vvEPA
United States
Environmental Protection
Agency
Economic and Benefits Analysis for
Proposed Section 316(b) Existing Facilities
Rule
EPA 821-R-11-003
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Contents
Table of Contents
1 Introduction 1-1
1.1 Background 1-1
1.2 Overview of the Economic and Benefits Analysis of the Proposed Existing Facilities Rule 1-1
1.2.1 Facilities Expected To Be Subject to the Proposed Existing Facilities Rule 1-1
1.2.2 Analyses Performed in Support of the Proposed Existing Facilities Rule 1-3
1.2.3 Regulatory Options Considered for the Proposed Existing Facilities Rule 1-4
1.2.4 Organization of the Economic and Benefits Analysis Report 1-5
2 Introduction to Industry Profiles 2-1
2A Profile of the Paper and Allied Products Industry 2A-1
2A.1 Summary Insights from this Profile 2A-3
2A.1.1 Likely Ability to Pass Compliance Costs Through to Customers 2A-3
2A.1.2 Financial Health and General Business Outlook 2A-3
2A.1.3 Domestic Production 2A-4
2A.1.4 Output 2A-5
2A.1.5 Prices 2A-8
2A.1.6 Number of facilities and firms 2A-10
2A.1.7 Employment and productivity 2A-12
2A.1.8 Capital expenditures 2A-14
2A.1.9 Capacity utilization 2A-15
2A.2 Structure and Competitiveness 2A-17
2A.2.1 Firm size 2A-18
2A.2.2 Concentration ratios 2A-18
2A.2.3 Foreign trade 2A-20
2A.3 Financial Condition and Performance 2A-25
2A.4 Facilities Operating Cooling Water Intake Structures 2A-26
2A.4.1 Waterbody and Cooling System Type 2A-27
2A.4.2 Facility Size 2A-27
2A.4.3 Firm Size 2A-28
2B Profile of the Chemicals and Allied Products Industry 2B-1
2B.1 Introduction 2B-1
2B.2 Summary Insights from this Profile 2B-4
2B.2.1 Likely Ability to Pass Compliance Costs Through to Customers 2B-4
2B.2.2 Financial Health and General Business Outlook 2B-5
2B.3 Domestic Production 2B-5
2B.3.1 Output 2B-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Contents
2B.3.2 Prices 2B-9
2B.3.3 Number of Facilities and Firms 2B-10
2B.3.4 Employment and Productivity 2B-11
2B.3.5 Capital Expenditures 2B-13
2B.3.6 Capacity Utilization 2B-14
2B.4 Structure and Competitiveness 2B-15
2B.4.1 Firm Size 2B-15
2B.4.2 Concentration Ratios 2B-16
2B.4.3 Foreign Trade 2B-19
2B.5 Financial Condition and Performance 2B-25
2B.6 Facilities Operating Cooling Water Intake Structures 2B-29
2B.6.1 Waterbody and Cooling System Type 2B-30
2B.6.2 Facility Size 2B-30
2B.6.3 Firm Size 2B-31
2C Profile of the Petroleum Refining Industry 2C-1
2C.1 Introduction 2C-1
2C.2 Summary Insights from this Profile 2C-2
2C.2.1 Likely Ability to Pass Compliance Costs Through to Customers 2C-2
2C.2.2 Financial Health and General Business Outlook 2C-2
2C.3 Domestic Production 2C-3
2C.3.1 Output 2C-3
2C.3.2 Prices 2C-6
2C.3.3 Number of Facilities and Firms 2C-6
2C.3.4 Employment and Productivity 2C-7
2C.3.5 Capital Expenditures 2C-9
2C.3.6 Capacity Utilization 2C-10
2C.4 Structure and Competitiveness 2C-11
2C.4.1 Firm Size 2C-12
2C.4.2 Concentration Ratios 2C-12
2C.4.3 Foreign Trade 2C-13
2C.5 Financial Condition and Performance 2C-16
2C.6 Facilities Operating Cooling Water Intake Structures 2C-18
2C.6.1 Waterbody and Cooling Water Intake System Type 2C-19
2C.6.2 Facility Size 2C-19
2C.6.3 Firm Size 2C-20
2D Profile of the Steel Industry 2D-1
2D.1 Introduction 2D-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Contents
2D.2 Summary Insights from this Profile 2D-2
2D.2.1 Likely Ability to Pass Compliance Costs Through to Customers 2D-3
2D.2.2 Financial Health and General Business Outlook 2D-3
2D.3 Domestic Production 2D-3
2D.3.1 Output 2D-4
2D.3.2 Prices 2D-7
2D.3.3 Number of Facilities and Firms 2D-8
2D.3.4 Employment and Productivity 2D-10
2D.3.5 Capital Expenditures 2D-12
2D.3.6 Capacity Utilization 2D-13
2D.4 Structure and Competitiveness 2D-14
2D.4.1 Firm Size 2D-15
2D.4.2 Concentration Ratios 2D-15
2D.4.3 Foreign Trade 2D-16
2D.5 Financial Condition and Performance 2D-18
2D.6 Facilities Operating Cooling Water Intake Structures 2D-20
2D.6.1 Waterbody and Cooling Water Intake System Type 2D-21
2D.6.2 Facility Size 2D-21
2D.6.3 Firm Size 2D-22
2E Profile of the Aluminum Industry 2E-1
2E.1 Introduction 2E-1
2E.2 Summary Insights from this Profile 2E-2
2E.2.1Likely Ability to Pass Compliance Costs Through to Customers 2E-2
2E.2.2Financial Health and General Business Outlook 2E-3
2E.3 Domestic Production 2E-3
2E.3.1Output 2E-4
2E.3.2Prices 2E-7
2E.3.3Number of Facilities and Firms 2E-8
2E.3.4Employment and Productivity 2E-11
2E.3.5Capital Expenditures 2E-13
2E.3.6Capacity Utilization 2E-14
2E.4 Structure and Competitiveness 2E-15
2E.4.1FirmSize 2E-16
2E.4.2Concentration Ratios 2E-16
2E.4.3Foreign Trade 2E-18
2E.5 Financial Condition and Performance 2E-21
2E.6 Facilities Operating Cooling Water Intake Structures 2E-23
2E.6.1Waterbody and Cooling Water Intake System Type 2E-24
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Contents
2E.6.2Facility Size 2E-24
2E.6.3Firm Size 2E-25
2F Profile of Food and Kindred Products Industry 2F-1
2F.1 Introduction 2F-1
2F.2 Summary Insights from this Profile 2F-2
2F.2.1Likely Ability to Pass Compliance Costs Through to Customers 2F-2
2F.2.2Financial Health and General Business Outlook 2F-3
2F.3 Domestic Production 2F-3
2F.3.1 Output 2F-4
2F.3.2Prices 2F-6
2F.3.3Number of Facilities and Firms 2F-7
2F.3.4Employment and Productivity 2F-9
2F.3.5Capital Expenditures 2F-11
2F.3.6Capacity Utilization 2F-12
2F.4 Structure and Competitiveness 2F-13
2F.4.1 Firm and Facility Size 2F-14
2F.4.2Concentration Ratios 2F-15
2F.4.3Foreign trade 2F-16
2F.5 Financial Condition and Performance 2F-18
2F.6 Facilities Operating Cooling Water Intake Structures 2F-20
2F.6.1Waterbody and Cooling System Type 2F-21
2F.6.2Facility Size 2F-21
2F.6.3Firm Size 2F-22
2G Profile of Facilities in Other Industries 2G-1
2G.1 Facilities Operating Cooling Water Intake Structures 2G-2
2G.1.1 Waterbody and Cooling System Types 2G-3
2G.1.2 Facility Size 2G-3
2G.1.3 Firm Size 2G-3
2H Profile of the Electric Power Industry 2H-1
2H.1 Introduction 2H-1
2H.2 Industry Overview 2H-1
2H.2.1 Industry Sectors 2H-1
2H.2.2 Prime Movers 2H-2
2H.2.3 Ownership 2H-4
2H.3 Domestic Production 2H-6
2H.3.1 Generating Capacity 2H-6
2H.3.2 Electricity Generation 2H-7
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2H.3.3 Geographic Distribution 2H-10
2H.4 Facilities Subject to the Proposed Existing Facilities Rule 2H-12
2H.4.1 Ownership Type 2H-13
2H.4.2 Ownership Size 2H-14
2H.4.3 Facility Size 2H-15
2H.4.4 Geographic Distribution 2H-16
2H.4.5 Waterbody and Cooling System Type 2H-17
2H.5 Industry Trends 2H-17
2H.5.1 Current Status of Industry Deregulation 2H-17
2H.5.2 Air Emissions Regulations 2H-21
2H.5.3 Renewable Portfolio Standards 2H-23
2H.5.4 Carbon Dioxide Emissions Regulations 2H-23
2H.6 Industry Outlook 2H-24
2H.6.1 Energy Market Model Forecasts 2H-24
2H.7 Glossary 2H-25
3 Development of Costs for Regulatory Options 3-1
3.1 Development of Costs to Existing Facilities 3-1
3.1.1 Components of Facility-Level Compliance Costs: Installing and Operating Compliance Technologies3-2
3.1.2 Components of Facility-Level Compliance Costs: Installing and Operating Compliance Technology3-4
3.1.3 Components of Facility-Level Compliance Costs: Energy Efficiency Penalty 3-6
3.1.4 Components of Facility-Level Compliance Costs: Installation Downtime 3-8
3.1.5 Components of Facility-Level Compliance Costs: Administrative Costs 3-13
3.1.6 Development of Compliance Years 3-16
3.1.7 Development of Total Compliance Costs 3-17
3.1.8 Uncertainties and Limitations 3-25
3.2 Development of Administrative Costs to State and Federal Governments 3-26
3.2.1 Administrative Costs to State and Territorial Governments 3-27
3.2.2 Administrative Costs to the Federal Government 3-28
3.2.3 Total Administrative Costs 3-28
3.2.4 Uncertainties and Limitations 3-28
3.3 Development of Costs for New Units 3-29
3.3.1 Estimating Costs for New Generating Units 3-29
Appendix 3A Use of Sample Weights in the Proposed Existing Facilities Rule Analyses 3A-1
3A.1 Facility-Level Weights 3A-1
3A.1.1 Manufacturers 3A-1
3A.1.2 Electric Power Generating Facilities 3A-2
3A.2 Entity-Level Analysis 3A-6
3A.2.1 Manufacturers 3A-6
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Contents
3A.2.2 Electric Power Generators 3A-6
3A.3 Summary 3A-8
Appendix 3B Mapping Manufacturers' Standard Industrial Classification Codes to North American
Industry Classification System Codes 3B-1
4 Cost and Economic Impact Analysis - Manufacturers 4-1
4.1 Analysis Overview 4-1
4.2 Overview and Data Sources 4-2
4.3 Facility-Level Impacts: Severe Impact Analysis 4-3
4.3.1 Analysis Approach and Data Inputs 4-3
4.3.2 Key Findings for Regulatory Options 4-6
4.4 Facility-Level Impacts: Moderate Impact Analysis 4-6
4.4.1 Analysis Approach and Data Inputs 4-6
4.4.2 Key Findings for Regulatory Options 4-8
4.5 Firm-Level Impacts 4-9
4.5.1 Analysis Approach and Data Inputs 4-9
4.5.2 Key Findings for Regulatory Options 4-10
4.6 Uncertainties and Limitations 4-12
Appendix 4A Cost Pass-Through Analysis 4A-1
4A.1 The Choice of Firm-Specific Versus Sector-Specific CPT Coefficients 4A-1
4A.2 Market Structure Analysis 4A-3
4A.2.1 Industry Concentration 4A-4
4A.2.2 Import Competition 4A-6
4A.2.3 Export Competition 4A-7
4A.2.4 Long-Term Industry Growth 4A-8
4A.3 Conclusions 4A-9
Appendix 4B Adjusting Baseline Facility Cash Flow 4B-1
4B.1 Background: Review of Overall Business Conditions 4B-2
4B.2 Framing and Executing the Analysis 4B-5
4B.2.1 Identifying the Financial Data Concept to Be Analyzed 4B-6
4B.2.2 Selecting Appropriate Data 4B-6
4B.2.3 Methodology for Development of ATCF Adjustment Factors 4B-7
4B.3 Analysis Results 4B-8
Appendix 4C Estimating Capital Outlays for Section 316(b) Manufacturing Sectors Discounted Cash
Flow Analyses 4C-1
4C.1 Analytic Concepts Underlying Analysis of Capital Outlays 4C-2
4C.2 Specifying Variables for the Analysis 4C-4
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4C.3 Selecting the Regression Analysis Dataset 4C-6
4C.4 Specification of Models to be Tested 4C-9
4C.5 Model Validation 4C-12
4C.6 Updating Inputs to Estimate Capital Outlays for the Proposed Rule Analyses 4C-19
Appendix 4D Analysis of Other Regulations 4D-1
4D.1 Regulations Potentially Affecting 316(b) Manufacturing Facilities 4D-1
4D.2 Methodology 4D-3
4D.2.1 Determination of Applicability to 316(b) Manufacturing Facilities 4D-3
4D.2.2 Extraction of Facility-Level Costs 4D-3
4D.3 Results 4D-4
4D.3.1 Baseline Analysis 4D-4
4D.3.2 Post-Compliance Analysis 4D-4
Appendix 4E Economic Impact Methodology - Manufacturers 4E-1
4E.1 Facility-Level Impacts: Severe Impact Analysis 4E-1
4E. 1.1 Calculation of Baseline Free Cash Flow and Performance of Baseline Closure Test 4E-1
4E.1.2Calculation of Post-Compliance Free Cash Flow and Performance of Post-Compliance Closure Test4E-5
4E.2 Facility-Level Impacts: Moderate Impact Analysis 4E-7
4E.2.1Calculation of Moderate Impact Metrics 4E-7
4E.2.2Developing Threshold Values for Pre-Tax Return on Assets (PTRA) 4E-9
4E.2.3Developing Threshold Values for Interest Coverage Ration (ICR) 4E-10
5 Cost and Economic Impact Analysis - Electric Generators 5-1
5.1 Analysis Overview 5-1
5.2 Cost-to-Revenue Analysis: Facility-Level Screening Analysis 5-1
5.2.1 Analysis Approach and Data Inputs 5-2
5.2.2 Key Findings for Regulatory Options 5-4
5.2.3 Uncertainties and Limitations 5-5
5.3 Cost-to-Revenue Screening Analysis: Parent Entity-Level Analysis 5-6
5.3.1 Analysis Approach and Data Inputs 5-6
5.3.2 Key Findings for Regulatory Options 5-9
5.3.3 Uncertainties and Limitations 5-11
5.4 Impact of Compliance Costs on Household Electricity Costs 5-12
5.4.1 Analysis Approach and Data Inputs 5-12
5.4.2 Key Findings for Regulatory Options 5-13
5.4.3 Uncertainties and Limitations 5-14
5.5 Impact of Compliance Costs on Electricity Prices 5-15
5.5.1 Analysis Approach and Data Inputs 5-15
5.5.2 Key Findings for Regulatory Options 5-16
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Contents
5.5.3 Uncertainties and Limitations 5-18
5.6 Assessment of Short-Term Reduction in Capacity Availability Due to Installation Downtime 5-18
5.6.1 Analysis Approach and Data Inputs 5-18
5.6.2 Key Findings for Regulatory Options 5-21
6 Assessing the Impact of the Existing Facilities Regulatory Options in the Context of National
Electricity Markets 6-1
6.1 Overview of the IPM Model and Its Use for the Market Model Analysis of the Existing Facilities Rule
Options 6-2
6.1.1 Key Specifications of the IPM V3.02 Update 6-2
6.1.2 Key Specifications for Analysis of the Proposed Existing Facilities Regulatory Options 6-5
6.2 Model Analysis Inputs and Outputs 6-7
6.2.1 Key Inputs to IPM V3.0 for the Proposed Phase II Rule Analyses 6-7
6.2.2 Key Outputs of the Market Model Analysis Used in Assessing the Effects of the Proposed Phase II
Regulatory Options 6-9
6.3 Regulatory Options Analyzed 6-10
6.4 Findings from the Market Model Analysis 6-11
6.4.1 Analysis Results for the Year 2028 - To Reflect Steady State, Post-Compliance Operations 6-11
6.4.2 Analysis Results for the Years 2015, 2020, and 2025 - To Capture the Effect of Installation
Downtime 6-26
6.5 Uncertainties and Limitations 6-29
Appendix 6A Market Model Analysis Results for the Years 2015,2020, and 2025 - To Capture the Effect
of Installation Downtime by NERC Region 6A-1
7 Assessing the Potential Impact of the Proposed Existing Facilities Rule on Small Entities -
Regulatory Flexibility Act (RFA) Analysis 7-1
7.1 Analysis of Manufacturers 7-2
7.1.1 Analysis Approach and Data Inputs 7-2
7.1.2 Key Findings for Regulatory Options 7-6
7.2 Analysis of Electric Generators 7-9
7.2.1 Analysis Approach and Data Inputs 7-9
7.2.2 Key Findings for Regulatory Options 7-16
7.3 Uncertainties and Limitations 7-20
8 Unfunded Mandates Reform Act (UMRA) Analysis 8-1
8.1 UMRA: Analysis of Impact on Government Entities 8-2
8.1.1 Compliance Costs 8-2
8.1.2 Administrative Costs 8-4
8.2 UMRA: Analysis of Impact on Small Governments 8-5
8.3 UMRA: Analysis of Impact on the Private Sector 8-7
8.4 UMRA: Analysis Summary 8-7
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Contents
9 Other Administrative Requirements 9-1
9.1 Executive Order 12866: Regulatory Planning and Review 9-1
9.2 Executive Order 12898: Federal Actions to Address Environmental Justice in Minority Populations and
Low-Income Populations 9-1
9.2.1 Presence of Low Income Populations in the Benefit Population 9-3
9.2.2 Assessment of Presence of Minority Populations in the Benefit Population 9-7
9.2.3 Overall Finding 9-7
9.3 Executive Order 13045: Protection of Children from Environmental Health Risks and Safety Risks 9-10
9.4 Executive Order 13132: Federalism 9-10
9.5 Executive Order 13158: Marine Protected Areas 9-11
9.6 Executive Order 13175: Consultation and Coordination With Indian Tribal Governments 9-14
9.7 Executive Order 13211: Actions Concerning Regulations That Significantly Affect Energy Supply,
Distribution, or Use 9-14
9.7.1 Impact on Electricity Generation 9-15
9.7.2 Impact on Electric Generating Capacity 9-15
9.7.3 Cost of Energy Production 9-16
9.7.4 Dependence on Foreign Supply of Energy 9-16
9.7.5 Overall E.G. 13211 Finding 9-16
9.8 Paperwork Reduction Act of 1995 9-16
9.9 National Technology Transfer and Advancement Act 9-17
10 Economy-Wide Output and Employment Effects 10-1
10.1 Economic Effects Due to Initial/One-Time Compliance Outlays 10-4
10.2 Economic Effects Due to Recurring Compliance Costs 10-5
10.3 Economic Effects Due to Changes in Electricity Rates 10-7
10.3.1 Residential Electricity Consumer Effects 10-8
10.3.2Business Electricity Consumer Effects 10-8
10.4 Economic Effects Due to Manufacturer's Compliance Costs 10-13
10.5 Results of the Economy-Wide Output and Employment Effects Analysis 10-14
10.6 Key Uncertainties and Limitations 10-17
11 Assessment of Total Social Costs 11-1
11.1 Overview of Social Costs 11-1
11.1.1 Costs of Regulatory Compliance 11-3
11.1.2 Government Administrative Costs 11-5
11.2 Key Findings for Regulatory Options 11-5
11.2.1 Costs of Regulatory Compliance 11-5
11.2.2 Costs of Government Administration of the Proposed Existing Facilities Rule 11-5
11.2.3 Total Social Cost 11-6
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Contents
11.2.4 Compliance Costs for New Generating Units 11-10
12 Social Costs and Benefits of the Proposed Rule 12-1
12.1 Summary of Benefits Estimation for the Proposed Regulation 12-1
12.2 Comparison of Benefits and Social Costs by Option 12-2
12.3 Incremental Analysis of Benefits and Social Costs 12-4
13 Cost and Economic Impact of Additional Regulatory Option (Option 4 - IM for Facilities with
Design Intake Flow Greater than 50 MGD) 13-1
13.1 Annualized Compliance Costs to Complying Facilities 13-1
13.2 Total Social Costs and Benefits 13-3
13.3 Unfunded Mandates Reform Act (UMRA) Analysis 13-5
13.3.1 Administrative Costs 13-5
13.3.2Compliance Costs 13-6
13.3.3 Analysis Impact on Small Governments 13-6
13.3.4Analysis Summary 13-7
13.4 Cost and Economic Impact Analysis - Manufacturers 13-8
13.5 Cost and Economic Impact Analysis - Electric Generators 13-9
13.5.1Cost-to-Revenue Analysis: Facility-Level Screening Analysis 13-9
13.5.2Cost-to-Revenue Analysis: Entity-Level Screening Analysis 13-10
13.5.3Impact of Compliance Costs on Household Electricity Costs 13-10
13.5.4Impact of Compliance Costs on Electricity Prices 13-11
13.6 Assessing the Potential Impact of the Proposed Existing Facilities Rule on Small Entities - Regulatory
Flexibility Act (RFA) Analysis 13-12
13.6.1 Analysis of Manufacturers 13-12
13.6.2 Analysis of Electric Generators 13-13
13.6.3 Overall Small Entity Impact Assessment for Option 4 13-14
References R-l
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Tables
Table of Tables
Table 1-1: Existing Electric Generators, Capacity, and Parent-Entities, by Ownership Type, 2007 1-2
Table 1-2: Existing Manufacturers, Value of Shipments, and Parent-Entities, by Industry 1-3
Table 2A-1: Existing Facilities in the Paper and Allied Products Industry (NAICS 322) 2A-2
Table 2A-2: Relationship between NAICS and SIC Codes for the Paper and Allied Products Industry (2007).2A-3
Table 2A-3: U.S. Pulp and Paper Industry Industrial Production Index (Annual Averages) 2A-8
Table 2A-4: Number of Facilities Owned by Firms in the Profiled Paper and Allied Products Segments 2A-11
Table 2A-5: Number of Firms in the Profiled Paper and Allied Products Segments 2A-12
Table 2A-6: Productivity Trends for Profiled Paper and Allied Products Segments ($2009) 2A-14
Table 2A-7: Capital Expenditures for Profiled Paper and Allied Products Segments (millions, $2009) 2A-15
Table 2A-8: Number of Firms and Facilities by Size Category for Profiled Paper and Allied Products Segments in
2006 2A-18
Table 2A-9: Selected Ratios for Profiled Paper and Allied Products Segments, 1987, 1992, 1997, and 2002.2A-20
Table 2A-10: Trade Statistics for Profiled Paper and Allied Products Segments (Millions, $2009) 2A-22
Table 2A-11: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by Waterbody
Type and Cooling Water Intake System for the Profiled Paper and Allied Products Segments 2A-27
Table 2B-1: Phase III Facilities in the Chemicals and Allied Products Industry (NAICS 325) 2B-2
Table 2B-2: Relationship between NAICS and SIC Codes for the Chemicals and Allied Products Industry (2007)
2B-4
Table 2B-3: Industrial Production Index for Chemicals and Allied Products Industry Segments 2B-8
Table 2B-4: Number of Facilities for Profiled Chemicals and Allied Products Industry Segments 2B-10
Table 2B-5: Number of Firms for Profiled Chemicals and Allied Products Industry Segments 2B-11
Table 2B-6: Productivity Trends for Profiled Chemicals and Allied Products Industry Segments ($2009) 2B-12
Table 2B-7: Capital Expenditures for Profiled Chemicals and Allied Products Industry Segments (in millions,
$2009) 2B-14
Table 2B-8: Number of Firms and Facilities by Firm Size Category for Profiled Chemical Segments, 2006.. 2B-16
Table 2B-9: Selected Ratios for SIC and NAICS Codes Within Profiled Chemicals and Allied Products Industry
Segments in 1987, 1992, 1997, and 2002 2B-18
Table 2B-10: Trade Statistics for Profiled Chemicals and Allied Products Industry Segments 2B-21
Table 2B-11: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by Waterbody
Type and Cooling Water Intake System for the Profiled Chemical Segments 2B-30
Table 2C-1: Existing Facilities in the Petroleum Refining Industry (NAICS 324110) 2C-1
Table 2C-2: Relationship between NAICS and SIC Codes for the Petroleum Refineries Industry (2007) 2C-2
Table 2C-3: U.S. Petroleum Refinery Product Production (million barrels per day) 2C-4
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Tables
Table 2C-4: Number of Firms and Facilities for Petroleum Refineries 2C-7
Table 2C-5: Productivity Trends for Petroleum Refineries ($2009) 2C-9
Table 2C-6: Capital Expenditures for Petroleum Refineries ($2009) 2C-10
Table 2C-7: Number of Firms and Establishments for Petroleum Refineries by Firm Employment Size Category,
2006 2C-12
Table 2C-8: Selected Ratios for Petroleum Refineries 2C-13
Table 2C-9: Foreign Trade Statistics for Petroleum Refining ($2009) 2C-14
Table 2C-10: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by Waterbody
Type and Cooling Water Intake System for the Petroleum Refining Segment 2C-19
Table 2D-1: Existing Facilities in the Steel Industry (NAICS 3311/2) 2D-1
Table 2D-2: Relationships between NAICS and SIC Codes for the Steel Industries (2007) 2D-2
Table 2D-3: U.S. Steel Production by Type of Producer 2D-5
Table 2D-4: Number of Facilities in the Profiled Steel Industry Segments 2D-9
Table 2D-5: Number of Firms in the Profiled Steel Industry Segments 2D-10
Table 2D-6: Productivity Trends for the Profiled Steel Industry Segments ($2009) 2D-12
Table 2D-7: Capital Expenditures for the Profiled Steel Industry Segments (millions, $2009) 2D-13
Table 2D-8: Number of Firms and Facilities by Employment Size Category in the Profiled Steel Industry Segments,
2006 2D-15
Table 2D-9: Selected Ratios for the Profiled Steel Industry Segments 2D-16
Table 2D-10: Import Penetration and Export Dependence: Profiled Steel Mills and Steel Products Segments ($2009)
2D-18
Table 2D-11: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by Waterbody
Type and Cooling Water Intake System for the Profiled Steel Industry Segments 2D-21
Table 2E-1: Existing Facilities in the Aluminum Industries (NAICS 33131) 2E-1
Table 2E-2: Relationships between NAICS and SIC Codes for the Aluminum Industries (2007) 2E-2
Table 2E-3: U.S. Aluminum Production 2E-5
Table 2E-4: Primary Stages of Aluminum Production - Number of Companies and Plants 2E-9
Table 2E-5: Number of Facilities for Profiled Aluminum Industry Segments 2E-10
Table 2E-6: Number of Firms for Profiled Aluminum Industry Segments 2E-11
Table 2E-7: Productivity Trends for Profiled Aluminum Segments ($2009) 2E-13
Table 2E-8: Capital Expenditures for Profiled Aluminum Segments (millions, $2009) 2E-14
Table 2E-9: Number of Firms and Facilities by Employment Size Category for the Profiled Aluminum Industry
Segments, 2006 2E-16
Table 2E-10: Selected Ratios for the Profiled Aluminum Segments, 1987, 1992, 1997, and 2002 2E-18
Table 2E-11: Import Share and Export Dependence for the Profiled Aluminum Segments ($2009) 2E-20
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Tables
Table 2E-12: Trade Statistics for Aluminum and Semi-fabricated Aluminum Products (Quantities in thousand
metric tons; Values in millions, $2009) 2E-21
Table 2E-13: Number of Facilities Estimated Subject to the 50 MOD All Option by Waterbody Type and Cooling
Water Intake System for the Profiled Aluminum Segments 2E-24
Table 2F-1: Existing Facilities in the Food and Kindred Products Industry (NAICS 311/3121) 2F-1
Table 2F-2: Relationship between NAICS and SIC Codes for the Petroleum Refining Industry (2007) 2F-2
Table 2F-3: U.S. Food and Beverage Manufacturing Industry Industrial Production Index 2F-6
Table 2F-4: Number of Facilities Owned by Firms in the Food and Beverage Manufacturing Segments 2F-8
Table 2F-5: Number of Firms in the Food and Beverage Manufacturing Segments 2F-9
Table 2F-6: Productivity Trends for Food and Beverage Manufacturing Segments ($2009) 2F-11
Table 2F-7: Capital Expenditures for Food and Beverage Manufacturing Segments (millions, $2009) 2F-12
Table 2F-8: Number of Firms and Facilities by Size Category for Food and Beverage Manufacturing Segments,
2006 2F-15
Table 2F-9: Selected Ratios for Food Manufacturing and Beverage Manufacturing Segments 2F-16
Table 2F-10: Trade Statistics for Profiled Food and Kindred Products Industry 2F-17
Table 2F-11: Number of Food and Kindred Products Facilities Estimated Subj ect to the 316(b) Existing Facilities
Regulation by Waterbody Type and Cooling Water Intake System 2F-21
Table 2G-1: Facilities in Other Industries by 2-digit SIC code Estimated Subj ect to Regulation Under the
Regulatory Analysis Options 2G-2
Table 2G-2: Other Industries Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by Water
Body and Cooling Water Intake System Type 2G-3
Table 2H-1: Number of Existing Utility and Nonutility Facilities by Prime Mover, 2007 2H-4
Table 2H-2: Net Generation by Energy Source and Ownership Type, 1997 to 2007 (TWh) 2H-8
Table 2H-3: Distribution of Existing Facilities and Total Capacity by NERC Region, 2007 2H-12
Table 2H-4: Existing Parent-Entities, Facilities, and Capacity by Ownership Type, 2010 2H-14
Table 2H-5: Existing Parent-Entities by Ownership Type and Size, 2010 2H-14
Table 2H-6: Section 316(b) In-Scope Facilities by Ownership Type and Size, 2010 2H-15
Table 2H-7: Section 316(b) In-Scope Facilities and Capacity by NERC Region, 2010 2H-17
Table 2H-8: Number of In-Scope Facilities by Waterbody and Cooling System Type 2H-17
Table 3-1: Estimated Average Net Downtime for Technology Modules 3-8
Table 3-2: Estimated Average Net Downtime for Technology Modules 3-11
Table 3-3: Cost of Initial Post-Promulgation NPDES Permit Application Activities ($2009) 3-14
Table 3-4: Cost of Subsequent Post-Promulgation NPDES Permit Application Activities ($2009) 3-15
Table 3-5: Cost of Subsequent Post-Promulgation NPDES Permit Application Activities ($2009) 3-15
Table 3-6: Annualized Compliance Costs by Industry Sector for Manufacturers (in millions, $2009, at 2012).. 3-20
Table 3-7: Annualized Compliance Costs by NERC Region (in millions, $2009, at 2012) 3-23
March 28, 2011 xlJT
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Tables
Table 3-8: Annualized Compliance Costs For Manufacturers and Electric Generators (in millions, $2009, at 2012)
3-25
Table 3-9: Administrative Activity Groups and Costs forNPDES Permitting Authorities ($2009) 3-28
Table 3-10: Annual Capacity Installation Subject to New Units EM Technology Requirement in New Generating
Units (MW) 3-30
Table 3A-1: Use of Weights in the Cost and Economic Impact Analysis for the Proposed Existing Facilities Rule
3A-4
Table 3A-2: Weights Applied to Each Cost Component 3A-5
Table 3A-3: Proposed Existing Facilities Rule Unique Parent Entities and Facilities (by Entity Type and Size)3A-7
Table 3A-4: Use of Weights in the Cost and Economic Impact Analysis for the Proposed Existing Facilities Rule
3A-9
Table 4-1: Summary of Baseline Closures by Sector for Manufacturers Segment Facilities Estimated Subject to the
316(b) Existing Facilities Rule 4-3
Table 4-2: Number of Facilities with Severe Impacts by Sector and Option 4-6
Table 4-3: Summary of Moderate Impact Thresholds by Manufacturers Industry based on 25th percentile value of
firms reporting data to RMA 4-8
Table 4-4: Number of Facilities with Moderate Impacts by Sector and Option 4-8
Table 4-5: Entity-Level Cost-to-Revenue Analysis Results, Assuming that Facilities Represented by Sample
Weights are Owned by the Same Firm that Owns the Sample Facility (Case 1) 4-11
Table 4-6: Entity-Level Cost-to-Revenue Analysis Results, Assuming that Facilities Represented by Sample
Weights are Owned by Different Firms than those Owning the Sample Facility (Case 2) 4-12
Table 4A-1: Proportion of Value of Shipments Potentially Subject to Compliance-Related Costs Associated with the
Proposed Existing Facilities Regulation (Millions; $2009) 4A-3
Table 4A-2: Herfmdahl-Hirschman Index for Six-Digit NAICS Sectors 4A-5
Table 4A-3: Herfmdahl-Hirschman Index by Industry 4A-6
Table 4A-4: Import Penetration by Industry, 2007 4A-7
Table 4A-5: Export Dependence by Industry, 2007 4A-8
Table 4A-6: Average Annual Growth Rate by Industry 4A-9
Table 4B-1: Analysis Sectors and Corresponding Sectors Covered by QFR 4B-8
Table 4B-2: Statistical Significance of Regression Results and Potential Adjustments 4B-9
Table 4B-3: : Potential ATCF Adjustment Factors 4B-10
Table 4C-1: Summary of Factors Influencing Capital Outlays 4C-3
Table 4C-2: Variables For Capital Expenditure Modeling Analysis 4C-5
Table 4C-3: Number of Firms by Industry Classifications 4C-9
Table 4C-4: Time Series, Cross-Sectional Model Results 4C-12
Table 4C-5: Estimation of Capital Outlays for 316(b) Manufacturers Sample Facilities: Median Facilities Selected
by Revenue and ROA Percentiles 4C-13
IcivMarch 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Tables
Table 4D-1: Regulations Potentially Affecting 316(b) Manufacturers 4D-2
Table 4D-2: Per Facility Cost of Regulations that Affect 316(b) Industries 4D-4
Table 5-1: Facility-Level Cost-to-Revenue Analysis Results by NERC Region and Regulatory Option 5-5
Table 5-2: Issues in Combining Facility- and Entity-Level Weights in the Small Entity Impact Analysis 5-8
Table 5-3: Entity-Level Cost-to-Revenue Analysis Results, Using Facility-Level Weights 5-10
Table 5-4: Entity-Level Cost-to-Revenue Analysis Results, Using Entity-Level Weights 5-11
Table 5-5: Average Annual Cost per Household in 2015 by NERC Region and Regulatory Option ($2009) 5-14
Table 5-6: Compliance Cost per KWh of Sales by NERC Region and Regulatory Option in 2015 ($2009) 5-16
Table 5-7: Projected 2015 Price (Cents per KWh of Sales) and Potential Price Increase Due to Compliance Costs by
NERC Region and Regulatory Option ($2009) 5-17
Table 5-8: Summary of Downtime Impact Analysis by NERC Region, Downtime Period, and Option 5-23
Table 5-9: Downtime Capacity for the ASCC and HICC NERC Regions, by Region, Compliance Year, and Option
5-27
Table 6-1: Crosswalk between NERC Regions and IPM Regions 6-4
Table 6-2: Impact of Regulatory Options on National and Regional Markets at the Year 2028 6-12
Table 6-3: Impact of Market Impact Analysis Options on In-Scope Facilities, as a Group, at the Year 2028 6-19
Table 6-4: Impact of Market Impact Analysis Options on Individual In-Scope Facilities at the Year 2028 (number of
in-scope facilities with indicated effect) 6-25
Table 6-5: Impact of Regulatory Options on National Electricity Market During Periods of Technology Installation
Downtime 6-27
Table 6A-1: Impact of Market Impact Analysis Options on National and Regional Markets at the Year 2015 .6A-2
Table 7-1: Summary of Small Entity Impact Analysis Findings for 316(b) Existing Facilities Rule Regulatory
Options 7-2
Table 7-2: Unique 6-Digit Firm-Level NAICS Codes and SBA Size Standards for Manufacturers 7-4
Table 7-3: Number of Firms by Sector and Size (assuming two different ownership cases) 7-6
Table 7-4: Number and Percentage of Small Manufacturers Firms Subject to the Proposed Regulation, by Industry
Sector 7-7
Table 7-5: Estimated Cost-To-Revenue Impact on Small Manufacturers Entities, by Industry 7-8
Table 7-6: NAICS Codes and SBA Size Standards for Entities Owning Electric Generators, With a Primary
Business Other Than Electric Power Generation 7-10
Table 7-7: Unique Parent Entities and Facilities for Electric Generators (by Entity Type and Size) 7-11
Table 7-8: Number of Small Electric Generators Parent Entities (Industry Total and Entities with In-Scope
Facilities) 7-15
Table 7-9: Estimated Cost-to-Revenue Impact on Small Electric Generators Entities, by Entity Type - Using
Facility-Level Weights 7-17
Table 7-10: Estimated Cost-to-Revenue Impact on Small Electric Generators Entities, by Entity Type - Using
Entity-Level Weights 7-18
March 28, 2011 xv
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Tables
Table 7-11: Estimated Cost-to-Revenue Impact on Small Entities - Comparing Findings from the Facility-Level
Weights and Entity-Level Weights Analyses 7-19
Table 8-1: Government-Owned Electric Generators and Their Parent Entities 8-2
Table 8-2: Compliance Costs to Government-Owned Electric Generators (Millions; $2009) 8-4
Table 8-3: Annualized Government Administrative Costs (Millions; $2009) 8-5
Table 8-4: Government-Owned Electric Generators and Their Parent Entities, by Size 8-6
Table 8-5: Compliance Costs for Electric Generators by Ownership Type and Size (Millions; $2009) 8-7
Table 8-6: Summary of UMRA Costs (Millions; $2009) 8-9
Table 9-1: Low-Income Population Participation in 316(b) Existing Facilities Rule Benefits by State3 9-5
Table 9-2: Minority Population Participation in 316(b) Existing Facilities Rule Benefits by State3 9-8
Table 9-6: 316(b) Facilities in Marine Protected Areas, and Improvements in IM&EM Technologies by Policy
Option 9-14
Table 10-1: Key RIMS 2006 Economic Impact Multipliers 10-4
Table 10-2: Average Annual Initial/One-Time Compliance Costs during Initial Compliance Achievement Periods
(Millions; $2009) 10-5
Table 10-3: Average Annual Recurring Compliance Costs by (Millions; $2009) 10-6
Table 10-4: Average Annual Electricity Rate Recovery for Generators (Millions; $2009) 10-7
Table 10-5: Allocation Factors for Distributing Business Customer Rate Effects Across Economic Sectors 10-9
Table 10-6: Price Elasticity of Demand, By Rate Impact Sector 10-11
Table 10-7: Price Elasticity of Supply, By Rate Impact Sector 10-13
Table 10-8: Manufacturer's Average Annual Compliance Cost (Millions; $2009) 10-14
Table 10-9: Output Effect, Reported as Average Annual Values by Effect Category for Indicated Time Periods
10-15
Table 10-10: Employment Effect, Reported as Average Annual Values by Effect Category for Indicated Time
Periods 10-16
Table 10-11: Total Present Value and Annualized Values of Output and Employment Effects 10-17
Table 11-1: Summary of Annualized Costs of Compliance (Millions; $2009) 11-5
Table 11-2: Summary of Annualized Government Administrative Costs (Millions; $2009) 11-6
Table 11-3: Summary of Total Social Costs (Millions; $2009) 11-6
Table 11-4: Time Profile of Costs to Society for Option 1: IM Everywhere (Millions; $2009) 11-7
Table 11-5: Time Profile of Costs to Society for Option 2: IM Everywhere and EM for Facilities with DIP > 125
MOD (Millions; $2009) 11-8
Table 11-6: Time Profile of Costs to Society for Option 3: I&E Mortality Everywhere (Millions; $2009) 11-9
Table 11-7: Annualized Costs of Compliance for New Generating Units (Millions; $2009) 11-10
Table 12-1: Weighted Number of In-Scope Facilities by Technology Standard 12-1
xvi March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Tables
Table 12-2: Total Benefits and Social Costs by Option (Millions; $2009) 12-3
Table 12-3: Time Profile of Benefits and Social Costs (Millions; $2009) 12-3
Table 12-4: Incremental Net Benefit Analysis (Millions; $2009) 12-5
Table 13-1: Option 4 - Annualized Compliance Costs for Manufacturers by Industry Sector (millions, $2009, at
2012) 13-2
Table 13-2: Option 4 - Annualized Compliance Costs for Electric Generators by NERC Region (millions, $2009, at
2012) 13-3
Table 13-3: Option 4 - Annualized Compliance Costs For Manufacturers and Electric Generators (millions, $2009,
at 2012) 13-3
Table 13-4: Option 4 -Total Social Costs (Millions; $2009) 13-4
Table 13-5: Option 4 - Total Benefits and Social Costs (Millions; $2009) 13-4
Table 13-6: Option 4 - Time Profile of Benefits and Social Costs (Millions; $2009) 13-4
Table 13-7: Option 4 - Compliance Costs to Government-Owned Electric Generators (Millions; $2009) 13-6
Table 13-8: Option 4 - Annualized Government Administrative Costs (Millions; $2009) 13-6
Table 13-9: Option 4 - Compliance Costs for Electric Generators by Ownership Type and Size (Millions; $2009)
13-7
Table 13-10: Option 4 - Summary of UMRA Costs (Millions; $2009) 13-8
Table 13-11: Option 4 - Entity-Level Cost-to-Revenue Analysis Results, Assuming that Facilities Represented by
Sample Weights are Owned by the Same Firm that Owns the Sample Facility (Case 1) 13-8
Table 13-12: Option 4 - Entity-Level Cost-to-Revenue Analysis Results, Assuming that Facilities Represented by
Sample Weights are Owned by Different Firms than those Owning the Sample Facility (Case 2) 13-9
Table 13-13: Facility-Level Cost-to-Revenue Analysis Results by NERC Region and Regulatory Option 13-9
Table 13-14: Entity-Level Cost-to-Revenue Analysis Results for Option 4: IM for Facilities with DIF>50 MGD
13-10
Table 13-15: Option 4 - Average Annual Cost per Household in 2015 by NERC Region ($2009) 13-11
Table 13-16: Compliance Cost per KWh of Sales by NERC Region for Option 4 in 2015 ($2009) 13-11
Table 13-17: Option 4 - Projected 2015 Price (Cents per KWh of Sales) and Potential Price Increase Due to
Compliance Costs by NERC Region ($2009) 13-12
Table 13-18: Option 4 - Estimated Cost-To-Revenue Impact on Small Manufacturers Entities, by Industry... 13-13
Table 13-19: Option 4 - Estimated Cost-to-Revenue Impact on Small Electric Generators Entities, by Entity Type
13-14
March 28, 2011 xvii
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Figures
Table of Figures
Figure 2A-1: Value of Shipments and Value Added for Profiled Paper and Allied Products Segments (Millions,
$2009) 2A-7
Figure 2A-2: Producer Price Indexes for Profiled Paper and Allied Products Segments 2A-9
Figure 2A-3: Employment for Profiled Paper and Allied Products Segments 2A-13
Figure 2A-4: Capacity Utilization Rate (Fourth Quarter) for Pulp and Paper Industry 2A-17
Figure 2A-5: Value of Imports and Exports for Profiled Paper and Allied Products Segments 2A-24
Figure 2A-6: Net Profit Margin and Return on Capital for Paper and Allied Products 2A-26
Figure 2A-7: Number of Facilities Estimated within Scope of the 316(b) Existing Facilities Regulation by
Employment Size for Profiled Paper and Allied Products Segments 2A-28
Figure 2B-1: Value of Shipments and Value Added for Profiled Chemicals and Allied Products Industry Segments
(millions, $2009) 2B-7
Figure 2B-2: Producer Price Indexes for Profiled Chemicals and Allied Products Industry Segments 2B-9
Figure 2B-3: Employment for Profiled Chemicals and Allied Products Industry Segments 2B-12
Figure 2B-4: Capacity Utilization Rates (Fourth Quarter) for Profiled Chemicals and Allied Products Industry
Segments 2B-15
Figure 2B-5: Value of Imports and Exports for Profiled Chemicals and Allied Products Industry Segments.. 2B-24
Figure 2B-6: Net Profit Margin and Return in Total Capital for the Chemicals and Allied Products Industry
Segments 2B-28
Figure 2B-7: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by Employment
Size for Profiled Chemicals and Allied Products Industry Segments 2B-31
Figure 2C-1: Value of Shipments and Value Added for Petroleum Refineries (millions, $2009) 2C-5
Figure 2C-2: Producer Price Index for Petroleum Refineries 2C-6
Figure 2C-3: Employment for Petroleum Refineries 2C-8
Figure 2C-4: Capacity Utilization Rates (Fourth Quarter) for Petroleum Refineries 2C-11
Figure 2C-5: Value of Imports and Exports for Petroleum Refining (millions, $2009) 2C-15
Figure 2C-6: Net Profit Margin and Return on Total Capital for Petroleum Refining 2C-18
Figure 2C-7: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by Employment
Size for the Petroleum Refinery Segment 2C-19
Figure 2D-1:Value of Shipments and Value Added for Profiled Steel Industry Segments (millions, $2009) ....2D-7
Figure 2D-2: Producer Price Index for Profiled Steel Industry Segments 2D-8
Figure 2D-3: Employment for Profiled Steel Industry Segments 2D-11
Figure 2D-4: Capacity Utilization Rates (Fourth Quarter) for Profiled Steel Industry Segments 2D-14
Figure 2D-5: Net Profit Margin and Return on Total Capital for the Iron and Steel Industry 2D-20
xviii March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Figures
Figure 2D-6: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by Employment
Size for Profiled Steel Industry Segments 2D-22
Figure 2E-1: Value of Shipments and Value Added for Profiled Aluminum Industry Segments (millions, $2009)
2E-6
Figure 2E-2: Producer Price Indexes for Profiled Aluminum Industry Segments 2E-8
Figure 2E-3: Employment for Profiled Aluminum Industry Segments 2E-12
Figure 2E-4: Capacity Utilization Rates (Fourth Quarter) for Profiled Aluminum Industry Segments 2E-15
Figure 2E-5: Net Profit Margin and Return on Total Capital for the Non-Ferrous Metals Industry 2E-23
Figure 2E-6: Number of In-Scope Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by
Employment Size for the Profiled Aluminum Segments 2E-24
Figure 2F-1: Value of Shipments and Value Added for Profiled Food Manufacturing and Beverage Manufacturing
Segments (millions, $2009) 2F-5
Figure 2F-2: Producer Price Indexes for Food Manufacturing and Beverage Manufacturing Segments 2F-7
Figure 2F-3: Employment for Food Manufacturing and Beverage Manufacturing Segments 2F-10
Figure 2F-4: Capacity Utilization for Food Manufacturing and Beverage and Tobacco Manufacturing 2F-13
Figure 2F-5: Value of Imports and Exports for Profiled Food and Kindred Products Industry (millions, $2009)
2F-18
Figure 2F-6: Net Profit Margin and Return on Total Capital for Food and Beverage Manufacturers 2F-20
Figure 2F-7: Number of Facilities Estimated Subject to the Proposed 316(b) Existing Facilities Regulation by
Employment Size for the Combined Food Manufacturing and Beverage Segments 2F-21
Figure 2G-1: Other Industries Facilities Estimated Subject to the Existing Facilities Regulation by Employment Size
2G-3
Figure 2H-1: Distribution of Facilities and Capacity by Ownership Type, 2007 2H-6
Figure 2H-2: Net Summer Capacity, 1997 to 2007 2H-7
Figure 2H-3: Percent of Electricity Generation by Primary Fuel Source and Facility Ownership Type, 2007... 2H-9
Figure 2H-4: 2009 North American Electric Reliability Corporation (NERC) Regions 2H-11
Figure 2H-5: Number of In-Scope Facilities by Size (in MW), 2007 2H-16
Figure 2H-6: Electricity Restructuring by State as of January 2010 2H-21
Figure 4B-1: Growth in Real Domestic Product, 1985-2009 4B-3
Figure 4B-2: Capacity Utilization in Manufacturing Industries, 1985-2009 4B-4
Figure 4B-3: Growth in Industrial Production, 1985-2009 4B-5
Figure 4B-4: ATCF Index Series and Calculated Trend -Aluminum 4B-11
Figure 4B-5: ATCF Index Series and Calculated Trend - Basic Chemicals, Resins and Synthetics 4B-12
Figure 4B-6: ATCF Index Series and Calculated Trend - Food and Kindred Products 4B-13
Figure 4B-7: ATCF Index Series and Calculated Trend- Paper and Allied Products 4B-14
March 28, 2011 xix
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Table of Figures
Figure 4B-8: ATCF Index Series and Calculated Trend- Pesticides and Fertilizers 4B-15
Figure 4B-9: ATCF Index Series and Calculated Trend- Petroleum Refining 4B-16
Figure 4B-10: ATCF Index Series and Calculated Trend- Pharmaceuticals 4B-17
Figure 4B-11: ATCF Index Series and Calculated Trend-Steel 4B-18
Figure 4C-1: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b) Manufacturers Survey
Facilities in the Paper and Allied Products Sector 4C-15
Figure 4C-2: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b) Manufacturers Survey
Facilities in the Chemicals and Allied Products Sector 4C-16
Figure 4C-3: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b) Manufacturers Survey
Facilities in the Petroleum and Coal Products Sector 4C-17
Figure 4C-4: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b) Manufacturers Survey
Facilities in the Primary Metal Industries Sector 4C-18
Figure 4C-5: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b) Manufacturers Survey
Facilities in the Food and Kindred Products Sector 4C-19
Figure 6-1: 2009 North American Electric Reliability Corporation (NERC) Regions 6-3
xx March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 1: Introduction
1 Introduction
1.1 Background
This document (the Economic and Benefits Analysis or EA) provides analytical support for development of EPA's
Proposed Existing Facilities Rule, which implements Clean Water Act (CWA) 316(b) requirements governing
cooling water intake structures at certain existing power producing facilities (Electric Generators) and
manufacturing facilities (Manufacturers). These requirements would apply to existing Electric Generators and
Manufacturers (existing facilities) with cooling water intake structures that are designed to withdraw two million
gallons per day (MGD) or more of water from rivers, streams, lakes, reservoirs, estuaries, oceans, or other waters of
the United States for cooling purposes. The national requirements, which will be implemented through National
Pollutant Discharge Elimination System (NPDES) permits upon promulgation, are based on the best technology
available to minimize the adverse environmental impact associated with the use of cooling water intake structures.
This is EPA's second attempt to develop CWA 316(b) requirements for existing Electric Generators and
Manufacturers. Two preceding efforts, the suspended 2004 Final Section 316(b) Phase II Existing Facilities Rule
(suspended 2004 Phase II Final Rule or Phase II Final Rule) applicable to existing generators with a design intake
flow (DIP) of greater than 50 MGD, and the 2006 Final Section 316(b) Phase III Existing Facilities Rule (2006
Phase III Final Rule or Phase III Final Rule) applicable to existing electric generators with a DIP of less than 50
MGD and existing manufacturing facilities, were challenged in court and subsequently remanded for further
rulemaking.
Specifically, in 2004, EPA published the Phase II Final Rule applicable to existing power plants (69 PR 41576 (July
9, 2004)). However, in response to court rulings, including a remand order from the Second Circuit Court of
Appeals in 2007, and a subsequent ruling by the Supreme Court in 2009, EPA suspended the Phase II regulations.
In a later rulemaking in 2006, EPA published the Phase III Final Rule, which establishes categorical regulations for
certain new offshore oil and gas extraction facilities, and establishes that 316(b) requirements for electric generators
with a DIP of less than 50 MGD and existing manufacturing facilities should be established through conditions
established by NPDES permit directors on a case-by-case basis using best professional judgment. In 2010, the Fifth
Circuit Court of Appeals accepted EPA's request to remand the existing facility portion of the Phase III Final Rule
to the Agency for further rulemaking.
In response to these court rulings, EPA suspended the previous existing facilities 316(b) rules and initiated
development of new CWA 316(b) requirements for existing electric generators and manufacturers. This proposed
regulation, the existing facilities rule, represents EPA's initial action to re-promulgate regulatory provisions that
will replace the suspended 316(b) requirements.
1.2 Overview of the Economic and Benefits Analysis of the Proposed Existing
Facilities Rule
1.2.1 Facilities Expected To Be Subject to the Proposed Existing Facilities Rule
The Proposed Existing Facilities Rule applies to existing Electric Generators and Manufacturers that have intakes
designed to withdraw two million gallons of water per day or more from waters of the United States and use at least
25 percent of this water for cooling purposes. EPA estimates that 559 Electric Generators and 593 Manufacturers
will be within the scope of this regulation.
March 28, 2011 1-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 1: Introduction
Electric Generators
The 559 in-scope Electric Generators have total electric generating capacity of 490,827 MW, which represents
approximately 45 percent of the national total of electric generating capacity (Table 1-1). EPA further estimates that
these 559 in-scope generating facilities are owned by 143 parent entities. The largest quantity of in-scope facilities
and generating capacity occurs in the Investor-Owned Utility category, with a total of 283 facilities and 291,051
MW of capacity estimated to be within the regulation's scope. This capacity represents over 71 percent of the total
generating capacity owned by Investor-Owned Utilities, and more than 25 percent of the generating facilities owned
by this ownership category. For more detail on the electric generating industry and the expected in-scope facilities
in that industry, see Chapter 2: Industry Profiles.
Table 1-1: Existing Electric Generators, Capacity, and Parent-Entities, by Ownership Type, 2007a
Ownership Type
Investor-Owned
Nonutility
Federal
State
Municipality
Cooperative
Political
Subdivision
Total
Parent-Entities
Industry
Total"
212
1,737
9
25
1,843
883
126
4,835
In-
Numb'er
43
37
T
4
35
20
3
143
Scope
"/ooffotai
20.3%
jf;!!
16.0%
1.9%
2.3%
2.4%
3.0%
Facilities
Industry
Total
1,117
2,784
197
104
869
205
93
5,369
In-S
Number0
283
171
14
9
44
31
7
559
cope
"/ooffotai
25.5%
f||
8.7%
5.4%
15.1%
7.5%
10.8%
Ca
Industry
Total
407,460
471,262
72^34
22,405
51,057
40,311
20,721
1,085,449
pacity (MW)
In-S
Number0
291,051
133,972
8,592
12,880
14,028
5,692
490,827
cope
%offotai
71.4%
28.4%
341%
38.3%
25.2%
34.8%
27.5%
45.2%
a. Individual values may not sum to totals due to independent rounding.
b. Information on the total number of parent-entities is based on data from Form EIA-861 and Form EIA-860. Information on facilities and capacity is
based on data from Form EIA-860. These data sources report information for non-corresponding sets of power producers. Therefore, the total number of
parent-entities is not directly comparable to the information on total facilities or total capacity.
c. EPA estimated the number of in-scope Electric Generators and their capacity using the original 316(b) survey weights. These weights account for
survey non-respondents (see Appendix 3.A for details).
Source: U.S. EPA, 2010; U.S. DOE, 2007a (EIA-860); U.S. DOE, 2007b (EIA-861).
Manufacturers
EPA identified six manufacturing industries, in addition to electric power generators, that use substantial amounts
of cooling water in their operations and that are likely to contain the largest numbers of facilities and cooling water
intake capacity within the scope of the Proposed Existing Facilities Rule: Paper and Allied Products, Chemicals and
Allied Products, Petroleum Refining, Steel, Aluminum, and Food and Kindred Products. Out of an estimated 593
in-scope Manufacturers, 576 are in these six primary manufacturing industries. The other 17 Manufacturers fall in a
wide range of businesses. These 17 facilities in other manufacturing industries also use cooling water and would
therefore also be subject to the Proposed Existing Facilities Rule; however, based on EPA's previous reviews of
industries' reliance on cooling water, the cooling water intake flow of these remaining industries is small relative to
that of the power industry and the six selected industries. Therefore, the cost and economic impact analyses
conducted for Manufacturers and presented in this document focus primarily on the six primary manufacturing
industries listed above.
Overall, EPA estimates that approximately 2 percent of facilities, and 23 percent of the total value of shipments for
the 6 primary manufacturing industries will be subject to today's proposed rule. The majority of Manufacturers
expected to be subject to the Proposed Existing Facilities Rule, or 225 facilities, are in the Pulp and Paper industry,
while facilities in the Chemicals and Allied Products make up the second largest category (179 facilities) (Table
1-2). In-scope Manufacturers in the Pulp and Paper and Petroleum industries represent the largest shares of their
respective industry facility totals at 38 percent and 11 percent, respectively. In terms of in-scope economic value,
in-scope Manufacturers in the Petroleum industry account for the largest quantity of value of shipments ($216
billion), followed by in-scope facilities in the Chemical industry ($75 billion), Pulp and Paper industry ($70 billion),
and Steel industry ($63 billion). These values also represent substantial shares of the total value of economic
activity in the various Manufacturing sectors: in-scope Manufacturers in the Pulp and Paper industry account for the
1-2
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 1: Introduction
largest share of total industry value of shipments (85 percent), followed by in-scope Manufacturers in the
Aluminum industry (61 percent), and Steel (49 percent). In-scope Manufacturers in the Food and Beverage industry
make up the smallest group in terms of absolute number of facilities and value of shipments as well as their shares
of total industry values. For more detail on the expected in-scope facilities in the manufacturing industries, see
Chapter 2: Industry Profiles.
Table 1-2: Existing Manufacturers, Value of Shipments, and Parent-Entities, by Industry
Industry
Sector
Aluminum
Chemicals
Food
Paper
Petroleum
Steel
Total
Numb
Sector
Total
333
4,433
28,938
597
352
1,525
36,178
er of Facil
In-iSc
Num
26
179
38
225
39
68
576
ities
ope"**
%offot
8%
4%
0%
38%
11%
4%
2%
Value of Sh
Sector Total
$36,557
$476,287
$697,164
$82,796
$59o';44"i
$128,082
$2,011,327
ipments (mill
IllllllttnSc
Value
$22,253
$74,822
$15,068
$70,142
j2l"6,320
$62,507
$461,112
2009 $)a'b
opea;<;
% of Tot
61%
16%
2%
85%
37%
49%
23%
Nun
Sector
Total
266
3,011
24,168
311
228
1,406
29,390
iber of Pareii
''IIIIIIIlM-§
Num
5-14
26-116
8-25
42-126
17-24
16-43
114-348
it Entities
cope**
% of f ot
1.9%-5.3%
0.9%-3.9%
0.0%-0.1%
13.5%-40.5%
J^O^IQ^O/0
1.1%-3.1%
0.4%-1.2%
a. For this analysis, facility revenue was used as a measure of value of output the absence of value of shipments for sample facilities.
b. To compare in-scope revenue values with the industry value of shipments, EPA brought in-scope revenue values forward to 2007 using industry-
specific Producer Price Index (PPI) published by the Bureau of Labor Statistics (BLS) and stated in 2009 dollars using GDP deflator published by the
Bureau of Economic Analysis (BEA).
c. Ranges of parent-entity counts and total shares represent different parent weighting schemes (see Appendix 3.A of the EBA for more details).
d. In-scope facility counts include baseline closures and exclude 17 facilities with NAICS codes that do not fall into any of these six primary
manufacturing industries (see Chapter 3 of the EBA).
e. Number of in-scope facilities is estimated using technology weights. In-scope revenue values are weighted estimates; these estimates were
generated using economic analysis weights. See Appendix 3. A of the EBA for information on weights development.
Source: U.S. EPA, 2000; U.S. Economic Census 2000; SUSB 2006.
1.2.2 Analyses Performed in Support of the Proposed Existing Facilities Rule
In developing costs and in performing analyses of the Proposed Existing Facilities Rule options, generally, EPA
followed closely the analysis approaches and impact evaluation concepts used in the analysis for the previous CWA
316(b) regulatory analyses, and to the extent possible relied on the same data sources.1 The discussion in the
following chapters provides an overall summary of the analytic approaches with emphasis on the differences in the
current analysis from the previous CWA 316(b) regulatory analyses and on the updating of information.
EPA performed the following analyses in support of the Proposed Existing Facilities Rule:
> Industry economic profiles (Chapter 2)
> Compliance cost assessment (Chapter 3)
> Facility-level severe and moderate impact analysis and firm-level cost-to-revenue analysis for
Manufacturers (Chapter 4)
> Facility-level cost-to-revenue analysis and electricity rate impact analysis for Electric Generators (Chapter
5}
> Electricity market model analysis (Chapter 6)
> Regulatory Flexibility Act (RFA) analysis (Chapter 7)
> Unfunded Mandates Reform Act (UMRA) analysis (Chapter 8)
> Analyses to address executive orders and other administrative requirements (Chapter 9)
> Assessment of economy-wide output and employment effects (Chapter 10)
> Assessment of total social costs (Chapter 11)
> Assessment of total social costs and benefits (Chapter 12)
For more details on these analyses see Chapter El: Summary of Compliance Costs in the suspended 2004 Phase II Final EA Report and
Chapter Cl: Summary of Cost Categories and Key Analysis Elements for Existing Facilities in the 2006 Phase III Final EA Report.
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 1: Introduction
In addition to these chapters and their respective analyses, the EA document also includes Chapter 13, which
addresses a regulatory option that was developed and analyzed late in the document preparation process. Chapter 13
includes all analytic findings for this additional regulatory option as described in the preceding EA chapters.
1.2.3 Regulatory Options Considered for the Proposed Existing Facilities Rule
EPA analyzed three regulatory options for its analysis of the Proposed Existing Facilities Rule, which vary in the
technology requirements and compliance schedules applicable to in-scope facilities:
> Option 1: Impingement Mortality at All Existing Facilities and Entrainment Mortality Controls for All New
Units at Existing Facilities; Determined Entrainment Controls for Facilities Greater than 2 MGD DIF On
a Site-Specific Basis (IMEverywhere2): Under this option, all in-scope existing facilities are required to
achieve either the design or the performance standard for impingement mortality. EPA has identified
modified traveling screens with a fish return system as the technology basis for these limits. The proposed
limitations on impingement mortality are a maximum of 31% mortality on a monthly basis and 12% on an
annual basis. Facilities would be required to meet the IM technology specifications within 5 years of rule
promulgation. In addition, entrainment controls would be established by the permitting authority on a case-
by-case basis for all facilities with at least 2 MGD DIF, and new units at an existing facility would be
required to reduce flow commensurate with closed cycle cooling. For details of the technologies, see the
Technical Development Document for the Proposed Section 316(b) Existing Facilities Rule (EPA-821-R-
11-0003), hereafter referred to as the Technical Development Document (TDD); see the Federal Register
notice and rule language for further discussion of the requirements of this option.
> Option 2: Impingement Mortality Everywhere and Entrainment Mortality for Existing Facilities with DIF
> 125 MGD and All New Units at Existing Facilities (IM Everywhere, EM for Facilities with DIF> 125
MGD): Under this option, in-scope existing facilities with a DIF exceeding 125 MGD are required to
achieve impingement and entrainment mortality reductions by reducing intake flows commensurate with
closed cycle cooling (i.e., these facilities are assigned the technology requirements from Option 3, below).
All other in-scope existing facilities are required to achieve numeric impingement mortality limits only (i.e.,
these facilities are assigned the technology requirements from Option 1, above). Facilities installing IM-
only technology would be required to meet this requirement within 5 years of rule promulgation. Facilities
with DIF exceeding 125 MGD and installing EM technology would be required to meet this requirement
within 10 years for non-nuclear electric generating facilities, and within 15 years for nuclear electric
generating facilities and manufacturing facilities. In addition, entrainment controls would be established by
the permitting authority on a case-by-case basis for all facilities with at least 2 MGD DIF but less than 125
MGD DIF, and new units at an existing facility would be required to reduce flow commensurate with
closed cycle cooling. For details of the technologies, see the Technical Development Document; see the
Federal Register notice for further discussion of this option.
> Option 3: Impingement and Entrainment Mortality Everywhere (I&E Mortality Everywhere): Underthis
option, in addition to requirements for all in-scope existing facilities to achieve numeric impingement
mortality limits, all facilities must achieve entrainment mortality reductions by reducing intake flows
commensurate with closed cycle cooling. EPA has identified wet cooling towers as the technology basis for
these limits. This option would establish optimized wet cooling towers as a design standard. Optimized wet
cooling would be demonstrated through flow monitoring and conductivity measurements. Optimized
cooling towers achieve flow reductions of 97.5 percent and 94.9 percent for freshwater and saltwater
The shorthand notation for this and the other option refers to the minimum direct requirements of the regulatory options. For example,
for Option 1, in addition to this minimum requirement (e.g., IM technology for all in-scope facilities), additional requirements for EM
technology may be determined on a case-by-case basis and all new units at existing facilities would be required to meet EM technology
standards.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 1: Introduction
sources, respectively. Alternatively, this option would allow facilities to demonstrate flow reductions
commensurate with closed cycle cooling based on optimized wet cooling towers. Facilities would be
required to meet this EM technology requirement within 10 years of rule promulgation for non-nuclear
electric generating facilities, and within 15 years for nuclear electric generating facilities and manufacturing
facilities. In addition, new units at an existing facility would be required to reduce flow commensurate with
closed cycle cooling. For details of the technologies, see the Technical Development Document; see the
Federal Register notice for further discussion of this option.
> Option 4: Uniform Impingement Mortality Controls at Existing Facilities with DIP of 50 MGD or more;
Best Professional Judgment-based Permits for Existing Facilities with DIP Less Than 50 MGD but more
than 2 MGD DIP; Uniform Entrainment Controls for All New Units at Existing Facilities (IMfor Facilities
with DIF>50 MGD). Option 4 is the same as Option 1: IM Everywhere, in all respects except that Option 4
requires only in-scope existing facilities with a DIP greater that 50 MGD to achieve the uniform national
impingement mortality design/performance standard. Existing facilities between 2 and 50 MGD would
remain subject to 316(b) permitting based on best professional judgment. EPA developed and analyzed this
option after completing the analysis and documentation of the other three regulatory options. As a result,
the analysis results for Option 4 are presented in a separate chapter of the EA document.
1.2.4 Organization of the Economic and Benefits Analysis Report
This Economic and Benefits Analysis Report (the EBA Report) follows a similar organizational structure to the
EBA report for the previous 316(b) regulations, with the exception that the detailed benefits analysis is presented in
a separate document, the Environmental and Economic Benefits Assessment. The EBA includes the following
chapters:
> Chapter 2: Industry Profiles provides background information on the electric power generation industry
and the six primary manufacturing industries, and specifically the characteristics of the in-scope facilities in
relation to other facilities in the respective industries.
> Chapter 3: Development of Costs for Regulatory Options details the methods used to develop and assign the
costs of compliance and administration for the Proposed Existing Facilities Rule to individual complying
facilities, and to NPDES permitting authorities and the Federal government.
> Chapter 4: Cost Impact Analyses -Manufacturers assesses the impacts of compliance on the Manufacturers
segment of in-scope facilities in terms of severe impacts (i.e., facility closures) and moderate impacts (i.e.,
adverse changes in a facility's financial position that are of lower severity than closure), and on their
owning entities based on a cost-to-revenue basis.
> Chapter 5: Cost Impact Analyses - Electric Generators assesses the impacts of compliance on the Electric
Generators segment of in-scope facilities and their owning entities based on a cost-to-revenue analysis. This
chapter also assesses the potential impact on consumer electricity rates in terms of increased electricity
prices for households and for other consumers of electricity.
> Chapter 6: Assessing the Impact of the Existing Facilities Regulatory Options in the Context of National
Electricity Markets analyzes the impacts of the rule using the output of the Integrated Planning Model
(IPM), which predicts impacts of the proposed rule in the context of changes to the entire electricity market,
including both in-scope and out of scope facilities.
> Chapter 7: Assessing the Potential Impact of the Proposed Existing Facilities Rule on Small Entities -
Regulatory Flexibility Act (RFA) Analysis addresses the requirements of RFA and assesses the impact of the
rule on small entities on the basis of a cost-to-revenue comparison.
March 28, 2011 1-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 1: Introduction
> Chapter 8: Unfunded Mandates Reform Act (UMRA) Analysis addresses the requirements of UMRA by
assessing the impact on government entities, both in terms of compliance costs to government-owned
Electric Generators and in terms of administrative costs to governments implementing the rule. This
analysis also compares the impacts to small governments with those of large governments and small private
entities.
> Chapter 9: Other Administrative Requirements addresses the requirements of Executive Orders that EPA is
required to satisfy for this proposal, notably Executive Order 13211, which requires EPA to determine if
this action will have a significant effect on energy supply, distribution, or use.
> Chapter 10: Assessment of Total Economic Impact looks at the economy-wide output and employment of
effects - direct, indirect, and induced - of the proposed regulation, accounting for inter-industry linkages at
the national level.
> Chapter 11: Assessment of Total Social Costs looks at the impact of the regulation in terms of its total
social cost, including costs to complying facilities, implementation costs to governments, and the costs to
society from potential reductions in electric generating capacity on a year-by-year basis.
> Chapter 12: Comparison of Social Costs and Benefits compares the estimated total costs of the regulation
with estimated benefits, on the bases of both a year-explicit schedule of costs and benefits, and annualized
costs and benefits, and also compares the incremental benefits across regulatory options.
> Chapter 13: Cost and Economic Impact of Additional Regulatory Option presents the cost, economic
impact and benefits analysis results for Option 4 (IMfor Facilities with DIF>50 MGD), which was
developed and analyzed after completion of analysis and EA documentation for Regulatory Options 1, 2,
and 3. For this reason, the EA findings for Option 4 are presented in a separate chapter.
This document includes seven appendixes:
> Appendix 3A: Use of Sample Weights in the Proposed Existing Facilities Rule Analyses describes the
development and use of sample weights for the cost and economic impact analysis of the proposed
regulatory options.
> Appendix 3B: Analysis of Short Term Reduction in Capacity Availability Due to Installation Downtime
assesses the potential impact of reduced generating capacity availability due to downtime of generating
units during technology installation.
> Appendix 3C: Mapping Manufacturers' Standard Industrial Classification Codes to North American
Industry Classification System Codes discusses the mapping of the facility-level 4-digit SIC codes for
which the 316(b) Survey-based facility information for Manufacturers was originally reported, onto 6-digit
NAICS codes for use in the current cost and economic impact analysis.
> Appendix 4A: Cost Pass-Through Analysis assesses the cost pass-through (CPT) potential for the six
Primary Manufacturing Industries sectors in which a substantial number of facilities are expected to be
subject to the Proposed 316(b) Existing Facilities Rule.
> Appendix 4B: Adjusting Baseline Facility Cash Flow describes EPA's development of adjustment factors to
bring certain survey-based financial data for the six Primary Manufacturing Industries to the present.
> Appendix 4C: Estimating Capital Outlays for Section 316(b) Manufacturing Sectors Discounted Cash Flow
Analyses describes the analysis used to estimate ongoing capital outlays for use in the facility-level cash
flow analyses for Manufacturers.
> Appendix 4D: Analysis of Other Regulations presents analysis of other environmental regulations that were
recently or will soon be promulgated, potentially imposing additional costs on 316(b) Manufacturing
Industries beyond those reflected in in-scope facilities' baseline financial statements.
Ti March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2: Introduction to Industry Profiles
2 Introduction to Industry Profiles
In this chapter, EPA presents economic profiles of the industries identified in the previous 316(b) rulemakings as
most reliant on cooling water in their operations and thus containing substantial numbers of facilities that are
expected to be within the scope of the Proposed Existing Facilities Regulation. These profiles review information
on the historical economic/financial performance, structure, and economic outlook for these industries and are
meant to provide insight on how the requirements of the Proposed Regulation will affect these industries. In
particular, the profiles assess the number of facilities that are expected to be within the scope of the Proposed
Regulation and the economic activity and employment in the in-scope segments, and review factors influencing
the ability of these industries to meet the Proposed Regulation's compliance requirements without undue adverse
economic impact.
These profiles cover the two broad categories of facilities that are within the Proposed Rule's scope:
1. Electric Generators
2. Manufacturers
In the previous rulemaking efforts, EPA identified the electric power sector - Electric Generators - as the industry
most reliant on cooling water in its operations. Within the Manufacturers category, EPA previously identified six
industries as having the largest total reliance on cooling water and the largest numbers of facilities that would
likely be subject to 316(b) regulations, including the Proposed Existing Facilities rule. These six industries,
referred to as the Primary Manufacturing Industries, are covered by this profile:
> Paper and Allied Products (NAICS 322)
> Chemicals and Allied Products (NAICS 325)
> Petroleum Refining (NAICS 324)
> Steel (NAICS 3311 and 3312)
> Aluminum (NAICS 3313)
> Food and Kindred Products (NAICS 311 and 3121).
Facilities in other industries also use cooling water and could therefore be subject to section 316(b) regulations;
however, based on EPA's previous reviews of industries' reliance on cooling water, the cooling water intake flow
of these remaining industries is small relative to that of the power industry and the six selected industries.
Therefore, the industry profiles presented in the following subchapters for the 316(b) Existing Facilities Rule
focus on the Electric Generators as well as the Manufacturers industries listed above.
This profile also reports information on certain facilities from which EPA received questionnaire responses in its
earlier 316(b) surveys that were found not to be part of Electric Generators or the Primary Manufacturing
Industries. EPA originally believed these facilities to be non-utility electric power generators; however, inspection
of their responses indicated that the facilities were better understood as cooling water-dependent facilities whose
principal operations lie in businesses other than the electric power industry or the manufacturing industries listed
above. This profile includes information for these facilities, referred to as "Other Industries."
The remainder of this chapter is divided into eight subchapters:
> 2A: Paper and Allied Products (NAICS 322),
> 2B: Chemicals and Allied Products (NAICS 325),
> 2C: Petroleum and Coal Products (NAICS 324),
> 2D: Steel (NAICS 3311 and 3312),
> 2E: Aluminum (NAICS 3313),
> 2F: Food and Kindred Products (NAICS 311 and 3121),
> 2G: Other Industries,
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2: Introduction to Industry Profiles
> 2H: Electric Power
Each Manufacturers industry subchapter, except for "Other Industries," is divided into the following five
subsections: (1) summary insights from this profile, (2) domestic production, (3) structure and competitiveness,
(4) financial condition and performance, and (5) facilities potentially subject to the 316(b) Existing Facilities
Rule. Data presented in these six sub-chapters span nearly two decades to ensure a review of industry trends since
the time of the Detailed Industry Questionnaire (1996-1998). The "Other Industries" section contains only
summary information for those facilities for which questionnaire responses were received; this section does not
include the industry specific discussions since the "Other Industry" facilities are in a variety of different
industries, which, as noted above, rely to a much less substantial degree on cooling water to support their
operations.
The Electric Power industry subchapter 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 existing facilities.
This profile uses the North American Industry Classification System (NAICS) as the primary framework for
analyzing and reporting information about the industries analyzed for the 316(b) Existing Facilities regulation.
However, older data were often reported in the Standard Industrial Classification (SIC) system, which the U.S.
Economic Census used for economic reporting until 1997 when data reporting switched to the NAICS system.
Where necessary, EPA converted information reported in the SIC framework to the NAICS framework using the
1997 Economic Census Bridge Between NAICS and SIC. In most instances, these translations are straightforward;
however, for some segments, the translation may introduce inconsistencies in data series at the point of
changeover from the SIC to the NAICS frameworks (see Appendix 3Cfor a more in-depth discussion). EPA
presents nearly twenty years of industry data to prevent any data anomalies at the time of the change in
classifications from affecting the longer-term understanding of trends in the profiled industries.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
2A Profile of the Paper and Allied Products Industry
EPA's Detailed Industry Questionnaire, hereafter referred to as the DQ, identified five 4-digit SIC codes in the
Paper and Allied Products manufacturing industry (SIC 26) with at least one existing facility that operates a
CWIS, holds a NPDES permit, withdraws at least two million gallons per day (MGD) from a water of the United
States, and uses at least 25 percent of its intake flow for cooling purposes (facilities with these characteristics are
hereafter referred to as "facilities potentially subject to the 316(b) Existing Facilities regulation" or "in-scope
facilities"). For the purpose of this analysis, EPA identified a six-digit NAICS code for each of these potential
facilities using the information from DQ and public sources (see Appendix 3. C: Conversion the Data from
Standard Industrial Classification (SIC) to North American Industry Classification System (NAICS)). As the
result of this mapping, EPA identified six 6-digit NAICS codes in the Paper and Allied Products manufacturing
industry (NAICS 322).
For each of these six analyzed 6-digit NAICS codes, Table 2A-1, following page, provides a description of the
industry segment, a list of primary products manufactured, the total number of detailed questionnaire respondents
(weighted to represent a national total of facilities that hold a NPDES permit and operate cooling water intake
structures), and the number of facilities estimated to be potentially subject to the proposed 316(b) Existing
Facilities Rule based on the minimum withdrawal threshold of 2 MGD (see Chapter 1: Introduction for more
details on the Rule applicability criteria).
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2A-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Table 2A-1: Existing Facilities in the Paper and Allied Products Industry (NAICS 322)
NAICS
322110
32212
322130
NAICS
Description
Pulp Mills
Paper Mills
Paperboard
Mills
Important Products Manufactured
Pulp from bagasse, linters, rags, straw, wastepaper, and wood manufactured by
chemical, mechanical, or semichemical processes without making paper for
paperboard.
Paper from wood pulp and other fiber pulp, converted paper products; integrated
operations of producing pulp and manufacturing paper if primarily shipping paper
or paper products.
Paperboard, including paperboard coated on the paperboard machine, from wood
pulp and other fiber pulp; and converted paperboard products; integrated
operations of producing pulp and manufacturing paperboard if primarily shipping
paperboard or paperboard products.
Total
Number of In-Scope
Facilities3
34
134
48
216
Other Paper and Allied Products Segments
322222
322224
322299
Coated and
Laminated Papei
Manufacturing
Uncoated Paper
and Multiwall
Bag
Manufacturing
All Other
Converted Paper
Products
Manufacturing
Cutting and coating paper, cutting a laminating paper and other flexible materials
(except plastics film), laminating aluminum and other metal foils for non-
packaging uses from purchased foil.
Uncoated, multiwall, paper bags manufactured from purchased paper.
Containers, bags, coated and treated paper, stationary products, and sanitary paper
products from paper and paperboard products; converted pulp products (i.e. egg
cartons, food trays, and other food containers) from molded pulp.
Total Other
3
3
3
9
Total Paper and Allied Products (NAICS 322)
Total NAICS Code 322\
225
" Number of weighted detailed questionnaire survey respondents.
b Individual numbers may not add up due to independent rounding.
Source: Executive Office of the President, 1987; U.S. EPA 2000; U.S. EPA analysis, 2010.
As shown in Table 2A-1, EPA estimates that out of an estimated total of 563 facilities3 with aNPDES permit and
operating cooling water intake structures in the Paper and Allied Products Industry (NAICS 322), that 225 (40
percent) are expected to be subject to the 316(b) Proposed Existing Facilities Regulation. EPA also estimated the
percentage of total industry production that occurs at facilities estimated to be subject to regulation under each
analysis option. Total value of shipments for the Paper and Allied Products industry from the 2007 Economic
Census is $82.8 billion ($2009). Value of shipments, a measure of the dollar value of production, was selected for
the basis of this estimate. Because the DQ did not collect value of shipments data, these data were not available
for the potential existing facilities. Total revenue, as reported on the DQ, was used as a close approximation for
value of shipments for these facilities. EPA estimated the total revenue of facilities in the paper industry expected
to be subject to the 316(b) Existing Facilities regulation is $70.1 billion. Therefore, EPA estimates that the
percentage of total production in the paper industry that occurs at facilities estimated to be subject to regulation is
85 percent.
The responses to the DQ indicate that three segments account for most of the existing Manufacturers in the Paper
and Allied Products industry: (1) Pulp Mills (NAICS 322110), (2) Paper Mills (NAICS 32212), and (3)
Paperboard Mills (NAICS 322130). The remainder of this profile therefore focuses on these three industry
segments.
This estimate of the number of facilities potentially subject to regulation is based on the universe of facilities that received the 1999
screener questionnaire.
2A-2
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Table 2A-2 provides the cross-walk between NAICS codes and SIC codes for the profiled paper NAICS codes.
The table shows that both Pulp Mills and Paperboard Mills have a 1 to 1 relationship to their SIC codes. A large
portion of SIC code 2621 (84 percent based on value of shipments) corresponds to Newsprint Mills. NAICS
322121, classified as Paper (except newsprint) Mills, corresponds to three SIC codes (2621, 2676, and 3842).
Table 2A-2: Relationship between NAICS and SIC Codes for the Paper and Allied Products Industry (2007)
NAICS
Code
322110
322121
322122
322130
NAICS
Description
Pulp mills
Paper (except
newsprint) mills
Newsprint mills
Paperboard mills
SIC
Code
2611
2621
2676
3842
2621
2631
SIC Description
Pulp mills
Paper Mills
Sanitary Paper Products
Surgical Appliances and Supplies
Paper Mills
Paperboard mills
Number of
Establishments
39
241
21
187
Value of
Shipments
(Millions;
$2009)
$5,196
$47,841
$3,556
$26,204
Employment
7,268
75,921
4,917
36,641
Sources: U.S. DOC. 2007Economic Census.
2A.1 Summary Insights from this Profile
A key purpose of this profile is to provide insight into the ability of pulp and paper firms to absorb compliance
costs under the Proposed 316(b) Existing Facilities Rule without material adverse economic/financial effects. The
industry's ability to withstand compliance costs is primarily influenced by the following two factors: (1) the
extent to which the industry may be expected to shift compliance costs to its customers through price increases
and (2) the financial health of the industry and its general business outlook.
2A.1.1 Likely Ability to Pass Compliance Costs Through to Customers
As reported in the following sections of this profile, the Paper and Allied Products industry is relatively
unconcentrated, which would suggest that firms in this industry may face difficulty in passing through to
customers a significant portion of their compliance-related costs. The domestic Pulp Mills industry segment also
faces significant competitive pressures from abroad, further curtailing the potential of firms in this industry to
pass through to customers a significant portion of their compliance-related costs. The domestic Paper Mills and
Paperboard Mills industry segments do not face as significant foreign competitive pressures, and, based on this
factor, would have more latitude in passing through to customers any increase in production costs resulting from
regulatory compliance. However, foreign pressure is likely to increase as capacity in foreign countries,
particularly China, continues to grow and exert pressure on the domestic market. As discussed above, given the
proportion of total value of shipments in the industry estimated to be subject to regulation under each analysis
option, EPA judges that in-scope facilities in the Paper and Allied Products industry subject to the 316(b) Existing
Facilities Regulation are not likely to be able to recover compliance costs through prices increases to customers.
For these reasons, in its analysis of regulatory impacts for the pulp and paper industry, EPA assumed that
complying firms would be unable to pass compliance costs through to customers: i.e., complying facilities must
absorb all compliance costs within their operating finances (see following sections and Appendix 4.A: Cost Pass-
Through Analysis for more information).
2A.1.2 Financial Health and General Business Outlook
Over the past two decades, the Paper and Allied Products industry, like other U.S. manufacturing industries, has
experienced a range of economic/financial conditions, including substantial challenges. Going into 2000, the
industry's financial performance started to improve from the erratic conditions of 1990s, but the subsequent
recession and global economic downturn, coupled with continuing overproduction, led to declining financial
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
results that persisted through 2003. Financial performance in 2004 through 2007 showed significant improvement
and steady growth. However, during the current economic recession, the Paper and Allied Products industry's
revenues and overall market value once again decreased significantly, but less so than the overall S&P 500 trend
(McNutt, 2009).
Throughout this decade, the Paper and Allied Products industry continued to face increased foreign competition,
global and domestic overcapacity, and difficulty adapting to changing business conditions (McNutt, Cenatempo &
Kinstrey, 2004). At the same time, with the expected recovery in U.S. economic conditions, the Paper and Allied
Products industry appears poised to achieve stronger financial performance from this point out. In 2009, the Paper
and Allied products sub-industry equity price index increased 134.4 percent, compared to a 24.3 percent increase
for the S&P 1500 (S&P, 201 Ob). Domestic demand for paper and paperboard products is expected to increase as
the economy rebounds as the result of large government stimulus packages, inventory rebuilding by end users,
and competitiveness of paper in certain markets. Overall world paper and paperboard usage is expected to return
to solid growth in 2010 to 2011, reaching 396 million tons in 2011, which is 3 million tons above the pre-
recession value. Much of this world demand growth will be fueled by strong economic development and a rising
middle class in developing countries, lead by China (Young, 2009). This should position businesses that
potentially within the scope of the Existing Facilities Rule to withstand additional regulatory compliance costs
without having a significant financial impact.
2A.1.3 Domestic Production
The Paper and Allied Products industry is one of the top ten U.S. manufacturing industries; the larger forest
products industry, which includes the paper and allied products subsector, accounts for approximately 5 percent of
the nation's GDP (AF&PA, 2009). Growth in the paper industry is generally tied closely to overall gross domestic
product (GDP) growth. Although, the domestic market consumes over 90 percent of total U.S. Paper and Allied
Products industry output, beginning in 2000, exports took on an increasingly important role, and growth in a
number of foreign paper and paperboard markets became a key factor in the health and expansion of the U.S.
industry (McGraw-Hill, 2000). The industry is considered mature, with growth slower than that of the GDP, and
over the years U.S. producers have continued to seek growth opportunities in overseas markets. Although exports
still represent a small share of domestic shipments for the paper and paperboard mills segment, they exert an
important marginal influence on capacity utilization. Prices and industry profits, which are sensitive to capacity
utilization, have therefore become increasingly sensitive to trends in global markets.
The U.S. Paper and Allied Products industry has a worldwide reputation as a high quality, high volume, and low-
cost producer. The industry benefits from many key operating advantages, including a large domestic market; the
world's highest per capita consumption; a modern manufacturing infrastructure; adequate raw material, water, and
energy resources; a highly skilled labor force; and an efficient transportation and distribution network (Stanley,
2000). Over the last two decades, U.S. producers have faced growing competition from new facilities constructed
overseas, however (McGraw-Hill, 2000). The 2009 AF&PA Annual Survey of Paper, Paperboard, and Pulp
Capacity reports that the average annual rate of contraction from 2001 to 2007 hovered around 1 percent, largely
as a result of foreign competition and more recently, the domestic economic recession. However, in 2008,
industry capacity declined by only 0.8 percent, and according to the survey, industry capacity is expected to
expand by 0.3 percent in 2010 and 2011 (AF&PA, 2009).
