT 7,309,073 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 FRCC 6,885,280 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 HI 351,229 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 MAAC 8,921,106 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 MAIN 8,366,132 $0.00 $0.05 $0.04 $0.06 $0.06 $0.08 MAPP 4,933,221 $0.00 $0.11 $0.00 $0.11 $0.12 $0.12 NPCC 12,676,283 $0.00 $0.03 $0.02 $0.05 $0.05 $0.09 SERC 20,550,922 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 SPP 5,002,020 $0.00 $0.06 $0.00 $0.06 $0.06 $0.06 WECC 23,085,962 $0.00 $0.00 $0.00 $0.00 $0.01 $0.01 U.S. 114,014,079 $0.00 $0.02 $0.01 $0.02 $0.03 $0.03

a Key to NERC regions: ASCC – Alaska Systems Coordinating Council; ECAR– East Central Area Reliability Coordination Agreement; ERCOT – Electric Reliability Council of Texas; FRCC – Florida Reliability Coordinating Council; HI – Hawaii; MAAC – Mid-Atlantic Area Council; MAIN – Mid-America Interconnect Network; MAPP – Mid-Continent Area Power Pool; NPCC – Northeast Power Coordinating Council; SERC – Southeaste rn Electric Reliability Council; SPP – Southwest Power Pool; WECC – Western Electricity Coordinating Council. Source: U.S. DOE, 2001; U.S. EPA Analysis, 2004.


### B5-3.3 Electricity Price Analysis EPA also considered potential effects of Phase III regulati on on electricity prices. EPA used three data inputs in this analysis: (1) total pre-tax compliance cost incurred by facilities subject to the national requirements; (2) total electricity sales projected for 2007 (the year the proposedrule would take effect), based on the Annual Energy Outlook (AEO) 2003; and (3) projected prices for 2007 by cons umer type (residential, commercial, industrial, and transportation), also from the AEO 2003. All thr ee data elements were calculated by NERC region. Table B5-10 shows total projected electricity sales (i n MWh) for 2007 and the average compliance cost per kilowatt hour (KWh) for each evaluated option, by NERC region. The average cost per kilowatt hour for each option was estimated by dividing the annualized pre- tax compliance costs for each NERC region (presented in Table B5-8 above) by the region’s total electricity sales. No Electric Generating facilities would incur compliance costs under the three proposed options. For a ll other evaluated options, the average cost ranges from no additional cost per KWh sales to a maximum of 0.0004 cen ts per KWh sales. The U.S. average is estimated to be 0.0001 additional cents per KWh sales or less under all options.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-12 Table B5-10: Compliance Cost per KWh of Sales by NERC Region

NERC Region a Total Electricity Sales (MWh; 2001) Annualized Pre-Tax Compliance Cost (Cents / KWh Sales) Proposed Options Option 3 Option 4 Option 2 Option 1 Option 6
ASCC --- ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ECAR 570,807,007 ¢0.0000 ¢0.0000 ¢0.0001 ¢0.0001 ¢0.0001 ¢0.0002 ERCOT 297,949,799 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 FRCC 208,035,233 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 HI --- ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 MAAC 280,251,282 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 MAIN 255,762,939 ¢0.0000 ¢0.0002 ¢0.0001 ¢0.0002 ¢0.0002 ¢0.0003 MAPP 172,704,269 ¢0.0000 ¢0.0003 ¢0.0000 ¢0.0003 ¢0.0003 ¢0.0004 NPCC 282,686,981 ¢0.0000 ¢0.0001 ¢0.0001 ¢0.0002 ¢0.0002 ¢0.0004 SERC 853,386,597 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 SPP 191,778,000 ¢0.0000 ¢0.0001 ¢0.0000 ¢0.0001 ¢0.0001 ¢0.0002 WECC 259,401,428 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0000 ¢0.0001 ¢0.0001 U.S. 3,845,085,938 ¢0.0000 ¢0.0001 ¢0.0000 ¢0.0001 ¢0.0001 ¢0.0001

a Key to NERC regions: ASCC – Alaska Systems Coordinating Council; ECAR– East Central Area Reliability Coordination Agreement; ERCOT – Electric Reliability Council of Texas; FRCC – Florida Reliability Coordinating Council; HI – Hawaii; MAAC – Mid-Atlantic Area Council; MAIN – Mid-America Interconnect Network; MAPP – Mid-Continent Area Power Pool; NPCC – Northeast Power Coordinating Council; SERC – Southeaste rn Electric Reliability Council; SPP – Southwest Power Pool; WECC – Western Electricity Coordinating Council. The Annual Energy Outlook does not include ASCC and HI. Source: U.S. DOE, 2003; U.S. EPA Analysis, 2004.
To determine potential effects on electricity prices as a result of compliance with the evaluated options, EPA compared the compliance cost per KWh of sales, presented in Table B5-10 above, to projected baseline electricity prices for different consumer types (projections for 2007). Table B5-11 below presents the estimated percentage changes in baseline electricity prices for Option 6, the most inclusive and most costly of the evaluated options. These results therefore represent the upper bound of potential electricity price effects under any of the evaluated options. Rounded to the nearest 100 thof a percent, the largest estimated percentage increases for any consumer type and in any region under Option 6 is 0.01%. For all other options, the resulting percentage increases in electricity pri ces would be less than, or equal to, those estimated for Option 6. Overall, EPA concludes that the compliance costs for none of the evaluated options would have an effect on electricity prices. This analysis assumes that Electric Generators fully recover compliance costs from consumers and that each sector (i.e., residential, commercial, industrial, and tran sportation) bears an equal burden of compliance costs per MWh of purchased electricity.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-13 Table B5-11: Estimated Price Increase as a Percen tage of 2007 Prices by Consumer Type and NERC Region – Option 6 (All costs and prices in cents per kilowatt hour; 2003$)

Region Annualized Pre-Tax Compliance Cost (Cents / KWh Sales) Residential Commercial Industrial Transportation All Sectors Average

Opt. 6 Price % Change Price % Change Price % Change Price % Change Price % Change
ECAR 0.0002 6.72 0.00% 5.95 0.00% 4.15 0.00% 5.65 0.00% 5.51 0.00% ERCOT 0.0000 8.30 0.00% 7.74 0.00% 5.08 0.00% 6.94 0.00% 7.22 0.00% FRCC 0.0000 8.37 0.00% 7.16 0.00% 5.33 0.00% 7.32 0.00% 7.65 0.00% MAAC 0.0000 7.48 0.00% 5.88 0.00% 5.35 0.00% 6.16 0.00% 6.34 0.00% MAIN 0.0003 7.60 0.00% 6.10 0.00% 4.18 0.01% 6.12 0.00% 6.01 0.00% MAPP 0.0004 6.91 0.01% 5.80 0.01% 3.99 0.01% 5.74 0.01% 5.53 0.01% NPCC 0.0004 10.64 0.00% 7.85 0.01% 5.47 0.01% 8.40 0.00% 8.37 0.00% SERC 0.0000 7.42 0.00% 6.55 0.00% 4.16 0.00% 6.29 0.00% 6.16 0.00% SPP 0.0002 7.18 0.00% 6.03 0.00% 4.06 0.00% 5.76 0.00% 5.91 0.00% WECC 0.0001 6.55 0.00% 5.90 0.00% 3.38 0.00% 5.67 0.00% 5.22 0.00% U.S. 0.0001 7.86 0.00% 6.87 0.00% 4.43 0.00% 6.71 0.00% 6.54 0.00% Source: U.S. EPA Analysis, 2004.



### B5-4 U NCERTAINTIES AND 

### L IMITATIONS 

###  ̃ Estimation of Private Compliance Costs EPA’s estimates of the compliance costs associated with the options evaluated in developing the proposed rule are subject to limitations because of uncertainties about the number and characteristics of Electric Generators that would potentially be subject to Phase III regulation under each option. Projecting the number of facilities that meet the design intake flow applicability thresholds is subject to uncertainties associated with the quality of data reported by the facilities in their Detailed Questionnaire (DQ) and Short Technical Questionnaire (STQ) surveys, and with the accuracy of the design flow estimates for th e STQ facilities. Characterizing the cooling systems and intake technologies in use at existing facilities is also s ubject to uncertainties associated with the quality of data reported by the facilities in their surveys and with the projected technologies for the STQ facilities. The estimated total compliance costs for the Electric Generating industry may be over- or understated if the projected number of Phase III existing facilities subject to the national categoricalrequirements is incorrect or if the characteristics of the facilities are different from those assumed in the analysis. Limitations in EPA’s ability to consider a full range of compliance responses may result in an overestimate of facility compliance costs. The Agency was not able to consider certain compliance responses, including the costs of using alternative sources of cooling water, the costs of some methods of changing the cooling system design, and the costs of restoration. Costs would be overstated if these excluded compliance responses are less expensive than the projected complian ce response for some facilities. Alternative less stringent requirements based on both cost s and benefits are allowed under the evaluated options. There is some uncertainty in predicting complian ce responses because the number of facilities requesting alternative less stringent requirements based on costs and benefits is unknown.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-14 There is also uncertainty associated with the estimates of facility revenues. Facility revenues are projected revenues from the IPM ® . The IPM ® is a forward looking model that simulates generator dispatch based on numerous assumptions about future conditions, including fu ture fuel prices, electricity demand, new capacity additions, heat rates, etc. Changing these assumptions might affect the projected facility revenues and the estimated cost of installation downtime for Electric Generators. 

###  ̃ Electricity Market Model Analysis Uncertainties and limitations associated with EPA’s IPM ® analysis are documented in the appendix to this chapter. 

###  ̃ Additional Impact Analyses There is uncertainty associated with EPA’s estimates of potential cost per household and electricity price changes. As noted in the sections above, EPA’s analyses are b ased on the assumption that Electric Generators would be able to pass on 100% of their compliance costs to thei r customers. For the cost per household analysis, EPA assumed that all costs would be passed on to all residential customers in the region. The results of this analysis might differ if less than 100% of compliance costs could be passed on, or if only a subset of residential consumers in a region bore the passed-on costs. For the electric ity price analysis, EPA assumed that all costs would be spread evenly among all customers. Again, the results of this analysis might differ if less than 100% of compliance costs could be passed on, or if the differe nt customer groups bore different shares of compliance costs. However, in both analyses, the two uncertainty factors would change results in opposite directions; it is therefore unclear whether EPA’s analyses might overstate or understate actual impacts. In addition, the impacts of both analyses are very minor; therefore, it is unlikely th at EPA’s findings would change, even if one or more of EPA’s assumptions were incorrect.§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-15 

### R EFERENCES Engineering News-Record (ENR). 2004. C onstruction Cost Index. Available at: http://enr.construction.com/features/c onEco/costIndexes/constIndexHist.asp. U.S. Department of Energy (U.S. DOE). 2003. Energy Information Administration (EIA). Annual Energy Outlook 2003 With Projections to 2025 . DOE/EIA-0383(2003). January 2003. U.S. Department of Energy (U.S. DOE). 2001. Form EIA-861. Annual Electric Utility Report for the Reporting Period 2001

. U.S. Department of Labor (U.S. DOL). 2004. Bureau of Labor Statistics (BLS). Producer Price Index - Commodities. Series ID: WPU0543. Not Seasonally Adjusted. Group: Fuels and related products and power. Item: Industrial electric power . Available at: http://www.bls.gov/ppi/. Accessed on July 23, 2004. U.S. Environmental Protection Agency (U.S. EPA). 2004a. Economics and Benefits Analysis for the Final Section 316(b) Phase II Existing Facilities Rule . EPA-821-R-04-005. February 2004. U.S. Environmental Protection Agency (U.S. EPA). 2004b. Technical Development Document for the Proposed Section 316(b) Rule for Phase III Facilities . EPA-821-R-04-015. November 2004. U.S. Environmental Protection Agency (U.S. EPA). 2003. Documentation Supplement for EPA Modeling Applications (V.2.1.6) Using the Integrated Planning Model . EPA 430/R-03-007. July 2003. U.S. Environmental Protection Agency (U.S. EPA). 2002. Documentation of EPA Modeling Applications (V.2.1) Using the Integrated Planning Model . EPA 430/R-02-004. March 2002. U.S. Environmental Protection Agency (U.S. EPA ). 2000. Section 316(b) Industry Survey. Detailed Industry Questionnaire: Phase II Cooling Water Intake Structures and Industry Short Technical Questionnaire: Phase II Cooling Water Intake Structures, January, 2000 (OMB Control Number 2040-0213). Industry Screener Questionnaire: Phase I Cooling Water Intake Structures , January, 1999 (OMB Control Number 2040-0203).§ 316(b) Proposed Rule: Phase III – EA, Part B: Economic Analys is for Existing Facilities B5: Impact Analysis for Generators

B5-16 

### T HIS 

### P AGE 

### I NTENTIONALLY 

### L EFT 

### B LANK§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

1 The IPM ® was also used for the Phase II Rule. At the time of the Phase II proposal EPA evaluated several models suitable for analysis of environmental policies that affect the electric power industry. For a fu ll discussion of the various models EPA co nsidered, refer to section B3-1 and Appendix B in Chapter B3 of theEconomics and Benefits Analysis for the Final Section 316(b) Phase II Existing Facilities Rule (U.S. EPA, 2004a).

2Please refer to Chapter D3: Other Admi nistrative Requirementsfor a discussion of this analysis.

B5A-1 

# Appendix 1 to Chapter B5: Electricity Market Model Analysis

I NTRODUCTION This appendix presents EPA’s analysis of impacts on Electric Generators potentially subject to Phase III regulation and to the Electric Generating Industry as a whole. While only a subset of facilities in the electric power generation industry would be subject to Phase III regulation under any option evaluated for this proposal, interdependencies within the electric power market, might result in indirect impacts throughout the industry. Direct impacts on plants subject to an evaluated option may include changes in capacity utilization, generation, and profitability. Potential indirect impacts on the electric power industry may include changes to the generation and revenue of facilities and firms not subject to Phase III regulation, changes to bulk system reliability, and regional and national impacts such as changes in the price of electricity and the construction of new generating capacity. Under the proposed options, the minimum applicability th reshold for national categorical requirements is 50 MGD or greater. Since Electric Generators with desi gn intake flows of 50 MGD or greater were covered by Phase II regulation, no Phase III Generator would be subject to the national categorical requirements under any of the proposed options; therefore there would be no direct impacts on any Electric Generators nor any indirect impacts on the Electric Generating Industry as a result of th e proposed rule. However, some of the other options evaluated by EPA would impose compliance costs on Electri c Generators. This chapter presents an analysis of the potential effects of Option 6, the most costly op tion considered by EPA, and the option with the highest potential impacts. Option 6 would impose national categorical requirements on all facilities with a DIF of 2 MGD or greater. EPA used ICF Consulting’s Integrated Planning Model (IPM ® ), an integrated energy market model, to conduct the economic analyses supporting this rule. 1 The model addresses the interdepe ndencies within the electric power market and accounts for both direct and indirect impacts of regulatory actions. EPA used the model to analyze two potential effects of Option 6: (1) potential energy e ffects at the national and regional levels, as required by Executive Order 13211 (“Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use”); 2and (2) potential economic impacts on facilities potentially subject to Phase III regulation. Option 6 was evaluated under the unadjusted electric ity demand from the Annual Energy Outlook (AEO) 2003. Section B5A-3 presents the results of the IPM ® analysis for Option 6. 

### A PPENDIX 

### C ONTENTS B5A-1 Integrated Planning Model Overview . . . . B5A-2 B5A-1.1 Modeling Methodology . . . . . . B5A-2 B5A-1.2 Specifications for the Section 316(b) Analysis . . . . . . . . . . . . B5A-5 B5A-1.3 Model Inputs . . . . . . . . . . . . . . B5A-6 B5A-1.4 Model Outputs . . . . . . . . . . . . . B5A-7 B5A-2 Economic Impact Analysis Methodology . B5A-8 B5A-2.1 Market-level Impact Measures . B5A-8 B5A-2.2 Facility-level Impact Measures (Potential Phase III Facilities Only) . . . . . . . . . . . . . . . . . . . B5A-10 B5A-3 Analysis Results for Option 6 . . . . . . . . . B5A-11 B5A-3.1 Market Analysis for 2013 . . . . B5A-12 B5A-3.2 Analysis of Potential Phase III Facilities for 2013 . . . . . . . . . B5A-18 B5A-4 Summary of IPM V.2.1.6 Updates . . . . . . B5A-24 B5A-5 Uncertainties and Limitations . . . . . . . . . B5A-30§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

3 EPA used the IPM ® to forecast operational changes, including changes in capacity, generation, revenues, electricity prices, and plant closures, resulting from the rule. In other policy analyses, the IPM ® is generally also used to de termine the compliance response for each model facility. This process involves selecting the optimalresponse from a menu of compliance options that will result i n the leastcost system dispatch and new re source investment decision. Compliance options specified by IPM ® may include fuel switching, repowering, pollution control retrofit, co-firing multiple fuels, di spatch adjustments, and economic retirement. EPA did not u se this capability to choose the compliance responses of the facilities subj ect to section 316(b) rulemaking. Rather EPA exogenously e stimated a compliance response using the costs of technol ogies capable of meeting the percentage reductions in impingement and entrainment required under the regulation. In the post-compliance analysis, th ese compliance costs were added as model inputs to the base case operating and capital costs.

B5A-2 

### B5A-1 I NTEGRATED 

### P LANNING 

### M ODEL 

### O VERVIEW This section presents a general overview of the capabilities of the IPM ® , including a discussion of the modeling methodology, the specification of the model for the secti on 316(b) analysis, and model inputs and outputs. When the analyses in support of the Phase II Rule were devel oped, the latest EPA specification of the U.S. power market, “EPA Base Case 2000,” was based on IPM ® Version 2.1 (U.S. EPA, 2002). In July 2003, a new version of the model, Version 2.1.6, was released (U.S . EPA, 2003). The Phase III proposal analyses utilize the specifications for the new “EPA Base Case 2003”. A su mmary table of model updates is presented in section B5A-4. B5A-1.1 Modeling Methodology a. General framework The IPM ® is an engineering-economic optimization model of the electric power industry, which generates leastcost resource dispatch decisions basedon user-specified constraints such as environmental, demand, and other operational constraints. The model can be used to anal yze a wide range of electric power market issues at the plant, regional, and national levels. In the past, applications of the IPM ® have included capacity planning, environmental policy analysis and compliance planni ng, wholesale price forecasting, and asset valuation.
The IPM ® uses a long-term dynamic linear programming framework that simulates the dispatch of generating capacity to achieve a demand-supply equilibrium on a seas onal basis and by region. The model seeks the optimal solution to an “objective function,” which is a linear equation equal to the present value of the sum of all capital costs, fixed and variable operation and maintenance (O &M) costs, and fuel costs. The objective function is minimized subject to a series of user-defined supply and demand, or system operating, constraints. Supply-side constraints include capacity constraints, availability of generation resources, plant minimum operating constraints, transmission constraints, and environmental constraint s. Demand-side constraints include reserve margin constraints and minimum system-wide load requirements. The optimal solution to the objective function is the least-cost mix of resources required to satisfy system wi de electricity demand on a seasonal basis by region. In addition to existing capacity, the model also consid ers new resource investment options, including capacity expansion or repowering at existing plants as well as investment in new plants. The model selects new investments while considering interactions with fuel markets, capacity markets, power plant cost and performance characteristics, forecasts of electricity demand, reliability criteria, and other constraints. The resulting system dispatch is optimized given the resource mix, unit operati ng characteristics, and fuel and other costs, to achieve the most efficient use of existing and new resources availabl e to meet demand. The model is dynamic in that it is capable of using forecasts of future cond itions to make decisions for the present. 3 b. Model plants The model is supported by a database of boilers and el ectric generation units which includes all existing utilityowned generation units as well as those located at plants owned by independent power producers and cogeneration facilities that contribute capacity to the el ectric transmission grid. Individual generators are aggregated into model plants with similar O&M co sts and specific operating characteristics including seasonal capacities, heat rates, maintenance schedules, outage rates, fuels, and transmission and distribution loss characteristics.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-3 

### PNW

CALI NWPE

AZNM RMPA MAPP

SPPN

SPPS

ERCT

FRCC ENTG 

### TVA

SOU MANO ECAO

VACA MACS MACE

LILC WUMS DSNYU

UPNY
MACW 

### NENG MECS

NYC The number and aggregation scheme of model plants can be adjusted to meet the speci fic needs of each analysis. The EPA Base Case 2003 contains 1,703 model plants.
c. IPM ®regions The IPM ® divides the U.S. electric power market into 26 regions in the contiguous U.S. It does not include generators located in Alaska or Hawaii. The 26 regi ons map into North American Reliability Council (NERC) regions and sub-regions. The IPM ® models electric demand, generation, transmission, and distribution within each region and across the transmission grid that connect s regions. For the analyses presented in this chapter, IPM ® regions were aggregated back into NERC regions. Figure B5A-1 provides a map of the regions included in the IPM ® . Table B5A-1 presents the crosswalk between NERC regions and IPM ® regions. Figure B5A-1: Regional Representation of U.S. Power System as Modeled in IPM ®

Source: U.S. EPA, 2002.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

4The IPM ® developed output for a total of five model run year s 2008, 2010, 2013, 2020, and 2026. Model run years 2020 and 2026 were specified for model balance, while run years 2008, 2010, and 2013 were selected to provide output across the compliance period. Output for 2026 was not used in this analysis. For a discussi on explaining the reasons for the selected model run years refer to section B3- 2.1d of the Economics and Benefits Analysis for the Final Section 316(b) Phase II Existing Facilities Rule (U.S. EPA, 2004a).

5 EPA estimates that Phase III fac ilities would comply between 2010 and 2014. For the analyses using the IPM ® only, EPA modified this assumption and used compliance years of 2008 through 2012 by s ubtracting two years from the estimated compliance year of e ach facility. This modification allowed EPA to analyze the output for 2013 as the year when all facilities are in compliance.

B5A-4 Table B5A-1: Crosswalk between NERC Regions and IPM ®Regions

NERC Region IPM ®Regions ASCC – Alaska Not Included ECAR – East Central Area Reliability Coordination Agreement ECAO, MECS ERCOT – Electric Reliability Council of Texas ERCT FRCC – Florida Reliability Coordinating Council FRCC HI – Hawaii Not Included MACC – Mid Atlantic Area Council MACE, MACS, MACW MAIN – Mid-America Interconnect Network MANO, WUMS MAPP – Mid-Continent Area Power Pool MAPP NPCC – Northeast Power Coordination Council DSNY, LILC, NENG, NYC, UPNY SERC – Southeastern Electricity Reliability Council ENTG, SOU, TVA, VACA SPP - Southwest Power Pool SPPN, SPPS WECC – Western Electricity Coordinati ng Council AZNM, CALI, NWPE, PNW, RMPA Source: U.S. EPA, 2002

.
d. Model run years The IPM ® models the electric power market over the 26-year period 2005 to 2030. Due to the data-intensive processing procedures, the model is run for a limited number of years only. Run years are selected based on analytical requirements and the necessity to maintain a balanced choice of run years throughout the modeled time horizon. EPA selected the following run years for the Phase II analysis: 2008, 2010, and 2013, and has chosen to retain them for the Phase III analysis. 4,5 The model assumes that capital investment decisions are only implemented during run years. Each model run year is mapped to several calendar years such that changes in variable costs, available capacity, and demand for electricity in the years between the run years are partially captured in the results for each model run year. Table B5A-2 below identifies the model run years specified for the analysis of Phase III options and the calendar years mapped to each.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-5 Table B5A-2: Model Run Year Mapping Run Year Mapped Years

2008 2005-2009 2010 2010-2012 2013 2013-2015 2020 2016-2022 2026 2023-2030 Source: IPM ® model specification for the Section 316(b) Base Case.
B5A-1.2 Specifications for the Section 316(b) Analysis The analysis for section 316(b) rulemaking required ch anges in the original specification of the IPM ®

. Specifically, the base case configuration of the model plan ts and model run years were revised according to the requirements of this analysis. Both modifications to the existing model specifications are discussed below.

< Changes in the Aggregation of Model Plants: As noted above, the IPM ® aggregates individual boilers and generators with similar cost and operational characteristics into model plants. Since the IPM ® model plants were initially created to support air policy anal yses, the original configuration was not appropriate for the section 316(b) analysis. As a result, the steam and non-steam electric generators at facilities subject to the Phase II and Phase III rules we re disaggregated from the existing IPM ® model plants and “run” as individual facilities along with the other existi ng model plants. This change increased the total number of model plants from 1,703 to 2,342.

< Use of Different Model Run Years: The original specification of the IPM ® ’s EPA Base Case 2003 uses five model run years chosen based on the require ments of various air policy analyses: 2005, 2010, 2015, 2020, and 2026. As explained above, EPA was interested in analyzing different years for the section 316(b) rulemaking effort. Therefore, EPA changed th e run years for the section 316(b) analysis in order to obtain model output throughout the compliance period (see discussion of run year selection in section B5A-1.1.d above). The change in run years and run year mappings are summarized below.