The Paper and Allied Products industry is a major energy user, second only to the chemicals and metals
industries. However, 56 percent of total energy used in 1998-99 was self-generated electricity (McGraw-Hill,
2000). The use of renewable resources (biomass, black liquor, hydroelectric, etc.) for energy production has
increased steadily over the past several decades, rising from 40 percent of total industry energy consumption in
1972 to 56 percent in 2000. Renewable re source-based energy was estimated to account for about 60 percent of
consumption in 2004 (Paper Age, 2004a).
2A-4 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
With the slowing of the U.S. economy in 2000, and the onset of recession in 2001, the resulting drop in demand
and prices put pressure on companies in the industry to eliminate excess capacity. Through aggressive
consolidation and streamlining of their operations, facilities sought to lower expenses through elimination of older
and less cost efficient operations. In 2002, paper companies eliminated three million tons of capacity, with similar
reductions expected in 2003 (Value Line, 2003). While this consolidation led to a balance in supply and demand
and subsequent relative financial soundness, the Paper and Paperboard industry segment suffered from the 2008-
2009 recession with nearly all grades and segments recording declines in global consumption. One exception,
tissue paper, grew 1.0 - 1.5 percent in 2009, with a full rebound to strong growth rates expected to occur in 2010;
9 percent growth is expected in China consumption alone. (Uutela, 2010).
The connection between business activity and office paper demand is eroding as electronic substitution, such as
online bill paying, email, internet publications, and electronic readers, become viable substitutes for several uses
of paper (S&P, 201 Ob). For instance, in 1999, newsprint demand was at its peak but with the advent and growing
popularity of the internet, domestic newsprint demand has fallen 57 percent in ten years (Timonen, 2010).
However, paper as a means for transmitting and storing information is far from being obsolete. Global paper
consumption increased dramatically in the decade prior to the economic recession, and will continue to rise
especially in developing countries (Environmental Paper Network, 2007). However, the newsprint industry is
most at risk from competition from substitutes.
2A. 1.4 Output
The Paper and Allied Products industry has experienced continued globalization and cyclical patterns in
production and earnings over the last two decades. Capital investments in the 1980s resulted in significant
overcapacity. U.S. producers experienced record sales in 1995. In 1996, lower domestic and foreign demand,
coupled with declining prices, caused the industry's total shipments to decline by 2.2 percent. Three consecutive
years of increasing demand and slowly increasing prices led to better industry performance at the end of the
1990s. During these years, domestic producers controlled operating rates to allow drawdown of high inventories
and to achieve higher capacity utilization. U.S. producers also placed a greater emphasis on foreign markets both
through export sales and investments in overseas facilities (McGraw-Hill, 2000). The Paper industry segment
recorded improved sales and stronger earnings in 1999 and early 2000, but began to experience declines in sales
in the second half of 2000, reflecting reduced paper and packaging demand due to the slowdown in the U.S.
economy and a growth in imports (S&P, 2001c). Most products were characterized by weak demand, reduced
production and price reductions in 2001, due to continuing reductions in domestic demand (Paperloop Inc., 2001).
Annual sales in the United States in 2001 dropped 1.5 percent, while earnings at the top 31 U.S. corporations fell
by nearly 75 percent, partly due to a decrease in prices of up to 15 percent (Paun et al. 2004).
Capacity for the U.S. Paper and Paperboard segment declined annually from 2001 to 2003, in contrast to annual
increases in capacity for the previous two decades. Capacity declined 1.9 percent in 2001, 1.3 percent in 2002,
and 0.4 percent in 2003, and remained largely unchanged from 2004 to 2006 due to increased foreign
competition, mature domestic markets, and competition from other media (Paper Age, 2004b). Overcapacity has
been a problem within the industry. As the world economy began to slow in the early 2000s, demand in the
United States and abroad waned, forcing producers to limit production to prevent oversupply and keep pricing
levels from dropping further (S&P, 2004b). In addition to production downtime, many older, less efficient, single
mill operations were permanently closed. In 2001, pulp production decreased 7.3 percent to 53 million tons, while
paper and paperboard production decreased 5.5 percent to 81 million tons (Paun et al. 2004). During the rest of
the decade, however, the overall production for the U.S. Paper and Allied Products industry remained relatively
flat until the recession of 2008-2009, when production of all grades began to decline.4 Only tissue production
remained strong during the recessionary period (McNutt, 2009). During 2009 alone, total printing-writing paper
4 Grades are product categories such as containerboard, packaging, printing & writing papers, newsprint, and tissue.
March 28, 2011 2A-5
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
shipments experienced a 17 percent decline, shipments for Kraft paper fell by 16 percent, and containerboard by
9.5 percent. (AF&PA, 2009). Although these industry segments showed decline in total output, the last quarter of
2009 saw relative production increases from the previous months, and signaled the potential beginning of
recovery from the economic downturn.
As the economy continues to improve, demand should pick up, with better financial performance expected in the
next few years, as long as the industry continues careful management of production levels and control of
inventories. In addition, the weakened dollar should help to improve performance in export markets in the short
run (Schwartz, 2009). These improving conditions should better position firms to manage any increase in
production costs resulting from regulatory compliance.
Figure 2A-1 shows the trend in value of shipments and value added for the three profiled segments.5 Value of
shipments and value added, two common measures of manufacturing output, provide insight into an industry's
overall economic health and outlook. Value of shipments is the sum of receipts from the sale of outputs; it
indicates the overall size of a market or the size of a firm in relation to its market or competitors. Value added
measures the value of production activity in a particular industry and is calculated as the difference between the
value of shipments and the value of inputs from other industries used to make the products sold.
Between 1987 and 2007, the Paper and Allied Products industry performed erratically, with swings in value of
shipments and value added generally following the performance trend of the aggregate U.S. economy. Of the
three profiled industry segments, the Paperboard Mills segment recorded an overall increase in the total value of
shipments and value added during the 20-year analysis period, while both the Paper Mills and the Pulp Mills
segments recorded real declines over the same period, with pulp mills faring the worst. Moreover, the recent
downturn in the housing market has been particularly disruptive for this industry. Stagnant new home sales have
left saw mills unable to sell lumber products, forcing many to shut down operations. As a result, these closings
have caused the price of inputs such as wood chips and kraft pulp to increase. The combination of rising input
prices and a sharp decline in demand has led manufacturers to sell their products at a loss thereby reducing the
total value of shipments for this industry in recent years (Great American Group, 2009).
5 Terms highlighted in bold and italic font are further explained in the glossary.
2A-6 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Figure 2A-1 : Value of Shipments and Value Added for Profiled Paper and Allied Products Segments
(Millions, $2009)a
Value of Shipments
® *C'?n nnn
§
-2
a
£
a>
5
•3 *cin nnn
^ '
$U -f
P
,-•-. .' '•
• • " ^^» .»-•
• Paperboard Mills (NAICS
,
' ""• * *""'*°°'~*>
f ^" SA f^ ^^^A ji
•4- '* '* *
322130)
NAICS)
^, A Pulp Mills (NAICS 3221 10)
- - - A" ' ' i'Ulp Mills (SIC to 1NA1CSJ
-A - ^A
* '* '*•* '" N^^-^^^r-
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
^O ^O ^O ^O ^O ^O ^O ^O ^O ^O ^O ^O ^O ^D ^D ^D ^D ^D ^D C
> bJ
5 O
5 O
Value Added
$40,000
$35,000
o $30,000
S $25,000
^ $20,000
4>
•o
^ $15,000
•| $10,000
$5,000
$0
P - •. ,' \ if
*••...; v-^s
...
7^*7^ *^^^^-
,*.
'"' 'A "A-.A.A'' '-A^
• Paper Mills (NAICS 32212)
1
• - - Panpr A/Tills f^TC tn MATC^
• Paperboard Mills (NAICS
322130)
A Pulp Mills (NAICS 3221 10)
*
...4... Pulp Mills (SIC to NAICS)
j
|
a Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic
Census Bridge Between SIC and NAICS.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007 Economic Census.
Table 2A-3 provides the Federal Reserve System's index of industrial production for the profiled pulp and paper
segments, which shows trends in production between 1990 and 2009. This index more closely reflects total output
in physical terms, whereas value of shipments and value added reflect the economic value of production. The
March 28, 2011
2A-7
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
production index is expressed as a percentage of output in the base year, 2002. Overall, production for all three
analyzed Paper and Allied Products industry segments declined between 1990 and 2009, with the Paper Mills
segment experiencing the largest decline. However, during the last decade, the Pulp Mills segment experienced a
slight increase in production of 3.1 percent, despite the waning global economy. During the same period, the
Paper Mills and the Paperboard Mills segments were not as successful in maintaining production, and both had
average annual growth rates of negative 3.0 percent. Following recovery from the most recent recession, the pulp
and paper industry production index could be expected to improve.
Table 2A-3: U.S. Pulp and Paper Industry Industrial Production Index (Annual Averages)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009a
Total Percent Change
1990-2009
Total Percent Change
2000-2009
Average Annual
Growth Rate 1990-
2009
Pulp
Index
2002=100
84.0
85.3
89.7
75.4
79.8
85.8
78.7
78.3
80.4
8l".0
'8"b"."l
8l".6
100.0
100.7
i"b"5'"."i
106.2
9"l"."5'
92".5
90.3
8276
Mills"
Percent
Change
-0.1%
r.6%
5.2%
-16.6%
579%
7.5%
-8.3%
-0.4%
2.7%
0.7%
-f."i%
r.9%
22.5%
0.7%
4.5%
i"."b%"
-10.9%
i"."i%
-2.4%
-876%
-1.7%
3.1%
-0.1%
Paper
Index
2002=100
108.6
iosTi
iois
loll
10976
11277"
10676
10576
10575
11674
10974
i'b"O
ib"b"b"
9271
953
9575
ioi""7"
9976
9672
8372
Mills"
Percent
Change
-2.1%
-373%
-T.2%"
-0.6%
576%
374%
-579%
-T.0%"
675%
4.7%
-679%
-7.5%
-T.2%"
-7.9%
375%
673%
7.1%
-2"."i"%"
-374%
If3";6"%
-23.4%
-24.0%
-1.4%
Paperboa
Index
2002=100
93.8
9279
97""l
9971
10478
10877
1033
10672
1072
10876
iosT
io'O
9979
9"7""l
9973
9779
9277
9377"
897'T
8"b"'i
rd Mills0
Percent
Change
0.4%
-i7b'%"
474%
27"i"%"
578%
377%
-478%,
276%
Tb'%"
'i""3%"
-372%
-376%
-T'4'%"
-278%
273%
-T"5"%"
-474%'
T""i'%"
-479%
-ioT%
-14.6%
-23.8%
-0.8%
a. NAICS 32211.
b. NAICS 32212.
c. NAICS 32213.
d. Average through 9/2009
Source: Economagic; Federal Reserve, Board of Governors, 2009b.
2A.1.5 Prices
The producer price index (PPI) measures price changes, by segment, from the perspective of the seller, and
indicates the overall trend of product pricing, and thus supply-demand conditions, within a segment.
Price levels in the U.S. Paper and Allied Products industry closely reflect domestic and foreign demand, and
industry capacity and operating rates, which determine supply (S&P, 2001c). Prices tend to be volatile due to
mismatches between short-term supply and demand. The industry is very capital intensive, and development of
new capacity requires several years. Prices therefore tend to increase when demand and capacity utilization rise,
and drop sharply when demand softens or when new capacity comes on line. In the past, producers have been
reluctant to cut production when demand declines because fixed capital costs are a substantial portion of total
2A-8
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
manufacturing costs; this reluctance has occasionally caused persistent oversupply. During the economic
slowdown of 2001, however, producers appeared more willing to cut output to prevent sharp reductions in prices
(Ince, 1999; S&P, 2001c).
As shown in Figure 2A-2, the Paper and Allied Products industry suffered from low prices throughout the early
1990s. The depressed prices resulted from the paper boom of the late 1980s. Prices recovered in the mid 1990s
before declining again in the latter part of that decade. Entering the 2000s decade, prices in the Paper and Allied
Products industry reversed course and rose, before experiencing declines in 2001 and 2002, as prices for most
paper grades dropped between 5 and 15 percent (Value Line, 2003). Faced with substantial declines in demand
during those years, producers cut production, endured downtime, and closed less efficient facilities to prevent
major price declines for paper products (S&P, 2001c). Prices started to level off near the end of 2002, and
proceeded to rise during 2003 through 2007.
In 2008, Paper and Allied Products industry prices reached near historical peak levels. Overall, following the
recession, prices remained comparable to the strong 2008 averages. Prices for many grades of paper trended
higher for most of 2008 due in part to capacity closures. Market pulp prices have fallen sharply and quickly in the
last year (McNutt, 2009). Paperboard prices have also decreased while prices for paper have flattened out.
Figure 2A-2: Producer Price Indexes for Profiled Paper and Allied Products Segments
\ovovovovovovovovovovovovooooooooooo
-PaperboardMills (NAICS
322130)
-Paper Mills (NAICS32212)
- Pulp Mills (NAICS 322110)
Source: BLS, 2009c.
Paper and Allied Products industry manufacturers have exhibited more resilient prices compared to other
industries during the current economic downturn (Cody, 2009). Overall, prices for pulp, paper, and paperboard
products are expected to increase slightly in 2010 due to gradually improving economic activity and employment
levels (S&P, 2010b).
March 28, 2011
2A-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
2A.1.6 Number of facilities and firms
Table 2A-4 and Table 2A-5 present the number of facilities and firms for the three profiled Paper and Allied
Products industry segments between 1990 and 2006. The Statistics of U.S. Businesses reports that the number of
facilities in the Pulp Mills segment decreased by 4.3 percent between 1990 and 2006, while the number of Pulp
Mill firms remained constant in the same period. One of the reasons for this decline in number of facilities was
the increase in the number of mills that produce de-inked recycled market pulp and thus displace demand for
virgin pulp mill product. These are secondary fiber processing plants that use recovered paper and paperboard as
their sole source of raw material. Producers of de-inked market pulp have experienced strong demand over the
past several years in both U.S. and foreign markets. In fact, U.S. de-inked recycled market pulp capacity more
than doubled between 1994 and 1998 (McGraw-Hill, 2000). The secondary fiber share of total papermaking fiber
production increased steadily during the decade, reaching 37 percent in 1999 (McGraw-Hill, 2000). In contrast,
the number of facilities and firms in the Paper Mills and Paperboard Mills segments declined.
Between 1990 and 2006, the number of facilities and parent firms in the Paper Mills industry segment decreased
by 22.1 percent and 17.4 percent, respectively. The numbers of facilities and firms in the Paperboard Mills
industry segment also declined by 9.3 and 14.7 percent, respectively. Overcapacity in the 1990s limited the
construction of new facilities. In 1998 and 1999, alone, 0.6 and 2.5 million tons of paper and paperboard capacity
were removed from the capacity base. Over the same period, more than one million tons of pulp capacity was
removed (Pponline, 1999). In 2001 and 2002, 8.2 million tons of capacity closed, mostly in containerboard,
market pulp, and print and writing papers (Paper Age, 2004c).
The number of Pulp Mill facilities and firms has not demonstrated the same level of decline as Paper and
Paperboard manufacturers. In particular, in 2004 the number of facilities grew by 13.2 percent and the number of
firms by 14.8 percent, suggesting that the Pulp Mills segment could be entering a period of long-term growth.
There has been extensive restructuring and consolidation in the Paper segment during the second half of 2000s
decade, especially for containerboard producers - resulting in a higher concentration of top producers. Boxboard
and newsprint manufacturers have also experienced a significant number of closures. Newsprint is perceived to be
the weakest subsector of the Paper and Allied Products industry, and may face additional consolidation in the
future (McNutt, 2009). Whereas it seems that other Paper and Allied Products industry product categories have
merely suffered from volatility in the U.S. and global economy, newsprint and graphic papers have demonstrated
long-term decline in demand and susceptibility to closures due to increasing competition from electronic products.
Overall, 41 Paper and Paperboard machine lines and 18 mills closed permanently in 2008 (AF&PA, 2009).
2A-10 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Table 2A-4: Number of Facilities Owned by Firms in the Profiled Paper and Allied Products Segments
Year"
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent Change
1990-2006
Total Percent Change
2000-2007
Average Annual
Growth Rate
Pulp Mills"
Number of
Facilities
46
53
44
46
52
53
62
41
44
45
48
51
44
38
43
43
44
Percent
Change
n/a
1572%
:f7;o%
45%
1376%
i"79"%"
1776%
l33;9%
73%
273%
677%
"6.3%
:f3;7%
:f3;6%
1372%
67'6%"
273%
-4.3%
-8.3%
-0.3%
Paper
Number of
Facilities
327
349
324
306
316
317
344
259
235
242
240
238
271
287
385
368
348
Mills0
Percent
Change
n/a
6.7%
-7.2%
-576%
3.3%
0.3%
8.5%
1247%
-9.4%
3.2%
-T.o%
-0.8%
1476%
'5.9%
2.4%
-4.4%
-574%
-22.1%
11.9%
-1.5%
Paperboa
Number of
Facilities
226
228
222
217
218
219
228
214
232
233
238
247
231
221
221
210
205
rdMills'1
Percent
Change
n/a
679%'
-276%
-273%
675"%"
63%
"4.1%
-6"7i"%"
874%
6'"4"%"
27"i"%"
378%
-675%
-43%
6'"6'%"
-576%
-274%
-9.3%
-13.9%
-0.6%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic Census Bridge Between
SIC and NAICS.
b. NAICS 322110.
c. NAICS 32212.
d. NAICS 322130.
Source: U.S. SBA, 1990-1997; SUSB, 1998-2006.
March 28, 2011
2A-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Table 2A-5: Number of Firms in the Profiled Paper and Allied Products Segments
Year3
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent Change
1990-2006
Total Percent Change
2000-2006
Average Annual
Growth Rate
Pulp:
Number of
Firms
31
37
29
32
37
32
43
27
32
33
36
40
27
27
31
30
31
yniis"
Percent
Change
19.4%
:2i".6%
10.3%
1576%
Ti375%
34.4%
:37;2%
18.5%
3.1%
9.T%
Ti.i%
13275%
0.0%
14.8%
-3.2'%
3.3%
0.0%
-13.9%
0.0%
Paper
Number of
Firms
158
186
161
153
163
163
186
131
124
133
134
140
174
162
226
211
197
Mills';
Percent
Change
1777%
:i374%
-576%
6.5%
0.0%
141%
:2976%
'-'5.3%
7.2%
0.7%
4.6%
2379%
-6.7%
7.6%
-6.6%
-676%
-17.4%
15.2%
-1.2%
Paperboi
Number of
Firms
102
102
95
99
96
93
lol
85
95
95
105
116
107
90
92
88
87
irdMillsd
Percent
Change
676%
-679%
42%,
-376%
-3'.T%
876%
:y578%
if.8%
676%
10.5%
10.5%
-7.8%
-1579
272%
-4.3%
-T.T%
-14.7%
-17.1%
-1.0%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic Census Bridge Between
NAICSandSIC.
b. NAICS 322110.
c. NAICS 32212.
d. NAICS 322130.
Source: U.S. SBA, 1990-1997; SUSB, 1998-2006.
2A.1.7 Employment and productivity
The U.S. Paper and Allied Products industry is among the most modern in the world. It has a highly skilled labor
force and is characterized by large capital expenditures, which have been principally aimed at productivity
improvements.
Employment in the three profiled Paper and Allied Products industry segments remained relatively constant from
1987 through the mid 1990s. Since then, employment at Pulp Mills has dropped considerably, decreasing by 46
percent by 2007; Paper Mills also saw a substantial reduction in the workforce of close to 43 percent in the same
period. Employment in Paperboard Mills fell the least over this period, but still declined by over 35 percent. Part
of this employment loss is attributable to firms closing older and higher cost facilities with lower employee
productivity (McNutt, Cenatempo & Kinstrey, 2004). Pulp, paper, and paperboard mills have faced serious losses
in employment in the latter part of the 2000s decade, losing roughly 81,000 jobs between January of 2000 and
December of 2009. The majority of layoff events occurred in 2001 and 2009 during recessionary periods, but
layoffs diminished considerably in the third and fourth quarters of 2009 (BLS, 2010). Figure 2A-3 presents
employment for the three profiled Paper and Allied Products industry segments between 1987 and 2007.
2A-12
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Figure 2A-3: Employment for Profiled Paper and Allied Products Segments
$
>*
o
X!
s
3
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
-A * * *-
-* A 1,
K>K>K>K>K>K>K>K>
• Paper Mills (NAICS 32212) - ------ Paper Mills (SIC to :
• Paperboard Mills (NAICS 322130) ---»--- Paperboard Mills (SIC to NAICS)
A Pulp Mills (NAICS 322110) ...4... Pulp Mills (SIC to NAICS)
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic
Census Bridge Between SIC and NAICS.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
Table 2A-6 on the following page presents the change in value added per labor hour, a measure of labor
productivity, for each of the profiled Paper and Allied Products industry segments between 1987 and 2007. The
table shows that labor productivity in the Pulp Mills segment has been relatively volatile, posting several double-
digit gains and losses between 1987 and 2007. These changes were primarily driven by fluctuations in value
added and production levels. Overall, productivity in Pulp Mills increased by 12.6 percent during this period,
while increasing by 65.4 and 51.4 percent in the Paper Mills and Paperboard Mills, respectively. The effect of the
current recession on productivity has been mixed, historically speaking (McNutt, 2009). The outlook for worker
and capital productivity coming out of the recession is therefore uncertain, but no dramatic movements upwards
or downwards have been observed thus far.
March 28, 2011
2A-13
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Table 2A-6: Productivity Trends for Profiled Paper and Allied Products Segments ($2009)
Year3
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total % Change
1987-2007
Total Percent
Change 2000-
2007
Average Annual
Growth Rate
Pulj
Value
Added
($ mil)
$3,867
$5j07
$6;2i9"
$5j94"
$3392"
$37664"
$27461"
$27877"
$5^273"
$27908
$^957-
$17802
$i;825"
$27262"
$17711
$27000
$1^936
$27104"
$^842"
$i;877"
$27349"
-39.3%
3.8%
-2.5%
Prod.
Hrs.
(mil)
24
24"
25"
28"
28"
26"
23"
22"
23"
24"
13'"
12"
12"
12"
12"
is"
is"
13"
12"
12"
is"
-46.0%
8.5%
-3.0%
) Mills
Va
Addec
S/hr
162
214"
245"
188
130"
139"
104
132"
233""
122"
152"
145"
156"
190
143"
159"
146
162"
150"
153"
182"
lue
/Hour
Percent
Change
n/a
32'J%"
14.6%
i23';4%
-30.6%
7"'i"%"
:2"5"';4"%
27".0%"
76.8%
:47";9%-
245%
-474%"
77%
22".0%"
:249%
11.2%
-872%"'
i"i'".'b%"
-773%"'
l""'9"%"
19.0%
12.6%
-4.3%
0.6%
Paper Mills
Value
Added
(S mil)
$29,712
""$"337309"
""$"3376"42"
""$"3l778"5"
""$"297784"
""$277797"
""$"26787"l"
""$277363""
$"367961
""$317543""
""$"327642"
""$"317852"
""$"32";280"
""$"3"3";455"
""$"3"a675"'
$"317686
3287668
""$28";4"70"
""$"297585"
""$"297745"
$"2"67999"
-9.1%
-19.3%
-0.5%
Prod.
Hrs.
(mil)
248
25"T"
249"
248"
250"
254"
252""
244"
238"
235"
236"
225"
218
202"
190
173"
164
155"
161
146
137"
-45.0%
-32.5%
-2.9%
Va
Addet
S/hr
120
133"
135"
128"
119
109
107"
112"
155"
134"
136"
141
148"
165"
159"
179"
172"
184
184
204"
198
lue
/Hour
Percent
Change
n/a
116%
i""5"%"
-576%"
-776%"
-873%"
-274%
576%
3877%
-1376%
(19%
474%""
477%
Il75%"
-476%
13"7l%"
-473%"
71%"
02%
i(i7%
-3""'i"%"
65.1%
19.6%
2.5%
Paperboard Mills
Value
Added (S
mil)
$11,719
ji4j8y
$i35893"
$12350"
$"T6,656
$il'7754"
$"l6,561
$"ll,9"3"6
$i7£38"
$127755"
$il'7737"
$"[27978"
$13j94"
$147753"
$13356"
$127853"
$11,915
$"ll,76"l
$"T6,9"65
$127528"
jY2'£6"3""
9.2%
-13.2%
0.4%
Prod.
Hrs.
(mil)
89
91
89
91"
87"
88""
90
94""
98
95"
93""
90
86
86
83"
75"
74""
67"
63"
62""
64""
-
27.9%
26.0%
-1.6%
Va
Addet
S/hr
132
158"
156""
136""
123"
133"'
117
127"
175""
134"
126
144
153""
171
160
170
160
T'75""
173"'"
203"
'2"6"i'"
lue
/Hour
Percent
Change
n/a
19.6%
-T"."7%""
:J2".'4"%"
-9"."7%"
7."9"%"
-li.8%"
8"'."5"%""
37;i%"
^3;o%
-6"'.'2"%"
14.1%
6.6%"
Ti.5%"
-6"'."3"%"
6"."3"%""
-6.1%
9.1%
-T.1%"
l"7"."7'%"
-F.4%"
51.4%
17.3%
2.1%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic Census Bridge Between
SIC and NAICS.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2A.1.8 Capital expenditures
The Paper and Allied Products industry is highly cyclical and capital intensive. Capital-intensive industries are
characterized by a large value of capital equipment per dollar value of production. New capital expenditures are
needed to modernize, expand, and replace existing capacity. The total level of capital expenditures for the Pulp,
Paper, and Paperboard industry segments was $3.8 billion in 2007. The Paper Mills and Paperboard Mills
segments accounted for approximately 95 percent of that spending (see Table 2/4-7). Most of the spending is for
production improvements (through existing machine upgrades, retrofits, or new installed equipment),
environmental concerns, and increased recycling (McGraw Hill, 2000). The total capital expenditure for recent
years has been considerably less, in real terms, than what was spent in the early 1990s, as producers became wary
of adding too much capacity that might lead to oversupply and depressed prices.
Overall, from 1987 to 2007, the annual value of capital expenditures decreased by 27 percent for Pulp Mills, 54
percent for Paper Mills, and 11 percent for Paperboard Mills. However, North American producers have
improved production asset quality in the latter half of the 2000s through incremental investment and closure of
uncompetitive lines. The median age of paper machine lines decreased by 23 percent between 1999 and 2007.
During the same time period, the average maximum speed of paper machine lines increased by 33 percent, the
2A-14
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
average width by 35 percent, and the average capacity by 20 percent (McNutt, 2009). It has been suggested that
some industries, such as containerboard producers, have been successful enough at matching supply to demand
that investment in new capital will be an attractive option for 2010 (Waghorne, 2010).
The Department of Commerce estimates that environmental spending accounted for about 14 percent of all capital
outlays made by the U.S. Paper and Allied Products industry since the 1980s, and the Cluster Rule promulgated in
1998 is expected to have encouraged increased environmental expenditures (S&P, 2001c).
Table 2A-7: Capital Expenditures for Profiled Paper and Allied Products Segments (millions, $2009)
Year3
1987
1988
1989
1996
1991
1992
1993
1994
1995
1996
1997
1998
1999
2666
2002
2003
2004
2005
2006
2007
Total Percent Change
1987-2007
Total Percent Change
2000-2007
Average Annual
Growth Rate 1987 -
2007
Pulp
Capital
Expenditures
$392
$507"
$7766
$"l",'6"6'2
$77455
$""i",l"68
$598
$433
$"622"
$7922
$447
$534
$236
$7293
$234
$223
$212
$213
$732
$386
$285
Mills
Percent
Change
n/a
2973%
457(5%
IIIIIII?-^!!
4375%
4872%
:5"Y;'5"%"
1973%
:5"5";8%
242%"
IIIIZ:2b73%7I
-4.6%
'-478'%"
67i%
~3o.U /o
1 OO QO/
:267i%
-27.2%
-2.7%
-1.6%
Paper
Capital
Expenditures
$5,077
$57823"
$"87794"
$67929
$57716
$47509"
$4497
$47765"
$47211
$47632
$47893
$"5'7i"i"i
$37862
'$"476976
$37815
$3,327
$37266
$27305
$27491
$27377
$27337
Mills
Percent
Change
n/a
1477%
:2"Y'.2%"
^Y!!
-673%
579%
T6.6%
576%
475%
:244%"
579%
-12.8%
IIIIZII-8%11
8.1%
-46%
777%
-54.0%
-42.9%
-3.8%
Paperbo
Capital
Expenditures
$1,309
$27487
$2,'6"i"l
$'4,'52"5'
$"'3'","i"60
j2',92"7"
$"'2"'3"b'6
$2,466
$2',8'i'6'
$3,118
$2,'6'9"6
$"l",'7"8"9
$"i",6io
$"l",'46"9
$l7247
$975
$"'892"
$"l",'6'5"2
$""i","i"66'
$"l",'6'5"6
$""i"j62"
irdMills
Percent
Change
n/a
8979%
576%
7373%"
-7"7%"
47"i"%"
173%
1677%
:32;'8%"
3Y46"%"
:YO;O%"
-87'8%"
-21.8%
-s7'5%
i"7""9"%"
5"]2%
-46%
i"b"b'%"
-11.3%
-20.9%
-0.6%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic Census Bridge Between
SIC and NAICS.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2A.1.9 Capacity utilization
Capacity utilization measures actual output as a percentage of total potential output given the available
capacity. Capacity utilization provides insight into the extent of excess or insufficient capacity in an industry, and
into the likelihood of investment in new capacity.
As shown in Figure 2A-4, capacity utilization fluctuated sharply in all three profiled segments over the analysis
period. Capacity utilization increased between 1989 and 1994, and then fell sharply in 1995. This sharp drop
resulted from an effort to reduce inventories, which began rising in 1995 in response to low demand and
oversupply (McGraw-Hill, 2000). As inventories were sold off and global economic activity strengthened,
March 28, 2011
2A-15
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
capacity utilization began to rise again in 1996, peaked in 1997, and again declined in 1998 due to reduced
demand from the Asian market (S&P, 2001c). With the global economic slowdown starting in 2000, paper
producers were forced to implement production cutbacks and downtime to prevent oversupply from further
depressing prices. As a result, utilization rates fell farther in 2000 and 2001 to values below those observed in the
prior decade. At the same time, overall capacity contracted as companies permanently closed less efficient
facilities. By 2004, capacity utilization in the Paperboard Mills and Pulp Mills industry segments had returned to
its 1990 level, while capacity utilization in the Pulp Mills industry segment increased between 2001 and 2002 and
remained relatively constant over 2003 to 2004.
In the second half of the 2000s decade, capacity utilization rose substantially for paperboard and pulp mills
previous to the economy collapse in 2008. During this same period, capacity utilization for paper mills fluctuated,
but remained fairly low. Producers of many grades curtailed production and capacity in those categories suffering
from overcapacity in an effort to improve the balance between supply and demand (S&P, 2010b). U.S. paper and
paperboard capacity edged down 0.8 percent in 2008 to 96.3 million tons, and declined 7.3 percent cumulatively
since its 2000 peak level (AF&PA, 2009). Boxboard and containerboard producers currently have experienced
increasing excess capacity, but still below 2001 to 2003 levels. The market pulp and printing and writing papers
sectors have also experienced relatively high levels of excess capacity/low capacity utilization, but this is
expected to be remedied by recovery from the economic recession. For the struggling newsprint industry, capacity
rationalization has been able to keep supply and demand in balance, but further cutbacks are expected (McNutt,
2009).
Overall, total paper and paperboard capacity is slated to expand by 0.3 percent in both 2010 and 2011, with
uncoated mechanical paper, tissue paper, linerboard, corrugating medium, and market pulp being forecast as the
most successful product grades (AF&PA, 2009).
2A-16 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Figure 2A-4: Capacity Utilization Rate (Fourth Quarter) for Pulp and Paper Industry
100
95
90
75
-»- - - Paperboard Mills (SIC to NAICS)
- A- - - Pulp Mills (SIC to NAICS)
- •- - - Paper Mills (SIC to NAICS)
- Paperboard Mills (NAICS 322130)
- Pulp Mills (NAICS 322110)
- Paper Mills (NAICS 32212)
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic
Census Bridge Between SIC and NAICS.
b. Before 2007, U.S. Census sampled every industry in a specific NAICS6. Beginning in 2007, U.S. Census only sampled certain industries within any
NAICS6, and therefore, the data collected before 2007 cannot be directly compared to the data collected in 2007 and beyond.
Source: U.S. DOC, Survey of Plant Capacity 1989-2006. 2007-2009 data -was obtained from the Census Bureau, however the data do not
meet the criteria outlined in the Census Bureau's Statistical Quality Standard: Releasing Information Products. Data is included here for
completeness as it is the only data available for Capacity Utilization for these years.
2A.2 Structure and Competitiveness
The Paper and Allied Products industry companies range in size from large corporations having billions of dollars
of sales, to small producers with revenue a fraction of the size of the large producers. Because all Paper and Allied
Products companies use the same base materials in their production, most manufacture more than one product. To
escape the extreme price volatility of commodity markets, many smaller manufacturers have differentiated their
products by offering value-added grades. The smaller markets for value-added products make this avenue less
available to the larger firms (S&P, 200 Ic).
The Paper and Allied Products industry consolidated through mergers and acquisitions and has closed older mills
during the last two decades as a way to improve profits in a mature industry. About six percent of North American
containerboard capacity was shut down (most were on a permanent basis) in late 1998 and early 1999. Companies
were reluctant to invest in any major new capacity, which might result in excess capacity (S&P, 2001c). In 1999,
new capacity additions in the Paper and Allied Products industry were at their lowest level of the past ten years
(Pponline.com, 2000); this caution in adding to capacity has continued through the 2000 to 2010 decade. Another
problem for the industry is the increasing capacity being brought online in foreign countries, which could result in
higher U.S. import levels and increased competition for U.S. products in export markets (S&P, 2004a). U.S. mills
have responded to this increased foreign competition by cutting capacity and retiring obsolete equipment and,
with help from private equity investors, have succeeded in constraining supply and improving average product
March 28, 2011
2A-17
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
quality, hoping to improve long-term returns. Moreover, the devaluation of the dollar over the last three years has
made domestic paper products more affordable than foreign goods (Great American Group, 2009).
Major mergers in the most recent decade include International Paper's acquisition of Champion International in
2000 and Union Camp in 1999, Georgia-Pacific's takeover of Fort James Corp. (itself a 1997 combination of
James River and Fort Howard), Weyerhaeuser's acquisition of Willamette industries Inc., the merger of Mead and
Westvaco, and Temple-Inland's takeover of Gaylord Container (S&P, 2001c, 2004b).
2A.2.1 Firm size
For this industry, the Small Business Administration defines a small firm as having fewer than 750 employees.
The size categories reported in the Statistics of U.S. Businesses (SUSB) do not correspond with the SBA size
criteria, therefore preventing precise use of the SBA size threshold in conjunction with SUSB data. The SUSB
data presented in Table 2A-8 show the following size distribution in 2006:
> 20 of 31 (65 percent) firms in the Pulp Mills segment had less than 500 employees. Therefore, at least 65
percent of firms were classified as small. These small firms owned 22 facilities, or 50 percent of all
facilities in the segment.
> 143 of 193 (74 percent) firms in the Paper Mills segment had less than 500 employees. These small firms
owned 149, or 43 percent of all Paper Mills.
> 55 of 87 (63 percent) firms in the PaperboardMills segment had less than 500 employees. Therefore, at
least 63 percent of paperboard mills were classified as small. These firms owned 57, or 28 percent of all
Paperboard Mills.
An unknown number of the firms with more than 500 employees have less than 750 employees, and would
therefore also be classified as small firms. Table 2A-8 below shows the distribution of firms and facilities for each
profiled segment by employment size of the parent firm.
Table 2A-8: Number of Firms and Facilities by Size Category for Profiled Paper and Allied Products
Segments in 2006
Employment Size
Category
0-19
20-99
100-499
500+
Total
Pulp
No. of Firms
10
6
4
11
31
Mills
No'Tof'
Facilities
10
6
6
22
44
Paper
No. of Firms
58
42
43
50
193
Mills
NoTof
Facilities
58
42
49
199
348
Paperbo
No. of Firms
18
19
18
32
87
ard Mills
NoTof'
Facilities
18
20
19
148
205
Source: U.S. DOC, Statistics of U.S. Businesses, 2006.
2A.2.2 Concentration ratios
Concentration is the degree to which industry output is concentrated in a few large firms. Concentration is
closely related to entry barriers, with more concentrated industries generally having higher barriers.
The four-firm concentration ratio (CR4) and the Herfindahl-Hirschman Index (HHI) are common measures of
industry concentration. The CR4 indicates the market share of the four largest firms. For example, a CR4 of 72
2A-18
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
percent means that the four largest firms in the industry account for 72 percent of the industry's total value of
shipments. The higher the concentration ratio, the less competition there is in the industry, other things being
equal. An industry with a CR4 of more than 50 percent is generally considered concentrated. The HHI indicates
concentration based on the largest 50 firms in the industry. It is equal to the sum of the squares of the market
shares for the largest 50 firms in the industry. For example, if an industry consists of only three firms with market
shares of 60, 30, and 10 percent, respectively, the HHI of this industry would be equal to 4,600 (602 + 302 + 102).
The higher the index, the fewer the number of firms supplying the industry and the more concentrated the
industry. Based on the U.S. Department of Justice's guidelines for evaluating mergers, markets in which the HHI
is under 1000 are considered unconcentrated, markets in which the HHI is between 1000 and 1800 are considered
to be moderately concentrated, and those in which the HHI is in excess of 1800 are considered to be concentrated.
Table 2A-9 shows that Pulp Mills have an HHI of 1,175, Paper Mills have an HHI of 721, and Paperboard Mills
have an HHI of 749 at 2002, the latest year for which concentration data. At these HHI levels, the Paper Mills and
Paperboard Mills segments are unconcentrated while the Pulp Mills segment is at the lower end of the moderately
concentrated range, all three industry segments appear relatively unconcentrated. With the majority of the firms in
this industry having relatively small market shares, this suggests limited potential for passing through to
customers any increase in production costs resulting from regulatory compliance.
The concentration ratios for the three profiled segments remained relatively stable between 1987 and 2002, with a
slight upwards jump for Paper and Paperboard manufacturers between 1997 and 2002. The Pulp Mills segment
has the highest concentration of the three profiled segments, with a CR4 of 61 percent and a HHI of 1,175 in
2002. Recent mergers and acquisitions have led to an increase in concentration in the Paper Mills and Paperboard
Mills segments. In the late 1990s, the top five U.S. firms controlled 38 percent of production capacity, with higher
concentrations in individual product lines due to targeted consolidation and specialization (Ince, 1999). The Paper
Mills and Paperboard Mills segments also account for most of the production of their primary products. The Pulp
Mills segment accounts for a lower percentage of all pulp shipments, with pulp also commonly produced by
integrated Paper and Paperboard Mills.
As described previously, this period of consolidation in the Paper and Allied Products industry continued
throughout the second half of the decade. Containerboard producers have in particular gone through a period of
extensive restructuring resulting in a higher concentration of top producers (McNutt, 2009). When more current
industry concentration data become available in late 2010/early 2011, the industry is likely to show higher
concentration levels than indicated by the 2002 data.
Note that the measured concentration ratio and the HHF are very sensitive to how the industry is defined. An industry with a high
concentration in domestic production may nonetheless be subject to significant competitive pressures if it competes with foreign
producers or if it competes with products produced by other industries (e.g., plastics vs. aluminum in beverage containers).
Concentration ratios based on share of domestic production are therefore only one indicator of the extent of competition in an
industry.
March 28, 2011 2A-19
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Table 2A-9: Selected Ratios for Profiled Paper and Allied Products Segments, 1987,1992,1997, and 2002
SIC (S) or
NAICS (N)
Code
S2611
N 3221 10
S2621
N 32212
S2631
N 322 130
Year
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
Total
Number
of Firms
26
29
24
21
122
127
139
187
91
89
81
80
4 Firm (CR4)
44%
48%
59%
61%
33%
29%
34%
50%
32%
31%
34%
49%
8 Firm (CR8)
69%
75%
86%
88%
50%
49%
55%
66%
51%
52%
53%
68%
Concentratioi
20 Firm
(CR20)
99%
98%
100%
100%
78%
77%
80%
81%
77%
80%
82%
88%
i Ratios
50 Firm
(CR50)
100%
100%
100%
100%
94%
94%
94%
97%
97%
97%
98%
99%
Herfindahl-Hirschman
Index
743
858
1406
i",175
432
392
467
721
431
438
485
749
Source: U.S. DOC, Economic Census, 1987, 1992, 1997, and 2002.
2A.2.3 Foreign trade
This profile uses two measures of foreign competition: export dependence and import penetration.
Import penetration measures the extent to which domestic firms are exposed to foreign competition in domestic
markets. Import penetration is calculated as total imports divided by total value of domestic consumption in that
industry: where domestic consumption equals domestic production plus imports minus exports. Theory suggests
that higher import penetration levels will reduce market power and pricing discretion because foreign competition
limits domestic firms' ability to exercise such power. Firms belonging to segments in which imports account for a
relatively large share of domestic sales would therefore be at a relative disadvantage in their ability to pass-
through costs because foreign producers would not incur costs as a result of the Existing Facilities regulation. The
estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) for 2007 is 27 percent.
For characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with
import ratios close to or above 27 percent would more likely face stiff competition from foreign firms and thus be
less likely to succeed in passing compliance costs through to customers.
Export dependence, calculated as exports divided by value of shipments, measures the share of a segment's sales
that is presumed subject to strong foreign competition in export markets. The Proposed Existing Facilities Rule
would not increase the production costs of foreign producers with whom domestic firms must compete in export
markets. As a result, firms in industries that rely to a greater extent on export sales would have less latitude in
increasing prices to recover cost increases resulting from regulation-induced increases in production costs. The
estimated export dependence ratio for the entire U.S. manufacturing sector for 2007 is 15 percent. For
characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with
export ratios close to or above 15 percent are at a relatively greater disadvantage in potentially recovering
compliance costs through price increases since export sales are presumed subject to substantial competition from
foreign producers.
Table 2A-10 presents trade statistics for the Pulp Mills and Paper and Paperboard Mills segments. Imports and
exports play a much larger role in the Pulp Mills segment than for the other two segments. Import penetration and
export dependence levels for the Pulp Mills segment were an estimated 82 and 83 percent, respectively, in 2007.
Import penetration and export dependence ratios for the Paper and Paperboard Mills segments in 2007 were 15
and 10 percent, respectively. For Pulp Mills, the large share of domestic production that is exported and domestic
consumption served by imports implies the industry faces significant foreign competition, limiting the industry's
ability to pass through to customers any increase in production costs resulting from regulatory compliance. For
Paper and Paperboard Mills, both measures of foreign competition are well below the U.S. manufacturing
2A-20
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
averages estimated for 2007. Given just these measures, it would be reasonable to assume that these segments do
not face significant foreign competitive pressures, and would have more latitude in passing through to customers
any increase in production costs resulting from regulatory compliance. However, foreign pressure is likely to
increase as capacity in foreign countries, particularly China, continues to grow and exert pressure on the domestic
market (McNutt, Cenatempo & Kinstrey, 2004). In addition, as noted above, the HHI of the Paper Mills and
Paperboard Mills segments is 721 and 749 respectively, suggesting firms in these segments have small market
shares, which would curtail their ability to pass through any increase in production costs.
In the later part of the decade, U.S. total Pulp, Paper, and Paperboard export growth outpaced imports, and this
trend continued in 2008. However, with a stronger U.S. dollar in 2009, export growth could begin to slow down
(McNutt, 2009). The biggest growth in paper consumption is predicted to take place in Asia (excluding Japan).
This growth, driven largely by India and China's rapidly increasing populations and developing markets, is
expected to rise dramatically in the next decade (Environmental Paper Network, 2007). In particular, China's
overall paper demand is projected to grow from approximately 60 million tons in 2005 to 143 million in 2021
(RISI 2007). The five largest importers of U.S. paper and paperboard articles from 2004 through 2009 were
Canada, Mexico, Japan, China, and the United Kingdom (U.S. DOC 2009).
March 28, 2011 2A-21
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Table 2A-10: Trade Statistics for Profiled Paper and Allied Products Segments (Millions, $2009)
Year3
Value of Imports
Value of Exports
Value of Shipments
Implied
Domestic
Consumption1"
Import
Penetration0
Export
Dependence*1
Pulp Mills
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent
Change 1989 - 2007
Total Percent
Change 2000 - 2007
Average Annual
Growth Rate 1989 -
2007
$4,952
$4544
$3346
$37198
$27805
$37327
$5246
$37621
$37512
$37241
$37387
$4247
$37313
$27922"
$37134
$37436
$37476
$37524
$37959
-12.9%
-6.8%
-0.8%
$5,752
$4999
$4287
$47641
$37483
$47660
$67325
$4^436
$4223
$37561
$37559
$4403'
$37449
$37322
$3213
$37427
$37716
$37917
$47309
-13.8%
-2.2%
-0.9%
$10,131
$97486
$7,825
$77839
$6,009
$67634
$97322
$7277
$4237
$4,018
$37939"
$4,583
$37922
$4208
$4,538
$47646
$47439"
$4526
$5496
-45.2%
13.4%
-3.5%
9,331
9,031
6,884"
6,396
5,331
5,'9"6l
8,243
6",462"
3,526
3,699
3,767
47427'
37786
37808
47460"
47655
4198
47133
47846"
53%
50%
49%"
50%
53%
56%
64%
56%
100%
88%
90%
96%
88%
77%
70%"
74"%"
83%
85%
82%
57%
53%
55%
59%
58%
61%
68%
61%
ioo'%"
89%
90%
96%
88%
79%
71%
74%
84%
87%
83%
-46.3%
9.5%
-3.6%
Paper and Paperboard Mills
1989
1990
1991
1992"
1993"
1994"
1995
1996"
1997
1998"
1999
2666
2001
2002"
2003"
2004"
2005"
2006"
2007'
Xotiil Percent
Change 1989 - 2007
Total Percent
Change 2000 - 2007
Average Annual
Growth Rate 1989 -
2007
$12,407
$127017"
$""l"6,816
$""i"o"3"i"5
$""l"6','8"6"5
$107952
$"147678
$127959
$12281
$13235
$13392
$"147524
$137606
$"127624
$""127618"
$""147164
$"147323
$"147290
$"127675
5.5%
-12.7%
0.3%
$5,005
$57579"
$67407
$67569"
$6377
$77190
$97269
$97050
$87309
$77806
$77441
$87084
'$"'77634
$57769
$57738
$67615
$67574
$67835
$77512
34.7%
-7.1%
1.8%
$93,842
$897990
$837414
$82,381
$79""637
$857422
$"1067555
$917529
$887635
$88379"
$897130
$917795
$83249
$797782
$757154
$767601
$787359
$79""378
$777601
-13.8%
-15.5%
-0.9%
101,244
967429
877823
86,126
8"47l25
897184
1117964
957438
927608
937808
957081
98235
897821
867638
827035
847750
867JQ7
867833
827763"
12%
12%
12%"
12%
13%
12%"
13"'%"
14%
13"'%"
14%
14%"
15%
15%
15%
15%
17"%"
17"%"
16%
15%
5%
6%
8%
8%
8%
8%
9%
10%
9%
9%
8%
9%
8%
7"%"
8%
8%
8%
9%
10%
-14.2%
-15.8%
-0.9%
2A-22
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic Census Bridge Between
SICandNAICS.
b. Calculated by EPA as shipments + imports - exports.
c. Calculated by EPA as imports divided by implied domestic consumption.
d. Calculated by EPA as exports divided by shipments.
Source: U.S. International Trade Commission, 1989-2007.
As shown in Figure 2A-5, the value of imports and exports peaked in the mid-1990s, before dropping and
rebounding in 2000. As expected, values of both dropped again in 2001 and 2002, as the global economy fell into
recession. Imports and exports grew steadily from 2003 to 2007 within the Pulp Mills industry segment, while the
Paper and Paperboard industry segments turned increasingly towards exporting product and showed a slight
decrease in imports.
March 28, 2011 2A-23
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Figure 2A-5: Value of Imports and Exports for Profiled
(millions, $2009)a
Paper and Allied Products Segments
Pulp Mills
J»T «
•
• * i
\ •' - ''
'• . ' • '. '.
\V*\ :/ \S A
z
\ ^ v; ^v/ \^z^
•jc *"
Is *"
^^ vo^^^^^^^^^^ooo
aevovovovovovovovovovoooo
> NAICS)
L • Exports (NAICS
322110)
NAICS)
— A Imports (NAICS
322110)
bJ bJ bJ bJ bJ bJ
o o o o o o
000000
trfJ ^ U\ O\ -^ 00
Paper and Paperboard Mills
§ $18 000
Oci /: nnn
o
^ c;i /i nnn
•_ ci 7 nnn
a
G $jn 000
hH '
— CQ nnn
+2 $6 000
o
D> 44 nnn
W
«*• $2 000
O
M «n
^ Ar_A_A
/ *^s^ ^^/^\^
'A-. ..•A-'A
^^^^•^ .fc t
,-'* ^^~
•• —
> ii^sslil^llill
---»--- Exports (SIC to
NAICS)
L • Exports (NAICS
322110)
NAICS)
> A Imports (NAICS
322110)
10 10 10 10 10 10
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic
Census Bridge Between SIC and NAICS.
Source: U.S. International Trade Commission, 1989-2007.
2A-24
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2A: Paper Industry Profile
2A.3 Financial Condition and Performance
Financial performance in the Paper and Allied Products industry is closely linked to macroeconomic cycles, both
in the domestic market and those of key foreign trade partners, and the resulting levels of demand. Many pulp
producers, for example, were not very profitable during most of the 1990s as chronic oversupply, cyclical
demand, rapidly fluctuating operating rates, sharp inventory swings, and uneven world demand plagued the global
pulp market for more than a decade (Stanley, 2000). The ability of Paper and Allied Products industry
manufacturers to withstand recession and react to changing global economic conditions will be critical in the
coming years.
Net Profit Margin is calculated as after-tax income before nonrecurring gains and losses as a percentage of sales
or revenue, and measures profitability, as reflected in the conventional accounting concept of net income. Over
time, the firms in an industry, and the industry collectively, must generate a sufficient positive profit margin if the
industry is to remain economically viable and attract capital. Year-to-year fluctuations in profit margin stem from
several factors, including: variations in aggregate economic conditions (including international and U.S.
conditions), variations in industry-specific market conditions (e.g., short-term capacity expansion resulting in
overcapacity), or changes in the pricing and availability of inputs to the industry's production processes (e.g., the
cost of energy to the pulp and paper process). The extent to which these fluctuations affect an industry's
profitability, in turn, depends heavily on the fixed vs. variable cost structure of the industry's operations. In a
capital intensive industry such as the Paper and Allied Products industry, the relatively high fixed capital costs as
well as other fixed overhead outlays, can cause even small fluctuations in output or prices to have a large positive
or negative affect on profit margin.
Return on Total Capital is calculated as annual pre-tax income divided by the sum of current portion of long-
term debt due in 1 year or less, long-term debt due in more that 1 year, all other noncurrent liabilities and total
stockholders' equity (total capital). This concept measures the total productivity of the capital deployed by a firm
or industry, regardless of the financial source of the capital (i.e., equity, debt, or liability element). As such, the
return on total capital provides insight into the profitability of a business' assets independent of financial structure
and is thus a "purer" indicator of asset profitability than return on equity. In the same way as described for net
profit margin, the firms in an industry, and the industry collectively, must generate over time a sufficient return on
capital if the industry is to remain economically viable and attract capital. The factors causing short-term variation
in net profit margin will also be the primary sources of short-term variation in return on total capital.
Figure 2A-6 below shows trends in net profit margins and return on total capital for the Paper and Allied Products
industry between 1989 and 2008. The figure shows considerable volatility in the trend. Profitability and return on
capital declined steadily between 1988 and 1993, reflecting oversupply in world markets and decreasing
shipments from U.S. producers (McGraw-Hill, 2000). By the mid-1990s, financial performance peaked as
demand rebounded, but weakened again in 1997 and 2001, reflecting slower growth in both the U.S. and the
world economy. Coupled with overproduction in the U.S. and global markets, these factors led to deteriorating
financial performance during these years. However, both net profit margins and return on capital improved
gradually from 2004 to early 2007. Since 2007, though, the industry's financial performance has declined
significantly owing to the current recession. Despite many significant obstacles, experts expect that demand for
Paper and Allied Products will reach 21.8 million tons in 2011, and that high value-added products will lead
growth in this segment as companies search for profit-earning ways to differentiate their product (Great American
Group, 2009).
During the entire decade, total shareholder returns for the Paper and Allied Products industry, indexed to year
2001, performed at a higher level than the S&P 500 index. However, at the start of the recession in 2008, total
shareholder returns began falling quickly back to S&P 500 levels. Ten of the largest public US-based forest and
paper companies posted earnings of US $1.2 billion in the third quarter of 2008. All but two companies posted
positive or improved earnings, reflecting an estimated US$1.1 billion of tax credits for the use of black liquor as
March 28, 2011 2A-25
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
a biofuel to generate energy (Pricewaterhouse Coopers, 2009). And the overall outlook for financial performance
in the near future looks promising given that during 2009, the paper products sub-industry index rose 134.4
percent compared to a 24.3 percent increase for the S&P 1500 index (S&P 2010). Industry analysts believe
pricing levels will increase modestly in the future due to gradually improving economic activity and employment,
but it is uncertain how long-term demand for some paper categories will evolve over time.
Figure 2A-6: Net Profit Margin and Return on Capital for Paper and Allied Products
Net Profit Margin
Return on Total Capital
Source: Quarterly Financial Report, 1988-2008; U.S. Census Bureau.
»
A.4 Facilities Operating Cooling Water Intake Structure
Point source facilities that use or propose to use a cooling water intake structure that withdraws cooling water
directly from a surface waterbody of the United States are potentially subject to Section 316(b) of the Clean Water
Act. In 1982, the paper and allied products industry withdrew 534 billion gallons of cooling water, accounting for
approximately 0.7 percent of total industrial cooling water intake in the United States. The industry ranked 5th in
industrial cooling water use, behind the electric power generation industry, and the chemical, primary metals, and
petroleum industries (1982 Census of Manufactures).
This section provides information for facilities in the profiled paper and allied products segments within the scope
of the regulatory options. Existing facilities that meet all of the following conditions are potentially subject to
regulation:
> Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or
arrangement with an independent supplier who has a cooling water intake structure; or their cooling water
intake structure(s) withdraw(s) cooling water from waters of the United States, and at least twenty-five
(25) percent of the water withdrawn is used for contact or non-contact cooling purposes;
> Have a National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;
and
> Meet the applicability criteria for regulatory analysis options in terms of design intake flow (i.e., 2 MGD).
2A-26
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
The regulatory analysis options also cover substantial additions or modifications to operations undertaken at such
facilities. EPA identified the set of facilities that were estimated to be potentially subj ect to the 316(b) Existing
Facilities regulation based on a minimum applicability threshold of 2 MGD; this section focuses on these facilities
in the profiled paper and allied products segments.
2A.4.1 Waterbody and Cooling System Type
Table 2A-11 reports the distribution of facilities in the profiled paper and allied products segments that are
potentially subject to the existing facilities regulation by type of waterbody and cooling water intake system. The
tables show that most of the facilities have either a once-through system or employ a combination of a once-
through and closed system.
Table 2A-11: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by
Waterbody Type and Cooling Water Intake System for the Profiled Paper and Allied Products Segments
Waterbody Type
Estuary /Tidal River
Ocean
Lake/Reservoir
Freshwater River/ Stream
Great Lake
Total3
Recirculating
No.
0
0
0
29
0
29
% of Total
0%
0%
0%
100%
0%
13%
Combination
No.
0
0
6
35
0
41
% of Total
0%
0%
15%
85%
0%
18%
Once-Through
No.
6
0
6
105
6
122
% of Total
5%
0%
5%
85%
5%
54%
Other
No.
0
0
11
19
3
33
% of Total
0%
0%
33%
58%
9%
14%
Total
6
0
23
188
9
225
Based on technical weights (See Appendix 3.A).
a. Individual numbers may not add up to total due to independent rounding.
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
2A.4.2 Facility Size
All of the pulp and paper facilities analyzed are relatively large, with no facilities employing fewer than 100
people. Figure 2A-7, shows the number of facilities in the profiled pulp and paper segments potentially subject to
the regulation by employment size category for each primary analysis option.
EPA applied sample weights to the sampled 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).
March 28, 2011
2A-27
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2A: Paper Industry Profile
Figure 2A-7: Number of Facilities Estimated within Scope of the 316(b) Existing Facilities Regulation by
Employment Size for Profiled Paper and Allied Products Segments
90
80
70
60
50
40
30
20
10
0
Less than 100-249 250-499 500-999 1000 and
100 greater
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
2A.4.3 Firm Size
EPA used the Small Business Administration (SBA) small entity size standards to determine the number of
facilities in the three profiled paper segments that are owned by small firms. Firms in this industry are considered
small if they employ fewer than 750 people. EPA estimates that 28 small entity-owned facilities and 187 large
entity-owned facilities in the Paper and Allied Products Segment are potentially subject to the 316(b) Existing
Facilities regulation.
2A-28
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
2B Profile of the Chemicals and Allied Products Industry
2B.1 Introduction
EPA's Detailed Industry Questionnaire, hereafter referred to as the DQ, identified 13 four-digit SIC codes in the
Chemical and Allied Products Industry (SIC 28) with at least one existing facility that operates a CWIS, holds a
NPDES permit, withdraws at least two million gallons per day (MGD) from a water of the United States, and uses
at least 25 percent of its intake flow for cooling purposes (facilities with these characteristics are hereafter referred
to as "facilities potentially subject to the 316(b) Existing Facilities regulation" or "in-scope facilities"). For this
analysis, EPA identified a six-digit NAICS code for each of these potential facilities using the information from
DQ and public sources (see Appendix 3.C: Conversion the Data from Standard Industrial Classification (SIC) to
North American Industry Classification System (NAICS)). As the result of this mapping, EPA identified 15 6-digit
NAICS codes in the Chemicals and Allied Products manufacturing industry (NAICS 325).
For each of the 15 NAICS codes, Table 2B-1, following page, provides a description of the industry segment, a
list of primary products manufactured, the total number of the DQ respondents (weighted to represent a national
total of facilities that hold a NPDES permit and operate cooling water intake structures), and the number of
facilities estimated to be potentially subject to Proposed 316(b) Existing Facilities Regulation based on the
minimum withdrawal threshold of 2 MGD (see Chapter 1: Introduction for more details on the Rule applicability
criteria).
March 28, 2011
2B-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Table 2B-1: Phase III Facilities in the Chemicals and Allied Products Industry (NAICS 325)
NAICS
NAICS
Description
Important Products Manufactured
Number of In-Scope
Facilities3
Basic Chemicals (NAICS 3251XX)
325110
325120
325131
325181
325188
325199
Petrochemical mfg
Industrial gas mfg
Inorganic dye &
pigment mfg
Alkalies &
chlorine mfg
All other basic
inorganic chemical
mfg
All other basic
organic chemical
mfg
Acyclic hydrocarbons such as ethylene, propylene, and butylene and cyclic
aromatic hydrocarbons such as benzene and toluene made from refined
petroleum or liquid hydrocarbons.
Industrial organic and inorganic gases in compressed, liquid, and solid forms.
Inorganic dyes and pigments such as antimony, copper, lead, and titanium
based pigments.
Alkalies such as chlorine, sodium, and hydroxide using an electrolysis process.
Basic inorganic chemicals except industrial gases, inorganic dyes and pigments,
alkalies and chlorine, and carbon black.
Basic organic chemical products, (except aromatic petrochemicals, industrial
gases, synthetic organic dyes and pigments, gum and wood chemicals, cyclic
crudes and intermediates, and ethyl alcohol).
Total Basic Chemicals
13
4
9
20
32
38
116
Resins and Synthetics (NAICS 3252XX)
325211
325221
325222
Plastics material &
resin mfg
Cellulosic organic
fiber mfg
Noncellulosic
organic fiber mfg
Resins, plastics materials, and nonvulcanizable thermoplastic elastomers and
mixing and blending resins on a custom basis; noncustomized synthetic resins.
Cellulosic (i.e. rayon and acetate) libers and filaments in the form of
monofilament, filament yam, staple, or tow.
Noncellulosic (i.e. nylon, polyolefm, and polyester) fibers and filaments in the
form of monofilament, filament yam, staple, or tow.
Total Resins and Synthetics
24
1
9
35
Pesticides and Fertilizers (SIC 3253XX)
325311
325312
Nitrogenous
fertilizer mfg
Phosphatic
fertilizer mfg
Nitrogenous fertilizer materials and mixing ingredients into fertilizer; fertilizer
from animal or sewage waste.
Phosphatic fertilizer material and phosphatic material mixed into fertilizer.
Total Pesticides and Fertilizers
9
1
10
Pharmaceuticals (3254XX)
325411
325412
Medicinal &
botanical mfg
Pharmaceutical
preparation mfg
Uncompounded medicinal chemicals and their derivatives (i.e. generally for use
by pharmaceutical preparation manufacturers); grading, grinding, and milling
uncompounded botanicals.
In-vivo diagnostic substances and pharmaceutical preparations (except
biological) intended for internal and external consumption in dose forms, such
as ampoules, tablets, capsules, vials, ointments, powders, solutions, and
suspensions.
Total Pharmaceuticals
2
6
8
Other Chemical Segments0
325611
325998
Soap & other
detergent mfg
All other
miscellaneous
chemical product
& preparation mfg
Soaps and other detergents, such as laundry detergents, dishwashing detergents,
toothpaste gels, tooth powders, and natural glycerin.
Chemical products excluding basic chemicals, resins, and synthetic rubber;
cellulosic and noncellulosic fiber and filaments; pesticides, fertilizers, and other
agricultural chemicals; Pharmaceuticals and medicines; paints, coating and
adhesives; soap, cleaning compounds, and toilet preparations; printing inks;
explosives; custom compounding of purchased resins; and photographic films,
paper, plates, and chemicals.
Total Other
4
9
13
Total Chemicals and Allied Products (NAICS 325)
Total NAICS Code 325
179
a. Number of weighted detailed questionnaire survey respondents.
b. Individual numbers may not add up due to independent rounding.
c. Not included in analysis.
Source: U.S. DOC Economic Census, 2007. Executive Office of the President, 1987; U.S. EPA 2000; U.S. EPA analysis, 2010.
2B-2
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2B: Chemicals Industry Profile
As shown in Table 2B-1, EPA estimates that, out of an estimated total of 6,9456 facilities with aNPDES permit
and operating cooling water intake structures in the Chemicals and Allied Products Industry (NAICS 325), 179
(or 3 percent) would be subject to the 316(b) Proposed Existing Facilities Rule. The total value of shipments for
the Chemicals and Allied Products Industry from the 2007 Economic Census is $494.0 billion ($2009). Value of
shipments, a measure of the dollar value of production, was selected for the basis of this estimate. Because the
DQ did not collect value of shipments data, these data were not available for in-scope facilities. Total revenue, as
reported on the DQ, was used as a close approximation for value of shipments for these facilities. EPA estimated
the total revenue of facilities expected to be subject to regulation to be $74.8 billion ($2009). Therefore, EPA
estimates that 15 percent of total production in the chemical industry occurs at facilities estimated to be subject to
regulation under the 316(b) Proposed Existing Facilities Rule.
The DQ responses indicate that four chemical segments account for a significant majority of the Chemicals and
Allied Products industry facilities subject to the 316(b) Proposed Existing Facilities Regulation: (1) Basic
Chemicals (including NAICS codes 325110, 325120, 325131, 328181, 325188, 325199); (2) Resins and
Synthetics (including NAICS codes 325211, 325221, and 325222); (3) Pesticides and Fertilizers (including
NAICS codes 325311 and 325312); and (4) Pharmaceuticals (including NAICS codes 325411, and 325412). This
profile therefore provides detailed information for these four industry segments.
Table 2B-2 on the following page provides the cross-walk between NAICS codes and SIC codes for the profiled
chemical NAICS codes. The table shows that some NAICS code industry segments have 1 to 1 relationships to
SIC codes, while the other NAICS codes in the four profiled chemical segments correspond to two SIC codes.
6 This estimate of the number of facilities potentially subject to regulation is based on the universe of facilities that received the 1999
screener questionnaire.
March 28, 2011 2B-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Table 2B-2: Relationship between NAICS and SIC Codes for the Chemicals and Allied Products Industry
(2007)
NAICS
Code
NAICS Description
SIC Code
SIC Description
#of
Establishmen
ts
Sales,
Shipments, or
Receipts
(Millions;
$2009)
#of
Employees
Basic Chemicals
325110
325120
325131
328181
325188
325199
Petrochemical manuf-g
Industrial gas manuf-g
Inorganic dye and pigment
manuf-g
Alkalies & chlorine manuf-g
All other inorganic chemical
manuf-g
All other organic chemical
manuf-g
2865
2869
2869
2813
2816
2819
2812
2819
2869
2869
2899
Cyclic crudes & intermediates
Industrial organic chemicals, n.e.c.
Industrial organic chemicals, n.e.c.
Industrial gases
Inorganic pigments
Industrial inorganic chemicals, n.e.c.
Alkalies & chlorine
Industrial inorganic chemicals, n.e.c.
Industrial organic chemicals, n.e.c.
Industrial organic chemicals, n.e.c.
Chemical preparations, n.e.c.
56
576
96
49
631
818
$80,262
$9,863
$5,880
$6,584
$23,593
$84,743
9,257
11,446
7,606
6,364
35,801
70,602
Resins and Synthetics
325211
325221
325222
Plastics material & resin
manuf-g
Cellulosic organic fiber
manuf-g
Noncellulosic organic fiber
manuf-g
2821
2823
2824
Plastics materials & resins
Cellulosic manmade libers
Organic libers, noncellulosic
1,059
15
109
$88,085
$957
$7,196
71,216
1,353
14,684
Pharmaceuticals
325311
325312
Nitrogenous fertilizer manuf-
a
Phosphatic fertilizer manuf-g
2873
2874
Nitrogenous fertilizers
Phosphatic fertilizers
156
80
$5,709
$6,694
3,920
6,264
Pesticides and Fertilizers
325411
325412
Medicinal & botanical
manuf-g
Pharmaceutical preparation
manuf-g
2833
2834
2835
Medicinals & botanicals
Pharmaceutical preparations
Diagnostic substances
399
963
$11,476
$145,245
23,848
159,420
Source: U.S. DOC Economic Census, 2007.
2B.2 Summary Insights from this Profile
A key purpose of this profile is to provide insight into the ability of chemicals firms to absorb compliance costs
under the Proposed 316(b) Existing Facilities Rule without material adverse economic/financial effects. The
industry's ability to withstand compliance costs is primarily influenced by two factors: (1) the extent to which the
industry may be expected to shift compliance costs to its customers through price increases, and (2) the financial
health of the industry and its general business outlook.
2B.2.1 Likely Ability to Pass Compliance Costs Through to Customers
As reported in the following sections of this profile, the chemicals industry has a variable level of concentration,
with some industry segments exhibiting relatively low concentration while others show somewhat higher
concentration. Regardless of the domestic industry concentration level and its implications for market power, the
U.S. Chemicals and Allied Products industry faces increasing competitive pressure from abroad, which
substantially limits any apparent ability of firms to pass a significant portion of their compliance-related costs
through to customers. In addition, the relatively low share of total industry output that is estimated subject to the
regulation under each analysis option also diminishes a firms' ability to shift compliance costs to customers. For
2B-4
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2B: Chemicals Industry Profile
these reasons, in its analysis of regulatory impacts for the chemicals industry, EPA judges that complying firms
would be unable to pass compliance costs through to customers; i.e., complying facilities must absorb all
compliance costs (see following sections, Appendix 3: Cost Pass-Through Analysis, and Chapter C3: Economic
Impact Analysis for Manufacturers, for further information).
2B.2.2 Financial Health and General Business Outlook
Over the last two decades, the Chemicals and Allied Products industry, like other U.S. manufacturing industries,
has experienced a range of economic/financial conditions and a number of substantial challenges. In the early
1990s, the domestic Chemicals and Allied Products industry was affected by reduced U.S. demand as the
economy entered a recessionary period. Although domestic market conditions improved by mid-decade, weakness
in Asian markets, along with other domestic economic factors, dealt a serious blow to the chemicals industry in
1998. A significant drop in demand for Chemicals and Allied Products during the economic recession of the early
2000s resulted in record low capacity utilization and a significant drop in capital expenditures. All profiled
Chemicals and Allied Products Industry segments except Pharmaceuticals saw significant declines in exports,
imports, value of shipments as well as value added. As the U.S. economy began to recover, the domestic
Chemicals and Allied Products industry saw continuous improvements in demand levels and consequent
improvement of financial performance during 2003 to 2005. By 2007, value of shipments significantly grew,
prices were at record highs, and labor productivity increased, with the Pharmaceuticals industry segment
performing especially well. Beginning in 2008, the Chemicals and Allied Products industry faced a substantial
drop in demand due to the economic recession. This economic downturn forced firms in the Chemicals and Allied
Products industry to realign their research and development capabilities, marking a shift in companies' long-term
strategies and prompting them to identify growth opportunities in areas such as energy, food and water (Jagger,
2009). With firms using this downturn as an opportunity for growth and innovation, the Chemicals and Allied
Products industry should be able to withstand additional regulatory compliance costs without a material financial
impact.
2B.3 Domestic Production
The U.S. Chemical and Allied Products industry includes a large number of companies that, in total, produce
more than 70,000 different chemical products. These products range from commodity materials used in other
industries to finished consumer products such as soaps and detergents. The industry accounts for over $630 billion
of total manufacturing value added (Bassi and Yudken, 2009).
The Chemical and Allied Products industry as a whole is highly energy-intensive. This is especially the case for
basic chemicals as well as certain specialty chemical segments (e.g., industrial gases). The industry relies upon
energy inputs not only for fuel and power for its operations, but also as raw materials in the manufacturing of
many of its products. For example, oil and natural gas are raw materials (termed "feedstocks") for the
manufacture of organic chemicals. However, various technology developments throughout the years have allowed
the industry to become less energy intensive; the U.S. chemical industry has reduced its fuel and power energy
consumed per unit of output by 53 percent since 1974 (ACC, 2009).
2B.3.1 Output
Figure 2B-1 shows constant dollar value of shipments and value added for the four profiled Chemicals and Allied
Products industry segments between 1988 and 2007.7 Value of shipments and value added are two common
measures of manufacturing output. Change in these values overtime provides insight into the overall economic
health and outlook for an industry. Value of shipments is the sum of receipts earned from the sale of outputs; it
7 Terms highlighted in bold and italic font are further explained in the glossary.
March 28, 2011 2B-5
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2B: Chemicals Industry Profile
indicates the overall size of a market or the size of a firm in relation to its market or competitors. Value added,
defined as the difference between the value of shipments and the value of inputs used to make the products sold,
measures the value of production activity in a particular industry.
Figure 2B-1 shows that between 1988 and 1993, the Basic Chemicals segment experienced a slight decrease in
both value of shipments and value added, followed by volatility through 1998. The mid 1990s were marked by
increased competition in the global market for petrochemicals, which comprise a large portion of basic chemical
products. The increased competition stems from the considerable capacity expansions for these products seen in
developing nations during that time (McGraw-Hill, 2000). Both value of shipments and value added declined in
2001 as the Basic Chemicals segment faced decreased demand due to the economic slowdown, but have risen
significantly and continuously since then. In 2007, value of shipments was nearly double the 2001 value and value
added reached peak levels.
The profiled Resins and Synthetics, and Pesticides and Fertilizers segments remained more stable during 1988
through 2007 than the Basic Chemicals segment. In the early 1990s, domestic producers benefited from the
relatively weak dollar, which made U.S. products more competitive in the global market. During the later part of
the 1990s, the strength of the U.S. economy bolstered domestic end-use markets, offsetting the effect of reduced
U.S. export sales, which resulted from increased global competition and a strengthened dollar (McGraw-Hill,
2000). The global economic slowdown that began in 2000 led to decreased production, in particular, of chemical
goods that are used in the production processes of other industries, notably steel, apparel, textiles, forest products,
and technology. During 2002 through 2007, the value of shipments and value added of both the Resins and
Synthetics and Pesticides and Fertilizers segments remained relatively stable.
Out of four profiled industry segments, the Pharmaceuticals segment saw the least volatility. Value of shipments
and value added in the Pharmaceuticals segment has been nearly steadily increasing since 1988, reaching peak
levels in 2006.
Overall, the Chemicals and Allied Products industry continues to be a strong contributor to the U.S. economy,
growing more than 150 percent over the past two decades. The composition of the industry, however, has changed
over time, with increasing emphasis being placed on high-technology fields such as pharmaceuticals,
biotechnology, and advanced materials. The recent recession caused declines in industry-wide output save for the
profiled Pharmaceuticals segment. However, this downturn is motivating companies to seek new ways to grow
and realign research and development capabilities to seek new growth opportunities in renewable energy and food
production, for example. Industry analysts predict that annual production in the Chemicals and Allied Products
industry will rise around 3 percent in 2010, and prices are expected to increase, especially for soda ash makers
and caustic soda producers (S&P, 2010a). This should better position in-scope facilities in the Chemicals and
Allied Products industry to absorb compliance costs of the Proposed Existing Facilities Regulation.
2B-6 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Figure 2B-1 : Value of Shipments and Value Added for Profiled Chemicals and Allied Products
Industry Segments (millions, $2009)a
Value of Shipments
$220,000
$200,000
^ $180,000
O
£< $160,000
a $140,000
-2 $120,000
|_ $100,000
'£
^ $80,000
o
J $60,000
~S
$40,000
$20,000
$0
A
/
/ ^ \
^^-*r ^
..-X^^^X ^/
••*-*...,...,"•* * y^^^^s
.-•><•' :. • •' ' ' V
i-"*-"X""K . '" ' ;' •"""""-••• • ,...•••-•"'
> * • • •-•*••••••• * -4
* * * • » • • • |
»oeioiojoioioioioioioiooooooooc
• • - A- • • Basic Chemicals (SIC to
NAICS)
A Basic Chemicals (NAICS
3251 XX)
Resins and Synthetics (SIC
to NAICS)
Resins and Synthetics
(NAICS 3252XX)
- - -4- - - Pesticides and Fertilizers
(SIC to NAICS)
• Pesticides and Fertilizers
(NAICS 3253XX)
• • -X- • • Pharmaceuticals (SIC to
NAICS)
X Pharmaceuticals (NAICS
3254XX)
Value Added
•a
•a
$30,000 -
i
^^ !
y^^r
/
/
^^
y^
V.-.X---X.--"*' "
„..-*•'*'* . ''
• -••»•- *— *— ^ ^-*~~-*---c
* * * »- * ^x^_^^^
i .-A---A--- i j^
* A * * A A A — ^ — A A • • A
Basic Chemicals (NAICS
325 1XX)
Basic Chemicals (SIC to
NAICS)
• Resins and Synthetics
i (NAICS 3252XX)
to NAICS)
A Pesticides and Fertilizers
(NAICS 3253XX)
(SIC to NAICS)
X Pharmaceuticals (NAICS
3254XX)
NAICS)
^0*0*0*0*0*0*0*0*0*0*0*000000000
33*0*0*0*0*0*0*0*0*0*000000000
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the
\Torth American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC
code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2003-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC,
1987, 1992, 1997, 2002, and 2007 Economic Census.
Table 2B-3 provides the Federal Reserve System's index of industrial production for the 4 profiled industry
segments, showing trends in production since 1990. This index reflects total output in physical terms, whereas
March 28, 2011
2B-7
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
value of shipments and value added reflect the value of output in economic terms. Table 2B-3 shows varying
trends in the four segments since 1990, but sharp declines in production in all segments except Pharmaceuticals
during 2000 through 2001. These declines were caused by the marked slowdown in the U.S. economy, which
affected demand in major chemical-using segments such as steel, apparel, textiles, forest products, and the
technology sectors (Chemical Marketing Reporter, 2001).
Between 1990 and 2009, the Basic Chemicals and Pesticides and Fertilizers segments experienced an overall
production decline of 1.5 and 16.3 percent, respectively. While production in the Basic Chemicals segment
changed little between 2000 and 2009, production in the Pesticides and Fertilizers segment dropped 10 percent.
Between 1990 and 2009, production in the Resins and Synthetics and Pharmaceuticals segments increased 6.5 and
101.4 percent, respectively. During the last decade, however, while Resins and Synthetics segment saw a
relatively modest 13 percent decline, the Pharmaceuticals segment production increased drastically - more than
34 percent. Production in the Pharmaceuticals segment was the least affected by swings in overall economic
conditions and is expected to outgrow the other chemical segments in 2010 (C&EN, 2010).
Table 2B-3: Industrial Production Index for Chemicals and Allied Products Industry Segments
(Annual Averages)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009=
Total Percent
Change 1990-
2009
Total Percent
Change 2000-
2009
Average
Annual Growth
Rate
Basic Chemicals3
Index
2002=100
104.8
100.2
101.3
97.4
98.3
98.2
98.1
105.5
102.0
107.0
10373
92.9
100.6
103.6
112.7
114.4
11577
119.7
110.1
103.3
Percent
Change
n/a
-'4.5%'
L2%
-378%
(19%
-b".T%
-6T%"
76%
-3.4%
49%
-375"%"
:joTo%
76%
376%
974%
15%
i""i"%
375%
-8.0%
-672%
-1.5%
0.0%
-0.1%
Resins and Synthetics
Index
2002=100
88.3
86.3'
9l".4"
92.2
99.6
ioo"2
98.1
1043
10877
10978
10777
97.2
i'bao
98.1
10278
i'b'97'i
1078
10876
96.8
94.0
Percent
Change
n/a
-2.3%
578%
176%
876%'
b"'6"%"
-2."i%
6l%
43%
679%
T.9%
-9.7%
'278%
-T.9%
48%
6"T%"
-i".'2"%
677'%"
-T679%
-278%
6.5%
-12.7%
0.3%
Pesticides and Fertilizers0
index
2002=100
113.2
10974
1140
1149
1148
1143
11676
12LO
12375
i'T'D
Tb"57i
9677
i'b'a'b
1048
10979
1144
12178
1140
10377
948
Percent
Change
n/a
'-374%
472%
b"."8"%"
-a'2%
-o"4%
2.0%
3.8%
2"'."l"%"
'-979%
-576%
-87'6%
374%
48%"
49"%"
41%
675%
'-674%
-97'6%
'-'876%
-16.3%
-9.9%
-0.9%
Pharmaceuticals
index
2002=100
57.4
6'"f.4
6"b"'<5
6"b"8
637T
6577
6976
7373
7977"
8279
8672
9277"
i"b'b".'b
10374
104.6
'i"b"8'"."i'
11576
1174
i"i"5"'.2"
Tile"
Percent
Change
n/a
6"'."9"%"
'-'il'%"
673%
379%
4"."b%
676%
574%
877%
46%
4"."b%
76%
78%
'3"'.4%
b"."6"%
4"."b%
674%
2"'."i%
-"i79"%"
674%
101.4%
34.2%
3.8%
a.NAICS3251.
b. NAICS 3252.
c. NAICS 3253
d. NAICS 3254
e. Average through 11/2009
Source: Economagic, Federal Reserve, Board of Governors, 2009a.
2B-8
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
2B.3.2 Prices
The Producer Price Index (PPI) measures price changes, by segment, from the perspective of the seller, and
indicates the overall trend of product pricing, and thus supply-demand conditions, within a segment.
Chemicals product prices fluctuate in large part as a result of varying energy prices. For instance, basic
petrochemicals, which comprise the majority of organic chemical products and are a part of the Basic Chemicals
segment, depend heavily on energy commodities as inputs to the production process - energy input costs may
account for up to 85 percent of total product costs. The prices of natural gas and oil therefore influence the
production costs and the selling price for these products. High basic petrochemical prices affect prices for
chemical intermediate and final end products. The cyclical nature of market supply and demand conditions also
significantly influence prices for commodity chemical products. Finally, all analyzed chemicals industry segments
are characterized by large existing capital investments and production capacity, which can lead to fluctuations in
prices in response to imbalances in supply and demand.
Figure 2B-2 shows PPI for the profiled Chemicals and Allied Products Industry segments for 1987 through 2009.
All profiled segments except Pharmaceuticals saw some volatility during that time in response to changing
economic conditions, energy prices, and changes in operating processes. For instance, the price jump for the
Resins and Synthetics and Basic Chemicals segments in 2000 is the result of an increase in the price of natural
gas - feedstock for 70 percent of U.S. ethylene production (Chemical Marketing Reporter, 2001). Price increases
for Resins and Synthetics also reflected a shift by U.S. producers away from production of commodity resins to
specialty and higher value-added products (McGraw-Hill, 2000). Overall, during 1987 through 2008, selling
prices increased for all four profiled chemicals industry segments, especially during the last decade. As the result
of the recent recession, prices for all profiled segments except Pharmaceuticals declined significantly in 2009 but
are expected to recover in 2010 (S&P, 2010a).
Figure 2B-2: Producer Price Indexes for Profiled Chemicals and Allied Products Industry Segments
350
250
200
- Basic Chemicals (NAICS
3251)
- Resins and Synthetics
(NAICS 3252)
- Pesticides and Fertilizers
(NAICS 3253)
- Pharmaceuticals (NAICS
3254)
Source: BLS, 2009a.
March 28, 2011
2B-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
2B.3.3 Number of Facilities and Firms
According to Statistics of U.S. Businesses, the number of facilities in the Basic Chemicals segment remained
relatively stable between 1990 and 1997, followed by five consecutive years of decreases in the number of
facilities. In 2003, however, the number of facilities increased again and remained flat for the next few years.
Overall, the Basic Chemicals segment experienced a 7.3 percent decline in the number of facilities over the 1990
to 2006 time period. The Resins and Synthetics and Pharmaceuticals segments saw overall increases of 50.7 and
41.2 percent, respectively in the number of facilities from 1990 to 2006. Above average increases in the number
of facilities in the Resins and Synthetics segment reported between 1993 and 1996 reflected growth in the demand
for plastics in a number of end-uses (McGraw-Hill, 2000). Table 2B-4 shows the downward trend in the number
of facilities since 1996 producing pesticides and fertilizer products. The recent increasing cost of feedstock
(largely crude oil) and other factors increasing production costs has led to consolidation and mergers of national
and multinational chemical companies (MBendi, 2010).
Table 2B-4: Number of Facilities for Profiled Chemicals and Allied Products Industry Segments
Year3
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent
Change 1990-2006
Total Percent
Change 2000-2006
Average Annual
Growth Rate
Basic Ch
Number of
Facilities
2,181
1111112,275
2,261
2,283
2,261
1111112^34
2,152
2,247
2,"f57
2J35
2,113
2,065
T,976
IIIIIIAM2
2,065
2,021
2,022
emicalsb
Percent
Change
n/a
43%
-0.6%
1.0%
-0".9%
IIIIII:I-2%
-3.7%
4.4%
-4.0%
IIIIII:I-0%
-1.0%
-2.3%
-473%
13%
1.1%
-2.1%
676%
-7.3%
-4.3%
-0.5%
Resins and
Number of
Facilities
601
11111111621
555
600
595
659
741
705
677
IIIIIIZzoo
714
744
806
IIIIIIII?07
905
924
906
Synthetics0
Percent
Change
n/a
373%
-10.6%
8.1%
-6"8%
1P:8%
12.4%
-4.9%
-4.0%
14%
2.0%
4.2%
81%
IIIIIj2-5%
-0.2%
2.1%
-L9%
50.7%
26.9%
2.6%
Pesticides an
Number of
Facilities
227
IIIIIII228
251
250
233
IIIIIII239
252
215
221
222
222
223
207
I??
193
193
188
d Fertilizers'1
Percent
Change
n/a
074%
10.1%
-0.4%
-6".8%
26%
5.4%
-14.7%
2.8%
0.5%
0.0%
0.5%
-7.2°/o
IIII]^7%
2.1%
0.0%
-276%
-17.2%
-15.3%
-1.2%
Pharmat
Number of
Facilities
933
9672
1,013
1,044
981
17005
1,142
1,190
U4l'
1111111^249
1,251
1,257
U44
U268
1,280
1,281
i"3"i7
euticals"
Percent
Change
n/a
11%
5.4%
3.0%
-6.0%
274%
13.7%
4.2%
473%
o7e%
0.2%
0.5%
-1.0%
1.9%
0.9%
0.1%
2.8%
41.2%
5.3%
2.2%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North
(NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using
SICandNAICS.
b. NAICS 3251.
c. NAICS 3252.
d. NAICS 3253
e. NAICS 3254
Source: U.S. SBA, 1990-1997: SUSB, 1998-2006.
American Industry Classification System
the 1997 Economic Census Bridge Between
Table 2B-5 shows the number of firms in the four profiled chemical segments between 1990 and 2006. The trend
in the number of firms between 1990 and 2006 is similar to the number of facilities. The number of firms in the
Basic Chemicals segment peaked in 1994, and then declined continuously during 1995 to 2002, before increasing
slightly in 2003 and then leveling off. The Resins and Synthetics segment followed a more positive trend and
increased 83.3 percent to 647 firms in 2006. The number of firms in the Pesticides and Fertilizers segment
2B-10
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
fluctuated over the period, falling steeply in 2002 and 2003 following the economic recessionary period. The
number of firms in the Pharmaceuticals segment increased substantially between 1995 and 1999, from 859 to
1,076 firms, before stabilizing through 2006.
Table 2B-5: Number of Firms for Profiled Chemicals and Allied Products Industry Segments
Year3
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent
Change 1990-2006
Total Percent
Change 2000-2006
Average Annual
Growth Rate
Basic Chemicals'"
Number of
Firms
1,189
U27
1=267
1,294
2,245
U51
L16J
1,222
1,136
1,096
l£?o
1,085
1,020
1,091
1£86
1,085
1,105
Percent
Change
n/a
3l%
373%
2.1%
73.5%
:443%
IIIZZ-2%
5.2%
-7.0%
-15%
-0.5%
-0.5%
-6.0%
70%
-0.5%
-0.1%
1.8%
-7.1%
1.4%
-0.5%
Resins and Synthetics0
Number of
Firms
353
380
319
350
595
409
477
434
395
411
429
456
518
635
IIIIIIIj>22
653
647
Percent
Change
n/a
7.6%
IIIII:]6J%
9.7%
70.0%
:3y;3%'
16-6%
-9.0%
-9.0%
4.1%
44%
6.3%
13.6%
22.6%
IIIIII:2jo%
5.0%
-0.9%
83.3%
50.8%
3.9%
Pesticides and Fertilizers'1
Number of
Firms
163
161
180
177
233
166
I8J
174
173
175
IIIIIIIJ74
178
165
146
150
154
152
Percent
Change
n/a
"-12%
11:8%
-1.7%
31.6%
:28;8%
9]0%
-3.9%
-0.6%
11%
111111^:6%
2.3%
-7.3%
:y'f;5%
27%
2.7%
-1.3%
-6.7%
-12.6%
-0.4%
Pharmaceuticals6
Number of
Firms
799
835
872
908
981
859
991
1,033
1,073
l",076
1>073
1,074
1,053
l",065
1=074
1,074
1,107
Percent
Change
n/a
44%
4.5%
4.1%
8.1%
:12T5%
15.3%
4.3%
3.8%
6"3%
111111:0:3%
0.1%
-2.0%
1.1%
IIIIIZP-8%
0.0%
3.1%
38.5%
3.2%
2.1%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the NAICS. For this analysis, EPA converted the SIC
classification data to the NAICS code classifications using the 1997 Economic Census Bridge Between SIC and NAICS.
b. NAICS 3251.
c. NAICS 3252.
d. NAICS 3253
e. NAICS 3254
Source: U.S. SBA, 1990-1997: SUSB, 1998-2006.
2B.3.4 Employment and Productivity
Figure 2B-3 provides information on employment from the Annual Survey of Manufactures and Economic
Census. With the exception of minor short-lived fluctuations, employment in the Basic Chemicals and Resins and
Synthetics segments remained relatively stable between 1988 and 1998 before seeing yearly declines through
2006. This decrease reflects the industry's restructuring and downsizing efforts, which are intended to reduce
costs in response to competitive challenges. However, in the last observed year of the analysis period, between
2006 and 2007, employment began increasing for both segments. Employment in the Pharmaceuticals segment
fluctuated between 1988 and 1997 and then experienced a period of strong growth through 2002, from 141,883 to
202,087 employees. Employment in this segment remained fairly constant over the next five years, dropping
slightly below peak 2002 levels. The Pesticides and Fertilizers segment experienced the least amount of
fluctuation over the two decades but had fairly significant employment losses compared to the small size of the
industry. From 1988 to 2007, only the Pharmaceuticals sector showed an overall increase in industry employment
March 28, 2011
2B-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
of 25 percent. The Pesticides and Fertilizers segment had the largest overall decrease in employment at 42
percent, while the Basic Chemicals and Resins and Synthetics segments' employment both declined 24 percent.
Figure 2B-3: Employment for Profiled Chemicals and Allied Products Industry Segments
220,000
200,000
180,000
160,000
140,000
Resins and Synthetics (NAICS
3252XX)
Resins and Synthetics (SIC to
NAICS)
Pesticides and Fertilizers
(NAICS 3253XX)
- - Pesticides and Fertilizers (SIC
to NAICS)
Basic Chemicals (SIC to
NAICS)
Basic Chemicals (NAICS
3251XX)
Pharmaceuticals (NAICS
3254XX)
Pharmaceuticals (SIC to
NAICS)
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North
American Industry Classification System (NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code
classifications using the 1997 Economic Census Bridge Between SIC and NAICS.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2003, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC,
1987, 1992, 1997, 2002, and 2007Economic Census.
Table 2B-6 presents the change in value added per labor hour, a measure of labor productivity, for each of the
profiled industry segments between 1988 and 2007. The trends in each segment show considerable volatility
through the 1990s into the 2000s. The gains in productivity in this early period for the Basic Chemicals segment
reflect firms' attempts to reduce costs by restructuring production and materials handling processes in response to
maturing domestic markets and increased global competition (S&P, 2001a). Over the 1988 to 2007 period, all
four segments saw significant increases in productivity. A great majority of this growth occurred from 2000 to
2007, where productivity increased 79 percent in the Basic Chemicals segment, 26 percent in the Resins and
Synthetics segment, 84 percent in the Pesticides and Fertilizers segment, and 37 percent in the Pharmaceuticals
segment. The complexity of the industry is increasing, requiring highly developed skills and workers with better
training and education. In addition, scientifically trained personnel - such as chemists, chemical engineers,
agronomists, toxicologists, and biologists - are in high demand. Increases in spending and productivity for the
chemical industry are not expected to reverse the loss in chemicals industry employment. Workforce losses in
2008 and 2009 are expected to continue into 2010 following additional productivity gains (C&EN, 2010).
2B-12
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Table 2B-6: Productivity Trends for Profiled Chemicals and Allied Products Industry Segments ($2009)
Year3
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1988-2007
2660-2667
Average
Annual
Growth
Rate
Basic Chemicals
Prod.
Hours
(mill.)
229
228
234
239
240
229
212
214
220
213
211
201
204
194
189
188
181
175
170
185
-19.4%
-9.4"%
-1.1%
Value Ad
S/hr.
244
265
252
222
222
223
246
270
230
294
290
269
244
216
248
268
356
437
481
437
ded/Hour
%
Change
n/a
8.6%
-5.0%
-11.8%
0.0%
0.3%
10.4%
9.9%
-14.9%
27.8%
-1.4%
-7.2%
-9.4%
-11.6%
14.9%
8.2%
32.8%
22.7%
10.1%
-9.2%
78.8%
79.0%
3.1%
Resins and Synthetics
Prod.
Hours
(mill.)
166
172
170
167
166
164
168
167
156
156
153
146
146
130
130
127
119
118
107
127
-23.4%
-12.8%
-1.4%
Value Ad
S/hr.
189
181
168
157
164
162
183
200
193
204
216
212
199
182
189
195
258
284
290
252
ded/Hour
%
Change
n/a
-4.6%
-7.1%
-6.6%
4.3%
-0.8%
12.9%
9.0%
-3.6%
6.2%
5.7%
-2.0%
-6.1%
-8.6%
3.7%
3.5%
31.8%
10.1%
2.4%
-13.3%
32.8%
26.4%
1.5%
Pesticides and Fertilizers
Prod.
Hours
(mill.)
25
26
27
27
26
25
26
26
25
22
22
21
19
18
17
17
16
15
14
15
-39.1%
-19.7%
-2.6%
Value Ad
S/hr
180
142
131
146
139
131
194
221
234
227
237
152
141
140
162
175
227
248
199
260
ded/Hour
%
Change
n/a
-21.1%
-7.8%
11.6%
-5.2%
-5.6%
47.9%
14.1%
5.6%
-2.8%
4.3%
-35.7%
-7.1%
-0.9%
15.6%
7.9%
29.7%
9.3%
-19.7%
30.6%
44.2%
83.7%
1.9%
Pharmaceuticals
Prod.
Hours
(mill.)
133
137
136
134
146
147
153
177
175
154
154
166
178
187
189
189
183
186
186
180
36.0%
i.5%
1.6%
Value A(
S/hr
353
369
395
424
406
414
411
352
370
456
504
501
487
511
574
630
643
637
653
669
Ided/Hour
%
Change
n/a
4.3%
7.0%
7.4%
-4.2%
1.9%
-0.7%
-14.4%
5.0%
23.4%
10.4%
-0.6%
-2.8%
5.1%
12.3%
9.6%
2.1%
-1.0%
2.6%
2.3%
89.2%
3X4%
3.4%
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the NAICS. For this analysis,
EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2003, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2B.3.5 Capital Expenditures
The Chemicals and Allied Products industry is relatively capital-intensive. According to the 2007 Economic
Census, facilities in NAICS 325 had aggregate capital spending of approximately $16.7 billion in 2007. Capital-
intensive industries are characterized by large, technologically complex manufacturing facilities, which reflect the
economies of scale required to manufacture products efficiently. New capital expenditures are needed to
extensively modernize, expand, and replace existing capacity to meet growing demand. Table 2B-7 on the
following page shows that all four profiled chemical industry segments experienced substantial increases in
capital expenditures through the 1990s. Much of the growth in capital expenditures was driven by investment in
capacity expansions to meet the increase in global demand for chemical products. Domestically, the continued
substitution of synthetic materials for other basic materials and rising living standards caused consistent growth in
the demand for chemical commodities (S&P, 200la). Expenditures declined somewhat during the early 2000s due
to a weakening economy, with the exception of the pharmaceuticals sector, which has remained relatively strong
March 28, 2011
2B-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
and continued to grow throughout the last two decades. As a whole, the industry increased spending in more
recent years and is looking towards new capital expenditure strategies for growth in the near future, hoping to
capitalize on long-term societal "megatrends," including increased use of renewable energy and the need for
improved food and water supplies (Jagger, 2009).
Table 2B-7: Capital Expenditures for Profiled Chemicals and Allied Products Industry Segments (in
millions, $2009)
Year3
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent
Change 1988 -
2007
Total Percent
Change 2000 -
2007
Average Annual
Growth Rate
Basic Chemicals
Capital
Expenditures
$5,850
$7,538
$8328
$8,405
$8,146
$6,296
'$5",670'
$7,549
$9,241
$8,896
$8/775
$7,776
$6,775
$6,012
'$5",'046'
$4,324
$4,680
$5,012
$5,855
$7465'
Percent
Change
n/a
28.9%
10"."5%"
0^9%
-3.1%
-22.7%
-979%''
3371%
22.4%
-3.7%
74%
1111110%
-12.9%
-11.3%
-16.1%
-14.3%
8.2%
7.1%
16.8%
275%
27.6%
10.2%
1.3%
Resins and Synthetics
Capital
Expenditures
$3,853
$4,361
'$5",'052"
$4,650
$3,616
$4,287
$"4449"
IIIIIZ*4l98
$3,677
$4,628
$5J93
$5,414
$3,433
$2,790
$27929
$2,023
$2,188
$2,746
'$'2','734
'$'3",'268'
Percent
Change
n/a
13.2%
15"."8%"
IIIIIlZ-9%
-22.2%
18.5%
18%
-5.6%
-12.4%
25.9%
12".2%"
43%
-36.6%
-18.7%
576'%'
-30.9%
8.1%
25.5%
-04%"
1975%
-15.2%
-4.8%
-0.9%
Pesticides and Fertilizers
Capital
Expenditures
$298
$402
$7360
$613
$741
$471
'$459"
11111111*498
$666
$1,038
$95'o'
$738
$436
$410
$406'
$319
$311
$335
'$409'
'$471
Percent
Change
n/a
35.1%
IYo.5%
7o7i%
20.9%
-36.4%
-276%"
8^6%
33.7%
55.9%
-8""5"%"
-22.3%
-41.0%
-6.1%
-1.0%
-21.4%
-2.4%
7.6%
2"T'."9%"
153'%"
58.1%
8.0%
2.4%
Pharmaceuticals
Capital
Expenditures
$3,093
$3,422
'$'3",'075
$3375
$4,414
$4,298
'$4,3"64'
111111*4330
$4,612
$4,696
$4,264
$4,557
$5,524
$6,085
$'6',060'
$5^992
$6,685
$5,179
$4,423
$5^538
Percent
Change
n/a
10.6%
-To.T%"
9^8%
30.8%
-2.6%
1.5%
10.7%
-4.5%
1.8%
-9.2%
6^9%
21.2%
10.2%
'-'6'".4'%
-1.1%
11.6%
-22.5%
-14.6%"
25".'2"%
79.0%
0.2%
3.1%
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the NAICS. For this analysis,
EPA converted the NAICS classification data to the SIC code classifications using the 1997Economic Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2003, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2B.3.6 Capacity Utilization
Capacity utilization measures actual output as a percentage of total potential output given the available capacity.
Capacity utilization reflects excess or insufficient capacity in an industry and is an indication of whether new
investment is likely. To take advantage of economies of scale, chemical commodities are typically produced in
large facilities. Capacity additions in this industry are often made on a relatively large scale and can substantially
affect the industry's capacity utilization rates.
Figure 2B-4 presents capacity utilization from 1990 to 2009 for the entire Chemicals and Allied Products industry
(NAICS 325). Capacity utilization for the industry fluctuated throughout the 1990s, dropping from 1990 through
1993, increasing gradually through 1997, and then dropping rapidly to a low of 71 percent in 2001. The next eight
years showed recovery, with increases in capacity utilization each year except during the recessions of 2001 and
2B-14
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
2008. Following a period of regular capacity utilization increases, the chemicals industry conserved cash by
cutting capital spending by 20.1 percent at the beginning of the 2008 recession, according to the American
Chemistry Council (C&EN, 2010). Overall, between 1990 and 2009, capacity utilization in the Chemicals and
Allied Products industry fell 7.4 percent, but has grown 2.2 percent since 2000. As the U.S. economy recovers,
companies in the Chemicals and Allied Products industry could still find themselves with significant excess
capacity, despite recent cuts in capacity investments, and may not return to making major investments until 2011
However (C&EN, 2010).
Figure 2B-4: Capacity Utilization Rates (Fourth Quarter) for Profiled Chemicals and Allied
Products Industry Segments
a,b
- Chemical Manufacturing
(NAICS 325)
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North
American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code
lassifications using the 1997 Economic Census Bridge Between NAICS and SIC.
3. Before 2007, U.S. Census sampled every industry in a specific NAICS6. Beginning in 2007, U.S. Census only sampled certain
industries within any NAICS6, and therefore, the data collected before 2007 cannot be directly compared to the data collected in 2007
and beyond.
Source: U.S. DOC, Survey of Plant Capacity 1989-2009, U.S. Census Bureau.
2B.4 Structure and Competitiveness
The Chemicals and Allied Products industry continues to restructure and reduce costs in response to competitive
challenges, including global oversupply for commodities. In the early 1990s, the chemical industry's cost-cutting
came largely from restructuring and downsizing. The industry has taken steps to improve productivity, and
consolidated to cut costs. Companies seeking growth within these relatively mature industry segments have made
acquisitions to achieve production or marketing efficiencies. The Resins and Synthetics segment, for example,
experienced sizable consolidations in the late 1990s into 2000 (S&P, 200la). In the most recent decade, there has
been a significant increase in trade activity for all profiled Chemicals and Allied Products industry segments, with
particularly notable growth in imports of pesticides, fertilizers, and pharmaceutical products. Consolidation and
restructuring efforts have also been very strong since 2000, as global chemical merger and acquisition activity
climbed from $33 billion to $55 billion in 2005 to 2007 alone (Chang, 2008).
2B.4.1 Firm Size
The Small Business Administration (SBA) defines small firms in the chemical industries according to the firm's
number of employees. Firms in the Basic Chemicals segment (325110, 325120, 325131, 328181, 325188, and
325199) and Resins and Synthetics (NAICS codes 325211, 325221, and 325222) are defined as small if they have
March 28, 2011
2B-15
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
1,000 or fewer employees (except for NAICS 325211, for which the threshold is 750 or fewer employees). Firms
in the NAICS industry 325311 and 325312 of the Pesticides and Fertilizers segment are considered small if they
have 1,000 or fewer and 500 or fewer employees, respectively. Firms in Pharmaceuticals (NAICS codes 325411
and 325412) are defined as small if they have 750 or fewer employees. The size categories reported in the
Statistics of U.S. Businesses (SUSB) do not correspond with the SBA size classifications, therefore preventing
precise use of the SBA size threshold in conjunction with SUSB data.
The SUSB data presented in Table 2B-8 show that in 2006, 872 of 1,105 firms in the Basic Chemicals segment
had less than 500 employees. Therefore, at least 79 percent of firms in this segment were classified as small.
These small firms owned 982 facilities, or 49 percent of all facilities in the segment. In the Resins and Synthetics
Industry segment, 537 of 647 firms, or 83 percent, had less than 500 employees in 2006. These small firms owned
589 of 906 facilities (65 percent) in this segment. In the Pesticides and Fertilizers segment, 86 percent of firms
(131 of 152) had fewer than 500 employees, owning 72 percent of all facilities in that segment. And for the
Pharmaceuticals segment, 966 of the 1,107 firms (87 percent) had less than 500 employees, and these firms
accounted for 76 percent of the total number of facilities.
Table 2B-8 below shows the distribution of firms and facilities in the four profiled segments by the employment
size of the parent firm.
Table 2B-8: Number of Firms and Facilities by Firm Size Category for Profiled Chemical Segments, 2006
Year
0-19
20-99
100-499
500+
Total
Basic Ch
Number of
Firms
495
234
143
233
1,105
•mitals
Number of
Facilities
495
254
233
1040
2,022
Resins and
Number of
Firms
269
184
84
110
647
Synthetics
Number of
Facilities
270
192
127
317
906
Pesticides ant
Number of
Firms
101
19
11
21
152
1 Fertilizers
Number of
Facilities
101
19
16
52
188
Pharmac
Number of
Firms
612
222
132
141
1,107
.•iititals
Number of
Facilities
613
237
151
316
1,317
Source: U.S. DOC, Statistics of U.S. Businesses, 2006 (U.S. DOC, 2006).
2B.4.2 Concentration Ratios
Concentration is the degree to which industry output is concentrated in a few large firms. Concentration is
closely related to entry barriers with more concentrated industries generally having higher barriers.
The four-firm concentration ratio (CR4) and the Herfindahl-Hirschman Index (HHI) are common measures of
industry concentration. The CR4 indicates the market share of the four largest firms. For example, a CR4 of 72
percent means that the four largest firms in the industry account for 72 percent of the industry's total value of
shipments. The higher the concentration ratio, the less competition there is in the industry, other things being
equal.3 An industry with a CR4 of more than 50 percent is generally considered concentrated. The HHI indicates
concentration based on the largest 50 firms in the industry. It is equal to the sum of the squares of the market
shares for the largest 50 firms in the industry. For example, if an industry consists of only three firms with market
shares of 60, 30, and 10 percent, respectively, the HHI of this industry would be equal to 4,600 (602 + 302 + 102).
The higher the index, the fewer the number of firms supplying the industry and the more concentrated the
industry. Based on the U.S. Department of Justice's guidelines for evaluating mergers, markets in which the HHI
is under 1,000 are considered unconcentrated, markets in which the HHI is between 1,000 and 1,800 are
considered to be moderately concentrated, and those in which the HHI is in excess of 1,800 are considered to be
concentrated.
The measured concentration ratio and the HHF are very sensitive to how the industry is defined. An industry with a high concentration
in domestic production may nonetheless be subject to significant competitive pressures if it competes with foreign producers or if it
competes with products produced by other industries (e.g., plastics vs. aluminum in beverage containers). Concentration ratios based
on share of domestic production are therefore only one indicator of the extent of competition in an industry.
2B-16
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2B: Chemicals Industry Profile
Of the profiled Chemicals and Allied Products segments, as shown in Table 2B-9, the following industry sub-
sectors were highly concentrated in 2002: Petrochemical Manufacturing (NAICS 325110), Phosphatic Fertilizer
Manufacturing (NAICS 325312), and Medicinal and Botanical manufacturing (NAICS 325411). HHI and CH4
values indicated that Industrial Gas Manufacturing (NAICS 325120), Inorganic Dye and Pigment Manufacturing
(NAICS 325131), Alkalies and Chlorine manufacturing (NAICS 325181), and Noncellulosic Organic Fiber
Manufacturing (NAICS 325222) were all moderately concentrated. In contrast, Plastics Material and Resin
Manufacturing (NAICS 325211), Nitrogenous Fertilizer Manufacturing (NAICS 325311), Pharmaceutical
Preparation Manufacturing (NAICS 325412), Other Basic Inorganic Chemical Manufacturing (NAICS 325188),
and Other Basic Organic Chemical Manufacturing (NAICS 325199) would be considered competitive. The
diversity of products in some of the profiled industry segments, however, makes generalizations about
concentration less reliable than in industry segments with a more limited product slate. That is, within a single
NAICS code, the numbers of producers may vary substantially by individual product - firms may possess
relatively high market power in products with a smaller number of competing producers even though the total
NAICS code would appear to have a relatively low concentration. On the basis of concentration information,
some industry segments would therefore appear to be moderately concentrated; accordingly, firms in these
segments might possess a moderate degree of market power and thus the ability to pass compliance costs through
to customers as price increases. However, as discussed above and more specifically in the following section,
competition from foreign producers in both domestic and export markets, increasingly restrains the discretionary
pricing power of U.S. firms in the profiled industry segments.
March 28, 2011 2B-17
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Table 2B-9: Selected Ratios for SIC and NAICS Codes Within Profiled Chemicals and Allied Products
Industry Segments in 1987, 1992, 1997, and 2002
SIC (S) or
NAICS (N) Code
Year3
4 Firm (CR4)
8 Firm (CR8)
Concentration Ri
20Firm
(CR20)
»tios
SbFirm
(CR50)
Herfindahl-
Hirschman Index
Basic Chemicals
S2869
N325110
S2813
N 325 120
S2816
N 325131
S2812
N 325181
S2819
N 325188
S2869
N 325199
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
31%
29%
60%
85%
77%
78%
64%
64%
64%
69%
67%
69%
72%
75%
78%
73%
38%
39%
31%
21%
31%
29%
25%
22%
48%
43%
83%
94%
88%
91%
85%
82%
76%
79%
79%
82%
93%
90%
92%
90%
49%
50%
42%
33%
48%
43%
38%
36%
68%
67%
98%
100%
95%
96%
96%
97%
94%
93%
95%
96%
99%
99%
100%
100%
68%
68%
63%
56%
68%
67%
57%
57%
86%
86%
100%
100%
98%
99%
99%
99%
99%
99%
100%
100%
100%
100%
100%
100%
84%
85%
82%
80%
86%
86%
80%
80%
376
336
1,187
27662
1,538
17629
1,223
17218
1,550
17916
1,848
17704
2,328
17994
2,870
17786
468
677
394
217
376
336
256
238
Resins and Synthetics
S2821
N 325211
S2823
N 325221
S2824
N 325222
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
20%
24%
26%
32%
NA
98%
100%
93%
76%
74%
69%
57%
33%
39%
39%
46%
100%
NA
NA
NA
92%
90%
87%
82%
61%
63%
64%
68%
NA
NA
NA
NA
98%
98%
98%
96%
89%
90%
89%
88%
NA
NA
NA
NA
100%
100%
100%
100%
248
284
304
443
NA
NA
NA
NA
2,403
2,158
1,708
F,262
Pesticides and Fertilizers
S2873
N 325311
S2874
N 325312
1987
1992
1997
2002
1987
1992
1997
2002
33%
48%
54%
54%
48%
62%
71%
78%
55%
67%
76%
79%
74%
83%
88%
93%
82%
91%
94%
95%
98%
98%
99%
100%
97%
99%
99%
98%
99%
99%
100%
100%
486
792
903
977
880
17528
1,675
17853
Pharmaceuticals
S2833
N 325411
S2834
N 325412
1987
1992
1997
2002
1987
1992
1997
2002
72%
76%
62%
64%
22%
26%
36%
36%
80%
84%
73%
73%
36%
42%
50%
53%
89%
91%
85%
83%
65%
72%
71%
76%
95%
97%
93%
92%
88%
90%
89%
89%
2,588
27999
2,059
27704
273
341
462
530
2B-18
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2B: Chemicals Industry Profile
Table 2B-9: Selected Ratios for SIC and NAICS Codes Within Profiled Chemicals and Allied Products
Industry Segments in 1987,1992, 1997, and 2002
SIC (S) or
NAICS (N) Code
Year3
4 Firm (CR4)
8 Firm (CR8)
Concentration R
lOFirm
(CR20)
atios
50 Firm
(CR50)
Herflndahl-
Hirschman Index
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the NAICS system. For this analysis, EPA converted
the NAICS classification data to the SIC code classifications using the 1997Economic Census Bridge Between NAICS and SIC.
Source: U.S. DOC, Economic Census, 1987, 1992, 1997, and 2002.
2B.4.3 Foreign Trade
The Chemicals and Allied Products industry is one of the largest exporters in the United States, with $106 billion
in annual exports accounting for more than 10 percent of total U.S. merchandise exports. In fact, U.S.
manufacturers produce 19 percent of the world's chemicals, more than any other country (ACC, 2009).
This profile uses two measures of foreign competition: export dependence and import penetration.
Import penetration measures the extent to which domestic firms are exposed to foreign competition in domestic
markets. Import penetration is calculated as total imports divided by total value of domestic consumption in that
industry: where domestic consumption equals domestic production plus imports minus exports. Theory suggests
that higher import penetration levels will reduce market power and pricing discretion because foreign competition
limits domestic firms' ability to exercise such power. Firms belonging to segments in which imports account for a
relatively large share of domestic sales would therefore be at a relative disadvantage in their ability to pass-
through costs because foreign producers would not incur costs as a result of the Proposed Existing Facilities
Regulation. The estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) for
2007 is 27 percent. For characterizing the ability of industries to withstand compliance cost burdens, EPA judges
that industries with import ratios close to or above 27 percent would more likely face stiff competition from
foreign firms and thus be less likely to succeed in passing compliance costs through to customers.
Export dependence, calculated as exports divided by value of shipments, measures the share of a segment's sales
that is presumed subject to strong foreign competition in export markets. The Proposed Existing Facilities
Regulation would not increase the production costs of foreign producers with whom domestic firms must compete
in export markets. As a result, firms in industries that rely to a greater extent on export sales would have less
latitude in increasing prices to recover cost increases resulting from regulation-induced increases in production
costs. The estimated export dependence ratio for the entire U.S. manufacturing sector for 2007 is 15 percent. For
characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with
export ratios close to or above 15 percent are at a relatively greater disadvantage in potentially recovering
compliance costs through price increases since export sales are presumed subject to substantial competition from
foreign producers.
Table 2B-10 presents trade statistics for each of the profiled Chemicals and Allied Products industry segments.
Both export dependence and import penetration experienced increases in each of these segments between 1990
and 2007.
Globalization of markets has become a key factor in the Basic Chemicals segment, with both import penetration
and export dependence growing substantially over the 18-year analysis period - imports tripled and exports
doubled. The greater growth in imports underscores the increasing competition from foreign producers in
domestic and world markets.
Increased globalization has also affected the Resins and Synthetics segment. Imports and exports of resins and
synthetics have increased significantly over the 18-year analysis period, reflecting the continued growth in the
global market. As with the Basic Chemical segment, this segment has shown substantial overall increases in
values of imports and exports with total growth of 243 percent and 137 percent, respectively during the last two
March 28, 2011 2B-19
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2B: Chemicals Industry Profile
decades. Import penetration grew more quickly than export dependence in this segment due to declining export
opportunities and increased foreign competition in the domestic markets. Nevertheless, the United States
remained a net exporter of resins and synthetics, despite these trends. The market for pesticides and fertilizers has
also become increasingly competitive. Significant capacity expansions for pesticides and fertilizers worldwide
increased competition in domestic markets from imports and began to limit export opportunities for U.S.
producers. Through 1999, the segment still exported more than it imported. However, this balance recently
changed as imports exceeded exports from 2000 through 2007. From 1990 through 2007, imports in the profiled
Pesticides and Fertilizers segment grew by 377 percent, while exports declined 10 percent. The Pharmaceuticals
segment had by far the largest surge in trade activity over the observed period, with imports growing over twelve-
fold, and exports increasing by 480 percent.
In 2007, import penetration ratios in the Basic Chemicals and Resins and Synthetics segments were 22 and 17
percent respectively, compared to 27 percent reported for the U.S. manufacturing industry as a whole. Therefore,
neither of these two profiled segments faces strong competition from foreign firms in U.S. markets. At the same
time, the import penetration ratio was 49 percent for the Pesticides and Fertilizers segment and 35 percent for the
Pharmaceuticals segment, suggesting that businesses in these segments do face strong competition from foreign
firms in the U.S. markets. Further, between 1990 and 2007 import penetration ratio for all profiled segments
except Basic Chemicals rose significantly (165 percent, 403 percent, and 149 percent, respectively), which could
indicate that foreign firms have begun aggressive pursuit of these U.S. markets.
In 2007, the export dependence ratio was 22 percent for the Basic Chemicals segment, 30 percent for the Resins
and Synthetics segment, 29 percent for the Pesticides and Fertilizers segment, and 18 percent for the
Pharmaceuticals segment compared to 17 percent reported for the U.S. manufacturing industry as a whole.
Therefore, all 4 segments likely face significant competitive pressure in retaining their positions in the foreign
markets. Further, for all profiled chemical industry segments except Pesticides and Fertilizers, export dependence
has been steadily increasing during the last two decades. Given these levels of exposure to competition from
foreign firms in domestic and export markets, the profiled chemicals industry segments likely have limited
discretionary power to recover compliance costs expected to be uncured as the result of the Proposed Existing
Facilities Regulation through price increases.
Recent trends in international chemicals markets imply that U.S. producers in the profiled Chemicals and Allied
Products industry will continue to face strong competition from foreign producers. However, trade is also
expected to play an important role in industry growth as increased importance is given to bilateral and multilateral
trade agreements. Free Trade Area of the Americas (FTAA) and other free trade agreements with Chile and
Vietnam offer U.S. chemical companies the opportunity to expand exports to these regions/countries. Trade
barriers such as higher tariff rates are falling in many countries as a result of commitment to the Chemical Tariff
Harmonization Agreement. These developments are favorable for increasing exports from the United States. At
the same time, industry exposure to fluctuations in regional and global economic conditions is on the rise due to
the increasing share of imports in domestic consumption (AllBusiness, 2009).
2B-20 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Table 2B-10: Trade Statistics for Profiled Chemicals and Allied Products Industry Segments
Year3
Value of
Imports
(millions, $2009)
Value of Exports
(millions, $2009)
Value of
Shipments
(millions, $2009)
Implied
Domestic
Consumption1"
Import
Penetration0
Export
Dependence*1
Basic Chemicals
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent
Change 1990-
2007
Total Percent
Change 2000-
2007
Average
Annual Growth
Rate
$14,964
$157095
$[57545
$[57237
$17379
$20,725
$217944
$24,083
$23,879
$25,442
$30,477
$29,500
$28,670
$3"l;571
$367345
$42,781
$44j3i
$47';654
214.4%
54.4%
7%
$19,875
$207321
$2o'Ji"6
$[97552
$217934
$26,527
$247896
$287590
$257976
$267247
$297356
$287099
$277100
$30,464
$357100
$36,728
$4i7552
$457375
128.3%
54.6%
5%
$120,460
$1147177
$1147i56
$1097039
$111,889
$119^249
$1177274
$1307827
$12"i77"6"b"
$1187993
$[25439
$111^12
$1135779
jl26,242
$1507479
$1707i42
$190,013
$2107924
75.1%
68.6%
3%
115,550
108,951
109,591
1047723"
1077533
[[37448
1147322
[26,320
119,664
118,189
1267260
libels
1157349
1277349"
[517724
1767i95
1927592"
2127604
13%
14%
14%
15%
16%
18%
19%
19%
20%
22%
24%
26%
25%
25%
24%
24%
23%
22%
16%
18%
18%
18%
20%
22%
21%
22%
21%
22%
23%
25%
24%
24%
23%
22%
22%
22%
84.0%
68.4%
4%
Resins and Synthetics
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent
Change 1990-
2007
Total Percent
Change 2000-
2007
Average
Annual Growth
Rate
$4,035
$3^938
$47463
$57361
$6,590
$7,658
$776'"5"5
$8,024
$8,137
$8,434
$9,593
$9,029
$9,062
$107619
$11^286
$147060
$147568
$13^848
243.2%
44.4%
8%
$11,995
$137328
$12,"i"6"3"
$127651
$137772
$167715
$167778
$16,883
$157707
$[57452
jl7,764
$167334
$167545
$17';427
$20,486
$227819
$257330
$287459
137.2%
60.2%
5%
$67,214
$61,883
$63,346
$627921
$69,639
$777396
$71jb"6"
$757639
j74^253
$737269
$777519
$677596
$66,182"
$,587451
$76,886
$91^428
$927910
$96^238
43.2%
24.1%
2%
59,253
52,494
557647
56,230
£27456
687338
62,582
66,780
66,683
66,251
697347
60,291
587700"
6l",043
677685
827669
827148
8J7627
7%
8%
8%
To%
11%
11%
12%
12%
12%
13%
14%
15%
15%
16%
17%
17%
18%
17%
18%
22%
19%
19%
20%
22%
23%
22%
21%
21%
23%
24%
25%
25%
27%
25%
27%
30%
37.8%
17.7%
2%
March 28, 2011
2B-21
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Table 2B-10: Trade Statistics for Profiled Chemicals and Allied Products Industry Segments
Year3
Value of
Imports
(millions, $2009)
Value of Exports
(millions, $2009)
Value of
Shipments
(millions, $2009)
Implied
Domestic
Consumption1"
Import
Penetration0
Export
Dependence*1
Pesticides and Fertilizers
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent
Change 1990-
2007
Total Percent
Change 2000-
2007
Average
Annual Growth
Rate
$1,782
$776ii
$7,624"
$7,893
$2,083
$2,181
$27139
$3,018
$3,022
$2,869
$3,350
$3,869
$3,135
$4'3'95
$57419
$7,282
$6,586
$87505
377.3%
153.9%
10%
$3,957
$"4,440
$37477
$2397
$"37771
$"4,369"
$4769
$4,072
$"47297'
$37855
j2,957
$2,654
$2,601
$2,840
$3,683
$3,321
$3",248"
$3379"
-9.6%
21.0%
-1%
$11,782
$127672
$107768
$97984
$127153
$137170
$137291
$127250
$117997
$97638"
$87845
$"87249
$8,556
$97607
$107822
$ii7o"4"i
$97795
$127403
5.3%
40.2%
0%
9,606
9"7243
87915
97280
167465
T6",982
117321
Il7l96
T6",722
87652
97238
97463
9,090
Ti73"6"T
137158
157663
137133
177328
19%
17%
18%
20%
20%
20%
19%
27%
28%
33%
36%
41%
34%
40%
41%"
49%
50%
49%"
34%
37%
32%
26%
31%
33%
31%
33%
36%
40%
33%
32%
30%
30%
28%
30%
33%
29%
80.4%
87.6%
4%
Pharmaceuticals
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2666
2001
2"662
2003
2"664
2005
2006
2007
Total Percent
Change 1990-
2007
Total Percent
Change 2000-
2007
Average
Annual Growth
Rate
$5,299
$57427
$7,564
$7,445
$87267
$167287
$137179
$177217
$2i796o
$28,491
$"3"4,328
$397693
$467255
$547847
$577190"
$587483"
$657632
$"6"9"78"4"6"
1218.0%
203.5%
16%
$4,994
$57423
$6,376
$6,486
$6",587"
$6,623"
$7,235
$107957
$127784
$1476"i7"
$167675
$197125
$19703l
$22,689
$257756
$26,688
$277623"
$287943"
479.5%
173.6%
11%
$75,140
$797735
$827830
$847342
$867992"
$897716
$947731
$1027669
$1137596
$1257i65
$1247879
$1357223
$1497641
$1567296
$1557815
$1577312
$1627697
$1567721
108.6%
125.5%
4%
75,446
867739"
83,964
85,36T
88,67"l
93,386
j'o'6'7'676
1087329
1227772
1347545
142332
1557191
1767865
1897547
1877249
18'9"7'708
266",767
1977624
7%
8%
9%
9%
9%
11%
13%
16%
18%
21%"
24%
25%
26%
29%
31%
31%
33%
35%
7%
7%
8%
8%
8%
7%
8%
11%
11%
12%
13%
14%
13%
14%
17%
17%
17%
18%
161.9%
138.7%
6%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled
the NAICS classification data to the SIC code classifications using the 1997Economic Census Br
in the NAICS system. For this analysis, EPA converted
Idge Between NAICS and SIC.
2B-22
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Table 2B-10: Trade Statistics for Profiled Chemicals and Allied Products Industry Segments
Year3
Value of
Imports
(millions, $2009)
Value of Exports
(millions, $2009)
Value of
Shipments
(millions, $2009)
Implied
Domestic
Consumption1"
Import
Penetration0
Export
Dependence*1
b. Calculated by EPA as shipments + imports - exports.
c. Calculated by EPA as imports divided by implied domestic consumption.
d. Calculated by EPA as exports divided by shipments.
Source: U.S. International Trade Commission, 1989-2007.
March 28, 2011
2B-23
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Figure 2B-5: Value of Imports and Exports for Profiled Chemicals and Allied Products Industry Segments3
Basic Chemicals
B $60,000
§ $55,000
-S2 $50,000
§, $45,000
J2 $40,000
"§ &? $35,000
& o
,2 ® $30,000
•_ V5
g_ $25,000
W $20,000
0 $15,000
•2 $10,000
/
^
£^S_
/^S
^r^
• ^x*^^— ^^^'^^^
..**' *---*^* *^
^O^O^O^O^O^O^O^O^O^OOOOOOOOOC
^o^o^o^o^o^o^o^o^o^ooooooooo^
k
!
Resins and Synthetics
0
o
5} $29 900
G $27 400
f, $24 900
•£ $22 400
O
Q, $19 900
H! $17 400
"a $14 900
!» $12 400 —
O $9 900
— '
H c;7 JAA
*S $4 900
3 $2 400
•3 5
1
*^
s
4/
s
A^^ ^^f
_.*---• *^»___#/' ^ — *^^"
yk^*~~^r—
* * ^^^
A— A— A— -X^* "* *^
,.*•'
-- *--•*'
t
h-h-h-h-h-h-h-h-h-KJKJKJKJKJKJKJKJKJ
^o ^o \o \o \o \o \o \o \o o o o o o o o o o
- - -*- - - Exports (SIC to NAICS) * Exports (NAICS 3252XX)
Pesticides and Fertilizers
2B-24
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Figure 2B-5: Value of Imports and Exports for Profiled Chemicals and Allied Products Industry Segments3
o\
3>13,UUU
fN
^
i
*ci 1 nnn
"E
o
jj cSi^OUU
—
5; 3>/,UUU
«
ifi
"^ *c^ nnn
| ,,---.> ^.--*-^^
p 1 Oh-*s*OJ^r/lO\iu,uuu . . -A • • ~ m ^^^
= n.,.^ = = 4= = = *"^"--»---«^
«
vovovovovovovovovovo
vovovovovovovovovovo
Oh-KJW*.tAO\
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2B: Chemicals Industry Profile
insight into the industry's financial performance and condition, EPA reviewed two key measures of financial
performance over the 21-year period, 1988-2008: net profit margin and return on total capital. EPA calculated
these measures using data from the Quarterly Financial Report for Manufacturing, Mining, and Trade
Corporations (QFR) published by the U.S. Census Bureau. Financial performance in the most recent financial
reporting period (2008) is obviously not a perfect indicator of conditions at the time of regulatory compliance.
However, examining the trend, and deviation from the trend, through the most recent reporting period gives
insight into where the industry may be, in terms of financial performance and condition, at the time of
compliance. In addition, the volatility of performance against the trend, in itself, provides a measure of the
potential risk faced by the industry in a future period in which compliance requirements are faced: all else equal,
the more volatile the historical performance, the more likely the industry may be in a period of relatively weak
financial conditions at the time of compliance.
Net profit margin is calculated as after-tax income before nonrecurring gains and losses as a percentage of sales
or revenues, and measures profitability, as reflected in the conventional accounting concept of net income. Over
time, the firms in an industry, and the industry collectively, must generate a sufficient positive profit margin if the
industry is to remain economically viable and attract capital. Year-to-year fluctuations in profit margin stem from
several factors, including: variations in aggregate economic conditions (including international and U.S.
conditions), variations in industry-specific market conditions (e.g., short-term capacity expansion resulting in
overcapacity), or changes in the pricing and availability of inputs to the industry's production processes (e.g., the
cost of energy to the chemical process). The extent to which these fluctuations affect an industry's profitability, in
turn, depends heavily on the fixed vs. variable cost structure of the industry's operations. In a capital intensive
industry such as the chemical and allied products industry, the relatively high fixed capital costs as well as other
fixed overhead outlays, can cause even small fluctuations in output or prices to have a large positive or negative
affect on profit margin.
Return on total capital is calculated as annual pre-tax income divided by the sum of current portion of long-term
debt due in 1 year or less, long-term debt due in more that 1 year, all other noncurrent liabilities and total
stockholders' equity (total capital). This concept measures the total productivity of the capital deployed by a firm
or industry, regardless of the financial source of the capital (i.e., equity, debt, or liability element). As such, the
return on total capital provides insight into the profitability of a business' assets independent of financial structure
and is thus a "purer" indicator of asset profitability than return on equity. In the same way as described for net
profit margin, the firms in an industry, and the industry collectively, must generate over time a sufficient return on
capital if the industry is to remain economically viable and attract capital. The factors causing short-term variation
in net profit margin will also be the primary sources of short-term variation in return on total capital.
Figure 2B-6 presents net profit margin and return on total capital for public-reporting firms in two chemical
industry segments - (1) Basic Chemicals, Resins, and Synthetics Manufacturing, which covers profiled segments
Basic Chemicals and Resins and Synthetics and (2) Pharmaceuticals and Medicines Manufacturing - for the 21-
year period, 1988 through 2008. Figure 2B-6 also presents net profit margin and return on total capital for public-
reporting firms in Other Chemicals segment - for the 17-year period, 1992 through 2008.8 The first segment
corresponds approximately to the profiled Basic Chemicals and Resins and Synthetics industry segments; the
second segment corresponds approximately to the profiled Pharmaceuticals industry segment; and the third
segment corresponds to the profiled Pesticides and Fertilizers industry segment. The financial performance
information reported in Figure 2B-6 confirms the trends and performance discussed above in this section.
8 For the Other Chemicals QFR segment, which includes the profiled Pesticides and Fertilizers segment, QFR data are available only
since 1992. In addition to NAICS 3253: Pesticide, Fertilizer, and Other Agricultural Chemical Manufacturing, which corresponds to
the profiled Pesticides and Fertilizers segment, the QFR Other Chemicals segment includes NAICS 3255: Paint, Coating, and
Adhesive Manufacturing; NAICS 3256: Soap, Cleaning Compound, and Toilet Preparation Manufacturing, and NAICS 3259: Other
Chemical Product and Preparation Manufacturing.
2B-26 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2B: Chemicals Industry Profile
As shown in Figure 2B-6, the Basic Chemicals and Resins and Synthetics segments have seen moderate volatility
of financial performance over the analysis period. Return on total capital moved off a post-recession low near -3
percent in 1992 to achieve levels of 7 to 10 percent during 1995-1997. Recovery of demand accompanied by
industry restructuring and downsizing accounted for the upturn in performance. During the latter part of the 1990s
decade, though, increased competition from foreign producers and demand weakness in Asian markets eroded this
performance. As a result, return on capital fell gradually through 2000. In 2001, a series of factors - high energy
and raw material prices at the start of the year, overcapacity, the terrorist attacks, and slowing U.S. and global
economies at the end of the year - led to a further sharp decline in return on capital performance of approximately
percent to less than one percent. Starting in 2002, however, return on total capital showed steady improvement,
increasing to nearly 10 percent by 2005 and then leveling out prior to the economic recession of 2008. Net profit
margin shows a similar, though less volatile, trend, with declines in 2000 through 2001, followed by steady
improvement between 2002 and 2005. In 2005, net profit margin reached a peak value of 6.6 percent, before
dipping in 2008 along with the general trend of the economy.
The same factors largely influenced performance in the Pharmaceuticals and Medicines Manufacturing segment
over the 21-year period. Performance in this segment was stronger than that in the other industry segments and
followed a less volatile pattern. Net profit margins rose from a low near 12 percent in 1993 to a peak of 15.9
percent in 1998. Since then, performance trended down to reach a low of approximately 14 percent in 2000. This
segment achieved steady, though moderate improvement during 2002 to 2004, and then rose rapidly to reach a
period high level of 21.7 percent in 2008. Return on total capital again shows a similar, though more volatile,
trend compared to net profit margin.
The Other Chemicals industry segment, which includes the profiled Pesticides and Fertilizers segment, was
susceptible to the same economic influences mentioned in the previous two paragraphs. The financial
performance of this segment was more volatile than the Pharmaceutical segment but more stable than the Basic
Chemicals segment. Both the net profit margin and return on total capital for this segment followed a similar
patter: performance was extremely transient for the first decade, peaking in 1996 and then falling sharply until
2001. In the 2000s decade, the financial health of this industry was much more stable and has been rising since
2001, with the exception of 2005. However, current levels of performance still have not reached the same peak
level they rose to in 1996.
Overall, the majority of profiled segments of the chemical industry remain at weaker levels of financial
performance than achieved during the mid 1990s but appear to have recovered from the downturn of 2001-2002.
As mentioned throughout this chapter, industry analysts predict that the chemicals sector will recover from the
2008 recession on all levels and is expected to provide improved ability to withstand additional regulatory
compliance costs without imposing significant financial impacts.
March 28, 2011 2B-27
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Figure 2B-6: Net Profit Margin and Return in Total Capital for the Chemicals and Allied Products Industry
Segments
Basic Chemicals, Resins, and Synthetics Manufacturing
20%
18%
Net Profit Margin
Return on T otal Capital
2B-28
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Pharmaceuticals and Medicines Manufacturing
k-Jk-Jk-Jk-Jk-Jk-Jk-Jk-Jk-J
Net Profit Margin
Return on Total Capital
Other Chemicals Manufacturing (Incl. Pesticides and Fertilizers)
^""
/ \
/ \
/
y
S£
vovovovovovovovoooooooooo
Net Profit Margin
Return on Total Capital
Source: Quarterly Financial Report, 1988-2008; U.S. Census Bureau.
2B.
6 Facilities Operating Cooling Water Intake Structures
Section 316(b) of the Clean Water Act applies to point source facilities that use or propose to use a cooling water
intake structure that withdraws cooling water directly from a surface waterbody of the United States. In 1982, the
Chemicals and Allied Products industry withdrew 2,797 billion gallons of cooling water, accounting for
approximately 3.6 percent of total industrial cooling water intake in the United States.4 The industry ranked 2nd in
industrial cooling water use behind the electric power generation industry (1982 Census of Manufactures).
4 Data on cooling water use are from the 1982 Census of Manufactures. 1982 was the last year in which the Census of Manufactures reported cooling water use.
March 28, 2011
2B-29
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
This section provides information for facilities in the profiled chemical and allied products segments estimated to
be subject to regulation under the regulatory analysis options. Existing facilities that meet all of the following
conditions could have been subject to regulation under the three regulatory analysis options:
> Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or
arrangement with an independent supplier who has a cooling water intake structure; or their cooling water
intake structure(s) withdraw(s) cooling water from waters of the United States, and at least twenty-five
(25) percent of the water withdrawn is used for contact or non-contact cooling purposes;
> Have a National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;
and
> Meet the applicability criteria for the specific regulatory analysis option in terms of design intake flow
(i.e., 2 MOD).
The regulatory analysis options also cover substantial additions or modifications to operations undertaken at such
facilities. EPA initially identified the set of facilities that were estimated to be potentially subject to the 316(b)
Existing Facilities Regulation based on a minimum applicability threshold of 2 MOD; this section focuses on
these facilities for the petroleum segment.5
2B.6.1 Waterbody and Cooling System Type
Table 2B-11, shows the distribution of facilities by type of water body and cooling system for each option.
Table 2B-11: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by
Waterbody Type and Cooling Water Intake System for the Profiled Chemical Segments
Waterbody Type
Estuary/ Tidal River
Ocean
Lake/Reservoir
Freshwater River/ Stream3
Great Lake
Total1'
Red
No.'
0
6
4
30
0
35
rculating
%offotai
0%
o%
12%
88%
0%
19%
Con
No.'
13
o
6
17
0
36
ibination
%offotai
36%
6'%"
17%
48%
0%
20%
Once
No".'
3
9
4
61
13
90
-Through
"/ooffotai
4%
10%
5%
68%
14%
50%
<
No'.'
0
o"
0
10
4
14
Mier
"/ooffotai
0%
o'%
0%
70%
30%
8%"
Total
16
9
15
123
17
179
Based on technical weights (See Appendix 3.A).
a. Four freshwater facilities' cooling water intake system types are unknown. These facilities are included in the total for Freshwater River/Stream.
b. Individual numbers may not add to total due to independent rounding.
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
2B.6.2 Facility Size
The facilities in the Inorganic Chemicals, Plastics Materials and Resins and Organic Chemicals segments that are
estimated subject to regulation under each analysis option are relatively large, with the vast majority of facilities
employing more than 100 employees. Figure 2B-7, shows the number of facilities in the profiled chemical
segments by employment size category for each analysis option.
EPA applied sample weights to the sampled 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).
2B-30
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2B: Chemicals Industry Profile
Figure 2B-7: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by
Employment Size for Profiled Chemicals and Allied Products Industry Segments
60
50
40
30
20
10
0
Less than
100
100-249
250-499
500-999
1000 and
greater
Source: U.S. EPA, 2000; U.S. EPA analysis2010.
2B.6.3 Firm Size
EPA used the Small Business Administration (SBA) small entity size standards to determine the number of
facilities in the three profiled chemical segments that are owned by small firms. Firms in the Basic Chemicals
segment (NAICS codes 325110, 325120, 325131, 325181, 325188, and 325199), firms in the Resins and
Synthetics sector (NAICS codes 325221, and 325222), and firms in the Pesticides and Fertilizer segment (NAICS
code 32311) are defined as small if they have 1,000 or fewer employees except firms in NAICS 32521 as well as
firms in the Pharmaceutical segment (NAICS codes 325411 and 325412), which are defined as small if they have
750 or fewer employees; remaining firms in the Pesticides and Fertilizer segment (NAICS 325312) are defined as
small if they have 500 or fewer employees.
EPA estimates that 21 small entity-owned facilities and 145 large entity-owned facilities in the Chemical segment
will be subject to the 316(b) Existing Facilities regulation. Insufficient survey data are available to classify the
entity size of an additional 13 in-scope facilities in this segment.
March 28, 2011
2B-31
-------
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
2C Profile of the Petroleum Refining Industry
2C.1 Introduction
EPA's Detailed Industry Questionnaire, hereafter referred to as the DQ, identified the Petroleum Refining
Industry (SIC 2911 or NAICS 324110) with at least one existing facility that operates a CWIS, holds a NPDES
permit, withdraws at least two million gallons per day (MGD) from a water of the United States, and uses at least
25 percent of its intake flow for cooling purposes (facilities with these characteristics are hereafter referred to as
"facilities potentially subject to the 316(b) Existing Facilities regulation" or "in-scope facilities").
Table 2C-1, below, provides a description of the industry segment, a list of primary products manufactured, the
total number of the DQ respondents (weighted to represent a national total of facilities that hold a NPDES permit
and operate cooling water intake structures), and the number of facilities estimated to be potentially subject to 316
(b) Existing Facilities Rule based on the minimum withdrawal threshold of 2 MGD (see Chapter 1: Introduction
for more details on the Rule applicability criteria).
Table 2C-1: Existing Facilities in the Petroleum Refining Industry (NAICS 324110)
NAICS
324110
NAICS
Description
Petroleum
Refineries
Important Products Manufactured
Gasoline, including finished base stocks and blending agents; jet fuel; kerosene;
light fuel oils; heavy fuel oils, including grades no. 5, 6, heavy diesel-type, heavy
gas-enrichment oils; lubricating oils and greases; unfinished oils and lubricating oil
base stock; asphalt; liquefied refinery gases, including other aliphatics (feed stock
and other uses); and other finished petroleum products, including waxes.
Number of In-Scope
Facilities3
39
a. Number of weighted detailed questionnaire survey respondents.
Source: Executive Office of the President, 1987; U.S. EPA 2000; U.S. EPA analysis, 2010.
As shown in Table 2C-1, EPA estimates that, out of an estimated total of 1639 facilities with a NPDES permit and
operating cooling water intake structures in the Petroleum Refining industry (NAICS 324110), 39 (or 24 percent)
would be subject to regulation under the 316(b) Proposed Existing Facilities Regulation. EPA also estimated the
percentage of total production that occurs at facilities estimated to be subject to regulation under each analysis
option. Total value of shipments for the Petroleum Refining Industry from the 2007 Economic Census is $590.4
billion ($2009). Value of shipments, a measure of the dollar value of production, was selected for the basis of this
estimate. Because the DQ did not collect value of shipments data, these data were not available for existing
facilities. Total revenue, as reported on the DQ, was used as a close approximation for value of shipments for
these facilities. EPA estimated the total revenue of facilities expected to be subject to regulation under the 316(b)
Existing Facilities Regulation to be $216.3 billion ($2009). Therefore, EPA estimates that the percentage of total
production in the petroleum refining industry that occurs at facilities estimated to be subject to the regulation is 37
percent.
Table 2C-2 provides the crosswalk between NAICS codes and SIC codes for the profiled petroleum NAICS
codes. For the Petroleum Refineries segment, the translation of SIC-reported data to the NAICS framework is
straightforward as these frameworks have a simple one-to-one match for Petroleum Refining: NAICS code
324110 and SIC code 2911.
This estimate of the number of facilities holding a NPDES permit and operating a cooling water intake structure is based on the
responses from facilities that received the 1999 screener questionnaire.
March 28, 2011
2C-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
Table 2C-2: Relationship between NAICS and SIC Codes for the Petroleum Refineries Industry (2007)
NAICS
Code
324110
NAICS
Description
Petroleum
Refineries
SIC
Code
2911
SIC Description
Petroleum Refining
Num.
Establishments
189
Value of Shipments
(Millions; $2009)
$590,441
Employment
(FTEs)
65,022
Sources: U.S. DOC. 2007Economic Census.
2C.2 Summary Insights from this Profile
A key purpose of this profile is to provide insight into the ability of Petroleum Refining firms to absorb
compliance costs under the Proposed 316(b) Existing Facilities Rule without material, adverse economic/financial
effects. The industry's ability to withstand compliance costs is primarily influenced by the following two factors:
(1) the extent to which the industry may be expected to shift compliance costs to its customers through price
increases and (2) the financial health of the industry and its general business outlook.
2C.2.1 Likely Ability to Pass Compliance Costs Through to Customers
As reported in the following sections of this profile, the Petroleum Refining industry is relatively unconcentrated,
which suggests that firms in this industry would have less power to pass a significant portion of their compliance-
related costs through to customers. As discussed above, given the small proportion of total value of shipments in
the industry estimated to be subject to regulation under each option, EPA judges that in-scope refineries subject to
the 316(b) Existing Facilities Regulation are not likely to be able to recover compliance costs through price
increases to customers. Even though the Petroleum Refining industry is not characterized by high competitive
pressure from foreign markets, the low market concentration leads EPA to judge that the market power held by
individual firms is likely to be quite small. For these reasons, in its analysis of regulatory impacts for the
Petroleum Refining segment, EPA assumed that complying firms would be unable to pass compliance costs
through to customers: i.e., complying facilities must absorb all compliance costs within their operating finances
(see following sections and Appendix 34.A: Cost Pass-Through Analysis, for further information).
2C.2.2 Financial Health and General Business Outlook
Over the past two decades, Petroleum Refining, like other U.S. manufacturing industries, has experienced a range
of economic/financial conditions, including substantial challenges. In the early 1990s, the domestic Petroleum
Refining industry was affected by reduced U.S. demand as the economy entered a recessionary period. Although
domestic market conditions improved by mid-decade, oversupply of crude oil, weakness in Asian markets, along
with other domestic factors, materially weakened refiners' financial performance in 1998. As petroleum
producing countries reduced crude oil supply and refiners cut production, prices rebounded in the late 1990's and
into 2000, before another U.S. recession, the attacks of 9/11, and global economic downturn again had a negative
effect on petroleum refiners. As the U.S. economy began recovery from its economic weakness caused by the
2001 recession, the domestic petroleum refining industry also recovered, with continuous improvements in
demand levels and financial performance during 2003 to 2007. Between July and December of 2008, however, at
the outset of the recent economic recession, the price of crude oil dropped more than $100 a barrel. Economists
predict that this slide in oil demand will rebound as the economy recovers in the coming years (Protec Fuel
Management, 2009). In fact, the 2009 Annual Energy Outlook, published by the U.S. Energy Information
Administration (EIA) of the U.S. Department of Energy (DOE), projects that petroleum production will increase
from 13.08 quadrillion Btu in 2008 to 15.51 in 2020, 15.68 in 2030, and 15.87 in 2035, showing gradual
expansion in domestic petroleum production (U.S. DOE, 2009a). In addition, according to the 2010 Annual
Energy Outlook, total liquid fuels consumption, including petroleum products, will grow by roughly 1 percent
annually until 2035, owing a majority of this increase to the transportation sector's growing demand (U.S. DOE,
2010). Although the Petroleum Refining industry has weathered difficult periods over the last two decades, the
expected strengthening of the industry's financial condition and general business outlook as the world and U.S.
2C-2
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2C: Petroleum Refining Industry Profile
economy recover from the current economic weakness, point to the ability of the in-scope facilities in the
Petroleum Refining industry to withstand additional regulatory compliance costs without imposing significant
financial impacts.
2C.3 Domestic Production
The Petroleum Refining industry accounts for about 11 percent of the value of shipments of the entire U.S.
manufacturing sector and employs 0.5 percent of the manufacturing sector's workers (U.S. Census Bureau,
2009a). According to the Annual Survey of Manufactures, in 2007, Petroleum Refineries achieved shipments of
approximately $590.4 billion dollars ($2009) and employed 60,022 people. Petroleum products constitute
approximately 37 percent of the total energy used in the United States, including virtually all of the energy
consumed in transportation (U.S. DOE, 2009b).
According to EIA, 150 Petroleum Refineries operated in the United States in 2008 (U.S. DOE, 2009b).l Some
data reported in this profile are taken from EIA publications. Readers should note that the Census data reported
for NAICS 324110 cover a somewhat broader range of facilities than do the U.S. DOE/EIA data, and the two data
sources are therefore not entirely comparable.2
The petroleum industry includes exploration and production of crude oil, refining, transportation, and marketing.
Petroleum refining is a capital-intensive process that converts crude oil into a variety of refined products.
Refineries range in complexity, depending on the types of products produced. Nearly half of all U.S. refinery
output is motor gasoline.
The number of U.S. refineries has declined by almost half since the early 1980s. The remaining refineries have
improved their efficiency and flexibility to process heavier crude oils by adding "downstream" capacity. While
the number of refineries has declined, the average refinery capacity and utilization has increased, resulting in an
increase in domestic refinery production overall.
2C.3.1 Output
Table 2C-3 shows trends in production of petroleum refinery products from 1990 through 2008. In general, output
of refined products grew over this period, reflecting growth in transportation demand and other end-uses. Output
fell in 1991 due to the domestic economic recession, and the early years of the 2000s experienced little or
negative growth because of the downturn of the U.S. economy and events of 9/11 (API, 2003). At the beginning
of 2002, petroleum products were in excess supply in the world market, and the focus was on the elimination of
excess supplies and stabilization of prices (U.S. DOE, 2004). In 2003, the industry rebounded, with refinery
processing increasing 2 percent, producing record or near record levels of gasoline and distillate (API, 2004).
Petroleum production continued to increase until the global recession hit in 2008, when overall U.S. production
fell slightly by 0.1 percent. U.S. demand for oil and gas refined products fell by more than three million b/d from
the peak in February 2008 to the trough in June 2009 during the global economic slowdown; as a result, refining
margins have narrowed and refiners have responded by reducing throughput rates, idling and closing less efficient
facilities, and cutting capital expenditures (S&P, 2010c). As the U.S. and global economy improves, Petroleum
In addition, one operating and one idle refinery were located in Puerto Rico and one operating refinery in the Virgin Islands.
2 For comparison, preliminary 1997 Census data included 244 establishments for NAICS 3241/SIC 2911, whereas U.S. DOE/EIA
reported 164 operable refineries as of January 1997.
The first step in refining is atmospheric distillation, which uses heat to separate various hydrocarbon components in crude oil. Beyond
this basic step are more complex operations (generally referred to as "downstream" from the initial distillation) that increase the
refinery's capacity to process a wide range of crude oils and increase the yield of lighter (low-boiling point) products such as gasoline.
These downstream operations include vacuum distillation, cracking units, reforming units, and other processes (U.S. DOE, 1999a).
March 28, 2011 2C-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
Refining firms are also likely to see improvements in their markets and earnings. This should place companies in
a better position to incur any costs associated with regulatory compliance.
Table 2C-3: U.S. Petroleum Refinery Product Production (million barrels per day)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Total Percent Change
1990-2008
Total Percent Change
2000-2008
Average Annual
Growth Rate
Motor
Gasoline
6.96
6.98
7.06
7.30
7."l8
7.46
7.57
7.74
7.89
7.93
7.95
8.02
8.18
8.19
8.23
8.32
8.36
8.36
874
20.7%
5.7%
1.1%
Distillate Fuel
Oil
2.93
2796
2797
3"7l3
3720
3"7l6
3732
3739
3742
3740
338
3770
339
3771
3782
3795
404"
413
429
46.4%
19.8%
2.1%
Jet Fuel
1.49
F.44
f.40
r.42
f.45
F.42
F.52
l'.55
l"53
l"."57
1.61
l"53
i'.5"i
l"50
l'.55
l"55
l"48
l".45
147
-1.3%
-8.7%
-0.1%
Residual Fuel
Oil
0.95
0.93
6.89
0.84
0.83
0.79
0.73
0.71
0.76
(170
(170
0.72
6.60
0.66"
0.65
0.63
b"."64
0.67
0.62"
-34.7%
-11.4%
-2.3%
Other
Products3
0.78
6"76
b""80
078
079
078
076
b"84
b"89
b"84
079
073
077
078
b"83
075
076
075
b"67
-14.1%
-15.2%
-0.8%
Total Output
15.27
15:26
15AO
F579
F579
i"5""99"
16732
1676
i"7"b3
16799
17724
T7729
17727
1749
1777
17"8
17798
17799
17798
17.7%
4.3%
0.9%
Percent Change in
Total Output
n/a
-61%
b"."9%"
275%
b"."b%"
i'.3%
21%
27%
F.6%
'-01%
l'."5"%"
03%
-61%
F.3%
F.6%
0.2%
i'."b%"
61%
-61%
a. Kerosene, lubricants, petrochemical feedstocks
Source: U.S. DOE, Annual Energy Review
i, waxes, and miscellaneous products.
2009b
Value of shipments and value added are two common measures of manufacturing output. They provide insight
into the overall economic health and outlook for an industry. Value of shipments is the sum of the receipts a
manufacturer earns from the sale of its outputs; it indicates the overall size of a market or the size of a firm in
relation to its market or competitors. Value added measures the value of production activity in a particular
industry. It is the difference between the value of shipments and the value of inputs (from other industries) used to
make the products that are sold.
Figure 2C-1 shows value of shipments and value added for petroleum products from 1987 to 2007. Value of
shipments rose through 1990; however, during and following the recession of 1991, value of shipments fell
through 1994. This was followed by some volatility over the next few years until experiencing a sharp drop in
1998, when a range of factors led to a dramatic decrease in petroleum prices. Increased production quotas by
OPEC, increased production from Iraq through the "oil-for-food" program, weak demand in Asia due to their
financial crisis, and a warm winter in the U.S. all increased the supply of petroleum products (U.S. DOE, 1999c).
Estimates of worldwide petroleum supply exceeding demand during 1998 range from 1.47 millions barrels per
day to 2.4 million barrels per day (World Oil, 1999).
As crude oil producers and refiners cut back on production, the industry was restored with significant
improvements in 1999 and 2000, before the global economic slowdown and weakening demand decreased the
Terms highlighted in bold and italic font are further explained in the glossary.
2C-4
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
value of shipments in 2001. From 2003 through 2007, however, value of shipments increased significantly,
peaking at nearly $600 billion in 2007. The average annual percentage change during this four-year period was
23.4 percent. Value added generally followed the path of value of shipments over the last two decades.
Figure 2C-1: Value of Shipments and Value Added for Petroleum Refineries (millions, $2009)a
Value of Shipments
^"~- ««7n nnn
o
o
M t^n nnn
^fi
— l^)W^C/lON)W^C/lON
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
2C.3.2 Prices
The producer price index (PPI) measures price changes from the perspective of the seller, and indicates the overall
trend of product pricing, and thus provides insight into supply-demand conditions, within a given industry.
Figure 2C-2 shows substantial fluctuations in petroleum product prices between 1987 and 2008. Through the
early 1990s, refiners faced declining prices due to the effects of the 1991 recession and weak demand before
rebounding somewhat in the mid 1990s. Prices fell in 1998 as a massive oversupply of petroleum products
coupled with decreased demand led to significant drops in petroleum prices. As the subsequent production
cutbacks took hold and the glut of supply dwindled, prices recovered in 1999 and 2000, as shown in Figure 2C-2.
The higher prices reflect low refinery product inventories and higher crude oil input prices (Value Line, 2010).
Excess supply, the global recession, impacts from 9/11, and the relatively warm winter of 2001-2002 led to
decreases in prices in subsequent years (U.S. DOE, 2004). During 2003 to 2008, however, prices rose
dramatically. By 2008, the price of petroleum products was over double the price seen in 2000, the previous peak
year during the 1987 to 2002 time period.
During the second half of 2008, petroleum industry prices began to decline as the result of economic recession
and continued to do so though the middle of 2009. Oil prices fell during 2008 due to a broad-based financial de-
leveraging occurring across all markets and investment asset classes. The drop in oil prices nearly exactly
corresponded to price movements in the collapsing stock market (Protec Fuel Management, 2008). Although
world oil prices declined sharply in 2008, they have generally risen throughout 2009. Prices are forecast to
rebound as the world economy recovers and global demand grows more rapidly than liquid supplies from
producers outside of the Organization of the Petroleum Exporting Countries (OPEC). In 2035, the average real
price of crude oil is expected to be $133 per barrel ($2008) (U.S. DOE, 2009a).
Figure 2C-2: Producer Price Index for Petroleum Refineries
350
300
250
Source: BLS, 2009d.
2C.3.3 Number of Facilities and Firms
The number of operable refineries fell substantially during the 1980s, with fluctuations in the number of refinery
firms and facilities through the 1990s and 2000s. The earlier decrease resulted in part from the elimination of the
2C-6
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
Crude Oil Entitlements Program in the early 1980s. The Entitlements Program encouraged smaller refineries to
add capacity throughout the 1970s. After the program was eliminated, surplus capacity and falling profit margins
led to the closure of less efficient capacity (U.S. DOE, 1999a). The decrease in the number of refineries
continued, as the industry consolidated to improve margins. After peaking in the early 1980s, refining capacity
decreased throughout the rest of the decade. Refining capacity has remained relatively stable since the decrease in
the 1980s, with a slight upward trend occurring in the latter part of the 1990s into the 2000s.
Table 2C-4 presents the numbers of refinery facilities and firms from 1990 to 2006 based on Statistics of U.S.
Businesses for NAICS 324110. As shown in the table, despite some significant losses in 1997 and 2003, both the
number of refinery facilities and the number of firms reporting Petroleum Refining as their primary business have
grown since 1990. The number of petroleum refinery firms grew 40.7 percent from 2000 to 2006, while the
number of facilities correspondingly grew by 18.1 percent. Since refinery operable distillation capacity is
projected to increase by 8,000 barrels per calendar day from 2009 to 2010, either new facilities or increased
efficiency can be expected in the next year for petroleum refineries (U.S. DOE, 2010). Most additional new
capacity in the United States is expected to be concentrated on the Gulf Coast, with some in the Midwest (Turner,
2006).
Table 2C-4: Number of Firms and Facilities for Petroleum Refineries
Year3
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent Change 1990-
2006
Total Percent Change 2000-
2006
Average Annual Growth Rate
Firms
Number
215
215
185
148
161
150
173
128
155
145
162
165
202
142
155
177
228
Percent Change
676%
:i4o%
:2o7o%
87s%
'-6.8%
1573%
l2'67o'%"
2i"."i%
-675%
i"i"."7%
i""9%"
224%
12977%
972%
142%
2878%
6.0%
40.7%
'674%
Facilities
Number
340
346
303
251
265
251
275
248
304
292
298
302
349
274
364
301
352
Percent Change
i'"."8"%"
:Y2'".4%"
:y772%
576%
"-53%
976%
-978"'%"
2276%
-379%
2"."i"%"
173%
1576%
:2j75%
3278%
:y773%
1679%
3.5%
18.1%
'672'"%
a Before 1998, these data were compiled in the Standard Industrial Classification (SIC) system; since 1998, these data have been compiled in the North
American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the
1997 Economic Census Bridge Between NAICS and SIC.
Source: U.S. SBA, 1990-1997; SUSB, 1998-2006.
2C.3.4 Employment and Productivity
Employment in the Petroleum Refining segment declined by 13 percent between 1987 and 2007, from 74,600 to
65,022 employees, shown in Figure 2C-3. After increasing in the early 1990s, employment at Petroleum
Refineries declined almost continuously through 2003, reflecting overall industry consolidation, before showing
slight recovery during the remainder of the decade. The declining level of employment is not so much an indicator
of financial success for the industry, but rather an indicator of the increasing mechanization of petroleum
March 28, 2011
2C-7
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
refineries. The industry has become highly automated, with the average annual revenue per worker currently at
over $3 million (First Research, 2009).
Figure 2C-3: Employment for Petroleum Refineries3
on nnn -r
77 ^nn
/ /,3UU
7^ nnn
4* 7"> ^nn
4> /Z,3UU
O 7n nnn
_ /u,uuu
c.
S/r'7 cnn
,. /:« nnn
0 '
£ 62,500
"5 AH nnn
'
55,000
c t\ nnn
3U,UUU 1
I
1
'",., >"S. !
*•* v. i
A i
\ 1
*v I (SIC 2911)
il. ^i /I A Petroleum Refineries
«r *>v /\y 1 (NAICS 3241 10)
1
i
1
I
1
I
a Before 1997, the Department of Commerce compiled data in the SIC system; since 1997 these data have been compiled in the North
American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code
classifications using the 1997 Economic Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007 Economic Census.
Table 2C-5 shows substantial year-to-year changes in labor productivity, measured by value added per production
hour. These fluctuations largely reflect volatility in value added, which in turn indicates variation in the
relationship between input prices (primarily crude oil) and refinery product prices. Changes in production hours
from year to year were less volatile, with a net reduction over the period 1987 to 2007, but with a slight increase
(1.4 percent) in the number of production hours in the last seven years since 2000. Value added was not
negatively affected, as it more than quadrupled over the same two-decade period.
2C-8
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
Table 2C-5: Productivity Trends for Petroleum Refineries ($2009)a
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2666
2001
2662
2003
2004
2005
2006
2007
Total Percent Change 1987-
2007
Total Percent Change 2006-
2007
Average Annual Growth
Rate 1987-2007
Value Added
(millions)
$24,100
$337966
$34.7675
$"347697
$297666
1277396
$267256
$"327717
$327281
$"34.7357
$467687
$367839
$397871
$457363
$487829
$"337312
$"457538
$637560
$"114"79"4"2"
$ii8^6i
$1147108
373.5%
151.5%
8.1%
Production Hours
(millions)
103
103
105
106
107
109
107
Tio
107
103
Too
98
94
92
94
84
83
83
89
88
94
-9.4%
1.4%
-0.5%
Value Ad
(S/hr)
$233
$329
$326
$328
$273
$251
$247
$297
$303
$335
$402
$315
$424
$491
$522
$395
$550
$769
$17294
$17348"
$17219
422.7%
148.2%
8.6%
ded/Hour
% Change in Value
Added/ Hour
n/a
4i72"%"
-i7T%
IIIIIIIIo^
-16.8%
-872%
-T.6%
20.6%
f.7%
107%
1979%
7273%
3473%
1676%
63%
:2473%
3972%
3978%
6872%
472%
-975%
a Before 1997, these data were compiled in the Standard Industrial Classification (SIC) system; since 1997, these data have been compiled
in the North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC
code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2C.3.5 Capital Expenditures
Petroleum industry capital expenditures increased substantially between 1987 and 1993, generally decreased
through the rest of the decade, then increased significantly in 2001, as shown in Table 2C-6. During 2001 through
2004, capital expenditures fluctuated somewhat, peaking at over 8.5 billion in 2002 before declining in both 2003
and 2004. The second half of the last decade showed a great increase in capital expenditures, reaching well over
$17 billion in 2007 - a 413 percent change from 1987 expenditures and 205 percent change since 2000. Much
recent investment in petroleum refineries has been to expand and de-bottleneck units downstream from
distillation, partially in response to environmental requirements. Changes in refinery configurations have included
adding catalytic cracking units, installing additional sulfur removal hydrotreaters, and using manufacturing
additives such as oxygenates. These process changes have resulted from two factors:
> processing of heavier crudes with higher levels of sulfur and metals; and
> regulations requiring gasoline reformulation to reduce volatiles in gasoline and production of diesel fuels
with reduced sulfur content (U.S. EPA, 1996b).
Environmentally related investments have also accounted for a substantial part of capital expenditures. Significant
expenditures for gasoline quality improvements occurred in the early 1990s and in 2002, and capital expenditure
activity is expected to continue to rise as oil and gas discoveries are being mad worldwide. In 2009 alone, over
March 28, 2011
2C-9
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
350 discoveries were announced, and in order to capitalize on these discoveries, companies are expecting to
increase their capital budgets for 2010 and beyond (NPC, 2004; Global Data, 2010).
Table 2C-6: Capital Expenditures for Petroleum Refineries ($2009)
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Percent Change 1987- 2007
Percent Change 2666- 2007
Average Annual Growth Rate
1987- 2007
Capital Expenditures
(millions)
$3,449
$3313
$47716
$5306
$87224
$8306
$87400
$77592
$7398
$6366
$57510
$57352
$47988
$5301
$87255
$87781
$77965
$77488
$11,552
311^39
317^77
412.5%
IIIIIIIIIIIIIIZ
8.5%
% Change
n/a
1675%
237%
237T%
4l76%
7.1%
-476%
-976%
476%
ri37i%
7j9j%"
-279%
-678%
1673%
4273%
674%
-973%
-676%
5473%
275%
4973%
a Before 1997, these data were compiled in the Standard Industrial Classification (SIC) system; since 1997, these data have been compiled
in the North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC
code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2C.3.6 Capacity Utilization
Refinery capacity is frequently measured in terms of crude oil distillation capacity. EIA defines refinery capacity
utilization as input divided by calendar day capacity, which is the maximum amount of crude oil input that can be
processed during a 24-hour period with certain limitations. Some downstream refinery capacities are measured in
terms of "stream days," which is the amount a unit can process when running full capacity under optimal crude
and product mix conditions for 24 hours (U.S. DOE, 1999a). Downstream capacities are reported only for specific
units or products, and are not summed across products, since not all products could be produced at the reported
levels simultaneously.
Figure 2C-4 below shows the fluctuation in capacity utilization rates over the period 1990-2009, based on the
U.S. Census Bureau data. Overall, capacity utilization fluctuated over a relatively low range over the last two
decades. Between 1990 and 1994, capacity utilization steadily increased, followed by a sharp drop in 1995. It
remained relatively stable until 2004 when excess supply, recession, and other factors led to decreases in rates
during the early part of this decade hitting particularly hard in 2005. The industry recovered very quickly,
however, as capacity utilization increased during the following two years before dropping slightly in 2008 and
2009 as an aftershock of the economic downturn that began in 2007. Overall, refinery utilization remained relative
2C-10
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
high during the last two decades. Capacity utilization for production of specific products may vary, however, as
the industry adjusts to changes in the desired product mix and characteristics.
Figure 2C-4: Capacity Utilization Rates (Fourth Quarter) for Petroleum Refineries3'15
100 n
Q0.
O/l
QA
00
88 (
sit*
Szt
on
5U
'TO
'?/:
1A
T)
•*
.'--' '- >F"\_. A '
j " \ A to NAICS)
\ 1 \ j
\ J \ \ (NAirS324110)
V
M
i i i i i i i i i i i i i i i i i i i
a Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
Census Bridge Between NAICS and SIC.
b Prior to 2007, U.S. Census sampled every industry in a specific NAICS6. Beginning in 2007, U.S. Census only sampled certain industries within any
NAICS6, and therefore, the data collected before 2007 cannot be directly compared to the data collected in 2007 and beyond.
Source: U.S. DOC, Survey of Plant Capacity 1989-2009, U.S. Census Bureau.
2C.4 Structure and Competitiveness
The U.S. Petroleum Refining industry is made up of integrated international oil companies, integrated domestic
oil companies, and independent domestic refining/marketing companies. In general, the petroleum industry is
highly integrated, with many firms involved in more than one stage of petroleum industry operations. Large
companies, referred to as the "majors," are fully integrated across crude oil exploration and production, refining,
and marketing. Smaller, nonintegrated companies, referred to as the "independents," generally specialize in one
segment of the industry.
Like the oil business in general, refining was dominated in the 1990s by integrated internationals, specifically a
few large companies such as Exxon Corporation, Mobil Corporation, and Chevron Corporation. These three
ranked in the top ten of Fortune 500 sales during this time period. Substantial diversification by major petroleum
companies into other energy and non-energy segments was financed by high oil prices in the 1970s and 1980s.
With lower profitability in the 1990s, the major producers began to exit unconventional energy operations (e.g.,
oil shale) as well as coal and non-energy operations in the 1990s. Some have recently ceased chemical production.
During the 1990s and into the early 2000s, several mergers, acquisitions, and joint ventures occurred in the
Petroleum Refining segment in an effort to cut cost and increase profitability. This consolidation took place
among the largest firms (as illustrated by the acquisition of Amoco Corporation by British Petroleum in 1999, the
merger of Chevron and Texaco in 2001, the merger of Conoco and Phillips in 2002, and the mega-merger of
Exxon and Mobil Corporation in 1998) as well as among independent refiners and marketers (e.g., the
independent refiner/marketer Ultramar Diamond Shamrock (UDS) acquired Total Petroleum North America in
March 28, 2011
2C-11
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2C: Petroleum Refining Industry Profile
1997) (U.S. DOE, 1999b, 2004). Merger activity slowed during the earlier part of the decade, possibly because
companies were trying to address financial issues after the 2001 recession and/or wanted to make sure that
economy was indeed recovering (U.S. DOE, 2004).
The oil industry has been becoming less vertically integrated in recent years. The share of U.S. refining capacity
owned by independent refiners with no production operations was eight percent in 1990 while by 2007, this share
exceeded 21 percent. Important mergers and acquisitions in the later part of the decade included: ChevronTexaco
and Unocal in 2005; Valero and Premcor in 2005; ConocoPhillips and Burlington Resources in 2006; Anadarko,
Kerr-McGee, and Western Gas Resources in 2006; and Occidental and Vintage Petroleum in 2006 (API, 2008).
2C.4.1 Firm Size
For NAICS 324110, the Small Business Administration defines a small firm as having 1,500 or fewer employees.
The size categories reported in the Statistics of U.S. Businesses (SUSB) do not correspond with the SBA size
classifications, therefore preventing precise use of the SBA size threshold in conjunction with SUSB data. Table
2C-7 below shows the distribution of firms and establishments in NAICS 324110 by the employment size of the
parent firm. The SUSB data show that 155 of the 352 NAICS 324110 establishments reported for 2006 (44
percent) are owned by larger firms (those with 500 employees or more), some of which may still be defined as
small under the SBA definition, and 197 (56 percent) are owned by small firms (those with fewer than 500
employees).
Table 2C-7: Number of Firms and Establishments for Petroleum
Refineries by Firm Employment Size Category, 2006a
Employment Size
Category
0-19
20-99
100-499
500+
Total
Number of Firms
148
21
19
40
228
Number of Establishments
148
26
23
155
352
a Based on NAICS 324110
Source: U.S.DOC, Statistics of U.S. Businesses, 2006.
2C.4.2 Concentration Ratios
Concentration is the degree to which industry output is concentrated in a few large firms. Concentration is closely
related to entry barriers, with more concentrated industries generally having higher barriers.
The four-firm concentration ratio (CR4) and the Herfindahl-Hirschman Index (HHI) are common measures of
industry concentration. The CR4 indicates the market share of the four largest firms. For example, a CR4 of 72
percent means that the four largest firms in the industry account for 72 percent of the industry's total value of
shipments. The higher the concentration ratio, the less competition there is in the industry, other things being
equal. An industry with a CR4 of more than 50 percent is generally considered concentrated. The HHI indicates
concentration based on the largest 50 firms in the industry. It is equal to the sum of the squares of the market
shares for the largest 50 firms in the industry. For example, if an industry consists of only three firms with market
Note that the measured concentration ratio and the HHF are very sensitive to how the industry is defined. An industry with a high
concentration in domestic production may nonetheless be subject to significant competitive pressures if it competes with foreign
producers or if it competes with products produced by other industries (e.g., plastics vs. aluminum in beverage containers).
Concentration ratios based on share of domestic production are therefore only one indicator of the extent of competition in an
industry.
"20^12 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
shares of 60, 30, and 10 percent, respectively, the HHI of this industry would be equal to 4,600 (602 + 302 + 102).
The higher the index, the fewer the number of firms supplying the industry and the more concentrated the
industry. Based on the U.S. Department of Justice's guidelines for evaluating mergers, markets in which the HHI
is under 1,000 are considered unconcentrated, markets in which the HHI is between 1,000 and 1,800 are
considered to be moderately concentrated, and those in which the HHI is in excess of 1,800 are considered to be
concentrated.
As shown in Table 2C-8, the CR4 and the HHI for NAICS 324110 are both below the benchmarks of 50 percent
and 1,000, respectively. For the Petroleum Refining segment, the HHI is 640, suggesting that as of 2002, the
sector was still fairly unconcentrated, although the trend during the previous decade had been toward becoming a
more concentrated industry. With the majority of the firms in this industry having small market shares, this
suggests limited potential for passing through to customers any increase in production costs resulting from
regulatory compliance.
Table 2C-8: Selected Ratios for Petroleum Refineries
SIC (S) or
NAICS (N)
Code
S2911
N 324110
Year
1987
1992
1997
2002
Total
Number of
Firms
200
132
122
88
Concentration Ratios
4 Firm
(CR4)
32%
30%
29%
41%
8 Firm
(CR8)
52%
49%
49%
64%
20 Firm
(CR20)
78%
78%
82%
89%
50 Firm
(CR50)
95%
97%
98%
99%
Herfindahl-
Hirschman Index
435
414
422
640
Source: U.S. DOC, Economic Census, 1987, 1992, 1997, and 2002.
2C.4.3 Foreign Trade
This profile uses two measures of foreign competition: export dependence and import penetration.
Import penetration measures the extent to which domestic firms are exposed to foreign competition in domestic
markets. Import penetration is calculated as total imports divided by total value of domestic consumption in that
industry: where domestic consumption equals domestic production plus imports minus exports. Theory suggests
that higher import penetration levels will reduce market power and pricing discretion because foreign competition
limits domestic firms' ability to exercise such power. Firms belonging to segments in which imports account for a
relatively large share of domestic sales would therefore be at a relative disadvantage in their ability to pass-
through costs because foreign producers would not incur costs as a result of the Proposed Existing Facilities
Regulation. The estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) for
2007 is 27 percent. For characterizing the ability of industries to withstand compliance cost burdens, EPA judges
that industries with import ratios close to or above 27 percent would more likely face stiff competition from
foreign firms and thus be less likely to succeed in passing compliance costs through to customers.
Export dependence, calculated as exports divided by value of shipments, measures the share of a segment's sales
that is presumed subject to strong foreign competition in export markets. The Proposed Existing Facilities
Regulation would not increase the production costs of foreign producers with whom domestic firms must compete
in export markets. As a result, firms in industries that rely to a greater extent on export sales would have less
latitude in increasing prices to recover cost increases resulting from regulation-induced increases in production
costs. The estimated export dependence ratio for the entire U.S. manufacturing sector for 2007 is 15 percent. For
characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with
export ratios close to or above 15 percent are at a relatively greater disadvantage in potentially recovering
compliance costs through price increases since export sales are presumed subject to substantial competition from
foreign producers.
March 28, 2011
2C-13
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
Table 2C-9 presents trade statistics for the profiled Petroleum Refining segment from 1990 to 2007. The table
shows that while export dependence has been relatively stable, import penetration decreased during the national
economic weakness of the early 1990s, before leveling off through the mid 1990s. Import penetration increased
steadily through 2000 and then dropped slightly in 2001. Since then, the industry has resumed a gradual increase
in import penetration through 2007, prior to the global economic slump which would occur in the next two years.
This cycle closely follows the periods of growth, stability, and decline of the U.S. economy during the volatile
two decades. Mexico received the largest amount of U.S. exported petroleum and coal products in 2008, followed
by Netherlands and Canada (U.S. Census Bureau, 2009b). Imports of refined petroleum products increased 40.9
percent from 1989 to 2008, with 46.3 percent of total imports coming from OPEC countries (U.S. DOE, 2009b).
The import penetration ratio for facilities in the Petroleum Refining segment in 2007 was only 16 percent, well
below the U.S. manufacturing segment average of 27 percent. The export dependence ratio for petroleum refiners
in 2007 was only five percent compared to the U.S. manufacturing average of 15 percent. Thus, based on the lack
of competitive pressures from foreign markets/firms, the petroleum industry appears to be in a position to pass-
through to consumers a significant portion of compliance-related costs associated with the Proposed Existing
Facilities Regulation. However, given the low HHI for this industry, EPA believes that existing market
competition among domestic firms most likely nullifies any favorable influence the lack of foreign competitors
would have on increasing the market power of firms in this industry.
Table 2C-9: Foreign Trade Statistics for Petroleum Refining ($2009)
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent Change
1990 - 2007
Total Percent Change
1990 - 2007
Average Annual
Growth Rate
Value of Imports
(millions)
$23,313
$17,332
$15,664
$14,792
$13,896
$12,825
$26,042
$28,244
$23,678
$29,505
$52,059
$45,200
$39,967
$48,338
$65,553
$93,858
$102,907
$109,568
370.0%
110.5%
10%
Value of Exports
(millions)
$9,104
$9,333
$8,260
$7,916
$6,814
$7,180
$8,323
$8,934
$6,659
$7,291
$10,808
$9,777
$9,216
$10,688
$13,994
$19,216
$27,247
$31,505
246.1%
191.5%
8%
Value of
Shipments
(millions)
$242,359
$213,478
$195,405
$182,372
$176,857
$183,327
$208,828
$205,984
$151,676
$182,543
$266,930
$242,075
$232,083
$260,951
$346,349
$489,475
$544,446
$590,441
143.6%
121.2%
5%
Implied Domestic
Consumption3
$256,568
$221,477
$202,809
$189,248
$183,939
$188,972
$226,547
$225,293
$168,695
$204,757
$308,181
$277,498
$262,834
$298,602
$397,908
$564,117
$620,107
$668,505
160.6%
116.9%
6%
Import
Penetration1"
9%
8%
8%
8%
8%
7%
11%
13%
14%
14%
17%
16%
15%
16%
16%
17%
17%
16%
Export
Dependence0
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
4%
5%
5%
a. Calculated by EPA as shipments + imports - exports.
b. Calculated by EPA as imports divided by implied domestic consumption.
c. Calculated by EPA as exports divided by shipments.