Table B5A-3: Modification of Model Run Years EPA Base Case 2003 Specification Section 316(b) Base Case Specification Run Year Run Year Mapping Run Year Run Year Mapping

2005 2005-2007 2008 2005-2009 2010 2008-2012 2010 2010-2012 2015 2013-2017 2013 2013-2015 2020 2018-2022 2020 2016-2022 2026 2023-2030 2026 2023-2030 Source: IPM ® model specifications for the EPA Base Case 2003 and the Section 316(b) Base Case.
EPA compared the base case results generated from the two different specifications of the IPM ® model. The base case results could only be compared for those run years that are common to both base cases, 2010 and 2020. This comparison identified little or no difference in the base case results:

< Base case total production costs (capital, O&M, and fu el) using the revised section 316(b) specifications are lower by 0.1% in 2010 and 2020.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

6Two of the 113 facilities potentially subject to Phase III regul ation and nine of the 543 facilities subject to the Phase II rule are either not modeled in the IPM ® or do not have steam-electric generators: one Phas e III facility is out-of-service; one Phase II facility is retired; five facilities, one Phase III and four Phase II, are on-s ite generators that do not provide electricity to the grid; three Phase II facilities are in Hawaii and one Phase II facility is in Alaska, neither of which is represented in the IPM ®

.

7 The capital charge rate is a function of capital structure (debt/e quity shares of an investment), pre-tax debt rate (or interest cost), debt life, after-tax return on equity, corporate income tax, depreciation schedule, book life of the investment, and other cost s including property tax and insurance. The discount rate is a function of capital structure, pre-tax debt rate, and after-tax return on e quity.

B5A-6 < Early retirements of base case oil and gas steam capacity and coal capacity under the section 316(b) specifications are higher by 3,192 megawatt (MW) and 383 MW in 2010 and 2020, respectively.

< The change in model specifications resulted in virtually no change in base case coal and gas use in 2010 and 2020. B5A-1.3 Model Inputs Compliance costs and compliance-related capacity reducti ons are the primary model inputs in the analysis of section 316(b) rulemaking. EPA determined compliance costs for each of the 113 facilities potentially subject to Phase III regulation and 534 facilities subject to th e Phase II regulation and modeled by the IPM ®

. 6For each facility, compliance costs consist of capital costs (incl uding costs for new screens or fish barrier nets, intake relocation, and intake piping modification), fixed O&M costs, variable O&M costs, permitting costs, and capacity reductions (for information on the costing methodology, see the Technical Development Document for the Proposed Section 316(b) Rule for Phase III Facilities ; U.S. EPA, 2004b).

< Capital cost inputs into the IPM ® are expressed as a fixed O&M cost, in dollars per kilowatt (KW) of capacity per year. The capital costs of compliance re flect the up-front cost of construction, equipment, and capital associated with the installation of required compliance technologies. The IPM ® uses two upfront cost values as model inputs (one each for technologies with a useful life of 10 and 30 years, respectively) and translates these values into a series of annual after-tax payments using a discount rate of 5.34% for medium risk investments and 6.74%for high risk investments, and a capital charge rate of 12% for medium risk investments and 13.4% for high risk investments for the duration of the book life of the investment (assumed to be 30 years for initial permitting costs and 10 years for the various compliance technologies) or the years remaining in the mode ling horizon, whichever is shorter. High risk investments include Integrated Gasification Combined Cycle (IGCC) and repowerings-to- IGCC. 7

< Fixed O&M cost inputs into the IPM ® are expressed in terms of dollars per KW of capacity per year.

< Variable O&M cost inputs are expressed in dollars pe r megawatt hour (MWh) of generation.

< Permitting costs consist of initial permitting costs, annual monitoring costs, repermitting costs (occurring every five years after issuance of the initial permit), and, for some facilities, pilot study costs. Permitting cost inputs are expressed as follows: initial permitti ng and pilot study activities are necessary for the ongoing operation of the plant and are therefore added to the capital costs for technologies with a 30-year useful life; annual monitoring and annualized repermitting costs are added to the fixed O&M costs.

< Capacity reductions consist of a one-time generator downtime. Generator downtime estimates reflect the amount of time generators are off-line while complian ce technologies are constructed and/or installed and are expressed in weeks. The generator downtime is a one-time event that affects several of the compliance technologies evaluated by EPA. Generato r downtime is estimated to occur during the year when a facility complies with the policy option. Si nce the years that are mapped into a run year are assumed to have the same characteristics as the run y ear itself, generator downtimes were applied as an§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

8 For example, a facility with a downtime in 2008 was modeled as if 1/5th of its downtime occurred in each year between 2005 an d 2009. A potential drawback of this approachof averaging downtimes over the mapped year s is that the snapshot of the effect of downtimes during the model run y ear is the average effect; this approach does not model potential worst case effects of above-average amounts of capacity being down in any one NERC region during any one year.

9 This information is provided in Schedul e IV - Generator Information, Question 3.A (Design flow rate for the condenser at 100% load). Design intake flow data at the generator level is not available for nonutilities nor for those utility owned plants wit h a steam generating capacity less than 100 MW. Generator-level design intake flow data were not available for 41 of 111 Phase III model ed facilities and 60 of the 534 Phase II modeled facilities.

10Repowering in the IPM ® consists of converting oil/gas or coal capacity to combined-cycle capacity. The modeling assumption is that each one MW of existing capacity is replaced by two MW of repowered capacity.

11 Under the Phase II analysis nuclear plants were evaluated for economic viability at the end of their license term. Nuclear units that, at age 30, did not make a major maintenan ce investment, were provided with a 10-year life extension, if they were economically viable. These same units could subsequently undertake a 20-year re-licensing option at age 40. Nuclear units that already had made a maintenance investment were provided with a 20-year re-licensing option at age 40, if they were economically viable. All nucle ar units were ultimately retired at age 60. Nuclear power plant retirements are no longer endogenous in the 2003 IPM ® , and are now consistent with AEO2003. All other nuclear plants are assu med to remain operating over the modeling time horizon.

B5A-7 average over the years that are mapped into each model run year. 8 Estimated generator downtimes due to construction and/or installation range from two to eleven weeks (see also Chapter B1: Summary of Cost Categories and Key Analysis Elements for Existing Facilities ).
The IPM ® operates at the boiler level. It was therefore n ecessary to distribute facility-level costs across affected boilers. EPA used the following methodology:

< Steam electric generators operating at each of the 645 modeled section 316(b) Phase II and Phase III facilities were identified using data from the IPM ®

.

< Generator-specific design intake flows we re obtained from Form EIA-767 (1998 and 2000). 9

< Facility-level compliance costs were distributed acr oss each facility’s steam generators. For facilities with available design intake flow data, this distri bution was based on each generator’s proportion of total design intake volume; for facilities without available design intake flow, this distribution was based on each generator’s proportion of total steam electric capacity.

< Generator-level compliance costs were aggregated to the boiler level based on the EPA’s Base Case 2003 cross-walk between boilers and generators. B5A-1.4 Model Outputs
The IPM ® generates a series of outputs on different levels of a ggregation (boiler, model plant, region, and nation). The economic analysis for Option 6 used a subset of the available IPM ® output. For each model run and for each model run year (2008, 2010, 2013, and 2020) th e following model outputs were generated:

< Capacity – Capacity is a measure of the ability to generate electricity. This output measure reflects the summer net dependable capacity of all generating units at the plant. The model differentiates between existing capacity, new capacity additions, and existing capacity that has been repowered. 10

< Early Retirements – The IPM ® models economic closures as a result of negative net present value of future operation. 11 Under the Phase II analysis, all power plan ts that retired after the compliance year continued to carry the compliance costs after retirement. This modeling assumption has been changed for the Phase III analysis such that power plants with a compliance year in the 2005, 2006, or 2007 model run years, if endogenously retired by the model in the 2008 run year, will not carry the cost of the compliance decision over their retired life.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

12Such short-term capacity reductions w ould not be expressed as changes in available capacity but might affect electricity generation, production costs, and/or prices.

B5A-8 < Energy Price – The average annual price received for the sale of electricity.

< Capacity Price – The premium over energy prices received by facilities operating in peak hours during which system load approaches available capacity. The capacity price is the premium required to stimulate new market entrants to construct additional capacity, cover costs, and earn a return on their investment. This price manifests as short term price spikes duri ng peak hours and, in long-run equilibrium, need be only so large as is required to justify investment in new capacity.

< Generation – The amount of electricity produced by each pl ant that is available for dispatch to the transmission grid (“net generation”).

< Energy Revenue – Revenues from the sale of electricity to the grid.

< Capacity Revenue – Revenues received by facilities operating in hours where the price of energy exceeds the variable production cost of generation for the next unit to be dispatched at that price in order to maintain reliable energy supply in the short run. At these peak hours, the price of energy includes a premium which reflects the cost of the required reservemargin and serves to stimulate investment in the additional capacity required to maintain a long run equilibrium in the supply and demand for capacity.

< Fuel Costs – The cost of fuel consumed in the generation of electricity.

< Variable Operation and Maintenance Costs – Non-fuel O&M costs that vary with the level of generation, e.g., cost of consumables, in cluding water, lubricants, and electricity.

< Fixed Operation and Maintenance Costs – O&M costs that do not vary with the level of generation, e.g., labor costs and capital expenditures for mainte nance. In post-compliance scenarios, fixed O&M costs also include annualized capital co sts of compliance and permitting costs.

< Capital Costs – The cost of construction, equipment, and capital. Capital costs are associated with investment in new equipment, e.g., the replacement of a boiler or condenser, installation of technologies to meet the requirements of air regulations, or the repowering of a plant.



### B5A-2 E CONOMIC 

### I MPACT 

### A NALYSIS 

### M ETHODOLOGY The outputs presented in the previous section were u sed to identify changes to economic and operational characteristics such as capacity, generation, revenue, cost of generation, and electricity prices associated with Option 6. EPA developed impact measures at two analytic levels: (1) the market as a whole, including all facilities and (2) the subset of facilities potentially subject to Phase III regulation. Both analyses were conducted by NERC region. The following subsections describe the im pact measures used for the two levels of analysis. B5A-2.1 Market-level Impact Measures The market-level analysis evaluates regional changes as a result of Option 6. Seven main measures are analyzed:

< (1) Changes in available capacity: This measure analyzes changes in the capacity available to generate electricity. A long-term reduction in availability may be the result of partial or full closures of plants subject to Phase III regulation. In the short term, te mporary plant shut-downs for the installation of Phase III compliance technologies may lead to reductions in available capacity. 12When analyzing changes in§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-9 available capacity, EPA distinguished between existing capacity , new capacity additions , and repowering additions . Under this measure, EPA also analyzed capacity closures . Only capacity that is projected to remain operational in the base case but is closed in the post-compliance case is considered a closure as the result of the analyzed option. An op tion may result in partial (i.e., unit) or full plant closures. An option may also result in avoided clos ures if a facility’s compliance costs are low relative to other affected facilities. An avoided closure is a unit or plant that would close in the base case but operates in the post-compliance case.

< (2) Changes in the price of electricity: This measure considers changes in regional prices as a result of Phase III regulation. In the long term, electricity prices may change as a result of increased production costs of the potential Phase III facilities. In the s hort-term, price increases may be higher if large power plants have to temporarily shut down to construct and/or install Phase III compliance technologies. This analysis considers changes in both energy prices and capacity prices

.

< (3) Changes in generation: This measure considers the amount of electricity generated. At a regional level, long-term changes in generation may be the r esult of plant closures or a change in the amount of electricity traded between regions. In the short term, temporary plant shut-downs to install Phase III compliance technologies may lead to reductions in ge neration. At the national level, the demand for electricity does not change between the base case and th e analyzed policy options (generation within the regions is allowed to vary). However, demand for electricity does vary across the modeling horizon according to the model’s underlying electricity demand growth assumptions.

< (4) Changes in revenues: This measure considers the revenues reali zed by all facilities in the market and includes both energy revenues and capacity revenu es (see definition in section B5A-1.4 above). A change in revenues could be the result of a changein generation and/or the price of electricity.

< (5) Changes in costs: This measure considers changes in th e overall cost of generating electricity, including fuel costs, variable and fixed O&M costs, and capital costs. Fuel costs and variable O&M costs are production costs that vary with the level of generation. Fuel costs generally account for the single largest share of production costs. Fixed O&M costs and capital costs do not vary with generation. They are fixed in the short-term and therefore do not affect the dispatch decision of a unit (given sufficient demand, a unit will dispatch as long as the price of electricity is at least equal to its per MWh production costs). However, in the long-run, these costs need to be recovered for a unit to remain economically viable.

< (6) Changes in pre-tax income: Pre-tax income is defined as total revenues minus total costs and is an indicator of profitability. Pre-tax income may decrease as a result of reductions in revenues and/or increases in costs.

< (7) Changes in variable production costs per MWh: This measure considers the regional change in average variable production cost per MWh. Variab le production costs include fuel costs and other variable O&M costs but exclude fixed O&M costs and capital costs. Production cost per MWh is a primary determinant of how often a power plant’s un its are dispatched. This measure presents similar information to total fuel and variable O&M costs under measure (5) above, but normalized for changes in generation. B5A-2.2 Facility-level Impact Measures (Potential Phase III Facilities Only)
EPA used the IPM ® results to analyze impacts on potential Phase III facilities at two levels: (1) changes in the economic and operational characteristics of the potential Phase III facilities as a group and (2) changes to individual facilities within the group of potential Phase III facilities. a. Potential Phase III facilities as a group§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

13 Such short-term capacity reductions w ould not be expressed as changes in available capacity but might affect electricity generation, production costs, and/or prices.