Note: Before 1997, these data were compiled in the Standard Industrial Classification (SIC) system; since 1997, these data have been
compiled in the North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data
to the SIC code classifications using the 1997 Economic Census Bridge Between NAICS and SIC.
Source: U.S. International Trade Commission, 1989-2007.
2C-14
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
The United States consumes more petroleum than it produces, requiring net imports of both crude oil and refined
products to meet domestic demand. In 2008, the United States imported 9.76 million barrels per day (MBD) of
crude oil and 3.12 MBD of refined products. These refined product imports represented roughly 16 percent of the
19.42 MBD of refined products supplied to U.S. consumers. The U.S. exported 1.80 MBD of refined products in
2008 (U.S. DOE, 2009b).
Imports of refined petroleum products have fluctuated since 1985. Imports rose to 2.3 MB in the early 1980s, due
to rapid growth in oil consumption, especially consumption of light products, which exceeded the growth in U.S.
refining capacity. Imports then declined as a result of the 1990-91 recession and increased upgrading of refinery
capacity resulting primarily from the 1990 Clean Air Act Amendments and other environmental requirements
(U.S. DOE, 1997). Since the 1995 low point, imports steadily increased through 2000 with the exception of 1998,
before dropping again, due to general economic weakness, in 2001 and 2002 (see Figure 2C-5). For the remainder
of the decade, both imports and exports have shown rapid growth, with value of imports surpassing 100 billion
dollars, and the value of exports reaching over 30 billion dollars.
Figure 2C-5: Value of Imports and Exports for Petroleum Refining (millions, $2009)
-*--- Exports (SIC 2911)
-A--- Imports (SIC 2911)
Exports (NAICS 3241 10)
Imports (NAICS 3241 10)
a Before 1 997, the Department of Commerce compiled data in the SIC system; since 1 997, these data have been compiled in the North
American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code
classifications using the 1 997 Economic Census Bridge Between NAICS and SIC.
Source: U.S. International Trade Commission, 1989-2007.
Petroleum exports include heavy products such as residual fuel oil and petroleum coke, which are produced as co-
products with motor gasoline and other light products. Production of these heavier products often exceeds U.S.
demand, and foreign demand absorbs the excess. Distillate fuel oil is the leading petroleum export product,
accounting for 29 percent of petroleum exports in 2008, followed by petroleum coke (almost 22 percent of
exports) and residual fuel oil (almost 20 percent) (U.S. DOE, 2009b). Exports generally reflect foreign demand,
but other factors influence exports as well. For example, exports of motor gasoline increased due to high prices in
Europe at the time of the 1990 Persian Gulf War (U.S. DOE, 1997). U.S. refiners and marketers have gained
experience in marketing to diverse world markets, and U.S. products are now sold widely abroad. As reported by
the International Trade Administration and shown in Figure 2C-5, the real value of petroleum exports fluctuated
March 28, 2011
2C-15
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2C: Petroleum Refining Industry Profile
between $5 and $10 billion during the years 1990 and 2002, and then increased over five-fold during the next five
year span.
2C.5 Financial Condition and Performance
The financial performance and condition of the Petroleum Refining segment are important determinants of its
ability to withstand the costs of regulatory compliance without material adverse economic/financial impact. To
provide insight into the industry's financial performance and condition, EPA reviewed two key measures of
financial performance over the 16-year period, 1992-2008: net profit margin and return on total capital. EPA
calculated these measures using data from the Quarterly Financial Report (QFR). Financial performance in the
most recent financial reporting period (2008) is obviously not a perfect indicator of conditions at the time of
regulatory compliance. However, examining the trend, and deviation from the trend, through the most recent
reporting period gives insight into where the industry may be, in terms of financial performance and condition, at
the time of compliance. In addition, the volatility of performance against the trend, in itself, provides a measure of
the potential risk faced by the industry in a future period in which compliance requirements are faced: all else
equal, the more volatile the historical performance, the more likely the industry may be in a period of relatively
weak financial conditions at the time of compliance.
Net profit margin is calculated as after-tax income before nonrecurring gains and losses as a percentage of sales
or revenue, and measures profitability, as reflected in the conventional accounting concept of net income. Over
time, the firms in an industry, and the industry collectively, must generate a sufficient positive profit margin if the
industry is to remain economically viable and attract capital. Year-to-year fluctuations in profit margin stem from
several factors, including: variations in aggregate economic conditions (including international and U.S.
conditions), variations in industry-specific market conditions (e.g., short-term capacity expansion resulting in
overcapacity), or changes in the pricing and availability of inputs to the industry's production processes (e.g., the
cost of energy to the petroleum refining process). The extent to which these fluctuations affect an industry's
profitability, in turn, depends heavily on the fixed vs. variable cost structure of the industry's operations. In a
capital intensive industry such as Petroleum Refining, the relatively high fixed capital costs as well as other fixed
overhead outlays, can cause even small fluctuations in output or prices to have a large positive or negative affect
on profit margin.
Return on total capital is calculated as annual pre-tax income divided by the sum of: current portion of long-term
debt due in 1 year or less, long-term debt due in more that 1 year, all other noncurrent liabilities and total
stockholders' equity (total capital). This concept measures the total productivity of the capital deployed by a firm
or industry, regardless of the financial source of the capital (i.e., equity, debt, or other liability element). As such,
the return on total capital provides insight into the profitability of a business' assets independent of financial
structure and is thus a "purer" indicator of asset profitability than return on equity. In the same way as described
for net profit margin, the firms in an industry, and the industry collectively, must generate, over time, a sufficient
return on capital if the industry is to remain economically viable and attract capital. The factors causing short-term
variation in net profit margin will also be important sources of short-term variation in return on total capital.
Figure 2C-6 below shows trends in net profit margins and return on total capital for the Petroleum Refining
segment between 1988 and 2008. Through the first half of the 1990s, unusually low product margins, low
profitability, and substantial restructuring characterized the petroleum industry. These low profit margins resulted
from three supply-side factors - (1) increases in operating costs as a result of governmental regulations; (2)
2C-16 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2C: Petroleum Refining Industry Profile
expensive upgrading of processing units to accommodate lower-quality crude oils; and (3) upgrading of
operations to adapt to changes in demand for refinery products8 - coupled with lower product prices, resulting
from competitive pressures (API, 1999). In the late 1990s, the petroleum industry pursued cost-cutting measures
throughout their operations (Rodekohr, 1999).9 These cost-cutting measures, along with increases in the prices of
petroleum refining products, resulted in significantly improved financial performance in the Petroleum Refining
industry. Refinery profits remained high in 2000 and the first half of 2001, due to low product inventories and
high operating rates. The latter half of 2001 and 2002 saw the effects of the global recession, the attacks of 9/11,
and a mild winter. These factors, coupled with world supply in excess of demand, led to decreases in refiner
margins, as crude oil prices increases and petroleum product prices decreased. In 2003, as the U.S. economy
began recovery from its economic hardship, the domestic Petroleum Refining segment returned to relatively
strong financial performance.
During the last decade, Petroleum Refining industry's performance continued to improve from 2004 through
2006, reaching the highest return on total capital and net profit margin observed over the time period. The
industry showed a decrease in both net profit margin and return on total capital in 2007 and 2008, trending along
with the beginning of the U.S. and global economy decline. The oil and gas refining and marketing sub-industry is
currently facing a challenging environment due to imbalance between supply and demand. This imbalance stems
from a fall in U.S. demand for refined products by more than 3 million b/d from February 2008 to June 2009, at
the same time that a minimum of 2 million b/d per year of new refining capacity worldwide is expected between
2009 and 2014 (S&P, 2010c; U.S. DOE, 2009a). However, world oil demand is expected to return to growth of
1.5 percent per year as the world economy recovers in 2010 and 2011, and these conditions should allow the net
profit margins for petroleum refineries to return to strong levels within the next five years (Auers, 2009).
Crude oils processed by U.S. refineries have become heavier and more contaminated with materials such as sulfur. This trend reflects
reduced U.S. dependence on the more expensive high gravity ("light") and low sulfur ("sweet") crude oils produced in the Middle
East, and greater reliance on crude oil from Latin America (especially Mexico and Venezuela), which is relatively heavy and contains
higher sulfur ("sour") (U.S. DOE, 1999a).
o
Demand for lighter products such as gasoline and diesel fuel has increased, and demand for heavier products has decreased.
9
Reductions in costs resulted from:
> divesting marginal refineries and gasoline outlets;
> divesting less profitable activities (e.g., gasoline credit cards);
> reducing corporate overhead costs, including eliminating redundancies through restructuring;
> outsourcing some administrative activities; and
> use of new technologies requiring less labor.
March 28, 2011 2C-17
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
Figure 2C-6: Net Profit Margin and Return on Total Capital for Petroleum Refining
24%
Net Profit Margin
Return on Total Capital
Source: Quarterly Financial Report, 1988-2008; U.S. Census Bureau.
2C.6 Facilities Operating Cooling Water Intake Structures
Section 316(b) of the Clean Water Act applies to point source facilities that use, or propose to use, a cooling water
intake structure that withdraws cooling water directly from a surface waterbody of the United States. In 1982, the
Petroleum and Coal Products industry (SIC 29) withdrew 590 billion gallons of cooling water, accounting for
approximately 0.8 percent of total industrial cooling water intake in the United States.10 The industry ranked 4th in
industrial cooling water use, behind the electric power generation industry and the chemical and primary metals
industries (1982 Census of Manufactures).
This section provides information for facilities in the petroleum segment estimated to be subject to regulation for
the regulatory analysis options. Existing facilities that meet all of the following conditions are expected to be
subject to regulation:
> Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or
arrangement with an independent supplier who has a cooling water intake structure; or their cooling water
intake structure(s) withdraw(s) cooling water from waters of the United States, and at least twenty-five
(25) percent of the water withdrawn is used for contact or non-contact cooling purposes;
> Have a National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;
and
> Meet the coverage criteria for the proposed regulation in terms of design intake flow - i.e., 2 MGD.
10
Data on cooling water use are from the 1982 Census of Manufactures. 1982 was the last year in which the Census of Manufactures
reported cooling water use.
EPA applied sample weights to the sampled 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).
2C-18
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
EPA initially identified the set of facilities that were estimated to be potentially subject to the 316(b) Existing
Facilities Regulation based on a minimum applicability threshold of 2 MGD; this section focuses on these
facilities for the petroleum segment.
11
2C.6.1 Waterbody and Cooling Water Intake System Type
Table 2C-10, shows the distribution of facilities by type of water body and cooling water intake system for each
option.
Table 2C-10: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by
Waterbody Type and Cooling Water Intake System for the Petroleum Refining Segment
Water Body Type
Estuary/ Tidal River
Ocean
Lake/Reservoir
Freshwater River/ Stream
Great Lake
Total3
Cooling Water Intake System
Recirculating
Number
0
0
1
21
0
22
% of Total
0%
0%
5%
95%
0%
56%
Combination
Number
3
0
0
6
2
12
% of Total
27%
0%
0%
54%
18%
31%
Once-Through
Number
2
1
0
2
0
5
% of Total
40%
20%
0%
40%
0%
14%
Total
5
1
1
29
2
39
Based on technical weights (See Appendix 3 A).
a. Individual numbers may not add up to total due to independent rounding.
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
According to the American Petroleum Institute and EPA, water use at Petroleum Refineries has been declining
because facilities are increasing their reuse of water (U.S. EPA, 1996a).
2C.6.2 Facility Size
All petroleum refinery facilities that are estimated to be subject to regulation under the regulatory analysis options
are relatively large. Figure 2C-7, shows the number of potentially regulated facilities by employment size
category.
March 28, 2011
2C-19
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2C: Petroleum Refining Industry Profile
Figure 2C-7: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by
Employment Size for the Petroleum Refinery Segment
18
16
14
12
10
8
6
4
2
Less than
100
100-249
250-499
500-999
1000 and
greater
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
2C.6.3 Firm Size
EPA used the Small Business Administration (SBA) small entity thresholds to determine the number of facilities
in the petroleum-refining segment that are owned by small firms. Firms in this industry are considered small if
they employ fewer than 1,500 people. EPA estimates that four small entity-owned facilities and 30 large entity-
owned facilities in the Petroleum Refining segment will be subject to the proposed regulation. In addition,
ownership status for four facilities is unable to be classified due to insufficient survey data.
2C-20
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
2D Profile of the Steel Industry
EPA's Detailed Industry Questionnaire, hereafter referred to as the DQ, identified five 4-digit SIC codes in the
Steel Works, Blast Furnaces, and Rolling and Finishing Mills Industries (SIC 331) with at least one existing
facility that operates a CWIS, holds a NPDES permit, withdraws at least two million gallons per day (MGD) from
a water of the United States, and uses at least 25 percent of its intake flow for cooling purposes (facilities with
these characteristics are hereafter referred to as "facilities potentially subject to the 316(b) Proposed Existing
Facilities regulation", "existing facilities", or "in-scope facilities"). For the purpose of this analysis, EPA
identified a six-digit NAICS code for each of these potential facilities using the information from DQ and public
sources (see Appendix 3.C: Conversion the Data from Standard Industrial Classification (SIC) to North American
Industry Classification System (NAICS)). As the result of this mapping, EPA identified five 6-digit NAICS codes
in the Steel and Allied Products manufacturing industry (NAICS 3311/2).
For each of the five NAICS codes, Table 2D-1, below, provides a description of the industry segment, a list of
primary products manufactured, the total number of detailed questionnaire respondents (weighted to represent a
national total of facilities that hold a NPDES permit and operate cooling water intake structures), and the number
of facilities estimated to be potentially subject to the 316(b) Existing Facilities Regulation based on the minimum
withdrawal threshold of 2 MGD.
Table 2D-1: Existing Facilities in the Steel Industry (NAICS
NAICS
NAICS
Description
Important Products
3311/2)
Manufactured
Number of In-
Scope Facilities3
Steel Mills (NAICS 3311)
331111
331112
[ron and Steel
Mills
Electrometailurgic
al ferroalloy
products
manufacturing
Hot metal, pig iron, and silvery pig iron from iron ore and iron and steel scrap;
converting pig iron, scrap iron, and scrap steel into steel; hot-rolling iron and steel into
basic shapes, such as plates, sheets, strips, rods, bars, and tubing; merchant blast
furnaces and byproduct or beehive coke ovens
Iron-rich alloys and more pure forms of elements added during the steel
manufacturing process. Ferroalloys add critical elements for low and high metal alloys.
42
2
Steel Products (NAICS 3312)
331210
331221
331222
[ron and steel pipe
and tubes
manufacturing
from purchased
steel
Rolled steel shape
manufacturing
Steel wire drawing
Production of welded or seamless steel pipe and tubes and heavy riveted steel pipe
from purchased materials
Cold-rolling steel sheets and strip from purchased hot-rolled sheets; cold-drawing steel
bars and steel shapes from hot-rolled steel bars; producing other cold finished steel
Drawing wire from purchased iron or steel rods, bars, or wire; further manufacture of
products made from wire; steel nails and spikes from purchased materials
Total Steel Products "
9
12
3
24
Total Steel (NAICS 3311/2)
Total NAICS Code 3311/2 "| 68
a. Number of weighted detailed questionnaire survey respondents.
b. Individual numbers may not add up due to independent rounding.
Source: Executive Office of the President, 1987; U.S. EPA 2000; U.S. EPA analysis, 2010.
March 28, 2011
2D-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
As shown in Table 2D-1, EPA estimates that, out of an estimated total of 476 facilities10 with a NPDES permit
and operating cooling water intake structures in the Steel Industry (NAICS 3311/2), 68 (14 percent) are expected
to be subject to the Proposed 316(b) Existing Facilities regulation. EPA also estimated the percentage of total
production that occurs at facilities estimated to be subject to the regulatory analysis options. Total value of
shipments for the steel industry from the 2007 Economic Census is $128.1 billion. Value of shipments, a measure
of the dollar value of production, was selected for the basis of this estimate. Because the DQ did not collect value
of shipments data, these data were not available for existing facilities. Total revenue, as reported on the DQ, was
used as a close approximation for value of shipments for these facilities. EPA estimated the total revenue of
facilities expected to be subject to the 316(b) Existing Facilities Regulation is $38.9 billion. Therefore, EPA
estimates that 30 percent of total production in the steel industry occurs at facilities estimated to be subject to
regulation.
The responses to the Detailed Questionnaire indicate that two main steel segments account for all of the potential
in-scope facilities: (1) Steel Mills (NAICS codes 331111 and 331112) and (2) Steel Products (NAICS codes
331210, 331221, and 331222).
Table 2D-2 provides the crosswalk between the new NAICS codes and the SIC codes for the profiled steel
NAICS codes. The table shows that electrometallurgical ferroalloy product manufacturing (NAICS 331112),
rolled steel shape manufacturing (NAICS 331221), steel wire drawing (NAICS 331222), and Iron and steel pipes
and tubes manufacturing from purchased steel (NAICS 331210) have a one-to-one relationship to SIC codes. The
remaining NAICS code - iron and steel mills (NAICS 331111) - corresponds to two SIC codes.
Table 2D-2: Relationships between NAICS and SIC Codes for the Steel Industries (2007)
NAICS Code
331111
331112
331221
331222
331210
NAICS
Description
Iron and steel mills
Electrometallurgical
ferroalloy product
manufacturing
Rolled steel shape
manufacturing
Steel wire drawing
Iron and steel pipes
and tubes
manufacturing from
purchased steel
SIC Code
3312
3399
3313
3316
3315
3317
SIC Description
Blast furnaces and steel
mills
Blast furnaces and steel
mills
Electrometallurgical
products
Cold finishing of steel
shapes
Steel wire and related
products
Steel pipe and tubes
Number of
Establishments
743
20
486
274
153
Value of
Shipments
(Millions;
$2009)
$105,608
$1,364
$6,784
$5,400
$8,927
Employment
114,315
2,144
10,391
15,156
17,408
Sources: U.S. DOC. 2007Economic Census.
2D.2 Summary Insights from this Profile
The key purpose of this profile is to provide insight into the ability of steel industry firms to absorb compliance
costs under the Proposed 316(b) Existing Facilities Rule without material adverse economic/financial effects. The
industry's ability to withstand compliance costs is primarily influenced by two factors: (1) the extent to which the
10 This estimate of the number of facilities holding a NPDES permit and operating a cooling water intake structure is based on the
responses from facilities that received the 1999 screener questionnaire.
2D-2
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2D: Steel Industry Profile
industry may be expected to shift compliance costs to its customers through price increases and (2) the financial
health of the industry and its general business outlook.
2D.2.1 Likely Ability to Pass Compliance Costs Through to Customers
As reported in the following sections of this profile, the profiled Steel industry is relatively unconcentrated, which
suggests that firms in this industry would have difficulty in passing a significant portion of their compliance-
related costs through to customers. In addition, the domestic Steel industry faces high competition from imports
into the U.S. market, further curtailing the potential of firms in this industry to pass through to customers a
significant portion of their compliance-related costs. As discussed above, given the relatively small proportion of
total value of shipments in the industry estimated subject to regulation under the primary analysis options, EPA
judges that in-scope facilities subject to the 316(b) Existing Facilities Regulation are not likely to be able to
recover compliance costs through price increases to customers and would have to absorb all compliance costs
within their operating finances (see following sections and Appendix 4.A: Cost Pass-Through Analysis, for further
information).
2D.2.2 Financial Health and General Business Outlook
Over the past two decades, the U.S. Steel industry, like other U.S. manufacturing industries, experienced a range
of economic/financial conditions, including substantial challenges. The U.S. Steel industry went through a
difficult restructuring process in the 1980s and early 1990s, including the closing of a number of inefficient mills,
substantial investment in new technologies, and reductions in the labor force. Although U.S. demand for steel was
strong in the late 1990s, low-priced imports increased substantially in 1998 because of the Asian financial crisis,
with the associated decline in Asian demand for steel and currency devaluations, thereby causing a number of
bankruptcies of U.S. Steel firms and steelworker layoffs. In addition to being affected by the increased inflow of
low-priced imported steel, the U.S. Steel industry was also negatively affected by economic recession in 2000 and
2001. Tariffs provided temporary relief through 2002, but were removed by the end of 2003. By 2003, the U.S.
steel industry's financial performance improved significantly, particularly for the Steel Mills industry segment,
and value of shipments and value added increased substantially. During this time, demand grew considerably, the
industry became more concentrated with high levels of productivity, and trade activity increased. The 2008
recession slowed growth of the U.S. Steel industry, with a substantial decrease in production in 2008. Overall
however, the current condition of the steel industry suggests that it is in a position for continued strong
performance following general recovery of the U.S. economy, despite the fact that it has weathered difficult
periods during the last two decades. The industry's more moderate recent fluctuation in financial condition
(compared to the other industries subject to the Proposed Existing Facilities Rule) suggests an average ability to
withstand additional regulatory compliance costs. This is particularly true for the profiled Steel Mills segment,
which has shown somewhat more resilience than the profiled Steel Products segment in light of the recent adverse
economic conditions.
2D.3 Domestic Production
Steel is one of the most important products of the U.S. industrial metals industry. For most of the twentieth
century, the U.S. steel industry consisted of a few large companies utilizing an integrated steelmaking process to
produce the raw steel used in a variety of commodity steel products. The integrated process requires a large
capital investment to process coal, iron ore, limestone, and other raw materials into molten iron, which is then
transformed into finished steel products. In recent decades, the integrated steel industry has undergone a dramatic
downsizing as a result of increased steel imports, decreased consumption by the auto industry, and the advent of
"minimills" (S&P, 200 Ib). While the traditional integrated facilities using basic oxygen furnaces (BOF) still
account for a substantial share of U.S. steel mill product production, the share of electric arc furnace (EAF)
March 28, 2011 2D-3
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2D: Steel Industry Profile
facilities using scrap steel as an input has grown steadily.11 By 2007, about 47 companies operating about 98
steelmaking plants, used the EAF steelmaking process; these non-integrated, minimill facilities produced 57
million metric tons of steel, an increase of about 1.7 percent compared with that of 2006, and accounted for 41.8
percent of total U.S. steelmaking (USGS, 2007f). The range of products produced by EAFs has also expanded
overtime. Initially, EAFs produced primarily lower-quality structural materials. Starting in the 1990s, EAFs
began producing higher quality sheet products as well. A majority of recent capacity additions have been at EAF
facilities.
Basic steel mill products include carbon steel, steel alloys, and stainless steel. Steel forming and finishing
operations may take place at facilities co-located with steelmaking or at separate facilities. These operations use
steel (in the form of blooms, billets, and slabs) in combination with heating, rolling or drawing, pickling, cleaning,
galvanizing, and electroplating processes in various combinations to produce finished bars, wire, sheets, and coils
(semifinished steel products). Establishments that produce hot rolled products, along with basic BOF and EAF
steelmaking facilities, are included in NAICS 331111 while establishments that primarily engaged in
manufacturing of electrometallurgical ferroalloys are included in NAICS 331112. NAICS codes 331222, 331221,
and 331212 perform additional processing of steel bars, wires, sheets, and coils (including cold-rolling of sheets)
to produce steel products for a variety of end-uses (U.S. EPA, 2000).
The steel industry represents about 3 percent of total U.S. energy demand, and the total cost of energy accounts
for approximately 15-20 percent of the total manufacturing cost (NEED, 2010). Steelmakers use coal, oil,
electricity, and natural gas to fire furnaces and run process equipment. Minimill producers require large quantities
of electricity to operate the electric arc furnaces used to melt and refine scrap metal, while integrated steelmakers
depend on coal-fired plants' coal and electricity for up to 60 percent of their total energy requirements (NEED,
2010). Because of its high energy intensity, the steel industry has invested over $60 billion in new technologies
since 1975 in an effort to improve energy efficiency and productivity. As a result of this effort as well as
increased use of recycled steel and older plant closures, the industry has been able to reduce its energy
consumption by 45 percent per ton of steel since 1973 (NEED, 2010).
2D.3.1 Output
Steel mill products are sold to service centers (which buy finished steel, often process it further, and sell to a
variety of fabricators, manufacturers, and construction industry clients), to vehicle producers, and to the
construction industry. The rapid growth in sales of heavy sport utility vehicles contributed to increased U.S. steel
consumption in the 1990s. However, recent efforts to increase the fuel efficiency of vehicles have eroded steel's
position in the automotive market as a whole, as aluminum and plastic have replaced steel in many automotive
applications. Other end-uses for steel include a wide range of agricultural, industrial, appliance, transportation,
and container applications. Use of steel in beverage cans has been largely replaced by aluminum.
Table 2D-3 shows trends in production from the two major groups of steel producers: BOF and EAF facilities.
1' Production from open hearth furnaces, which dominated production until the early 1950s, ended in 1991. BOF facilities have
traditionally been referred to as integrated producers, because they combined iron-making from coke, production of pig iron in a blast
furnace, and production of steel in the BOF. In recent years, some facilities have closed their coke ovens. These BOF facilities are no
longer fully integrated.
2D-4March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Table 2D-3: U.S. Steel Production by Type of Producer
Year
1990a
i'99"'i'F
1992
1993"
1994
1995
1996
1997
1998"
1999
2066
2001
2002
2003
2004
2005
206(5
2007
2008
Percent Change 1990-2008
Percent Change 2666-2668
Average Annual Growth
Rate
Steel Pro
89.7
7977
84"'."3"
88"'."8"
9172
9572
95."5"
98"'."5"
9876
97".'4"
102.0
9"o"."i
9l'."6
9377
9977
949
9872
98"7l
9779
2.45%
0.15%
duction
% Change
n/a
-11.1%
578%"
573%
277%
44"%"
673%"
37T%
671%
772"%"
47%
:jY'.7%
i""7%
273%
674%"
-48%
375%
:QJ%
-673%
Percent from BOFC
59.1%
6"676'%"
627o%"
6(16%"
6(17%"
5976%
574%
5672%
549"%"
53""7%"
5"3"76'%"
52"76"%"
49"76"%"
4"'976%"
477'8"'%"
4576%
4279%
4l'"."8"%"
4"276%
Percent from EAFd
37.3%
384%"
3"s76%"
394%"
393'%"
404%
4276%
43""g'%
45"J%
4673"'%"
4776%
47".4%"
504%
5"T76%"
52"I%"
5"570%"
57"7i"%"
5"8"'^2%"
574%
a. 3.5 percent of 1990 production was from open hearth furnaces.
b. 1.6 percent of 1991 production was from open hearth furnaces.
c. Basic oxygen furnaces
d. Electric arc furnaces
Source: Mineral Commodity Summaries, Aluminum 1995, 1999, 2002, 2006, 2010
This table shows the cyclical nature of the U.S. steel industry, with variations in growth from year to year
reflecting general domestic and world economic conditions, persistent excess production capacity worldwide, the
competitive strength of imports, and trends in steel's share of the automotive and other end-use markets for steel.
The U.S. steel industry went through a difficult restructuring process in the 1980s and early 1990s, including the
closing of a number of inefficient mills, substantial investment in new technologies, and reductions in the labor
force. Following this difficult transition, the United States became a world leader in low-cost production, lead by
the minimill producers. Although U.S. demand for steel was strong in the late 1990s, low-priced imports
increased substantially in 1998, which led to a number of U.S. steel bankruptcies and steelworker layoffs. The
increased imports resulted from the Asian financial crisis, with the associated decline in Asian demand for steel
and currency devaluations. The U.S. government initiated the Steel Action Program in response to the crisis,
focusing on strong enforcement of trade laws through the World Trade Organization and bilateral efforts to
address market-distorting practices abroad.12 The industry began to show signs of recovery in the second half of
1999, and by early 2000, capacity utilization recovered to above 90 percent and earnings were up for most major
steel companies (U.S. DOC, 2000).
However, beginning in 2000, the weakening of the U.S. economy significantly reduced steel demand and total
U.S. steel production fell by nearly 12 percent in 2001. In March 2002, the U.S. steel industry received temporary
relief under Section 201 of the 1974 Trade Act with three years of tariffs ranging up to 30 percent on certain steel
imports. Relief from imports was nullified to some extent when the U.S. Department of Commerce exempted 727
12 World steel trade is characterized by noncompetitive practices in a number of countries, which have resulted in substantial friction
over trade issues since the late 1960s. Since 1980, almost 40 percent of the unfair trade practice cases investigated in the U.S. have
been related to steel products (U.S. DOC, 2000).
March 28, 2011
2D-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2D: Steel Industry Profile
imported steel products from the tariff in June 2002. By year-end, 2002 was the fourth highest steel import year in
U.S. history (USGS, 2002f). Removal of all tariffs occurred on December 4, 2003 (S&P, 2004c). The steel
industry recovered, but slowly, from the import penetration in the late 1990s followed by the economic recession
in 2001. In 2003, the integrated steel industry had poor operating results, as high raw material costs outweighed
increased sales and higher volumes. As a result, most domestic steel producers instituted a raw material surcharge
to offset sharply rising costs for raw materials such as scrap, iron ore and coke.
Between 2000 and 2005, world steel demand increased by 6 percent, and China surpassed Japan, Russia, and the
United States to become the number one steel producer (British Geological Survey, 2005). During this period the
two different methods for producing steel - integrated (ore-based) and electric arc furnace (scrap-based) - began
converging in response to the changing cost balance of raw materials, scrap and energy (AISI, 200 la). The
combination of rising GDP, a smaller decline in nonresidential construction, a recovery in auto sales, and
rebuilding of distributor inventory is expected to lead to an increase in the volume of steel shipped in 2010 (S&P,
2010e).
Value of shipments and value added are two common measures of manufacturing output.13 Change in these
values overtime provides insight into the overall economic health and outlook for an industry. Value of
shipments is the sum of receipts earned from the sale of outputs; it indicates the overall size of a market or the
size of a firm in relation to its market or competitors. Value added, defined as the difference between the value of
shipments and the value of inputs used to make the products sold, measures the value of production activity in a
particular industry.
Figure 2D-1 presents trends in constant-dollar value of shipments and value added for the profiled Steel Mills and
Steel Products segments. Value of shipments and value added from Steel Mills declined in the early 1990s, and
recovered through 1997, prior to the 1998 import crisis and the later U.S. economic recession. The segment's
value of shipments began to decline in 1997 and continued to do so through 2001. However, from 2001 through
2007, the Steel Mills segment experienced continuous growth, peaking at over $105 billion at the end of that
period. Steel Mills value added also continued to decline until 2001 and increased drastically in 2004. However,
between 2004 and 2007, value added for the Steel Mills segment experienced only a moderate growth. Value of
shipments and value added for Steel Products were less volatile, increasing gradually during 1990 through 1995
and 1996, respectively, when both value of shipments and value added began to decline, bottoming in 2003. Since
then through 2007, both value of shipments and value added for the profiled Steel Products segment experienced
overall moderate growth.
13 Terms highlighted in bold and italic font are further explained in the glossary.
2D-6 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Figure 2D-1:Value of Shipments and Value Added for Profiled Steel Industry Segments (millions, $2009)a
Value of Shipments
o
o
I
•
O
4*
-A- - - Steel Mills (SICto NAICS)
-Steel Mills (NAICS3311)
Steel Products (SIC to
NAICS)
- Steel Products (NAICS
3312)
$20,000 f-
$10,000
KIKIKIKIKI
vovovovovovovovovovovovovoooooo
OOOOOOVOVOVOVOVOVOVOVOVOVOOOOOO
KIKIKI
ooo
OOO
Value Added
$40,000
$35,000
^a
§ $30,000
$25,000
$20,000
-A.--- Steel Mills (SIC to
NAICS)
-± Steel Mills (NAICS
3311)
-•- - - Steel Products (SIC to
NAICS)
-• Steel Products (NAICS
3312)
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
'lassification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007 Economic Census.
2D.3.2 Prices
The producer price index (PPI) measures price changes, by segment, from the perspective of the seller, and
indicates the overall trend of product pricing, and thus supply-demand conditions, within a segment.
March 28, 2011
2D-7
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
As shown in Figure 2D-2, below, prices increased from 1987 to 1989 and then dropped slightly in the early
1990s, due to depressed domestic economy and the resulting decline in demand for steel. During the 1990s, prices
in both profiled segments remained on average unchanged, with prices in the Steel Mills segments rising
temporarily in the middle of the decade. As the U.S. and world economies began to recover in 2002, so did steel
prices, which began to rise and continued to do so through 2008, with a significant jump in the middle of this
decade. Overall, during the last two decades, prices in the profiled Steel Mills segment showed a slightly higher
degree of volatility compared to those in the profiled Steel Products segment.
Figure 2D-2: Producer Price Index for Profiled Steel Industry Segments
270
255
240
225
- Steel Mills (NAICS 3311)
- Steel Products (NAICS
3312)
Source: BLS, 2009.
2D.3.3 Number of Facilities and Firms
The number of operating Steel Mills fluctuated significantly between 1990 and 2006, as the U.S. industry
underwent a substantial restructuring. Table 2D-4 shows substantial decreases in the number of facilities in the
profiled Steel Mills segment in 1992 and 1993 due to a significant decrease in global demand and resulting
overcapacity. This decrease was followed by a significant recovery in 1995 and 1996, and another significant drop
in 1997. The number of facilities continued to rise through 2001, with the largest increase around 1999. This
increase may have resulted in part from the advent of minimills, as discussed above. The import crisis during
1997-1998 ultimately led to bankruptcy for a number of U.S. producers, including LTV and Bethlehem Steel
(S&P, 2001b). Additionally, seven major bankruptcies occurred over 2002 and early 2003, including Bayou Steel
Corp, Kentucky Electric Steel Inc, Slater Steel Inc, and Weirton Steel Corp (USGS, 2004b). Between 2000 and
2006, the number of facilities in the Steel Mills and Steel Products segments dropped by approximately 18
percent and 25 percent, respectively. Nonetheless, the Steel Mills segment saw an overall 43 percent increase in
the number of facilities during 1990 to 2006 with average annual growth rate of 2.3 percent. While the Steel
Products segment also grew during the same analysis period, this growth was much more moderate -
approximately 6 percent with an average annual growth rate of less than 1 percent.
2D-8
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Table 2D-4: Number of Facilities in the Profiled Steel Industry Segments
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent Change
1990-2006
Total Percent Change
2000-2006
Average Annual Growth
Rate
Steel Mills
Number of
Facilities
579
609
499
436
431
477
555
377
410
702
T',oo3
T',374
U59
876
799
839
827
Percent Change
n/a
573%
:y87i%
:Y2.7%
-T7i%
i'o.7%"
16.4%
:327i%
877%
7l".2%
4279%
37.0%
-874%
i3o74%
-878%
576%
-L4%
42.9%
-17.5%
2.3%
Steel Products
Number of
Facilities
659
782
807
808
779
766
748
705
769
824
933
939
870
828
734
716
698
Percent Change
n/a
1877%
3.1%
o7T%
-375%
T.6%
-2774%
-578%
9.1%
7.2%
1372%
0.6%
-773%
-478%
:ff;4%
-275%
-275%
5.9%
-25.2%
0.4%
a. Before 1998, data were compiled in the SIC system; since 1998, these data have been compiled in the North American
Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code
classifications using the 1997Economic Census Bridge Between NAICS and SIC.
Source: U.S. SBA, 1990-1997; SUSB, 1998-2006.
Table 2D-5 shows the number of firms in the two profiled steel segments between 1990 and 2006. The trend in
the number of firms over the period between 1990 and 2006 is similar to the trend in the number of facilities in
the profiled Steel Mills industry segment. The number of firms in this segment decreased to a period-low of 288
in 1997, before increasing significantly during 1998 through 2001, to 1,269 firms. This rise in the number of Steel
Mill firms was followed by declines during 2002 through 2004, and then a slight recovery in 2005. Between 2000
and 2006, the number of firms in the Steel Mills segment fell by nearly 21 percent. Overall, however, between
1990 and 2006, the number of Steel Mill firms increased by nearly 47 percent with an average annual growth rate
of approximately 2 percent. The number of firms in the Steel Products segment also decreased between 1992 and
1997, before rising steadily through 2001, and then declining slightly between 2002 and 2006. However, unlike
the Steel Mills segment, the number of firms in the Steel Products segment experienced a decline not only in the
last decade - nearly 22 percent - but an overall decline of nearly 2 percent between 1990 and 2006.
March 28, 2011
2D-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Table 2D-5: Number of Firms in the Profiled Steel Industry Segments
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent Change
1990-2006
Total Percent Change
2000-2006
Average Annual
Growth Rate
Steel
Number of Firms
482
505
401
345
342
388
462
288
320
603
900
T7269
T7l49
758
684
718
708
Mills
Percent Change
n/a
47%
:2o;6%
ii4o%
-679%
1376%
19'7i'%"
I3777%'
FE'b'%"
884%
4973%
4LO%
-975%
1340%
-978%
576%
-T74%
46.9%
-21.3%
2.4%
Steel P
Number of Firms
578
615
642
622
599
588
567
528
577
628
725
729
681
684
598
580
568
•oducts
Percent Change
n/a
674%
43%
"-3.1%
-377%
-18%
-375%'
'-679'%"
973%
878%
1574%
(16%
-676%
(14%
:i276%
-3""b"'%"
"-2.1%
-1.8%
-21.7%
-0.1%
a Before 1998, data were compiled in the SIC system; since 1998, these data have been compiled in the North American
Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code
classifications using the 1997Economic Census Bridge Between NAICS and SIC.
Source: U.S. SBA, 1990-1997: SUSB, 1998-2006.
2D.3.4 Employment and Productivity
Figure 2D-3, following page, provides information on employment from the Annual Survey of Manufactures and
the Economic Census for the profiled Steel Mills and Steel Products segments. As shown in the figure, between
1987 and 2007, employment levels in the Steel Mills segment decreased by a total of nearly 40 percent at an
average annual rate of approximately 3 percent. Employment is a significant cost component for steelmakers.
Labor cost reductions enabled Steel Mills to improve profitability and competitiveness in the face of limited
opportunities for price increase in the highly competitive market of Steel Products. A steady decline in
employment in the 1990s reflects a smaller number of Steel Mill facilities and firms, in conjunction with
aggressive efforts to improve worker productivity in order to cut labor costs and improve profits (McGraw-Hill,
1998). Employment declined further as a result of the 1997-1998 import crisis, with almost 26,000 U.S.
steelworkers reportedly losing their jobs (AISI, 200 Ib). During the 2000s decade, employment in the Steel Mills
segment declined until 2006 when the industry had a sudden rise in number of employees. Employment in the
Steel Products segment also declined, largely steadily, at an average annual rate of 2 percent resulting in a total
decline of approximately 27 percent over the period 1987-2007 (approximately 31 percent between 2000 and
2007).
2D-10
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Figure 2D-3: Employment for Profiled Steel Industry Segments
220,000
200,000
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
-A- - - Steel Mills (SIC to NAICS)
-A Steel Mills (NAICS 3311)
-•- - - Steel Products (SIC to NAICS)
-• Steel Products (NAICS 3312)
acacvevevevevevevevevevecccccccc
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001; and 2003-2004; U.S. DOC, 1987, 1992, 1997, and 2002.
Table 2D-6 presents the change in value added per labor hour, a measure of labor productivity, for the Steel Mills
and Steel Products segments between 1987 and 2007. Labor productivity at Steel Mills increased significantly
over this period. Between 1987 and 2007, value added per labor hour increased nearly 120 percent, with most
growth - 86 percent - taking place since 2000. Much of this increase in labor productivity can be attributed to the
restructuring of the U.S. steel industry and the increased role of minimills in production. Minimills are capable of
producing rolled steel from scrap with substantially lower labor needs than integrated mills (McGraw-Hill, 1998).
Labor productivity in the Steel Products segment has also experienced an overall growth between 1987 and 2007,
although less so compared to that in the Steel Mills segment; labor productivity grew by nearly 20 percent
between 1987 and 2007, with most of this growth - approximately 30 percent - taking place between 2000 and
2007.
March 28, 2011
2D-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Table 2D-6: Productivity Trends for the Profiled Steel Industry Segments ($2009)
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent Change
1987-2007
Total Percent Change
2000-2007
Average Annual
Growth Rate
Steel Mills
Value Added
(millions)
$27,340
$7327896
$317361
$28,508
$217982
$247430
$25/754
$297i58
$307491
$29,823
$33,016
$317343
$277655
$25345
$19,940
$22,123
$20',681
$36,674
$36,150
$36,535
$38,647
41.4%
51.3%
1.7%
Production
Hours
(millions)
313
333
357
323
287
285
276
275
271
268
259
252
243
248
289
200
185
191
183
178
202
-35.5%
-18.6%
-2.2%
Value Ac
S/hr
$87
$99
$88
$88
$77
$86
$93
$106
$113
$111
$128
$124
$114
$103
$69
$111
$112
$192
$197
$205
$192
Ideci/Hour
Percent
Change
n/a
1279%
-Tf6%
0"4%
:j3j%
i"i""8%
879%
1377%
"6.1%
-12%
1479%
-278%
-872%
'-975%
l33"J%
6075%
i""2%
7175%
277%
379%
-675%
119.3%
85.8%
4.0%
Steel Products
Value Added
(millions)
$9,045
$9,280
$9,048
$7,993
$8,047
$7,695
$8,858
$8,804
$9,062
$9,189
$8,779
$8,438
$8,119
$8,271
$6,590
$6,782
$5,904
$7,804
$7,886
$7,708
$7,087
-21.6%
-14.3%
-1.2%
Production
Hours
(millions)
105
91
109
89
104
84
106
88
110
130
106
108
103
104
92
86
80
73
73
71
69
-34.6%
-34.2%
-2.1%
Value Ac
S/hr
$86
$103
$83
$89
$78
$92
$84
$100
$82
$71
$83
$78
$79
$79
$71
$79
$73
$107
$108
$108
$103
deci/Hour
Percent
Change
n/a
1877%
-19.6%
776%
:i37o%
1779%
-8.8%
20.0%
-1876%
11472%
18.6%
-579%
r.2%
673%,
:io72%
i"6"."6"%
-679%
4579%
i'"."i%
-6.1%
-45%
19.7%
30.2%
0.9%
a Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997Economic
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2D.3.5 Capital Expenditures
New capital expenditures are needed to modernize, expand, and replace existing capacity to meet growing
demand. Capital expenditures in the profiled Steel Mills and the Steel Products segments between 1987 and 2007
are presented in Table 2D-7, following page. As shown in the table, capital expenditures in both the Steel Mills
and the Steel Products segments fluctuated significantly during this analysis period. Steel mill capital outlays
increased in the late 1980s and early 1990s, rising by a total of 87 percent between 1987 and 1991. This
substantial increase coincides with the advent of thin slab casting, a technology that allowed minimills to compete
in the market for flat rolled sheet steel. The significant decreases in capital expenditures by Steel Mills that
followed this expansion reflect the bottoming out of the demand for Steel Products in the early 1990s. The
recovery in capital expenditures in the mid 1990s reflected increased demand and higher utilization rates
(McGraw-Hill, 1998). The import crisis of the late 1990s and later weakening of the U.S. economy put pressure
on the domestic steel industry, and expenditures for new capacity began to decline in 1997 in both segments
(McGraw-Hill, 2000). However, capital expenditures in the Steel Mills segment recovered during the 2000s,
increasing by approximately 31 percent, while the Steel Products segments showed continuing drops in capital
expenditures resulting in a total decline of approximately 23 percent. Overall, between 1987 and 2007, capital
expenditures increased by nearly 62 percent in the Steel Mills segment and dropped by approximately 51 percent
in the Steel Products segment.
2D-12
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Table 2D-7: Capital Expenditures for the Profiled Steel Industry Segments (millions, $2009)
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent Change
1987-2007
Total Percent Change
2000-2007
Average Annual Growth
Rate
Steel Mills
Capital Expenditures
$2,115
$3,180
$4,006
$3,930
$4,413
$3,222
$2,531
$3,622
$3,725
$3,760
$3,471
$3,379
$2,866
$2,605
$1,878
$1,627
$1,118
$1,690
$1,979
$1,948
$3,414
Percent Change
n/a
50.4%
26.0%
-1.9%
12.3%
-27.0%
-21.4%
43.1%
2.8%
1.0%
-7.7%
-2.7%
-15.2%
-9.1%
-27.9%
-13.3%
-31.3%
51.2%
17.1%
-1.6%
75.3%
61.5%
31.0%
2.4%
Steel Products
Capital Expenditures
$911
$689
$773
$773
$554
$570
$625
$718
$699
$750
$693
$664
$557
$580
$444
$482
$490
$509
$396
$418
$450
Percent Change
n/a
-24.3%
12.1%
0.0%
-28.3%
2.8%
9.6%
15.0%
-2.7%
7.2%
-7.6%
-4.1%
-16.2%
4.2%
-23.6%
8.6%
1.8%
3.7%
-22.2%
5.7%
7.7%
-50.6%
-22.4%
-3.5%
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997Economic
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2D.3.6 Capacity Utilization
Capacity utilization measures actual output as a percentage of total potential output given the available capacity.
Capacity utilization provides insight into the extent of excess or insufficient capacity in an industry, and into the
likelihood of investment in new capacity. Figure 2D-4 presents capacity utilization index for 1990 through 2009
for the profiled Steel Mill and Steel Products segments. Capacity utilization followed a similar trend for both
industry segments. Capacity utilization in the Steel Mills and the Steel Products segments declined by more than
35 and 38 percent, respectively, during the last two decades and by nearly 25 and 28 percent, respectively, during
the last decade alone. The most dramatic drops in capacity utilization took place around the 2001 and the 2008
economic recessions; in fact, the 2009 drop in capacity utilization marked the most drastic drop in capacity
utilization in the last two decades. For the Steel Mills segment, capacity utilization dropped by 17 percent and 31
percent, respectively, while for the Steel products segment it fell by 12 percent and 32 percent, respectively. With
March 28, 2011
2D-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
global steelmaking capacity outstripping the growth of steel consumption in recent years, and as steel demand is
now contracting due to global economic downturn, it appears that the global steel industry could be facing
overcapacity and poor financial performance in the next few years (OECD, 2009).
Figure 2D-4: Capacity Utilization Rates (Fourth
<)>
QO
85 i
l
80
7^
70
65
60
«
50
d^
Quarter) for Profiled Steel Industry Segments3'15
A
t^-^^^-A A ^ / \
^y\\ / v s\^\
V T^V «
V
•
—A— Steel Mills (NAICS 3311)
— •— Steel Products (NAICS 33 12)
^o ^o ^o ^o ^o ^o ^o ^o ^o ^o o o o o o o o o o o
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
Census Bridge Between NAICS and SIC.
b Prior to 2007, U.S. Census sampled every industry in a specific NAICS6. Beginning in 2007, U.S. Census only sampled certain industries within any
NAICS6, and therefore, the data collected before 2007 cannot be directly compared to the data collected in 2007 and beyond.
Source: U.S. DOC, Survey of Plant Capacity 1989-2009, U.S. Census Bureau.
2D.4 Structure and Competitiveness
The Steel Mill segment is comprised of two different kinds of facilities, integrated mills and minimills. The
integrated steelmaking process requires expensive plant and equipment purchases that will support production
capacities ranging from two million to four million tons per year. Until the early 1960s, integrated steelmaking
was the dominant method of U.S. steel manufacturing. Since then, the integrated steel business underwent
dramatic downsizing due to competition from minimills and imports. These trends reduced the number of
integrated steelmakers (S&P, 2001b). Minimills vary in size, from capacities of 150,000 tons at small facilities to
larger facilities with annual capacities of between 400,000 tons and two million tons. Integrated companies have
significant capital costs of approximately $2,000 perton of capacity compared with minimills' $500 per ton.
Because minimills do not require as much investment in capital equipment as integrated steelmakers, minimills
have been able to lower prices during periods of weak demand, driving integrated companies out of many of the
commodity steel markets (S&P, 200 Ib). The advent of minimills, with their lower initial capital investments, has
made it easier for new producers to enter the market.
2D-14
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
2D.4.1 Firm Size
For both the Steel Mills and Steel Products segments, the Small Business Administration defines a small firm as
having 1,000 or fewer employees (750 or fewer employees for NAICS 331112). The size categories reported in
the Statistics of U.S. Businesses (SUSB) do not correspond with the SBA size classifications, therefore preventing
precise use of the SBA size threshold in conjunction with SUSB data. Table 2D-8 below shows the distribution of
firms, facilities, and receipts by the employment size of the parent firm.
The SUSB data presented in Table 2D-8 show that in 2006, 640 of 708 firms in the Steel Mills segment had less
than 500 employees. Therefore, at least 90 percent of firms in this segment were classified as small. These small
firms owned 654 facilities, or 80 percent of all facilities in the segment. Of the 568 firms with facilities that
manufacture Steel Products, 482, or 85 percent, employ fewer than 500 employees, and are therefore considered
small businesses. Small firms own 73 percent of facilities in the industry.
Table 2D-8: Number of Firms and Facilities by Employment Size Category in the
Profiled Steel Industry Segments, 2006
Employment Size
Category
0-19
20-99
100-499
500+
Total
Steel Mills
Number of Firms
473
109
58
68
708
Number of Facilities
475
no
69
173
827
Steel Products
Number of Firms
272
120
90
86
568
Number of Facilities
272
123
114
189
698
Source: U.S.DOC, Statistics of U.S. Businesses, 2006.
2D.4.2 Concentration Ratios
Concentration is the degree to which industry output is concentrated in a few large firms. Concentration is
closely related to entry barriers with more concentrated industries generally having higher barriers.
The four-firm concentration ratio (CR4) and the Herfindahl-Hirschman Index (HHI) are common measures of
industry concentration. The CR4 indicates the market share of the four largest firms. For example, a CR4 of 72
percent means that the four largest firms in the industry account for 72 percent of the industry's total value of
shipments. The higher the concentration ratio, the less competition there is in the industry, other things being
equal.14 An industry with a CR4 of more than 50 percent is generally considered concentrated. The FfHI indicates
concentration based on the largest 50 firms in the industry. It is equal to the sum of the squares of the market
shares for the largest 50 firms in the industry. For example, if an industry consists of only three firms with market
shares of 60, 30, and 10 percent, respectively, the FfHI of this industry would be equal to 4,600 (602 + 302 + 102).
The higher the index, the fewer the number of firms supplying the industry and the more concentrated the
industry. Based on the U.S. Department of Justice's guidelines for evaluating mergers, markets in which the HHI
is under 1000 are considered unconcentrated, markets in which the HHI is between 1000 and 1800 are considered
to be moderately concentrated, and those in which the HHI is in excess of 1800 are considered to be concentrated.
Table 2D-9 shows that Steel Mills, comprised of NAICS 331111 and 331112, have HHIs of 657 and 2,196,
respectively and that Steel Products, comprised of NAICS 331222, 331221, and 331210, have HHIs of 326, 491,
and 279, respectively. Consequently, the Steel Products segment is considered competitive, based on standard
14 Note that the measured concentration ratio and the HHF are very sensitive to how the industry is defined. An industry with a high
concentration in domestic production may nonetheless be subject to significant competitive pressures if it competes with foreign
producers or if it competes with products produced by other industries (e.g., plastics vs. aluminum in beverage containers).
Concentration ratios based on share of production are therefore only one indicator of the extent of competition in an industry.
March 28, 2011
2D-15
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
measures of concentration. Because the Steel Mills segment is mostly comprised of the firms in NAICS 331111
industry sector, this segment is also mostly competitive. Other than for electrometallurgical products
manufacturing, the CR4 and the HHI for all but one relevant NAICS code - NAICS 331112 - are below the
benchmarks of 50 percent and 1,000, respectively. The relatively low concentration values suggest low overall
ability of the industry to pass through compliance costs as price increases to customers.
Table 2D-9: Selected Ratios for the Profiled Steel Industry Segments
SIC (S) or
NAICS (N) Code
Year
Total Number
of Firms
Concentration Ratios
4 Firm (CR4)
8 Firm (CR8)
20 Firm
(CR20)
50 Firm
(CR50)
Herfindahl-
Hirschman Index
Steel Mills
S3312a
N331111
S3313
N331112
1987
1992
1997
2002
1987
1992
1997
2002
271
135
191
285
25
31
19
19
44%
37%
33%
44%
55%
56%
61%
75%
63%
58%
53%
59%
78%
77%
82%
92%
81%
81%
75%
78%
99%
98%
100%
100%
94%
96%
94%
93%
100%
100%
100%
100%
607
551
445
657
1,208
i7io3
1,123
27196
Steel Products
S3315
N 33 1222
S3316
N 331221
S3317
N331210
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
274
271
199
270
156
158
153
121
155
166
166
133
21%
19%
21%
30%
45%
43%
44%
34%
23%
19%
20%
26%
34%
32%
36%
42%
62%
60%
60%
51%
34%
31%
30%
39%
54%
54%
56%
61%
82%
81%
81%
73%
58%
53%
52%
61%
78%
80%
80%
85%
95%
96%
96%
93%
85%
80%
82%
86%
212
201
223
326
654
604
631
491
242
194
200
279
a. SIC code represents
Source: U.S. DOC,
largest percentage of facilities and value of shipments within this NAICS based on the 1997 Bridge Between SIC and NAICS
1987, 1992, 1997, and 2002.
2D.4.3 Foreign Trade
This profile uses two measures of foreign competition: export dependence and import penetration.
Import penetration measures the extent to which domestic firms are exposed to foreign competition in domestic
markets. Import penetration is calculated as total imports divided by total value of domestic consumption in that
industry: where domestic consumption equals domestic production plus imports minus exports. Theory suggests
that higher import penetration levels will reduce market power and pricing discretion because foreign competition
limits domestic firms' ability to exercise such power. Firms belonging to segments in which imports account for a
relatively large share of domestic sales would therefore be at a relative disadvantage in their ability to pass-
through costs because foreign producers would not incur costs as a result of the 316(b) Proposed Existing
Facilities Rule. The estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) for
2007 is 27 percent. For characterizing the ability of industries to withstand compliance cost burdens, EPA judges
that industries with import ratios close to or above 27 percent would more likely face stiff competition from
foreign firms and thus be less likely to succeed in passing compliance costs through to customers.
Export dependence, calculated as exports divided by value of shipments, measures the share of a segment's sales
that is presumed subject to strong foreign competition in export markets. The Proposed Existing Facilities
regulation would not increase the production costs of foreign producers with whom domestic firms must compete
2D-16
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2D: Steel Industry Profile
in export markets. As a result, firms in industries that rely to a greater extent on export sales would have less
latitude in increasing prices to recover cost increases resulting from regulation-induced increases in production
costs. The estimated export dependence ratio for the entire U.S. manufacturing sector for 2007 is 15 percent. For
characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with
export ratios close to or above 15 percent are at a relatively greater disadvantage in potentially recovering
compliance costs through price increases since export sales are presumed subject to substantial competition from
foreign producers.
The global market for steel continues to be extremely competitive. From 1945 until 1960, the U.S. steel industry
enjoyed a period of prosperity and was a net exporter until 1959. However, by the early 1960s, foreign steel
industries had thoroughly recovered from World War II and had begun construction of new plants that were more
advanced and efficient than the U.S. integrated steel mills. Foreign producers also enjoyed lower labor costs,
allowing them to take substantial market share from U.S. producers. This increased competition from foreign
producers, combined with decreased consumption in some key end use markets, served as a catalyst for the
restructuring and downsizing of the U.S. steel industry. The industry emerged from this restructuring considerably
smaller, more technologically advanced and internationally competitive (S&P, 200Ib). Global steel trade fell
during the economic recession of 2008, trade imbalances narrowed, and governments responded with an increase
in trade policy measures to support the steel industry such as tariff increases, non-tariff barriers in emerging Asia,
export-facilitating measures, and trade remedy measures (OECD, 2009).
Table 2D-10 presents trade statistics for the profiled steel industry segments from 1990 to 2007. As shown in the
table, although the trend in export dependence has been relatively stable, import penetration increased almost
continuously. Historically, the U.S. steel industry has exported a relatively small share of shipments compared to
the steel industries of other developed nations (McGraw-Hill, 2000). U.S. steel exports rose in 1995 to the highest
level since 1941, and dropped slightly until 2003 before nearly tripling in the following four years. Import
penetration rose to 19 percent in 1994, 1996, and 2000 and reached another peak of 27 percent in 2006, after
hovering around 15 percent in the early 1990s. This increase in imports reflected excess steel capacity worldwide
and the competitiveness of foreign steel producers, as described previously. Canada received the largest amount
of U.S. exported steel in 2007, followed by Mexico. Brazil, China, the EU, Germany, Japan, the Republic of
Korea, Mexico, Russia, and Ukraine were major sources of steel mill product imports (USGS, 2008f).
The steel industry's import penetration ratio in 2007 was 25 percent (compared to the 27 percent penetration for
the entire U.S. manufacturing industry), implying that domestic steel producers face moderate competition from
foreign firms in setting prices on the domestic market. The steel industry's export dependence ratio in 2007 was
10 percent (compared to the 15 percent export dependence for the entire U.S. manufacturing industry), suggesting
that this industry's overall cost pass-through potential is not significantly affected by its foreign market sales.
The combination of moderate import penetration and relatively low export dependence suggest that international
trade considerations are not a strong factor in determining the cost pass-through potential of firms facing
compliance requirements under the Proposed 316(b) Existing Facilities Rule. However, potential changes in
tariffs and other international trade policies that were implemented during the recent recession, as well as the
global recession, itself, may have altered the overall balance of international competitiveness factors affecting the
U.S. steel industry. These potential effects are not able to be accounted for in this analysis because 2007 is the
latest year of trade data.
March 28, 2011 2D-17
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Table 2D-10: Import Penetration and Export Dependence: Profiled Steel Mills and Steel Products
Segments ($2009)a
Year
1990
1991
1992
1993
1994
1995
1996
1997"
1998
1999
2666
2"66T
2002
2003
2004
2005
2006
2007
Total Percent
Change 1990 -2007
Xotiil Percent
Change 1990 - 2007
Average Annual
Growth Rate
Value of Imports
(millions)
$15,990
$147435
$147288"
$1571637
$21,116
$"207563"
$217343
$177331
$207826
$157552
$197327
$147882
$157699
$147l56
$297547
$3i7i96
$397802
$377814
136.5%
95.7%
5.2%
Value of Exports
(millions)
$4,651
$57819
$47736
$"47337
$47536
$6,762
$57925
$57373
$5,669"
$47676
$57360
$57lTi
$47837
$57823
$7,395
$9,862
$107942
$137684"
181.3%
144.1%
6.3%
Value of
Shipments
(millions)
$94,002
$82,298
$83,440
$87,223
$955679
$"T66/i98"
$987147
$997832
$977395
$88,667
$877262
$747826
$757286
$737627
$1077341
$1127393
$1207045
$128,082
36.3%
46.8%
1.8%
Implied
Domestic
Consumption1"
105,341
967914
927992"
987649
112,258
114-299
1137565
TlT,79"6
1137152
1007543"
1017229"
847597
867148
817960
1297493
1337727
1487905
1527812
Import
Penetration0
15%
16%
15%
15%
19%
18%
19%
16%
18%
16%
19%
18%
18%
17%
23%
23%
27%
25%"
Export
Dependence*1
5%
7%
6%
5%
5%
7%
6%
5%
5%
5%
6%
7%
6%
8%
7%
9%
9%
10%
45.1%
51.0%
2.2%
a. Before 1997, data were compiled in the SIC system; since 1998, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997Economic Census Bridge Between
NAICSandSIC.
b Calculated by EPA as shipments + imports - exports.
c. Calculated by EPA as imports divided by implied domestic consumption.
d. Calculated by EPA as exports divided by shipments.
Source: U.S. DOC, 2006; U.S. DOC, 1988-1991, 1993-1996, 1998-2001; and 2003-2004; U.S. DOC, 1987, 1992, 1997, and 2002.
2D.5 Financial Condition and Performance
The financial performance and condition of the U.S. steel industry are important determinants of its ability to
withstand the costs of regulatory compliance without material, adverse economic/financial impact. To provide
insight into the industry's financial performance and condition, EPA reviewed two key measures of financial
performance over the 21-year period, 1992-2008: net profit margin and return on total capital. EPA calculated
these using data from the Quarterly Financial Report (QFR) (see Appendix 4.B: Adjusting Baseline Facility Cash
Flow). Financial performance in the most recent financial reporting period (2008) is obviously not a perfect
indicator of conditions at the time of regulatory compliance. However, examining the trend, and deviation from
the trend, through the most recent reporting period gives insight into where the industry may be, in terms of
financial performance and condition, at the time of compliance. In addition, the volatility of performance against
the trend, in itself, provides a measure of the potential risk faced by the industry in a future period in which
compliance requirements are faced: all else equal, the more volatile the historical performance, the more likely the
industry may be in a period of relatively weak financial conditions at the time of compliance.
Net profit margin is calculated as after-tax income before nonrecurring gains and losses as a percentage of sales
or revenue, and measures profitability, as reflected in the conventional accounting concept of net income. Over
time, the firms in an industry, and the industry collectively, must generate a sufficient positive profit margin if the
industry is to remain economically viable and attract capital. Year-to-year fluctuations in profit margin stem from
2D-18
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2D: Steel Industry Profile
several factors, including: variations in aggregate economic conditions (including international and U.S.
conditions), variations in industry-specific market conditions (e.g., short-term capacity expansion resulting in
overcapacity), or changes in the pricing and availability of inputs to the industry's production processes (e.g., the
cost of energy to the steel production process). The extent to which these fluctuations affect an industry's
profitability, in turn, depends heavily on the fixed vs. variable cost structure of the industry's operations. In a
capital intensive industry such as the steel industry, the relatively high fixed capital costs as well as other fixed
overhead outlays, can cause even small fluctuations in output or prices to have a large positive or negative affect
on profit margin.
Return on total capital is calculated as annual pre-tax income divided by the sum of current portion of long-term
debt due in 1 year or less, long-term debt due in more that one year, all other noncurrent liabilities and total
stockholders' equity (total capital). This concept measures the total productivity of the capital deployed by a firm
or industry, regardless of the financial source of the capital (i.e., equity, debt, or liability element). As such, the
return on total capital provides insight into the profitability of a business' assets independent of financial structure
and is thus a "purer" indicator of asset profitability than return on equity. In the same way as described for net
profit margin, the firms in an industry, and the industry collectively, must generate over time a sufficient return on
capital if the industry is to remain economically viable and attract capital. The factors causing short-term variation
in net profit margin will also be the primary sources of short-term variation in return on total capital.
Figure 2D-5, following page, presents trends in net profit margins and return on total capital for the steel industry
between 1988 and 2008. The graph shows considerable volatility in the trend over this analysis period. After
registering improvement in financial performance in the first half of the 1990s, steel industry financial
performance declined markedly between 1995 and 2002/2003, due first to increasing imports resulting from Asian
financial crisis with the associated decline in Asian demand for steel and currency devaluations, and later, general
economic weakness. Financial performance improved in 2002 slightly when the U.S. steel industry received
temporary relief with tariffs ranging up to 30 percent on certain steel imports. However, in 2003 the integrated
steel industry again saw poor operating results, as high raw material costs outweighed increased sales and higher
volumes. In 2004, the steel industry's financial performance improved strongly, with returns on total capital and
net profit margins peaking in 2006. In 2007, at the beginning of the recent economic recession, financial
performance of the steel industry began to deteriorate. However, in 2009, financial conditions in the steel industry
began to improve; as a result, the steel sub-industry equity price index rose 37.9 percent versus a 24.3 percent
gain in the S&P 500 index. Longer term, experts expect the industry will benefit from greater pricing power
stemming from further expected consolidation, a lower production cost structure, and continuing decline in the
U.S. dollar (S&P, 2010e).
March 28, 2011 2D-19
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Figure 2D-5: Net Profit Margin and Return on Total Capital for the Iron and Steel Industry
-10%
- Net Profit Margin
- Return on Total Capital
Source: Quarterly Financial Report, 1988-2008; U.S. Census Bureau.
2D.6 Facilities Operating Cooling
Section 316(b) of the Clean Water Act applies to point source facilities that use or propose to use a cooling water
intake structure that withdraws cooling water directly from a surface waterbody of the United States. In 1982, the
Primary Metals industries as a whole (including Nonferrous and Steel producers) withdrew 1,312 billion gallons
of cooling water, accounting for approximately 1.7 percent of total industrial cooling water intake in the United
States.15 The industry ranked third in industrial cooling water use, behind the electric power generation industry,
and the chemical industry (1982 Census of Manufactures).
This section provides information for facilities in the profiled steel segments estimated to be subject to regulation
under the primary analysis options. Existing facilities that meet all of the following conditions would have been
subject to regulation under the three regulatory analysis options:
> Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or
arrangement with an independent supplier 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;
> Have an National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;
and
> Meet the applicability coverage criteria for the proposed regulation specific regulatory analysis option in
terms of design intake flow (i.e., 2 MGD).
15 Data on cooling water use are from the 1982 Census of Manufactures. 1982 was the last year in which the Census of Manufactures
reported cooling water use.
2D-20
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
EPA initially identified the set of facilities that were estimated to be potentially subject to the 316(b) Existing
Facilities Regulation based on a minimum applicability threshold of 2 MGD; this section focuses on these
facilities for the petroleum segment.16
2D.6.1 Waterbody and Cooling Water Intake System Type
Minimills use electric-arc-furnaces (EAF) to make steel from ferrous scrap. The electric-arc-furnace is extensively
cooled by water, which is in turn recycled through cooling towers (U.S. EPA, 1995). This is important to note
since most new steel facilities are minimills.
Table 2D-11, shows the distribution of in-scope facilities in the profiled Steel industry by type of water body and
cooling water intake system. As reported in the table, most in-scope facilities employ a combination of a once-
through and recirculating system. In addition, most in-scope facilities in the Steel industry draw water from a
freshwater stream or river.
Table 2D-11: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by
Waterbody Type and Cooling Water Intake System for the Profiled Steel Industry Segments
Cooling Water Intake Systems
Water Body Type
Lake/Reservoir
Freshwater River/ Stream
Great Lake
Total3
Recirc
Number
0
13
o
13
ulating
% of Total
0%
100%
o%
18%
Comb
Number
0
19
9
27
nation
% of Total
0%
69%
31%
40%
Once-T
Number
1
18
i'
21
hrough
% of Total
6%
89%
6%
31%
Ot
Number
0
7
o
7
ler
% of Total
0%
100%
o%
11%
Total
1
57
10
68
Based on technical weights (See Appendix 3.A).
a. Individual numbers may not add up to total due to independent rounding.
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
2D.6.2 Facility Size
Figure 2D-6, shows the number of in-scope facilities by employment size category. The in-scope facilities in the
Steel Mills and Steel Products segments are on-average relatively large.
16 EPA applied sample weights to the sampled 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).
March 28, 2011
2D-21
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2D: Steel Industry Profile
Figure 2D-6: Number of Facilities Estimated Subject to the 316(b) Existing Facilities Regulation by
Employment Size for Profiled Steel Industry Segments
35
30
25
20
15
10
5
0
Less than
100
100-249
250-499
500-999
1000 and
greater
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
2D.6.3 Firm Size
EPA used the Small Business Administration (SBA) small entity size standards to determine the number of
Section 316(b) profiled steel industry facilities owned by small firms. Firms in the Steel Mills and Steel Products
segments are defined as small if they have 1000 or fewer employees (except for facilities with NAICS code
331112 which are defined as small if they have 750 or fewer employees). EPA estimates that eight small entity-
owned facilities and 57 large entity-owned facilities in the Steel industry segment will be subject to the proposed
regulation. In addition, the ownership size of three facilities was unable to be classified due to insufficient survey
data.
2D-22
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2E: Aluminum Industry Profile
2E Profile of the Aluminum Industry
EPA's Detailed Industry Questionnaire, hereafter referred to as the DQ, identified two 3-digit SIC codes in the
Nonferrous Metals manufacturing industry (SIC codes 333/335) with at least one existing facility that operates a
CWIS, holds a NPDES permit, withdraws at least two million gallons per day (MOD) from a water of the United
States, and uses at least 25 percent of its intake flow for cooling purposes (facilities with these characteristics are
hereafter referred to as "facilities potentially subject to the proposed Existing Facilities regulation" or "in-scope
facilities"). For the purpose of this analysis, EPA identified a six-digit NAICS code for each of these potential
facilities using information from the DQ and public sources (see Appendix 3. C: Conversion the Data from
Standard Industrial Classification (SIC) to North American Industry Classification System (NAICS)}. As the
result of this mapping, EPA identified four 6-digit NAICS codes in the Nonferrous Metals manufacturing industry
(NAICS 331311-5).
For these four NAICS codes, Table 2E-1, below, provides a description of the industry segment, a list of primary
products manufactured, and the number of facilities estimated to be potentially subject to the 316(b) Existing
Facilities regulation based on the minimum withdrawal threshold of 2 MGD.
Table 2E-1: Existing Facilities in the Aluminum Industries (NAICS 33131)
NAICS
Code
NAICS Description
Important Products Manufactured
Number of In-
Scope Facilities3
Primary Stages of Production (Primary Aluminum)
331311
331312
Alumina refining
Primary aluminum
production
Refining alumina (i.e. aluminum oxide) generally from bauxite.
Aluminum from alumina and/or aluminum from alumina and rolling, drawing,
extruding, or casting the aluminum they make into primary forms (i.e. bar,
billet, ingot, plate, rod, sheet, strip).
6
7
Secondary Stages of Production (Secondary Aluminum)
331314
331315
Secondary smelting and
alloying of aluminum
Aluminum sheet, plate,
and foil manufacturing
Recovered aluminum and aluminum alloys from scrap and/or dross (i.e.
secondary smelting) and billet or ingot (except by rolling); manufactured alloys,
powder, paste, or flake from purchased aluminum.
Flat-rolling or continuous casting sheet, plate, foil, and welded tube from
purchased aluminum; recovered aluminum from scrap.
Total NAICS 331311-5"
3
9
25
a. Number of weighted detailed questionnaire survey respondents.
b. Individual numbers may not add up due to independent rounding.
Source: Executive Office of the President, 1987; U.S. EPA 2000; U.S. EPA analysis, 2010.
As shown in Table 2E-1, EPA estimates that, out of an estimated total of 8817 facilities with a NPDES permit and
operating cooling water intake structures in the Aluminum industry (NAICS 331311-5), 25 (or 28 percent)
facilities are estimated to be subject to the 316(b) Proposed Existing Facilities Rule. EPA also estimated the
percentage of total production that occurs at facilities estimated to be subject to the regulatory analysis options.
The total value of shipments for the profiled Aluminum Industry (NAICS 331311-5) from the 2007 Economic
Census is $36.6 billion ($2009). Value of shipments, a measure of the dollar value of production, was selected for
the basis of this estimate. Because value of shipments data were not collected using the DQ, these data were not
available for the sample of manufacturing facilities potentially subject to the regulatory analysis. Total revenue, as
reported on the DQ, was used as a close approximation for value of shipments for these facilities. EPA estimates
the total revenue of facilities in the Aluminum industry subject to the 316(b) Proposed Existing Facilities Rule is
17 This estimate of the number of facilities holding a NPDES permit and operating a cooling water intake structure is based on the
responses from facilities that received the 1999 screener questionnaire.
March 28, 2011
2E-1
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
$22.3 billion ($2009). Therefore, EPA estimates that 61 percent of total domestic aluminum production occurs at
facilities estimated to be subject to the proposed regulation.
Table 2E-2 provides the crosswalk between NAICS codes and SIC codes for the profiled Aluminum NAICS
codes. The table shows that of the profiled 6-digit NAICS codes in the Aluminum industry, alumina refining
(NAICS 331311), primary aluminum production (NAICS 331312), and aluminum sheet, plate, and foil
manufacturing (NAICS 331315) have a one-to-one relationship to SIC codes. Secondary smelting and alloying of
aluminum (NAICS 331314) represents two SIC codes: secondary nonferrous metals (3341) and primary metal
products (3399).
Table 2E-2: Relationships between NAICS and SIC Codes for the Aluminum Industries (2007)
NAICS
Code
331311
331312
331314
331315
NAICS Description
Alumina refining
Primary aluminum
production
Secondary smelting and
alloying of aluminum
Aluminum sheet, plate, and
foil manufacturing
SIC
Code
2819
3334
3341
3399
3353
SIC Description
Industrial inorganic chemicals
Primary aluminum
Secondary nonferrous metals
Primary metal products, n.e.c.
Aluminum sheet, plate, and
foil
Number of
Establishments
16
54
138
115
Value of
Shipments
(Millions;
$2009)
$1,382
$6,880
$9 010
$19,285
Employment
1,611
9,355
8 286
17,626
Source: U.S. DOC, 2007Economic Census
2E.2 Summary Insights from this Profile
The key purpose of this profile is to provide insight into the ability of aluminum industry firms to absorb
compliance costs under the Proposed 316(b) Existing Facilities Rule without material adverse economic/financial
effects. The industry's ability to withstand compliance costs is primarily influenced by two factors: (1) the extent
to which the industry can shift compliance costs to its customers through price increases, and (2) the financial
health of the industry and its general business outlook.
2E.2.1 Likely Ability to Pass Compliance Costs Through to Customers
As reported in the following sections of this profile, the Aluminum industry on average has a moderate-to-high
degree of market concentration, with the profiled Primary Aluminum production segment being slightly more
concentrated than the profiled Secondary Aluminum Production segment. This potentially supports the notion that
firms in the Primary Aluminum production segment may be able to pass some portion of their compliance-related
costs through to consumers while firms in the Secondary Aluminum production segment may not. However, the
domestic Primary Aluminum production segment faces significant competition from imports into the U.S. market,
which has increased overtime and is likely to continue doing so going forward. Further, the Secondary Aluminum
production segment has been persistently and notably reliant on sales into foreign markets, although to a lesser
degree than the Primary Aluminum production segment. Substantial competitive pressure from abroad weakens
the potential of firms in this industry to pass through to customers a significant portion of their compliance-related
costs. As discussed above, given the relatively small proportion of total value of shipments in the Aluminum
industry, in addition to the moderate-to-high degree of concentration in the profiled Aluminum industry, and
strong competitive pressures from abroad, EPA judges that in-scope facilities in the profiled Aluminum industry
subject to the 316(b) Existing Facilities Regulation are not likely to be able to recover compliance costs through
price increases to customers and would have to absorb all compliance costs within their operating finances (see
following sections and Appendix 4.A: Cost Pass-Through Analysis, for further information).
2E-2
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2E: Aluminum Industry Profile
2E.2.2 Financial Health and General Business Outlook
Over the last two decades, the aluminum industry, like other U.S. manufacturing industries, has experienced a
range of economic/financial conditions, including substantial challenges. In the early 1990s, the domestic
aluminum industry was adversely affected by reduced U.S. demand and the dissolution of the Soviet Union,
which resulted in substantially increased Russian aluminum exports. Although domestic market conditions
improved by middle of that decade, weakness in Asian markets, along with growing Russian exports, dampened
performance during the latter half of the 1990s. Demand for aluminum industry products declined again during
2000 through 2002, reflecting recessionary weakness in both the U.S. and world economies, and again resulted in
oversupply of aluminum and declining financial performance of facilities in the aluminum industry. As the U.S.
economy began to show signs of recovery in 2003, so did the overall aluminum industry with higher demand
levels and improving financial performance over the course of 2004 and 2006. Despite increasing costs of energy
and other aluminum production inputs, which lead to lower aluminum production levels and higher aluminum
prices during that time, demand for aluminum grew; increasing prices of steel and copper compared to aluminum
lead to aluminum substitution in the manufacturing of certain goods like cable, beverage cans, and automobile
parts (USGS, 2006c). Higher demand for aluminum also lead to smelter restarts and substantial increases in
primary aluminum production throughout 2007 and the first half of 2008. The recent recession, however, resulted
in lower demand for aluminum, leading to significantly lower aluminum prices and consequent production cuts by
aluminum smelters. By June 2009, 54 percent of domestic production smelting capacity was idle (USGS, 2008a).
Decreased consumption of aluminum in developed economies as a result of the economic events of 2008 could
keep U.S. aluminum production below the 2008 level for the next several years. Moreover, relatively high
electricity rates in the United States compared to those in other nations diminishes the likelihood that domestic
smelters will reopen in the near term (USGS, 2008a).
2E.3 Domestic Production
The Primary stages of aluminum production involve mining bauxite ore and refining it into alumina, one of the
feedstocks for aluminum metal. Direct electric current is used to split the alumina into molten aluminum metal
and carbon dioxide. The molten aluminum metal is then collected and cast into ingots. Technological
improvements over the years have improved the efficiency of aluminum smelting, with a particular emphasis on
reducing energy requirements. Currently, no commercially viable alternative exists to the electrometallurgical
process (Aluminum Association, 2001).
Secondary stages of aluminum production involve recovering aluminum and aluminum alloys from scrap and/or
dross, making billet and ingot, and manufacturing of alloys, powder, paste, of flake from purchased aluminum. In
2009, aluminum recovered from purchased scrap was about three million tons, of which about 60 percent came
from new (manufacturing) scrap and 40 percent from old scrap (discarded aluminum products). Aluminum
recovered from old scrap was equivalent to about 35 percent of apparent consumption (USGS, 2010c).
Reclamation of used aluminum beverage cans continues to be a major source of supply for the U.S. aluminum
industry, generating large savings in production energy costs (USGS, 2009c). In contrast to the steel industry,
aluminum minimills have had limited impact on the profitability of traditional integrated aluminum producers.
Aluminum minimills are not able to produce can sheet of the same quality as that produced by integrated
facilities. As a result, they are able to compete only in production of commodity sheet products for the building
and distributor markets, which are considered mature markets.
In addition, the Secondary stages of aluminum production include manufacturing of semi-fabricated aluminum
products. Examples of semi-fabricated aluminum products include (Aluminum Association, undated):
> sheet (cans, construction materials, and automotive parts);
> plate (aircraft and spacecraft fuel tanks);
March 28, 2011 2E-3
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule Chapter 2E: Economic Profile of the Aluminum Industry
> foil (household aluminum foil, building insulation, and automotive parts);
> rod, bar, and wire (electrical transmission lines); and
> extrusions (storm windows, bridge structures, and automotive parts).
U.S. aluminum companies are generally vertically integrated. Major aluminum companies own large bauxite
reserves, mine bauxite ore and refine it into alumina, produce aluminum ingot, and operate the rolling mills and
finishing plants used to produce semi-fabricated aluminum products. As noted above, the Primary stages of
aluminum production is an electrometallurgical process, which is extremely energy intensive. Electricity accounts
for approximately 30 percent of total production costs for primary aluminum smelting. The Aluminum industry is
therefore a major industrial user of electricity, spending more than two billion dollars annually. Throughout the
years aluminum facilities have been pursuing opportunities to reduce its use of electricity as a means of lowering
costs. Consequently, in the last 50 years, the average amount of electricity needed to make a pound of aluminum
has declined from 12 kilowatt hours to approximately 7 kWh (Aluminum Association, undated).
2E.3.1 Output
The transportation sector is the largest North American market for aluminum, accounting for 6.2 billion pounds,
or 28.1 percent of total consumption. Other major markets include: containers and packaging (22.2 percent);
building and construction (28.1 percent); electrical (7.0 percent); machinery and equipment (6.8 percent); and
consumer durables (6.0 percent) (Aluminum Association, 2009).
Demand for aluminum reflects the overall state of domestic and world economies, as well as long-term trends in
aluminum products use in major end-use sectors. Because aluminum production involves large fixed investments
and capacity adapts slowly to fluctuations in demand, the industry has experienced alternating periods of excess
capacity and tight supplies. The early 1980s was a period of oversupply, high inventories, and excess capacity. By
1986, excess capacity was closed, inventories were low, and demand increased substantially. The early 1990s
were affected by reduced U.S. demand and the dissolution of the Soviet Union, resulting in large increases in
Russian exports of aluminum. By the mid-1990s, global production declined, demand rebounded, and aluminum
prices rose. Subsequent increased production reflected an overall increase in the demand for aluminum with
stronger domestic economic growth, driven by increased consumption by the transportation, container, and
construction segments. The economic crises in Asian markets in the later 1990s, along with growing Russian
exports, again resulted in a period of oversupply, although U.S. demand for aluminum remained strong.
Demand declined again in 2000 through 2002 due to slower growth in both the domestic and world economies,
resulting in oversupply. In addition, production in China increased during this period, and although increased
Chinese consumption helped reduce the surplus slightly, the country switched from being a net importer to a net
exporter. The U.S. aluminum surplus was mitigated somewhat as demand in the automotive and housing markets
remained relatively high through mid-2003. In addition, the California energy crisis in 2000 and 2001 reduced
production from primary smelters located in the Pacific Northwest (Aluminum Association, 1999; USGS, 1999a;
USGS, 1998d; USGS, 1994a; Value Line, 2001). However, as the U.S. economy began to recover in 2003, the
aluminum industry saw higher demand levels, but also higher input prices, which contributed to a slight
production decline during 2003 through 2006. In 2009, following the recession of 2008, sales and net profits in
the aluminum industry were low; however, recovery in the global economy along with a mild rebound in several
key markets will likely result in higher demand for aluminum in 2010 compared to the estimated drop of 9 percent
in 2009. In particular, industry analysts expect that U.S. construction spending will increase 1.7 percent and U.S.
car sales will rise 7.7 percent during 2010, giving a boost to aluminum production (S&P, 2010).
Table 2E-3 shows trends in output of aluminum by Primary and Secondary stages of aluminum production.
Secondary aluminum production grew from 24 percent to just over 34 percent of total domestic production over
the period from 1991 to 2008. Primary production of aluminum recorded a net decrease over the 18-year period,
with a particularly sharp decline in 2001. As noted above, this decrease reflects reduced domestic and world
2E-4 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2E: Aluminum Industry Profile
demand for aluminum, and curtailed production at a number of Pacific Northwest mills caused by the California
energy crisis (S&P 2001; USGS, 2001c). Production has remained fairly constant in recent years.
Table 2E-3: U.S. Aluminum Production
Year
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Total percent change 1991-
2008
Total percent change 2000-
2008
Average annual growth
rate
Aluminum Ingot
Primary Stages of Production
Thousand MT
4,121
4,042
37'6"9"5'
'3,299
'3,375
3377
37603
3",713
'37779
37688"
2"',637
27707
27703
27516
2"',4"'8"'l
2"',2"'8"4
2"354
2"',6"5"8
% Change
na
-"f"9"%
-876"%
-Tb77'%"
2"73%
6'"'b%
a'7°/o
37"l"%
L8%"
-2A%
-283%
17%
-6'T%
-679"%
-T'74%
-779%
i"i"."8"%"
4"i"%
-35.5%
i27;9%
-275%
Secondary Stages of Production
Thousand MT
1,320
1,610
17630
i"3'b"o
i"3"i"b
i"3"8"b
i"3"3'b
i"3'b"b
i"3'7'b
i",'3"70
17210
i","i7'b
i",'b"7'b
1,160
1,080
17,260
1,600
17340
% Change
na
2"2"'.'b"%
l'".'2"%
-8"'."6%
b"."7%
476%
-372%
-2"'."6%
4"'."7%
:i2"."7%
:fi77%
-373%
-8"'."5"%
8".'4"%
-6"'."9"%
l"6"'."7%
'27"."b%
:f6";3%
1.5%
-272%
67i%
Total Production
Thousand MT
5,441
5",'6"5"2
5'73"2"5
'4"','7"9"9
'4"','88'"5'
5'7l57
5","l"3"3
5",2'i"3"
5",'3"4"9
5",'b"5"8
3"',84"7
37877
37773
37676"
3"','5"6"T
3"',54"4"
4',"l"5"4
3"','9"'9"'8
% Change
na
379%
-578%
-979%
i""8%
576"%'
-(15%
i""6"%
2""6%
-5"^4%
:23;9%
'b'"8%'
-277%
-27(5%
-3""i"%'
-0"5"%'
i"7""2"%
'-3"'"8%'
-26.5%
^^0
-18%
Source: USGS Mineral Commodity Summaries, Aluminum 1995-201 Oc
Value of shipments and value added are two common measures of manufacturing output.18 Change in these
values overtime provides insight into the overall economic health and outlook for an industry. Value of
shipments is the sum of receipts earned from the sale of outputs; it indicates the overall size of a market or the
size of a firm in relation to its market or competitors. Value added, defined as the difference between the value of
shipments and the value of inputs used to make the products sold, measures the value of production activity in a
particular industry.
Figure 2E-1 reports constant dollar value of shipments and value added for the Primary and Secondary stages of
aluminum production between 1987 and 2007.
Terms highlighted in bold and italic font are further explained in the glossary.
March 28, 2011
2E-5
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
Figure 2E-1 : Value of Shipments and Value Added for Profiled Aluminum Industry Segments (millions,
$2009)a
Value of Shipments
5- $18 000
/
••••-.
/'•\ ^ /
^""~-"— * /
" \ .-•'" \ X
A-..
''A,
*•"*
••*..-A ••*•"*— ^__^ ~
"* ^^^^—^
- - - A- - - Primary Stages of
Production (SIC to NAICS)
A Primary Stages Production
(NAICS331311 and
331312)
Production (SIC to NAICS)
• Secondary Steage of
Production (NAICS 33 1 3 1 4
and331315)
iiiiiiiiiiiiiiiiiiiii
Value Added
$8,000
$7,000
$6,000
/— V
0
o
S $5,000
^ $4,000
u $3,000
_s
$2,000
$1,000
$0 -
A /
/\ / \ /
* .*> ^•••* \ y
(.*.../";•< * A V^^
A'-. •' 'i'''*~^X.
"* AyAV
i i i i i i i i i i i i i i i i i
Production (SIC and NAICS)
A Primary Stages of
Production (NAICS 331311
and331312)
• • •*• • • Secondary Stages of
Production (SIC to NAICS)
• Secondary Stages of
Production (NAICS 33 13 14
and331315)
^0*0*0*0*0*0*0*0*0*0*0*0*000000000
333*0*0*0*0*0*0*0*0*0*000000000
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2003-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007 Economic Census.
The value of Primary Aluminum shipments shows generally the same pattern as the quantity data shown in Table
2E-3. Trends in production over 1987 to 2003 reflect trends in demand for aluminum; both production and value
of shipments fell with increases in the percentage of domestic demand provided by imports. A similar trend can
2E-6
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2E: Aluminum Industry Profile
be observed for the Secondary Aluminum production during this period, which substitutes in some but not all
markets for Primary Aluminum. In recent years however, value of shipments for both Primary and Secondary
Aluminum has risen steadily (with a significantly steeper increase in the Secondary Aluminum segment) due to
higher demand, increased domestic production, and declining reliance on imports for consumption. In 2008, the
United States became a net exporter of aluminum, increasing net exports by 34 percent from the previous year as
a result of a weak dollar, low labor costs, and low-cost shipping rates. Value added by aluminum production
excludes the value of purchased materials and services (including electricity). Figure 2E-1 shows more
fluctuation in value added during the last decade than in value of shipments for both Primary and Secondary
Aluminum production segments, which could be attributed to fluctuating input prices without the industry being
able to implement significant price adjustments due to stiff competition from foreign markets. However,
beginning in 2003, both value of shipments and value added for Primary and Secondary Aluminum production
segments experienced growth, which could be attributed to an overall increase in market demand for aluminum,
both domestically and world-wide. The recent fluctuations seen in the Primary Aluminum production industry
segment can be attributed to rising cost of inputs, particularly energy and alumina (USGS, 2009c).
Value of shipments in the Secondary Aluminum production segment declined from late 1980s through 1993, and
then recovered by mid-decade, before declining again in the late 1990s. As described above, the profiled
Secondary Aluminum production segment is comprised of secondary smelting and alloying of aluminum and
production of semi-finished aluminum products such as aluminum sheet, plate, and foil. Demand for secondary
smelting and alloying of aluminum is primarily driven by demand from semi-finished aluminum products
manufacturing firms. Demand for secondary and semi-finished aluminum products reflects demand from
transportation, container, construction, and auto industries. Despite the rising cost of aluminum production during
most of the last decade, which resulted in higher aluminum prices, world demand for aluminum continued to
increase; prices for copper and steel experienced more significant increases compared to those of aluminum,
leading to greater aluminum substitution in production of various goods such as cable, beverage cans, and
automobile parts. Consequently, increasing demand for aluminum products during the last decade through the
recession of 2008 resulted in increased value of shipments (USGS, 2006a). As discussed in the next section,
however, prices for the Primary and Secondary Aluminum segment products dropped in 2009 as the result of the
2008 recession.
Overall, while the Primary Aluminum production segment shows lower values for the constant dollar value of
shipments and value added at the end of the 21-year analysis period than at the beginning of the period, the
Secondary Aluminum production segment shows higher values. The declining value of shipments and value
added in the Primary Aluminum production segment reflect the increasing role of imports in meeting total U.S.
demand and the increased competition this segment faces from foreign markets. Over time, the U.S. producers of
Primary Aluminum products have been forced to absorb the cost of rising input costs due to increasing pressure
from foreign markets.
2E.3.2 Prices
The producer price index (PPI) measures price changes, by segment, from the perspective of the seller, and
indicates the overall trend of product pricing, and thus supply-demand conditions, within a segment.
The price trends shown for Primary Aluminum in Figure 2E-2 reflect the fluctuations in world supply and
demand discussed in the previous section. During the early 1980s, the aluminum industry experienced oversupply,
high inventories, excess capacity, and weak demand, resulting in falling prices for aluminum. By 1986, much of
the excess capacity had permanently closed, inventories had been worked down, and worldwide demand for
aluminum increased strongly. This resulted in price increases through 1988, as shown in Figure 2E-2.
In the early 1990s, the dissolution of the Soviet Union had a major impact on aluminum markets. Large quantities
of Russian aluminum that formerly had been consumed internally, primarily in military applications, were sold in
March 28, 2011 2E-7
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
world markets to generate hard currency. At the same time, world demand for aluminum was decreasing. The
result was increasing inventories and depressed aluminum prices. In response to declining aluminum prices, the
United States and five other primary aluminum producing nations signed an agreement in January 1994 to curtail
global output, At the time of the agreement, there was an estimated global overcapacity of 1.5 to 2.0 million
metric tons per year (S&P, 2000).
By the mid-1990s, production cutbacks, increased demand, and declining inventories led to a sharp rebound of
prices. Prices declined again during the late 1990s, however, when the economic crises in Asian markets reduced
the demand for aluminum (USGS, 2001e). During 2000, prices rebounded sharply despite the continuing trend of
high Russian production and exports. However, economic recession caused prices to fall again through 2002
(S&P, 2001-2004). Prices seen by both profiled segments increased significantly between 2003 and 2007. An
increase in global demand, especially in emerging markets like China with cheap shipping and labor rates
contributed to price increases during 2006 and 2007. But in 2009, prices dropped in response to the financial
crisis and recession that began in 2008. Industry analysts expect the average price of aluminum to rise to $1.05 a
pound in 2010, from the average price of $0.76 in 2009; this estimate relies on the expectation that global GDP
will increase by 2.8 percent growth in 2010 (S&P, 2010). This prediction suggests that aluminum prices are
expected to recover coming out of the current recession.
Figure 2E-2: Producer Price Indexes for Profiled Aluminum Industry Segments
225
200
175
150
125
100
- Primary Aluminum
Production (NAICS
331312)
- Aluminum Plate, Sheet,
and Foil Manufacturing
(NAICS 331315)
300000*0*0*0*0*0*0*0
^oo*ooh-(JW.uuic\
a. Data source does not provide Producer Price Indices for NAICS 331311 and NAICS 331314.
Source: BLS, 2009e.
2E.3.3 Number of Facilities and Firms
U.S. Geological Survey data indicate that between 1995 and 2010 the number of Primary Aluminum facilities and
the number of domestic firms that own them declined, as shown in Table 2E-4. The number of domestic firms and
plants they own declined sharply in 2002 and dropped again in 2004. The bulk of the idled capacity in the
beginning of this decade resulted from curtailed production at a number of Pacific Northwest mills caused by the
California energy crisis. Most of the smelters outside of this region continued to operate at or near their
engineered capacities (S&P 2001; USGS, 2001c; USGS, 2002a). However, by 2007, the amount of idled capacity
2E-8
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2E: Aluminum Industry Profile
decreased because new power contracts were obtained by producers, which led to a slight increase in production.
Domestic smelters operated at 69 percent of their capacity (USGS, 2008a). Because of the 2008 recession and the
resulting decrease in demand for aluminum during the first half of 2009 smelter closures took place in Alcoa, TN;
Massena, NY; and Ravenswood, WV, and by the beginning of the fourth quarter of 2009, domestic smelters were
operating at only 49 percent of rated or engineered capacity (USGS, 2010c).
Table 2E-4: Primary Stages of Aluminum Production - Number of
Companies and Plants
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Number of Companies
13
13
13
13
12
12
12
7
7
6
6
6
5
6
6
6
Number of Plants
22
22
22
23
23
23
23
16
15
14
15
15
13
14
14
13
Source: USGS, 1995-2010c
Table 2E-5 shows that the number of Primary Aluminum production facilities generally decreased every year
between 1990 and 1999 and have generally risen every year after that until 2005. The number of facilities in the
Secondary Aluminum production segment showed a more consistent trend, increasing nearly every year.
March 28, 2011
2E-9
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
Table 2E-5: Number of Facilities for Profiled Aluminum Industry Segments
Year3
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent
Change 1990-2006
Total Percent
Change 2000-2006
Average Annual
Growth Rate
Primary Stages of A
Number of
Establishments
61
64
60
52
49
47
58
41
37
39
44
49
57
63
78
73
69
u mi nil m Production1"
Percent Change
na
573%
-73%
:j3";5"%
-578%'
-275%
2716%,
329""2"%
-979%
574%
12.8%
iT.4%
16.3%
10.5%
2378%
-674%
-575%
12.7%
56.8%
0.7%
Secondary Stages of t
Number of
Establishments
229
241
238
224
227
227
207
214
226
247
276
289
236
243
250
251
264
i In m ilium Production0
Percent Change
na
573%
-To%
'-672%
T'75%
61%
-879%
372%
578%
973%
il'77%
477%"
3jg"3^
376%
279%
674%
572%
15.4%
-4.3%
0.9%
a. Before 1998, these data were compiled in the Standard Industrial Classification (SIC) system; since 1998, these data have been compiled
in the North American Industry Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC
code classifications using the 1997Economic Census Bridge Between NAICS and SIC.
b. NAICS 331311-2
c. NAICS 331314-5
Source: U.S. SBA, 1990-1997; SUSB 1998-2006.
The trend in the number of Primary Aluminum production firms over the period 1990 through 2006 is similar to
the trend in the number of facilities. However, the number of Secondary Aluminum production firms prior to
1997 fell by a larger percentage than the number of Secondary Aluminum production facilities. Table 2E-6
presents SUSB information on the number of firms in each segment between 1990 and 2006.
2E-10
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2E: Aluminum Industry Profile
Table 2E-6: Number of Firms for Profiled Aluminum Industry Segments
Year3
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent Change
1990-2006
Total Percent Change
2000-2006
Average Annual Growth
Rate
Primary Stages of Ah
Number of Firms
42
46
41
38
38
35
44
27
27
29
32
38
50
51
63
62
57
mi mi m Production1"
Percent Change
na
7.7%
Tio".4%
-7.6%
-0.9%
-7.5%
:27';8%
:3876%
-0.9%
7.4%
10.3%
18.8%
31.6%
2.0%
23.5%
-T.6%
-81%
34.2%
78J%
L9%
Secondary Stages of A
Number of Firms
192
206
204
190
185
182
161
172
182
199
225
239
190
197
201
194
209
uminum Production0
Percent Change
na
7".'2"%"
-'i"."6'%
I'6"."9"%
-24%
-16%
-114%
7"."i%
5"'."7%
9^3%
131%
6".'2"%
:2o"'.5%
17%
2"'."6%
-15%
7:7%
8.8%
-7J%
0.5%'
a. Before 1998, these data were compiled in the SIC system; since 1998, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the
1997Economic Census Bridge Between NAICS and SIC.
b. NAICS 331311-2
c. NAICS 331314-5
Source: U.S. SBA, 1990-1997; SUSB 1998-2006.
2E.3.4 Employment and Productivity
Figure 2E-3, below, provides information on employment for the profiled Primary and Secondary Aluminum
production segments. Trends in Primary Aluminum employment reflect efforts by both production and producers'
efforts to compete with less labor-intensive minimills through improvements in labor productivity (McGraw-Hill,
2000). Between 1992 and 2006 employment in the Primary Aluminum segment declined almost steadily, even
when production increased. Employment in the Secondary Aluminum segment declined from 1987 through 1994,
rose between 1995 and 1997, before declining until 2003 when it began to rise again.
March 28, 2011
2E-11
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
Figure 2E-3: Employment for Profiled Aluminum Industry Segments
Number of Employees
z,uuu,uuu
n
...***.
*.. . AT -A.
* * * Nv^
4"*'A ^""X /^-^
.*•' '-. » — *
i' *-.
'A-- A--^
^A
^^*\^ ^
-A- - - Primary Stages of
Production (SIC to NAICS)
-A Primary Stages of
Production (NAICS331311
and 331312)
- ^- - - Secondary Stages of
Production (SIC to NAICS)
—• Secondary Stages of
Production (NAICS331314
and331315)
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the SIC classification data to the NAICS code classifications using the 1997 Economic
Census Bridge Between SIC and NAICS.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
Table 2E-7: presents year-over-year changes in value added per labor hour, a measure of labor productivity, for
the Primary and Secondary Aluminum production segments between 1987 and 2007. The trend in labor
productivity in both segments shows volatility over the entire period, reflecting variations in capacity utilization.
Value added per hour in the Primary Aluminum segment showed a 14.0 percent net increase over the entire period
1987 through 2007, while value added per hour in the Secondary Aluminum segment saw a 124.4 percent
increase over the same period.
2E-12
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2E: Aluminum Industry Profile
Table 2E-7: Productivity Trends for Profiled Aluminum Segments ($2009)
Year3
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2666
2001
2662"
2663"
2664"
2665"
2666
2007'
Total Percent Change
1987-2007
Total Percent Change
2000- 2007
Average Annual
Growth Rate
Primar
Value Added
(millions)
$4,409
$7j39
$"5",65"7"
$1J7415
$"3326
$"'3'"3'24"
$2,621"
$"'37338
$47614
$3,3"3"3
$'3'"3"8"6"
$3,469
$3,664
$27915
$37129
$2,835"
$27108"
$2,92"6
$37365"
$27957"
-32.9%
1.4%
-2.0%
y Stages of Alu
Production
Hours
(millions)
32
37
35"
37
38
38
35
32
34
34"
31"
32
30
29
24"
24
21
19
17
16
19
-41.2%
-34.6%
-2.6%
minum Production
Value Added/Hour
(S/hour)
137
194"'
162
119
93
87
76
104
136
99
115
IDS"
99
i'b'i"
l"30
118
102
151
145"
201"
156
Percent
Change
n/a
4176%
11679%
22675%
-672%
:y278%"
3679%"
3""i""3"%"
1578%
-674%
-875%
272%
2973%"
-973%
-14"76%"
4875%
-3"'"9"'%"
3876%
12273%
14.0%
55.1%
0.7%
Seconda
Value Added
(millions)
$3,921
$"47272'
$4,6'9"9
$47747
$47458'
$"'47864"
$57179
$5",'28"6
$5",4'8"9
$5",'6"3"9
$6,84"6"
$7",'6"6l"
$47962
$47184
$"4','86"8
W',687"
$4,'6"8"l"
$57447
$6",24"6
$77549
92.5%
52.1%
3.3%
ry Stages of Al
Production
Hours
(millions)
51
53
54
52
51"
52
51"
49
52
53
52
51"
49
46"
43
41"
41
4"i"
43
41"
44
-14.2%
-5.7%
-0.8%
uminum Production
Value Added/Hour
(S/hour)
77
8"'i"
77
9"i"
88"
108"'
96"
105"
l"6'2
105"
108"'
136"
i"57"
107"
97
120
T'i'4
Ti4
126
154
173
Percent
Change
n/a
5""5"%"
-5""6"%"
l"8""5"%"
2"2""7"%"
:io'"7"%
9"'"2"'%"
-27'7"%"
2l%
376%
1671%
:327o%
-973%
2"'3"'"3"'%"
-47'9%"
672%
123%
124.4%
61.3%
4.1%
a.Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997Economic
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2E.3.5 Capital Expenditures
Aluminum production is a highly capital-intensive process. Capital expenditures are needed to modernize,
replace, and when market conditions warrant, expand capacity. Environmental requirements may also require
substantial capital expenditures.
Table 2E-8 presents capital expenditures in the Primary and Secondary Aluminum production segments during
1987 through 2007. As shown by the table, capital expenditures in the Primary Aluminum segment fluctuated in
the early 1990s, but steadily increased beginning in 1995 and through the remainder of the decade, eventually
increasing more than 200 percent. In the last ten years however, this segment has shown large fluctuations in
capital expenditures from one year to the next, rising and falling as much as 50 percent in a single year. These
changes resulted from the production surges and cutbacks, and capacity fluctuations, in response to supply and
demand conditions prevalent in the market for aluminum.
Capital expenditures in the Secondary Aluminum production segment also fluctuated considerably between 1987
and 2007, peaking in 1990, ten years earlier than in the Primary Aluminum segment. Between 1991 and 1993
producers of Secondary Aluminum reduced capital expenditures by approximately 80 percent. Capital
expenditures in this segment fluctuated during the remainder of the decade until 2002 where expenditures
decreased more than 70 percent in two years. However, between 2005 and 2007, outlays increased by 106 percent
March 28, 2011
2E-13
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
in response to the increase in world demand. Lack of credit to aluminum companies as a result of the recession
beginning in 2007/2008 is expected to cause delays in expansion projects in many parts of the world. Projects in
places with lower power costs are still expected to move forward (USGS, 2008a).
Table 2E-8: Capital Expenditures for Profiled Aluminum Segments (millions, $2009)
Year3
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent Change
1987-2007
Total Percent Change
2000-2007
Average Annual Growth
Rate
Primary Stages of A
Capital Expenditures
$437
$336
$376
$355
$376
$377
$297
$231
$250
$318
$542
$626
$570
$814
$453
$220
$123
$170
$125
$161
$211
uminum Production
Percent Change
n/a
12372%
I27i%
-577%"
578%
674%,
:2i72%
12274%
876%
277o"%"
703%
1576%
:97i%
47279%
14473%
:j]7g%
:4"47i%
3876%
:2"67i"%"
2876%
3679%,
-51.8%
-74.1%
-3.6%
Secondary Stages of i
Capital Expenditures
$804
$925
$966
$l"7J2"2"
$893
$718
$423
$455
$583
$596
$543
$493
$544
$570
$768
$433
$298
$298
$416
$472
$718
.In mi nil m Production
Percent Change
n/a
1576%
44%
167!%
:2o74%
3i97g%
:4f72%
7"."8"%"
2876%
273%
-976%,
-972%
i'b"."3"%"
478%
3478%
14377%'
:3"i7o%
-61%
3976%
1375%
5272%
-10.7%
26.1%
-0.6%
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2E.3.6 Capacity Utilization
Capacity utilization measures actual output as a percentage of total potential output given the available capacity.
Capacity utilization reflects excess or insufficient capacity in an industry and is an indication of whether new
investment is likely. Capacity utilization is also closely linked to financial performance for industries with
substantial fixed costs, such as the aluminum industry. Like integrated steel mills, the aluminum manufacturing
process requires a large capital base to transform raw material into finished product. Because of the resulting high
fixed costs of production, earnings can be very sensitive to production levels, with high output levels relative to
capacity needed for plants to remain profitable.
Figure 2E-4 shows capacity utilization from 1989 through 2009 for the two profiled Aluminum industry
segments. As shown, capacity utilization fluctuated substantially throughout the 12-year analysis period for both
segments. Between 1989 and 1998, capacity utilization in the Secondary Aluminum production segment increased
on average, largely due to high demand for rolled aluminum products, which account for more than 50 percent of
all shipments from the aluminum industry. Increased consumption by the transportation segment, the largest end-
use segment for the Secondary Aluminum production segment, is responsible for bringing idle capacity into
2E-14
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2E: Aluminum Industry Profile
production (McGraw-Hill, 1999). At the same time, capacity utilization in the profiled Primary Aluminum
production segment remained approximately the same after some fluctuations during that decade. However,
between 1998 and 2001, the general weakening of demand for aluminum products during the Asian economic
crisis and later, general economic weakness in domestic and world economies, resulted in a marked fall-off in
capacity utilization in both profiled segments. Again, reflecting the economic recovery that began in 2002,
capacity utilization in both profiled segments began to rise and by 2007 had risen substantially. While capacity
utilization in both profiled segments fluctuated during this decade, the Primary Aluminum production segment
experienced larger fluctuations. Contrary to the Primary Aluminum production segment, utilization for the
Secondary Aluminum production segment fluctuated much less throughout this decade, staying on average within
a 5 percent margin. More recently, both profiled segments began to experience a decline in capacity utilization as
a result of the economic recession that began in 2007/2008.
Figure 2E-4: Capacity Utilization Rates (Fourth Quarter) for Profiled Aluminum Industry Segments3 b)C
a. Before
Class ific
Census L
5. Befon
NAICS6
c. Capac
33131H
Source.
i (\(\
1UU
oc
yj
on
VU
Off
I
on i
oU
/j
7O.
/re
/rn
££
A
* .•*• A. ^ /\ /
.- > A; / *•* ^-4 / i
"* J. t . A A \ / 1 I \
: 2 •'••'••-••' \\ J-UA
^ w
V
*«o *«o *«o *«o *«o *«o *«o *«o *«o *«o *«o o o o o o o o o o $
00 *«O *«O *«O *«O *«O *«O *«O *«O *«O *«O O O O O O O O O O t
^ooh-K)W4^tyio\^ioo^ooh-K)W4^tyio\^ioov
---*--- Primary Stages of Aluminum
Production (SIC to NAICS)
» Primary Stages of Aluminum
Production (NAICS
331311/2)
Aluminum Production (SIC
to NAICS)
A Secondary Stages of
Aluminum Production
(NAICS 33 13 14/5)
j
1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
ation System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
bridge Between NAICS and SIC.
; 2007, U.S. Census sampled every industry in a specific NAICS6. Beginning in 2007, U.S. Census only sampled certain industries within any
, and therefore, the data collected before 2007 cannot be directly compared to the data collected in 2007 and beyond.
ity Utilization for the Primary Aluminum production segment (NAICS 33131 1/2) for 2007-2009 are for NAICS 331312; 2007-2009 data for NAICS
were not available from the Census Bureau at the time of the analysis.
U.S. DOC, Survey of Plant Capacity 1989-2009, U.S. Census Bureau.
2E.4 Structure and Competitiveness
On average, the U.S. Aluminum industry has moderate-to-high industry concentration, with the Primary
Aluminum production segment being slightly more concentrated than the Secondary Aluminum production
segment. A number of large mergers among aluminum producers have increased the degree of concentration in
the industry in recent years. For example, Alcoa (the largest aluminum producer) acquired Alumax (the third
March 28, 2011
2E-15
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
largest producer) in 1998 and Reynolds (the second largest producer) in May 2000. Alcan acquired Algroup in
2000 and Pechiney in 2004. As the result of these acquisitions, three companies accounted for 41 percent of
primary global aluminum output. In 2007, Rusal and Sual and Rio Tinto acquired Alcan, thereby increasing
concentration in the aluminum industry. Industry analysts speculate that with a greater degree of concentration,
capacity will be more closely managed during varying market conditions, which will likely reduce volatility of
industry prices and profits (USGS, 2008a).
2E.4.1 Firm Size
The Small Business Administration (SBA) defines a small firm for Primary Aluminum production (NAICS
331311 and 331312) as a firm with 1,000 or fewer employees and for Secondary Aluminum Production (NAICS
331314 and 331315) as a firm with 750 or fewer employees. The Statistics of U.S. Businesses (SUSB) provides
employment data for firms with 500 or fewer employees and does not specify data for companies with 500-750
employees for the Primary Production industry and 500-1000 for the Secondary Production industry. Therefore,
based on 2006 data for firms with up to 500 employees,
> 45 of the 57 firms in the Primary Aluminum production segment had less than 500 employees. Therefore,
at least 79 percent of this segment's firms are classified as small. These small firms owned 48 facilities, or
70 percent of all facilities in the segment.
> 182 of the 209 firms in the Secondary Aluminum production segment had less than 500 employees.
Therefore, at least 87 percent of this segment's firms are classified as small. These small firms owned 196
facilities, or 74 percent of all facilities in the segment.
Table 2E-9 below shows the distribution of firms and facilities in the Primary Aluminum production segment
(NAICS 331311 and 331312) and the Secondary Aluminum production segment (NAICS 331314 and 331315) by
the employment size of the parent firm.
Table 2E-9: Number of Firms and Facilities by Employment Size Category for the Profiled
Aluminum Industry Segments, 2006
Employment
Size Category
0-19
20-99
100-499
500+
Total
Primary Stages of A
Number of Firms
30
8
7
12
57
uminum Production
Number of Facilities
30
8
10
21
69
Secondary Stages of i
Number of Firms
101
49
32
27
209
.In mi nil m Production
Number of Facilities
101
52
43
68
264
Source: U.S.DOC, Statistics of U.S. Businesses, 2006 (U.S. DOC, 2006).
2E.4.2 Concentration Ratios
Concentration is the degree to which industry output is concentrated in a few large firms. Concentration is closely
related to entry barriers with more concentrated industries generally having higher barriers.
The four-firm concentration ratio (CR4) and the Herfindahl-Hirschman Index (HHI) are common measures of
industry concentration. The CR4 indicates the market share of the four largest firms. For example, a CR4 of 72
percent means that the four largest firms in the industry account for 72 percent of the industry's total value of
shipments. The higher the concentration ratio, the less competition there is in the industry, other things being
2E-16
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2E: Aluminum Industry Profile
equal.19 An industry with a CR4 of more than 50 percent is generally considered concentrated. The HHI indicates
concentration based on the largest 50 firms in the industry. It is equal to the sum of the squares of the market
shares for the largest 50 firms in the industry. For example, if an industry consists of only three firms with market
shares of 60, 30, and 10 percent, respectively, the HHI of this industry would be equal to 4,600 (602 + 302 + 102).
The higher the index, the fewer the number of firms supplying the industry and the more concentrated the
industry. Based on the U.S. Department of Justice's guidelines for evaluating mergers, markets in which the HHI
is under 1,000 are considered unconcentrated, markets in which the HHI is between 1,000 and 1,800 are
considered to be moderately concentrated, and those in which the HHI is in excess of 1,800 are considered to be
concentrated.
Table 2E-10 shows that the concentration ratios for the profiled Primary Aluminum production segment (NIACS
331311 and 331312) have increased for the top four and eight firms since 1997 (particularly for NAICS 331312).
In 2002, the eight and four largest firms in this segment accounted for 97 and 85 percent of total U.S. primary
capacity, respectively. Consolidation in the industry since the early 1990s has increased market concentration.
With the merger of Alcoa, Inc. and Reynolds in May 2000, the single merged company accounted for 50 percent
of domestic primary aluminum capacity, and the four largest U.S. producers controlled 72 percent of domestic
capacity (Alcoa Inc. for 50 percent, Century Aluminum Co. for almost 10 percent, and Noranda Aluminum Inc.
and Ormet Primary Aluminum Corp. for 6 percent each) reported at the end of 2002 (USGS, 2002a). While no
HHI is available in 2002 for these NAICS codes, the recent merger history in this segment and the concentration
ratios indicate a moderate to high degree of market concentration.
As reported in Table 2E-10, the profiled Secondary Aluminum production segment had an HHI of 694 for NAICS
331313 and 1,856 for NAICS 331314 in 2002. On average, this segment as a whole can be considered moderately
concentrated. Thus, based on these ratios and indices, firms in the profiled Primary Aluminum production
segment on average enjoy higher market power than those in the profiled Secondary Aluminum production
segment. Consequently, while the firms in the Primary Aluminum production segment may be able to pass some
of their compliance costs onto their consumers, the firms in the Secondary Aluminum production segment are less
likely to be able to do so. However, an accurate assessment of the cost pass-through potential of firms in the
Aluminum industry must be considered in conjunction with other measures of market power.
19 Note that the measured concentration ratio and the HHF are very sensitive to how the industry is defined. An industry with a high
concentration in domestic production may nonetheless be subject to significant competitive pressures if it competes with foreign
producers or if it competes with products produced by other industries (e.g., plastics vs. aluminum in beverage containers).
Concentration ratios based on share of domestic production are therefore only one indicator of the extent of competition in an
industry.
March 28, 2011 2E-17
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
Table 2E-10: Selected Ratios for the Profiled Aluminum Segments, 1987,1992,1997, and 2002
SIC (S) or
NAICS (N)
Code
S2819a
N 331311
S3334
N 331312
S3341
N 331314
S3353
N 331315
Yearb'c
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
1987
1992
1997
2002
Total
Number of
Firms
427
446
5
8
34
30
13
26
365
346
87
124
39
45
41
79
4 Firm (CR4)
38%
39%
NA
97%
74%
59%
59%
85%
24%
28%
41%
45%
74%
68%
65%
71%
C
8 Firm (CR8)
49%
50%
100%
160%
95%
82%
82%
98%
36%
41%
54%
58%
91%
86%
85%
87%
oncentration Rati
20 Firm (CR20)
68%
68%
NA
NA
99%
99%
100%
ibb%
52%
60%
76%
79%
99%
99%
98%
97%
)S
50 Firm (CR50)
84%
85%
NA
NA
100%
100%
NA
ibb%
74%
79%
94%
96%
100%
100%
100%
ibb%
Herfindahl-
Hirschman Index
468
677
NA
NA
1,934
M56
1,231
NA
251
300
630
694
1,719
U533
1,447
1^856
a. SIC code represents largest percentage of facilities and value of shipments within this NAICS based on the 1997 Bridge Between SIC and NAICS
b. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the NAICS system. For this analysis, EPA converted
the NAICS classification data to the SIC code classifications using the 1997Economic Census Bridge Between NAICS and SIC.
c The 2002 Census of Manufactures is the most recent concentration ratio data available.
Source: U.S. DOC, 1987, 1992, 1997, and 2002.
2E.4.3 Foreign Trade
This profile uses two measures of foreign competition: export dependence and import penetration.
Import penetration measures the extent to which domestic firms are exposed to foreign competition in domestic
markets. Import penetration is calculated as total imports divided by total value of domestic consumption in that
industry: where domestic consumption equals domestic production plus imports minus exports. Theory suggests
that higher import penetration levels will reduce market power and pricing discretion because foreign competition
limits domestic firms' ability to exercise such power. Firms belonging to segments in which imports account for a
relatively large share of domestic sales would therefore be at a relative disadvantage in their ability to pass-
through costs because foreign producers would not incur costs as a result of the Proposed Existing Facilities
Regulation. The estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) for
2007 is 27 percent. For characterizing the ability of industries to withstand compliance cost burdens, EPA judges
that industries with import ratios close to or above 27 percent would more likely face stiff competition from
foreign firms and thus be less likely to succeed in passing compliance costs through to customers.
Export dependence, calculated as exports divided by value of shipments, measures the share of a segment's sales
that is presumed subject to strong foreign competition in export markets. The Proposed Existing Facilities
Regulation would not increase the production costs of foreign producers with whom domestic firms must compete
in export markets. As a result, firms in industries that rely to a greater extent on export sales would have less
latitude in increasing prices to recover cost increases resulting from regulation-induced increases in production
costs. The estimated export dependence ratio for the entire U.S. manufacturing sector for 2007 is 15 percent. For
characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with
export ratios close to or above 15 percent are at a relatively greater disadvantage in potentially recovering
compliance costs through price increases since export sales are presumed subject to substantial competition from
foreign producers.
Table 2E-11 reports export dependence and import penetration for both the Primary and Secondary Aluminum
production segments, from 1990 through 2007. Imports of Primary Aluminum rose dramatically in 1994,
2E-18
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2E: Aluminum Industry Profile
primarily due to the large exports from Russian producers. Representatives of major aluminum producing
countries met in late 1993 and 1994 to address the excess global supply of primary aluminum. Those discussions
resulted in the Russian Federation's agreement to reduce production by 500,000 MTs per year, and plans for other
producers to cut their production and to assist Russian producers to improve their environmental performance and
stimulate the development of internal demand for the Russian production (USGS, 1994a). Nonetheless, imports
have continued to represent a substantial and growing share of total U.S. consumption, and Canada and Russia
have continued to be the top aluminum suppliers to the United States, accounting for 56 percent and 17 percent of
all aluminum imports, respectively. During 2006 and 2007, imports for consumption increased to more than 60
percent to fill the supply deficit created by increased demand and decrease in domestic production. As domestic
production began to increase in 2008, imports for consumption declined, and exports increased, and the United
States became a net exporter of aluminum (USGS, 2009c).
Between 1990 and 2007, imports in the Primary Aluminum production segment on average grew by more than 7
percent each year while exports and value of shipments declined by nearly 2 percent each year, thereby indicating
a continuous growth in dependence of the U.S. economy on aluminum imports and a steady decline of U.S.
competitiveness on the world aluminum market. In 2007, the import penetration ratio for the Primary Aluminum
production segment was 60 percent, which is more than double the U.S. manufacturing industry average of 27
percent. The export ratio for the Primary Aluminum production segment in 2007 was 21 percent compared to the
national manufacturing average of 15 percent. This shows that the in-scope facilities in the profiled Primary
Aluminum production segment are subject to significant international competitive pressures, largely manifesting
though the increasing penetration of foreign product into domestic markets as well as declining competitiveness
of domestically produced aluminum on world aluminum markets. Consequently, these facilities are not very
likely to be able to pass a material share of compliance costs through to consumers.
Facilities in the profiled Secondary Aluminum production segment face lower competition from foreign producers
in domestic and foreign markets than facilities in the profiled Primary Aluminum production segment. Unlike the
Primary Aluminum production segment, between 1990 and 2007 exports, imports, and value of shipments in the
Secondary Aluminum production segment experienced an annual average growth of approximately 2, 4, and 2
percent respectively. In 2007, the import penetration ratio for the Secondary Aluminum production segment was
13 percent, which is one-half of the U.S. manufacturing industry average of 27 percent. The export ratio for the
Secondary Aluminum production segment in 2007 was 13 percent, or just below the average for the U.S.
manufacturing industry. Consequently, in-scope facilities in the profiled Secondary Aluminum production
segment would probably be in a better position to recover regulation-induced increases in production costs
through price increases compared to in-scope facilities in the profiled Primary Aluminum production segment.
Overall, the competitive pressures from foreign firms/markets may offset the finding stated above, that the
aluminum industry would appear to possess market power from being a moderately concentrated industry. As a
result, from a total market perspective, the industry is not likely to possess any substantial market power
advantage in being able to pass compliance costs through to customers as price increases.
March 28, 2011 2E-19
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
Table 2E-11: Import Share and Export Dependence for the Profiled Aluminum Segments ($2009)
Year3
Value of Imports
(millions)
Value of Exports
(millions)
Value of
Shipments
(millions)
Implied
Domestic
Consumption1"
Import
Penetration0
Export
Dependence*1
Primary Stage of Aluminum Production
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent Change
1990 - 2007
Total Percent Change
2000 - 2007
Average Annual
Growth Rate
$2,969
$27598
$27640
$37516
$57395
$57702"
$'4774!'
$57943
$67095"
$67'l32
$67481'
$57815
$57775
$57798"
$7';405
$87617
$l'o''3'6"'i'
$97666
225.6%
49.1%
7.2%
$2,432
$2',490"
$17704
$17205
$U38
$1350
$7/ii"i
$"l",440
$7,286"
$17246
$17298
$T;o35
$897'
$821"
$Yj4i
'$17424
$1,880
$17714'
-29.5%
32.0%
-2.0%
$12,433
$107882
'$"l"6',07"8"
$8';'9"i"5"
$9"',"l"06"
'$""l"o',2"62"
$97004
$97663
$9,660
$87842
$8,858
$77543"'
$77471"
$"'67226"
$67968
$6",'7"0'5"
$77921
$87262
-33.5%
-6.7%
-2.4%
12,970
107990
'i"i7b"'i'4
fiTzIs'
life's"
14747.4
i'27333"
147167
147469"
i'3'77'2'8"
1476"4""l
i'27322
i'27349"
117203"
137232
l"37'8"99
i'6"34"3
Te^T's
23%
24%
24%"
31"%"
4"'i"%"
40%
38"'%"
42'%"
42'%"
45"%"
46"%"
47"%"
47%
52%"
56%
62"'%"
63%"
60%
20%
23%"
17%
14%
14%"
T'5%
16%
15%
13%
14%"
T'5%
14%"
12%
13%
16%
2"'T%"
24%"
21%
25.0%
15.5%
1.3%
Secondary Stages of Aluminum Production
1990
1991
1992"
1993"
1994"
1995'
1996"
1997'
1998"
1999"
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent Change
1990 - 2007
Total Percent Change
2000 - 2007
Average Annual
Growth Rate
$2,428
$"i",'6"9"T
$17791
$1,714
$1,887
$27643
$"272l2
$77734
$17859
$1,898
$27088
$1,819
$17954
$"27029
$27471
$"3"',35"8"
$37871
$"3"3'92"
47.9%
72.1%
2.3%
$1,824
$17924
$17969
$17860
$2"33"5
$"3,196
$"278"5"5
$3^212
$"'3"',b2"'8
$"'2"','8"'6"4
$"'27904
$2"'3"i'6"
$2^202
$"2,211
$"'2,'6"8"7
$"'3"',b'6"6
$"'37603
$3,646
99.8%
25.6%
4.2%
$21,754
$19,681
$"19,912
$177798
$19,665
$"24,138
3217641
$22"7i64
$217336"
$26,737
$20308"
$17,974
3177835
$"i"777"6"7
$19,753
j2276'4"'5
$26"'i"5"6"
$28,295
30.1%
38.0%
1.6%
22,358
i"9"74"8"
19734
17352
i"972"i"7
2"3"7'5"85"
2"b79"98"
2"b79"87
2"o7'i"6"6"
i"9j'7"b
f976"92"
YJ^JJ
i'7'3'8'7
i'7'3'2'5"
i"9"3'3"7
'22^37
2"6"724"
28^24'i
11%
9%
9%
i"b%
i'b'%
i"i"%
i"i"%
8%
9%
i'b'%
i"i"%
ii%
i"i"%
12%
i"3"%
i'5%
i'5%
i'3%
8%
i"b%"
i"b'%"
i"b'%"
12%
i'3%
13%
14%
14%
14%
14%
14%
12%
12%
14%
14%
14%
i'3%"
26.3%
43.4%
1.4%
a. Before 1998, the Department of Commerce compiled data in the SIC system; since 1998, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
Census Bridge Between NAICS and SIC.
b. Calculated by EPA as shipments + imports - exports.
c. Calculated by EPA as imports divided by implied domestic consumption.
d. Calculated by EPA as exports divided by shipments.
Source: U.S. International Trade Commission 1990-2007.
2E-20
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2E: Aluminum Industry Profile
Table 2E-12 shows trends in exports and imports separately for the profiled Primary and Secondary Aluminum
production segments separately. U.S. aluminum companies have a large overseas presence, which makes it
difficult to analyze import data. Reported import data may reflect shipments from an overseas facility owned by a
U.S. firm. The import data therefore do not provide a completely accurate picture of the extent to which foreign
companies have penetrated the domestic market for aluminum. This table shows that imports have grown
substantially in both categories between 1993 and 2008. Exports of primary aluminum have generally declined,
with some fluctuation over the period. Exports of secondary aluminum rose steadily until 1999, before declining
during 2000 to 2003. Exports did, however, rebound in 2004 and continued to rise or remain steady until 2008.
Table 2E-12: Trade Statistics for Aluminum and Semi-fabricated Aluminum Products (Quantities in
thousand metric tons; Values in millions, $2009)
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Total Percent Change
1993-2008
Average Annual Growth
Rate
Primary Alumii
Import3
X]
Quantity
1,840
2,480
T',930
1,910
2,060
2,400
2,650
2,490
2,560
2,790
2,870
3,250
3,660
3,440
2,950
2,790
51.63%
57.95%
Value
3,017
47783"
47968"
47616
47'544
47698
47757
47996
47759
47814
47981
67670
77838
97610
87568
77903
161.93%
76.17%
mm Production
Exportb
K
Quantity
400
339
369
417'
352
265
sis"
273
192
206
214
298
329
346
349
308
-23.00%
-235.18%
Value
759
737
929
901
787
576
652
580
387
402
409
641
731
963
985
901
18.62%
39.11%
Secondary Alum
Import3
X.
Quantity
400
507
622
498
562
649
735
791
683
796
653
724
927
914
801
693
73.25%
46.04%
Value
1,175
17487
2,212"
17695
17972
2,202"
27248
27586
27134
2,290
17761
2,212"
2,975
3,434
37173
2,863"
138.63%
63.73%
nil m Production
Exportb
JC7
Quantity
594
719
812
760
882
893
907
845
751
706
690
795
886
923
887
929
56.40%
47.35%
Value
2,078
27538
37526
37147
37565
37496
37244"
27'947
27567
27246
27216
27766
37194
37806
37855
47668
95.73%
65.93%
a. Table 10: U.S. Imports for Consumption of Aluminum, by
b. Table 9: U.S. Exports of Aluminum, by Class
Source: USGS, Minerals Yearbook, 1993-2008a.
Class
2E.5 Financial Condition and Performance
The financial performance and condition of the aluminum industry are important determinants of its ability to
withstand the costs of regulatory compliance without material, adverse economic/financial impact. To provide
insight into the industry's financial performance and condition, EPA reviewed two key measures of financial
performance over the 21-year period, 1988-2008: net profit margin and return on total capital. EPA calculated
these measures using data from the Quarterly Financial Report for Manufacturing, Mining, and Trade
Corporations (QFR) published by the U.S. Census Bureau. Financial performance in the most recent financial
reporting period (2008) is obviously not a perfect indicator of conditions at the time of regulatory compliance.
However, examining the trend and deviation from the trend through the most recent reporting period gives insight
into where the industry may be in terms of financial performance and condition, at the time of compliance. In
addition, the volatility of performance against the trend, in itself, provides a measure of the potential risk faced by
the industry in a future period in which compliance requirements are faced: all else equal, the more volatile the
historical performance, the more likely the industry may be in a period of relatively weak financial conditions at
the time of compliance.
March 28, 2011
2E-21
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule Chapter 2E: Economic Profile of the Aluminum Industry
Net profit margin is calculated as after-tax income before nonrecurring gains and losses as a percentage of sales
or revenues, and measures profitability, as reflected in the conventional accounting concept of net income. Over
time, the firms in an industry, and the industry collectively, must generate a sufficient positive profit margin if the
industry is to remain economically viable and attract capital. Year-to-year fluctuations in profit margin stem from
a several factors, including: variations in aggregate economic conditions (including international and U.S.
conditions), variations in industry-specific market conditions (e.g., short-term capacity expansion resulting in
overcapacity), or changes in the pricing and availability of inputs to the industry's production processes (e.g., the
cost of energy to the aluminum production process). The extent to which these fluctuations affect an industry's
profitability, in turn, depends heavily on the fixed vs. variable cost structure of the industry's operations. In a
capital intensive industry such as the aluminum industry, the relatively high fixed capital costs as well as other
fixed overhead outlays, can cause even small fluctuations in output or prices to have a large positive or negative
affect on profit margin.
Return on total capital is calculated as annual pre-tax income divided by the sum of current portion of long-
term debt due in one year or less, long-term debt due in more that 1 year, all other noncurrent liabilities and total
stockholders' equity (total capital). This concept measures the total productivity of the capital deployed by a firm
or industry, regardless of the financial source of the capital (i.e., equity, debt, or liability element). As such, the
return on total capital provides insight into the profitability of a business' assets independent of financial structure
and is thus a "purer" indicator of asset profitability than return on equity. In the same way as described for net
profit margin, the firms in an industry, and the industry collectively, must generate over time a sufficient return on
capital if the industry is to remain economically viable and attract capital. The factors causing short-term variation
in net profit margin will also be the primary sources of short-term variation in return on total capital.
Figure 2E-5, following page, shows net profit margin and return on total capital for the aluminum industry
between 1998 and 2008. The graph shows considerable volatility in both metrics. Financial performance declined
significantly between 1988 and 1992, reflecting general economic weaknesses and oversupply in the market
(McGraw-Hill, 2000). By the mid-1990s, performance improved as demand recovered and aluminum prices
increased. Between 2000 and 2002 financial performance declined again, reflecting economic downturn in both
the United States and world economies. Financial health of the Aluminum industry began to improve after that
and continued to do so until it significantly deteriorated in 2008 as a result of recession that affected every
industry nationwide. During the fourth quarter of 2008 and early 2009, a number of smelters closed and the price
of aluminum continued to decline. Going forward, higher electricity prices in the United States relative to other
nations could prevent domestic smelters from reopening in the near term. World demand for aluminum remained
low throughout 2009 owing to declines in automobile manufacturing and construction. Overall, decreased
consumption of aluminum as a result of the recent economic recession could keep aluminum production at low
levels for the next several years (USGS, 2008a).
2E-22 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2E: Aluminum Industry Profile
Figure 2E-5: Net Profit Margin and Return on Total Capital for the Non-Ferrous Metals Industry
20%
-10%
£t fjl O\
• Net Profit Margin
• Return on Total Capital
Source: Quarterly Financial Report. 1988-2008; U.S. Census Bureau.
2E.6 Facilities Operating Cooling Water Intake Structures
Section 316(b) of the Clean Water Act applies to point source facilities that use or propose to use a cooling water
intake structure that withdraws cooling water directly from a surface waterbody of the United States. In 1982, the
Primary Metals industries as a whole (including Steel and Non-ferrous producers) withdrew 1,312 billion gallons
of cooling water, accounting for approximately 1.7 percent of total industrial cooling water intake in the United
States.20 The industry ranked 3rd in industrial cooling water use, behind the electric power generation industry,
and the chemical industry (1982 Census of Manufactures).
This section provides information for facilities in the profiled aluminum segments estimated to be subject to
regulation under the regulatory analysis options. Existing facilities that meet all of the following conditions would
have been subject to the regulation under the three regulatory analysis options:
> Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or
arrangement with an independent supplier who has a cooling water intake structure; or their cooling water
intake structure(s) withdraw(s) cooling water from waters of the United States, and at least twenty-five
(25) percent of the water withdrawn is used for contact or non-contact cooling purposes;
> Have an National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;
and
> Meet the applicability coverage criteria for the proposed regulation specific regulatory analysis option in
terms of design intake flow (i.e., 2 MGD).
20 Data on cooling water use are from the 1982 Census of Manufactures. 1982 was the last year in which the Census of Manufactures
reported cooling water use.
March 28, 2011
2E-23
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2E: Economic Profile of the Aluminum Industry
EPA initially identified the set of facilities that were estimated to be potentially subject to the 316(b) Existing
Facilities Regulation based on a minimum applicability threshold of 2 MGD; this section focuses on these
facilities for the petroleum segment.21
2E.6.1 Waterbody and Cooling Water Intake System Type
Table 2E-13, shows the distribution of facilities by type of water body and cooling system.
Table 2E-13: Number of Facilities Estimated Subject to the 50 MGD All Option by Waterbody Type and
Cooling Water Intake System for the Profiled Aluminum Segments
Water Body Type
Estuary /Tidal River
Freshwater Stream/River
Lake or Reservoir
Great Lake
Total
Cooli
Recirculating
Number % of Total
0
3
1
o
4
0%
73%
27%
o%
17%
tig Water Intake System
Once-Through
Number % of Total
4
14
o
3
21
21%
64%
15%
o%
83%
Total
4
17
T
3
26
Based on technical weights (See Appendix 3.A).
a. Individual numbers may not add up to total due to independent rounding.
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
2E.6.2 Facility Size
The 316(b) facilities in the aluminum industry subject to the regulation under the regulatory analysis options are
relatively large. Figure 2E-6, shows the number of regulated facilities by employment size category.
Figure 2E-6: Number of In-Scope Facilities Estimated Subject to the 316(b) Existing Facilities Regulation
by Employment Size for the Profiled Aluminum Segments
Source: U.S. EPA, <
14
12
10
8
6
4
2-
0
/
/
/
1/1
1
) > -ft
^-^
10
' f
Less than 100-249 250-499 500-999 1000 or
100 greater
'000; U.S. EPA analysis, 2010.
21 EPA applied sample weights to the sampled 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).
2E-24
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2E: Aluminum Industry Profile
2E.6.3 Firm Size
EPA used the Small Business Administration (SBA) small entity size standards to determine the number of
Section 316(b) profiled aluminum industry facilities owned by small firms. Firms in the Primary Production of
Aluminum segment are defined as small if they have 1000 or fewer employees; firms in the Secondary Production
segment are defined as small if they have 750 or fewer employees. EPA estimates there are seven small entity-
owned facilities, and 18 large entity-owned facilities in the Aluminum industry subject to the regulation.
March 28, 2011 2E-25
-------
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
2F Profile of Food and Kindred Products Industry
2F.1 Introduction
EPA's Detailed Industry Questionnaire, hereafter referred to as the DQ, identified five 4-digit SIC codes in the
Food and Kindred Products manufacturing industry (SIC 20) with at least one existing facility that operates a
CWIS, holds a NPDES permit, withdraws at least two million gallons per day (MGD) from a water of the United
States, and uses at least 25 percent of its intake flow for cooling purposes (facilities with these characteristics are
hereafter referred to as "facilities potentially subject to the 316(b) Existing Facilities regulation" or "in-scope
facilities"). For the purpose of this analysis, EPA identified a six-digit NAICS code for each of these potential
facilities using the information from DQ and public sources (see Appendix 3. C: Conversion the Data from
Standard Industrial Classification (SIC) to North American Industry Classification System (NAICS)). As the
result of this mapping, EPA identified five 6-digit NAICS codes in the Food and Kindred Products manufacturing
industry (NAICS 322).
For each of these five analyzed 6-digit NAICS codes, Table 2F-1 following page, provides a description of the
industry segment, a list of primary products manufactured, the total number of detailed questionnaire respondents
(weighted to represent a national total of facilities that hold a NPDES permit and operate cooling water intake
structures), and the number of facilities estimated to be potentially subject to the section 316(b) Proposed Existing
Facilities Rule based on the minimum withdrawal threshold of 2 MGD (see Chapter 1: Introduction for more
details on the Rule applicability criteria). Although the respondent DQs fall in only five of the 48 four-digit SIC
codes that map onto 52 NAICS codes within the Food and Kindred Products industry, EPA knows of no basis to
exclude any of the remaining four-digit SIC codes (or six-digit NAICS codes) from consideration in this profile.
Accordingly, this profile focuses on the entirety of SIC 20 that map onto NAICS 311/3121, Food and Kindred
Products.
Table 2F-1: Existing Facilities in the Food and Kindred Products Industry (NAICS 311/3121)
NAICS
311211
311221
311312
311313
312140
NAICS
Description
Flour milling
Wet corn
milling
Cane sugar
refining
Beet sugar mfg
Distilleries
Important Products Manufactured
Milling flour or meal from grains (except rice) or vegetables; milling flour and
preparing flour mixes or doughs.
Com oil cake and meal; com starch; com syrup; dextrose, fructose; glucose; high
fructose syrup; starches
Cane sugar; molasses; granulated sugar; raw sugar; cane syrup (all made from
sugarcane); molasses, blackstrap; granulated sugar; refined sugar; syrup (all made
from purchased raw cane or sugar syrup)
Beet sugar; molasses; granulated sugar; liquid sugar; powdered sugar; syrup (all
made from sugar beets)
Distilled and blended liquors, except brandy; gin; rum; vodka; whiskey; cocktails;
cordials; eggnog; grain alcohol for medicinal and beverage purposes
Total NAICS 311/3121
Number of In-scope
Facilities
3
13
12
6
3
37
Source: Executive Office of the President, 1987; U.S. EPA 2000; U.S. EPA analysis, 2010.
The Food and Kindred Products industry includes facilities that process or manufacture food and beverages for
human consumption, feed for animals, and other related products. Statistics for the industry were previously
recorded under the Standard Industry Classification (SIC) code of 20, for Food and Kindred Products. SIC 20
included nine industry groups at the three-digit SIC level, and 48 industries at the four-digit SIC level. Under the
SIC system, beverage manufacturing was included in SIC 20, the Food and Kindred Products sector. In 1997, the
U.S. Census Bureau began reporting economic activity in the North American industry Classification System
(NAICS), which replaced the SIC system (U.S. DOC, 1997). Under NAICS, the previous SIC 20 sector is
March 28, 2011
2F-1
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2F: Economic Profile of the Food and Beverage Industry
recorded in one 3-digit NAICS sector (NAICS 311) and one 4-digit NAICS sector (NAICS 3121), Beverage
Manufacturing. Because the analysis period for this profile extends across the SIC-to-NAICS transition, most of
the data series presented in the profile include data both the SIC and NAICS frameworks: in general, for years
prior to 1997, data are in the SIC framework; for 1997 and after, data are in the NAICS framework. Table 2F-1
summarizes the relationship between SIC and NAICS codes used for this profile and provides summary
information on the relevant NAICS sectors from the 2007 Economic Census.
Table 2F-2: Relationship between NAICS and SIC Codes for the Petroleum Refining Industry (2007)
NAICS Code
311(Excl.
311811a)
3121
NAICS
Description
Food
Manufacturing
3everage
Manufacturing
SIC Code
20- (excl. 2082,
2084-6, and
2097)
2082
2084
2085
2086
2097
SIC Description
Food and Kindred
Products
Vlalt Beverages
Wines, Brandy, and
3randy Spirits
Distilled and Blended
Liquors
Bottled ."and" Canned Soft
Drinks and Carbonated
Waters
Vlanufactured Ice
Establishments
19,219
3,703
Value of Shipments
(Millions; $2009)
$606,694
$90,470
Employment
1,466,762
134,407
a. NAICS 311811: Retail Bakeries is not apart of manufacturing sectors in the SIC framework. Because Annual Survey of Manufacturers used to analyze
Food and Kindred Products manufacturing sector provides data only for manufacturing sectors, EPA excluded NAICS 311811 from the totals to the Food
and Kindred Products sector.
Sources: U.S. DOC. 2007Economic Census.
2F.2 Summary Insights from this Profile
The key purpose of this profile is to provide insight into the ability of firms in the Food and Kindred Products
industry to absorb compliance costs from the regulatory analysis options without material adverse
economic/financial effects. The industry's ability to withstand compliance costs is primarily influenced by two
factors: (1) the extent to which the industry may be expected to shift compliance costs to its customers through
price increases and (2) the financial health of the industry and its general business outlook.
2F.2.1 Likely Ability to Pass Compliance Costs Through to Customers
As reported in the following sections of this profile, the Food Manufacturing and Beverage Manufacturing
segments face somewhat limited foreign competitive pressures, and, based on this factor, would have some
latitude to pass through to customers any increase in production costs resulting from regulatory compliance.
However, within the U.S. market, the Food Manufacturing and Beverage Manufacturing segments have relatively
low concentrations. Although niche product and/or regional segments are likely to face lighter overall
competition, the lack of industry concentration, as described later in this profile, suggests that firms in this
industry may have little ability to recover compliance costs through increased prices - particularly if the increased
costs do not occur in a relatively uniform way throughout the industry. Given the likelihood that only a relatively
small subset of facilities and firms in this industry will face additional costs as a result of the regulatory options
considered for the section 316(b) Existing Facilities Regulation, EPA judges that a conservative assumption of
no-cost-pass-through is appropriate for analysis of the impact on this industry. Consequently, for the cost and
economic impact analysis, EPA assumed that in-scope facilities would absorb all compliance costs within their
operating finances (see following sections and Appendix 4.A: Cost Pass-Through Analysis, for further
information).
2F-2
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2F: Food and Kindred Products Industry Profile
2F.2.2 Financial Health and General Business Outlook
Unlike the more cyclical sectors in the other profiled Primary Manufacturing Industries, the profiled Food and
Kindred Products industry, being a consumer staples industry, was not as strongly affected by the economic
downturns that occurred in the early 2000s and in 2008-2009. During the last two decades, this industry has
maintained relatively healthy financial performance and steady growth despite economic fluctuations, increasing
government regulations, and changing consumer preferences and behavior. To remain competitive, firms in the
Food and Kindred Products industry have been able to promptly respond to changing economic, business, and
regulatory environment by offering a greater variety of products while consistently and cost-effectively producing
high quality products (Rockwell Automation, 2008). Extremely high prices for many food commodities brought a
cash windfall in much of 2006-2008 for the industry. However, more recently, the global financial crisis has
created new challenges as consumers move to cheaper food options and increasingly cook at home (Plunkett
Research, 2010). The industry has exhibited substantially less fluctuation in capacity utilization and financial
performance than more cyclical industries, such as the other five Primary Manufacturing Industries. Although
foreign competition increased, the industry also experiences significantly less international competition than firms
in the other Primary Manufacturing Industries, as indicated by the industry's lower reliance on export sales and
the lower extent of import penetration in domestic markets.
On the whole, the Food and Kindred Products industry has maintained a steadily increasing level of capital
expenditures during the last two decades and has correspondingly recorded moderately increasing labor
productivity. These factors suggest that the industry's capital equipment base has been maintained and regularly
improved during the last two decades, and that the business faces no inordinate needs for capital expenditure due,
for example, to offset a period in which capital outlays substantially retrenched because of declining business
performance. Within the broader Food and Kindred Products industry, the Food Manufacturing segment has
generally achieved more stable growth and financial performance than the Beverage segment. Nevertheless, the
general financial health and outlook for the overall industry appear positive. Favorable product demand trends,
efficient production capability, and effective management of production costs and supply chains all point to a
favorable industry outlook, both near and longer term.
Given the proven ability of the profiled Food and Kindred Products industry to withstand economic fluctuations,
regulatory changes, and constantly changing consumer behavior and business environment together with recent
industry trends may suggest that going forward, the profiled Food and Kindred Products industry is very likely to
continue its moderate steady growth accompanied by relatively healthy financial performance. Further, EPA
judges that the profiled Food and Kindred Products industry is currently in better economic/financial condition
overall than the other profiled Primary Manufacturing Industries and that this industry should be able to withstand
the cost of Proposed Existing Facilities Rule compliance requirements without material adverse financial impact.
2F.3 Domestic Production
At the beginning of this decade, the profiled Food and Kindred Products industry was one the largest
manufacturing industries in the United States, with the Food Manufacturing and Beverage segments accounting
for approximately one-sixth of U.S. industrial activity in 2000 (McGraw-Hill, 2000). In 2009, U.S. total food
sales exceeded $1.5 trillion (Plunkett Research, 2010), and the Food Manufacturing segment alone accounts for
over 10 percent of all manufacturing shipments (U.S. DOC, 2008). The industry is considered mature, however,
and firms are constantly seeking new avenues for increased sales in domestic and foreign markets. With total food
industry shipments growing more slowly than GDP, U.S. producers have actively sought growth opportunities in
overseas markets. Although exports still make up a small share of domestic shipments, changes in global food
consumption could lead to increased demand and trade for processed food products going forward. As developing
countries experience growth in income, the demand for higher quality food products, such as meat products,
March 28, 2011 2F-3
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule Chapter 2F: Economic Profile of the Food and Beverage Industry
present an opportunity for U.S. firms to increase exports. In developed countries, consumer demand for food is
driven mainly by convenience and specialty food products (U.S. DOC, 2008).
2F.3.1 Output
Figure 2F-1, following page, shows trends in constant dollar value of shipments and value added for the Food
Manufacturing and Beverage Manufacturing segments.22 Change in these values over time provides insight into
the overall economic health and outlook for an industry. Value of shipments is the sum of receipts earned from
the sale of outputs; it indicates the overall size of a market or the size of a firm in relation to its market or
competitors. Value added, defined as the difference between the value of shipments and the value of inputs used
to make the products sold, measures the value of production activity in a particular industry.
Over-time trends in value of shipments and value added show that both the Food Manufacturing and Beverage
Manufacturing segments have achieved generally stable performance over the 1987-2007 analysis period: these
industries have not been substantially affected by fluctuations in the performance trend of the U.S. economy. The
lack of major swings in shipments and value added results largely from the consumer staple-character of the
industry. At the end of the 1987-2007 analysis period, both profiled segments ended with a higher total value of
shipments and value added: constant dollar value of shipments in the profiled Food Manufacturing and Beverage
Manufacturing segments increased by 87 percent and 26 percent, respectively, while value added increased by 99
percent and 44 percent, respectively. The general trends indicate that firms in these industry segments have been
able to increase shipments and value added, which is a sign that these firms have been successful in finding ways
to expand their market and continue to grow.
Terms highlighted in bold and italic font are further explained in the glossary.
2F-4 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
Figure 2F-1 : Value of Shipments and Value Added for Profiled Food Manufacturing and Beverage
Manufacturing Segments (millions, $2009)a
Value of Shipments
$625,01
$600,01
$575,01
$550,0(
$525,0(
. $500,0(
§ $475,0(
"o $450,0(
O
fe $425,0(
$400,0(
$375,0(
$350,0(
$325,0(
)0
)0
10
/
)0 /
10 4-*^
in •-•*•*' *' •
.-f.. ":'-"-.'••
)0
)0
)0
^o^o^o^o^o^o^o^o^o^o^o
oeoeoe^o^o^o^o^o^o^o*o
>^\
Z A
/
v , /
^0*0000000
\o*ooooooo
P -- $91 500
. J 7 $90000
"* -- $87.500
-- $85,000
-- $82,500
-- $80,000
-- $77,500
-- $72,500
-- $70,000
-- $67,500
-- $65,000
- $62,500
0 0
o o
03 » FoodMfg.(NAICS311)
^
S5
Og • Beverage Mfg. (NAICS3121)
(J?
Value Added
$260 000
$240 000
$220 000
$200 000
OX) $180 000
"O $160 000
o
o
fe «i /m nnn
$120 000
$100 000
$80 000
$60 000
^^-
_^^
f
*-*'''*J
/v
,-.••*•<•*
I
• -•'
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
$55 000
$52 500
$50 000
$47 500
03
$45 000 ?
$42 500 jq
^
«/m nnn H*<
TO
$37 500
$35 000
$32 500
$30 000
* FoodMfg.( NAICS 311)
— • Beverage Mfg. (NAICS 3121)
— H — Beverage Mfg (SIC to
NAICS)
vovovovovovovovovovovovovooooooooo
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been compiled in the North American Industry
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007 Economic Census.
Table 2F-3 provides the Federal Reserve System's index of industrial production for the profiled Food
Manufacturing and Beverage Manufacturing segments, showing trends in production between 1990 and 2009.
This index more closely reflects total output in physical terms, whereas value of shipments and value added
reflect the economic value of production. The production index is expressed as a percentage of output in the base
March 28, 2011
2F-5
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2F: Economic Profile of the Food and Beverage Industry
year, 2002. With the exception of modest decreases in production during 1996, 2003, and 2009, the Food
Manufacturing segment has seen year-to-year production increases over the analysis period, with an overall
increase in production of approximately 34 percent (13 percent during the last decade). Being less of a consumer
staple industry segment, the Beverage Manufacturing segment saw slightly more fluctuations during the analysis
period in response to the economic recessions of the early 1990s and early and late 2000s, but also experiencing
an overall increase of 21 percent for the entire period (11 percent during the last decade). Food manufacturers
continue to invest in greater automation in manufacturing processes with budgeted spending for plant equipment,
upgrades, computers, and automation remaining at steady levels. With the recent national concerns over food
safety and increasing food safety regulations, food manufacturers will likely also begin investing in additional
technological processes to meet increasing food safety requirements (U.S. DOC, 2008).
Table 2F-3: U.S. Food and Beverage Manufacturing Industry Industrial Production Index
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009s
Total Percent Change
1990-2009
Total Percent Change
2000-2009
Average Annual
Growth Rate
Food Man
j^j^QQjZjQQ
82.3
8378
854
876
88772
904
8876
9l7b
95"0
96"0
977
977
ioao
ioTo
TofT
1042
10574
1093
fill
lias
ifacturing3
Percent Change
n/a
l7"8%
L9%
2"6%
0"6%
276%
-27T%
278%
474%
T7b%
T'77%
b7b%
274%
T7b%
bT%
371%
T7i%
379%
T'75%
-b7<5%
34.3%
13.2%
1.6%
Beverage Mi
^de^ 2002=100
91.7
9279
9374
9373
9772
9779
10271
103777
105773
Tbb7b
9978
9977
Tbb7b
10574
109773
115773
ii"6."b
11974
1146
iiTb
nufacturingb
Percent Change
n/a
F.3%
b7s%
-b7'2%
472%
b77%
473%
F.6%
f.5%
-57o%
-b7'2%
-b7T%
b7"3%
s74%
378%
575%
b7<5%
279%
-47b%
-371%
21.0%
11.3%
1.0%
a. NAICS311
b. NAICS3121
c. Average through 9/2009
Source: Economagic; Federal Reserve, Board of Governors,
2009.
2F.3.2 Prices
The producer price index (PPI) measures price changes, by segment, from the perspective of the seller, and
indicates the overall trend of product pricing, and thus supply-demand conditions, within a segment.
As shown in Figure 2F-2, price levels in the profiled Food Manufacturing and Beverage Manufacturing segments
have risen steadily between 1987 and 2009, with an average annual growth rate of more than 2 percent. Total
spending on food makes up about 13 percent of a household's total average annual expenditures. Of the average
$6,111 in food spending, $3,417 is used for food to be consumed in the home and $2,694 is used for food
consumed away from home. Prepared meals, ready-to-serve products, ethnically diverse food products, and
organic food are showing increased demand as the U.S. population becomes older, more frugal, more diverse, and
2F-6
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
increasingly concerned about nutrition (U.S. DOC, 2008). The Beverage Manufacturing segment has also seen a
steady increase in consumer spending over the last two decades despite being more susceptible to economic
fluctuations. Further, industry experts expect the Beverage Manufacturing industry segment to continue the
modest but stable upward trend as manufacturers address consumer concerns about appropriate beverage size and
environmentally friendly packaging (CID, 2010).
Figure 2F-2: Producer Price Indexes for Food Manufacturing and Beverage Manufacturing Segments
180
• Food Manufacturing
• Beverage Manufacturing
100
GCGCGCVOVOVOVOVOVOVOVOVOVOOOOOOOOOOO
Source: BLS, 2009b.
2F.3.3 Number of Facilities and Firms
Table 2F-4 and Table 2F-5 present the number of facilities and firms for the Food Manufacturing and Beverage
Manufacturing segments between 1990 and 2006. As reported in the Statistics of U.S. Businesses, between 1990
and 2006, the number of facilities in the Food Manufacturing segment increased by 14 percent. The number of
firms in this segment grew by about 13 percent during this time period. During the same analysis period, the
number of facilities and number of firms in the Beverage Manufacturing segment increased even more
dramatically, by 62 percent and 68 percent, respectively. During the last decade, however, the Food
Manufacturing saw a number of mergers and acquisitions (U.S. DOC, 2008). Consequently, while the number of
facilities and firms in the Beverage Manufacturing grew by 29 percent and 32 percent, respectively, during the
last decade, the Food Manufacturing segment saw a decrease in both of approximately 4 percent and 9 percent,
respectively.
March 28, 2011
2F-7
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2F: Economic Profile of the Food and Beverage Industry
Table 2F-4: Number of Facilities Owned by Firms in the Food and Beverage Manufacturing Segments3
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent Change 1990-
2006
Total Percent Change 2066-
2006
Average Annual Growth
Rate
Food Manufacturing
Number of Facilities
16,740
16J90
177824
18jl4
177795
177726
18387
187558
20,088
197954
197962"
267340
19436"
19,873
197667
197339
19426
Percent Change
n/a
0.3%
6.2%
i".6%
-18%
-674%
4.9%
-61%
8.2%
-677%
1673%
272%
-579%
19%
-Io%
-T'77%
-T7i%
14.3%
-3.9%
0.8%
Beverage Manufacturing
Number of Facilities
2,200
27211
27287
27281
27293
27333
27576
27660
2,601
27671
27748
37633
3,099
3,082
37222
37376
37556"
Percent Change
n/a
675%
374%
-673%
67s%
177%
104%
373%
-272%
2.7%
279%
10.4%
2.2%
-67s%
4.5%
4.8%
5.3%
61.6%
29.4%
3.0%
a. Before 1998, data were compiled in the SIC system; since 1998, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic Census Bridge Between
NAICS and SIC.
Source: U.S. SBA, 1990-1997; SUSB, 1998-2006.
2F-8
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
Table 2F-5: Number of Firms in the Food and Beverage Manufacturing Segments3
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Total Percent Change
1990-2006
Total Percent Change
2000-2006
Average Annual
Growth Rate
Food Manufacturing
Number of Firms
13,346
137418
J4lJO"9"
14398
147378
147330
j"5j89
157189
16356
16^559
16333
16^960
157796
16361
15'lj"ll
'i'5'^'74
T'5'393"
Percent Change
n/a
(15%
74%
7272%
-673%
6.0%
676%
977%
'-676"%"
-672%
276%
-679%
48%"
-63%
-"i'72%"
13.1%
-8.7%
0.8%
Beverage Manufacturing
Number of Firms
1,789
17818
T7867
T7893
T7954"
2"7l92"
27235
2"7T37
2"7l96"
2"7267
27558
27616
27576"
27692
27839
27998
Percent Change
n/a
1 .O /O
J . 1 /O
-04%
14%
372%
1272%
276%
-44%
278%
372%
1278%
273%
rf5%
45%
575%
576%
67.6%
32.2%
3.3%
a. Before 1998, data were compiled in the SIC system; since 1998, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic Census Bridge Between
NAICS and SIC.
Source: U.S. SBA, 1990-1997; SUSB, 1998-2006.
2F.3.4 Employment and Productivity
The U.S. Food and Kindred Products industry is among the most modern in the world. A steady trend of industry
growth and accompanying capital outlays have both increased production capacity and led to installation of
increasingly modern and more efficient, higher technology, production equipment. Indeed, spending for
production, packaging and process control equipment is the most robust automation capital area (see Section
2F.3.5, below). The more advanced technology production equipment requires a more skilled labor force;
therefore, the key to future productivity gains are said to lie in better skills training of line operators and
supervisors. At the same time, more advanced technology equipment has resulted in more automated production
process and has reduced the number of employees needed per dollar of production (Higgins, 2005).
Figure 2F-3 presents employment for the two profiled segments between 1987 and 2007. As shown in Figure
2F-3, between 1987 and 2007, employment exhibited different behavior in the two profiled segments. Other than
sharp increases from in 1988 and 1997, employment in the Food Manufacturing segment was relatively stable,
decreasing by no more than 2.5 percent and increasing by no more than 4 percent. Over the entire analysis period,
employment in the Food Manufacturing segment increased by 58 percent. During the last decade, however,
employment in this segment fell by nearly 3 percent. This drop in employment is likely the result of heavy
investments in technology and increased automation and production improvements, which persisted in the Food
Manufacturing segment in the last decade and have allowed companies to increase output while relying on fewer
employees. This trend is likely to continue going forward which could potentially lead to further employment
reductions in the Food Manufacturing segment (U.S. DOC, 2008).
The Beverage Manufacturing segment has experienced more volatility over the last two decades. Between 1987
and 1994, employment in the Beverage Manufacturing segment fell nearly every year, before reversing this trend
and experiencing gains through 2001. These employment gains, however, were followed by consecutive
March 28, 2011
2F-9
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2F: Economic Profile of the Food and Beverage Industry
significant declines of nearly 10 and 9 percent in 2002 and 2003, respectively. After relatively stable few years,
2007 saw a significant employment increase of over 8 percent - a promising sign for the industry's future
employment outlook.
Figure 2F-3: Employment for Food Manufacturing and Beverage
1 f(\t\ nnn
1 zinn nnn
tori i inn nnn
.3 1,200,000
0
1 nnn nnn
onn nnn
r^^ . ^
Manufacturing Segments3
* ^/\ t\t\t\
— ^
/ A
\ J
\l
E _•__••
I I I I I I I I I I I I I I I I
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC
Census Bridge Between NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Si
1992, 1997, 2002, and 2007 Economic Census.
CO
re
143,UUU Jg
140,000 era
' re
1 1^ nnn ^
tp5
1 in nnn
1 *>^ nnn
Kl Kl Kl Kl
o o o o
o o o o
o NAICS)
SIC to NAICS)
been compiled in the North American ]
code classifications using the 1997 Ecor
irvey of Manufacturers; U.S. DOC,
ndustry
lomic
1987,
2F-(5 presents the change in value added per labor hour, a measure of labor productivity, for the two
profiled industry segments between 1987 and 2007. As shown in this table, labor productivity in the Food and
Beverage Manufacturing segments has generally grown steadily and at an average annual rate of approximately 1
and 2 percent, respectively. However, labor productivity in the Beverage Manufacturing segment has shown a
greater degree of fluctuation, with both annual increases and decreases in productivity exceeding 10 percent
during the last two decades. Overall, the Beverage manufacturing segment saw a greater increase in productivity
during the last two decades, nearly 45 percent, compared to a 15 percent productivity gain in the Food
Manufacturing segment, with substantial gains occurring during the last decade. Technology improvement in the
industry is playing an important role in increasing production in recent years, as automation allows output levels
to increase without significant increases in employment (U.S. DOC, 2008).
2F-10
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
Table 2F-6: Productivity Trends for Food and Beverage Manufacturing Segments ($2009)a
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent Change
1987- 2007
Total Percent Change
2000 - 2007
Average Annual
Growth Rate
Food Manufacturing
Value Added
($ millions)
$124,685
$168,020
jj"64;302
$17^954
jY68,847
$i8i;o77
$187^79
$189^47
ji'9^429
$186,869
$212,485
$222,614
$223,642
$229,049
$233,984
$24i;304
$249425"
$2555095
$2587698
$2487129
$247,970
98.9%
8.3%
3.5%
Production
Hours
(millions)
1,325
i",7iT
1/708
17788
1,776
l",877
1,901
17933
17938
1,911
27234
27315
27310
27327
27303
2,279
27282
27246
27238
27T97
27287
72.7%
-1.7%
2.8%
Value Added/Hour
S/hr
94
98
96
96
95
96
99
98
101
98
95
96
97
98
102
106
109
114
116
Ti3
108
Percent
Change
n/a
4.3%
-27T%"
6.6%
-TT%
F.5%
2.3%
-679%
3.6%
-376%
-277%
1.1%
677%
17%
372%
4.2%
3."i%
4.0%
1.8%
7273%
-476%"
15.2%
10.1%
0.7%
Beverage Manufacturing
£?. .£»
Value Added
(S millions)
$32,780
$33,643
$327969
$"327238"
$337444
$34;5J6
$"3"4,073"
J35J41
$367663
$387396
$"3"8,6l"3"
$397758
$387386
$37j59
$387223"
$387621
$43,8"l6
$4555"l5
$47",68"6
$45;612
$477269
44.0%
27.0%
1.8%
Production
Hours
(millions)
148
145
142
140
139
140
144
138
139
139
149
148
140
153
150
139
138
132
135
138
147
-0.6%
-3.8%
0.0%
Value Added/Hour
S/hr
221
231
233
230
240
246
237
258
259
276
260
269"
274
243"
255
278
317
344
352
331
321
Percent
Change
n/a
4.5%
677%
-ll%
4.4%
276%
-379%
9."i%
674%
6.5%
-6"T%"
377%
F.9%
-114%
5'.T%
878%
146%
8.6%
2.4%
-579%
-372%
44.9%
32.1%
1.9%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic Census Bridge Between
NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2F.3.5 Capital Expenditures
The profiled Food and Kindred Products Manufacturing industry is capital intensive, and has invested
substantially in capital to implement automation, introduce process controls, and reduce inventories in order to
ultimately improve yield and reduce labor costs. Capital-intensive industries are characterized by a large value of
capital equipment per dollar value of production. In order to modernize, expand, and replace existing capacity,
new capital expenditures are needed. In 2007, total capital expenditures for the Food Manufacturing and
Beverage Manufacturing segments amounted to $16.7 billion. Approximately 82 percent of that spending (see
Table 2F-7) occurred in the Food Manufacturing segment.
Between 1987 and 2007, capital expenditures in the Food Manufacturing segment increased by nearly 78 percent
at an average annual rate of approximately 10 percent, peaking at $16.2 billion in 1999. The Beverage
Manufacturing segment has also seen substantial growth in capital expenditures during this time period. Between
1987 and 2007, expenditures in this segment increased by nearly 51 percent at an average annual rate of 2 percent,
peaking at $3.6 billion in 2002. During the last decade, however, capital expenditures in the Food and Beverage
Manufacturing industry segments declined by approximately 10 and 6 percent, respectively, and appear to have
leveled off at relatively constant values since the middle of the 2000s decade.
March 28, 2011
2F-11
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2F: Economic Profile of the Food and Beverage Industry
Table 2F-7: Capital Expenditures for Food and Beverage Manufacturing Segments (millions, $2009)a
Yciir
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Total Percent Change
1987-2007
Total Percent Change
2000 - 2007
Average Annual
Growth Rate
Food Manufacturing
Capital Expenditures
$7,653
$97726
j"ioljl9
$Yi;o7"9
$107967
$"Yi;628
$10;937
$Y]7278
$137664"
$127361
$147626
$147778
$16^210
$i5Too7
$147668
$137136
$127736
$127663
$137247
$137454
$l'37585
77.5%
-9.5%
2.9%
Percent Change
n/a
271%
77T%"
673%'
-16%
676'%"
-579%
37"i"%"
1573%
-54%
146%
574%"
977%
-74%
-677%
-672"%
'-3'7"i"%
-675%
476%
i""6"%"
i""6'%"
Beverage Manufacturing
Capital Expenditures
$2,034
$2"7l07
$"27635'
$17796
$27618
$27677
'$'"i7'8"3""i
$27142"
$27462
$27466
$37692"
$27852
$27896
$37254
$3"7642"
$3"763"5
$27766'
$27742"
$37285
$37256
$37670
50.9%
-5.6%
2.1%
Percent Change
n/a
376%
-34%
:i2.o%"
1278%
279%
:"i"i"."8"%
1679%
1576%
-275%
2879%
-778%"
i"."3"%
1276%
-675%"
1975%
12379%
-679%
1978%
-679%
-577%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997 Economic Census Bridge Between
NAICS and SIC.
Source: U.S. DOC, 1988-1991, 1993-1996, 1998-2001, 2002-2004, and 2005-2006 Annual Survey of Manufacturers; U.S. DOC, 1987,
1992, 1997, 2002, and 2007Economic Census.
2F.3.6 Capacity Utilization
Capacity utilization measures output as a percentage of total potential output from available capacity. Capacity
utilization reflects excess or insufficient capacity in an industry and is an indication of whether new investment is
likely. The degree of fluctuation in capacity utilization is also an indicator of the relative stability of demand and
business conditions in an industry.
As shown in Figure 2F-4, between 1990 and 2008, capacity utilization in the Food Manufacturing and Beverage
and Tobacco Manufacturing23 industry segments generally trended downward. The Food Manufacturing segment,
however, did not experience the volatility that the Beverage and Tobacco Manufacturing segment experienced
over the same period. Food Manufacturing capacity utilization rates have generally remained within 80 and 85
percent range, while the Beverage and Tobacco Manufacturing segment experienced a high of over 85 percent in
1996, followed by a significant decline to below 70 percent by 2002. Further, the Beverage and Tobacco
Manufacturing segment was significantly affected by economic downturns in the early 1990s and early and late
2000s, when its capacity utilization significantly dropped to 72, 69, and 70 percent, respectively. Between 1990
and 2008, capacity utilization increased in both segments, although the Beverage and Tobacco Manufacturing
23 The Census Bureau provides capacity utilization data are available only for the 3-digit NAICS sector NAICS 312: Beverage and
Tobacco Manufacturing sector. The Census Bureau does not provide capacity utilization data for the 4-digit NAICS sector NAICS
3121: Beverage Manufacturing.
2F-12
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
segment experienced a more substation drop: while capacity utilization in the Food Manufacturing segment
declined by a little over 4 percent, capacity utilization in the Beverage and Tobacco Manufacturing declined by
nearly 17 percent.
Again, significantly less fluctuation in capital utilization in the profiled Food Manufacturing segment during the
analysis period, suggests that this segment is characterized by a lower degree of susceptibility to economic
changes compared to the profiled Beverage Manufacturing segment. This pattern is likely to continue going
forward. That overall capacity utilization remained at a moderate level throughout the analysis period for both
profiled segments - low 70s to mid 80s percent - implies that the profiled Food and Beverage Manufacturing
segments do not face requirements for large outlays for capital expansion in the near term.
Figure 2F-4: Capacity Utilization for Food Manufacturing and Beverage and Tobacco Manufacturing
65
- Food Manufacturing (NAICS 311)
• Beverage & Tobaco Manufacturing (NAICS 312)
a. The Federal Reserve provides capacity utilization data for the combined NAICS 312 (Beverage and Tobacco Manufacturing) sectors. The Federal Reserve
does not provide capacity utilization data for just the Beverage Manufacturing sector.
Source: U.S. DOC, Survey of Plant Capacity 1989-2009, U.S. Census Bureau.
2F.4 Structure and Competitiveness
Food Manufacturing and Beverage Manufacturing companies range in size from multi-billion dollar corporations
to small producers with revenues a fraction of the size of the large producers. Many of the companies in these
segments are diversified producers of multiple food or beverage products. Because food is a necessary purchase,
demand is less affected by the ups and downs of the economy than for other industries.
The Food Manufacturing segment has consolidated over the last two decades as companies moved to diversify
their product offerings and gain market share. This segment has also looked abroad to tap into the emerging
markets of foreign countries. According to the Food Institute, 99 mergers and acquisitions occurred among food
processing companies in 2006, up from 94 in 2005, but down from 168 in 2000 (U.S. DOC, 2008). These
acquisitions and mergers permit companies to acquire more efficient manufacturing plants, close inefficient
plants, expand product lines, and increase market share in a mature market (U.S. DOC, undated). Some recent
mega-mergers in the Food Manufacturing segment include the Kraft Foods' acquisition of Nabisco, General
Mills' acquisition of Pillsbury, and Tyson's bringing beef and pork firm IBP into its lineup. In 2008 and 2009,
March 28, 2011
2F-13
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule Chapter 2F: Economic Profile of the Food and Beverage Industry
mergers and acquisitions were concentrated in the restaurant industries with limited M&A activity among food
processing companies (The Food Institute Report, 2009).
The Beverage Manufacturing segment recorded acquisitions and mergers during the last decade, although not
nearly as many as the Food Manufacturing segment. Product differentiation is a key strategy for larger firms to
increase brand awareness and market share (Yahoo, 2005a). As sales in the United States slowed, firms in the
non-alcoholic beverage industry saw their largest gains from non-U.S. markets. In fact, in 2008 alone, PepsiCo
had three international deals (Value Line, 2004).
In the alcoholic beverage sub-segment, Anheuser-Busch lost the rank of world's largest brewer due to the merger
of Inbrew and Brazil's Ambev. The merger between Adolph Coors and Molson further consolidated the industry.
Brewers began to look for acquisitions in China, which is seen as an untapped market. Constellation Brands
purchased the Robert Mondavi Corporation, a leader in wine making, and began to work in a joint venture with
the French vintner Domaines Barons de Rothschild. Diageo and France's Pernod Ricard bought Seagrams
Company, after outbidding the tandem of Bacardi and Brown-Forman. In recent years, Sazerac Company has
purchased Constellation Brands' value spirits business, and Sabmiller and Molson Coors Brewing Company have
merged (Yahoo, 2005a).
2F.4.1 Firm and Facility Size
For almost all NAICS codes in the Food Manufacturing and Beverage Manufacturing segments, the Small
Business Administration defines a small firm as having fewer than 500 employees. The exceptions are NAICS
codes 311221, 311312, 311313, 311821, and 312140, which are considered small if the firm has fewer than 750
employees, and NAICS codes 311223, 311225, 311230, and 311422, which are deemed small if the firm employs
fewer than 1,000 employees. The size categories reported in Statistics of U.S. Businesses (SUSB) do not
correspond with the SBA size classifications, therefore preventing precise use of the SBA size threshold in
conjunction with SUSB data. Table 2F-8 reports the size distribution of firms and facilities in the Food
Manufacturing and Beverage Manufacturing segments for 2006. As shown in the table, small establishments
dominate both segments:
> 20,625 of 21,170 (97 percent) firms in the Food Manufacturing segment had fewer than 500 employees.
These small firms owned 21,675 facilities, or 85 percent of all facilities in the segment.
> 2,921 of 2,998 (97 percent) firms in the Beverage Manufacturing segment had fewer than 500 employees.
These small firms owned 2,995 facilities, or 84 percent of all Beverage Manufacturing facilities.
Because some six-digit NAICS codes within the Food Manufacturing and Beverage Manufacturing segments
have small business size thresholds of greater than 500 employees, the reported numbers and percentages of
businesses with fewer than 500 employees represent lower bounds of the number and percentage of small
businesses in these industry segments.
In general, the percentage of small firms in the food and beverage industry is comparable to the percentage of
small firms in all manufacturing industries combined. In 2006, approximately 97 percent of the firms in NAICS
311 and 3121 had fewer than 500 employees, compared to almost 99 percent for all manufacturing firms (U.S.
SBA, 2006). However, compared to the Primary Manufacturing Industries, the Food Manufacturing and Beverage
Manufacturing industries have a significantly higher percentage of firms within the industry identified as small.
As noted below, however, the larger companies within each segment dominate in terms of producing the majority
of shipments for each segment, with the 50 largest firms in Food Manufacturing accounting for 53 percent of
shipments, while the 50 largest companies in Beverage Manufacturing producing an even greater share of
shipments, at 82 percent of the total (see Table 2F-9, following page).
2F-14 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
Table 2F-8: Number of Firms and Facilities by Size Category for Food and Beverage Manufacturing
Segments, 2006
Employment Size
Category
0-19
20-99
100-499
500+
Total
Food Manufacturing3
No. of Firms
15,214
4,091
1,320
545
21,170
No. of Facilities
15,278
4,422
1,975
3,707
25,382
Beverage Manufacturing1"
No. of Firms
2,330
463
128
77
2,998
No. of Facilities
2,333
486
176
561
3,556
a. NAICS311
b. NAICS3121
Source: U.S.DOC, Statistics of U.S. Businesses, 2006.
2F.4.2 Concentration Ratios
Concentration is the degree to which industry output is concentrated in a few large firms. Concentration is closely
related to entry barriers, with more concentrated industries generally having higher barriers.
The four-firm concentration ratio (CR4) and the Herfindahl-Hirschman Index (HHI) are common measures of
industry concentration. The CR4 indicates the market share of the four largest firms. For example, a CR4 of 72
percent means that the four largest firms in the industry account for 72 percent of the industry's total value of
shipments. The higher the concentration ratio, the less competition there is in the industry, other things being
equal.24 An industry with a CR4 of more than 50 percent is generally considered concentrated. The HHI indicates
concentration based on the largest 50 firms in the industry. It is equal to the sum of the squares of the market
shares for the largest 50 firms in the industry. For example, if an industry consists of only three firms with market
shares of 60, 30, and 10 percent, respectively, the HHI of this industry would be equal to 4,600 (602 + 302 + 102).
The higher the index, the fewer the number of firms supplying the industry and the more concentrated the
industry. Based on the U.S. Department of Justice's guidelines for evaluating mergers, markets in which the HHI
is under 1,000 are considered unconcentrated, markets in which the HHI is between 1,000 and 1,800 are
considered to be moderately concentrated, and those in which the HHI is in excess of 1,800 are considered to be
concentrated.
As shown in Table 2F-9, based on the most recent data, the Food Manufacturing segment has an HHI of 119, and
the Beverage Manufacturing segment has an HHI of 512. At these HHI levels, the two industry segments,
especially the Food Manufacturing segment, appear unconcentrated. With relatively low concentration in the
affected industries, firms are unlikely to possess the market power to recover regulatory compliance costs through
price increases, particularly if those costs do not apply relatively uniformly and broadly throughout the industry.
The concentration ratios also show that each profiled segment operates in unconcentrated markets. The Beverage
Manufacturing segment has the higher concentration of the two segments, with a CR4 of 40 percent. This is
slightly lower than the 50 percent threshold, which would indicate some market concentration. The CR4 for the
Food Manufacturing segment is considerably lower at only 17 percent. In this segment, the top 50 companies
control roughly half of the market, indicating a relatively unconcentrated market segment. As noted above,
however, mergers and acquisitions are occurring in both segments, which will likely lead to increased
concentration in the future. Also, certain sub-segments within each segment can be highly concentrated. For
Note that the measured concentration ratio and the HHF are very sensitive to how the industry is defined. An industry with a high
concentration in domestic production may nonetheless be subject to significant competitive pressures if it competes with foreign
producers or if it competes with products produced by other industries (e.g., plastics vs. aluminum in beverage containers).