B5A-10 The analysis of the potential Phase III facilities as a group is largely similar to the mark et-level analysis described in Section B5A-2.1 above, except that the base case and policy option totals only include the economic activities of the 111 potentially regulated Phase III facilities represented by the model. In addition, a few measures differ: (1) new capacity additions and prices are not relevant at the facility level, (2) the number of potential Phase III facilities that experience closure of all their steam electric capacity is presented, and (3) repowering changes are not explicitly analyzed at the facility level. Followi ng are the measures evaluated for the group of potential Phase III facilities:

< (1) Changes in available capacity: This measure considers the capacity available at the 111 potentially regulated Phase III facilities. A long-term reduction in availability may be the result of partial or full plant closures, a change in the decision to repower, or a change in the choice of air pollution control technologies. In the short term, temporary plant s hut-downs for the installation of Phase III compliance technologies may lead to reductions in available capacity. 13 Under this measure, EPA also analyzed regulatory closures . Only capacity that is projected to re main operational in the base case but is closed in the post-compliance case is considered a closure as the result of the rule. An option may result in partial (i.e., unit) or full plant closures. An opti on may also result in avoided closures if a facility’s compliance costs are low relative to other affected f acilities. An avoided closure is a unit or plant that would close in the base case but operates in the pos t-compliance case. At the facility-level, both the number of full regulatory closure facilities and closure capacity are analyzed.

< (2) Changes in generation: This measure considers the generation at the 111 potential Phase III facilities. Long-term changes in generation may be the result of a reduction in available capacity (see discussion above) or the less frequent dispatch of a plant due to higher production cost as a result of the policy option. In the short term, temporary plant shut-downs may lead to reductions in generation at some of the 111 potential Phase III facilities. For some 316(b) fac ilities, Phase III regulation may lead to an increase in generation if their compliance costs ar e low relative to other affected facilities.

< (3) Changes in revenues: This measure considers the revenues realized by the 111 potential Phase III facilities and includes both energy revenues and cap acity revenues (see definition in section B5A-1.4 above). A change in revenues could be the result of a change in generation and/or the price of electricity. For some 316(b) facilities, Phase III regulation may leadto an increase in revenues if their generation increases as a result of the rule, or if the rule leads to an increase in electricity prices.

< (4) Changes in costs: This measure considers changes in the ove rall cost of generating electricity for the 111 Phase III facilities, including fuel costs, variable and fixed O&M costs, and capital costs. Fuel costs and variable O&M costs are production costs that vary with the le vel of generation. Fuel costs generally account for the single largest share of production costs. Fixed O&M costs and capital costs do not vary with generation. They are fixed in the short-term and therefore do not affect the dispatch decision of a unit (given sufficient demand, a unit will dispatch as long as the price of electricity is at least equal to its per MWh production costs). However, in the long-run, these costs need to be recovered for a unit to remain economically viable.

< (5) Changes in pre-tax income: Pre-tax income is defined as total revenues minus total costs and is an indicator of profitability. Pre-tax income may decrease as a result of reductions in revenues and/or increases in costs.

< (6) Changes in variable production costs per MWh: This measure considers the plant-level change in the average annual variable production cost per MWh. Variable production costs include fuel costs and other variable O&M costs but exclude fixed O&M costs and capital costs. b. Individual Phase III facilities§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-11 To assess potential distributional impacts among individual Phase III facilities, EPA analyzed facility-specific changes to a number of key measures. For each measure, EPA determined the number of potential Phase III facilities that experience an increase or a reduction, respectiv ely, within three ranges: 1% or less, 1 to 3%, and more than 3%. EPA conducted this analysis for the following measures:

< (1) Changes in capacity utilization: Capacity utilization is defined as a unit’s actual generation divided by its potential generation, if it ran 100% of the time (i.e., generation / (capacity * 365 days * 24 hours)). This measure indicates how frequently a unit is disp atched and earns energy revenues for its owner.

< (2) Changes in generation: See explanation in subsection a. above.

< (3) Changes in revenues: See explanation in subsection a. above.

< (4) Changes in variable production costs per MWh: See explanation in subsection a. above.

< (5) Changes in fuel costs per MWh: See explanation in subsection a. above.

< (6) Changes in pre-tax income: See explanation in subsection a. above.



### B5A-3 A NALYSIS 

### R ESULTS FOR 

### O PTION 

### 6 The remainder of this section presents the results of th e economic impact analysis of Option 6 for the ten NERC regions modeled by the IPM ® . Analyzed characteristics include changes in electricity prices, capacity, generation, revenue, cost of generation, and income. These chang es were identified by comparing outcomes in the postcompliance scenario with outcomes in the base case. Because of the interrelationships between the final Phase II rule (promulgated in July 2004) and regulation of potential Phase III facilities, EPA developed two base cases for this analysis: the first base case (referred to as Base Case 1) models operational characteristics of the electricity market in the absence of any section 316(b) rulemaking (i.e., pre-Phase II regulation); the second base case (referred to as Base Case 2) models operational characteristics of the electricity market including compliance costs of the final Phase II rule (but pre-Phase III regulati on). Results are presented at the market level and the Phase III facility level. For the market-level analysis of Option 6, EPA compar ed the post-compliance scenario (after the implementation of Phase III compliance requirements) with Base Case 2 (i ncluding Phase II compliance costs). This comparison allows EPA to identify the incremental market-level e ffects of Phase III requirements, beyond the effects of Phase II regulation. In contrast, for the analysis of fac ilities subject to Phase III regulation, EPA compared the postcompliance scenario with Base Case 1 (excluding Phase II compliance costs). This comparison was done to determine the “true” effect of Phase III regulation, net of any temporary effects that might be introduced as the result of the staggering of the three section 316(b) ph ases. Because Phase II facilities have to comply before Phase III facilities (on average by two years), Phase III facilities may experience a short-term competitive advantage during the time when Phase II facilities incur section 316(b) compliance costs while Phase III facilities do not. The post-compliance economic performance of Phase III facilities should not be compared to this potential short-term improvement in operating characteristics but to their steady-state, pre-section 316(b) rulemaking economic condition. The following subsections present the market-level analys is (including all facilities, by NERC region) and the facility-level analysis (including analyses of the in-scope Phase III facilities as a group and of individual Phase III facilities). The results are presented using data from mode l run year 2013. It should be noted that the results presented in this section are based on Option 6; EPA did not conduct an IPM ® analysis for the other options considered for this proposal. Since Option 6 is the most in clusive and costly of any of the considered options, the results represent the upper bound estimate of potential economic impacts as a result of proposed Phase III regulation. And, as noted above, none of the thr ee proposed options would apply national categorical§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-12 requirements to any electric generating facilities; thus the proposed options have no effects to be considered in an IPM ® analysis. B5A-3.1 Market Analysis for 2013 This section presents the results of the IPM ® analysis for all facilities modeled by the IPM ® . The market-level analysis includes results for all generators located in each NERC region including facilities that are potentially subject to Phase III regulation and facilities that are not subject to Phase III regulation. Table B5A-4 presents the market-level impact measures discussed in section B5A-2.1 above: (1) capacity changes, including changes in existing capacity, new add itions, repowering additions, and closures; (2) electricity price changes, including changes in energy prices and capacity prices; (3) generation changes; (4) revenue changes; (5) cost changes, including changes in fuel costs, variable O& M costs, fixed O&M costs, and capital costs; (6) changes in pre-tax income; and (7) changes invariable production costs per MWh of generation. For each measure, the table presents the results for Base Case 2 and Option 6, the absolute difference between the two cases, and the percentage difference. Table B5A-4: Market-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 2 Option 6 Difference % Change National Totals (1) Total Domestic Capacity (MW) 992,564 992,549 (15) 0.0% (1a) Existing 944,254 944,168 (86) 0.0% (1b) New Additions 38,766 39,008 241 0.6% (1c) Repowering Additions 9,544 9,373 (171) (1.8)% (1d) Closures 23,213 23,386 173 0.7% (2a) Energy Prices ($2003/MWh) n/a n/a n/a n/a (2b) Capacity Prices ($2003/KW/yr) n/a n/a n/a n/a (3) Generation (GWh) 4,592,198 4,592,191 (7) 0.0% (4) Revenues (Millions; $2003) $181,098 $181,026 ($72) 0.0% (5) Costs (Millions; $2003) $112,839 $112,863 $23 0.0% (5a) Fuel Cost $64,060 $64,075 $14 0.0% (5b) Variable O&M $8,393 $8,394 $1 0.0% (5c) Fixed O&M $35,689 $35,692 $3 0.0% (5d) Capital Cost $4,696 $4,702 $5 0.1% (6) Pre-Tax Income (Millions; $2003) $68,259 $68,164 ($95) (0.1)% (7) Variable Production Cost s ($/MWh) $15.78 $15.78 $0.00 0.0% East Central Area Reliability Coordination Agreement (ECAR) (1) Total Domestic Capacity (MW) 129,375 129,381 6 0.0% (1a) Existing 127,266 127,264 (3) 0.0% (1b) New Additions 2,109 2,117 8 0.4% (1c) Repowering Additions 0 0 0 0.0% (1d) Closures 1,699 1,703 4 0.2% (2a) Energy Prices ($2003/MWh) $28.81 $28.81 $0.01 0.0% (2b) Capacity Prices ($2003/KW/yr) $59.50 $59.55 $0.05 0.1% (3) Generation (GWh) 711,535 711,438 (97) 0.0% (4) Revenues (Millions; $2003) $28,304 $28,313 $9 0.0%§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5A-4: Market-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 2 Option 6 Difference % Change

B5A-13 (5) Costs (Millions; $2003) $15,091 $15,086 ($4) 0.0% (5a) Fuel Cost $8,423 $8,423 $0 0.0% (5b) Variable O&M $1,703 $1,702 ($1) (0.1)% (5c) Fixed O&M $4,471 $4,471 $0 0.0% (5d) Capital Cost $494 $491 ($3) (0.6)% (6) Pre-Tax Income (Millions; $2003) $13,213 $13,226 $14 0.1% (7) Variable Production Cost s ($/MWh) $14.23 $14.23 $0.00 0.0% Electric Reliability Council of Texas (ERCOT) (1) Total Domestic Capacity (MW) 88,456 88,456 0 0.0% (1a) Existing 80,603 80,722 119 0.1% (1b) New Additions 6,622 6,966 345 5.2% (1c) Repowering Additions 1,231 768 (463) (37.6)% (1d) Closures 178 291 113 63.4% (2a) Energy Prices ($2003/MWh) $39.84 $41.91 $2.07 5.2% (2b) Capacity Prices ($2003/KW/yr) $14.69 $5.48 ($9.21) (62.7)% (3) Generation (GWh) 343,397 343,397 0 0.0% (4) Revenues (Millions; $2003) $14,972 $14,873 ($98) (0.7)% (5) Costs (Millions; $2003) $10,490 $10,504 $14 0.1% (5a) Fuel Cost $6,834 $6,844 $10 0.1% (5b) Variable O&M $698 $700 $2 0.2% (5c) Fixed O&M $2,309 $2,311 $2 0.1% (5d) Capital Cost $649 $650 $1 0.1% (6) Pre-Tax Income (Millions; $2003) $4,481 $4,369 ($112) (2.5)% (7) Variable Production Cost s ($/MWh) $21.93 $21.97 $0.03 0.2% Florida Reliability Coordinating Council (FRCC) (1) Total Domestic Capacity (MW) 56,655 56,655 0 0.0% (1a) Existing 52,822 52,676 (146) (0.3)% (1b) New Additions 1,463 1,316 (146) (10.0)% (1c) Repowering Additions 2,370 2,662 292 12.3% (1d) Closures 145 145 0 0.0% (2a) Energy Prices ($2003/MWh) $34.46 $34.43 ($0.04) (0.1)% (2b) Capacity Prices ($2003/KW/yr) $50.55 $50.82 $0.28 0.5% (3) Generation (GWh) 231,180 231,180 0 0.0% (4) Revenues (Millions; $2003) $10,831 $10,838 $7 0.1% (5) Costs (Millions; $2003) $7,173 $7,177 $4 0.1% (5a) Fuel Cost $4,633 $4,634 $1 0.0% (5b) Variable O&M $432 $432 $0 0.0% (5c) Fixed O&M $1,870 $1,868 ($1) (0.1)% (5d) Capital Cost $238 $243 $5 2.1% (6) Pre-Tax Income (Millions; $2003) $3,658 $3,661 $3 0.1%§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5A-4: Market-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 2 Option 6 Difference % Change

B5A-14

(7) Variable Production Cost s ($/MWh) $21.91 $21.91 $0.00 0.0% Mid-Atlantic Area Council (MAAC) (1) Total Domestic Capacity (MW) 70,973 70,973 0 0.0% (1a) Existing 68,977 68,977 0 0.0% (1b) New Additions 1,997 1,997 0 0.0% (1c) Repowering Additions 0 0 0 0.0% (1d) Closures 949 949 0 0.0% (2a) Energy Prices ($2003/MWh) $31.55 $31.56 $0.01 0.0% (2b) Capacity Prices ($2003/KW/yr) $44.06 $44.06 $0.01 0.0% (3) Generation (GWh) 314,261 314,253 (8) 0.0% (4) Revenues (Millions; $2003) $13,039 $13,044 $5 0.0% (5) Costs (Millions; $2003) $8,131 $8,131 $0 0.0% (5a) Fuel Cost $3,744 $3,744 $0 0.0% (5b) Variable O&M $537 $537 $0 0.0% (5c) Fixed O&M $3,619 $3,619 $0 0.0% (5d) Capital Cost $230 $230 $0 0.0% (6) Pre-Tax Income (Millions; $2003) $4,908 $4,913 $5 0.1% (7) Variable Production Cost s ($/MWh) $13.62 $13.62 $0.00 0.0% Mid-America Interconnected Network (MAIN) (1) Total Domestic Capacity (MW) 69,770 69,770 0 0.0% (1a) Existing 67,013 67,013 0 0.0% (1b) New Additions 2,757 2,757 0 0.0% (1c) Repowering Additions 0 0 0 0.0% (1d) Closures 0 0 0 0.0% (2a) Energy Prices ($2003/MWh) $27.09 $27.09 $0.00 0.0% (2b) Capacity Prices ($2003/KW/yr) $51.01 $51.05 $0.04 0.1% (3) Generation (GWh) 332,292 332,359 68 0.0% (4) Revenues (Millions; $2003) $12,556 $12,561 $5 0.0% (5) Costs (Millions; $2003) $7,690 $7,692 $2 0.0% (5a) Fuel Cost $3,405 $3,406 $1 0.0% (5b) Variable O&M $544 $544 $0 0.0% (5c) Fixed O&M $3,424 $3,425 $1 0.0% (5d) Capital Cost $317 $317 $0 0.0% (6) Pre-Tax Income (Millions; $2003) $4,866 $4,870 $3 0.1% (7) Variable Production Cost s ($/MWh) $11.88 $11.88 $0.00 0.0% Mid-Continent Area Power Pool (MAPP) (1) Total Domestic Capacity (MW) 37,368 37,368 0 0.0% (1a) Existing 37,336 37,336 0 0.0% (1b) New Additions 32 32 0 0.0% (1c) Repowering Additions 0 0 0 0.0%§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5A-4: Market-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 2 Option 6 Difference % Change

B5A-15 (1d) Closures 0 0 0 0.0% (2a) Energy Prices ($2003/MWh) $25.58 $25.58 $0.00 0.0% (2b) Capacity Prices ($2003/KW/yr) $40.39 $40.42 $0.03 0.1% (3) Generation (GWh) 190,058 190,058 0 0.0% (4) Revenues (Millions; $2003) $6,404 $6,406 $2 0.0% (5) Costs (Millions; $2003) $3,884 $3,885 $1 0.0% (5a) Fuel Cost $1,968 $1,968 $0 0.0% (5b) Variable O&M $370 $370 $0 0.0% (5c) Fixed O&M $1,541 $1,542 $1 0.0% (5d) Capital Cost $5 $5 $0 0.0% (6) Pre-Tax Income (Millions; $2003) $2,520 $2,521 $1 0.0% (7) Variable Production Cost s ($/MWh) $12.30 $12.30 $0.00 0.0% Northeast Power Coordinating Council (NPCC) (1) Total Domestic Capacity (MW) 77,994 77,982 (12) 0.0% (1a) Existing 74,198 74,151 (47) (0.1)% (1b) New Additions 3,586 3,621 35 1.0% (1c) Repowering Additions 210 209 0 (0.1)% (1d) Closures 3,531 3,578 47 1.3% (2a) Energy Prices ($2003/MWh) $34.95 $34.95 $0.00 0.0% (2b) Capacity Prices ($2003/KW/yr) $30.21 $30.21 $0.00 0.0% (3) Generation (GWh) 306,579 306,608 30 0.0% (4) Revenues (Millions; $2003) $13,053 $13,054 $1 0.0% (5) Costs (Millions; $2003) $9,535 $9,538 $3 0.0% (5a) Fuel Cost $5,248 $5,248 $0 0.0% (5b) Variable O&M $439 $439 $0 0.0% (5c) Fixed O&M $3,426 $3,426 $0 0.0% (5d) Capital Cost $422 $424 $3 0.6% (6) Pre-Tax Income (Millions; $2003) $3,518 $3,516 ($2) 0.0% (7) Variable Production Cost s ($/MWh) $18.55 $18.55 $0.00 0.0% Southeastern Electric Reliability Council (SERC) (1) Total Domestic Capacity (MW) 218,915 218,915 0 0.0% (1a) Existing 207,416 207,416 0 0.0% (1b) New Additions 11,499 11,499 0 0.0% (1c) Repowering Additions 0 0 0 0.0% (1d) Closures 8,824 8,824 0 0.0% (2a) Energy Prices ($2003/MWh) $30.48 $30.47 $0.00 0.0% (2b) Capacity Prices ($2003/KW/yr) $47.76 $47.77 $0.01 0.0% (3) Generation (Gwh) 1,065,456 1,065,456 0 0.0% (4) Revenues (Millions; $2003) $42,915 $42,912 ($3) 0.0% (5) Costs (Millions; $2003) $25,995 $25,997 $2 0.0%§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5A-4: Market-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 2 Option 6 Difference % Change

B5A-16 (5a) Fuel Cost $14,586 $14,588 $2 0.0% (5b) Variable O&M $1,839 $1,839 $0 0.0% (5c) Fixed O&M $8,468 $8,468 $0 0.0% (5d) Capital Cost $1,102 $1,102 $0 0.0% (6) Pre-Tax Income (Millions; $2003) $16,921 $16,915 ($6) 0.0% (7) Variable Production Cost s ($/MWh) $15.42 $15.42 $0.00 0.0% Southwest Power Pool (SPP) (1) Total Domestic Capacity (MW) 57,806 57,797 (9) 0.0% (1a) Existing 57,806 57,797 (9) 0.0% (1b) New Additions 0 0 0 0.0% (1c) Repowering Additions 0 0 0 0.0% (1d) Closures 179 188 9 5.0% (2a) Energy Prices ($2003/Mwh) $28.05 $28.05 $0.00 0.0% (2b) Capacity Prices ($2003/KW/yr) $13.96 $13.97 $0.01 0.1% (3) Generation (GWh) 239,392 239,392 0 0.0% (4) Revenues (Millions; $2003) $7,520 $7,521 $1 0.0% (5) Costs (Millions; $2003) $5,505 $5,506 $1 0.0% (5a) Fuel Cost $3,582 $3,583 $1 0.0% (5b) Variable O&M $472 $472 $0 (0.1)% (5c) Fixed O&M $1,444 $1,443 $0 0.0% (5d) Capital Cost $8 $8 $0 (1.9)% (6) Pre-Tax Income (Millions; $2003) $2,015 $2,015 $0 0.0% (7) Variable Production Cost s ($/MWh) $16.93 $16.94 $0.00 0.0% Western Electricity Coordinating Council (WECC) (1) Total Domestic Capacity (MW) 185,252 185,252 0 0.0% (1a) Existing 170,817 170,817 0 0.0% (1b) New Additions 8,702 8,702 0 0.0% (1c) Repowering Additions 5,733 5,733 0 0.0% (1d) Closures 7,708 7,708 0 0.0% (2a) Energy Prices ($2003/MWh) $32.62 $32.62 $0.00 0.0% (2b) Capacity Prices ($2003/KW/yr) $20.37 $20.37 $0.00 0.0% (3) Generation (GWh) 858,050 858,050 0 0.0% (4) Revenues (Millions; $2003) $31,504 $31,504 $0 0.0% (5) Costs (Millions; $2003) $19,346 $19,347 $1 0.0% (5a) Fuel Cost $11,638 $11,638 $0 0.0% (5b) Variable O&M $1,360 $1,360 $0 0.0% (5c) Fixed O&M $5,116 $5,117 $1 0.0% (5d) Capital Cost $1,232 $1,232 $0 0.0% (6) Pre-Tax Income (Millions; $2003) $12,158 $12,157 ($1) 0.0% (7) Variable Production Cost s ($/MWh) $15.15 $15.15 $0.00 0.0%§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5A-4: Market-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 2 Option 6 Difference % Change

B5A-17 Source: IPM ® Analysis: Model runs for Section 316(b) Base Case 2 and Option 6.
Summary of Market Results at the National Level. The results presented in Table B5A-4 show that capac ity closures are estimated to increase by 173 MW, which represents 0.7% of total baseline capacity. Repowering additions are estimated to experience a net decrease of 171 MW or 1.8%. However, an estimated increase of 241 MW in new additions would offset the lost capacity. Total costs of electricity generation would not change, but capital costs are estimated to rise by 0.1%. All other measures are estimated to change by less than 1%. Summary of Market Results at the Regional Level. At the regional level, Option 6 is estimated to result in the following changes:

< ERCOT is estimated to experience the most notable ch anges in electricity prices, repowering additions, new capacity, and closures among the ten NERC regions. Energy prices increase by $2.07/MWh, which represents a 5.2% increase. Capacity prices decrease by $9.21/KW/year, or approximately 63%. This is partially due to the increase in energy prices, which allows facilities to bid their undispatched capacity at a lower price. This may also be, in part, due to an increase of 345 MW of new additions. The increased new additions are offset by a large decrease in re powering additions (463 MW, or 37.6%). Capacity closures increase by 113 MW. However, these closures occur in facilities that do not fall under Phase III regulation. While not subject to regulation these generators retire because there is a decrease in the capacity price they are able to receive for existing cap acity. As a result of lower capacity prices, pre-tax income is also estimated to decrease in ERCOT (2.5 %). All other measures are predicted to change by less than 1%.

< FRCC is the only region estimated to experience a reduc tion in new additions (146 MW, or 10%). It is also estimated to lose 146 MW of existing capacity. A projected 292 MW increase in repowering, however, would completely offset these reductions. FRCC is estimated to have an increase in capacity prices and a decrease in energy prices. However, both changes are less than 1%. All other measures are also estimated to change by less than 1%.

< SPPand NPCC are the only regions that are estimated to experience an increase in post-compliance capacity closures. In SPP, the 9 MW increase in cl osures (5% of Base Case 2 capacity) is due to the partial retirement of a potential Phase III facility. In NPCC, the 47 MW increase in closures (1.3% of Base Case 2 capacity) is the result of combination of partial facility closures and an avoided partial facility closure. Specifically, three facilities (two potential Phase III and one Phase II) retire 73 MW of capacity while one potential Phase III facility opts to keep 26 MW of capacity on-line which was retired under the baseline. The net result of these chang es is a 47 MW increase in closures. There are no additional changes in capacity in SPP. However, NPCC is estimated to have an additional 35 MW of new additions. The changes in all other measures are less than 1%.

< ECARis estimated to have the largest decrease in generation (97 GWh). However, this decrease is negligible in comparison to total base case generation (less than 0.1%). Overall capacity (6 MW), new additions (8 MW), and closures (4 MW, all of wh ich is potential Phase III capacity) increase slightly. Capacity prices also increase slightly (0.1%), as do es pre-tax income (0.1%). All other measures do not change.

< MAAC, MAIN, MAPP, SERC, and, WECC are not estimated to have any significant impacts for any of the measures analyzed. There are no changes in capacity for each region; there is no new or repowered capacity, and there are no additional closures as a result of Option 6. MAAC is estimated to have a slight increase in energy prices. Energy prices remain constant in each of the other regions. Each region except§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-18 WECC experiences a slight increase in capacity prices ( on average 0.1%). Generation, revenue, and costs are not expected to significantly change for any regi on. MAAC and MAIN are estimated to have a 0.1% increase in pre-tax income. B5A-3.2 Analysis of Potential Phase III Facilities for 2013 This section presents the results of the IPM ® analysis for facilities that are potentially subject to Phase III regulation and that are modeled by the IPM ® . Four of the 111 potential Phase III facilities are closures in Base Case 1, and five facilities are closures under Option 6. These facilities are not represented in the results described in this section.
EPA used the IPM ® results from model run year 2013 to analyze impacts on potential Phase III facilities at two levels: (1) changes in the economic and operational ch aracteristics of the potential Phase III facilities as a group and (2) changes to individual facilities within the group of potential Phase III facilities. a. Potential Phase III facilities as a group This section presents the analysis of the impacts of Option 6 on the potential Phase III facilities as a group. This analysis is similar to the market-level analysis describ ed above but is limited to facilities subject to the national requirements of Option 6. Table B5A-5 presents th e impact measures for the group of potential Phase III facilities discussed in section B5A-3.2 above: (1) capacity changes, including changes in the number and capacity of closure facilities; (2) generation changes; (3) revenuechanges; (4) cost chang es, including changes in fuel costs, variable O&M costs, fixed O&M costs, and capital costs; (5) changes in pre-tax income; and (6) changes in variable production costs per MWh of generation, where variable production cost is defined as the sum of fuel cost and variable O&M cost. For each measure, the ta ble presents the results for the Base Case 1 and Option 6, the absolute difference between the tw o cases, and the percentage difference. Two points should be kept in mind when interpreting these results:

< The percentage changes are calculated relative to baseline values of potential Phase III facilities in each region. In some regions, very few facilities are poten tially subject to Phase III regulation. If these percentage changes were calculated relative to the total for all electric power facilities in each region, the observed percentage changes would be much smaller.