Concentration ratios based on share of domestic production are therefore only one indicator of the extent of competition in an
industry.
March 28, 2011
2F-15
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2F: Economic Profile of the Food and Beverage Industry
example, within the soft drink market, Coca-Cola claims around 50 percent of the global market, followed by
Pepsi with roughly 21 percent and Cadbury-Schweppes with 7 percent (Yahoo, 2005a).
Table 2F-9: Selected Ratios for Food Manufacturing and Beverage Manufacturing Segments
NAICS Code
311
3121
Year
1997
2002
1997
2002
Total Number
of Firms
21958
23334
2169
2445
Concentration Ratios
4 Firm (CR4)
14%
17%
41%
40%
8 Firm (CR8)
22%
25%
52%
53%
20 Firm (CR20)
35%
40%
66%
69%
50 Firm (CR50)
51%
53%
79%
82%
Herfindahl-
Hirschman
Index
91
119
532
512
Source: U.S. DOC, Economic Census, 1987, 1992, 1997, and 2002.
2F.4.3 Foreign trade
This profile uses two measures of foreign competition: export dependence and import penetration.
Import penetration measures the extent to which domestic firms are exposed to foreign competition in domestic
markets. Import penetration is calculated as total imports divided by total value of domestic consumption in that
industry: where domestic consumption equals domestic production plus imports minus exports. Theory suggests
that higher import penetration levels will reduce market power and pricing discretion because foreign competition
limits domestic firms' ability to exercise such power. Firms belonging to segments in which imports account for a
relatively large share of domestic sales would therefore be at a relative disadvantage in their ability to pass-
through costs because foreign producers would not incur costs as a result of the Proposed Existing Facilities Rule.
The estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) for 2007 is 27
percent. For characterizing the ability of industries to withstand compliance cost burdens, EPA judges that
industries with import ratios close to or above 27 percent would more likely face stiff competition from foreign
firms and thus be less likely to succeed in passing compliance costs through to customers.
Export dependence, calculated as exports divided by value of shipments, measures the share of a segment's sales
that is presumed subject to strong foreign competition in export markets. The Proposed Existing Facilities Rule
would not increase the production costs of foreign producers with whom domestic firms must compete in export
markets. As a result, firms in industries that rely to a greater extent on export sales would have less latitude in
increasing prices to recover cost increases resulting from regulation-induced increases in production costs. The
estimated export dependence ratio for the entire U.S. manufacturing sector for 2007 is 15 percent. For
characterizing the ability of industries to withstand compliance cost burdens, EPA judges that industries with
export ratios close to or above 15 percent are at a relatively greater disadvantage in potentially recovering
compliance costs through price increases since export sales are presumed subject to substantial competition from
foreign producers.
Table 2F-10 presents trade statistics for the profiled Food and Kindred Products industry.25 Imports and exports
play a small role in this industry, with 2007 import penetration and export dependence ratios of 7.7 and 6.2
percent, respectively. Both measures of foreign competition are well below the 2007 U.S. manufacturing
averages. Given just these measures, it would be reasonable to assume that these segments do not face significant
foreign competitive pressures, and would have more latitude in passing through to customers any increase in
production costs resulting from regulatory compliance. However, as noted above, the HHI of the Food
Due to data limitations, it is not possible to accurately separate the Food and Beverage Manufacturing segments.
2F-16
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
Manufacturing and Beverage Manufacturing segments is 119 and 512, respectively suggesting firms in these
segments have low market power, limiting their ability to pass through any increase in production costs.
Table 2F-10: Trade Statistics for Profiled Food and Kindred Products Industry
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2666
2001
2002
2003
2004
2005
2006
2007
Xotiil Percent
Change 1990 - 2007
Total Percent
Change 2000 - 2007
Average Annual
Growth Rate
Value of Imports
(millions, $2009)
$25,713
$"247105
$257633"
$247198
$257560
$"267405
$"297465
$367784
$3^904
$347017
$357214
$357795
$"3"872"7"'i
$427144
$467575
$487909
$5J7578
$547656
112.6%
55.2%
5%
Value of Exports
(millions, $2009)
$24,368
$"257554
$287i98
$287776
$"317725
$"357621
$357717
$357669"
$337766"
$317786
$"327833"
$347669
$317919
$337359"
$31,735
$337974
$367921
$437212
77.3%
31.6%
3%
Value of Shipments
(millions, $2009)
$506,181
$49f;448
$5"ll76"42"
$517,776
$5227957
$5357438
$"5417974
$6267558
$6367115
$"6167891
$gl7j76"
$"62"5""583
$"62"i77"i"8
$6517626
$6667127
$"6757964"
$6587450
$6977164
37.7%
13.0%
2%
Implied Domestic
Consumption1"
$507,527
$4897'9"99
$5087476
$513,199
$5167792
$5267822"
$5357723
$6217734
$6287319
$6197123
$6i"97557
$6277370
$628767"l
$"6607405
$6"8"b7'9"6"7
$"69"67899"
$"673"7i6"8
$7"6'876"6'8
Import
Penetration0
5.1%
'479%
49"%"
47"%"
49"%"
576%
575%
576%
5""i"%"
575%
577%
577%
6""i"%"
674%
678%
7""i"%"
777%
777%
Export
Dependence*1
4.8%
572%"
575%
576%
6""i"%"
675%
67(5%
577%
573%
572'%"
573%
54%
5""i"%"
5""i"%"
478%
s76%
576%
672%
39.6%
14.4%
2%
a. Before 1997, data were compiled in the SIC system; since 1997, these data have been compiled in the North American Industry Classification System
(NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code classifications using the 1997Economic Census Bridge Between
NAICSandSIC.
b. Calculated by EPA as shipments + imports - exports.
c. Calculated by EPA as imports divided by implied domestic consumption.
d. Calculated by EPA as exports divided by shipments.
Source: U.S. International Trade Commission, 1989-2007.
As shown in Figure 2F-5, between 1990 and 2007, imports of Food and Kindred Products steadily increased at an
average annual rate of over 5 percent leading to an overall increase of approximately 113 percent (approximately
55 percent during the last decade). Exports of Food and Kindred Products also increased during this time period at
an average annual rate of approximately 3 percent leading to an overall increase of approximately 77 percent
(approximately 32 percent during the last decade). While imports experienced a relatively steady increase, exports
fluctuated significantly during the analysis period: Exports increased between 1990 and 1996, declined for the
next three years, remained relatively steady through 2004, when they increased through 2007. During most of the
decade of the 1990s, the Food and Kindred Products industry recorded a trade surplus, even though the value of
imports was steadily growing. However, in 1999, this trend reversed itself and during the last decade, the Food
and Kindred Products industry was characterized by trade deficit. Starting in 2005, however, exports have been
growing at a higher rate than imports, thereby shrinking the deficit.
March 28, 2011
2F-17
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2F: Economic Profile of the Food and Beverage Industry
Figure 2F-5: Value of Imports and Exports for Profiled Food and Kindred Products Industry (millions,
$2009)a
o
C4
V5
ft| ft
GO
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^^
:,*•':*.. ..-••-•'
>• ^' A
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
vovovovovovovovovovoooooooos
vovovovovovovovovovoooooooos
Oh-KJW*.CAO\<100VOOh-KJW*.CAa\~
a. Before 1997, the Department of Commerce compiled data in the SIC system; since 1997, these data have been com
Classification System (NAICS). For this analysis, EPA converted the NAICS classification data to the SIC code class
Census Bridge Between NAICS and SIC.
Source: U.S. International Trade Commission, 1989-2007.
J
( • Exports (NAICS 3 1 1 &
3121)
— A Imports (NAICS 3 1 1 &
3121)
j
5
5
i
ailed in the North American Industry
ifications using the 1997 Economic
.5 Financial Condition and Perforr
As discussed above, the profiled Food and Kindred Products industry overall is not as susceptible to economic
fluctuations and, consequently, its financial performance is not as closely linked to macroeconomic cycles as it is
in other, more cyclical manufacturing industries. As products from these segments are generally "consumer
staples," they are not as strongly affected by swings in the U.S. economy as the other 5 Primary Manufacturing
industries. As a result, businesses in these segments have been able to maintain a moderate level of positive
financial performance over the analysis time period, including the U.S. recessions of early 1990 and early and late
2000s, which more substantially affected other profiled Primary Manufacturing industries such as Pulp and Paper
Manufacturing and Steel Manufacturing.
This profile uses two measures of financial condition and performance: Net Profit Margin and Return on Total
Capital.
Net profit margin is calculated as after-tax income before nonrecurring gains and losses as a percentage of sales
or revenue, and measures profitability, as reflected in the conventional accounting concept of net income. Over
time, the firms in an industry, and the industry collectively, must generate a sufficient profit margin if the industry
is to remain economically viable and attract capital. Year-to-year fluctuations in profit margin stem from several
factors, including: variations in aggregate economic conditions (including international and U.S. conditions),
variations in industry-specific market conditions (e.g., short-term capacity expansion resulting in overcapacity), or
changes in the pricing and availability of inputs to the industry's production processes (e.g., the cost of energy to
2F-18
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2F: Food and Kindred Products Industry Profile
the manufacturing process). The extent to which these fluctuations affect an industry's profitability, in turn,
depends heavily on the fixed vs. variable cost structure of the industry's operations. In a capital intensive industry
such as the food and beverage industry, the relatively high fixed capital costs as well as other fixed overhead
outlays, can cause even small fluctuations in output or prices to have a large positive or negative affect on profit
margin.
Return on total capital is calculated as annual pre-tax income divided by the sum of current portion of long-term
debt due in 1 year or less, long-term debt due in more that one year, all other noncurrent liabilities and total
stockholders' equity (total capital). This concept measures the total productivity of the capital deployed by a firm
or industry, regardless of the financial source of the capital (i.e., equity, debt, or liability element). As such, the
return on total capital provides insight into the profitability of a business' assets independent of financial structure
and is thus a "purer" indicator of asset profitability than return on equity. In the same way as described for net
profit margin, the firms in an industry, and the industry collectively, must generate over time a sufficient return on
capital if the industry is to remain economically viable and attract capital. The factors causing short-term variation
in net profit margin will also be the primary sources of short-term variation in return on total capital.
Figure 2F-6 shows a trend in net profit margins and return on total capital for Food and Kindred Products
industry firms between 1988 and 2008. Despite some fluctuations in response to recessions in 1993, 2001, and
2008, when both profit margins and return on total capital fell slightly but recovered shortly after, this industry
reported positive profit margins and return on total capital over the entire analysis period.
Industry analysts expect retail food prices to be higher in 2010 and U.S. packaged food companies' production
volumes to remain relatively flat. With improved consumer demand and benefits from restructuring and other
cost-saving activities, industry profit margins are expected to widen in 2010. That demand for food and beverages
remains high during otherwise weak economic conditions, indicates that the profiled industry segments should be
able to continue robust financial performance over the foreseeable future, thus suggesting strong ability to
withstand the costs associated with the Proposed Existing Facilities Rule. In the long term, the Food and Beverage
Manufacturing industry will continue to focus on and adjust to consumer lifestyles and tastes, including both
opportunities in developing international markets and the particular needs of an aging U.S. population. Future
growth opportunities might include introduction and distribution of products that appeal to consumers' interest in
healthier eating and environmental sustainability (S&P, 2010d).
March 28, 2011 2F-19
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule
Chapter 2F: Economic Profile of the Food and Beverage Industry
Figure 2F-6: Net Profit Margin and Return on Total Capital for Food and Beverage Manufacturers
Source.
lo /o
16%
14/0 \
Iz /o
lUvo
6o/
4%
2o/
._*__.»
A^~A
~ - \
V ^A
~ /V *- N
« *^^ ^^»
X. *— ""^^
V ^*^*- X_
^«r
11111111 i i i i i i i i i
\G\G\G\G\G\G\G\G\G\G ^O^OOOOOOOOOC
OOOO^O^O^O^O^O^O^O^O ^O^OOOOOOOOOC
OO^OOh-*siOJ^r/lO\
j
s
s
e
Quarterly Financial Report, 1988-2008; U.S. Census Bureau.
2F.
6 Facilities Operatinq Coolinq Water Intake Stn
Point source facilities that use or propose to use a cooling water intake structure that withdraws cooling water
directly from a surface water body of the United States are potentially subject to Section 316(b) of the Clean
Water Act. In 1982, the Food and Kindred products industry withdrew 272 billion gallons of cooling water,
accounting for approximately 5 percent of total manufacturing cooling water intake in the United States. The
industry ranked sixth in industrial cooling water use, behind the electric power generation industry, chemical,
primary metals, petroleum and coal products, and paper and allied products industries (U.S. DOC, 1982).
This section provides information for the facilities in the Food and Kindred Products industry that EPA estimates
to be subject to regulation under the regulatory analysis options. Existing facilities that meet all of the following
conditions would have been subject to regulation under the three regulatory analysis options:
> Have a National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;
> Use or propose to use one or more cooling water intake structures to withdraw water from waters of the
United States;
> Use at least twenty-five (25) percent of the water withdrawn exclusively for contact or non-contact
cooling purposes; and
> Meet the applicability coverage criteria for the proposed regulation specific regulatory analysis option in
terms of design intake flow (i.e., 2 MGD).
2F-20
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2F: Food and Kindred Products Industry Profile
EPA initially identified the set of facilities that were estimated to be potentially subject to the 316(b) Existing
Facilities Regulation based on a minimum applicability threshold of 2 MGD; this section focuses on these
facilities for the petroleum segment).26
2F.6.1 Waterbody and Cooling System Type
Table 2F-11, reports the distribution of the Food and Kindred Products industry facilities by type of water body
and cooling water intake system.
Table 2F-11: Number of Food and Kindred Products Facilities Estimated Subject to the 316(b) Existing
Facilities Regulation by Waterbody Type and Cooling Water Intake System
Waterbody Type
Estuary /Tidal River
Freshwater River/ Stream
Great Lake
Total3
Reci
No^
0
14
0
14
rculating
% of Total
0%
100%
0%
35%
Con
No^
0
4
3
8
ibination
% of Total
0%
56%
44%
20%
Once
No^
7
7
0
14
-Through
% of Total
50%
50%
0%
35%
(
No^
0
3
0
3
3ther
% of Total
0%
100%
0%
9%
Total
6
28
3
38
Based on technical weights (See Appendix 3.A).
a. Individual numbers may not add up to total due to independent rounding.
Source: Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
2F.6.2 Facility Size
Figure 2F-7, shows the employment size category for the Food and Kindred Products industry facilities estimated
subject to regulation under the regulatory analysis options.
Figure 2F-7: Number of Facilities Estimated Subject to the Proposed 316(b) Existing Facilities Regulation
by Employment Size for the Combined Food Manufacturing and Beverage Segments
20
15
10
5-
0
/
A
9
m
! J
19
^ —
_
?
\
6
-
n
Less than 100-249 250-499 500-999 1000 or
100 greater
Source: U.S. EPA, 2000; U.S. EPA analysis, 2010.
26 EPA applied sample weights to the sampled 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).
March 28, 2011
2F-21
-------
Economic and Benefits Analysis for 316(b) Existing Facilities Rule Chapter 2F: Economic Profile of the Food and Beverage Industry
2F.6.3 Firm Size
EPA used the Small Business Administration (SBA) small entity size standards to determine the number of
facilities in the Food and Kindred Products facility dataset that are owned by small firms. EPA estimates that
three small entity-owned facilities and 34 large entity-owned facilities in this industry segment will be subject to
the proposed regulation.
2F-22 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2G: Other Industries
2G Profile of Facilities in Other Industries
The preceding profile sections focus on the six Primary Manufacturing Industries - Paper and Allied Products,
Chemicals and Allied Products, Petroleum Refining, Steel, Aluminum, and Food and Kindred Products -
identified, after electric power generators, as using the largest amount of cooling water in their operations and
whose facilities are most likely, after electric power generators, to be within the scope of the 316(b) Existing
Facilities regulation. However, facilities in other industries use cooling water and would therefore also be subject
to the final regulation if they meet the regulation's specifications. This section of the profile provides information
on a sample of facilities in these Other Industries.
Although EPA targeted its Detailed Industry Questionnaire at the electric power industry and manufacturing
industries that use large amounts of cooling water, the Agency received 13 questionnaire responses from facilities
with business operations in industries other than these major cooling water-intensive industries. EPA originally
believed these facilities to be non-utility Electric Generators; however, inspection of their responses indicated that
the facilities were better understood as cooling water-dependent facilities whose principal operations lie in
businesses other than Electric Generators or the Primary Manufacturing Industries. Unlike the sample facility
observations for the six Primary Manufacturing Industries, the sample of observations from Other Industries is not
based on a scientifically framed sample and the information from this sample of observations may not be reliably
extrapolated beyond these facilities. As a result, EPA's profile of information for the Other Industries facilities is
restricted to these 13 sample facilities and is not presented as national estimates.
All of the 13 Other Industries facilities withdraw at least 2 million gallons of water a day and meet other in-scope
criteria, and thus would be subject to regulation under the regulatory options considered for existing facilities.
These facilities fall in a wide range of businesses, as defined by three-digit NAICS industry group. Table 2G-1,
presents the number of responses received from facilities in the Other Industries by industry group. The
information summarized in the following sections focuses on these Other Industries facilities that EPA estimates
will be subject to regulation under the Existing Facilities Rule options.
March 28, 2011
2G-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2G: Other Industries
Table 2G-1: Facilities in Other Industries by 2-digit SIC code Estimated Subject to Regulation Under the
Regulatory Analysis Options
No. of
Facilities
1
4
1
2
1
1
1
NAICS
Code
111
212
313
321
331
336
339
SIC Description
Crop production
Mining (except oil and
gas)
Textile mills
Wood product mfg.
Primary metal mfg
Transportation
equipment mfg.
Miscellaneous mfg.
Important Operations
Establishments, such as farms, orchards, groves, greenhouses, and nurseries,
primarily engaged in growing crops, plants, vines, or trees and their seeds.
Including biological and physiological characteristics and economic
requirements, the length of growing season, degree of crop rotation, extent of
input specialization, labor requirements, and capital demands production
activities.
Mining, mine site development, and beneficiating (i.e., preparing) metallic
minerals and nonmetallic minerals, including coal. Also includes ore extraction,
quarrying, and beneficiating (e.g., crushing, screening, washing, sizing,
concentrating, and flotation), customarily done at the mine site.
Transforming a basic fiber (natural or synthetic) into a product, such as yarn or
fabric, that is further manufactured into usable items, such as apparel, sheets
towels, and textile bags for individual or industrial consumption.
Wood products, such as lumber, plywood, veneers, wood containers, wood
flooring, wood trusses, manufactured homes (i.e., mobile home), and
prefabricated wood buildings. Includes sawing, planing, shaping, laminating, and
assembling of wood products starting from logs that are cut into bolts, or lumber
that then may be further cut, or shaped by lathes or other shaping tools.
Making (i.e., the primary production) nonferrous metals by smelting ore and/or
the primary refining of nonferrous metals by electrolytic methods or other
processes (except copper and aluminum).
Equipment for transporting people and goods for each mode of transport - road,
rail, air and water. Land use motor vehicle equipment not designed for highway
operation (e.g., agricultural equipment, construction equipment, and materials
handling equipment).
A wide range of products that cannot readily be classified in specific NAICS
subsectors in manufacturing. Processes used by these establishments vary
significantly, both among and within industries.
Source: Executive Office of the President, 1987; U.S. EPA 2000; U.S. EPA analysis, 2010.
2G.1 Facilities Operating Cooling Water Intake Structures
Section 316(b) of the Clean Water Act applies to point source facilities that use or propose to use a cooling water
intake structure and that withdraws cooling water directly from a surface waterbody of the United States. This
section provides information for facilities in Other Industries subject to regulation under the regulatory analysis
options. The regulatory analysis options apply to existing facilities that meet all of the following conditions:
> Use a cooling water intake structure or structures, or obtain cooling water by any sort of contract or
arrangement with an independent supplier 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;
> Have an National Pollutant Discharge Elimination System (NPDES) permit or are required to obtain one;
and
> Meet the applicability criteria for regulatory coverage in terms of design intake flow (i.e., 2 MGD).
The regulatory options also cover substantial additions or modifications to operations undertaken at such
facilities.
2G-2
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2G: Other Industries
2G.1.1 Waterbody and Cooling System Types
Table 2G-2 summarizes information on the Other Industries facilities by type of water body and cooling system
for each option.
Table 2G-2: Other Industries Facilities Estimated Subjec
Water Body and Cooling Water Intake System Type
Waterbody Type
Estuary/ Tidal River
Freshwater Stream/River
Great Lake
Lake/Reservoir
Ocean
Total3
Recirculating
Number
1
2
0
0
0
3
% of Total
33%
67%
0%
0%
0%
18%
Once-Through
Number
1
8
2
1
1
13
% of Total
11%
45%
22%
11%
11%
77%
Other
Number
0
1
0
0
0
1
% of Total
0%
100%
0%
0%
0%
6%
Total3
2
11
2
1
1
17
Based on technical weights (See Appendix 3.A).
a. Individual numbers may not sum to total due to independent rounding
Source: U.S. EPA, 2000; U.S. EPA Analysis, 2010.
2G.1.2 Facility Size
Figure 2G-1 shows the employment size category for the Other Industries facilities that EPA estimates will be
subject to the regulation under each analysis option.
Figure 2G-1: Other Industries Facilities Estimated Subject to the Existing Facilities Regulation by
Employment Size
Less than 100-249
100
250-499 500-999 Greater
than 1000
Source: U.S. EPA, 2000; U.S. EPA Analysis, 2010.
2G.1.3 Firm Size
EPA used the Small Business Administration (SBA) small entity size standards to determine the number of the
Other Industries facilities that are owned by small firms. Depending on their SIC code, firms are defined as small
based on either revenues or number of employees. EPA estimates that four small entity-owned facilities and six
large entity-owned facilities in the Other Industries facility group will be subject to the 316(b) Existing Facilities
March 28, 2011
2G-3
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2G: Other Industries
regulation. Insufficient survey data are available to classify the entity size of an additional three in-scope Other
Industries facilities.
2G-4 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
2H Profile of the Electric Power Industry
2H.1 Introduction
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 describes important trends that
may influence the nature and magnitude of the economic impacts from the Proposed CWA Section 316(b)
Existing Facilities Regulation (Proposed 316(b) Regulation for Existing Facilities or Existing Facilities
Regulation).
The electric power industry is one of the most extensively 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 Existing Facilities Regulation.
The remainder of this profile is organized as follows:
> Section 2H.2 provides a brief overview of the industry, including descriptions of major industry segments,
types of generating facilities, and the entities that own generating facilities.
> Section 2H.3 provides data on industry production, capacity, and geographic distribution.
> Section 2H.4 focuses on the Section 316(b) Existing Facilities Regulation facilities; this section provides
information on their physical, geographic, and ownership characteristics.
> Section 2H.5 provides a brief discussion of factors affecting the future of the electric power industry,
including the status of electric utility regulatory restructuring and ongoing changes in air quality
regulations.
> Section 2H. 6 summarizes forecasts of market conditions through the year 2030 from the Annual Energy
Outlook 2009.
> Section 2H. 7 provides a glossary of key terms used throughout the chapter.
2H.2 Industry Overview
This section provides a brief overview of the industry, including descriptions of major industry sectors, types of
generating facilities, and the entities that own generating facilities.
2H.2.1 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,
2000):
> The generation sector includes the facilities that produce, or "generate," electricity. Electric power is
usually produced by a mechanically driven rotary generator. 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.
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2H: Electric Power Industry Profile
> The transmission sector is the large, high-voltage power lines that deliver electricity from facilities to
local areas. Electricity transmission involves the "transportation" of electricity from facilities 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 facilities to balance the demand and supply of electricity in real time; and
managing the system for equipment failures, network constraints, and interaction 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 provide electricity 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 functions, 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)
regulations. The remainder of this profile will focus on the generation sector of the industry.
2H.2.2 Prime Movers
Electric power facilities use a variety of prime movers to generate electricity. The type of prime mover used at a
given facility is determined based on the type of load the facility is designed to serve, the availability of fuels, and
energy requirements. Most prime movers use fossil fuels (coal, oil, and natural gas) as an energy source and
employ some type of turbine to produce electricity. According to the Department of Energy, the most common
prime movers are (U.S. DOE, 2000):
> Steam Turbine: "Most of the electricity in the United States is produced with 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 shaft 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 uranium 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 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) unit, 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 electricity. 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, gas-turbine units are suitable for peak load, emergency, and reserve-
power requirements. The gas turbine, as is typical with peaking units, has a lower efficiency than the
steam turbine used for base load power."
> Combined Cycle Turbine: "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 recovery 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 turbine. Thus, the supplementary steam-turbine generator
2H-2 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2H: Electric Power Industry Profile
may be operated with 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 Units: "Hydroelectric power is the result of a process in which flowing water is
used to spin a turbine connected 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 drive 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 their ability to start quickly and make rapid changes in power
output, hydroelectric generating units are suitable for serving peak loads and providing immediately
available back-up reserve power (spinning reserve), as well as serving base load 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 hydroelectric
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 to those listed above there are a number of other less common prime movers:
> Other Prime Movers: "Other methods of electric power generation, which presently contribute only small
amounts to total power production, have potential 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-flows close enough to the surface of the earth to produce steam.
That steam can then be harnessed for use in conventional steam-turbine plants. Solar power is derived
from the energy (both light and heat) of the sun. Photovoltaic conversion generates electric power directly
from the light of the sun; whereas, solar-thermal electric generators use the heat from the sun to produce
steam to drive turbines. Wind power 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."
The section 316(b) regulation is only relevant for electric generators that use substantial amounts of cooling
water, and not all prime movers require substantial amounts of 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 Proposed
Existing Facilities Rule. This profile, therefore, differentiates between steam-based generating capacity and other
March 28, 2011 2H-3
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
prime movers. EPA identified steam-electric prime movers using data collected by the EIA (U.S. DOE, 2007b).27
For this profile, the following prime movers, including both steam turbines and combined cycle technologies, are
classified as steam-electric:
> Steam Turbine, including coal, gas, oil, waste, 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)
Table 2H-1 provides data on the number of existing utility and nonutility power facilities by prime mover. This
table includes all facilities that have at least one non-retired unit and that submitted Form EIA-860 (Annual
Electric Generator Report) in 2007. For the purpose of this analysis, facilities were classified as "steam turbine"
or "combined cycle" if they have at least one generating unit of that type; facilities with both steam turbine- and
combined cycle-based capacity are classified as steam turbine capacity. Facilities that have no steam-electric units
were classified under the prime mover that accounts for the largest share of the facility's total generating capacity.
EPA identified a facility as a utility or a nonutility based on their regulatory status. An electric power generator
operating under the traditional rate regulation framework is classified as a utility; a generator operating as a
producer and seller of electricity outside of the traditional rate regulation framework is classified as a nonutility.
Table 2H-1: Number of Existing Utility and Nonutility Facilities by Prime Mover, 2007
Prime Mover
Number of Facilities
'"Utility" ] Nonutiiity"
Total
Steam Electric Prime Movers
Steam Turbine
Combined Cycle
513
113
799
338
1,312
451
Other Prime Movers
Gas Turbine
Internal Combustion
Hydroelectric
Other
Total
414
649
882
38
2,609
454
379
518
296
2,784
868
1,028
1,400
334
5,393
a. See definition of utility and nonutility in Section 2H.2.3.
Source: U.S. DOE, 2007b.
2H.2.3 Ownership
The U.S. electric power industry consists of two broad categories of firms that own and operate electric
generating facilities: utilities and nonutilities. Generally, they can be defined as follows (U.S. DOE,
2009c):
> Generating Utility: A regulated entity providing electric power in a rate regulation framework in which a
government regulatory authority sets prices at which the regulated entity sells generated electricity or
other electricity-related services. Electric utilities have traditionally operated in a vertically integrated
framework including power generation, transmission and distribution. However, generating utilities,
which are the focus of this profile within the utility segment, in some instances may provide only power
generation and transmission services and not provide local distribution services. Vertically integrated
utilities - i.e., those that include power generation, transmission and distribution - deliver electric energy
to customers in a designated franchise service territory. Other electric utility segments include
27 U.S. DOE collects data (EIA Form 860, Annual Electric Generator Report 2007) 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.
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March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2H: Electric Power Industry Profile
"transmission utilities," which refers to the regulated owners/operators of transmission systems, and
"distribution utilities," which refers to the regulated owners/operators of distribution systems serving
retail customers.
> Nonutility: Entities that generate power for their own use and/or for sale to utilities and others in a non-
regulated pricing environment. Nonutility power producers include independent power producers and
cogenerators (combined heat and power producers). Nonutilities do not have a designated franchised
service area and do not transmit or distribute electricity.
For this profile, the key distinction between utilities and nonutilities is that utilities operate in a rate regulation
framework in which a regulatory body sets prices at which the regulated entity sells generated electricity or other
electricity-related services, while nonutilities operate in a non-regulated pricing environment.
Generating utilities can be further divided into three major ownership categories: investor-owned utilities,
publicly-owned utilities, and rural electric cooperatives. EPA identified a facility's ownership using data collected
from EIA forms 860 and 861 (U.S. DOE, 2007b; U.S. DOE, 2007c; U.S. DOE, 2006). Each category is discussed
below (adapted from U.S. DOE, 2000):
> Investor-owned utilities: Investor-owned utilities (lOUs) are for-profit businesses that can take two basic
organizational forms: the individual corporation and the holding company. An individual corporation is a
single utility company with its own investors; a holding company is a business entity that owns one or
more utility companies and may have other diversified holdings as well. Like all businesses, the objective
of an IOU is to produce a return for its investors. lOUs are entities with designated franchise areas. They
are required to charge reasonable and comparable prices to similar classifications of consumers and to
give consumers access to services under similar conditions. Most lOUs engage in generation,
transmission, and distribution. In 2007, lOUs operated 1,117 facilities, which accounted for
approximately 37 percent of all U.S. electric generation capacity (U.S. DOE, 2007b; U.S. DOE, 2007c;
U.S. DOE, 2006).
> Publicly-owned utilities: Publicly-owned electric utilities can be State authorities, municipalities, and
political subdivisions (e.g., public power districts, irrigation projects, and other State agencies established
to serve their local municipalities or nearby communities). This category also includes Federally-owned
facilities. Excess funds or "profits" from the operation of these utilities are put toward reducing rates,
increasing facility efficiency and capacity, and funding community programs and local government
budgets. Smaller municipal utilities, which make up the majority municipal utilities, are nongenerators
engaging solely in the purchase of wholesale electricity for resale and distribution. Larger municipal
utilities, as well as State and Federal utilities, usually generate, transmit, and distribute electricity. 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 lOUs. In 2007, the Federal government operated 197
facilities (accounting for 7 percent of total U.S. electric generation capacity), States owned 104 facilities
(2 percent of U.S. capacity), and municipalities owned 869 facilities (5 percent of U.S. capacity) (U.S.
DOE, 2007b; U.S. DOE, 2007c; U.S. DOE, 2006).
> Rural electric cooperatives: Cooperative electric utilities ("coops") are member-owned entities created to
provide electricity to those members. These utilities, established under the Rural Electrification Act of
1936, provide electricity to small rural and farming communities (usually fewer than 1,500 consumers).
The National Rural Utilities Cooperative Finance Corporation, the Federal Financing Bank, and the Bank
of Cooperatives are important sources of financing for these utilities. In 2007, rural electric cooperatives
operated 205 generating facilities and accounted for approximately 4 percent of all U.S. electric
generation capacity (U.S. DOE, 2007b; U.S. DOE, 2007c; U.S. DOE, 2006).
March 28, 2011 2H-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
Figure 2H-1 presents the number of generating facilities and their capacity in 2007, 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 electric power generating facilities that have at least one non-retired unit and that submitted Form EIA-860
for 2007. To determine the ownership type for each of these facilities, EPA relied on the information reported in
the 2006 EIA-860 and the 2007 EIA-861 databases and additional research.28 The chart shows that nonutilities
account for the largest percentage of facilities (2,784, or 52 percent), but represent only 43 percent of total U.S.
generating capacity. Investor-owned utilities operate the second largest number of facilities, 1,117, and account
for 37 percent of total U.S. capacity.
Figure 2H-1: Distribution of Facilities and Capacity by Ownership Type, 2007
Non utility
Investor Owned
Municipal
Cooperative
Federal
State
Political Subdivision
Unknown/Other
471,262 l\
2,784
| 51, 057 P
~| 40,311 MW
205
| 72,234 I
~~n 197
~\ 22,405 MW
I 104
1 20,721 MW
J 93
2,342 MW
I 24
1,117
/IW
869
A\N
407,460 MW
IW
D Capacity (MW)
• Number of Plants
0% 20% 40% 60% 80% 100%
Source: U.S. DOE, 2007b; U.S. DOE, 2007c; U.S. DOE, 2006
'
H.3 Domestic Productior
This section presents an overview of generating capacity and electricity generation. Section 2H.3.1 provides data
on capacity, and Section 2H.3.2 provides data on generation. Section 2H.3.3 presents an overview of the
geographic distribution of generation facilities and capacity.
2H.3.1 Generating Capacity
The rating of a generating unit, expressed in megawatts (MW), is a measure of its ability to produce electricity.
Capacity and capability are the two most common measures. Nameplate capacity, which is generally greater than
a generating unit's net summer or winter capacity, is the maximum rated (i.e., full-load) output of a generating
28 Prior to 2007, ownership information at the utility/operator level was reported in the EIA-860 database; this information was reported
for more facilities than in the EIA-861 database, which covers regulated facilities only.
2H-6
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
unit under specified conditions, as designated by the manufacturer. Net summer capacity is the maximum output
that a generating unit can supply to system load at the time of summer peak demand; it reflects a reduction in
capacity due to electricity use for station service or auxiliaries.29 Net winter capacity is the maximum output that a
generating unit can supply to system load at the time of winter peak demand; it also reflects a reduction in
capacity due to electricity use for station service or auxiliaries.30 Because, in most of the United States, summer
peak demand exceeds winter peak demand, aggregate net summer capacity exceeds net winter capacity. Net
capability is the steady hourly output that a 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 higher ambient-air and cooling-water temperatures, which cause generating units to perform less
efficiently in converting the input energy source to usable electricity (U.S. DOE, 2000).
In 2007, utilities owned and operated the majority of net summer capacity (57 percent), in the United States, with
nonutilities owning the remaining 43 percent. Nonutility ownership of net summer capacity increased
substantially in the last few years, following the passage of state legislation aimed at increasing competition in the
electric power industry. Nonutility ownership of net summer capacity increased by 535 percent between 1997 and
2007, compared with a decrease in utility ownership of net summer capacity of 20 percent over the same time
period, as traditional regulated utilities sold generating capacity to nonutility power producers to meet state-based
deregulation requirements. Overall, total net summer capacity increased during this period, from approximately
776,000 MW in 1996 to 995,000 MW in 2007 (see Figure 2H-2).
Figure 2H-2: Net Summer Capacity, 1997 to 2007
s
i
cu
O)
<& rS> «N ^V ^> «!>> ^> rS> .A
Source: U.S. DOE, 2009c
2H.3.2 Electricity Generation
The production of electricity is referred to as generation and is measured in units of produced energy such as
kilowatt-hours (kWh) or megawatt-hours (MWh). Generation can be measured by gross generation, net
generation, or electricity available to consumers. Gross generation is the total amount of electricity produced by
In the United States, this is the period of June 1 through September 30.
In the United States, this is the period of December 1 through February 28(29).
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
an electric power facility whereas net generation is the electricity available to the transmission system beyond
that needed to operate facility equipment. For example, approximately 7 percent of electricity generated by steam-
electric units provides power for operation of the power generating station, including, for example, lights at the
facility, operation of fuel supply systems, and cooling water intake-related equipment. An additional 8 to 9
percent of net generation is lost during the transmission and distribution process. Electricity available to
consumers is the electricity available for sale to customers after accounting for these factors (U.S. DOE, 2000).
Total net electricity generation in the United States for 2007 was 4,157 TWh.31 Utility-owned facilities accounted
for 60 percent of this amount. Total net generation has increased by 19 percent over the 11-year period from 1997
to 2007. During this period, nonutilities increased their electricity generation by 347 percent while utilities
decreased their generation by 20 percent (U.S. DOE, 2009c). This trend is expected to continue in the coming
years, as more facilities are built by nonutility power producers or purchased from traditional integrated utilities
(see Table 2H-2), which summarizes the change in net generation between 1997 and 2007 by energy source and
ownership type).
Table 2H-2: Net Generation by Energy Source and Ownership Type, 1997 to 2007 (TWh)
Energy Source
Coal
Hydropower
Nuclear
Petroleum
Natural Gas
Other Gases
Renewablesa
Other5
Total
1997
1,788
337
629
78
284
0
7
o
3,123
Utilities
2007
1,491
221
428
41
314
0
9
1
2,504
% Change
-16.6%
-34.3%
-32.0%
-47.6%
10.6%
NA
20.0%
NA"
-19.8%
1997
57
15
0
15
196
13
70
4
370
Nonutilities
2007
525
19
379
25
583
13
96
12'
1,653
% Change
818.5%
26.5%
NA
69.0%
197.7%
-0.3%
38.1%
222:4%
347.1%
1997
1,845
352
629
93
479
13
77
4
3,492
Total
2007
2,016
241
806
66
897
13
105
12
4,157
% Change
9.3%
-31.7%
28.3%
-29.0%
87.0%
0.8%
36.3%
238:6%
19.0%
a Renewables include wind, solar thermal and photovoltaic, wood and wood derived fuels, geothermal, and other biomass.
b Other includes non-biogenic municipal solid waste, batteries, chemicals, hydrogen, pitch, purchased steam, sulfur, tire-derived fuels and
miscellaneous technologies.
Source: U.S. DOE, 2009c.
As shown in Table 2H-2, not accounting for the "other" energy source category, natural gas-based electricity
generation experienced the highest growth (87 percent) among fuel sources between 1997 and 2007. Although
coal-based generation experienced the lowest growth (9 percent), it is still the largest energy source for electricity
generation. Petroleum experienced the largest decline (29 percent). For utilities, generation using natural gas as a
fuel source was relatively constant. Utility-owned generation from other sources fell, mostly because of sales of
capacity to nonutilities. Nonutility generation grew quickly between 1997 and 2007. Nonutility coal generation
grew the fastest among the energy source categories, increasing by over 800 percent during this period. Of these
energy sources, coal, nuclear, natural gas, and petroleum are the sources that always (coal, nuclear) or frequently
(natural gas, petroleum) depend on cooling water as part of the electricity production process.
Coal accounted for the largest share of total electricity generation (49 percent) in 2007, followed by natural gas at
approximately 22 percent of total generation, and nuclear power at 19 percent. Other energy sources accounted for
comparatively smaller amounts of total generation, with hydropower representing 6 percent; renewable energy,
2.5 percent; and petroleum, 1.6 percent (see Figure 2H-3).
Overall, regulated utilities accounted for 60 percent of total electricity generation in 2007, with nonutilities
accounting for 40 percent. However, the distribution of generation between utilities and nonutilities varies
considerably by energy source. Energy inputs for which utilities had higher shares of generation than observed at
the total generation level are as follows:
One terawatt-hour is 1012 watt-hours.
2H-8
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
> Hydropower, with 92 percent of generation from regulated utilities and 8 percent from nonutilities
> Coal, with 74 percent of generation from utilities and 26 percent from nonutilities
> Petroleum, with 62 percent of generation from utilities and 38 percent from nonutilities.
Energy inputs for which nonutilities had higher shares of generation than observed at the total generation level are
as follows:
> Renewables, with 91 percent of generation from nonutilities and 9 percent from utilities
> Natural gas, with 65 percent of generation from nonutilities and 35 percent from utilities
> Nuclear, with 47 percent of generation from nonutilities and 53 percent from utilities.
The Other Gas and Other categories are also dominated by nonutilities, which account for 100 of generation in
these categories; however, these energy source categories represent negligible shares of total generation.
The Proposed 316(b) Existing Facilities Regulation will affect electric power generating facilities differently
based on the fuel sources and prime movers that facilities use to generate electricity. Only prime movers with a
steam-electric generating cycle use substantial amounts of cooling water; consequently, these are the only units
that will be directly affected by the Existing Facilities Proposed Rule. In addition, the Proposed Regulation
specifies different compliance schedules based on the energy input source of the affected facilities.
Figure 2H-3: Percent of Electricity Generation by Primary Fuel Source and Facility Ownership Type, 2007
^
Source: U.S. DOE, 2009c.
March 28, 2011
2H-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2H: Electric Power Industry Profile
2H.3.3 Geographic Distribution
Electricity is a commodity that cannot be stored or easily transported over long distances. As a result,
the geographic distribution of power facilities 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 covers the largest portion of the United States, from the eastern end of
the Rocky Mountains and the northern borders to the Gulf of Mexico states (including parts of northern
Texas) on to the Atlantic seaboard. This system contains six of the NERC regions defined below (the
FRCC - Florida Reliability Coordinating Council, the MRO - Midwest Reliability Organization, the
NPCC - Northeast Power Coordinating Council (U.S. component), the RFC - Reliability First
Corporation, the SERC - Southeastern Electric Reliability Council, and the SPP - Southwest Power
Pool).
> The Western Interconnected System covers nearly all of areas west of the Rocky Mountains, including the
Southwest. The only NERC region within this system is the WECC - Western Energy Coordinating
Council (U.S. component).
> The Texas Interconnected System, the smallest of the three, covers the majority of Texas. The only NERC
region within this system is TRE - Texas Regional Entity.
The Texas system is not connected with the other two systems, while 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 connections that allow for power transmission 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 Corporation
(NERC) is responsible for the overall reliability, planning, and coordination of the power grids. This voluntary
organization was formed in 1968 by electric utilities, following a 1965 blackout in the Northeast. NERC is
organized into eight regional organizations that cover the 48 contiguous States, and two affiliated councils that
cover Hawaii, part of Alaska, and portions of Canada and Mexico.32 These regional organizations are responsible
for the overall coordination of bulk power policies that affect their regions' reliability and quality of service. As
discussed above, interconnection between the bulk power networks is limited in comparison to the degree of
interconnection within the major bulk power systems. Further, the degree of interconnection between NERC
regions even within the same bulk power network is also limited. Consequently, each NERC region deals with
electricity reliability issues in its own region, based on available capacity and transmission constraints. The
regional organizations also 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.
Figure 2H-4 provides a map of the 2009 NERC regions, which include:33
> ASCC - Alaska Systems Coordinating Council
32 Energy concerns in the States of Alaska, Hawaii, the Dominion of Puerto Rico, and the Territories of American Samoa, Guam, and the
Virgin Islands are not under reliability oversight by NERC.
33 This chapter provides NERC region data by the 2009 NERC regions. Some NERC regions have been re-defined over the past few
years; the NERC region definitions used in the proposed Existing Facilities regulation analyses vary by analysis depending on which
region definition aligns better with the data elements underlying the analysis.
2H-10 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
FRCC - Florida Reliability Coordinating Council
HICC - Hawaii Coordinating Council
MRO - Midwest Reliability Organization
NPCC - Northeast Power Coordinating Council (U.S.)
RFC - Reliability First Corporation
SERC - Southeastern Electric Reliability Council
SPP - Southwest Power Pool
TRE - Texas Regional Entity
WECC - Western Energy Coordinating Council (U.S.)
Figure 2H-4: 2009 North American Electric Reliability Corporation (NERC) Regions
FRCC
a The ASCC and HICC regions are not shown.
Source: U.S. DOE, 2009c.
The Proposed Existing Facilities Regulation may affect facilities located in different NERC regions differently.
Because of differences in the economic characteristics of in-scope facilities across NERC regions and in the
baseline economic characteristics of the NERC regions themselves, together with the market segmentation due to
limited interconnectedness among NERC regions, the proposed regulation will have a different effect on
profitability, electricity prices, and other impact measures across NERC regions.
Table 2H-3 shows the distribution of all existing facilities and total capacity by NERC region. As presented in
Table 2H-3, 1,407 facilities (approximately 26 percent of all facilities in the United States) are located in WECC.
However, these facilities account for only approximately 18 percent of total national capacity. Conversely, only
17 percent of generating facilities are located in the SERC, yet these facilities account for approximately 27
percent of total national capacity.
March 28, 2011
2H-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
Table 2H-3: Distribution of Existing Facilities and Total Capacity by NERC Region, 2007
NERC Region
ASCC
FRCC
FflCC
MRO
NPCC
RFC
SERC
SPP
TRE
WECC
TOTAL
Fac
Number
105
126
40
680
708
899
912
286
230
1,407
5,393
ilities
%0f Total
1.9%
2.3%
0.7%
12.6%
13.1%
16.7%
16.9%
5.3%
4.3%
26.1%
100%
Caps
j^ai"]VIW
2,163
60,457
2,674
53,467
78,757
248,159
288,625
63,221
93,789
196,480
1,087,791
icity
%0fTotaj
0.2%
5.6%
0.2%
4.9%
7.2%
22.8%
26.5%
5.8%
8.6%
18.1%
100%
Source: U.S. DOE, 2007b.
2H.4 Facilities Subject to the Proposed Existing Facilities Rule
Section 316(b) of the Clean Water Act applies to point source facilities that 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 facilities, only those facilities employing a steam-based generating technology - i.e., steam turbine and
combined cycle turbine generating units - require sufficient amounts of cooling water within scope of this
proposed rulemaking and therefore are of interest to this analysis.
The following sections describe electric power facilities that are expected to be subject to the Proposed Existing
Facilities Rule. This rule applies to existing steam-electric power generating facilities that meet the applicability
criteria in section 316(b):
> Is a point source that uses or proposes to use a cooling water intake structure;
> Has at least one cooling water intake structure that uses at least 25 percent of the water it withdraws for
cooling purposes;
> Has a National Pollutant Discharge Elimination System (NPDES) permit or is required to obtain one; and
> Has a design intake flow of two million gallons per day (MGD) or greater.
The Proposed Rule also covers substantial additions or modifications to operations undertaken at such facilities.
Based on (1) data collected from EPA's Section 316(b) 2000 industry Surveys and (2) the above rule scoping
requirements, EPA identified 559 facilities to which the Proposed Existing Facilities Rule is expected to apply
(the "in-scope facilities").34'35 All of these facilities are in the set of 671 facilities that were subject to the EPA's
Section 316(b) Survey. However, according to the 2007 EIA database, 38 of these 671 facilities have retired, and
15 facilities will do so by 2012; in addition, 39 facilities are baseline closures according to the Integrated Planning
The 2000 Industry Short Technical Questionnaire (STQ) and the 2000 Detailed Industry Questionnaire (DQ). As described in the 2002
Phase II Proposed and suspended 2004 Phase II Final Regulation analyses, these surveys collected technical and economic
information from 372 STQ facilities and 284 DQ facilities that were expected to be within the scope of the Phase II Regulation. For
more information on EPA's Section 316(b) Industry Surveys, see U.S. EPA, 2000.
EPA developed the estimates of the number and characteristics of facilities expected to be within the scope of the Proposed 316(b)
Existing Facilities Rule, based on the original Section 316(b) Industry Survey facility sample weights that were developed for the
earlier 316(b) analyses. These original survey weights account for survey non-respondents and provide comprehensive estimates for
the total of expected in-scope facilities based on the full set of facilities sampled in the Section 316(b) Industry Surveys. See Chapter
3: Development of Costs for Regulatory Options and Appendix 3.A: Use of Sample Weights in the Proposed Existing Facilities Rule
Analyses for further discussion of the sample weights used in this analysis.
2H-12
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2H: Electric Power Industry Profile
Model (IPM) baseline case analysis (see Chapter 6: Electricity Market Model Analysis}^6 EPA also excluded 19
Electric Generators that are located in California and that use coastal and estuarine waters for power plant cooling.
These facilities are already required by the State of California to comply with standards that are similar to those
under the Proposed Existing Facilities Rule and thus for regulatory analysis purposes, are not expected to be
affected by the proposed rule. In particular, according to the California requirements, water intake velocity at
these facilities must not exceed 0.5 feet per second and the intake flow rate at each unit must not exceed the level
commensurate with the level that can be attained by a closed-cycle wet cooling system.37 Based on the 2007 EIA
database, EPA estimates that 388 of these in-scope facilities are owned by utilities and 171 in-scope facilities are
owned by nonutilities.
The following sections present information on the physical and geographic characteristics, as well as, ownership
of the facilities expected to be within the scope of the Proposed 316(b) Existing Facilities Rule. Topics discussed
include:
> Ownership type: Section 2H.4.1 presents a discussion on the distribution of all facilities and their parent-
entities in the industry, as well as, facilities subject to this proposal and their parent-entities across
ownership categories.
> Parent-entity size: Section 2H.4.2 presents an assessment of the distribution of parent-entities across
ownership categories by parent-entity size for the entire industry, as well as, parent-entities owning
facilities subject to the Proposed Existing Facilities Rule.
> Facility size: Section 2H.4.3 contains a size assessment for the in-scope Electric Generators based on
generating capacity.
> Geographic distribution: Section 2H.4.4 presents information on geographic distribution of in-scope
Electric Generators across NERC regions.
> Waterbody and cooling system type: Section 2H. 4.5 presents information on the type of waterbody
from which in-scope Electric Generators draw their cooling water and the type of cooling system they
operate.
2H.4.1 Ownership Type
As described above, utilities can be divided into six major ownership categories: investor-owned utilities,
nonutilities, federally-owned utilities, state-owned utilities, municipalities, and rural electric cooperatives. This
classification is important because EPA has to assess the impact of the Proposed Rule on state, local, and tribal
governments in accordance with the Unfunded Mandates Reform Act (UMRA) of 1995 (see Chapter 8: UMRA
Analysis).
Table 2H-4 reports the number of parent-entities, facilities, and capacity by ownership type for the total industry
and for the subset of estimated in-scope facilities (for a discussion of the determination of parent-entities for in-
scope facilities, see Chapter 5: Cost and Economic Impact Analyses; for a discussion of the determination of
parent-entities for the entire industry, see Chapter 7: Regulatory Flexibility Analysis (RFA)}. Overall, EPA
estimates that 3 percent of all parent-entities, approximately 11 percent of all facilities, and over 45 percent of all
electric power sector capacity will be subject to the Proposed Existing Facilities Rule. The majority of facilities
expected to be subject to the Proposed Existing Facilities Rule, or 283 facilities, are investor-owned utilities,
while nonutilities make up the second largest category. In-scope investor-owned and cooperative facilities
36 For the purposes of this analysis an Electric Generator is considered retired even if it no longer operates any steam electric units even
though it may still be operating non-steam electric units.
37 Individual values do not sum to reported totals due to rounding as a result of the application of statistical weights.
March 28, 2011 2H-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
represent the largest shares of their respective industry totals at over 25 percent and over 15 percent. In terms of
in-scope capacity, investor-owned utilities account for the largest absolute quantity (291,051 MW) and also the
largest single share of total capacity by ownership category, at over 71 percent. Substantial shares of the capacity
in the other ownership categories- ranging from approximately 28 to 38 percent- are also estimated to be within
the scope of the Proposed Existing Facilities Rule.
Table 2H-4: Existing Parent-Entities, Facilities, and Capacity by Ownership Type, 2010
Ownership Type
Investor Owned
Nonutilityd
Federal
State
Municipality
Cooperative
Political Subdivision
Total
P
Total"
212
1,737
9"
25"
T7843
883"
126'"
4,835
arent-Entiti
IIZZI^
Number
43
37
!""
4"""
35""
20""
3""
143
BS
:opea
"/oofTotai
20.3%
2.1%
lT7l%
16T6%
l"9%"
273%"
274%"
3.0%
Total"
1,117
2,784
197"
104"
869
205""
93"
5,369
Facilities
]to:S
Number0
283
171
14"
9"
44"
31""
7"
559
copea
"/ooffotai
25.5%
6.6%
7.T%"
8.7%"
574%"
J5"J%"
775%"
10.8%
C
Total"
407,460
471,262
72^34"
22,40"5""
5i;057"
40,311
207721""
1,085,449
jpacity (AT
IIZZI^
Number0
291,051
133,972
24,612"
8,592"""
12,880
14,028
57692"""
490,827
W)
copea
%ofTotai
71.4%
28.4%
34.T%"
3873%
2572%
3478%
275%
45.2%
a. Numbers may not add up to totals due to independent rounding.
b. Information on the total number of parent-entities is based on data from the 2007 EIA-861 database (U.S. DOE, 2007c). Information on facilities and
capacity is based on data from the 2007 EIA-860 database (U.S. DOE, 2007b). These data sources report information for non-corresponding sets of power
producers. Therefore, the total number of parent-entities is not directly comparable to the information on total facilities or total capacity.
c. The numbers of facilities and capacity are calculated on a sample-weighted basis.
d. Form EIA-861 does not provide information for nonutilities. This is the total number of non-regulated operators from the 2007 EIA-860 database.
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2007b; U.S. DOE, 2007c
2H.4.2 Ownership Size
EPA estimates that 33 of the 143 entities owning in-scope facilities (23 percent) are small-entities according to
Small Business Administration business size criteria (Table 2H-5). The size distribution varies considerably by
ownership type: only about 5 percent of 316(b) investor-owned utilities and slightly over 13 percent of 316(b)
nonutilities are small-entities, compared to over 48 percent of 316(b) municipalities, 40 percent of 316(b)
cooperatives, and over 33 percent of other political subdivisions. Across ownership categories, as well as in total,
parent-entities that own in-scope facilities are on average larger than parent-entities in the whole industry: large
parent-entities make up only 57 percent of all parent-entities in the industry compared to nearly 77 percent of
entities owning in-scope facilities.
Of the 559 in-scope facilities, EPA estimates that 38 (nearly 7 percent) are owned by small entities (Table 2H-6).
The majority of the in-scope facilities owned by small entities are owned by municipalities (over 40 percent),
while cooperatives, investor-owned, nonutilities, and other political subdivisions own the remaining 60 percent.
By definition, States and the Federal government are considered large parent-entities. For a detailed discussion of
the identification and size determination of parent-entities see Chapter 7: Regulatory Flexibility Analysis (RFA).
Table 2H-5: Existing Parent-Entities by Ownership Type and Size, 2010
Ownership Type
Investor-owned
Nonutility0
Federal
State
Other Political
Subdivision
Municipality
Total
Small
18
131
o
o
113
968
Number of
Large
194
1,606
9
25
13
875
jParent-E
Total
212
1,737
9
25
126
1,843
ntities"
% Small
8.7%
775%
676%
676%
89.7%
5275%
Total Ni
Own Sec
Small
2
5
6
6
i
17
imber of ]
tion 316(b
Large
41
32
1
4
2
18
Jarent-En
) In-Scop<
Total
43
37
1
4
3
35
tities That
; Facilities'"
% Small
4.7%
1375%
676%
676%
33.3%
4876%
% of Small
Entities
Owning In-
Scope
Facilities
10.9%
378%
NA
NA
0.9%
178%
2H-14
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
Cooperative
Total
848
2,078
35
2,757
883
4,835
96.0%
43.0%
8
33
12
110
20
143
40.0%
23.1%
0.9%
1.6%
a. The total number of parent entities that own generation utilities is based on data from the 2007 EIA-861 database (U.S. DOE, 2007c). The total number of
parent entities that own nonutilities, is based on data from the 2007 EIA-860 database (U.S. DOE, 2007b). Because these two databases report data for
differing sets of facilities, the information in this table is not directly comparable to the other information presented in this profile.
b. Numbers may not add up to totals due to independent rounding.
c. Form EIA-861 does not provide data on nonutilities. This is the total number of nonregulated operators from the 2007 EIA-860 database; the number of
small parent entities for nonregulated operators was determined using the 2007 EIA-906/920/923 database (U.S. DOE, 2007d).
Source: U.S. EPA Analysis, 2010; U.S. EPA, 2000; U.S. DOE, 2007b; U.S. DOE, 2007c; U.S. DOE, 2007d
Table 2H-6: Section 316(b) In-Scope Facilities by Ownership Type and Size, 2010
Ownership Type
Investor-owned
Nonutility
Federal
State
Municipality
Other Political Subdivisions
Cooperative
Total
Small
3
8
o
0
18
1
8
38
Large
280
162
14
9
26
6
23
521
Total
283
171
14
9
44
7
31
559
% Small
1.1%
5.0%
oTo%
0.0%
40.3%
143%
25.8%
6.8%
a Numbers may not add up to totals due to independent rounding.
b The numbers of facilities and capacity are calculated on a sample-weighted basis.
Source: U.S. EPAAnalysis, 2010; U.S. DOE, 2007b; U.S. DOE, 2007c
2H.4.3 Facility Size
EPA also analyzed the estimated in-scope facilities with respect to their generating capacity. Facility size is
important because it partly determines the need of a given facility for cooling water, and its importance in meeting
electricity demand and reliability needs. The majority of facilities expected to be subject to the Proposed Existing
Facilities Rule (63 percent) are of relatively moderate size with capacity of less than 1,000 MW, while only a few
facilities (4 percent) have a capacity of greater than 2,500 MW (Figure 2H-5).
March 28, 2011
2H-15
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
Figure 2H-5: Number of In-Scope Facilities by Size (in MW), 2007 '
240
220
$ 200
+3
•= 180
B
160
140
120
100
80
60
40
20
0
214
137
80
33
18
V>
a. Numbers may not add up to totals due to independent rounding.
Vi n/i n/i «
cv c\' c\' cv
rv' rv' rv' rv'
<§><§><§><§>
> N" I/" I?"
Facility Size (MW)
b. The numbers of facilities and capacity are calculated on a sample-weighted basis using the original 316(b) Survey sample weights to account for non-
respondents.
Source: U.S. EPA Analysis, 2000; U.S. DOE, 2007b
2H.4.4 Geographic Distribution
To assess the potential regional impact of installation downtime - the requirement of facilities to temporarily shut
down their electricity generating operations due to the installation of certain compliance technologies - which
may accompany regulatory compliance, EPA assessed the distribution of in-scope facilities and their capacity
across NERC regions. As reported in Table 2H-7, considerable differences are present across the NERC regions
in terms of the number of in-scope facilities and their capacity and the percentages of facilities and capacity
represented by in-scope facilities. In the RFC region, in-scope facilities have the greatest regional capacity
representation (over 62 percent of total RFC capacity), followed by SERC (over 54 percent of total SERC
capacity); consequently, the potential downtime effect in these NERC regions is likely to be the greatest. In
ASCC, in-scope facilities have the smallest representation of total regional capacity (approximately 1 percent of
total ASCC capacity), followed by WECC (11 percent of total WECC capacity); therefore, the downtime effect in
these NERC regions is likely to be of least consequence. Not all of the in-scope facilities will experience
downtime; therefore, the percentage of facilities and regional capacity actually affected by downtime may be
overstated by this assessment.38
38 In particular, nuclear generating facilities are not expected to incur any additional downtime for installing technology for compliance
with the Proposed Existing Facilities Rule.
2H-16
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
Table 2H-7: Section 316(b) In-Scope Facilities and Capacity by NERC Region, 2010
NERC Region
ASCC
FRCC
FflCC
MRO
NPCC
RFC
SERC
SPP
TRE
WECC
TOTAL
Total Number
of Facilities
105
126
40
680
708
899
912
286
230
1,407
5,393
Facilities
IlllljnnSc
Number
1
24
3
49
60
172
147
31
43
27
559
ope**'
"/ooffotai
in Region
1.0%
19.2%
7.5%
7.3%
8.5%
19.2%
16.1%
10.8%
18.7%
1.9%
10.4%
Ci
Total Capacity
2,163
60,457
2,674
53,467
78,757
248,159
288,625
63,221
93,789
196,480
1,087,791
ipacity (MW)
... .. .JtonSc
MW
28
30,923
1,086
20,512
39,348
154,077
156,807
24,634
41,840
21,571
490,827
ope**'
"/ooffotalin
Region
1.3%
51.1%
40.6%
38.4%
50.0%
62.1%
54.3%
39.0%
44.6%
11.0%
45.1%
a. Numbers may not add up to totals due to independent rounding.
b. The numbers of facilities and capacity are calculated on a sample-weighted basis.
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2007b
2H.4.5 Waterbody and Cooling System Type
As reported in Table 2H-8, most of the in-scope facilities draw water from a freshwater river (306 facilities or
nearly 55 percent), followed by lakes or reservoirs (117 facilities or 21 percent) and estuaries ortidal rivers (83
facilities or nearly 15 percent). The table also shows that most of the in-scope facilities (355 facilities or over 63
percent) employ a once-through cooling system.
Table 2H-8: Number of In-Scope Facilities by Waterbody and Cooling System Type3
Waterbody Type
Estuary /Tidal River
Ocean
Lake/Reservoir
Freshwater Stream/River
Great Lake
Total
Recirc
Number
5
0
36
102
4
148
ulating
%'of
Total
3.5%
0.0%
24.7%
69.2%
2.7%
100.0%
Once-Tl
Number
69
9
73
166
37
355
trough
%"of
Total
19.5%
2.6%
20.5%
46.9%
10.4%
100%
Comb
Number
8
0
7
32
2
49
nation
%"of
Total
16.3%
0.0%
14.3%
65.3%
4.1%
100%
Ot
Number
1
0
1
5
0
7
tier
%"of
Total
14.3%
0.0%
14.3%
71.4%
0.0%
100%
To
Number
83
9
117
306
43
559
talb
% of Total
14.9%
1.7%
21.0%
54.7%
7.7%
100.0%
a. The numbers of facilities and capacity are calculated on a sample-weighted basis.
b. Numbers may not add up to totals due to independent rounding.
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2007b
2H.S Industry Trends
Deregulation, along with several environmental regulations and programs, has had a significant impact on the
electric power industry in recent years. Section 2H.5.1 discusses the current status of industry deregulation,
Section 2H.5.2 discusses air emissions regulations, Section 2H.5.3 discusses renewable portfolio standards, and
Section 2H.5.4 discusses carbon emissions regulations, all of which have affected and/or will affect the electric
power industry.
2H.5.1 Current Status of Industry Deregulation
The electric power industry has been evolving from a highly regulated industry with traditionally-structured
electric utilities to a less regulated, more competitive industry. Several key pieces of Federal legislation have
made the changes in the industry's structure possible. The industry has traditionally been regulated based on the
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premise that the supply of electricity 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. During the last two decades, the
relationship between electricity consumers and suppliers has undergone substantial change, as governments and
regulatory agencies recognized that electricity generation does not meet the definition of a natural monopoly. As a
result, substantial steps have been undertaken to promote competition, thereby achieving better electricity
production efficiency among electricity generators, while recognizing that the delivery of electricity via
transmission and distribution systems does remain within the definition of a natural monopoly. A key step in this
effort is the required unbundling of the traditional vertically integrated electric power business, with the electricity
generation business (and therefore the electricity generating assets) being separated from the electricity
transmission and distribution business. Electricity restructuring has two essential aspects: wholesale access and
retail access. Wholesale access refers to the ability of electric power generating entities - utilities and independent
power producers - to access transmission systems to compete for wholesale markets, i.e., distribution utilities and
independent marketers buying and selling electricity. Retail access refers to the ability of marketers and retailing
businesses of utilities to obtain access to distribution systems to sell electricity to end-use consumers, thereby
introducing consumer choice of electricity supplier (or retail choice).
The initial actions promoting competition in the wholesale electric power markets began with the Public Utility
Regulatory Policies Act of 1978 (PURPA), which established business terms by which certain nonutility
electricity-generators - "qualifying facilities" or QFs - could sell electricity to utilities. Later, the Energy Policy
Act of 1992 (EPACT) made it easier for nonutilities to enter the wholesale electricity market by creating a new
category of nonutility power producers - exempt wholesale generators or EWGs - which were exempt from the
Public Utility Holding Company Act of 1935 (PUHCA) regulation (EEMCTF, 2007).39 In 1996, the Federal
Energy Regulatory Commission (FERC) issued Order 888, promoting wholesale electric competition, by ensuring
non-discriminatory open access transmission service, and, in some states, the introduction of retail choice. Order
888 also established guidelines for the formation of independent system operators (ISOs), independent, federally
regulated entities established to coordinate regional transmission in a non-discriminatory manner.
Nearly a decade later, the Energy Policy Act of 2005 (EPAct 2005) repealed the original PUHCA of 1935, while
enacting provisions to encourage investment in energy infrastructure and transfer certain consumer protection
oversight authorities from the Security and Exchange Commission (SEC) to FERC and the states. Specifically,
EPAct 2005 enacted a new PUHCA (PUHCA of 2005), which gives FERC, as opposed to SIC, jurisdiction over
holding companies. EPAct 2005 also modified PURPA of 1978, removing some pricing requirements that had
resulted in consumers paying above-market prices for some electricity. In addition, EPAct 2005 created the
Electric Reliability Organization (ERO), now certified as the NERC, to enforce mandatory electric reliability rules
on all users, owners, and operators of the transmission systems (FERC, 2006).
Key Changes in the Electric Power Industry Structure
Industry deregulation has already changed and continues to 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 competition in the generation sector of the industry. Increased
competition has resulted in a separation of power generation, transmission, and retail distribution services.
39 PUHCA of 1935 was passed by the United States Congress to facilitate regulation of electric utilities, by either limiting their
operations to a single state, and thus subjecting them to effective state regulation, or forcing divestitures so that each company became
a single integrated system serving a limited geographic area. In addition, PUHCA of 1935 required holding companies to obtain
permission from the Securities and Exchange Commission (SEC) prior to engaging in a non-utility business and further required that
such businesses be kept separate from the regulated businesses.
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Utilities that provide transmission and distribution services continue to be regulated and are required to
divest their generation assets. In the deregulated framework, entities that generate electricity are no longer
subject to rate regulation and do not operate in protected franchise markets.
> 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 consumers continue to receive power through their
local distribution company (LDC), retail competition has allowed some consumers to select the company
that generates the electricity they purchase.
> Electricity prices: Under the traditional system, State and Federal authorities regulated many aspects of
utilities' business operations, including, in particular, their prices. Electricity prices were determined
administratively for each utility, based on the cost of producing and delivering power to customers and a
reasonable rate of return on invested capital (i.e., under the cost-of-service framework). As a result of
deregulation, competitive market forces set prices for generated electricity. Buyers and sellers of power
negotiate through power pools or one-on-one to set the price of electricity. As in all competitive markets,
prices reflect the interaction of supply and demand for electricity. During most time periods, the price of
electricity in a given competitive wholesale electricity market (e.g., an integrated dispatch region) is set
by the generating unit with the highest energy production cost that is dispatched to meet spot market
electricity demand - i.e., the unit with the highest production cost determines the "marginal cost" of
production and therefore the short-run energy price (Beamon, 1998).
New Industry Participants
As discussed above, PURPA and EPACT set business terms by which nonutility generators - QFs and EWGs,
respectively - could enter the wholesale power market. Under PURPA, utilities are required to buy power that is
produced by QFs (usually cogeneration or renewable energy) in their service area at a price equal to the avoided
production cost of a buying utility. EPACT did not require utilities to purchase power from EWGs. Instead,
EPACT gave FERC the authority to order utilities to provide access to their transmission systems on a case-by-
case basis. However, access to the systems proved to be slow and burdensome. In response, FERC issued Order
888, which provides open access to the transmission systems by utilities that have filed open-access transmission
tariffs by a specific deadline (OATTs). Furthermore, in 1999, FERC issued Order 2000, calling for the
development of Regional Transmission Organizations (RTOs), which independently control and operate the
transmission systems (EEMCTF, 2007).40
State Activities
The current status of electricity restructuring varies across states. Out of 50 states, 22 had initiated efforts to
design restructured electricity markets and pass enabling legislation. However, eight of these 22 states - Virginia,
Arkansas, New Mexico, Arizona, Nevada, California, Oregon, and Montana - experienced difficulties during the
transition to a competitive electricity market, such as lack of competition for residential customers and substantial
rate increases that have occurred or are anticipated to occur; consequently, these eight states suspended the
restructuring process. As of January 2010, only 14 states and the District of Columbia were operating with some
degree of competitive wholesale and retail electricity markets, in which some or all of the energy portion of the
40 RTO is similar to ISO, with the main difference being the ability of RTO control and monitor of the electric power transmission
system over a wider area across state borders.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2H: Electric Power Industry Profile
retail electricity price is determined in a deregulated market.41 The remaining 28 states have not introduced any
electricity restructuring legislation. Figure 2H-6, provides a national map of the status of electricity restructuring.
The 36 states in which electricity generation remains regulated under the cost-of-service framework, host 3,636
facilities (67 percent of all electric power generating facilities in the United States) and 693 GW of generating
capacity (64 percent of total generating capacity in the United States) (U.S. DOE, 2007b).
The state of restructuring of the electric power industry is an important factor to consider when assessing the
impact of the proposed Existing Facilities Regulation on in-scope electric power generating facilities and
electricity consumers discussed in Chapter 5: Cost Impact Analysis - Electric Power Generating Facilities. In
particular, the degree of competition affects the ability of in-scope facilities to pass cost increases to consumers
via electricity rate increases, and consequently, affects their profitability and business viability (for more detail
see Chapter 5: Cost Impact Analysis - Electric Power Generating Facilities). Most of the in-scope Electric
Generators (316 out of 559 or 57 percent) are located in the states where electricity generation remains regulated
under the cost-of-service framework; these facilities account for a large share of total in-scope generating capacity
(293 GW out of 514 GW or 57 percent) and total in-scope generation (1,568 TWh out of 2,646 TWh or 59
percent).42'43 EPA judges that these facilities should be able to recover any increases in their production costs
resulting from compliance with the Proposed Existing Facilities Rule through higher electricity rates approved by
utility regulatory authorities. The other 242 in-scope facilities (43 percent) are located in states in which
electricity generation is deregulated and cost recovery is less certain; these facilities account for approximately
221 GW of total in-scope generating capacity (43 percent) and 1,078 TWh of total in-scope generation (41
percent) (U.S. DOE, 2007b).
41 Maine, New Hampshire, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, Pennsylvania, Delaware, Maryland,
Ohio, Michigan, Illinois, Texas, and the District of Columbia.
42 EPA developed the estimates of the number and characteristics of facilities expected to be within the scope of the Proposed Existing
Facilities Rule, based on the original 316(b) survey facility sample weights. These weights provide comprehensive estimates for the
total of expected in-scope facilities based on the full set of facilities sampled in the Section 316(b) 2000 Industry Surveys. See
Chapter 3: Development of Costs for Regulatory Options sad Appendix 3. A: Use of Sample Weights in the Proposed Existing
Facilities Rule Analyses for further discussion of the sample weights used in this analysis.
43 Capacity values are from the 2007 EIA-860 database. EPA calculated generation values as a 5-year average (2003-2007) using
generation values from the EIA-906/920 database. In using the year-by-year generation values to develop an average over the data
years, EPA set aside from the average calculation, generation values that are anomalously low. Such low generating output would
likely result from a generating unit being out of service for maintenance.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 2H: Electric Power Industry Profile
Figure 2H-6: Electricity Restructuring by State as of January 2010
Electricity Restructuring by State
Source: U.S. DOE, 2009d
2H.5.2 Air Emissions Regulations
A number of recent air emission regulations significantly affect electric power generators. Under these
regulations, power generators must meet emission limits by physically reducing emissions via air emission control
technology or adjusting operations to reduce emissions (e.g., using lower sulfur coal), and/or by purchasing
emissions allowances that permit release of pollutant emissions. These programs have significantly reduced
emissions of sulfur dioxide (SO2) and nitrogen oxides (NOx) from electricity generation. In some instances, these
programs have caused, or are expected to cause in the future, relatively substantial changes in electric power
sector operations, including increased use of lower pollution fuels, repowering of existing production capacity
(e.g., converting natural gas-based steam capacity to a more energy efficiency combined cycle operation, which
includes a steam and non-steam electricity production capability), accelerated development of new capacity, and
earlier retirement of older, higher air pollution-intensive capacity for which substantial investments to reduce
emissions are not economic to undertake. This Proposed Rule will overlap with these ongoing air emission
regulatory programs in requiring further changes to facility operations and further affecting the economics of
power production.
In 1995, Phase I of the Acid Rain Program was implemented to achieve significant environmental and health
benefits by reducing SO2 and NOx emissions and ambient concentrations. The Program affects over 2,000 electric
utility facilities powered by coal, oil, or natural gas. The Program was the first to implement an allowance trading
program in the United States. Instead of the standard command and control regulatory approach, the allowance
trading program is market-based, allocating SO2 emission credits to each utility and allowing the credits to be
bought, sold, or banked (as long as emissions levels are met) for future use. The Acid Rain Program allows
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2H: Electric Power Industry Profile
flexibility in selecting the most cost-effective approach to reduce emissions. While allowing flexibility in the
approach to reducing emissions, the Program did not implement an allowance trading system for NOx emissions.
During Phase II of the program (starting in 2000), the Program set a cap on the number of allowances, ensuring
achievement of the intended reductions in pollutant emissions (U.S. EPA, 2009c).
Similar to the Acid Rain Program, the Clean Air Interstate Rule (CAIR) was promulgated to further reduce SO2
and NOx emissions in 28 eastern states and the District of Columbia through an allowance trading program. On
July 11, 2008, the U.S. Court of Appeals for the D.C. Circuit ruled to vacate CAIR. However, on December 23,
2008, the U.S. Court of Appeals issued a new ruling that repealed the vacatur and instead, remanded CAIR,
noting that: "allowing CAIR to remain in effect until it is replaced by a rule consistent with our opinion would at
least temporarily preserve the environmental values."44 EPA was tasked with modifying CAIR to address the
issues raised by the Court in its July 11th decision (U.S. EPA, 2010d).
Other rulemakings are based in part on the expected emissions reductions from CAIR.45 Promulgated in 2005,
CAIR established Phase I caps for NOx and SO2 for 2009 and 2010, respectively, and Phase II caps for NOx and
SO2 for 2015. For SO2 allowances, CAIR allocated the allowances that are used within the Acid Rain Program.
However, since a NOx trading program was not in place in the Acid Rain Program, EPA provided new NOx
emission allowances under CAIR. Each of the 28 eastern states and the District of Columbia were allowed to
achieve emissions reductions by their own selected method. Most are expected to achieve the required levels by
mandating reduced emissions from the power generation sector (U.S. EPA, 2009a).
On July 6, 2010, EPA proposed the Transport Rule to replace CAIR. If finalized, the Transport Rule will require
31 states and the District of Columbia to reduce electric power sector emissions that contribute to ozone and fine
particle pollution in these and other states. Twenty-eight states would be required to reduce both annual SO2 and
NOx emissions. In addition, 26 states would be required to reduce NOx emissions during the summer, because
these emissions contribute to downwind states' ozone pollution during the summer "smog season." EPA believes
that by reducing the emissions from the upwind states, the Transport Rule would help downwind states attain air
quality standards, specifically the 24-hour PM25 standards established in 2006, the 1997 annual PM25 standards,
and the 1997 ground-level ozone standard. EPA proposed that the initial reductions occur during 2012 and 2013,
with the second phase, which calls for additional emission reductions in some states, starting in 2014 (U.S. EPA,
2010a).46
The Clean Air Mercury Rule (CAMR), also promulgated in 2005, planned to build on CAIR to significantly
reduce mercury emissions from coal-fired power facilities. However, on February 8, 2008, the D.C. Circuit Court
vacated CAMR. Additionally, on February 6, 2009, the Department of Justice, on behalf of EPA, asked the
Supreme Court to dismiss EPA's request that the Supreme Court review the D.C. Circuit Court's vacatur of
CAMR. In addition, on February 23, 2009, the Supreme Court denied the Utility Air Regulatory Group's request
for a review of the D.C. Circuit Court's decision (U.S. EPA, 2009c). In CAMR, states and tribes were given the
option to adopt an allowance trading system, similar to the Acid Rain Program system, or to adopt emission
regulations that would achieve CAMR requirements. During the first phase of the program, emissions were
expected to be reduced by taking advantage of mercury reductions due to CAIR. During the second phase, coal-
fired power facilities were to be subject to another mercury emissions cap. New facilities (construction starting on
or after January 30, 2004) would also be subject to stringent new source performance standards (U.S. EPA,
2009a).
44 United States Court of Appeals for the District of Columbia Circuit, No. 05-1244
45 Emissions reductions under the national ambient air quality standards (NAAQS) and the new source review (NSR) program are
dependent in part to emissions reductions from CAIR.
46 For more information on the Transport Rule see http: //www.epa. go v/airtransport/actions.html#jul 10.
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Because of the vacatur of CAMR's mercury emissions requirements, by March 2009, approximately one-half of
the states (including most states in the Northeast) had moved to implement mercury emissions reduction programs
independent of CAMR. However, without Federal monitoring requirements, many states have made or will have
to make modifications to their guidelines. The other states without mercury emissions standards currently allow
electricity generating units to emit mercury without limitations ((U.S. DOE, 2009d).
Also building off CAIR, the Clean Air Visibility Rule (CAVR), finalized on June 15, 2005, requires emission
controls to reduce SO2 and NOx emissions using Best Available Retrofit Technology (BART) for industrial and
power generation facilities. Results of an EPA analysis showed that the CAIR allowance trading program for the
power generation sector would result in greater visibility improvements than the BART requirements would
provide. Therefore, states that are active in the CAIR allowance trading program are allowed to apply CAIR
controls as an alternative for BART requirements under CAVR.47
Much like the requirements of the Proposed Existing Facilities Regulation, compliance with the above mentioned
air emissions standards may require the owners of existing plants to install compliance technology and, as a
consequence, change their operating practices. For example, according to the North American Electric Reliability
Corporation's (NERC) report, 2008 Long-Term Reliability Assessment, plants may need to be retired earlier than
expected or need to be retrofitted with compliance technology, which can increase the generating facilities' own
need for electricity and reduce the net electricity available for delivery to consumers. If these changes are
necessary, according to NERC, "capacity margins would be reduced, increasing the need for more resources to
meet resource adequacy requirements" (NERC, 2008).
2H.5.3 Renewable Portfolio Standards
In many states, Renewable Portfolio Standards (RPS) require electric utilities to generate a certain percentage of
power from renewable sources. States have increasingly adopted RPS since the late 1990s: currently 31 states and
Washington B.C. have mandatory RPS policies in effect, and four have voluntary utility commitments (PCGCC,
2009a). While the focus of most RPS activity in the United States has been at the state level, the U.S. House of
Representatives and Senate have each, at different times, passed versions of Federal RPS; however, a Federal RPS
has not yet been signed into law (U.S. DOE, 2008c). Typically, RPS aim to achieve 1 to 5 percent renewable
power generation in the first year and then require increasing percentages every year thereafter, with most aiming
for around 15 to 25 percent renewable power generation (PCGCC, 2009a). The definition of renewable sources
differs among states. Some states allow only new renewables (renewable sources built after a certain year) while
some allow all renewables, new and existing. Some RPS also involves credit trading programs, similar to the
programs used in the air emissions regulations mentioned in Section 2H.5.2. Investors and power generators make
the decision on what source of renewable energy to acquire or whether to purchase additional credits. Eventually,
RPS should result in increased competition, efficiency, and innovation among the renewable energy sectors and
should distribute renewable energy at the lowest possible cost (AWEA, 1997).
2H.5.4 Carbon Dioxide Emissions Regulations
Though not as prevalent as programs regulating emissions of SO2 and NOx, carbon dioxide (CO2) emissions
reduction programs are beginning to surface among states and on the national agenda. In the absence of federal
action, five states48 have adopted CO2 caps or offset requirements for the power generation sector and regional
cap and trade programs have begun to be implemented. Both the Northeast Regional Greenhouse Gas Initiative
47 For more information on CAVR, see Regulatory Impact Analysis for the Final Clean Air Visibility Rule or the Guidelines for Best
Available Retrofit Technology (BART) Determinations Under the Regional Haze Regulations available at:
http://www.epa.gov/visibilitv/pdfs^art ria 2005 6 15.pdf (U.S. EPA, 2005)
48 Oregon, Washington, California, Montana, and Illinois (PCGCC, 2009b).
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(RGGI)49 and the Western Climate Initiative (WCI)50 were formed by groups of states in a given region to achieve
reductions in CO2. The RGGI program held its first auction of CO2 credits on September 25, 2008, with the
seventh auction planned for March, 2010. According to RGGI, these states have capped and will reduce
CO2 emissions from the power sector by 10 percent by 2018. The WCI looks to reduce greenhouse gas emissions
to levels 15 percent below 2005 emissions by 2020 (U.S. DOE, 2009d). While several states are beginning to
develop similar programs, Congress and the EPA are still deliberating on whether and how to implement
mandatory CO2 reduction programs at the federal level.
Looking to the future, national CO2 emissions cap and trade programs may be administered at the federal level. In
addition, state and federal programs are likely to require increased reliance on renewable energy for electricity
generation.
2H.6 Industry Outlook
Section 2H.6.1 presents a summary of forecasts from the Annual Energy Outlook 2009 (AEO2009) (U.S. DOE,
2009d).
2H.6.1 Energy Market Model Forecasts
This section discusses forecasts of electric energy supply, demand, and prices based on data and modeling by the
EIA and presented in the AEO2009 (U.S. DOE, 2009d). AEO2009 projects future market conditions through the
year 2030, 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), reflecting all Federal, State, and local laws and regulations in effect as of November
2008.51
Electricity Demand
AEO2009 projects electricity demand to grow by approximately 0.5 percent annually between 2007 and 2030.
This growth is driven by an estimated 1.1 percent annual increase in commercial sector demand for electricity
stemming from increases in demand for office equipment and growth in commercial floor space. Residential
demand is expected to increase by 0.4 percent annually over the same forecast period; this increase is
driven by a growing number of U.S. households, greater use of personal computers, and a shift to larger
television formats; however, energy efficiency improvements offset this increased demand to a degree. EIA
expects electricity demand from the industrial sector to increase by 0.1 percent annually.
Capacity Retirements
AEO2009 projects fossil fuel-fired generation to be the greatest share of capacity retirement. Overall, EIA
forecasts 25.8 thousand MW of total fossil-steam capacity retirements between 2007 and 2030, including 18.3
thousand MW of oil and natural gas fired steam capacity. An additional 4.4 thousand MW of nuclear facility
capacity are expected to retire during this period.
49 The RGGI consists of Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode
Island and Vermont.
50 The WCI consists of Arizona, California, Montana, New Mexico, Oregon, Utah, and Washington.
51 The electricity market analysis undertaken by EPA for the analysis of this rule is based on AEO projections (see Chapter 6: Electricity
Market Model Analysis).
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Capacity Additions
Due to the estimated growth in electricity demand and the need to offset the retirement of 30 GW of existing
capacity, AEO2009 expects that 259 thousand MW of new generating capacity will be needed by 2030. AEO2009
projects that these capacity requirements will be met by natural gas, coal, renewable energy, and nuclear power
sources - in order of expected contribution. Of the new capacity projected to come on line between 2007 and
2030, approximately 53 percent is projected as natural gas-fired capacity, 22 percent is expected to be fueled by
renewables, 18 percent by coal-fired plants, and 5 percent by nuclear energy. The increase in renewable capacity
results in part from Renewable Portfolio Standards, as described in Section 2H.5.3.
Electricity Generation
AEO2009 projects increased electricity generation from both natural gas- and coal-fired facilities to meet growing
demand and to offset lost capacity due to facility retirements. AEO2009 projects that coal-fired facilities will
remain the largest source of generation throughout the forecast period. Natural gas-fired power facilities are
expected to make up much of the new capacity over the next ten years, and coal-fired generation is predicted to
decrease slightly between 2007 and 2030, reducing its share of total generation from 49 percent to an estimated 47
percent. The anticipated decrease in the share of coal generation results from concern regarding greenhouse gas
emissions and the potential for emissions limits on CO2. The share of total generation associated with natural gas-
fired technologies is projected to stay approximately the same, increasing to 21 percent in 2027 but decreasing
back to the 2007 level of 20 percent by 2030. The share of total generation from renewable power sources is
expected to more than double over the analysis period, eventually accounting for 14 percent of total generation in
2030. Nuclear power generation, however, is expected to decrease from 19 percent to 18 percent as a share of
total generation.
Electricity Prices
According to AEO2009, EIA expects the average inflation-adjusted price of electricity to stabilize in 2010 at an
annual average of 9 cents per kilowatt-hour ($2007). After 2010, electricity prices are expected to stabilize and
then rise steadily after 2015. This steady rise is due to the need for new capacity and the requirement for more
renewables due to state mandates. Retail electricity prices are expected to reach an average price of 10.4 cents per
kilowatt-hour in 2030.
2H.7 Glossary
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.
(http://www.eia.doe.gov/cneaf/electricity/page/prim2/chapter2.html)
Combined Cycle Turbine: An electric generating technology in which electricity is produced from otherwise lost
waste heat exiting from one or more gas (combustion) turbines. 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 portion of an electric system that is dedicated to delivering electric energy to an end user.
Electricity Available to Consumers: Power available for sale to customers. Approximately 8 to 9 percent of net
generation is lost during the transmission and distribution process.
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 the turbine. The hot gases
expand to drive the generator and are then used to run the compressor.
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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.
Intermediate load: Intermediate-load generating units meet system requirements that are greater than baseload but
less than peakload. Intermediate-load units are used during the transition between baseload and peak load
requirements, (http://www.eia.doe.gov/cneaf/electricity/page/prim2/chapter2.html)
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.
Kilowatt-hours (kWh): A measure of electric energy generated or consumed. The amount of energy generated
from one Kilowatt of fully utilized capacity during one hour. A Megawatt-hour (MWh) is also an energy measure
and equals 1,000 Kilowatt-hours.
Load: Refers to either demand for electricity or total electricity generated.
Megawatt (MW): Unit of power equal to one million watts. A watt is a measure of power, or the potential to
produce or consume electricity (or other energy).
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.
Net Generation: Gross generation minus electricity used by the electricity generating facility (or company).
Nonutility: A corporation, person, agency, authority, or other legal entity or instrumentality that owns electric
generating capacity and does not produce or sell electricity under a rate-regulation framework. Nonutility power
producers include qualifying cogenerators, qualifying small power producers, and other nonutility generators
(including independent power producers) without a designated franchised service area that do not file forms listed
in the Code of Federal Regulations, Title 18, Part 141. (http://www.eia.doe.gov/emeu/iea/glossary.html)
Other Prime Movers: Methods of power generation other than steam turbines, combined cycles, gas combustion
turbines, internal combustion engines, and hydroelectric generating units. Other prime movers include:
geothermal, solar, wind, and biomass.
Peakload: A peakload generating unit, normally the least energy efficient of the three unit types, is used to meet
requirements during the periods of greatest, or peak, load on the system.
(http://www.eia.doe.gov/cneaf/electricity/page/prim2/chapter2.html)
Prime Movers: The engine, turbine, water wheel or similar machine 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).
Reliability: Electric system reliability has two components: adequacy 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. (http:/www.eia.doe.gov/cneaf/electricity/epavl/glossary.html)
Spinning Reserve: Reserve generating capacity running at a zero load and synchronized to the electric system. It is
the unloaded section of synchronized generation that is able to respond immediately to serve load.
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
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 2H: Electric Power Industry Profile
mechanical energy is used to power electric generators, including combined cycle electric generating units that
convert the mechanical energy to electricity.
System: Physically connected generation, transmission, and distribution facilities operated as an integrated unit
under one central management or operating supervision.
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 territories, or Puerto Rico for the generation, transmission, distribution, or
sale of electric energy primarily for use by the public, and that 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. Utility power generators produce
and sell electricity at cost-based prices that are set under the traditional electricity rate regulation framework.
http://www.eia.doe.gov/emeu/iea/glossary.html)
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 3: Development of Costs for Regulatory Options
3 Development of Costs for Regulatory Options
In estimating the total cost of the proposed section 316(b) regulation for existing Electric Power Facilities (or
Electric Generators) and Manufacturers, EPA developed costs both to facilities complying with the Proposed
Section 316(b) Existing Facilities Rule (the Proposed Existing Facilities Rule, Proposed Rule, or Existing
Facilities Rule) and to the State and federal governments to administer this rule. This chapter presents the details
of the methodology and data inputs used in developing compliance costs for the regulatory options considered by
EPA for the existing facilities rule. The discussion focuses specifically on cost development for the following
three regulatory options that are presented in this chapter and the following chapters:
> Option 1: IM Everywhere
> Option 2: IM Everywhere and EM for Facilities with DIF > 125 MGD
> Option 3: I&E Mortality Everywhere
The following sections of this chapter describe:
1. The development of costs to existing facilities for complying with these regulatory options,
including the compliance outlays of certain administrative activities incurred by complying
facilities (Section 3.1)
2. The development of costs to State and Federal governments for administering the regulatory
options (Section 3.2)
3. The development of costs to new units, reflecting the cost of installing EM technology for newly
constructed generating units or increasing electric generating capacity at existing units (Section
3.3).
In developing compliance costs for Electric Generators and Manufacturers, and in performing the analysis of the
Existing Facilities Rule options, generally, EPA followed closely the analysis approaches and impact evaluation
concepts used in the analysis for the previous CWA 316(b) regulatory analyses, and to the extent possible relied
on the same data sources.52
3.1 Development of Costs to Existing Facilities
EPA estimated costs to facilities for complying with the requirements of the Proposed Rule and the alternative
regulatory options. The following discussion reviews four overall aspects of compliance cost development:
1. Determining the set of facilities potentially installing compliance technologies
2. Development of facility-level costs, which are broken into four main components:
• The cost of installing and operating compliance technology
• The cost of energy penalties
• The cost of installation downtime
• The cost of administrative activities.
3. Development of an estimated facility compliance schedule based on the period of years during
which facilities would be required to meet regulatory requirements, which may vary depending
For more details on these analyses, see Chapter Bl: Summary of Compliance Costs in the suspended 2004 Economic and Benefits
Analysis for the Final Section 316(b) Phase II Existing Facilities Rule report (U.S. EPA, 2004a) and Chapter Cl: Summary of Cost
Categories and Key Analysis Elements for Existing Facilities in the 2006 Economic and Benefits Analysis for the Final Section 316(b)
Phase III Existing Facilities Rule report (U.S. EPA, 2006).
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
on the specific compliance technology adopted by a facility, facility type, and their NPDES
permit expiration dates. This schedule supports analysis of the timing of compliance costs,
benefits, and the potential impact on electricity supply resulting from shutdown of generating
units during compliance technology installation.
4. Development of total costs to complying entities for each of the regulatory options.
3.1.1 Components of Facility-Level Compliance Costs: Installing and Operating Compliance
Technologies
The three regulatory options would apply to existing facilities - Electric Generators and Manufacturers - with a
design intake flow for cooling water exceeding 2 MGD (for more details on application of this Rule, see Chapter
1: Introduction). The following two sections describe the sets of facilities (potentially in-scope facilities) for
which costs were estimated in order to assess the total impact on the electric power generating and manufacturing
sectors.
Determination of In-Scope Manufacturers
EPA relied on information on cooling water systems and intake structures already in place collected in the Section
316(b) Industry Surveys (the 1999 Industry Screener Questionnaire (ISQ) and the 2000 Detailed Industry
Questionnaire (DQ)) to estimate the number of manufacturing facilities that would potentially be in-scope of the
regulatory options considered for the Proposed Existing Facilities Rule. Based on the criteria summarized in
Chapter 1, EPA estimated that 592 manufacturing facilities would be potentially in-scope of the existing facilities
rule.
Because the DQs were sent to a sample of the manufacturing industries that use cooling water, the respondents
were assigned sample weights designed to represent other facilities that were not covered in the survey. Survey
responses indicating that a facility was in-scope for the regulation are thus assumed to represent a sample-
weighted number of facilities in the industry that would also be in-scope. Therefore, the compliance technology
requirements and associated costs are weighted to represent the costs to these implicitly analyzed facilities. For
more details on the manufacturers sample weighting, see Appendix 3. A: Use of Sample Weights in the Proposed
Existing Facilities Rule Analysis.
EPA determined that out of 592 Manufacturers, 72 facilities were baseline closures; therefore, the Agency
excluded these Manufacturers from the cost and economic impact analyses.53 EPA determined that 27 (weighted
estimate) in-scope Manufacturers already have cooling towers installed and meet the water intake velocity
requirement (see Chapter 1: Introduction), and thus will not have any further technology requirements under the
Proposed Rule. These facilities would still incur administrative costs required to demonstrate compliance with the
regulation.
Determination of In-scope Electric Generators
For the analysis of in-scope Electric Generators, EPA used information on cooling water systems and intake
structures already in place, from 656 in-scope facilities that responded to the 2000 Section 316(b) Industry Short
Technical Questionnaire (STQ) or the Detailed Industry Questionnaire (DQ): 284 facilities responded to the DQ
and 372 facilities responded to the STQ. EPA also used photos and maps to supplement the questionnaire-based
information where applicable and possible. Based on more recent data on these facilities from the Department of
Energy's Energy Information Administration (EIA), EPA determined that 37 out of 656 facilities have terminated
their steam operations since the time of the 316(b) Surveys and 15 out of 656 facilities are expected to retire
53 A baseline closure is a facility that shows materially inadequate financial performance in the baseline and is at substantial risk of
financial failure regardless of the 316(b) regulation (see Chapter 10: Manufacturers Impact Analysis for a more detailed discussion).
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
before the Rule's scheduled promulgation, i.e., 2012.54 An additional 39 facilities are projected to close by the
Integrated Planning Model (IPM), discussed in Chapter 6: Electricity Market Analysis ,55 All of these facilities
were set aside from the analysis for the Proposed Rule. In addition, EPA excluded 19 Electric Generators located
in California that use coastal and estuarine waters for power plant cooling. These facilities are already required by
the State of California to comply with standards at least as stringent as the Proposed Existing Facilities Rule and
thus are not expected to incur any compliance costs under any of the regulatory options considered in this
economic analysis.56 The Proposed Existing Facilities Rule analysis thus focused on the remaining 548 facilities.57
EPA determined that certain DQ and STQ Electric Generators would already meet compliance requirements
under specific regulatory options, based on their having a recirculating cooling water system in place and/or
meeting impingement and entrainment (I&E) mortality technology requirements in the baseline, as follows:
> Option 1: IM Everywhere. EPA estimates that 92 DQ and STQ Electric Generators already meet the IM
technology requirements of this Option and thus will not incur additional compliance technology costs.
Out of these 92 facilities, 39 have recirculating systems in their baseline and intake velocity less than or
equal to 0.5 feet per second. The other 53 facilities do not have recirculating systems in their baseline, but
have intake velocity equal to or less than 0.5 feet per second.
> Option 2: IM Everywhere and EM for Facilities with DIF > 125 MGD. EPA estimates that 58 DQ and
STQ Electric Generators already meet the requirements of this option and thus will incur no additional
compliance technology costs. Of these 58 facilities, 39 have recirculating systems in their baseline and
intake velocity less than or equal to 0.5 feet per second and therefore fully meet the proposed I&E
mortality requirements in the baseline and will incur no technology installation costs. The other 19
facilities have no baseline recirculating system, but have intake velocity equal to or less than 0.5 feet per
second and DIF less than or equal to 125 MGD.
> Option 3: I&E Mortality Everywhere. For this option, EPA estimates that 39 DQ and STQ facilities
already meet both the cooling tower and IM technology requirements and thus will not incur additional
compliance technology costs. These facilities have recirculating systems in their baseline and intake
velocity of less than or equal to 0.5 feet per second.
While these facilities will be subject to the requirements of the Proposed Rule, they are not expected to incur
compliance technology costs as outlined above for the various regulatory options analyzed. However, all of these
facilities would still incur administrative costs required to demonstrate compliance with the Proposed Rule.
In its analysis of facility-level cost and economic impacts, EPA estimated technology costs explicitly only for the
DQ facilities that do not already meet the proposed I&E mortality technology requirements in their baseline: 189
under Option 1, 210 under Option 2, and 218 under Option 3. Estimated technology costs and impacts for the
remaining 267, 280, and 291 STQ facilities under Options 1, 2, and 3, respectively, which do not already meet the
proposed performance requirements in their baseline operations, were assessed by application of sample weights
54 For this analysis, facilities that no longer operate steam capacity are considered retired for this analysis as long as any remaining water
intake is for purposes other than cooling.
55 For the purpose of this analysis, EPA identified a facility as fully closed if all of its steam electric generating units are reported as
retired in IPM even if one or more units' capacity is reported as only partially retired in IPM. For the cost analyses presented in this
report, EPA peformed an alternative analysis using a different unit closure defintion in which "partially retired" units were assumed to
be fully operating. The results of this alternative analysis are reported in a memorandum to the record.
56 Water intake velocity at these facilities must not exceed 0.5 feet per second and intake flow must not exceed the level commensurate
with the level attained by a closed-cycle wet cooling system.
57 These are non-retired Electric Generators that responded to either DQ or STQ, excluding coastal and estuarine Electric Generators
located in California. This number is not a weighted estimate. See Appendix 3.A: Use of Sample Weights in the Proposed Existing
Facilities Rule Analyses for information on facility-level weights.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
to the estimates developed for the explicitly analyzed DQ facilities. EPA developed and applied three sets of
sample weights according to the specific cost element or other facility characteristic for which the sample weights
are intended to provide estimates. Specifically, the Agency developed sample weights on three extrapolation
bases: (1) facility count, (2) electric generating capacity, and (3) design intake flow (DIP) (for a discussion on
weights development and application refer to Appendix 3. A: Use of Sample Weights in the Proposed Existing
Facilities Rule Analyses).
EPA also performed a market-level assessment of cost and economic impacts (see Chapter 6: Electricity Market
Analysis). For this analysis, EPA used technology costs that were estimated for 533 existing Electric Generators
that: (1) responded to the DQ andSTQ, (2) lack a recirculating system and do not meet IM requirements in their
baseline operation, and (3) were included in the Market Model Analysis system. EPA recognizes that the cost
estimates for the STQ facilities contain greater uncertainty than those estimated for the DQ facilities. However,
because the market-level analysis requires compliance cost data for each individual in-scope facility and cannot
be undertaken on the basis of a sample of facilities with application of sample weights to the in-scope population,
this approach was necessary.
As described in Chapter 6: Electricity Market Model Analysis, EPA performed a Market Model Analysis using
the Integrated Planning Model (IPM), to assess the economic impact of the regulatory options within the context
of regional and national electricity markets. The market model analysis baseline, which involves a projection of
electricity markets and facility operations into the future, indicated that some of the currently operating in-scope
electric generating units, and in some instances, the whole facilities, would close by the time those facilities
would be expected to begin achievement of compliance with the proposed regulation. Consequently, for the
Market Model Analysis, EPA treated these generating units as baseline closures and set them aside from that
analysis. In addition, EPA removed facilities that IPM projects to retire all steam generating units from all other
cost and economic impact analyses. Because compliance cost estimates were developed at the level of an intake
structure and EPA was not able to link intake structures to the generating units, the Agency kept facilities
projected to retire some but not all of their steam generators in the non-IPM analyses.
3.1.2 Components of Facility-Level Compliance Costs: Installing and Operating Compliance
Technology
For each of the three regulatory options, EPA estimated compliance costs at manufacturing and power generating
facilities based on the extent to which current technologies already comply with the requirements of a given
option, and the additional technology that EPA estimated would be needed to meet requirements for that facility.
When EPA judged that a facility already had technology in-place that would meet the performance requirements
of a given regulatory option, EPA assigned no additional technology requirements or associated costs to that
facility.
The specific technologies, while varying across different regulatory options considered by EPA, reduce
impingement mortality and entrainment through one of two methods:
1. Exclusion through implementation of design and construction technologies to reduce IM
2. Flow reduction through conversion of cooling systems from once-through to re-circulating
operation to reduce the design intake flow and impingement and entrainment.
EPA developed the following costing modules for assessing model-facility compliance costs under the three
regulatory options:
> CT Cooling Tower
> #1 Add Fish Handling and Return System
> #3 Add New Larger Intake Structure with Fine Mesh, Handling and Return
> #4 Relocate Intake to Submerged Near-shore with passive fine mesh screen
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
> #5 Add Fish Barrier Net
> #8 Add Velocity Cap at Inlet
> #10.2 Modules 3 and 5
> #10.3 Module 1 plus Module 5
These costing modules are described in detail in the Technical Development Document for the Proposed Section
316(b) Existing Facilities Rule (EPA-821-R-11-003), hereafter referred to as the Technical Development
Document (TDD).
The major components of technology costs are:
> Capital costs. These costs include the cost of designing and installing the assigned compliance
technology. Facilities assigned cooling towers are assumed to incur these costs over two years starting
one year before the year in which they achieve compliance, discussed in Section 3, below. Facilities
assigned other compliance technologies incur all capital costs during the compliance year. Capital costs
may repeat during the compliance analysis period - assumed 30 years - based on the estimated service
life of the capital equipment.58
> Recurring O&M costs. These costs include regular annual maintenance and upgrading activities and
consist of fixed and variable costs. In addition, these costs include the cost of initial and follow-up
entrainment studies that Electric Generators and Manufacturers with average intake flow (AIF) exceeding
125 MOD are required to perform. Electric Generators with DIP exceeding 50 MOD are required to
conduct the initial entrainment study over a three year period starting six months after rule promulgation;
Manufacturers and all other Electric Generators are required to conduct the initial entrainment study over
a three year period starting a year and a half after promulgation of the Rule. Although these initial study
costs occur only once, they are included in the O&M cost pool. In addition, these facilities will submit
follow-up entrainment studies of one year duration every third year after completion of the initial
entrainment study (see the Technical Development Document).
In addition to these technology cost items, which were estimated as specific dollar values for each facility, EPA
also accounted for two additional technology-related cost and operating effects for facilities assigned compliance
technology:
1. Energy Penalty. Energy penalty effects arise from two factors: (1) an increase in auxiliary power
required to operate an assigned compliance technology and (2) a reduction in the energy
conversion efficiency of the power generating system, which occurs with operation of retrofitted
recirculating system compliance technologies. Depending on generating unit type and baseline
operating circumstances, the combination of these effects (referred to as the "energy penalty") is
assessed as (1) a reduction in the generated electricity that is available for sale or (2) an increase
in the production cost of sold electricity. The energy penalty effect is discussed in Section 3.1.3,
below.
2. Installation downtime. Installation of certain compliance technologies will require a one-time,
temporary downtime period for the facility; costs associated with this connection outage are
discussed in Section 3.1.4, below.
For further discussion on compliance cost development, see the Technical Development Document.
58 As discussed later in the report, 30 years is the analysis period used for all facility-related analyses and reflects the useful life of the
longest lived 316(b) compliance technology - cooling tower and certain IM technologies.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
3.1.3 Components of Facility-Level Compliance Costs: Energy Efficiency Penalty
Facilities assigned cooling towers will incur a permanent reduction in the electricity production efficiency of
affected power generating units: for a given level of energy input to the generating unit, the quantity of electricity
that is available to be sold for revenue or used beneficially by the power generator for onsite services (e.g.,
electricity for onsite offices) is reduced. As described above, the energy penalty assessed in this analysis includes
two effects:
1. Increased auxiliary power requirement. Cooling towers require some of the facility's power
output to operate the compliance technology (e.g., pumps and fans). This effect manifests itself as
a reduction in produced power that is available for sale, given a baseline level of power
generation. This effect is more substantial for cooling towers than for the IM technologies. For
the analysis of cooling tower installations, the auxiliary power requirement was assessed as a
percentage reduction in the generating capacity and electric generating output for any given level
of energy input. For the analysis of IM technologies, the cost of the auxiliary power requirement
was included in the estimated cost of technology operation and maintenance.
2. Reduction in unit generating efficiency. Operation of retrofitted recirculating or dry cooling
systems causes an increase in turbine back-pressure, which reduces the amount of electricity that
is produced by the electric generating unit for the same energy input. For this analysis, the
reduction in unit generating efficiency was also assessed as a percentage reduction in the
generating capacity and electric generating output for any given level of energy input.
The following sections explain the accounting for energy penalty effects for Electric Generators and
Manufacturers.
Electric Generators
EPA assessed the impact of the energy penalty effects differently for Electric Generators depending on the type of
generating unit affected and the unit's baseline operating circumstances in terms of capacity utilization:
> For generating units that operate at high capacity utilization (namely, nuclear units and base load fossil
fuel units with capacity utilization exceeding 62 percent), EPA assumed that the energy penalty will
manifest as a loss in generating capacity available for production of revenue. As a result, the financial
effect of the energy penalty is to reduce the revenue otherwise received by the generating unit, but with
no change in the cost of energy inputs to the generating unit.
> For units that operate at lower capacity utilization (i.e., less than or equal to capacity utilization rate of 62
percent), EPA assumed that the energy penalty effect can be offset by increasing the energy input to the
unit, thereby avoiding a loss in revenue. In this case, although the generating unit does not lose revenue,
the cost of generating electricity for sale from the unit will increase, and the financial effect is a reduction
in the operating margin for electricity sales from the affected unit.
Regardless of the method for accounting for the energy penalty effect, EPA combined the separate operating
effects to yield the total energy penalty effect as follows:
Total Penalty = Aux. Requirement + Eff. Loss + (Aux. Requirement * Eff. Loss) (3-1)
Where:
Total Penalty = Total percentage loss in generating unit production capability for a given level
of energy input to the generating unit
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
Auxiliary Power = Energy required for operation of the compliance system, as a percentage of
Requirement baseline generating unit capacity
Reduction in Unit = Reduction in generating unit energy conversion efficiency, as a percentage of
Generating Efficiency baseline generating unit capacity.
EPA estimates that increased auxiliary power requirements will range from 0.0013 to 9.11 percent59 of baseline
steam generating capacity for Electric Generators. EPA estimated unit generating efficiency loss as 1.5 percent
and 2.5 percent of baseline steam generating capacity for each non-nuclear and nuclear facility assigned a cooling
tower, respectively. As described above, EPA assumed that nuclear Electric Generators and fossil Electric
Generators with capacity utilization rate exceeding 62 percent will not to be able to increase their electricity
generation to make-up this efficiency loss on site and that other Electric Generators supplying to the grid would
have to increase their electricity production to ensure the adequate electricity supply. For these Electric
Generators, EPA accounted for the energy penalty as revenue loss - i.e., revenue was reduced by the amount of
the Total Penalty percentage as follows:
Adjusted Revenue = Baseline Revenue x (1 - Total Penalty Percentage) (3-2)
For all other in-scope Electric Power facilities, EPA assumed that the generating units would have sufficient
excess generating capacity to be able to make up the potential loss in electricity generation on site. For these
facilities EPA accounted for the energy penalty effect as an increase in fuel and other variable operation and
maintenance costs. The increased value of these costs was calculated as follows:
Fue,
In these calculations, the baseline revenue, fuel and variable O&M are assumed to be as of the year of compliance
(see Section 3.1.4, below, for discussion of revenue estimation and Section 3.1.4, for discussion of cost
estimation).
Manufacturers
While the calculation of the Total Penalty Percentage loss is the same for both Electric Generators and
Manufacturers, the economic value of this loss for Manufacturers is calculated differently. For Manufacturers,
EPA estimates that increased auxiliary power requirements range from 0 to 48.5 percent60 of baseline steam
generating capacity and estimated the same unit generating efficiency loss, 1.5 percent of baseline steam
generating capacity, for Manufacturers as estimated for Electric Generators
For Manufacturers, the economic value of energy is calculated in one of two ways, depending on the facility's
characteristics. If a Manufacturer sells electricity to the grid, the cost of the energy penalty is valued as a
reduction in revenue from electricity sales. If the manufacturer only generates power for its own use and does not
sell electricity to the grid, EPA assumed that the energy penalty's reduction of the facility's electricity generation
will be made up by purchasing an equivalent amount of electricity, effectively supplementing the facility's
reduced output to keep its total electricity usage constant.
Facilities reported revenue from electricity sales, if any, for 1996, 1997, and 1998 on the 2000 316(b) DQ. EPA
used the average of these reported values, adjusted for inflation to 2009, as the facilities' electricity sales revenue
59 Of the 559 Electric Generators, only 8 have increased auxiliary power requirements exceeding 5 percent..
60 Of the 592 Manufacturers, only 13 have increased auxiliary power requirements exceeding 10 percent.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
values. For facilities that sold electricity, EPA multiplied the energy penalty percentage calculated in Equation (3-
3) by this revenue value to determine the value of energy penalty losses.
Facilities also reported total electricity generated for 1996-1998 in the DQ, and EPA used the average of these
values as facility-level generation in this analysis. For facilities that did not sell electricity and are assumed to
need to purchase power to make up losses to energy penalty, EPA multiplied this generation value by the energy
penalty percentage calculated in Equation (3-3) to determine the amount of electricity a given facility would have
to purchase to meet its energy needs. EPA then used EIA-reported electricity prices for the industrial sector, by
state for the year 2007 and adjusted for inflation to 2009, to determine the cost of purchasing this electricity,
resulting in the value of the energy penalty at these facilities.
For a detailed discussion of the development of these energy penalty values, see the Technical Development
Document.
3.1.4 Components of Facility-Level Compliance Costs: Installation Downtime
Installation of certain compliance technologies will require facilities to shut down temporarily their business
operations (installation downtime). This downtime will lead to a loss in facility revenue and net income, which
constitutes an additional regulation-induced cost to complying facilities.61 In addition, specifically for electricity
generation, depending on the extent and scheduling of installation downtime, the occurrence of these temporary
reductions in electricity supply could create local electricity market imbalances, with undesirable reductions in
system reliability reserve margins and/or short-term electricity price increases. EPA estimated downtime in weeks
based on the type of compliance technology to be installed and the type of facility.
Manufacturers
The required downtime for other compliance technologies varies by module.62 Table 3-1, below, presents the
number of downtime weeks by technology module assigned to Manufacturers.
Table 3-1: Estimated Average Net Downtime for Technology Modules
Module
1
3
4
5
8
I Description
Add Fish Handling and Return System
Add New Larger Intake Structure with Fish Handling and Return
Relocate Intake to Submerged Near-shore with passive fine mesh screen
Add Fish Barrier Net
Add Velocity Cap at Inlet
Estimated Net Downtime (Weeks)
1 o
| o,"i
1 3,7
1 o
1 o
IP.-.? M°^l? 3 phis Module 5 | 0
iol' .......................... poduiel'''^^^']^^^!^''^ [[[ | [[[ b"
Source: U.S. EPA analysis, 2010
Installation downtime may affect business operations at a manufacturing facility in several ways:
1 . The facility may be unable to perform production or other business operations that depend on
cooling water.
61 EPA used a different method for calculating the social cost of installation downtime. The social cost analysis method recognizes that
the cost to society of downtime is the differential cost of energy production from use of other electric generating units to meet energy
demand instead of the 316(b) compliance units that are temporarily out of service during the period of technology installation. The
social cost may differ, perhaps substantially, from the private cost to the entity incurring the installation downtime. EPA's estimate of
the social cost of downtime is derived from the Market Model Analysis findings, which are documented in Chapter 6: Market Model
Analysis. See Chapter 11: Assessment of Total Social Costs for discussion of the use of the Market Model Analysis findings in
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
2. The facility may lose revenue from the production and sale of the goods and services that
otherwise would have been produced by the affected production operations during the period of
downtime.
3. The facility may shed the variable cost of producing the goods and services not able to be
produced during the period of installation downtime. However, the facility will continue to incur
the fixed costs of production associated with the affected operations.
4. If, as part of its cooling water dependent operations, the facility generates electricity for its own
use, and some part of this self-generated electricity continues to be needed during the period of
installation downtime, the facility may need to purchase replacement electricity.
Together, these effects may lead to a loss in pre-tax income, which EPA calculated and used as the cost of
installation downtime in its analysis of facility impacts. For the options considered for this proposal, EPA
estimates that facilities will not need to interrupt any manufacturing processes other than electricity generation.
Thus the impact of downtime for Manufacturers is calculated as revenue lost from electricity sales and the cost of
replacing electricity normally generated and consumed at the facility.
EPA used information from the 316(b) DQs to calculate the income loss in electric power-related operations. This
includes: (1) annual electric revenue reported as cooling water dependent, (2) the fuel cost of electric power
generation, which is assumed to be shed during the period of curtailed operations, (3) the quantity of electricity
consumed by the facility, and (4) the quantity of electricity generated by the facility. The remaining key input
required for this analysis is the unit price of replacement electricity: for this item, EPA used the average electricity
price for industrial customers by state, using data reported by the EIA, for 2007 (the latest year available),
adjusted to 2009 using the EIA's projection of electricity prices. EPA calculated the pre-tax income loss effect for
electric power generation activities as follows.
1. Average annual electric revenue from cooling water-dependent generation is obtained from the
facility questionnaire and adjusted for inflation to 2009. This value is assumed to be the annual
revenue loss in electric power generation, from curtailment of cooling water-dependent
operations.
2. Average annual fuel cost of electric power generation is obtained from the facility questionnaire
and adjusted for inflation to 2009. EPA assumes that this value is shed during the period of
curtailed operations.
3. Calculate self-generated electricity that is consumed by the facility as the lesser of (a) the
facility's own electricity generation or (b) the electricity used within the facility.
4. Calculate the quantity of replacement electricity to be purchased by the facility, by multiplying
the quantity of own-generated electricity that is consumed by the facility by the fraction of non-
electric revenue that is cooling water dependent but subject to a maximum reduction in electricity
need of 75 percent. That is, the facility is assumed to need replacement electricity in proportion
to the fraction of non-electric revenue that is not cooling water-dependent.
5. Calculate the cost of electricity purchased to replace self-generated electricity used by the facility
by multiplying the quantity of replacement electricity by the average electricity price, by state, for
industrial customers.
6. Calculate annual loss in pre-tax income for electric power-related operations as estimated
revenue loss from cooling water-dependent generation less estimated annual fuel cost of electric
power generation plus cost of electricity purchased to replace own-generated electricity.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
7. Calculate pre-tax income loss in electric power-related operations, from installation downtime, by
multiplying the annual pre-tax income loss by the fraction of the year indicated as the net
downtime required for installing compliance equipment.
In certain cases, the cost of replacement electricity was estimated to be less than a facility's indicated fuel costs,
resulting in a negative cost of downtime. However, in order to avoid potentially understating the burden of
installation downtime, EPA set a floor of $0 for the cost of downtime.
Electric Generators
Table 3-2, below, presents the number of downtime weeks by technology module assigned to Electric Generators.
EPA assumed that in-scope Electric Generators would install 316(b) compliance technology - cooling tower
and/or IM technology - during the spring and fall, when electricity demand is on average at its lowest. In
assessing the impact of installation downtime for fossil fuel Electric Generators, EPA assumed that cooling tower
and IM technology installation would occur at the same time as customary annual maintenance, which typically
requires facilities to shut down their electricity generating units for a minimum duration of 4 weeks. As a result,
for fossil fuel Electric Generators, EPA calculated the net additional downtime due to regulatory compliance as
total downtime outage less the 4 weeks of customary annual maintenance.
In assessing the impact of installation downtime for nuclear Electric Generators, EPA assumed that IM
technology installation would take place during In-Service Inspections (ISIs). ISIs occur at 5-year intervals and
typically last 8 to 16 weeks; consequently, the Agency calculated the net additional downtime for IM technology
installation as total downtime outage less the 8 weeks of periodic ISIs (i.e., the minimum ISI duration).63 Because
the total number of weeks of downtime required to install IM technology at a nuclear facility is expected to be
less than 8 weeks, nuclear facilities are not expected to incur any additional downtime for IM installation. EPA
assumed that cooling tower installation would take place during either extended capacity upratings (ECUs) or
during ISIs. ECU takes place at most once during the life of a nuclear facility and lasts several months.EPA
assumed that nuclear facilities that have not applied for ECUs with the Nuclear Regulatory Commission (NRC)
will do so in the future and will install cooling towers during their ECUs; EPA expects that the length of the ECU
will be sufficient to install a cooling tower and did not assign additional downtime to these nuclear facilities. The
Agency assumed that nuclear facilities that applied for and were given permission by the Nuclear Regulatory
Commission (NRC), have either gone through ECU or have completed all of the engineering developments for
the ECU; EPA expects these facilities will install cooling towers during their ISIs; consequently, the Agency
calculated the net additional downtime for IM technology installation as total downtime outage less the 8 weeks
of periodic ISIs. To the extent that an ISI would require 16 weeks, EPA's estimate of downtime costs is an
overestimate, (see Technical Development Document for more detail).
The required net downtime for compliance technology installation varies by module and facility type.64 EPA
assumed that nuclear facilities will incur no net downtime due to 316(b) technology installation (see Technical
Development Document for more detail). Facilities required to install both cooling tower and IM reduction
technologies are expected to do so during the same time. For details on the compliance schedule, see Section
3.1.6, below.
63 For details see United States Nuclear Regulatory Commission. Generic Environmental Impact Statement for License Renewal of
Nuclear Plants (NUREG-1437 Vol. 1). Available online at: http://www.nrc.gov/reading-rm/doc-
collections/nuregs/staff/srl437/vl/part02.html#_l_49
64 For information on how these technology modules were assigned to in-scope facilities, see the Technical Development Document.
3-10 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
Table 3-2: Estimated Average Net Downtime for Technology Modules
Module | _ Description _ Estimated Net Downtime (Weeks)
CT pooling Tower | 0 or 24 for nuclear, 4 for non-nuclear
1 Add Fish Handling and Return System | 0
3 Add New Larger Intake Structure with Fine Mesh, Handling and Return | 2
4 Relocate Intake to Submerged Near-shore with passive fine mesh screen | 0, 3, 9
5 Add Fish "Barrier Net | 0
8 AM Velocity Cap at Inlet | 0
l'0.2 ......................... [Moduje S^and Mtodule 5 [[[ 1 [[[ 273 [[[
.......................... — — — — — [[[ | [[[ - [[[
Source: U.S. EPA analysis, 2010
EPA calculated the financial loss to complying facilities from installation downtime as lost revenue less variable
production costs not incurred during the net installation downtime period, as follows:
> For facilities modeled in the Market Model Analysis system, EPA calculated the average of each
downtime cost element - i.e., winter energy revenue, total annual capacity revenue, winter fuel costs, and
winter variable O&M costs - over the years 2015, 2020, 2025, and 2028, for each generating unit.65'66
EPA used these averages to develop downtime costs at the level of a generating unit, and then summed
these estimates to the facility. For 216 out of the 223 explicitly analyzed facilities with assigned
technology costs, installation downtime costs are based on the Market Model Analysis.
> To estimate potential plant-specific revenue loss for the remaining 1 facilities, EPA used generator-level
data on electricity generation and utility-level electricity sales and revenue data, from two Energy
Information Administration databases: EIA-906/920/923 (for steam generation) and EIA-861 (for
electricity sales and revenue) databases. EPA used the utility-level revenue and sales quantity data to
estimate electricity prices (revenue per MWh of sales) for each in-scope Electric Generator. As the
measure of price, EPA used the average of 'wholesale prices (wholesale revenue per MWh of wholesale
sales) for the preceding five years, 2003 through 2007, if these prices were below 2003-2007 average
retail prices (retail revenue per MWh of retail sales); however, '^wholesale prices exceeded retail prices,
the Agency used total prices (total revenue from all sources per MWh of total sales). For the measure of
generating output, EPA used the average of 2003-2007 prime mover-level steam generation values, which
EPA then aggregated to the level of the facility to calculate facility-level steam generation.67 EPA
estimated the share of total power disposition sold through retail and wholesale operations for each
facility using EIA-861 data as a 2003-2007 average and used these shares to adjust facility-level
65 In calculating these averages over the data years, EPA set aside from the averaging calculation values for years that are anomalously
low, i.e., more than 30 percent below the 4-year average values.
66 The Integrated Planning Model (IPM), which is used for the Market Model Analysis (see Chapter 6: Electricity Market Model
Analysis), reports generation, energy revenue, variable O&M and fuel costs for winter and summer seasons, but does not report
information for the shoulder season demand periods - fall and spring - which are the periods when EPA expects that installation
downtime would generally occur. EPA used information for the winter season because, for the United States, winter is generally a
lower demand season than summer and therefore, would provide a better basis for assessing the impact of downtime-based capacity
reductions that would actually be expected to occur during the lower demand shoulder season operating periods. IPM reports capacity
revenue for the entire year, and not by season; thus, the potential capacity revenue loss was assessed on the basis of the annual value
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
electricity generation values. The product of the adjusted electricity generation and price values is the loss
in revenue on an annual basis.68
> For the 7 facilities not modeled in IPM, EPA estimated variable that would be shed during downtime cost
using the average of IPM-generated total annual variable cost values - fuel and variable O&M - by North
American Electric Reliability Council (NERC) region and/or steam plant type, expressed on a per-unit of
generated energy basis, and facility-level 2003-2007 average steam generation from the EIA-906/920/923
database. Facilities are assumed to be able to shed the variable cost of energy production for affected
capacity during the installation downtime period; the product of electricity generation and variable cost
per MWh is unincured variable costs on an annual basis.
> EPA performed all revenue and cost effect calculations on a per-week basis (i.e., annual values divided by
the number of weeks a given facility is available for electricity generation in a given year, which assumes
that all revenue and cost values occur uniformly over this availability time period).69 Subtracting variable
cost reduction from revenue, on a per-week basis, yields the net income loss per week from installation
downtime.
> EPA multiplied these per-week net revenue loss values by the estimated net downtime weeks to yield the
one-time net income loss from installation downtime.
EPA decided not to assign any downtime costs to facilities with very low capacity utilization rates, i.e., less than
15 percent.70 Given the low frequency of utilization of these units, the Agency determined that these facilities
would very likely be able to schedule downtime at a time that will not result in any revenue loss. For facilities
modeled by IPM, to estimate capacity utilization rates, EPA used the average of the IPM projections of electricity
generation and capacity for 2015, 2020, 2025, and 2028 (see Chapter 6: Electricity Market Model Analysis}?1 For
facilities not modeled in IPM, EPA calculated capacity utilization rates based on two EIA databases: 2007 EIA-
860 (for capacity) and 2003-2007 EIA-861 (for generation).72
For this analysis and other dollar value analyses, EPA expressed all annualized cost and revenue values as of 2015
in 2009 dollars. EPA selected 2015 as the adjustment year for two reasons. First, 2015 is approximately mid-way
through the period in which facilities assigned only IM technology are expected to achieve compliance (2013-
2017), which is the first 5-year compliance window and therefore closely reflects the operating conditions of these
in-scope facilities at the time of compliance (see Section 3.1.6, below, for discussion of compliance schedule).
Second, although facilities assigned cooling towers are expected to comply during windows of time that are
farther into the future (2018-2022 for non-nuclear Electric Generators and 2023-2027 for nuclear Electric
Generators and Manufacturers), EPA was not confident in the reliability of projecting compliance cost and
revenue values beyond 2015; consequently, the Agency used 2015 as the adjustment year for all in-scope facilities
regardless of assigned compliance technology.
68 The EIA-861 database does not provide data by season; consequently, EPA calculated cost of installation downtime for the 7 DQ
facilities not modeled in IPM on an annual, as opposed to seasonal, basis.
69 For facilities for which downtime costs were estimated using IPM data, the number of availability weeks may vary across the
generating units within a facility. For all other facilities, EPA assumed that generating units are available 48 weeks, i.e., total number
of weeks in the entire year (i.e., 52 weeks) less 4 weeks of assumed baseline customary maintenance downtime.
70 Only steam generating units were included in the calculation of capacity utilization rate.
71 In the same way as described above for calculating installation downtime costs, in developing average generation and capacity values
for CUR generation, EPA set aside from the averaging calculation, values for years that are anomalously low, i.e., more than 30
percent below the 4-year average values.
72 EPA set aside from the averaging calculation, values for years that are anomalously low, i.e. more than 30 percent below the 4-year
average values.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
The adjustments of cost and revenue values to state them as of 2015 in 2009 dollars account for two concepts of
price and/or cost change over time:
1. The historical or expected change in prices or costs for outlays of a given cost category (e.g.,
electricity prices, construction costs) from the year at which costs or prices were initially
estimated to the estimated year of their occurrence. The methodology of bringing all cost and
revenue values to 2015 is based on the assumption of zero real growth rate after 2015.
2. A general inflation adjustment to state values in constant 2009 dollars.
The net effect of these adjustments is to account for the possibility that certain cost categories (e.g., electricity
prices) may change over time at rates that differ, perhaps substantially, from the general rate of inflation.
These adjustments were performed as follows:
> For facilities for which electricity revenue values were developed from EIA data (i.e., not obtained from
the Market Model Analysis), EPA developed these values using the average of utility-level or NERC-
level, depending on data availability, 2003-2007 electricity prices. Because these individual yearly prices
were in dollars of each of the reporting years, EPA's first step in this calculation was to state these prices
in dollars of the year 2009 using the GDP Deflator published by the U.S. Bureau of Economic Analysis
(BEA) of the U.S. Department of Commerce (DOC). These individual yearly values were then averaged
and brought forward to 2015, using electricity price projections from the Annual Energy Outlook
publication for 2009 (AEO2009).73 Because the AEO2009 electricity price projections are in constant
dollars, these adjustments yield revenue values as of 2015, in dollars of the year 2009.
> For facilities for which electricity revenue and variable cost values were obtained from the Market Model
Analysis, the values are assumed to be as of 2015 but in dollars of the year 2006. Thus, no adjustment
was needed to bring the values to 2015. However, as described below, a further adjustment was needed to
state these revenue estimates in 2009 dollars.
> Compliance technology cost values, which were originally estimated as of February of 2009, were
adjusted over time to 2015 for all facilities regardless of technology and/or facility type using the
Construction Cost Index (CCI) from McGraw Hill Construction. The average of the year-to-year changes
in the CCI over the most recent ten-year reporting period was used to estimate these values in the year in
which the costs would be incurred. The resulting values are as of 2015 and in 2015 dollars.
> The above adjustments yield revenue and cost values as of 2015 but in dollars of varying years,
depending on the underlying estimation approach and adjustment concept. For example, the revenue
values based on EIA data are in 2009 dollars while the Market Model Analysis-based revenue and
variable cost values are in 2006 dollars. Because EPA performed the cost and economic impact analysis
in constant 2009 dollars, a further adjustment was needed to restate these projected cost and revenue
values in 2009 dollars. For this adjustment, EPA used the average of the year-to-year changes in the GDP
Deflator over the most recent ten-year reporting period to restate dollars of a future year to 2009 dollars.
3.1.5 Components of Facility-Level Compliance Costs: Administrative Costs
The suspended 2004 Final Section 316(b) Phase II Existing Facilities Rule included a range of administrative
activities that in-scope facilities would need to perform to establish compliance requirements, obtain needed
Annual Energy Outlook is published by the Energy Information Administration (EIA). AEO2009 contains projections and analysis of
US energy supply, demand, and prices through 2030; these projections are based on results from the Energy Information
Administration's National Energy Modeling System.
March 28, 2011 3-13
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 3: Development of Costs for Regulatory Options
permits, and perform periodic monitoring, reporting, and re-permitting subsequent to initial compliance efforts.74
For the Proposed 316(b) Existing Facilities Rule, EPA significantly reduced the administrative burden on
facilities, eliminating many of the activities required by the suspended 2004 Final Section 316(b) Phase II
Existing Facilities Rule. For the remaining administrative requirements, EPA used the hourly burdens estimated
for these activities in the suspended 2004 Phase II Final analysis and updated labor rates and other costs as
described at the end of this subsection. EPA applied the same methodology and assumptions to the Electric
Generators with design intake flow (DIP) of 2 to 50 MGD and Manufacturers, which were not part of the
suspended 2004 Phase II Final Rule (U.S. EPA, 2004a). The principal areas of change in administrative
requirements and the updating of the cost estimation framework for this proposed rule are summarized below.
Administrative activities
EPA reviewed activities associated with the initial NPDES permit application and permit renewal as well as
annual monitoring, record-keeping, and reporting activities (monitoring activities). To accommodate the revised
language of the Proposed Existing Facilities Rule, EPA modified the assignment of administrative costs based on
the requirements of the regulation. The two factors that determine administrative costs of a given in-scope facility
are: (1) its waterbody type (freshwater or marine) and (2) whether it has/is required to install a cooling tower or
will install other BTA.75 Facilities that have or are required to install cooling towers are exempted from the
monitoring requirements.
76
Table 3-3 below presents a list of initial permitting activities and estimated administrative costs associated with
these activities; these costs are the same for all options and for all categories, as all facilities are required to
perform these activities. All of these costs are expected to be incurred one year before the facility's compliance
year. EPA estimates that initial post-promulgation permit application will cost $33,737 per facility.
Table 3-3: Cost of Initial Post-Promulgation NPDES Permit Application Activities ($2009)
Activity
Start-up activities
Permit application activities
Source water Baseline Biological Characterization
Total
Fresh
iSi
Requirements
$2,958
$14j76
$16^603
$33,737
water
Cooling Tower
$2,958
$14j76
SILOS'
$33,737
Ma
iSi
Requirements
$2,958
$14j76
$16^603
$33,737
•ine
Cooling Tower
$2,958
$14j76
SILOS'
$33,737
Source: U.S. EPA analysis, 2010
Table 3-4 below lists relevant NPDES permit renewal activities and estimated administrative costs associated
with these activities. These activities are the same as those for the initial post-promulgation permit, but are
estimated to cost less in subsequent permit applications. As with the cost for the initial post-promulgation permit,
these costs do not vary by option or by category, as all facilities are required to perform these activities. EPA
assumed that facilities will incur these costs in the year prior to the application for a permit renewal. EPA
estimates that subsequent post-promulgation permit applications will cost $14,789 per facility.
74 For details see Economic and Benefits Analysis for the Final Section 316(b) Phase II Existing Facilities Rule report available online at
http://water.epa.gov/lawsregs/lawsguidance/cwa7316b/phase2/upload/2009_03_26_316bjihase2_econbenefits_final_toc.pdf
75 Freshwaters includes streams, rivers, lakes, and reservoirs, except for the Great Lakes. Marine waters include estuaries and tidal rivers,
oceans, and the Great Lakes.
76 For the suspended 2004 Final Section 316(b) Phase II Existing Facilities Rule, these facilities were assigned additional administrative
costs (U.S. EPA, 2004a).
3-14
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 3: Development of Costs for Regulatory Options
Table 3-4: Cost of Subsequent Post-Promulgation NPDES Permit Application Activities ($2009)
Activity
Start-up activities
Permit application activities
Source water Baseline Biological Characterization
Total
Fresh
IM
Requirements
$989
$8313
$47986
$14,789
water
Cooling Tower
$989
$8313
$4,986
$14,789
Ma
IM
Requirements
$989
$8313
$4^986
$14,789
fine
Cooling Tower
$989
$8313
$47986
$14,789
Source: U.S. EPA analysis, 2010
Table 3-5 below lists monitoring, record-keeping, and reporting activities and estimated administrative costs
associated with these activities. Facilities required to install only cooling towers and facilities with baseline
recirculating systems in place and no IM requirements are not required to perform monitoring. For all other
facilities with monitoring requirements, the cost per facility is estimated to be approximately $60,176 for
freshwater facilities and $66,542 for marine facilities.
Table 3-5: Cost of Subsequent Post-Promulgation NPDES Permit Application Activities ($2009)
Activity
Biological Monitoring for Impingement
Visual or Remote Inspections
Yearly Status Report Activities
Total
Fresh
IM
Requirements
$23,382
$13/136
$237364
$60,176
water
Cooling Tower
$0
$6
$6
$0
Ma
EVI
Requirements
$29,747
$737436
$23364
$66,542
rine
Cooling Tower
$0
$6
$6
$0
Source: U.S. EPA analysis, 2010
Several activities that were required for the suspended 2004 Phase II regulation are not required under the current
regulatory options, and were excluded from the analysis. In addition, monitoring for entrainment is only required
for certain compliance alternatives, with which facilities may elect to comply, but are not required; thus, these
costs also are not included in the analysis.
EPA developed unit labor costs as of 2015, i.e., adjustment year, in 2009 dollars, as follows:
> EPA obtained unloaded wage rates (i.e., wages and salaries not including benefits, overhead, or fee) for
all facility and contracted employees from Bureau of Labor Statistics Occupational Outlook Handbook,
2008-2009 Edition (http://www.bls.gov/oco/home.htm). Most wages were presented as annual earnings,
which EPA converted into hourly wages assuming 2080 hours per year. EPA brought these hourly wages
forward from Ql 2006 to Q4 2009 using the Bureau of Labor Statistics' Employment Cost Index
(http://www.bls.gov/ncs/ect/home .htm).
> EPA obtained all state government wage rates from the BLS Employer Costs for Employee
Compensation as of March 2001.77 EPA brought hourly wage rates forward from Ql 2001 to Q4 2009
using the Bureau of Labor Statistics Employment Cost Index.
http://www.bls.gov/news.release/History/ecec_06292001 .txt
> In the same way as for the suspended Final Phase II Rule analysis, EPA assumed indirect costs to be 15
percent of the unloaded wage values for facilities and states, and 50 percent of the unloaded wage values
for contract services.
> In the same way as for the suspended Final Phase II Rule analysis, EPA assumed a contractor fee of 8
percent of unloaded wage values.
More recent data do not contain appropriate occupation categories.
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
> Non-labor costs (laboratory analysis costs and other direct costs) were adjusted from 2002 to 2009 using
the BEA's GDP Deflator.
> EPA brought the resulting administrative costs forward to the adjustment year of 2015 using the
Employment Cost Index (ECI) published by the Bureau of Labor Statistics. This adjustment was
performed using the average of the year-to-year changes in the ECI over the most recent ten-year
reporting period. The resulting values are as of 2015 and in 2015 dollars. The costs presented above do
not reflect this adjustment, as it was applied after these individual activity costs were summed.
> Because EPA performed the cost and economic impact analysis in constant dollars of the year 2009, a
further adjustment was needed to restate costs that were developed in a different dollar year, in 2009
dollars. For this adjustment, EPA used the average of the year-to-year changes in the GDP Deflator over
the most recent ten-year reporting period.
3.1.6 Development of Compliance Years
The estimated compliance years for facilities subject to the Proposed 316 (b) Existing Facilities Rule are
important for two reasons:
1. Compliance years are used to determine by how much compliance costs are discounted in the
national cost estimate.
2. A high concentration of facilities estimated to be out of service for cooling tower connection or
other compliance technology installation at the same time in a given electricity production region
(North American Electric Reliability Council Region or NERC Region) could lead to reduced
reserve margins and jeopardize the reliability of power operations in that NERC region.
Electricity production costs could also increase in the short term in an affected region if a
substantial fraction of lower production cost capacity were out of service at the same time. Thus,
the years in which in-scope plants are expected to achieve compliance and potentially be out of
service during technology installation are important for the impact analysis.
Analysis Approach and Data Inputs
The development of compliance years varies depending on whether a facility is assigned a cooling tower or a non-
cooling tower technology or is assumed to meet compliance requirements in its baseline operations as well as
facility type.
1. EPA expects facilities - Electric Generators and Manufacturers - assigned only non-cooling
tower technologies to comply within 5 years after this rule is promulgated, i.e., by 2017. For the
compliance cost analysis, EPA assumed that nuclear electric generators would comply in the year
of their first post-promulgation ISI and all other facilities would comply in the year of their first
post-promulgation NPDES permit renewal, which results in a 5-year compliance schedule of
2013 through 2017. EPA performed the cost and economic impact analyses using 2015 as a proxy
compliance year for these facilities because this year is approximately mid-way through the
compliance window, i.e., 2013-2017, for this category of facilities, and therefore reflects the
operating conditions of in-scope facilities at the time of compliance.
2. Under the two options that would require cooling towers, EPA assumed non-nuclear Electric
Generators assigned cooling towers to achieve compliance with this rule within 10 years after
promulgation, i.e., by 2022. EPA assumed Manufacturers and nuclear Electric Generators
assigned cooling towers to achieve compliance with this rule within 15 years after promulgation,
i.e., 2027. For the cost and economic impact analysis, EPA assumed that non-nuclear Electric
Generators would comply during the 5 years of their second post-promulgation NPDES permit
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
period - i.e., between 2017 and 2022 - and Manufacturers and nuclear Electric Generators would
comply during the 5 years of their third post-promulgation NPDES permit period and during their
third post-promulgation ISI, respectively - i.e., between 2023 and 2027. Further, EPA assumed
that two years would be needed for these facilities to complete cooling tower installation.78 For
the cost and economic impact analyses, EPA used 2020 for non-nuclear Electric Generators and
2025 for nuclear Electric Generators and Manufacturers to represent facility-specific compliance
years. These years are approximately mid-way through the compliance windows of 2017-2022
and 2023-2027, respectively, and therefore reflect the operating conditions of in-scope facilities at
the time of compliance. EPA notes that these assumed compliance years will not generally be the
actual years in which facilities would be specified to meet compliance in permits. However, these
assumptions reflect the approximate years in which compliance would reasonably be expected to
occur, and thus provide a practical basis for the cost and economic impact analysis.
3. EPA assumed that facilities assigned both cooling towers and IM technologies would install both
technologies at the same time and, therefore, comply during their cooling tower compliance
window, i.e., during 2017 through 2022 for non-nuclear Electric Generators and during 2023 and
2027 for Manufacturers and nuclear Electric Generators. Consequently, the Agency used 2020
and 2025 as proxy compliance years, respectively, for these installations.
4. For facilities with baseline re-circulating systems in place and no IM requirements or that are
otherwise assumed to meet BTA in their baseline, the Agency also used 2015 as a proxy
compliance year; because these facilities are not required to install any compliance technology,
they need no extended period of time to comply.
These assumptions result in an overall compliance window of 15 years, 2013 through 2027, for all in-scope
facilities.
For the Market Model Analysis, EPA needed to assign an individual compliance year to each Electric Generator.
Consequently, for the Market Model Analysis the Agency assumed that non-nuclear and nuclear Electric
Generators assigned either only IM technology or no compliance technology would comply in the year of their
first post-promulgation permit and first post-promulgation ISI, respectively. Further, the Agency assumed that
non-nuclear and nuclear Electric Generators assigned cooling towers regardless of whether they were also
assigned IM technologies would comply in the year of their second post-promulgation permit and third post-
promulgation ISI, respectively (see Chapter 6: Electricity Market Model Analysis}.
For Electric Generators, EPA also performed a separate assessment of the impact of downtime on capacity
availability in each NERC region using a 5-year compliance window of 2013 through 2017 for Electric
Generators assigned non-cooling tower technologies, a 5-year compliance window of 2018 through 2022 for non-
nuclear Electric Generators assigned cooling towers or cooling towers together with IM technologies, and a 5-year
compliance window of 2023 through 2027 for nuclear Electric Generators assigned cooling towers or cooling
towers together with IM technologies. This analysis is discussed in Chapter 5: Cost and Economic Impact
Analysis - Electric Generators.
3.1.7 Development of Total Compliance Costs
EPA aggregated compliance cost components as described in the preceding sections (i.e., compliance technology
costs (one-time and periodic recurring costs), administrative costs (one-time and periodic recurring costs), and
installation downtime costs) to develop total costs of compliance with this proposed rule, on the basis of costs as
78 As assumed in the previous 316(b) rule analyses.
March 28, 2011 3-17
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
incurred and recognized by complying facilities. The development of total compliance costs follows the same
approach for both electric power generating facilities and manufacturers.
Analysis approach and data inputs
EPA aggregated compliance cost components for the assessment of facility impact based on the following
methodology and assumptions:
> First, EPA calculated total compliance costs for the 460 in-scope facilities (257 Electric Generators and
203 Manufacturers) that were explicitly analyzed in the compliance assessment and cost estimation
process.79
> The Agency calculated compliance costs on a "year-explicit" basis relative to the assumed promulgation
year of 2012 for the entrainment study portion of O&M costs, and relative to the assumed compliance
year of 2015, 2020 or 2025 for all other compliance cost components. The assignment of year-explicit
cost for these compliance cost components is based on the assigned technology and facility type,
accounting for the specific years in which facilities were estimated to undertake pre-compliance studies,
obtain necessary permits, implement compliance technology, and undertake periodic recurring activities
after initial outlays (e.g., for re-permitting and replacement/reinstallation of compliance technology
components with shorter useful lives than the full analysis period).
> All cost values were discounted to 2012, the assumed year of rule promulgation, at a 7 percent discount
rate. The 7 percent discount rate was used in the cost impact analysis since it is intended to reflect the
opportunity cost of capital to society, per Office of Management and Budget guidance.
> EPA analyzed costs over a 30-year post-compliance operating period for each in-scope facility.
> Within the 30-year period, EPA annualized one-time and recurring (on other than an annual basis) cost
components over specific useful life, implementation, and/or event recurrence periods, using a 7 percent
discount rate:
• Capital costs of non-cooling tower technologies: 20, 25, or 30 years
• Capital costs of cooling tower technologies: 30 years
• Downtime, the initial entrainment study portion of the O&M costs, and initial permitting costs: 30
years
• Re-permitting costs: 5 years
• The follow-up entrainment study portion of the O&M costs: 3 years
> EPA than added annualized capital, downtime, permitting and re-permitting costs to annual O&M and
administrative costs to derive total annualized compliance costs, where costs are expressed on an
equivalent annual cost basis.
> To calculate annualized costs as of the compliance year or a different analysis year, depending on the
analysis, EPA first discounted the year-explicit stream of costs for each facility to each facility's
compliance year, i.e., 2015, 2020, or 2025, or the different analysis year, depending on the analysis, and
then annualized these costs over the assumed 30 years of technology operation. EPA discounted and
annualized these costs at a 7 percent discount rate.
> EPA applied sample weights to these cost values to extend the analysis to the total of 1,077 estimated in-
scope facilities - 518 Manufacturers and 559 Electric Generators - as follows (see Appendix 3.A: Use of
Sample Weights in the Proposed Existing Facilities Rule Analyses):
• Manufacturers:
79 Facility counts exclude baseline closures.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
D Tech Weights were applied to industry profiles, cost compliance assessment, UMRA analysis,
Executive Order analysis, and social cost assessment.
D Econ Weights were applied to cost and economic impact assessment and RFA analysis.
• Electric Generators:
D Facility Count-Based Weights were applied to initial permitting, re-permitting, monitoring, and
pilot study costs.
D DIF-Based Weights were applied to downtime costs and energy penalty.
D Capacity-Based Weights were applied to capital and O&M costs.
For the facility impact assessment, EPA considered costs on both a pre-tax and after-tax basis. EPA calculated the
after-tax value of compliance costs by applying combined federal and State tax rates to the pre-tax cost values for
privately owned facilities.80 EPA used after-tax costs, which is a more meaningful measure of compliance impact
on privately-owned facilities, to estimate compliance costs to private, for-profit facilities as well as to
approximate capital depreciation treatment. For this adjustment, EPA used State corporate rates from the
Federation of Tax Administrators (http://www.taxadmin.org/) combined with federal corporate tax rate schedules
from the Department of the Treasury, Internal Revenue Service.
The methodology used for the assessment of total social costs differs slightly from the methodology used for the
assessment of compliance costs to facilities. For a detailed discussion on development of social costs, see Chapter
11: Assessment of Total Social Costs.
Key findings for regulatory options - Manufacturers
Table 3-6 presents total compliance costs for Manufacturers by industry sector.
80 Government-owned entities and cooperatives are not subject to income taxes. To distinguish among the government-owned, privately
owned, and cooperative ownership categories, EPA relied on the 2006 EIA-860, and 2007 EIA-861 databases and additional research.
See Chapter 5: Cost and Economic Impact Analyses - Electric Generators for further discussion of these determinations.
March 28, 2011 3-19
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 3: Development of Costs for Regulatory Options
Table 3-6: Annualized Compliance Costs by Industry Sector for Manufacturers (in millions, $2009, at 2012)
Sector
One-Time Costs
Capital | Connection | Initial Permit
Technology | Outage | Application
Recurring
I Monitoring, |
| Record I
| Keeping, and |
O&M I Reporting [
Costs
Energy
Penalty
Permit
Renewal
Total
Option 1: IM Everywhere
Pre-Tax Compliance Costs
Aluminum
Chemicals and Allied
Products
Food and Kindred
Products
Paper and Allied
Products
Petroleum Refining
Steel
Other (Misc)
Total
$0.31
$8.73
$1.50
$6.26
$1.96
$3.57
$0.43
$22.76
$0.04 1
$0.00
$0.01
$1.26 I
$0.00
$0.00
$0.03
$1.34
$0.06
$0.44
$0.08
$0.49
$0.08
$0.12
$0.04
$1.31
$0.68 1
$6.13
$1.16
$5.97 I
$2.46 '
$4.80 '
$0.71
$21.91
$0.54 |
$6.85 |
$1.26 |
$7.95 |
$1.56 1
$1.91 |
$0.64 i
$20.71 1
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.05
$0.42
$0.07
$0.47
$0.08
$0.11
$0.03
$1.24
$1.68
$22.57
$4.08
$22.38
$6.14
$10.51
$1.89
$69.26
After-Tax Compliance Costs
Aluminum
Chemicals and Allied
Products
Food and Kindred
Products
Paper and Allied
Products
Petroleum Refining
Steel
Other (Miscj
Total
$0.20
$5.37
$0.87
$3.76
$17l7
$216
$6726
$13.79
$0.03 1
$0.00
$0.01
$0.75 |
$6766 ?
$"67'6'o
$6762
$0.80 i
$0.04
$0.27
$0.05
$0.30
$6765
$6767
$"6"76'2
$0.79
$0.44 1
$3.77
$0.67
$3.59
$1747 !
$2.88 '
$6.42 '
$13.25 i
$0.34 |
$4.18 |
$0.74 |
$4.80 |
$6794 1
$1.14 |
$0.39 |
$12.53 1
$0.00
$0.00
$0.00
$0.00
$6766
$6766
$6766
$0.00
(h/"v /"vo
$0.03
$0.25
$0.04
$0.28
$6765
$6767'
$6762
$0.75
$1.07
$13.84
$2.39
$13.48
$3768
$6732
$T7"i"3
$41.92
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
Pre-Tax Compliance Costs
Aluminum
Chemicals and Allied
Products
Food and Kindred
Products
Paper and Allied
Products
Petroleum Refining
Steel
Other (Misc)
Total
$1.51
$38.27
$9.75
$8.47
$8.72
$"29.58
$1.39
$97.69
$0.02 i
$0.00 i
$0.01 i
$1.26 1
$0.00
$o7oo
$0.02
$1.31 !
$0.06
$0.42
$0.07
$0.48
$0.08
$o7i"6
$0.03
$1.24
$0.37 i
$4.63 i
$1.10 i
$4.56 I
$1.03
$1.99 '
$0.36
$14.04 !
$0.48
$6.31
$1.17
$7.71
$1.34
$1.41
$0.56
$18.97
$1.88
$29.66
$7.97
$0.80
$2.41
$4.14
$0.16
$47.03
$0.05
$0.40
$0.06
$0.46
$0.07
$6.09
$0.03
$1.17
$4.37
$79.68
$20.13
$23.74
$13.66
$37731
$2.55
$181.44
After-Tax Compliance Costs
Aluminum
Chemical's and Allied
Products
Food and Kindred
Products
Paper and Allied
Products
Petroleum Refining
Steel
Other (Miscj
Total
$0.98
$23.43
$5.75
$5.11
$5"7l8
$17762
$6782
$58.89
$0.01 1
$0.00 1
$0.01 j
$0.75 !
$"67'6'o
$"67'6'o
$"67'6"i 1
$0.78 i
$0.03
$0.25
$0.04
$0.29
$6765
$6766'
$6762
$0.75
$0.24 1
$2.84 1
$0.64 1
$2.75 !
$6762
$17 19
$6722 |
$8.50 i
$0.30
$3.84
$0.69
$4.65
$0.81
$0.84
$0.34
$11.48
$1.22
$18.67
$4.76
$0.49
$1.43
$2.46
$6.10
$29.14
$0.03
$0.24
$0.04
$0.28
' $6764
" $6766
' $6762
$0.71
$2.82
$49.28
$11.93
$14.32
$87"i"4
$22723
$1752
$110.24
3-20
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 3: Development of Costs for Regulatory Options
Table 3-6: Annualized Compliance Costs by Industry Sector for Manufacturers (in millions, $2009, at 2012)
Sector
One-Time Costs
Capital | Connection | Initial Permit
Technology | Outage | Application
Recurring
I Monitoring, |
| Record I
| Keeping, and |
O&M I Reporting [
Costs
Energy
Penalty
Permit
Renewal
Total
Option 3: I&E Mortality Everywhere
Pre-Tax Compliance Costs
Aluminum
Chemicals and Allied
Products
Food and Kindred
Products
Paper and Allied
Products
Petroleum Refining
Steel
Other (Misc)
Total
$3.75
$64.61
$12.78
$32.00
$12.82
$3278
$2.71
$161.45
$0.02 |
$0.00 |
$0.01 |
$0.67 I
$0.00 |
$0.06 1
$0.02 |
$0.72 i
$0.03
$0.26
$0.05
$0.28
$0.07
$0.07
$0.02
$0.76
$0.17 |
$3.98 |
$0.90 |
$3.11 I
$0.83 |
$170 1
$0.21 |
$10.91 i
$0.00
$2.25
$0.53
$2.04
$0.94
$6762
$0.07
$6.46
$1.88
$31.74
$8.70
$16.10
$2.45
$469
$0.30
$65.86
$0.03
$0.24
$0.04
$0.26
$0.06
$6"06
$0.02
$0.72
$5.88
$103.08
$23.01
$54.46
$17.17
$39.93
$3.35
$246.88
After-Tax Compliance Costs
Aluminum
Chemicals and Allied
Products
Food and Kindred
Products
Paper and Allied
Products
Petroleum Refining
Steel
Other (Misc)
Total
$2.37
$39.48
$7.57
$19.34
$'7768
$19753
$1761
$97.57
$0.01 |
$0.00
$0.01 :
$0.40
$0.00
$0.00
$676"i I
$0.43 !
$0.02
$0.16
$0.03
$0.17
$0.04
$0.04
$6"."6"i
$0.46
$0.11 |
$2.45
$0.53 !
$1.88
$0.50
$1.02 '
$67i2 |
$6.61 !
$0.00
$1.38
$0.31
$1.23
$0.57
$0.37
$6764
$3.90
$1.22
$19.93
$5.20
$9.67
$1.46
$2.79
$67i8
$40.45
$0.02
$0.15
$0.03
$0.16
$0.04
$0.04
$6761
$0.44
$3.74
$63.54
$13.67
$32.85
$10.28
$23.79
$1799
$149.86
Source: U.S. EPA analysis, 2010
Key findings for regulatory options - Electric Power Generating Facilities
As explained in Chapter 2.H: Profile of the Electric Power Industry, North American Electric Reliability
Corporation (NERC) is responsible for the overall reliability, planning, and coordination of the power grids;
NERC is organized into regional organizations that are responsible for the overall coordination of bulk power
policies that affect their regions' reliability and quality of service. Each NERC region has full responsibility for
dealing with electricity reliability issues in its region, based on available capacity and transmission constraints.
Service areas of the member facilities determine the boundaries of the NERC regions. Because of differences in
economic characteristics of in-scope facilities across NERC regions, as well as differences in the baseline
economic characteristics of the NERC regions themselves, this proposed rule may have a different impact on
profitability, electricity prices, and other impact measures across NERC regions (Chapter 2.H: Profile of the
Electric Power Industry). Consequently, EPA evaluated compliance costs of the Proposed 316(b) Existing
Facilities Rule at both the national level and by NERC region. The NERC regions used for the analysis of
compliance costs to existing Electric Generators include:81
> ASCC - Alaska Systems Coordinating Council
> ERCOT - Electric Reliability Council of Texas
> FRCC - Florida Reliability Coordinating Council
> HICC - Hawaii Coordinating Council
> MRO - Midwest Reliability Organization
As noted previously, NERC region defitions have changed recently.
March 28, 2011
3-21
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
> NPCC - Northeast Power Coordinating Council
> RFC - ReliabilityFirst Corporation
> SERC - Southeastern Electric Reliability Council
> SPP - Southwest Power Pool
> WECC - Western Energy Coordinating Council
As reported in Table 3-7, EPA estimates that the 559 in-scope Electric Generators will incur annualized costs of
complying with the proposed regulatory options of $406 million on a pre-tax basis and $264 million on an after-
tax basis under the proposed Option 1, $4.9 billion on a pre-tax basis and $3.3 billion on an after-tax basis under
Option 2, and $5.1 billion on a pre-tax basis and $3.4 billion on an after-tax basis under Option 3. The burden of
these compliance costs is expected to be the highest in the RFC and SERC region and the lowest in the WECC
region under all three options. These annualized costs are calculated on a present value basis as of the
promulgation year of 2012, in 2009 dollars; these costs are then annualized over a period of 30 years, the assumed
"compliance life" in this regulatory analysis. The 30-year annualization period is the longest expected service life
of the technology equipment components that would be expected to be installed for compliance with the
regulatory options.
All three regulatory options include a provision for required installation of EM technology at new Electric
Generators - i.e., newly constructed generating units at existing units. However, Table 3-7 does not include costs
associated with this New Unit EM technology requirement. Instead the new unit costs are discussed and presented
in Section 3.3.
3-22 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 3: Development of Costs for Regulatory Options
Table 3-7: Annualized Compliance Costs by NERC Region (in millions, $2009, at 2012)
NERC
Region
One-Time Costs
Capital
Technology
Connection
Outage
Initial Permit
Application
Recurring Costs
O&M
Monitoring,
Record
Keeping, and
Reporting
Energy
Penalty
Permit
Renewal
Total
Option 1: IM Everywhere
Pre-Tax Compliance Costs
ASCC
ERCOT
FRCC
HICC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
$0.00
$12767
$15784
$738
$7679
$1431
$46785
$35749
$6756
$To5
$139.73
$0.00
$3.59
$6.21
$0.00
$5.82
$o."6b"
$"'6'"."i"5
$6.60
$3l".T7
$044
$59.99
$0.00
$6".Ti
$0.06
$0.01
$0."l2
$0."l6
$0.41
$0.40
$0.09
$0.06
$1.41
$0.00
$15763
$io746
$T'9i
$8791
$2489"
$56l6
$4763
$12762
$"b""83
$178.53
$0.00
$179
$T"6"5
$7677
$2709
$2766
$7785
$6778
$"1757
$"'b"'"8"'5
$24.80
$0.00
$"b"b"b"
$b"bb
$b"bb
$"b"bb
$"b"bb
$"b"bb
$b"bb
$b"bb
$"'b"bb
$0.00
$0.00
$7676
$lib6
$"b"oi
$7671
$7675
$639
$7638
$lib8
$"b""b"6"
$1.33
$0.00
$32768
$33768
$348
$723724"
$427i6
$117791
$97.28
$52.09
$3728
$405.78
After-Tax Compliance Costs
ASCC
ERCOT
FRCC
lice"
MRO
NPCC
RFC
SERC
SPP
WECC
Total
$0.00
$8743
$9775
$b"84
$468
$8754
$28714
$25717
$4722
$072
$90.49
$0.00
$2.33
$3.81
$"b"'.'b"b
$5.76
$b'."6b
$3.72
$4.00
$19.05
$6726"
$38.94
$0.00
$6768
$"6764"
$"6'."66
$6768
$616
$6726"
$6728"
$"6766"
$6764
$0.94
$0.00
$fi7J2
$6744
$""i"l"6"
$5799
$1482
$3404
$34725
$8"7l4
$1155
$116.51
$0.00
$1"33
$ll69
$767fi
$138
$761
$496"
$"473"
$""i'"'b"3
$ll56
$16.40
$0.00
$61)6
$61)6
$"'b"bb
$61)6
$b"66
$61)6
$61)6
$"b"bb
$61)6
$0.00
$0.00
$6"6'7
$lio4
$"b""b"b
$"6"6'7
$lib9
$li25
$"b"26
$lio5
$lib4
$0.89
$0.00
$2336
$26777
$"2."l2"
$17.96
$25716
$7"f.37
$68776
$32755
$2"."i'7'
$264.16
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
Pre-Tax Compliance Costs
ASCC
ERCOT
FRCC
HICC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
$0.00
$302799
$140.87
$1782
$114759
$332.23
$8i"9.o5
$822.70
$183797"
$2798
$2,737.20
$0.00
$11791
$1137
$1744
$16159
$34723
$6J755
$133762
$1459"
$044
$279.84
$0.00
$61)8
$61)5
$"b"bi
$616
$b"i2
$'6733"
$b"3""i
$"b"b'7'
$61)6
$1.12
$0.00
$34.32
$16784
$2762
$137376
$3833
$94.39
$9459
$2130
$"6799
$316.09
$0.00
$"6"."i"9"
$"6"."i"8"
$"b".bb
'$"b"."8"9
$635
$"2"."5"6
$2^64
$037
$"6774"
$7.46
$0.00
$142744
$85768
$489
$"62.32"
$72|b234
$453."51
$573796
$65.54
$039
$1,590.47
$0.00
$b76'7
$'b"6'5'
$"b"b"i
$"b"b'9"
$76712
$632
$030
$"b7b"7
$"b"b'5'
$1.06
$0.00
$492766
$254744
$"'26"'."i"8"
$7ibi799"
$6"b'7"."9'"3"
$J743i;64
$l-;6''2'7"."5"'3"
'$28"'5"'."9"b
$5^66
$4,933.26
After-Tax Compliance Costs
ASCC
ERCOT
FRCC
HICC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
$0.00
$2i47o3
$88744
$76784
$7948
$197.67
$491793
$60235
$117720
$"'i'"."9"2
$1,803.87
$0.00
$8775
$7763
$b788
$7758
$2031
$37767
$164769
'$"'9707'
$'b"2'6'
$195.05
$0.00
$b"b'6'
$"b"b'3"
$"b"bb
$'b"6'6'
$b"b'8"
$611
$"b"2"'2
$b7b"5
$'b"b'4
$0.75
$0.00
$2425
$lb".56
$T".23
$9720
$22.82
$56^82
$69.24
$l"3.66
$6^65
$208.42
$0.00
$"ai2
$"b"."i"'i
'$"b".bb
$633
$636
$173
$148
$6".27
$6750
$5.10
$0.00
$100725
$5237
$2.97
$4332
$12047'
$27243
$42'i".'2"8"
$4l7o"7
$6724
$1,054.61
$0.00
$b"b'5'
$"b"b'3"
$"b"bb
$b"b'6'
$b"b'7'
$616
$"b'"2"i
'$"b"b'4"
$'b"b'4
$0.71
$0.00
$347.51
$158778
$15793
$140723
$36178
$86039
$l7l98.87
$18136
$"37<55'
$3,268.50
March 28, 2011
3-23
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 3: Development of Costs for Regulatory Options
Table 3-7: Annualized Compliance Costs by NERC Region (in millions, $2009, at 2012)a
NERC
Region
One-Time Costs
Capital
Technology
Connection
Outage
Initial Permit
Application
Recurring Costs
O&M
Monitoring,
Record
Keeping, and
Reporting
Energy
Penalty
Permit
Renewal
Total
Option 3: I&E Mortality Everywhere
Pre-Tax Compliance Costs
ASCC
ERCOT
FRCC
fflcc"
MRO
NPCC
RFC
SERC
SPP
WECC
Total
$0.00
$302:99
$14216
$1782
$l23":57
$345715
$831715
$83734
$183797
$401
$2,788.16
$0.00
$11.91
$12.04
$7.44'
$11.69
$37.42"
$63."89
$742744
$14.59
$0.54
$295.96
$0.00
$0.08
$"'6"."6"5
$(i6T
$0.09
$"'6"."i"i'
$"'6"."32"
$"'6"."3"b"
$"b"."6'7
$"b"."6"5
$1.06
$0.00
$3432
$16795
Sib!
$7713
$3972
'$"'9481
$95762
$2""O"b
$0:'93
$319.20
$0.00
$(119
$(108
$"'6T6"b
$0:22
$(io6
$147
$122"
$(137
$(153
$4.07
$0.00
$142744
$"87779
$489
$6777
$223:94
$46"97g3
$606:98
$65754
$6:'93
$1,669.82
$0.00
$0'."07
$0.04
$0.01
$0.08
$6.10
$0.30
$(128
$0.07
$"1165
$1.01
$0.00
$492766
$259711
$2618
$2i77gg
$645:86
$]7467746
$l76847l7
$285:90
$705
$5,079.28
After-Tax Compliance Costs
ASCC
ERCOT
FRCC
lice
MRO
NPCC
RFC
SERC
SPP
WECC
Total
$0.00
$214703
$89:23
$76:84
$8485
$205:80
$566:98
$614770
$117720
$2:76
$1,840.33
$0.00
$"'8'"."7"5
$7.44
$"'6'"."8"8"
$8.2"l
$22":25
$"'3'"8'"."6'"9"
$fll767
$9."67
$6:32
$207.29
$0.00
$"b'"."6"6"
$"b"."b"3
$"b"."bb
$6:66
$6:67
$"b".'2"b
$"b".'2"'i'
$6:6s
$6:64
$0.71
$0.00
$2425'
$76:63
$T:23
$9:69
$23:33
$57717
$7671
$73:66
$(160
$210.67
$0.00
$672
$(105
$6:66
$"'b:"i"3"
$6:66
$(193
$6:86
$(127
$0:33
$2.63
$0.00
$100:25'
$5424
$2:97
$46767
$133769
$28431
$448766
$"4"I'b'7
$6:60
$1,112.45
$0.00
$"b"."b"5
$"b"."b"3
$"b"."bb
$"b"."b"5
$6.66
$"b"."i"9"
$"b".'2"b
$"b"."b'4"
$6:64
$0.67
$0.00
$3477'5J
$161:65
$15:93
$149766
$385:20
$882:47
$17246734
$181:36
$462
$3,374.74
a. EPA data indicate that no DQ in-scope facilities are located in the ASCC NERC region; an STQ facility in ASCC facility was grouped with STQ facilities
in the WECC region (see Appendix 3.A: Use of Sample Weights in the Proposed Existing Facilities Rule Analyses).
Source: U.S. EPA analysis, 2010
Table 3-8 presents total compliance costs for Manufacturers and Electric Generators.
3-24
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 3: Development of Costs for Regulatory Options
Table 3-8: Annualized Compliance Costs For Manufacturers and Electric Generators (in millions, $2009, at
2012)a
Facility
Group
Capital
Technology
One-Time Costs
| Connection I
i Outage i
Initial Permit
Application
Recurring
I Monitoring, |
| Record |
(Keeping, and|
O&M I Reporting I
Costs
Energy
Penalty
Permit
Renewal
Total
Option 1: IM Everywhere
Pre-Tax Compliance Costs
Manufacturers
Generators
Total
$22.76 |
$139773 1
$162.49 |
$1.34 |
$59799 1
$61.33 |
$1.31
$141
$2.72
$21.91 |
$178753 1
$200.44 |
$20.71 |
$24.80 1
$45.51 |
$0.00 |
$o."6b" 1
$0.00 |
$1.24
$7733
$2.57
$69.26
$405778
$475.04
After-Tax Compliance Costs
Manufacturers
Generators
Total
$13.79 !
$9049 |
$104.28 !
$0.80 !
$38794 |
$39.74 !
$0.79
$6794
$1.73
$13.25 I
$7l6T51 !
$129.76 I
$12.53 I
$1646 |
$28.93 I
$0.00 !
$6766 |
$0.00 !
$0.75
$6789
$1.64
$41.92
$7264716
$306.08
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
Pre-Tax Compliance Costs
Manufacturers
Generators
Total
$97.69 !
$27737726 |
$2,834.89 |
$1.31 !
$279784 |
$281.15 |
$1.24
$712
$2.36
$14.04 I
$7316769 !
$330.13 |
$18.97 I
$746 |
$26.43 |
$47.03 !
$f;579o747 1
$1,637.50 |
$1.17
$106
$2.23
$181.44
$47933726
$5,114.70
After-Tax Compliance Costs
Manufacturers
Generators
Total
$58.89 |
$17803787 |
$1,862.76 |
$0.78 |
$195705 |
$195.83 |
$0.75
$075
$1.50
$8.50 |
$208742 1
$216.92 |
$11.48 |
$5.To |
$16.58 |
$29.14 |
$^054.61 |
$1,083.75 |
$0.71
$071
$1.42
$110.24
$17268750
$3,378.74
Option 3: I&E Mortality Everywhere
Pre-Tax Compliance Costs
Manufacturers
Generators
Total
$161.45 |
$727788716 1
$2,949.61 I
$0.72 |
£2957% |
$296.68 !
$0.76
$1766
$1.82
$10.91 |
$319720 1
$330.11 I
$6.46 |
$4.07 |
$10.53 !
$65.86 |
$^7669782 1
$1,735.68 !
$0.72
$To"i
$1.73
$246.88
$757079728
$5,326.16
After-Tax Compliance Costs
Manufacturers
Generators
Total
$97.57 |
$1784613 1
$1,937.90 |
$0.43 |
$207.29 1
$207.72 !
$0.46
$671
$1.17
$6.61 |
$210767 1
$217.28 |
$3.90 !
$2763 1
$6.53 !
$40.45 |
$1711245 |
$1,152.90 !
$0.44
$0767
$1.11
$149.86
$37374774
$3,524.60
Source: U.S. EPA analysis, 2010
3.1.8 Uncertainties and Limitations
> Data on cooling water systems at in-scope facilities may not reflect the current circumstances of some
facilities, given the passage of time since completion of the 316(b) facility survey. In addition, it is
possible that the set of facilities in the earlier survey may differ from the set of facilities that would be
within the scope of the regulatory options, due either to retirement of facilities from operation or addition
of generating units since that time.
> As the detailed questionnaire was administered by EPA in 1999-2000 for the original 316(b) rulemakings,
the data may no longer accurately represent the business conditions or cooling water usage of the sampled
facilities. For generators, EPA supplemented the survey information with the most recent information
available from the EIA. However, for manufacturers, no public or private source of data contains the type
of information collected by the survey, so EPA used the original survey data, updating it where possible
based on overall trends in the regulated industries.
> To the extent that EPA used the same set of facilities for the analysis of this regulation as the one used for
the previous 316(b) analyses, the same set of uncertainties regarding the facility sample and cost estimates
apply. In particular, EPA's compliance cost estimates are subject to uncertainties about the number and
characteristics of the existing facilities that will be subject to the rule. Projecting the number of existing
facilities that meet the design intake flow threshold is subject to uncertainties associated with the quality
March 28, 2011
3-25
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
of data reported by facilities in the original questionnaire surveys (see Chapter Bl: Summary of
Compliance Costs in the suspended 2004 Final Section 316(b) Phase II Existing Facilities Rule report).
> Given the large number of implicitly analyzed electric power facilities, it is impossible to develop sample
weights that accurately account for all economic and operating differences of these facilities (see
memorandum dated June 18, 2008). Consequently, the estimated national facility compliance costs may
be over- or under-estimated due to statistical error in the facility sample weights.
> Additional uncertainties are associated with downtime cost estimates. In its analysis of connection outage,
EPA relied on either IPM-projected estimates of electricity generation, prices, and variable production
costs or historical EIA electricity generation and prices, which may not be representative of actual
electricity market conditions when facilities comply with this proposed regulation. Further, to the extent
that technology installation occurs during the shoulder months of spring and fall, when electricity demand
is on average below that during winter, the downtime costs for IPM-modeled Electric Generators, which
are estimated using winter cost and revenue values, are likely to be over-stated. For IPM-modeled Electric
Generators there is also an uncertainty of how much capacity revenue complying facilities will actually
lose as the result of addition downtime; to the extent that this value is less than the amount calculated
from total annual capacity revenue, the impact of technology installation downtime for these facilities
may be overstated. Overall, these uncertainties point to overestimation of the impact of downtime on
complying facilities.
> Due to time constraints, EPA was not able to perform a full reassessment of the administrative costs that
will be required of facilities and state and federal governments for the proposed regulation. Though EPA
was able to eliminate activities related to initial and subsequent permit issuance for facilities with cooling
towers, the assumption that administrative activities required under this proposed regulation would be the
same as those under the suspended 2004 Final Section 316(b) Phase II Existing Facilities Rule, may be
inaccurate. Further, to the extent that EPA used suspended 2004 Phase II Final Administrative Cost
framework, the uncertainties with that framework still apply (Chapter Bl: Summary of Compliance Costs
in the suspended 2004 Phase II Final EA Report).
3.2 Development of Administrative Costs to State and Federal Governments
This section presents the estimated costs to State and federal governments administering the Proposed 316 (b)
Existing Facilities Rule. EPA developed costs of administering the requirements of the Proposed Rule and the
alternative regulatory options.
In developing these costs, EPA closely followed the methodologies used in support of the suspended 2004 Final
Section 316(b) Phase II Existing Facilities Rule and to the extent possible relied on the same data sources.82 For
those parts of analysis related to cooling tower technologies, which were not a part of the suspended 2004 Phase
II Final Rule, but were a part of the original 2002 Proposed Section 316(b) Phase II Existing Facilities Rule, EPA
closely followed these methodologies, and to the extent possible, relied on the same data sources.83
Administrative costs to State and federal governments are closely related to the administrative costs to complying
facilities, and are primarily based on labor costs to review information produced by complying facilities and to
write the necessary permits. State governments incur start-up costs in the year of promulgation, i.e., 2012, costs
82 For details fee Economic and Benefits Analysis for the Final Section 316(b) Phase II Existing Facilities Rule. U.S. EPA, 2004. Office
of Water. EPA-821-R-04-005 available online at
http://water.epa.gov/lawsregs/lawsguidance/cwa7316b/phase2/upload/2009_03_26_316b_phase2_econbenefits_final_toc.pdf
83 For details see Economic and Benefits Analysis for the Proposed Section 316(b) Phase II Existing Facilities Rule. U.S. EPA, 2002.
Office of Water. EPA-821-R-02-001 available online at http://water.epa.gov/lawsregs/lawsguidance/cwa/316b/phase2/upload/toc.pdf
3-26 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
for permit issuance in and after the compliance year, annual monitoring costs, and costs for permit renewal. The
federal government is assumed to incur only costs for permit review, which occurs in the compliance year. Details
of these costs are presented below.
3.2.1 Administrative Costs to State and Territorial Governments
EPA assessed total administrative costs for National Pollutant Discharge Elimination System (NPDES) permitting
authorities for states (45) and territories (1) under section 402(c) of the Clean Water Act (CWA).84'85 EPA
evaluated costs associated with three distinct periods of rule administration:
> Initial Permitting activities: costs including start-up activities, review of permit applications and other
information compiled by complying facilities during the permitting process, and are incurred for every
permit
> Annual activities: costs incurred to review annual monitoring data and other information produced by
complying facilities on an annual basis, which are incurred for every permit
> Permit renewal activities: includes activities similar to permitting activities, incurred every 5 years as
facilities renew their NPDES permits.
Individual activities associated with each activity group have been modified to correspond to the activities now
required of the facilities; costs to review activities that are no longer required have been excluded from the
analysis of this proposed regulation. Costs were updated from the values used for the suspended Phase II Final
regulation using the same methods as for private administrative costs described in Section 3.1.5. As with the
administrative costs to facilities, the administrative costs to governments presented below do not reflect the
adjustment for real changes in labor costs using the Employment Cost Index; this adjustment was applied after
summing the individual activity costs.
Table 3-9 presents the groups of activities required for NPDES permitting authorities for each period of
administration. Costs per permit vary based on a facility's performance requirement (I&E mortality, cooling
tower). Start-up costs are estimated to be $5,024 for each of the 46 NPDES authorities. Permit issuance activities
are estimated to cost $5,089 for a facility either having or required to install a cooling tower only and $67,168 for
a facility required to either install new IM technology only or both IM technology and cooling tower. Annual
activities are estimated to cost approximately $2,375 per facility. Facilities with AIF exceeding 125 MOD and not
installing a cooling tower are required to performance an Initial Entrainment Study, which is estimated to impose
a cost on the permitting authority of approximately $26,912 per facility. In addition, these facilities are expected
to conduct Follow-Up Entrainment studies every third year after they complete the Initial Entrainment Study;
processing of these Follow-Up Entrainment Studies is estimated to result in a cost to the NPDES permitting
authority of approximately $8,971 per facility per year in which the Follow-Up Entrainment Study is performed.
Permit reissuance costs vary based on the same facility characteristics as the permitting costs, and are expected to
be $1,786 for facilities with cooling towers only and $20,444 for facilities having installed either new IM
technology only or both IM technology and cooling tower, recurring every 5 years after the initial post-
promulgation permit is issued.
84 EPA incurs the costs assigned to facilities located in states without NPDES permitting authority. The labor costs are assumed to be the
same as those for the State governments incurring these administrative costs.
85 Since the time of this analysis, Alaska was also granted NPDES permitting authority. Only one in-scope generator is located in
Alaska.