< The post-compliance scenario reflect s compliance costs of both Phase II and Phase III regulation, while the base case reflects conditions before either Phase II or Phase III regulation. While Phase II compliance costs do not directly affect the analyzed measures for poten tial Phase III facilities, they do have an indirect effect on all facilities within the NERC region, through potential increases in electricity prices and changes in fuel demand. As a result, measures such as changes in variable production cost/MWh and pre-tax income might be influenced by Phase II compliance co sts, rather than Phase III regulation.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-19 Table B5A-5: Facility-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 1 Option 6 Difference % Change National Totals (1) Total Domestic Capacity (MW) 62,075 62,157 0 0.1% (1a) Closures - Number of Facilities 3 4 1 33.3% (1b) Closures - Capacity (MW) 1,047 964 (82) (7.8)% (2) Generation (GWh) 409,687 408,609 (1,078) (0.3)% (3) Revenues (Millions; $2003) $14,165 $14,104 ($60) (0.4)% (4) Costs (Millions; $2003) $8,500 $8,468 ($33) (0.4)% (4a) Fuel Cost $4,798 $4,761 ($37) (0.8)% (4b) Variable O&M $1,129 $1,127 ($2) (0.2)% (4c) Fixed O&M $2,406 $2,412 $6 0.2% (4d) Capital Cost $168 $168 $0 0.2% (5) Pre-Tax Income (Millions; $2003) $5,664 $5,637 ($28) (0.5)% (6) Variable Production Costs ($2003/MWh) $14.47 $14.41 ($0.06) (0.4)% East Central Area Reliability Coordination Agreement (ECAR) (1) Total Domestic Capacity (MW) 11,536 11,532 (4) 0.0% (1a) Closures - Number of Facilities 0 0 0 0.0% (1b) Closures - Capacity (MW) 0 4 4 n/a (2) Generation (GWh) 83,922 83,922 0 0.0% (3) Revenues (Millions; $2003) $3,042 $3,039 ($3) (0.1)% (4) Costs (Millions; $2003) $1,601 $1,601 $1 0.0% (4a) Fuel Cost $907 $907 $0 0.0% (4b) Variable O&M $233 $233 $0 0.0% (4c) Fixed O&M $386 $387 $1 0.2% (4d) Capital Cost $74 $74 $0 0.0% (5) Pre-Tax Income (Millions; $2003) $1,441 $1,438 ($4) (0.3)% (6) Variable Production Cost s ($2003/MWh) $13.59 $13.59 $0.00 0.0% Electric Reliability Council of Texas (ERCOT) (1) Total Domestic Capacity (MW) 3,900 3,900 0 0.0% (1a) Closures - Number of Facilities 0 0 0 0.0% (1b) Closures - Capacity (MW) 0 0 0 0.0% (2) Generation (GWh) 16,418 16,482 64 0.4% (3) Revenues (Millions; $2003) $684 $657 ($27) (3.9)% (4) Costs (Millions; $2003) $445 $448 $3 0.7% (4a) Fuel Cost $246 $249 $3 1.0% (4b) Variable O&M $65 $65 $0 0.5% (4c) Fixed O&M $123 $123 $0 0.1% (4d) Capital Cost $11 $11 $0 3.1% (5) Pre-Tax Income (Millions; $2003) $239 $209 ($30) (12.7)% (6) Variable Production Cost s ($2003/MWh) $18.94 $19.04 $0.10 0.5% Florida Reliability Coordinating Council (FRCC)§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5A-5: Facility-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 1 Option 6 Difference % Change

B5A-20

(1) Total Domestic Capacity (MW) 2,447 2,447 0 0.0% (1a) Closures - Number of Facilities 0 0 0 0.0% (1b) Closures - Capacity (MW) 0 0 0 0.0% (2) Generation (GWh) 13,227 12,714 (513) (3.9)% (3) Revenues (Millions; $2003) $577 $555 ($22) (3.8)% (4) Costs (Millions; $2003) $317 $300 ($17) (5.5)% (4a) Fuel Cost $200 $183 ($17) (8.4)% (4b) Variable O&M $36 $36 ($1) (1.7)% (4c) Fixed O&M $81 $81 $0 0.0% (4d) Capital Cost $0 $0 $0 0.0% (5) Pre-Tax Income (Millions; $2003) $260 $255 ($5) (1.8)% (6) Variable Production Costs ($2003/MWh) $17.85 $17.21 ($0.65) (3.6)% Mid-Atlantic Area Council (MAAC) (1) Total Domestic Capacity (MW) 6,420 6,420 0 0.0% (1a) Closures - Number of Facilities 0 0 0 0.0% (1b) Closures - Capacity (MW) 0 0 0 0.0% (2) Generation (GWh) 41,996 41,996 0 0.0% (3) Revenues (Millions; $2003) $1,515 $1,529 $14 0.9% (4) Costs (Millions; $2003) $813 $812 ($1) (0.1)% (4a) Fuel Cost $439 $438 ($1) (0.2)% (4b) Variable O&M $118 $118 $0 0.0% (4c) Fixed O&M $232 $232 $0 0.0% (4d) Capital Cost $24 $24 $0 0.0% (5) Pre-Tax Income (Millions; $2003) $702 $717 $15 2.1% (6) Variable Production Costs ($2003/MWh) $13.27 $13.25 ($0.02) (0.2)% Mid-America Interconnected Network (MAIN) (1) Total Domestic Capacity (MW) 3,234 3,234 0 0.0% (1a) Closures - Number of Facilities 0 0 0 0.0% (1b) Closures - Capacity (MW) 0 0 0 0.0% (2) Generation (GWh) 22,247 22,247 0 0.0% (3) Revenues (Millions; $2003) $778 $777 ($1) (0.1)% (4) Costs (Millions; $2003) $443 $444 $1 0.2% (4a) Fuel Cost $267 $267 $0 0.0% (4b) Variable O&M $56 $56 $0 0.0% (4c) Fixed O&M $95 $96 $1 0.8% (4d) Capital Cost $25 $25 $0 0.0% (5) Pre-Tax Income (Millions; $2003) $335 $333 ($2) (0.6)% (6) Variable Production Cost s ($2003/MWh) $14.54 $14.54 $0.00 0.0% Mid-Continent Area Power Pool (MAPP) (1) Total Domestic Capacity (MW) 4,379 4,379 0 0.0%§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5A-5: Facility-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 1 Option 6 Difference % Change

B5A-21 (1a) Closures - Number of Facilities 0 0 0 0.0% (1b) Closures - Capacity (MW) 0 0 0 0.0% (2) Generation (GWh) 30,897 30,897 0 0.0% (3) Revenues (Millions; $2003) $955 $953 ($2) (0.2)% (4) Costs (Millions; $2003) $687 $687 $1 0.1% (4a) Fuel Cost $342 $341 $0 0.0% (4b) Variable O&M $83 $83 $0 0.1% (4c) Fixed O&M $263 $263 $1 0.3% (4d) Capital Cost $0 $0 $0 0.0% (5) Pre-Tax Income (Millions; $2003) $268 $265 ($3) (1.0)% (6) Variable Production Cost s ($2003/MWh) $13.73 $13.73 $0.00 0.0% Northeast Power Coordinating Council (NPCC) (1) Total Domestic Capacity (MW) 845 940 95 11.3% (1a) Closures - Number of Facilities 0 1 1 n/a (1b) Closures - Capacity (MW) 112 16 (95) (85.6)% (2) Generation (GWh) 791 647 (144) (18.2)% (3) Revenues (Millions; $2003) $62 $54 ($8) (12.7)% (4) Costs (Millions; $2003) $40 $39 ($1) (3.5)% (4a) Fuel Cost $17 $13 ($4) (24.8)% (4b) Variable O&M $2 $2 $0 (18.1)% (4c) Fixed O&M $21 $24 $3 15.8% (4d) Capital Cost $0 $0 $0 0.0% (5) Pre-Tax Income (Millions; $2003) $21 $15 ($6) (30.3)% (6) Variable Production Costs ($2003/MWh) $24.53 $22.77 ($1.75) (7.2)% Southeastern Electric Reliability Council (SERC) (1) Total Domestic Capacity (MW) 11,967 11,967 0 0.0% (1a) Closures - Number of Facilities 1 1 0 0.0% (1b) Closures - Capacity (MW) 207 207 0 0.0% (2) Generation (GWh) 88,265 88,265 0 0.0% (3) Revenues (Millions; $2003) $3,104 $3,106 $2 0.1% (4) Costs (Millions; $2003) $1,859 $1,857 ($2) (0.1)% (4a) Fuel Cost $1,211 $1,209 ($2) (0.2)% (4b) Variable O&M $210 $210 $0 0.0% (4c) Fixed O&M $403 $403 $0 0.0% (4d) Capital Cost $34 $34 $0 0.2% (5) Pre-Tax Income (Millions; $2003) $1,245 $1,249 $4 0.3% (6) Variable Production Costs ($2003/MWh) $16.10 $16.08 ($0.02) (0.1)% Southwest Power Pool (SPP) (1) Total Domestic Capacity (MW) 4,391 4,382 (9) (0.2)% (1a) Closures - Number of Facilities 0 0 0 0.0%§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5A-5: Facility-Level Impacts of Option 6 (by NERC Region; 2013) Economic Measures Base Case 1 Option 6 Difference % Change

B5A-22 (1b) Closures - Capacity (MW) 0 9 9 n/a (2) Generation (GWh) 14,995 14,510 (485) (3.2)% (3) Revenues (Millions; $2003) $457 $441 ($15) (3.4)% (4) Costs (Millions; $2003) $381 $364 ($17) (4.4)% (4a) Fuel Cost $211 $196 ($15) (7.2)% (4b) Variable O&M $41 $40 ($1) (3.4)% (4c) Fixed O&M $129 $129 $0 (0.1)% (4d) Capital Cost $0 $0 $0 0.0% (5) Pre-Tax Income (Millions; $2003) $76 $77 $1 1.7% (6) Variable Production Costs ($2003/MWh) $16.81 $16.22 ($0.59) (3.5)% Western Electricity Coordinating Council (WECC) (1) Total Domestic Capacity (MW) 12,956 12,956 0 0.0% (1a) Closures - Number of Facilities 2 2 0 0.0% (1b) Closures - Capacity (MW) 728 728 0 0.0% (2) Generation (GWh) 96,928 96,928 0 0.0% (3) Revenues (Millions; $2003) $2,992 $2,995 $2 0.1% (4) Costs (Millions; $2003) $1,916 $1,916 $0 0.0% (4a) Fuel Cost $957 $957 $0 0.0% (4b) Variable O&M $284 $284 $0 0.0% (4c) Fixed O&M $674 $675 $0 0.0% (4d) Capital Cost $0 $0 $0 0.0% (5) Pre-Tax Income (Millions; $2003) $1,077 $1,079 $2 0.2% (6) Variable Production Cost s ($2003/MWh) $12.81 $12.81 $0.00 0.0% Source: IPM ® analysis: Model runs for Section 316(b) Base Case 1 and Option 6. Summary of Potential Phase III Facility Results at the National Level. The results presented in Table B5A-5 show that Option 6 would lead to one facility closure, and 82 MW (0.1% of Base Case 1 capacity) of avoided capacity closures. This outcome is the net result of two avoided partial facility closures (potential Phase III facilities with relatively low compliance costs that become more competitive relative to facilities with which they compete), one full policy closure, and two partial policy closures. It should be noted that all four facilities estimated to experience partial or full closures under Opti on 6 did not generate any electricity under Base Case 1. All four facilities are oil and gas-fueled facilities that served only reliability purposes. In addition, generation, revenues, and overall costs would all decrease under Option 6 but by less than 1%. Fixed O&M costs, which include the capital cost of compliance, are projected to increase by 0.2%. Pre-tax income for the group of potential Phase III facilities would decrease by 0.5%. Summary of Potential Phase III Facility Results at the Regional Level. Results vary somewhat by region. For many regions, impacts follow the general pattern described in the comparison to the market level above: generation, revenues, and pre-tax income decrease. Overall costs decrease in many regions due to lower levels of generation but increase in other regions where the additional compliance costs outweigh the reduction in generation. In addition to these general patterns, EPAestimates that Option 6 would result in the following changes:

< NPCC is the only region estimated to experience an increase in total capacity, gaining 95 MW (11.3% of Base Case 1 capacity) under Option 6. This outcome is the net result of two avoided partial facility§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-23 closures, and one full policy closure. Potential Phase III facilities in NPCC are estimated to experience the largest relative reductions in generation and revenues of any NERC region (18.2% and 12.7%, respectively). The reduction in generation is attributab le to two facilities that are projected to experience an increase in variable production costs. All potential Phase III facilities in NPCC are estimated to experience at least some reduction in revenues due to the estimated decrease in capacity prices (see Table B5A-4). Potential Phase III facilities in NPCC are al so estimated to experience the largest relative reduction in pre-tax income (30.3%) of any region. Though the aforementioned changes are significant on percentage basis, they are relatively minor in ab solute terms and consistent with the changes seen in the other regions. The only measure for which NPCC experiences the largest change on both a percentage basis and in absolute value is variable production costs.

< ECARand SPPare the only regions projected to experience a net reduction in capacity due to Option 6. In ECAR 4 MW are estimated to retire, or less than 0.1% of ECAR’s Base Case 1 capacity. In SPP 9 MW are estimated to retire, or 0.2% of SPP’s B ase Case 1 capacity. Neither region experiences changes in generation as a result of these partial closures. Inaddition, none of the 13 MW of retired capacity were dispatched under the Base Case 1.

< ERCOT is the only region projected to experience an increase in Phase III generation under Option 6, gaining 64 GWh, or 0.4%. However, potential Phase III facilities in ERCOT are also estimated to see the largest reductions in revenues ($27 million) and pre-tax incomes ($30 million). Revenues decrease even though generation in the region increases due to the large drop in capacity prices (see Table B5A-4). Specifically, the projected $93 million increase in energy revenues are offset by the projected $120 million decrease in capacity revenues.

< Potential Phase III facilities in FRCC are estimated to experience a 513 GWh reduction in generation (3.9%) due to Option 6. As a result, revenues d ecrease in by 3.8%, fuel costs decrease by 8.4%, and variable O&M costs decrease 1.7%.

< MAAC , MAIN , and MAPP , SERC and WECC are estimated to experience relatively small changes in pre-tax income (between -1.0% and 2.1%). The changes in all other measures are less than 1% in these regions. b. Individual facilities potentia lly subject to Phase III regulation In addition to the effects of Option 6 on potential Phase III facilities as a group, there may be shifts in economic performance among individual facilities potentially subjectto Phase III regulation. To assess such potential shifts, EPA analyzed facility-specific changes in (1 ) capacity utilization (defined as generation divided by capacity multiplied by the number of hours per year – 8,760) ; (2) generation; (3) revenues; (4) variable production costs per MWh of generation (defined as variable O&M cost plus fuel cost divided by generation); (5) fuel cost per MWh of generation; and (6) pre-tax income. Fo r each measure, EPA determined the number of potential Phase III facilities that experience no changes, or an increase or a reduction within three ranges: 1% or less, 1 to 3%, and more than 3%. Table B5A-6 presents the total number of potential Phase III facilities with different estimated degrees of change due to Option 6. This table excludes four facilities w ith estimated significant status changes in 2013: three facilities are baseline closures, and one facility is a full closure as a result of Option 6. These facilities are either not operating at all in either Base Case 1 or the post-co mpliance case, or they experience fundamental changes in the type of units they operate; therefore, the measur es presented in Table B5A-6 would not be meaningful for these facilities. In addition, the change in variab le production cost per MWh of generation could not be developed for six facilities with zero generation in eith er Base Case 1 or the post-compliance scenario. For these facilities, the change in variable production cost per MWh is indicated as “n/a.”§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-24 Table B5A-6: Number of Potential Phase III Facilities with Operational Changes (2013)

Economic Measures Reduction Increase No Change N/A </= 1% 1-3% > 3% </= 1% 1-3% > 3% (1) Change in Capacity Utilization 0 0 8 0 0 4 95 0 (2) Change in Generation 0 0 8 0 0 4 95 0 (3) Change in Revenues 42 2 15 9 4 4 31 0 (4) Change in Variable Production Costs/MWh 15 2 1 16 0 1 66 6
(5) Change in Fuel Costs/MWh 16 2 1 10 1 1 70 6 (6) Change in Pre-Tax Income 29 8 32 13 10 1 14 0

a For all measures percentages used to assign facilities to impact categories have been rounded to the nearest 10th of a percent. bThe change in capacity utilization is th e difference between the capacity utilizati on percentages in the base case and postcompliance case. For all other measures, the change is expre ssed as the percentage change between the base case and postcompliance values. Source: Model runs for Section 316(b) Base Case 1 and Option 6.
Table B5A-6 indicates that the majority of potential Phase III facilities would not experience changes in capacity utilization, generation, fuel costs per MWh, or variab le production costs per MWh due to compliance with Option 6. Of those facilities with changes in post-compliance capacity utilization, generation, fuel costs per MWh, and variable production costs per MWh, most would experi ence decreases in these measures. Changes in revenues at most potential Phase III facilities would also not exceed 1.0%. The largest effect of Option 6 would be on facilities’ pre-tax income: about 64% of facilities woul d experience a reduction in pre-tax income, with 30% experiencing a reduction of 3% or greater. These reducti ons are due to a combination of reduced revenues and increased compliance costs.



### B5A-4 S UMMARY 

### IPM ® 

### V.2.1.6 U PDATES Table B5AA-1 below presents a summary of the series of updates that were incorporated in EPA modeling applications using the Integrated Planning Model (IPM ® ) in the Spring of 2003. Designated Version 2.1.6, the latest available data were used to update key model parameters in the EPA Base Case and associated policy cases in preparation for performing analyses in conjunction w ith Congressional consideration of the Administration’s Clear Skies Initiative. This table and its accompanying report, Documentation Supplement for EPA Modeling Applications (V.2.1.6) Using the Integrated Planning Model (U.S. EPA, 2003), is a supplement to the comprehensive documentation of EPA’s applications of IPM ® as reported in Documentation of EPA Modeling Applications (V.2.1) Using the Integrated Planning Model (U.S. EPA, 2002). The supplementary report consists of the summary table presented below and a series of attachments providing details of speci fic updates. To help readers track the parameters that were updated, Table B5AA-1 contains cross referen ces to the earlier documentation report. Parameters not included in Table B5AA-1 remained unchanged. Boththe supplemental and comprehensive documentation is available for viewing and downloadi ng at www.epa.gov/airmarkets/epa-ipm.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-25 Table B5AA-1 Summary Table of IPM ®V.2.1.6 Updates
ID Feature Description Doc. Report Section 1 Power System Operations Assumptions 1 Revised aggregation scheme (“Documentation for v.2.1" refers to the report Documentation of EPA Modeling Applications (V.2.1) Using the Integrated Planning Model , EPA 430/R-02-004 (March 2002), which is available for viewing and downloading atwww.epa.gov/airmarkets/epaipm. The aggregation scheme was revi sed to enable modeling emission scenarios in geographical areas most likel y to be of future interest. Table A-1 in Attachment A updates the crosswalk between actual and model plants that was previously presente d as Table 4.7 in the documentation for v.2.1. Table A-2 and the accompanying map provides details on the geographical aggregation sche me used in the v.2.1.6. 3.1 4.2.6 Appendix A4.1

2 Electricity Demand Growth: @ 1.55% indexed on AEO 2003 electricity sales projections. (AEO 2003 refers to Annual Energy Outlook 2003 with Projections to 2025, DOE/EIA-0383(2003), released by the U.S. Department of Energy’s Energy Information Administration on January 9, 2003.) 1. As was done in EPA’s previous applications of IPM ® , calculations were performed to account for effici ency improvements not factored into AEO 2003's projections of electricity sales. This resulted in a 2000-2020 adjusted electricity growth rate of 1.55% per year. Attachment B provides details. 3.2.1 3.2.2 Appendix A3.1

3 State Multi-Pollutant Regulations Attachment C lists the state multi pollutant programs incorporated in v.2.1.6. 3.9
4 New Source Review (NSR) Settlements Attachment D shows the settlements under New Source Review provisions of the Clean Air Act that were included in v. 2.1.6. 3.9.3 5 State Renewable Energy Programs V. 2.1.6 incorporates the capacity shown in Table 76 in the AEO 2003 assumptions document. Entitled “P lanned 2002+ U.S. Central Station Generating Capacity Using Renewable Resources,” the table captures the effects of state renewable energy programs in its projection of both existing and forecasted renewable capacity. Table 76 appears on pp. 131- 133 of the document “Assumptions for the Annual Energy Outlook 2003," which can be found on the Web at www.eia.doe.gov/oiaf/aeo/a ssumption/pdf/0554(2003).pdf. 3.9.4 (Not covered)

6 State Renewable Portfolio Standards (RPS) V. 2.1.6 does not endogenously mode l RPS beyond the capacity already implicit in Table 76 “Planned 2002+ U.S. Central Station Generating Capacity Using Renewable Resour ces.” (See previous item for information on locating this table.) 3.9.4 (Not covered)

7 Emission and removal rate assumptions for potential units. The emission and removal rates are the same as in AEO 2003, i.e., NOx Rates SO2 Rates Conventional Pulverized Coal (CPC) 0.11 lb/mmBtu 95% Removal Integrated Gasification Combined Cycle (IGCC) 0.02 lb/mmBtu 99% Removal Combined Cycle (CC) 0.02 lb/mmBtu — Combustion Turbine (CT) 0.08 lb/mmBtu —
These differ from the removal rates inv. 2.1 (also called EPA Base Case 2000). See Attachment E for a detailed breakdown of the differences. 3.9.5§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5AA-1 Summary Table of IPM ®V.2.1.6 Updates
ID Feature Description Doc. Report Section 1

B5A-26

Generating Resources

8 National Electric Energy Data System (NEEDS) Changes 4.1 4.2 The following changes were made to NEEDS, the data base that serves as the source of all currently operating and planned/committed un its represented in v.2.1.6. 8a AES Deepwater Unit The AES Deepwater ge nerating unit in Texas (ID #10670_G_GEN1) was identified as combusting fossil wast e in NEEDS 2000 (used for the EPA Base Case 2000, v2.1) but as com busting oil in EPA’s Emissions and Generation Resource Integrated Database (EGRID). Further investigation revealed that this unit burned petroleum coke and some oil. To give a more accurate representation of its mercury emissions, in v. 2.1.6 the unit was designated as combusting pe troleum coke and assigned a corresponding mercury emission rate of 23.18 lb/TBtu (dry). 8b Mercury Emission Rates for Existing Geothermal Units Based on recent information obtained by EPA, mercury emission rates were updated to 2.97 lbs/TBtu for exis ting geothermal units in California and 3.65 lbs/TBtu for existing geothermal units in the IPM ® model region NWPE. In addition, 29 MW of existing geothermal capacity was identified in the AZNM model re gion and 8 MW in the PNW model region and assigned an emission rate of 3.70 lbs/TBtu, the same emission rate as assigned to new potential geothermal units in v.2.1.6. (See item #10 below.) 8c Hawthorn Unit 5 This 550 MW coal un it was added to NEEDS, v. 2.1.6. 8d Updated information on unit closures Units that were shown as retired or out of service in 2000 EIA 860a were removed from the NEEDS database as part of the v.2.1.6 update. Based on supplemental information, Ashta bula units 8, 10 and 11, Arapahoe units 1 and 2, Arkwright units 1 - 4, 5A, 5B,and Mitchell units 1 and 2 were also removed from the NEEDS population, either because they were retired or out of service. 4.2

8e Life Extension Costs A life extension cost of $5/kW-yr is added to every fossil plant that reaches an age of 30 years. This assumption is based on AEO 2003. 4.2.4 and 4.3.4

8f SO 2 , NO x , and Particulate Controls The inventory of SO 2 , NO x , and particulate controls in v.2.1.6 was derived from U.S. EPA’ s Emission Tracking System, 2002, Quarter 2, supplemented by corroborated information obtained from utilities, control technology vendors, state and regional regulatory agencies, and trade publications and announcements. Attachment F shows the inventory of emission controls on existing generating units that are included in v.2.1.6. 4.2.5

8g Updated planned/committed capacity Existing and planned/committed units in NEEDS 2.1.6 were derived from the following data sources: Period Source 1998 and earlier NEEDS 2000 All planned/committed capacity after 1998 in NEEDS 2000 was removed and replaced with the following data. 1999-2000 EIA 860, as released in year 2000. EIA 860 shows operating units for these years. 2001 RDI. (Updated through the July 2002 release of the RDI database.) 2002-2005 AEO 2003 or RDI. AEO 2003 was used for renewable (biomass, geothermal, landfill gas, hydro,pumped storage, solar, and wind) and nonconventional generating units (fuel cells) due to the Energy Information Administration’s (EIA) extensive 4.3§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5AA-1 Summary Table of IPM ®V.2.1.6 Updates
ID Feature Description Doc. Report Section 1

B5A-27 research in this area for AEO 2003. The RDI database (up through the July 2002 release) was used for conventional generating units (coal steam, combined cycle turbines, combustion turbines, fossil and non-fossil waste) since it was more current than AEO 2003. Attachment G lists the planned/committed units included in NEEDS 2.1.6 and gives a detailed summary of the data sources used. 9 Cost and Performance of Potential (New) Capacity from Conventional Generating Units The cost and performance assumptions for new (potential) conventional pulverized coal, integrated gasificati on combined cycle, combined cycle, advanced combined cycle, and co mbustion turbine units were updated based on AEO 2003. See Attachment H for details. 4.4.2

10 Mercury emissions for new (potential) geothermal units Based on recent information obtained by EPA, the mercury emission rate for new (potential) geothermal plan ts was updated to 3.70 lbs/TBtu in v.2.1.6, compared to 4.08 lbs/TBtu in v.2.1. (See item 8b above for a description of related updates of th e mercury emission rates for existing geothermal plants.) 4.4.3 5.3.1

Existing Nuclear Units 11a Cost and performance 1. To provide maximum granularity in forecasting the behavior of nuclear units, 102 out of the 103 exis ting nuclear units in v.2.1.6 are represented by separate model plants. (Note: All nuclear generating units, except Browns Ferry units 1 and 2 are represented by a separate model plant. In the v.2.1.6 base case, Browns Ferry Unit 1, which is projected to be brought out of mothballs, is represented by the same model plant as Browns Ferry Unit 2. See item 11c below for further details.) In v.2.1, the 103 existing nuclear units were represented by 47 model plants. 2. AEO 2003 cost and performance assumptions were implemented. These include (a) Variable operations and mainte nance (VOM), fixed operations and maintenance (FOM), and fuel co st assumptions as in AEO 2003. Attachment I details the cost a ssumptions included in v. 2.1.6. (b) AEO 2003 assumption of cost incurred from age 30, i.e., an addition of $50/Kw/yr to annual FOM costs starting at age 30. (c) Availability assumptions are expressed in terms of capacity factors, which are based on AEO 2003. As inAEO 2003, v. 2.1.6 assumes two vintages of existing nuclear units, based on whether a unit’s start date occurs before or after 1982. For the older vintage, the capacity factor increases 0.5 percentage point per year through age 25, stays flat from 25- 40, and then declines by 0.5% point after 40. The capacity factor of a newer vintage unit increases by 0.7 percentage point per year through age 30, is flat from 30-40, and declines by 0.5% point after age 40. The maximum capacity factor is assumed to be 90%. Any plant starting with a capacity factor above 90% just remains at its current level, at least until it is old enough to start declining. 3. In v.2.1.6 existing nuclear units are constrained to retain the same retirement pattern as in AEO 2003. 4.5 Appendix 4.4

11b Upratings All the nuclear capacity uprating assumptions that are in AEO 2003 were incorporated in NEEDS 2.1.6. A listing of all upratings appears in Attachment J. 4.5 Appendix 4.4

11c Browns Ferry Unit 1 V. 2.1.6 uses the sameassumptions about this TVA unit being brought out of mothballs as in AEO 2003, i.e., 1. The unit has a zero capacity factor (availability) until 2007. Starting 4.5 Appendix 4.4§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5AA-1 Summary Table of IPM ®V.2.1.6 Updates
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B5A-28 in 2007, it can operate up to a 75% capacity factor. 2. Like other existing nuclear units its capacity factor grows by 0.7% per year until it reaches a maximum of 90%. 3. Its costs were assumed to be th e same as those for Browns Ferry Unit 2. Emission Control Technologies 12 Selective Non-Catalytic Reduction (SNCR) Control of NOx Emissions In v. 2.1.6 SNCR is available as an emission control retrofit option for all coal plants $ 25 MW and < 200 MW rather than to all plants $ 25, as in v.2.1. In both v.2.1 and v.2.1.6 SNCR is available to all oil/gas steam units $ 25 MW. 5.2.2

13 Gas Reburn Option for NOx Control at coal fired plants To reduce model size, this option, which was provided in v 2.1, was not offered in v2.1.6. 5.2.2 14 Mercury Emission Modification Factors (EMFs) Mercury emission modification factors are multipliers that represent the extent of mercury removal achieved by various configurations of NO X , SO 2and particulate emission controls at coal fired generating units. Based on additional information received on the performance of these controls, mercury EMFs were updated. Attachment K shows the mercury EMFs used in v. 2.1.6. 5.3.2 5.3.3 Appendix A5.4

15 Mercury Control Using Activated Carbon Injection (ACI) Instead of modeling ACI with an 80% mercury removal rate, as was done in v. 2.1, v.2.1.6 has the capability to provide two concurrent ACI options of 60% and 90% mercury removal. The two options could be used for special mercury analyses. However, v. 2.1.6 will use an ACI mercury removal rate of 90% for typical anal yses. Due to constraints on model size and run time, the 60% removal op tion is intended to be applied only on selected sensitivity analysis runs. 5.3.3 Appendix A5.3

16 Mercury Control Costs Using ACI Based on information received from ACI vendors as an outgrowth of the Mercury MACT FACA process, the co st and injection rates for ACI were revised. (“Mercury MACT FACA pr ocess” refers to the advisory committee set up under the Federal Advisory Committee Act (FACA) to enable EPA to obtain input on propos ed regulations governing maximum achievable control technology (M ACT) for mercury removal from electric generating units.) (See Attachments L1 and L2 for a complete development of the ACI cost equations used in v. 2.1.6.) Appendix A5.3.2

Financial Assumptions 17 Revised financial assumptions for Integrated Gasification Combined Cycle (IGCCs) plants. With the following exceptions, the financial assumptions in v.2.1.6 are the same as in EPA Base Case 2000 (v.2.1): IGCCs and Repowerings-toIGCCs are assigned the discount rate (DR) and capital charge rate (CCR) associated with high (rather than me dium) risk investments, i.e., DR = 6.74%, not 6.14%. CCR = 13.4%, not 12.9% 7

Fuel Assumptions 18 Coal Supply Curves To provide greater cons istency between the v.2.1.6 and the AEO 2003 coal supply curves, the regional co al supply curves in v.2.1.6 were adjusted to reflect the percentage change in labor productivity assumed in AEO 2003. The coal transportati on cost escalation rates in v.2.1.6 were also made consistent withthose assumed in AEO 2003. See Attachment M for a presentation of the AEO 2003 labor productivity and transportation escalator assumptions. 8.1

19 Natural Gas Supply Curves Updated gas supply curv es were generated using ICF Consulting Inc.’s North American Natural Gas Analys is System (NANGAS) model. Key activities included: 1. Gas supply curves were de veloped for the 2005-2025, modeling 8.2 Appendix 8.1§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5 Table B5AA-1 Summary Table of IPM ®V.2.1.6 Updates
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B5A-29 horizon, rather than the 2005-2020 period used earlier. 2. Earlier optimistic technology assumptions, developed for the Department of Energy’s National Energy Technology Laboratory’s (NETL), were reviewed and revised resulting in a somewhat less optimistic technology perspective. 3. The Gulf of Mexico East drilling moratorium was incorporated in NANGAS. 4. EIA success rates for Gulf of Mexico offshore were adopted. 5. Pipeline links were checked to ensure correct gas flow, e.g., making sure the Rockies-Southwest link show s gas flows from the Rockies to the Southwest rather than the reverse. 6. Seasonal transportation adders were updated. 7. Four initial NANGAS runs were performed to cover the range of anticipated electric demand growth rates. A separa te NANGAS run was performed at electric demand annual gr owth rates of 1.1%, 1.55% (EPA’s CCAP adjusted growth rate), 1.88% (approximating the AEO 2003 Reference Case electricity sales growth rate), and 2.2%. 8. Outputs from the four runs were used to produce an initial set of natural gas supply curves for incorporation in IPM ®

. 9. A series of iterations wa s performed between NANGAS and IPM ® until convergence was achieved in the IPM ® and NANGAS electric sector results. The gas supply curves generated by this process were incorporated in v.2.1.6. Attachments N contains the natural gas supply curves used in v. 2.1.6 for each model run year and the seasonal transportation adders. 20 Oil prices consistent with AEO 2003 1. V. 2.1.6 fuel prices for distillate oil and high and low sulfur residual oil were based on the AEO 2003. The prices used in v.2.1.6 are shown in Attachment O together with the AEO 2003 source data from which the prices were derived. 2. The sulfur content for these fuel s were defined to be consistent with AEO 2003, i.e., Fuel Sulfur Content Distillate 0.3 Residual: Low Sulfur 1.08 Residual: High Sulfur 2.69 8.3

Miscellaneous Other Features 21 SO 2allowance bank An SO2 allowance bank of 6.414 million tons (going into 2005) was assumed. 22 Feasibility constraint on the maximum amount of SO 2 scrubbers that can be built in 2005 under the v.2.1.6 Clear Skies run The maximum amount of SO 2scrubbers that could be built in 2005 was limited to 5066 MW in the Clear Skies run. This is consis tent with recent EPA assessments of the short-term feasibility of scrubber installations.

1 This column indicates the most closely related sections in Documentation of EPA Modeling Applications (V. 2.1) Using the Integrated Planning Model (U.S. EPA, 2002). Source: U.S. EPA, 2003.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

14EPA also considered conducting an analysis under a third ba se case adjusted to account for demand reductions resulting from implementation of the C limate Change Action Plan (CCAP) as was done for the Phase II analysis.

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There are uncertainties associated with EPA’s electric power market and economic impacts analyses conducted in developing the proposed rule: Demand for electricity: The IPM ® assumes that electricity demand at the national level would not change between the base case and the policy case (g eneration within the regions is allowed to vary). Under the base case specifications, electricity demand is based on the AEO 2003 forecast. 14The IPM ® model, as specified for this analysis, does not capture changes in demand that may result from electricity price increases associated with Option 6. While this constraint may overestimate total demand in policy options that have high compliance cost and that may therefore lead to significant price increases, EPA believes that it does not affect the results obtained in developing the proposed rule. As described in S ection B5A-3 above, the price increases associated with the Option 6 in most NERC regions are relatively small. EPA therefore concludes that the assumption of inelastic demand-responses to changes in prices is reasonable.

< International imports: The IPM ® assumes that imports from Canada and Mexico would not change between the base case and the policy case. Holding international imports fixed would provide a conservative estimate of production costs and electricity prices under Option 6, because imports are not subject to Phase III regulation and may therefore beco me more competitive relative to domestic capacity, displacing some of the more expensive domestic ge nerating units. On the other hand, holding imports fixed may understate effects on marginal domestic un its, which may be displaced by increased imports. However, EPA concludes that fixed imports do not mate rially affect the results of the analyses. In 2013 only four of the ten NERC regions import electricity (ECAR, MAPP, NPCC, and WECC) and the level of imports compared to domestic generation in each of these regions is very small (from less than 0.01% in ECAR, to 2.75% in NPCC).

< Repowering: For the section 316(b) analysis, EPA is not using the IPM ® function that allows the model to pick among a set of compliance responses. As a result, there is no iterative process that would adjust the compliance response (and as a result the cost of compliance) if a facility chooses to repower. Repowering in the IPM ® typically consists of the conversion of existi ng oil/gas or coal capacity to new combined-cycle capacity. The modeling assumption is that each one MW of existing capacity is replaced by two MW of repowered capacity. This change in plant type and size might lead to a change in intake flow and potentially to different compliance requirements and costs. Since combined-cycle facilities require substantially less cooling water than other oil/gas or coalfacilities, the effect of repowering is likely to be a reduction in cooling water requireme nts (even considering the doubling of the plant’s capacity). As a result, not allowing the model to adjust the compliance response or cost is likely to lead to a conservative estimate of compliance costs and potential economic impacts from Option 6.

< Downtime associated with insta llation of compliance technologies: EPA estimates that the installation of several compliance technologies would require the steam electric generators of facilities that are projected to install such technologies to be off-line. Downtimes under Option 6 are estimated to be either 2 or 9 weeks, depending on the technology. Generator downtime is estimated to occur during the year when a facility complies with Option 6. Since the years that are mapped into a run year are assumed to have the same characteristics as the run year itself, generator downtimes were applied as an average over the years that are mapped into each model run year. For example, years 2010 to 2012 are all mapped into 2010. Therefore, a facility with a downtime in 2011 was modeled as if 1/3rd of its downtime occurred in each year between 2010 and 2012. A potential drawback of this approach of averaging downtimes over the mapped years is that the snapshot of the effect of downtimes during the model run year is the average§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

B5A-31 effect; this approach does not model potential wo rst case effects of above-average amounts of capacity being down in any one NERC region during any one year.§ 316(b) Proposed Rule: Phase III -- EA, Part B: Economic Analysis for Existing Facilities Appendix 1 to Chapter B5

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