March 28, 2011 3-27
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
Table 3-9: Administrative Activity Groups and Costs for NPDES Permitting Authorities ($2009)
Activity Group
Facilities Installing IM
Technology Only
Facilities Installing
Cooling Towers
First Post-Promulgation Permit Issuance
Start-up Activities8
Permit Issuance Activities6
$5,024
67J68 | 5;089
Annual
Annual Monitoring Review
2,375
2,375
Entrainment Study
Initial0
Follow-Up3
$26,912
$8,971
None
None
Permit Reissuance
Permit Issuance Activities
20,444
1,786
a. Start-up Activities are incurred for each of the 46 Permitting Authorities in the year before facility compliance activities begin, while all other
activities are incurred per permit
b. Incurred in the compliance year
c. Permitting authority costs for the Initial Entrainment Study costs are incurred once for all Electric Generator and Manufacturers.
d. The Follow-Up Entrainment Study costs are incurred every third year after completion of the Initial Entrainment Study.
Source: U.S. EPA analysis, 2010
3.2.2 Administrative Costs to the Federal Government
The federal government is expected to incur costs for program oversight only in the initial compliance year for
facilities. Its role is to review certain permit issuance activities performed by the NPDES permitting authorities,
and it incurs labor costs and O&M costs for these activities. The federal government is expected to incur costs of
$830 per facility before adjustment for real changes in labor costs.86
3.2.3 Total Administrative Costs
Total State and federal administrative costs were calculated for the social cost analysis. For more details see
Chapter 11: Assessment of Total Social Costs.
3.2.4 Uncertainties and Limitations
> There is a significant uncertainty associated with the estimates of time required to complete each
administrative activity, the number of these activities, the type of personnel administrating these activities
as well as associated labor rates and/or other costs.
> Annualized cost of administrative activities depends on when they are incurred. If facilities come into
compliance later or earlier than assumed in this analysis, permitting authorities' administrative activities
will also occur in later or earlier years, respectively. Consequently, the annualized costs of the Proposed
Existing Facilities Rule to permitting authorities will be lower or higher because administrative costs
incurred in later years have lower or higher, respectively, net present values.
> The incremental administrative burden on States will also depends on the extent of each State's current
practices for regulating cooling water intake structures (CWIS). States that currently require relatively
modest analysis, monitoring, and reporting of impacts from CWIS in NPDES permits may require more
permitting resources to implement the Proposed Existing Facilities Rule than are required under their
current programs. Conversely, States that currently require very detailed analysis may require fewer
permitting resources to implement the Proposed Rule than are currently required.
86 In addition to these costs, the federal government (EPA) may also incur costs for permitting-related activities on behalf of the five
states that have not been delegated NPDES permitting authority under the Clean Water Act (see footnote 84, above). The initial and
ongoing permitting costs are accounted for under state costs. EPA is assumed not to incur additional start-up costs for these otherwise
state-assigned activities.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
> To the extent that EPA used the 2004 Phase II Final Administrative Cost framework, the uncertainties of
that framework still apply (see Chapter B5: UMRA Analysis in the suspended 2004 Phase II Final EA
Report).
3.3 Development of Costs for New Units
The regulatory options analyzed for the Proposed Existing Facilities Rule include provisions for installation of
entrainment mortality technology (closed cycle cooling system) at newly constructed generating units at existing
facilities. The practical effect of the new facilities provision is the same for all three regulatory options; all new
generating units at existing facilities would be subject to the EM technology requirement.
Analysis of the cost of the new facilities provision involves:
> Estimating the occurrence of new facilities over time that would be subject to the requirement for
installation of EM technology.
> Estimating the unit costs that would be incurred for EM technology installation and operation for the new
units.
> Estimating the total costs based on the occurrence of new unit EM technology installation.
The Agency expects that for Manufacturers compliance costs associated with new units will be negligible.
Consequently, the discussion of the New Unit provision cost development in this Section 3.3 focuses on costs for
Electric Generators only.
3.3.1 Estimating Costs for New Generating Units
Estimating the Occurrence of New Generating Units Subject to the New Unit EM Technology
Requirement
For estimating the occurrence of new generating units that would be subject to the new units EM technology
requirement, EPA began from projections of new steam generating capacity as reported in the Integrated Planning
Model baseline for the existing facilities rule analysis (see Chapter 6). This baseline projection of new capacity
covers the period 2012 through 2035, and includes three steam capacity types: coal, combined cycle, and nuclear.
EPA used the annual average of new capacity additions in these capacity types as the starting point for the new
generating units analysis. EPA assumed that this rate of new capacity addition would continue on a constant
annual basis beginning with the first year after promulgation, i.e., 2013, and through the remainder of the rule
analysis period:
> Coal: 3,573 MW
> Combined cycle: 1,491 MW
> Nuclear: 1,938 MW.
These values overstate the quantity of capacity in new units that would be subject to the new units EM technology
requirement of the existing facilities rule for several reasons:
> Some of this capacity will be subject to the 316(b) Phase I requirement for closed cycle cooling system
installation at new generating facilities; the requirement in the existing facilities rule will thus not affect
these facilities.
• New coal and combined cycle capacity: EPA estimates that approximately 70 percent of this capacity
will be subject to the Phase I requirements, with the residual of 30 percent potentially affected by the
new units EM technology requirement.
March 28, 2011 3-29
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
• New nuclear unit capacity: EPA estimates that all of this capacity will occur at existing nuclear
facility sites, and therefore not be subject to 316(b) Phase I requirements. Thus, all of this capacity
would be potentially affected by the new units EM technology requirement.
> Within the new generating unit capacity that would be subject to the new units EM technology
requirement, EPA further estimates that approximately half of this capacity would be required to install
closed cycle cooling systems independent of the new facility requirements of the existing facilities rule.
Accordingly, this capacity would not incur additional costs because of the new units EM technology
requirement since the new capacity is already expected to install closed cycle cooling system based on
local permitting requirements. The remaining 50 percent of this capacity would be affected by the new
units EM technology requirement
> Finally, EPA set aside from the remaining residual, the new capacity that is expected to be accomplished
via capacity increases at existing units. Specifically, EPA estimated that approximately 90 percent of the
remaining new coal capacity would be accomplished in new generating units; 15 percent of the remaining
new combined cycle capacity would be accomplished in new generating units; and none of the remaining
new nuclear capacity would be accomplished in new generating units. In each case, the residual of new
capacity is expected to occur in new generating units and represents the annual average addition to
capacity that would be subject to the new units provision of the existing facilities rule.
These estimates and the associated calculations yield the following estimates of annual capacity installation as
new generating units that would be subject to the new units EM technology requirement (see Table 3-10). In
summary, EPA estimates that 482 MW of coal capacity, 34 MW of combined cycle, and no nuclear capacity will
occur in new generating units. These estimates of new generating unit capacity that would be subject to the new
units EM technology requirement do not vary by regulatory option since these capacity additions occur as new
generating units, and thus do not have a baseline intake flow, etc., which could lead to varying applicability of the
regulatory options' direct requirements to these new facility capacity installations. (See Technical Development
Document for additional information on the estimation of these capacity values).
Table 3-10: Annual Capacity Installation Subject to New Units EM Technology Requirement in New
Generating Units (MW)
Coal
Combined Cycle
Nuclear
Projected
Annual
Average New
Capacity
3,573
i",49"i
U938
Subject to
Phase I New
Facility
Requirements
2,501
F,044
Residual
Subject to
Existing
Facilities Rule
Requirements
1,072
447
U938
Install EM
Technology
Independent
of Rule
Requirements
536
224
969
Residual
Incurring Cost
due to Existing
Facilities Rule
Requirements
536
224
969
Capacity
Increase in
Existing Units
54
190
969
Capacity
Increase in
New Units
482
34
Source: U.S. EPA Analysis, 2010
Estimating Unit Costs for New Generating Units Subject to the New Units EM Technology
Requirement
EPA estimated that new generating units subject to the new units EM technology requirement would incur
additional fixed and variable O&M costs, and additional costs from auxiliary energy requirements from
installation and operation of EM technology. Auxiliary energy requirements are assumed to be factored into the
planning and development of new generating units and are valued as an increment to baseline energy costs. EPA
estimated that these generating units would not incur additional capital costs, downtime, or costs from reduced
energy conversion efficiency in the generating system due to installation of EM technology. Accordingly, all cost
effects occur in annually recurring cost elements. EPA estimated these costs and energy penalty effects on a per
3-30 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 3: Development of Costs for Regulatory Options
MW basis. The cost and energy penalty effects vary by fuel type (see Technical Development Document for
additional information on this cost estimation).
Estimating Total Costs for New Generating Units Subject to the New Units EM Technology
Requirement
EPA combined the estimates of annual average capacity installation in new generating units and the unit cost
estimates to yield the streams of costs that occur overtime from installation of EM technology at new generating
units. In each year following rule promulgation, the estimated installation of new capacity initiates a stream of
annually recurring costs (fixed O&M, variable O&M, and auxiliary energy requirements) estimated to occur over
the life of the analysis. As a result, the aggregate costs from installation and operation of EM technology at new
generating units cumulate into the future as new cohorts of generating units come on line in each successive year.
EPA tallied these costs on a present value basis for each year of new generating unit installation activity -
beginning with the first year following rule promulgation, i.e., 2013, and continuing through the remainder of the
regulatory analysis period - and then calculated a total present value and annualized cost as the year of rule
promulgation.
The resulting annualized cost values for the new units EM technology requirement at new generating units are
$11.3 million pre-tax and $7.72 million after-tax ($2009, at 2012). These values are not included in the summary
of rule cost totals presented in Table 3-7 earlier in this chapter because these costs would not be incurred for
installation and operation of compliance technology at existing units, but would occur only as a result of the
construction of new generating units, as described above.
March 28, 2011 3-31
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 3A: Sample Weights Development and Use
Appendix 3A Use of Sample Weights in the Proposed Existing Facilities
Rule Analyses
In the analyses for the Proposed Section 316(b) Existing Facilities regulation, EPA used sample weights to
estimate costs and impacts on the Electric Generator and Manufacturer facilities for which detailed analysis was
not performed. Sample weights were also used to estimate impacts on entities that own 316(b) in-scope facilities.
The first section of this appendix discusses facility-level weights; the second section discusses entity-level
weights.
At the time of the 2000 316(b) Detailed Industry Questionnaire (DQ), survey sample weights were developed for
Manufacturer facilities and for Electric Generator facilities, and were used in the earlier analyses for the 316(b)
Phase II and Phase III regulations. These weights, which accounted for non-sampled facilities and non-
respondents, are referred to in this appendix as the original survey weights. For manufacturing facilities, EPA
continues to use these original weights in the current rule analyses. However, because of differences in some of
the current rule analyses for electric power generating facilities, it was necessary to develop additional weights
for the electric power facility analyses. In particular, it was necessary to develop new weights to account for
different analytic approaches for Electric Generators that received the DQ from those that received the Short
Technical Questionnaire (STQ). These new weights, which provide a basis for extrapolating analyses from
facilities that received the DQ to represent facilities that received the STQ, are referred to as the new DQ weights.
Development of these weights and their use in the current rule analyses are explained in this appendix (see Section
3A.1.2).
Table 3A-4 at the end of this appendix, summarizes the various weighting concepts used in the current analyses
and in the relevant chapters of this report.
3A.1 Facility-Level Weights
3A.1.1 Manufacturers
Original survey weights
EPA applied sample weights to the Manufacturers survey respondents to account for non-sampled facilities and
non-responding facilities. For more information on EPA's Section 316(b) Industry Surveys, please refer to the
Information Collection Request (U.S. EPA, 2000). For the analyses presented in this document, EPA continues to
use the weights developed for the 2006 Final Section 316(b) Phase III Existing Facilities Rule. These weights
consist of two parallel subsets of weights. One set is used for analyses that use the engineering information from
the 316(b) Manufacturers Questionnaire, including assessment of the number of affected facilities and the costs of
installing new technology, and are referred to as the "technical weights." The second set of weights is used in
analyses that rely on facility financial information, including analyses that assess the impact of the rule on a
facility's financial health. These weights are referred to as the "economic weights."
In the responses to the 316(b) Manufacturers Questionnaire, EPA found that 14 facilities provided insufficient
financial information to support impact analyses. Therefore, these 14 facilities were removed from the economic
impact assessment; these facilities plus the sample-weighted facilities that they would otherwise represent were
redistributed among the remaining facilities in their sector.
March 28, 2011
3A-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 3A: Sample Weights Development and Use
Facilities in other industries
As discussed in the earlier 316(b) Phase III rule analyses, EPA received survey questionnaires from facilities with
business operations in manufacturing industries other than the set of industries on which EPA had stratified the
2000 Section 316(b) Industry Survey (referred to in the Phase III rule analysis documents as the Primary
Manufacturing Industries). EPA originally believed these facilities to be non-utility electric power generators;
however, inspection of their responses indicated that the facilities were better understood as cooling water-
dependent facilities whose principal operations lie in businesses other than the electric power industry or the
Primary Manufacturing Industries listed above. These surveys included 12 questionnaires from facilities in the
Food and Kindred Products industry and 10 additional questionnaires from facilities in a range of other
manufacturing and non-manufacturing industries. In the earlier Phase III rule analysis documents, EPA referred to
these additional industries as the "Other Industries" and the facilities as the "Other Industries facilities."
Because the questionnaire responses for these Other Industries facilities were not received through the structured
sample framework, EPA did not apply sample weights to these facilities in the earlier 316(b) analyses and treated
them as "additional known facility" observations with a sample weight of one. Except for the facilities in the Food
and Kindred Products industry, EPA followed this same convention in its analysis for the Proposed Existing
Facilities Rule - i.e., assigning a sample weight of one to these observations.
For the analysis conducted for the 2006 Final Section 316(b) Phase III Existing Facilities Rule, EPA included the
Food and Kindred Products industry in the set of Primary Manufacturing Industries and used the cooling water
usage-based multiplier of 3.11 to estimate the industry-level costs and impacts of Phase III regulatory compliance
for the Food and Kindred Products industry. Therefore, these 12 sampled facilities represent 37 facilities in the
Food and Kindred Products Industry.
For the current analysis, EPA kept the Food and Kindred Products industry in the set of Primary Manufacturing
Industries. However, because EPA did not have sufficient survey data for 1 of the 12 sampled facilities, EPA
adjusted both "economic" and "technical" weights to reflect the fact that only 11 of the 12 questionnaires had
sufficient information for completing the impact analysis.
3A.1.2 Electric Power Generating Facilities
For the facility-level Electric Generator analyses, EPA used a combination of weights from the earlier 316(b)
Phase II and Phase III analyses (Original Survey Weights), and sample weights that were newly developed to
support the Proposed Rule analyses.
Original survey weights
As described in the regulatory analysis documents for the earlier 316(b) regulations, EPA collected technical and
economic information from the expected in-scope facilities through the Short Technical Questionnaire (STQ) and
the Detailed Questionnaire (DQ). Based on these survey responses, EPA developed facility-level sample weights
that account for those facilities in the total in-scope population that either were not surveyed or did not respond.
These weights use the total of DQ and STQ facilities as the underlying sample facility set, and account only for
the non-respondents to the original 316(b) survey. In general, these weights are numerically close to one, as EPA
had either DQ or STQ information for 656 facilities out of the 671 facilities presumed to be in-scope of the earlier
316(b) regulations.87
For the 2010 Proposed Rule analyses that did not rely on cost information for facilities, such as the Industry
Profile, EPA continued to use these Original Survey Weights, excluding weights for facilities that have since
closed or are expected to close according to either the Energy Information Administration (EIA) or the Integrated
87 Includes all Electric Generators in the 2000 316(b) Survey as opposed to only those facilities that are within the scope of the Proposed
Existing Facilities Rule.
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Planning Model (IPM), which EPA used to assess the impact of the Proposed Rule on electricity market (see
Chapter 6: Electricity Market Model Analysis). As noted in Table 3A-4, EPA also incorporated these weights into
the sample weights used for the economic analysis of the Existing Facilities Proposed Rule (New DQ Weights,
described below), for those DQ and STQ facilities that are known to have a re-circulating system in place or have
water intake velocity less than or equal to 0.5 feet per second, and therefore will have no technology-related costs
for compliance. Assigning the Original Survey Weights to these DQ and STQ facilities eliminates the need to
extrapolate information for the STQ facilities in this facility group, and ensures that these facilities are not
disproportionately represented.
New facility-level weights for the Proposed Existing Facilities Rule analyses
As described above, the earlier 316(b) rule analyses were based on facility-level data obtained through the STQs
and DQs. For the current analysis, EPA explicitly estimated costs for installing and operating compliance
technology for only those facilities that responded to the DQ. To extrapolate these compliance cost estimates to
all expected in-scope facilities (i.e., including the STQ facilities and survey non-respondents), EPA therefore
developed and applied the New DQ Weights to estimate costs and other facility-level information (e.g., facility
counts, generating capacity, design intake flow (DIP)) from the DQ facilities.
Throughout this document, EPA refers to those facilities for which compliance costs were specifically estimated
as the "explicitly analyzed" facilities. The facilities for which compliance costs were not specifically estimated are
referred to as the "implicitly analyzed" facilities. As described in this Appendix, explicitly analyzed facilities
include (1) all DQ facilities and (2) STQ facilities with a re-circulating system in the baseline and intake velocity
of less than or equal to 0.5 feet per second. The implicitly analyzed facilities include all other STQ facilities. In
the analysis of cost and economic impacts for the Proposed Existing Facilities Rule, the implicitly analyzed
facilities are accounted for by applying the appropriate facility-level weights to the findings for the explicitly
analyzed facilities.88
Development of Proposed Existing Facilities Rule facility-level weights
Extrapolating costs and other information from DQ facilities to STQ facilities required the development of a new
set of facility-level weights, as the Original Survey Weights were designed only to account for survey non-
respondents because, in the previous 316(b) rule analyses, EPA developed costs for STQ facilities.
In developing weights for the current rule analyses, EPA considered several approaches attempting to account
simultaneously for:
Three Control Variables And Four Classification Variables
> Generating capacity > NERC region
> Number of facilities > Capacity/fuel type (coal steam, combined cycle,
etc.)
> Design intake capacity > Ownership (investor-owned, nonutility, etc.)
> Baseline cooling water intake structure
specifications and related compliance
requirements (Technology Group)
EPA was unable to develop a single set of weights that accurately accounted for all of the control variables
according to each of the classification variables, and therefore chose to develop three sets of weights, one based
on each of the three control variables. Even with this approach, EPA was unable to develop weights that
88 The DQ and STQ facilities with a re-circulating systems in place and intake velocity of less than or equal to 0.5 feet per second are
assumed to meet compliance requirements in their baseline; therefore, these facilities are not expected to incur any technology costs
and no extrapolation was necessary.
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Appendix 3A: Sample Weights Development and Use
accurately accounted for facilities in all four of the classification variables, and chose to focus on weights that
represented the NERC region classification and Compliance Requirements as accurately as possible, which EPA
believes are the more important classifications for understanding the economic implications of this action.89'90
For the extrapolation of compliance costs to the full set of expected in-scope facilities, EPA developed weights to
account for only the DQ and total of DQ/STQ facilities that are estimated to be in-scope of the Proposed Rule and
that may need to undertake a compliance technology response to the regulatory options considered for this
analysis.91 The three sets of sample weights differ according to the specific cost element or other facility
characteristic for which the sample weights are intended to provide estimates.
To ensure proper representation of STQ facilities by DQ facilities in terms of 316(b) compliance requirements
under each of the three proposed options, EPA grouped in-scope Electric Generators into seven Technology
Groups (Table 3A-1)92
Table 3A-1: Use of Weights in the Cost and Economic Impact Analysis for the Proposed Existing
Facilities Rule
Technology Group
Compliance Requirements
Has Baseline
Recirculating
System
Yesa
Yes
Yes
No
No
No
No
Water Intake
Velocity
<=0.5
<=(X5
<=(X5
DIP Group
2+ MOD
""<=125"MGD""
""<=125"MGD""
Option 1
No Technology
IM Assigned
IM Assigned
No Technology
IM Assigned
No Technology
IM Assigned
Option 2
No Technology
IM Assigned
IM Assigned
No Technology
IM Assigned
CT Assigned
CT Assigned
Option 3
No Technology
IM Assigned
IM Assigned
CT Assigned
CT Assigned
CT Assigned
CT Assigned
a. Because these facilities are assumed to be in compliance with the requirements of all three options, EPA did not have to extrapolate compliance costs
for these facilities. These facilities are explicitly analyzed DQ and STQ facilities.
EPA developed sample weights on three extrapolation bases (the control variables listed above): (1) facility count,
(2) electric generating capacity, and (3) design intake flow. Although the underlying set of DQ Electric
Generators and the set of total in-scope Electric Generators on which these weights were developed are the same
for each weight set, the weights for any DQ facility may differ by weight set: each weight set is intended to
provide an accurate estimate for only one facility concept. For each weighting concept, EPA developed weights
that accurately account for that control variable (i.e. number of facilities, total generating capacity, or total intake
flow) in each NERC region and Technology Group. For example, using facility count-based weights accurately
89
91
For more details of the approaches considered by EPA see memorandum dated June 18, 2008
90 Accounting for NERC regions is particularly important for the electricity rate and household impact analyses (see Chapter 5: Cost and
Economic Impact Analysis - Electric Generators). Consequently, to develop New DQ Weights EPA relied on sets of NERC region
assignments for Electric Generators facilities in these analyses. As noted previously, NERC region definitions have changed recently.
In developing these sample weights, EPA accounted for known changes in the universe of DQ and STQ facilities that are expected to
be within the scope of the Proposed Existing Facilities Rule options. In particular, EPA set aside from the weights development effort,
and the cost and impact analysis generally, facilities that are documented in 2007 EIA database to retire by 2012 (15 facilities) or to
have already retired all cooling water-dependent (steam-based) electric generating capacity (38 facilities). In addition, EPA excluded
39 facilities projected to retire all steam-based electric generating capacity by IPM.
Because 23 in-scope STQ facilities did not have a DQ representation in their respective NERC regions and compliance requirements
groups, EPA re-assigned these STQ facilities to the NERC regions with relatively more substantial DQ representation in their
respective compliance requirements groups. In addition, to ensure a better representation of 5 STQ facilities in the WECC NERC
region in Technology Groups 5 and 6, EPA assigned these facilities to other NERC regions in these Technology Groups with
substantial DQ representation.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 3A: Sample Weights Development and Use
represents the number of facilities in each region and requirements group, but may misrepresent the region's total
capacity or intake flow, whereas using capacity-based weights will accurately represent the total capacity in a
given NERC region and compliance group, but may distort its number of facilities and total flow. Cost elements
were thus weighted based on the concept corresponding to the parameters underlying the cost. A schedule of
weight concepts employed for each cost element is provided in Table 3A-2, below. The use of these separate
weight sets for extrapolating from the DQ-facility set to the total in-scope facility set improves the overall
accuracy of the sample-weighted estimates.
Based on the DQ and STQ responses, EPA determined that some in-scope DQ and STQ facilities already operate
a re-circulating system and have intake flow of not more than 0.5 feet per second, and that these facilities would
therefore not be expected to require additional compliance technology for compliance with any of the regulatory
options. Although these facilities would not be expected to incur costs for installation of compliance technology,
they would incur certain administrative costs as a result of the Proposed Existing Facilities Rule. For these
facilities, EPA used the Original Survey Weights, as described in Section 3A.1. Thus, a facility with a re-
circulating system in place and intake flow of no more than 0.5 feet per second receives its sample weight from
the Original Survey Weights in all three of the weight sets used for the Existing Facilities Rule analysis,
regardless of whether the facility replied to the DQ or STQ.
Use of newly developed facility-level weights
The different weight sets are used to estimate technology and other compliance-related costs or other complying
facility characteristics according to the primary driver of a given cost element or of a given facility characteristic.
For example, once a given compliance technology has been assigned to a facility, the primary driver of
technology capital cost is the facility's design intake flow. Accordingly, EPA used the design intake flow-based
weight set for extrapolating technology capital costs from the DQ facility set to the total of in-scope facilities.
Similarly, to estimate total affected intake flow for a given regulatory option, EPA again used the design intake
flow-based weight set to extrapolate from the DQ facility set to the total of in-scope facilities. On the other hand,
some cost elements depend more on the affected electric generating capacity - for example, installation downtime
costs. For estimating these costs and for estimating total affected electric generating capacity, EPA used the
electric generating capacity-based weights. Finally, for estimating cost elements that are facility count-dependent
(e.g., a fixed cost of compliance activity, such as initial permitting) and for estimating total affected facilities,
EPA used fas facility count-based weights. Table 3A-2 details the weight set used for each component of costs
considered by EPA in analyzing the costs of the proposed regulation.
Table 3A-2: Weights Applied to Each Cost Component
Weight Set
Capacity-based
DIF-based
Facility Count-based
Cost Component
Downtime Impact Costs
Energy Penalty Turbine (Auxiliary Requirements and Backpressure)
Capital Costs
O&M Costs
Initial Permitting Costs
Monitoring Costs
Permit Reissuance Costs
State initial Permitting Costs
State Monitoring Costs
State Permit Reissuance Costs
Federal Initial Permitting Costs
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3A.2 Entity-Level Analysis
3A.2.1 Manufacturers
EPA's sample-based facility analysis supports specific estimates of (1) the number of facilities expected to be
subject to the regulation and (2) the total compliance costs expected to be incurred in these facilities. However,
the sample-based analysis does not support specific estimates of the number of entities - or firms - that own
Manufacturer facilities. In addition, the sample-based analysis does not support specific estimates of the number
of regulated facilities that may be owned by a single firm, or the total of compliance costs across regulated
facilities that may be owned by a single firm.
For the firm-level analysis, EPA therefore considered two cases based on the sample weights developed from the
facility survey. These cases provide approximate upper and lower bound estimates on: (1) the number of firms
incurring compliance costs and (2) the costs incurred by any firm owning a regulated facility.93 The cases are as
follows:
Case 1: Lower bound estimate of number of firms owning facilities that face requirements under
the regulation; upper bound estimate of total compliance costs that a firm may incur.
For this case, EPA assumed that any firm owning a regulated sample facility(ies), owns the known sample
facility(ies) and all of the sample weight associated with the sample facility(ies). This case minimizes the count of
affected firms, as the weight for each known affected firm is 1, while tending to maximize the potential cost
burden to any single firm as they are assumed to own all the facilities represented by the sample weights of the
facility(ies) they are known to own.
Case 2: Upper bound estimate of number of firms owning facilities that face requirements under
the regulation; lower bound estimate of total compliance costs that a firm may incur.
For this case, which is an inversion of the assumption underlying Case 1, EPA assumed (1) that a firm owns only
the regulated sample facility(ies) that it is known to own from the sample analysis and (2) that this pattern of
ownership, observed for sampled facilities and their owning firms, extends over the facility population
represented by the Manufacturers sample facilities. This case minimizes the possibility of multi-facility ownership
by a single firm and thus maximizes the count of affected firms, but also minimizes the potential cost burden to
any single firm. In this case, the parent firms are weighted based on the weight(s) of the facility(ies) they own;
details on the analytical methods behind this procedure are described in Chapter 4, Section 4.5.1.
3A.2.2 Electric Power Generators
In addition to the use of facility-based weights as described in Section 3A. 1.2, EPA also developed and used
sample weights for estimating owning entity-level effects for Electric Generators that extend from the DQ
facilities, and the identified parent entities that own them, to the estimated population of parent entities that own
in-scope DQ and STQ facilities. These entity-level weights are needed because a number of owning entities own
only implicitly analyzed facilities. The parent entities that own only these implicitly analyzed facilities would
therefore not be accounted for by an analysis that focuses only on explicitly analyzed facilities and, and as a
result, only on their parent entities. The use of entity-level weights allows EPA to more precisely estimate the
impacts on entities owning only implicitly analyzed facilities by taking into account important entity
characteristics (such as business size size and type) in the development of the weights. The assessment of impact
at the level of the owning entity is important in EPA's analysis of firm/owning entity-level effects for Electric
93 The application of sample weights in the firm-level analyses for manufacturing facilities is the same as that followed in the earlier
316(b) Phase III rule analyses.
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Appendix 3A: Sample Weights Development and Use
Generators as part of the general cost and economic impact analysis (Chapter 5: Cost and Economic Impact
Analyses - Electric Generators) and for the analysis of the small entity impacts for the Regulatory Flexibility Act
analysis (Chapter 7: Regulatory Flexibility Act (RFA) Analysis).
Development of entity-level weights
EPA developed these weights from research to identify the current owning parent entity for all DQ and STQ
facilities currently in operation. In this effort, EPA also identified the entity-ownership type - such as private
firm, municipality, co-operative, etc. - and whether the owning parent entity would be classified as a small entity
based on Small Business Administration entity size criteria. The sample weights were developed in accordance
with this classification framework - by entity-ownership type and by entity size classification. Table 3A-3
presents the number of entities falling into each classification both for entities owning at least one explicitly
analyzed facility and for all entities.
Table 3A-3: Proposed Existing Facilities Rule Unique Parent Entities and Facilities (by Entity Type and
Size)
Parent Entity Type
Small Entity Size
Standard
Number of Parent Entities3 b
Large | Small I Total
Number of Facilities
Large | Small | Total0
Parent Entities Owning at Least One Explicitly Analyzed Facility
Rural Electric Cooperative
Federal
Investor-Owned Utilities
Municipality
Nonutility
Other Political Subdivision
State
4,000 MWh output
assumed large
4,000 MWh output
50,000 population served
4,000 MWh output
50,000 population served
assumed large
Total
9
1
38
7
26
0
4
85
2
0
1
6
3
0
0
12
11
1
39
13
29
0
4
98
11 i
7
137
7
73
0
8
243 i
2
0
1
6
5
0
0
14
i 13
7
138
13
82
0
8
! 257
All Known Parent Entities - Le., Parent Entities Owning Only Implicitly Analyzed Facilities or at Least One Explicitly Analyzed
Facility
Rural Electric Cooperative
Federal
Investor-Owned Utilities
Municipality
Nonutility
Other Political Subdivision
State
4,000 MWh output
assumed large
4,000 MWh output
50,000 population served
4,000 MWh output
50,000 population served
assumed large
Total
12
1
41
18
32
2
4
110
8
0
2
17
5
1
0
33
20
1
43
35
37
3
4
143
23 i
14 |
280
26
153
6
9
511 i
8
0
3
17
8
1
0
37
i 31
1 14
283
43
161
7
9
! 548
a. For 8 parent entities, EPA was unable to find the entity revenue values needed to determine the size of these entities; consequently, EPA used the total
revenue for all facilities owned by these entities to determine entity size
b. In 3 instances, an Electric Generator is owned by a joint venture of two entities.
c. 548 facilities include (1) explicitly analyzed facilities and (2) implicitly analyzed facilities that responded to the 2000 316(b) Surveys.
d. These counts are unweighted and reflect the known universe of facilities and their parent entities expected to be in scope of Proposed Existing Facilities
Rule.
Source: U.S. EPA Analysis, 2010
EPA developed the entity-level weights for each combination category of entity size and entity type by dividing
the number of parent entities at the total population level (i.e., parent entities owning at least one explicitly
analyzed facility and parent entities owning only implicitly analyzed facilities) by the number of parent entities
owning at least one explicitly analyzed facility. Applying these entity-level weights to the numbers of explicitly
analyzed facility-based parent entities assessed in the various cost impact categories yields an estimate of the
number of parent entities including the implicitly analyzed facility-based parent entities.
Application of entity-level weights
EPA applied entity-level weights in assessing impacts on owning entities for the cost impact analysis (Chapter 5,
Section 3) and in the RFA analysis (Chapter 7). Thus, the findings of impacts to entities owning explicitly
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 3A: Sample Weights Development and Use
analyzed facilities (i.e., the number of parent entities in a given impact category) were extrapolated to entities
owning implicitly analyzed facilities with the same characteristics by multiplying by the appropriate weight.
As described in Chapter 5, Section 3 and Chapter 7, the entity-level impact analyses were performed in two
weighting configurations: (1) using only entity-level weights and (2) using only facility-level weights. The
Agency notes that using only entity-level weights may understate the impact on an individual entity, while using
only facility-level weights may overstate the impact on a given entity. For this reason, EPA performed and
presents results for entity-level analyses using both of these weighting concepts. EPA chose not to combine the
entity-level weights with the facility level weights, as this has the potential to overestimate the effects on both the
facilities and their owning entities. The relevant chapters present more information on how entity-level weights
were used in the analysis.
3A.3 Summary
Table 3A-4, following page, shows which weights were used in each of the analyses conducted for Electric
Generators and Manufacturers.
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Appendix 3A: Sample Weights Development and Use
Table 3A-4: Use of Weights in the Cost and Economic Impact Analysis for the Proposed Existing Facilities
Rule
Chapter
2: Industry Profile
3: Compliance Cost Assessment
4: Cost and Economic Impact Assessment
> Facility-Level Analysis
> Entity-Level Analysis
5: Market Model Analysis
6: Regulatory Flexibility Act (RFA) Analysis
> Facility-Level Analysis
> Entity-Level Analysis
7: Unfunded Mandates Reform Act (UMRA)
Analysis
8: Other Administrative Requirements
9: Social Cost Assessment
10: Cost and Benefits
Weights Used
Generators3
> Original Survey Weights
> New DQ Weights for all facilities except those with re-
circulating systems in the baseline and intake velocity of less
than or equal to 0. 5 feet per second, which use Original
Survey Weights
> New DQ Weights for all facilities except those with re-
circulating systems in the baseline and intake velocity of less
than or equal to 0. 5 feet per second, which use Original
Survey Weights
> New DQ Weights (Original Survey Weights for Facilities
with re- circulating systems in the baseline and intake velocity
of less than or equal to 0.5 feet per second), without using
Entity-level weights
> Entity-level weights, without using Facility-level weights
> No weights'
> New DQ Weights (Original Survey Weights for Facilities
with re- circulating systems in the baseline and intake velocity
of less than or equal to 0.5 feet per second)
> New DQ Weights (Original Survey Weights for Facilities
with re-circulating systems in the baseline and intake velocity
of less than or equal to 0.5 feet per second), without using
Entity-level weights
> Entity-level weights, without using Facility-level weights
> New DQ Weights (Original Survey Weights for Facilities
with re- circulating systems in the baseline and intake velocity
of less than or equal to 0.5 feet per second) for impacts to
facilities owned by governments and small governments
> New DQ Weights (Original Survey Weights for Facilities
with re- circulating systems in the baseline and intake velocity
of less than or equal to 0.5 feet per second) for E.O. 13132:
Federalism
> No weights for E.O. 132 1 1 : Energy Effects
> Original Survey Weights for Sort-Term Reliability
Assessment
> New DQ Weights (Original Survey Weights for Facilities
with re- circulating systems in the baseline and intake velocity
of less than or equal to 0.5 feet per second) for cost
development
> New DQ Weights (Original Survey Weights for Facilities
with re- circulating systems in the baseline and intake velocity
of less than or equal to 0.5 feet per second) used in Social
Cost Assessment
Manufacturers'"
"? Original Survey Weights (T)
"? Original Survey Weights (T)
> Original Survey Weights (E)
> Original Survey Weights (E)
> Not included
> Original Survey Weights (E)
> Original Survey Weights (E)
> Original Survey Weights (T)
> Original Survey Weights (T)
> Original Survey Weights (T)
> Original Survey Weights (T)
a. "DQ" refers to the Detailed Questionnaire.
b. Manufacturers survey sample weights consist of two sets, one used for economic impact analysis (denoted by an E), and another set used for all other
analyses (denoted by a T). For details on these two weight sets, see Section 3A.1.1.
c. "No weights" means that the analyses in a chapter do not use weights.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 3B: SIC to NAICS Data Conversion
Appendix 3B Mapping Manufacturers' Standard Industrial Classification
Codes to North American Industry Classification System Codes
At the time of the 2000 316(b) Detailed Industry Questionnaire (DQ), industry information was collected and
analyzed within the Standard Industry Classification (SIC) framework, which was the standard until 1997. In that
year, the United States switched to the North American Industry Classification System (NAICS) framework for
industrial classification. In the earlier 316(b) rulemaking analyses, data from years after 1997 were translated
from the NAICS framework back into the SIC framework for reporting historical industry trends. Now that more
than a decade of historical data is available in the NAICS framework, EPA determined that it was appropriate to
use the NAICS framework for all industry-level analyses. At the time of the 316(b) Industry Survey, surveyed
Manufacturers provided their primary SIC codes, which EPA used to categorize them into industries. To use the
316(b) Survey-based facility information in the current analyses, the Agency mapped facility-level 4-digit SIC
codes onto 6-digit NAICS codes to determine the industry to which to assign in-scope Manufacturers and for
which public industry data to collect.
Because there is not always a one-to-one relationship between a SIC and NAICS code, EPA first used a
Manufacturer's NPDES permit identification number to obtain current information about the facility, including
the facility's primary NAICS code. In the event that these data were not available or are unclear in the NPDES
database, EPA used the SIC code provided in the facilities' survey responses and mapping provided by the U.S.
Census Bureau to determine the appropriate NAICS code. In cases where the mapping between SIC and NAICS
was not one-to-one, EPA assigned the NAICS code with the highest value of shipments share according to the
1997 Economic Census: Bridge Between NAICS and SIC published by the U.S. Census.94
This bridge is available online at http://www.census.gov/epcd/ec97brdg/
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 4: Manufacturers Impact Analyses
4 Cost and Economic Impact Analysis - Manufacturers
4.1 Analysis Overview
This chapter assesses the expected economic impact of the 316(b) Existing Facilities Rule options on the
Manufacturers segment of in-scope facilities. In the same way as undertaken for the previous 316(b) Phase III
regulatory analyses, the analysis for the Manufacturers segment of the 316(b) existing facilities rule focuses on
impacts in six key manufacturing industries - Paper, Chemicals, Petroleum, Aluminum, Steel, and Food and
Kindred Products (the "Primary Manufacturing Industries") - in which a substantial number of facilities are
expected to be subject to regulation. EPA's analysis of the regulation's expected impact in these industries is
based on a statistically valid sample survey of facilities in these six industries. EPA's previous industry surveys
indicate that the regulation would potentially bring as many as 569 facilities in the Primary Manufacturing
Industries under national requirements.95
This chapter also considers the effect of the regulation on facilities in other industries ("Other Industries") that
would be within the scope of the regulatory options. The facility impact analysis for Other Industries is restricted
to a sample of 6 facilities for which EPA received surveys, but which are not part of the statistically valid sample.
As a result, EPA's analysis for the Other Industries group is limited to these known facilities. EPA has not
estimated the number of facilities in the Other Industries group that may be subject to the regulation because EPA
does not believe that this number can be reliably extrapolated from the sample of known facilities in this group.
However, because the statistically valid survey group of industries (i.e., Electric Generators and the six Primary
Manufacturing Industries) reflects 99 percent of total estimated cooling water withdrawals, EPA believes that few
additional facilities in the Other Industries group are potentially subject to the regulatory analysis options.
Although EPA was able to undertake impact analysis for the Other Industries group using only the sample of
known facilities for this group, EPA believes that its analysis for the Other Industries group provides a sufficient
basis for regulation development. EPA's review of the engineering characteristics of cooling water intake and use
in the Other Industries group indicates that cooling water intake and use in these industries do not differ
materially from cooling water intake and use in the electric power industry and the Primary Manufacturing
Industries.
Based on the sum of the sample-weighted estimate of 569 facilities in the Primary Manufacturing Industries and
the 10 known facilities in the Other Industries group, EPA included a total of 579 potentially regulated facilities in
the economic impact analysis for the Manufacturer. The total number of Manufacturer facilities considered in the
economic impact analysis (579) differs from the number of facilities potentially subject to regulation (592), as
reported in Chapter 1: Introduction, and as used as the basis for calculating the social costs of the regulatory
analysis options. EPA determined that the survey responses of 1496 sample facilities lacked certain financial data
needed for the facility impact analysis while containing sufficient data to support estimates of facility counts and
compliance costs. EPA therefore retained these sample facilities (37 sample weighted facilities) in the analyses to
estimate the total number of Manufacturers facilities potentially subject to regulation and for the calculation of
social cost but excluded them from the economic impact analysis. When these sample facilities were excluded
from the impact analysis, the sample weights for remaining facilities within the affected sample frames were
adjusted upwards to account for their removal. The difference in the reported facility totals in the impact and
95 EPA applied sample weights to 222 sample 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, 1999).
96 The numbers of facilities reported in the social cost chapter and in this chapter may differ due to independent rounding.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 4: Manufacturers Impact Analyses
social cost analyses reflects the removal of these 14 facilities and the use of adjusted sample weights. Both values
are valid statistical estimates of the same, but unknown, value of the Manufacturers' facility population.
EPA undertook the economic impact analysis for the Manufacturers segment to aid in assessing the economic
impact of alternative regulatory options and, on the basis of that assessment, to aid in defining a potential
regulation. Measures of economic impact for this segment include facility closures and associated losses in
employment, financial stress short of closure ("moderate impacts"), and firm-level impacts. Severe impacts are
facility closures and the associated losses in jobs at facilities that would close due to the regulation. EPA also
assessed moderate economic impacts to support its evaluation of regulatory options and to understand better the
regulation's economic impacts. Moderate impacts are adverse changes in a facility's financial position that are
not threatening to its short-term viability. The firm impact analysis assesses whether firms that own multiple
facilities are likely to incur more significant impacts than indicated by the facility impact analysis. Impacts may
be more significant at the firm level than at the facility level if a firm owns a number of facilities that incur
significant cost. In addition, a firm-level analysis is needed to assess impacts on small businesses, as required by
the Regulatory Flexibility Act. Other chapters consider the impacts on small entities (Chapter 7: Regulatory
Flexibility Analysis)91
In conducting these analyses, EPA closely followed the methodologies used to conduct analyses in support of the
previous 316(b) rule analyses and, to the extent practicable, relied on similar data sources.
4.2 Overview and Data Sources
The economic impact analyses for the Manufacturers segment of the 316(b) existing facilities rule rely on data
provided in the financial portion of the detailed questionnaires distributed by EPA to facilities potentially subject
to the previous 316(b) Phase III regulation. The survey financial data included facility and parent firm income
statements and balance sheets for the three years 1996, 1997, and 1998.
In addition to the survey data, a number of secondary sources were used to characterize economic and financial
conditions in the industries for this regulatory analysis. Secondary sources used in the analyses include:
> Department of Commerce economic census and survey data, including the Census of Manufactures,
Annual Surveys of Manufactures, international trade data; and quarterly financial reports (QFR);
> U.S. Industry and Trade Outlook, published by McGraw-Hill and the U.S. Department of Commerce;
> Annual Statement Studies, published by Risk Management Association (RMA); and
> Statistics of U.S. Businesses (SUSB).
For the facility-level impact analysis, EPA first eliminated from analysis those facilities showing materially
inadequate financial performance in the baseline, that is, in the absence of the regulation. EPA judged these
facilities, which are referred to as baseline closures, to be at substantial risk of financial failure regardless of any
financial impacts of the 316(b) regulation (see Table 4-1). Second, for the remaining facilities, EPA evaluated
how compliance costs would likely affect facility financial health. A facility is identified as a regulatory closure
if it would have operated under baseline conditions but would fall below an acceptable financial performance
level when subject to the new regulatory requirements. The closure test, or test of severe impacts, is detailed in
Section 4.3. EPA's analysis also identified facilities that would likely incur moderate impacts from compliance
with the regulation. EPA anticipates that these facilities would experience moderate deterioration of financial
performance but not at a level sufficient to cause the facility to fail financially. The test of moderate impacts is
detailed in Section 4.4.
97 This chapter also includes 4 appendixes, which address particular elements of the Manufacturers cost and economic impact analysis.
T2 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 4: Manufacturers Impact Analyses
Table 4-1: Summary of Baseline Closures by Sector for Manufacturers Segment Facilities Estimated
Subject to the 316(b) Existing Facilities Rule
Sector
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Total Facilities in Primary
Manufacturing Industries
Additional known facilities in
Other Industries"
Total Number of
Facilities
230
171
36
68
27
37
569
10
Number of Baseline
Closures
32
4
5
22
3
6
73
3
Percentage Closing in
Baseline
14%
3%
15%
32%
12%
17%
13%
30%
Number Operating in
Baseline
198
167
30
46
24
31
496
7
a. Totals may not sum due to independent rounding.
Source: U.S. EPA analysis, 2010
For the assessment of firm-level effects, EPA compared annualized after-tax compliance cost to firm revenue and
reports the estimated number and percentage of firms incurring compliance cost in three cost-to-revenue ranges:
less than one percent; at least one percent but less than three percent; and three percent or greater. Although
EPA's sample-based data support specific estimates of the number of facilities, these data do not support a
specific estimate of the number of entities that own these facilities. As a result, EPA estimated the number of
entities owning facilities in the Manufacturers segment as a range, based on alternative assumptions about the
potential ownership of regulated facilities. In its comparison of compliance cost to firm revenue, EPA also used
this same range concept, which yields approximate upper and lower bound estimates of the value of compliance
cost that might be incurred by an entity, based on the number of regulated facilities that it owns.
The following sections detail the calculations and results of the severe and moderate facility-level impact
assessments and the firm-level impact assessment.
4,3 Facility-Level Impacts; Severe Impact Analysis
4.3.1 Analysis Approach and Data Inputs
The assessment of severe impacts for facilities in the Manufacturers segment is based on the change in the
facility's estimated business value, as determined from a discounted present value analysis of baseline cash flow
and the change in cash flow resulting from regulatory compliance. If the estimated discounted cash flow value of
the facility is positive before considering the effects of regulatory compliance but becomes negative as a result of
compliance outlays, then the facility is considered a regulatory closure. In this impact test, the estimated ongoing
business value of the facility is compared with a threshold value of zero for the closure decision: as long as the
discounted cash flow value of the facility is greater than zero, the business is earning its cost of invested capital
and continuation of the business is warranted. If the discounted cash flow value of the facility is less than zero in
the baseline or becomes less than zero as a result of compliance outlays, then the business would not earn its cost
of invested capital and the business owners would be better off financially by terminating the business. As noted
in earlier discussion, facilities for which EPA estimated a negative baseline value were considered baseline
closures and were not tested for additional adverse impacts from regulatory compliance.
In an alternative formulation of this concept, business owners would compare the discounted cash flow value of
the facility with the value that the facility's assets would bring in liquidation. In this case, the estimated ongoing
business value would be compared with a value that may be different from zero: liquidation value could be
positive or negative. When liquidation value is positive, business owners might benefit financially by terminating
a business and seeking its liquidation value even when the ongoing business value is positive but less than the
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 4: Manufacturers Impact Analyses
estimated liquidation value. With negative liquidation value - which generally would result from business
termination liabilities (e.g., site clean-up) - the opposite result could occur: business owners may find it
financially advantageous to remain in business even though the business earns less than its cost of invested
capital, if the liquidation value of the business is "more negative," and thus less in value, than the ongoing
business based on the discounted cash flow analysis. EPA considered this alternative impact test formulation for
the previous 316(b) Manufacturers analyses. However, EPA judges that the liquidation value estimates are
substantially speculative and subject to considerable error because such an assessment requires detailed facility-
specific financial and operational history, and projections of future asset values and liabilities that are
considerably uncertain. For these reasons, EPA decided against using liquidation value for comparison with
ongoing business value in the closure test.
The cash flow concept used in calculating ongoing business value for the closure analysis is free cashflow
available to all capital. Free cash flow is the cash available to the providers of capital - both equity owners and
creditors - on an after-tax basis from business operations, and takes into account the cash required for ongoing
replacement of the facility's capital equipment. Free cash flow is discounted at an estimated after-tax total cost of
capital to yield the estimated business value of the facility. The baseline and post-compliance cash flow concepts
are outlined below. Details of these calculations can be found in Appendix 4E: Economic Impact Methodology -
Manufacturers.
Calculation of Baseline Free Cash Flow and Performance of Baseline Closure Test
Calculation of baseline free cash flow and performance of the baseline closure test involved the following steps:
> Average survey income statement data over response years and convert to 2009 dollars.
> Adjust after-tax income to exclude the effects of financial structure.
> Calculate after-tax cash flow from operations, before interest, by adjusting income for non-cash charges
such as depreciation and amortization.
> Remove the implied cash flow benefit of any negative taxes, as reported in the facility's income statement
after adjustment for removal of interest. This assumption is consistent with a later step in the post-
compliance analysis in which EPA limited the cash flow benefit of tax deductions on compliance outlays
not to exceed the amount of taxes paid as reported in the baseline income statement (and adjusted for
interest).
> Adjust after-tax cash flow to reflect estimated real change in business performance, as reflected in
baseline cash flow, from the time of survey data collection to the present (see Appendix 4.B: Adjusting
Baseline Facility Cash Flow}. This adjustment is intended to address two concerns: (1) that facility
survey data might have been collected during a period that deviated cyclically from the longer-term trend
of business performance for the 316(b) manufacturing industries; and (2) that some of the industries
might be experiencing a longer-term trend of deteriorating economic performance. In both cases, using
the survey-based data for the current analysis - without accounting for these possible effects - could lead
to misleading estimates of the affected industries' ability to withstand the compliance cost burdens of the
proposed existing facilities rule.
> Calculate free cash flow by adjusting after-tax cash flow from operations for estimated ongoing capital
equipment outlays (see Appendix 4.B).98
98 In the primary analysis, the cash flow analysis did not consider potential costs from other environmental regulations that might be
affect these industries at approximately the same time that the 316(b) regulatory requirements would come into effect. Recognizing
this potential impact, EPA also undertook an alternative case analysis in which it further adjusted baseline cash flow to reflect costs
that facilities might incur from compliance with Federal environmental regulations that were recently promulgated and whose costs
are not likely to be reflected fully in the ATCF adjustment analysis. This analysis, which is documented in Appendix 4.D: Analysis of
4~4March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 4: Manufacturers Impact Analyses
> Calculate baseline facility value as the present value of free cash flow over a 30-year analysis horizon,
using an estimated real (i.e., excluding the effects of inflation), after-tax cost of capital of 7.0 percent. The
use of 30 years as the time horizon reflects the facility-level analysis period for the 316(b) existing
facilities rule.
As explained above, EPA considered a facility to be a baseline closure if its estimated business value was
negative before incurring regulatory compliance costs. Baseline closures were neither tested for adverse impact in
the post-compliance impact analysis nor were their compliance costs included in the tally of total costs of 316(b)
regulatory compliance.
Calculation of Post-Compliance Free Cash Flow and Performance of Post-Compliance Closure
Test
For the post-compliance closure analysis, EPA recalculated annual free cash flow, accounting for changes in
revenue, annual expenses and taxes that are estimated to result from compliance-related outlays. EPA combined
the post-compliance free cash flow value and the estimated compliance capital outlay in the present value
framework to calculate business value on a post-compliance basis.
For the post-compliance analysis, EPA considered whether in-scope Manufacturers would be able to pass forward
compliance costs to customers through increased prices. From the analyses presented in Appendix 4A: Cost Pass-
Through Analysis, EPA concluded that an assumption of zero cost pass-through is appropriate for analyzing the
impact of the regulatory analysis options on facilities in the six Primary Manufacturing Industries (this is the same
assumption as applied in the previous analysis conducted in support of the 2006 Final Section 316(b) Phase III
Existing Facilities Rule). Performance of the impact analysis under this assumption means that facilities must
absorb all compliance-related costs and operating effects (e.g., income loss from facility shutdown during
equipment installation) within their baseline cash flow and financial condition. To the extent that facilities would
be able to pass on some of the compliance costs to customers through price increases, the analysis may overstate
the potential impact on complying facilities.
Calculation of post-compliance free cash flow and performance of the post-compliance closure test involved the
following steps:
> Adjust baseline annual free cash flow to reflect compliance outlay effects. As outlined previously,
compliance cost and other operating effects include annual change in revenue; annually recurring
operating and maintenance costs; the annual equivalent of permitting and re-permitting costs, which recur
on other than an annual basis over the life of the analysis; the annual equivalent of the income loss from
installation downtime; and related changes in taxes."
> Limit tax adjustment not to exceed taxes as reported in baseline financial statement.
Other Regulations, found no material effect on the facility impact analysis, as reported in this chapter. The alternative case analysis,
which incorporated estimated compliance costs from the recent Federal environmental regulations, found one additional baseline
closure and no change in post-compliance closures.
99 For the facility cash flow analysis, EPA treated the income loss from installation downtime on an annual equivalent basis even though
this financial event occurs only once, and at the beginning of the assumed analysis period. EPA treated the installation downtime on
an annualized basis for two reasons. First, the installation downtime is assumed to have a useful "financial life" of 30 years to reflect
the total potential business life of the facility with the installed compliance technology (note that reinstallation of the basic capital
equipment other than cooling towers, which is assumed to recur on a 10-, 20-, or 25-year interval depending on the specific
technology, does not require a new round of downtime). Since compliance capital equipment is assumed to have a specific useful life
and the discounted cash flow analysis is accordingly structured around this period, including the income loss from installation
downtime (which is assumed to have a 30-year useful life) as a one-time up-front cost would overstate its impact in the discounted
cash flow calculation. Second, calculation of the downtime cost on an annual basis allows the tax effect from the one-time income loss
to be summed with other annual tax effects for applying the limit to tax offsets, as explained in the next step of the analysis.
March 28, 2011 4T
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 4: Manufacturers Impact Analyses
> Calculate post-compliance facility value, including post-compliance free cash flow and the compliance
capital outlay. As in the baseline analysis, EPA calculated post-compliance facility value as the present
value of free cash flow and accounting for the compliance capital outlay as an undiscounted cash outlay
in the first analysis period. As before, a 7 percent discount rate was used in this present value calculation.
EPA considered a facility to be a post-compliance closure if its estimated business value was positive in the
baseline but became negative after adjusting for compliance-related cost, revenue and tax effects. In addition to
tallying closure impacts in terms of the number of estimated facility closures, EPA also measured the significance
of closures in terms of losses in employment and output. Employment losses equal the number of employees
reported by closure facilities in survey responses; output losses equal total revenue reported for regulatory closure
facilities. EPA estimated national results by multiplying facility results by facility sample weights.100
4.3.2 Key Findings for Regulatory Options
Table 4-2 reports the estimated severe impacts of the proposed rule on Manufacturing facilities by option. Of the
504 Manufacturers facilities potentially subject to regulation after excluding baseline closures, EPA estimated that
no facilities would close or incur employment losses as a result of the proposed Options.
Table 4-2: Number of Facilities with Severe Impacts by Sector and Option
Sector
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Total Facilities in Primary
Manufacturing Industries
Additional known facilities in
Other Industries
Total
Operating in
Baseline
198
167
30
46
24
31
496
7
Opti
Number
0
o
o
o
o
o
0
0
Numb
on 1
Percentage
0%
o%
o%
o%
o%
o%
0%
0%
er of Facilities
Opti
Number
0
o
6
6
6
o
0
0
with Severe Ii
on 2
Percentage
0%
o%
6%
6%
6%
o%
0%
0%
ipacts
Opt
Number
0
o
o
o
o
o
0
0
on 3
Percentage
0%
o%
o%
o%
o%
o%
0%
0%
Source: U.S. EPA analysis, 2010
4,4 Facility-Level Impacts; Moderate Impact Analysis
4.4.1 Analysis Approach and Data Inputs
EPA also conducted an analysis of financial stress short of closure to assess the occurrence of moderate impacts
on facilities in the Manufacturers segment. Facilities incurring moderate impacts are not projected to close due to
the regulatory analysis options. The regulation, however, might reduce their financial performance to the point
where they incur greater difficulty and higher costs in obtaining financing for future investments. As above, the
following discussion outlines the calculations undertaken for this assessment; detailed discussion of this analysis
is contained in Appendix 4E: Economic Impact Methodology-Manufacturers.
The analysis of moderate impacts examined two financial measures:
Pre-Tax Return on Assets (PTRA): ratio of pre-tax operating income - earnings before interest and taxes
(EBIT) - to assets. This ratio measures the operating performance and profitability of a business' assets
independent of financial structure and tax circumstances. PTRA is a comprehensive measure of a firm's
100 For the analysis of options presented in this chapter, none of these impact measures (e.g., employment loss, output loss) were in fact
relevant because none of the three regulatory analysis options resulted in regulatory closures.
4-6
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 4: Manufacturers Impact Analyses
economic and financial performance. If a firm cannot sustain a competitive PTRA on a post-compliance
basis, it will likely face difficulty financing its investments, including the outlay for compliance equipment.
Interest Coverage Ratio (ICR): ratio of pre-tax operating cash flow - earnings before interest, taxes, and
depreciation (EBITDA) - to interest expense. This ratio measures the facility's ability to service its debt on
the basis of current, ongoing financial performance and to borrow for capital investments. Investors and
creditors will be concerned about a firm whose operating cash flow does not comfortably exceed its
contractual obligations. As ICR increases, the firm's general ability to meet interest payments and carry
credit also increases. ICR also provides a measure of the amount of cash flow available for equity after
interest payments.
Creditors and equity investors review the above two measures as criteria to determine whether and under what
terms they will finance a business. PTRA and ICR also provide insight into a firm's ability to generate funds for
compliance investments from internally generated equity, i.e., from after-tax cash flow.
Calculation of Moderate Impact Metrics
EPA calculated a firm's PTRA and ICR measures using data collected from the 316(b) industry survey, adjusted
for inflation to 2009. EPA calculated these measures on a baseline and post-compliance basis. In calculating the
baseline values of the PTRA and ICR measures, EPA applied the same cash flow adjustments as described above
for the Manufacturers facility closure analysis, to the numerators of the PTRA and ICR measures. In the same
way as described for the closure analysis, these adjustments are intended to capture the change in the financial
performance of firms in the Primary Manufacturing Industries between the time of the 316(b) Phase III survey
and the present (see Appendix 4.B: Adjusting Baseline Facility Cash Flow).
Developing Threshold Values for Pre-Tax Return on Assets (PTRA) and Interest Coverage Ratio
(ICR)
For evaluating 316(b) manufacturing facilities according to the moderate impact measures, EPA compared
baseline and post-compliance PTRA and ICR to 316(b) industry-specific thresholds that were developed from
data compiled by Risk Management Association, Inc. (RMA). RMA compiles and reports financial statement
information by industry as provided by member commercial lending institutions. The threshold values represent
the lowest 25th percentile values of PTRA and ICR for statements received by RMA for the 11 years from 1998 to
2008 within relevant industries (RMA, 2009). EPA developed 316(b) industry-level values by weighting and
summing the RMA industry values according to the definition of 316(b) industries. Thresholds by sector ranged
from 0.5 percent to 2.8 percent for PTRA and from 1.5 to 2.7 for ICR. Because the financial statements received
by RMA are for businesses applying for credit from member institutions, the data do not represent a random
sample. In particular, the RMA data likely exclude representation from the financially weakest businesses, which
are unlikely to seek financing from RMA member lending institutions. As a result, EPA views the threshold
values as somewhat likely to overestimate the occurrence of moderate impacts.
Both measures are important to financial success and firms' ability to attract capital. Facilities failing at least one
of the moderate impact measures in the baseline were deemed to be already experiencing moderate financial
weakness and were not tested for additional financial impact in the moderate impact analysis. Facilities that
passed both moderate impact tests in the baseline but failed one or both threshold comparisons, post-compliance,
were considered to incur moderate financial impacts short of closure as a result of the final Section 316(b)
regulation.
The 6-digit NAICS code data were consolidated into weighted industry averages, weighted by 2002 value of
shipments from the Economic Censuses (U.S. DOC, 2002).101 For each industry and impact measure, a separate
101 2002 is the most recent year for which value of shipments is available for all industries, as the 2007 economic census data was not yet
available at the time of this writing.
March 28, 2011 4T
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 4: Manufacturers Impact Analyses
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 described in detail in Appendix 4E: Economic Impact Methodology -
Manufacturers.
Summary of Threshold Values
Table 4-1 reports the resulting threshold values for PTRA and ICR by industry. The PTRA values range from 0.5
percent for the Other industries to 2.8 percent for Petroleum. The ICR values range from 1.5 for Other industries
to 2.7 for Petroleum.
Table 4-3: Summary of Moderate Impact Thresholds by Manufacturers
Industry based on 2!?h percentile value of firms reporting data to RMA
Industry
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Pre-Tax Return on Assets (PTRA)
1.3
0.8
2.8
1.5
1.1
0.8
0.5
Interest Coverage Ratio (ICR)
2.4
1.9
2.7
2.0
1.7
1.8
1.5
Source: RMA, 2009; U.S. Economics Census, 2002; U.S. EPA Analysis, 2010.
Calculation of Moderate Impacts
In a similar way as described for the analysis of severe impacts, EPA compared the baseline and post-compliance
values for the two moderate impact measures to the moderate impact thresholds summarized above. Facilities
falling below one or both thresholds in the baseline were considered baseline failures and removed from the post-
compliance impact analysis. Facilities failing one or both thresholds in the post-compliance analysis are
considered post-compliance failures for the moderate impact test.
4.4.2 Key Findings for Regulatory Options
Table 4-4 reports the estimated moderate impacts of the proposed rule on Manufacturing facilities by option. Of
the 504 Manufacturers facilities potentially subject to regulation after excluding baseline closures, EPA estimated
that no facilities would incur moderate impacts under Options 1 and 2, and 17 facilities would incur moderate
impacts under Option 3.
Table 4-4: Number of Facilities with Moderate Impacts by Sector and Option
Sector
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Total Facilities in Primary
Manufacturing Industries
Additional known facilities in
Other Industries
Total
Operating in
Baseline
198
167
30
46
24
31
496
7
Number of Facilities with Moderate Impacts
Option 1: IM
Number |
0 1
0 1
o 1
0 1
0 1
0 1
o I
0 |
Everywhere
Percentage
0%
0%
0%
0%
0%
0%
0%
0%
Option 2: EVI Everywhere
and EM for Facilities with
DIP > 125 MGD
Number
0
0
0
0
0
0
0
0
I Percentage
1 0%
1 0%
I 0%
I 0%
1 0%
I 0%
| 0%
| 0%
Option 3: I&E Mortality
Everywhere
Number
3
13
1
0
0
0
17
0
I Percentage
1 0.6%
1 2.6%
| 0.2%
I 0%
1 0%
| 0%
| 3.4%
| 0%
Source: U.S. EPA analysis, 2010
4-8
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 4: Manufacturers Impact Analyses
4.5 Firm-Level Impacts
The analysis of impact on Manufacturers segment firms builds on the facility impact analysis to assess whether
firms that own multiple facilities are likely to incur impacts in a way that is not revealed by the facility impact
analysis. For the assessment of firm-level effects, EPA calculated annualized after-tax compliance costs as a
percentage of firm revenue and reports the estimated number and percentage of affected firms incurring
compliance costs in three cost-to-revenue ranges: less than one percent; at least one percent but less than three
percent; and three percent or greater. These ranges are accepted by EPA for screening of firm-level impacts.
EPA's sample-based facility analysis supports specific estimates of (1) the number of facilities expected to be
subject to the regulation and (2) the total compliance costs expected to be incurred in these facilities. However,
the sample-based analysis does not support specific estimates of the number of firms that own manufacturing
facilities. In addition, the sample-based analysis does not support specific estimates of the number of regulated
facilities that may be owned by a single firm, or the total of compliance costs across regulated facilities that may
be owned by a single firm.
For the firm level analysis, EPA therefore considered two cases based on the sample weights developed from the
facility survey. These cases provide approximate upper and lower bound estimates on: (1) the number of firms
incurring compliance costs and (2) the costs incurred by any firm owning a regulated facility. The cases are laid
out in the following sections.
4.5.1 Analysis Approach and Data Inputs
Case 1: Lower bound estimate of number of firms owning facilities that face requirements under the
regulation; upper bound estimate of total compliance costs that a firm may incur.
For this case, EPA inverted the prior assumption and assumed that any firm owning a regulated sample
facility(ies), owns the known sample facility(ies) and all of the sample weight associated with the sample
facility(ies). This case minimizes the count of affected firms, while tending to maximize the potential cost burden
to any single firm.
For this case, EPA grouped together all facilities with a common parent firm from the surveys and sample
weighted the facility compliance costs. EPA calculated the firm-level compliance cost as:
/ t
(4-4)
where:
CCfirm = firm-level compliance cost
CQ = compliance cost for surveyed facility /' owned by the firm
Wt = sample weight for surveyed facility /' owned by the firm
As stated above, for the analysis of firm-level impacts, EPA calculated annualized after-tax compliance costs as a
percentage of firm revenue. EPA judged that firms with annualized after-tax compliance cost of less than one
percent of revenue would not be materially affected by the regulation. EPA identified firms as subject to
potentially more serious impacts if annualized compliance cost exceeded three percent of revenue.
Case 2: Upper bound estimate of number affirms owning facilities that face requirements under the
regulation; lower bound estimate of total compliance costs that a firm may incur.
For this case, EPA assumed (1) that a firm owns only the regulated sample facility(ies) that it is known to own
from the sample analysis and (2) that this pattern of ownership, observed for sampled facilities and their owning
March 28, 2011 £J
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 4: Manufacturers Impact Analyses
firms, extends over the facility population represented by the sample facilities. This case minimizes the possibility
of multi-facility ownership by a single firm and thus maximizes the count of affected firms, but also minimizes
the potential cost burden to any single firm.
For each firm that owns one sample facility, no firm is assumed to own more than one regulated facility, and the
analysis is straightforward: the firm owns one regulated facility and incurs compliance costs only for that facility.
This configuration is assumed to exist as many as times as the facility's sample weight. However, EPA found that
28 percent of the firms identified as owning a sample facility, own more than one sample facility. Where the
multiple facilities owned by the same firm have the same sample weight, the analysis is also straightforward: the
firm is assumed to own and incur the compliance costs of the identified sample facilities, and the configuration is
assumed to exist as many times as many times as the uniform sample weight of the multiple facilities.
In some instances, however, the sample facilities that are owned by the same firm have different sample weights.
In these cases, which required a more complex analysis, EPA accounted for the ownership of multiple sample
facilities by a single firm, but restricted the count of the multiple facilities and their configuration of ownership
for the firm-level cost analysis based on the sample weights of the individual sample facilities. Specifically, the
firm is assumed to exist on a sample-weighted basis as many times as the highest of the sample weights among
the sample facilities known to be owned by the firm. However, sample facilities with a smaller sample weight,
and their compliance costs, can be included in the total instances of ownership by the firm for only as many times
as their sample weights. Otherwise, the total facility count implied in the firm analysis would exceed the sample-
based estimated total of facilities; correspondingly, the total of compliance costs accounted for in the firm level
analysis would exceed the sample-based estimated total of facility compliance costs. For implementation, this
concept means that all of the sample facilities known to be owned by the same firm, and their compliance costs,
can be included in the ownership configuration for only as many sample weighted instances as the smallest
sample weight among the multiple facilities owned by the firm. Once the sample weight of the smallest sample
weight facility is "used up," a new multiple facility ownership is configured including only the costs for those
facilities with weights greater than the weight of the smallest sample weight facility. This configuration is
assumed to exist for as many sample weighted instances as the difference between the lowest sample weight and
the next higher sample weight among the facilities owned by the firm. This process is repeated - with successive
removal of the new lowest sample weight facility, and its compliance cost- as many times as necessary until only
the highest sample weight facility remains in the ownership configuration.
The survey asked respondents to provide firm-level revenue for the parent firm. For single-facility firms, firm
revenue and compliance costs are identical to those for the facility. For multi-facility firms, EPA grouped together
all facilities with a common parent firm from the surveys. For each firm in the analysis, firm-level compliance
cost is:
/ t
(4-5)
where:
CCfirm = firm-level compliance cost
CQ = compliance cost for the surveyed facility /', known to be owned by the firm
4.5.2 Key Findings for Regulatory Options
Table 4-5 summarizes the results from the firm impact analysis assuming that facilities represented by sample
weights are owned by the same firm that owns the sample facility (Case 1). The following table, Table 4-6,
reports the results from the firm impact analysis assuming that the facilities presented by sample weights are
owned by different firms than that owning the sample facility (Case 2). Both tables show the number of firms that
4-10 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 4: Manufacturers Impact Analyses
incur costs in three ranges: less than 1 percent of a firm's revenue, within 1 and 3 percent of revenue, and greater
than 3 percent of revenue.
Under Case 1 Options 1, 2, and 3, of the 123 entities subject to the proposed regulation, 118 incur costs less than
1 percent of revenue, zero incur costs between 1 and 3 percent of revenue, and one incurs costs greater than 3
percent revenue. Under Case 2 Option 1, 2, and 3, 356 entities are subject to regulation and 346 incur costs less
than 1 percent revenue, zero incur costs between 1 and 3 percent revenue, and one incurs costs greater than 3
percent revenue.
Table 4-5: Entity-Level Cost-to-Revenue Analysis Results, Assuming that Facilities
Sample Weights are Owned by the Same Firm that Owns the Sample Facility (Case
Represented by
1)
Entity Type
Total Number of
Facilities
Total Number of
Entities
Number of Firms with a Ratio of
<1% | 1-3% | >3% | Unknown"
Minimum
Ratio
Maximum
Ratio
Option 1: IM Everywhere
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Total
31
198
30
167
46
24
7
504
8
42
17
26
16
5
9
123
8 | 0 | 0 | 0
40 I 0 1 0 I 2
15 0 [ 1 ' 1
26 0 1 0 0
16 0 I 0 | 0
5 0 1 0 1 0
9 0 | 0 | 0
118 1 0 1 1 1 3
0
0
6
0
0
0
0
0
0.01
0.37
1L73
0.35
0.09
0.02
0.25
11.73
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Total
31
198
30
167
46
24
7
504
8
42
17
26
16
5
9
123
8 0 1 0 I 0
40 0 | 0 | 2
15 0 ! 1 [ 1
26 I 0 1 0 I 0
16 0 1 0 1 0
5 0 I 0 | 0
9 I 0 1 0 I 0
118 ! 0 I 1 ! 3
0
0
6
0
0
0
0
0
0.01
0.37
1L73
0.35
0.09
0.02
0.25
11.73
Option 3: I&E Mortality Everywhere
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Total
31
198
30
167
46
24
7
504
8
42
17
26
16
5
9
123
8 0 | 0 ! 0
40 I 0 1 0 I 2
15 0 [ 1 i 1
26 0 1 0 1 0
16 0 I 0 | 0
5 I 0 1 0 I 0
9 | 0 | 0 | 0
118 1 0 1 1 1 3
0
0
6
0
0
0
0
0
0.01
0.03
1L73
0.01
0.07
0.00
0.00
11.73
a. EPA was unable to determine revenues for 3 parent entities.
Source: U.S. EPA Analysis, 2010
March 28, 2011
4-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 4: Manufacturers Impact Analyses
Table 4-6: Entity-Level Cost-to-Revenue Analysis Results, Assuming that Facilities Represented
Sample Weights are Owned by Different Firms than those Owning the Sample Facility (Case 2)
by
Entity Type
Total Number of
Facilities
Total Number of
Entities
Number of Firms with a Ratio of
<1% I 1-3% I >3% I Unknown"
Minimum
Ratio
Maximum
Ratio
Option 1: IM Everywhere
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Total
31
198
30
167
46
24
7
504
24
126
24
116
43
14
9
356
24 ! 0 ! 0 ! 0
118 0 I 0 8
22 0 1 1 i 1
116 0 1 0 i 0
43 0 I 0 i 0
14 i 0 1 0 i 0
9 i 0 | 0 i 0
346 i 0 1 1 i 9
0
0
o
0
0
0
0
0
0.00
0.13
TEi2
0.08
0.05
0.02
0.25
11.12
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Total
31
198
30
167
46
24
7
504
24
126
24
116
43
14
9
356
24 i 0 1 0 i 0
118 0 | 0 8
22 ' 0 i T 1
116 i 0 1 0 i 0
43 0 1 0 i 0
14 0 1 0 i 0
9 0 I 0 i 0
346 ! 0 I 1 ! 9
0
0
o
0
0
0
0
0
0.00
0.13
TEi2
0.08
0.05
0.02
0.25
11.12
Option 3: I&E Mortality Everywhere
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Total
31
198
30
167
46
24
7
504
24
126
24
116
43
14
9
356
24 ! 0 ! 0 ! 0
118 | 0 | 0 8
22 loll! 1
116 0 1 0 i 0
43 0 I 0 i 0
14 i 0 1 0 i 0
9 i 0 | 0 i 0
346 i 0 1 1 i 9
0
0
o
0
0
0
0
0
0.00
0.01
Till
0.00
0.03
0.00
0.00
11.12
a. EPA was unable to determine revenues for 9 parent entities.
Source: U.S. EPA Analysis, 2010
4.6 Uncertainties and Limitations
The analyses of facility-level and firm-level impacts for the Manufacturers segment are subject to a range of
uncertainties and limitations, including:
> The facility-level data for these analyses is based on surveys conducted by EPA in 1999 and reflects
reporting years of 1996, 1997, 1998. Recognizing the length of time since collection of these data, EPA
adjusted facility financial data to account for changes in overall economic conditions and industry
performance since the time of the original survey (see Appendix 4.B: Adjusting Baseline Facility Cash
Flow). This adjustment concept improves the validity of using these data for the current analyses, but
introduces uncertainty and inevitably cannot account for all facility-level financial and overall
economic/financial changes since the time of the original 316(b) Phase III survey.
> The analyses of facility-level and firm-level costs and impacts rely on sample surveys of estimated in-
scope facilities, as outlined above. The use of data from surveyed facilities to support the cost and
economic impact analysis is an appropriate and valid approach for assessing the impact of the proposed
316(b) existing facilities rule: the sampled facilities serve as models for assessing cost and impact across
the rule's expected universe of in-scope facilities. Inevitably, however, use of sampled facilities as the
basis for the analysis introduces uncertainty in the estimates of the number of in-scope facilities and the
estimates of total costs and impacts across the in-scope facility universe.
4-12
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 4: Manufacturers Impact Analyses
> The assessment of firm-level impacts relies on approximate upper and lower bound concepts of the
number of affected parent entities and the numbers of in-scope facilities that these entities may own. EPA
judges that the range of results from these analyses provides appropriate insight into the overall extent of
firm-level effects.
> The use of RMA data as the basis for the moderate impact thresholds provides an approximate basis for
the assessment of moderate financial impacts. As described, the RMA data are not based on a statistically
valid sample, and, further, may introduce bias in the quartile values, given the characteristics of
businesses that are represented in the RMA data. Finally, the 25th percentile value is not a perfect
indicator of the occurrence of adverse financial condition, and therefore occurrence of adverse impact
from the 316(b) regulatory options. The value is indicative of weak financial condition and performance
relative to other businesses present in the RMA data, but is not an absolute indicator of financial
weakness.
March 28, 2011 4-13
-------
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4A: Cost Pass-Through Analysis
Appendix 4A Cost Pass-Through Analysis
The impact of the existing facilities rule's compliance requirements on Manufacturers will depend on the extent to
which affected facilities are able to pass forward compliance costs to customers in increased prices (cost pass-
through). This appendix presents the assessment of cost pass-through (CPT) potential for six Primary
Manufacturing industry sectors in which a substantial number of facilities are expected to be subject to the
Proposed 316(b) Existing Facilities Rule. This analysis considered the following six industry sectors:
> NAICS 311/3121: Food and kindred products
> NAICS 322: Paper and allied products
> NAICS 325: Chemicals and allied products
> NAICS 3241: Petroleum Refining
> NAICS 3311/2: Steel
> NAICS 3313: Aluminum
In performing this analysis, EPA closely followed the methodology and, to the extent possible, relied on the same
data sources used for the CPT analysis in support of the 2006 Phase III Final Rule. This appendix begins with a
review of approaches for assessing CPT potential associated with market-wide cost increase scenarios. Next, a
description of the methodology and specific metrics used to assess CPT potential are discussed and the results for
each sector provided. Finally, conclusions are presented.
From this analysis, as was the case with the analysis conducted in support of the 2006 Phase III Final Rule, EPA
concluded that an assumption of zero cost pass-through is appropriate for analyzing the impact of the regulatory
analysis options on facilities in these six manufacturing industries. Performance of the financial impact analysis
under this assumption means that facilities must absorb all compliance-related costs and operating effects (e.g.,
income loss from facility shutdown during equipment installation) within their baseline cash flow and financial
condition. To the extent that facilities would be able to pass on some of the compliance costs to customers
through price increases, the analysis may overstate the potential impact on complying facilities.
4A.1 The Choice of Firm-Specific Versus Sector-Specific CPT Coefficients
One method of examining the ability of a firm to pass-through compliance-related cost increases associated with
the Proposed Existing Facilities Regulation is to review the firm's historical performance in passing on previous
cost increases to consumers. For example, Ashenfelter et al. (1998) estimate the cost pass-through rate facing an
individual firm, and distinguish that rate from the rate at which a firm passes through cost changes common to all
firms in an industry, by regressing the price a firm charges on both its costs and the costs of another firm in the
industry. The firm-specific CPT rate would relate a change in the prices charged by a specific firm to a change in
its production costs, assuming no changes in the production cost for rival producers of that product. However,
estimating firm specific CPT rates is extremely complex. For example, in order to estimate firm-specific CPT
rates for every manufacturing firm included in the sample of 316(b) Detailed Industry Questionnaire (DQ)
respondents, EPA would require, for each firm, detailed information on the products sold, the markets in which
these products are sold, as well as information identifying major competitors in each market. The DQ did not
obtain this information from surveyed facilities. And even if such information were available, the analysis would
remain highly challenging and subject to significant analytic error. As such, it is neither possible nor practical to
develop firm-specific CPT coefficients for the sample of analyzed manufacturers.
Moreover, even if the Agency possessed the data necessary to estimate firm-specific CPT rates, it is questionable
whether these rates would be the appropriate measure of CPT potential for compliance-related cost increases
stemming from the proposed regulation. This regulation would force multiple firms in each of the industry sectors
March 28, 2011
4A-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4A: Cost Pass-Through Analysis
considered in this analysis to incur compliance-related cost increases, which implies that for most firms the cost
increases would not only apply to them, but also to several of their competitors. Not surprisingly, previous studies
have found that the CPT rate for changes to an individual firm's cost differs from the rate at which a firm would
pass through cost changes that are common to all, or a substantial fraction of, firms in an industry (Ashenfelter et
al., 1998). It can be reasonably expected that the higher the share of firms incurring the cost increase, or more
appropriately the higher the share of total output produced by such firms, the greater the ability of those firms to
pass on a greater portion of those costs to the consumer.
In cases where an industry-wide cost shock occurs, an industry-wide CPT rate would be an appropriate and
practical way of assessing the potential of all firms in that industry to pass through that cost increase to consumers
(EPA, 2003). An industry-wide CPT rate provides an estimate of the change in each facility's output prices as a
function of the increase in its production costs, assuming that the same cost increase is experienced by all firms in
the industry. Such an industry-wide rate is relatively easier to estimate than firm-specific cost pass-through rates if
one assumes that perfect competition exists in the industry. Among other things, perfect competition implies the
existence of product homogeneity within the industry, homogeneity of production technology among firms in the
industry, and homogeneity of production costs among firms (i.e., pricing is at marginal cost). Under these
conditions, the price response to a general industry-wide change in production costs is likely to be industry-wide
and similar across all firms. For example, in support of the Economic, Environmental, and Benefits Analysis of
the Final Metal Products & Machinery Rule (MP&M), promulgated in 2003,102 EPA estimated industry-specific
CPT rates since a large fraction of establishments in these industries were expected to be subject to the regulation.
EPA estimated these CPT rates by regressing annual output price indices on annual input cost indices for the
MP&M industry (U.S. EPA, 2003). The estimated CPT coefficients were validated by a market structure analysis
that assessed, for each industry, the potential market power enjoyed by firms in the industry and the consequent
implications it had on their ability to pass through compliance-related costs.
Industry-wide CPT rates can be estimated for the analyzed manufacturing sectors based on the methodology used
for deriving industry-wide CPT rates for industries covered by the MP&M regulation and the 2006 Phase III Final
Rule. As was the case with the 2006 Phase III Final Rule, because the regulatory analysis options will affect only
those facilities that operate a CWIS to withdraw cooling water from surface water bodies, only a subset of
facilities in each industry sector would incur compliance-related cost increases. As the cost increase associated
with the regulatory analysis options is not industry-wide, it is questionable whether industry-wide CPT rates are
appropriate for estimating the price response of firms in the five industry sectors considered in the analysis of
Existing Facilities Rule impacts. If a substantial portion of production in each industry occurs at facilities not
subject to the regulation under each analysis option, then the use of industry-wide CPT rates may grossly
overestimate the ability of firms in these industries to pass-through compliance-related costs to consumers.
To assess the reasonableness of using industry-wide CPT rates in the analysis of impacts to in-scope
manufacturers, EPA estimated the percentage of total production in each of the six Primary Manufacturing
Industries sectors that occurs at facilities that are estimated to be subject to the regulatory analysis options. Value
of shipments, a measure of the dollar value of production, was selected for the basis of this estimate. Because
value of shipments data were not collected using the DQ, these data were not available for the sample of Existing
Facilities Rule Manufacturers facilities potentially subject to the regulatory analysis options. As such, total
revenue, as reported on the DQ, was used as a close approximation to value of shipments for these facilities. EPA
estimated the total revenue subject to the regulatory options by multiplying the revenue of facilities (in $2009) in
the sample of Manufacturers that were determined to be potentially subject to each option by their facility sample
weights and summing across all facilities. Total value of shipment estimates for each industry were obtained from
the 2007 Economic Census. Table 4A-1 summarizes the findings of this analysis.
102 For details see Economic, Environmental, and Benefits Analysis of the Final Metal Products & Machinery Rule report available online
athttp://water.epa.gov/scitech/wastetech/guide/mpm/upload/2003_l_31_guide_mpm_eebaj)artl.pdf
4Al March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4A: Cost Pass-Through Analysis
Table 4A-1: Proportion of Value of Shipments Potentially Subject to Compliance-Related Costs
Associated with the Proposed Existing Facilities Regulation (Millions; $2009)
NAICS
311/3121
322
3241
325
33TT/2
3313
Industry
Food and Kindred Products
Paper and Allied Products
Chemicals and Allied Products
Petroleum Refining
Steel
Aluminum
Revenue for
Manufacturers Subject
to Proposed Existing
Facilities Regulationa'b
$11,603
$6YJ28
$68^42
$174378
$55^241
$16';792
Total Value of
Shipments
$697,164
jYsT^Ti
$748;681
$626^293
$128^082
$45313
Proportion of Total
Value of Shipments
Subject to Regulation
1.7%
316%
97i%
271%
43.1%
36.9%
a. For the purpose of this analysis, facility revenue was used as an appropriate surrogate in the absence of value of shipments for sample facilities.
Revenue estimates are the sum of weighted facility-level revenue values and includes revenue for baseline closures.
b. To compare in-scope revenue values with the industry value of shipments, EPA brought in-scope revenue values forward to 2007 using industry-
specific Producer Price Index (PPI) values published by the Bureau of Labor Statistics (BLS) and stated in 2009 dollars using GDP deflator
published by the Bureau of Economic Analysis (BEA).
Source: Economic Census, 2007; U.S. EPA, 2000
As shown in Table 4A-1, the proportion of total value of shipments estimated subject to the regulation under the
regulatory analysis options ranges from 1.7 percent to 43.1 percent depending on the industry. Given that less
than 45 percent of the total value of shipments in any of the six industries considered in this analysis would be
subject to regulation induced compliance costs, EPA believes that the theoretical threshold for justifying the use
of industry-wide CPT rates in the Phase II impact analysis has not been met. As it was the case with the 2006
Phase III Final Rule, the Agency believes that using industry-wide CPT rates in the analysis of Proposed Existing
Facilities Rule impacts would overestimate the cost pass-through ability of firms incurring regulation-induced
compliance costs, and thus underestimate impacts. At the other end of the spectrum, however, an assumption of
zero CPT would avoid the risk of understating impacts, as it would assume that all in-scope facilities absorb one
hundred percent of cost impacts.
Given the inability to estimate firm-specific CPT rates and the finding that the use of industry-wide CPT rates
would not be appropriate, EPA next conducted a market structure analysis to investigate the extent to which firms
in the six industry sectors enjoy sufficient market power to pass compliance-related costs on to consumers in the
form of higher prices.
4A.2 Market Structure Analysis
Information on the competitive structure and market characteristics of an industry provide insight into the likely
ranges of supply and demand elasticities and the sensitivity of output prices to input costs. For example, when
input costs increase, the profit-maximizing firm attempts to maintain its profits by increasing output prices, to the
extent permitted by market power. The amount of the cost increase that the firm can pass on as higher prices
depends on the relative market power of the firm and its customers. The market structure analysis described in
this section attempts to measure the market power enjoyed by firms in each of the six industries. This analysis is
combined with information from industry review documents such as McGraw-Hill's U.S. Industry and Trade
Outlook to reach conclusions regarding the CPT ability of firms in each industry. The market structure analysis
consists of a review of economic data for the following four indicators of market power: industry concentration;
import competition; export competition; and long term growth. Each of these indicators is discussed in detail
below. EPA notes that the impact of each of these four indicators of market power varies from industry to
industry. Furthermore, the results presented for each indicator must be interpreted with caution because even
though for a particular industry an indicator may predict high cost pass-through potential, the specific features of
the industry may result in the indicator having diminished significance in predicting market power.
March 28, 2011
4A-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4A: Cost Pass-Through Analysis
4A.2.1 Industry Concentration
The extent of concentration among a group of market participants is an important determinant of that group's
market power. A group of many small firms typically has less market power than a group of a few large firms,
because the latter are in a more advantageous position to collude with each other. All else being equal, highly
concentrated industries are therefore expected to pass-through a higher proportion of the compliance costs that
would result from the proposed regulation.
This analysis uses the Herfindahl-Hirschman Index (HHI) as a measure of market concentration. The HHI is
calculated by squaring the market share of each firm competing in the market and then summing the resulting
numbers.103 For example, for a market consisting of four firms with shares of thirty, thirty, twenty and twenty
percent, the HHI is 2600 (302 + 302 + 202 + 202 = 2600). The HHI takes into account the relative size and
distribution of the firms in a market and approaches zero when a market consists of a large number of firms of
relatively equal size. The HHI increases both as the number of firms in the market decreases and as the disparity
in size between those firms increases. Based on the U.S. Department of Justice's guidelines for evaluating
mergers, markets in which the HHI is under 1,000 are considered unconcentrated, markets in which the HHI is
between 1,000 and 1,800 are considered to be moderately concentrated, and those in which the HHI is in excess
of 1,800 are considered to be concentrated.
The accuracy of any analysis of market power originating from industry concentration depends to a large extent
on properly defining the relevant market. A well-defined market requires the inclusion of all competitors and the
exclusion of all non-competitors. Defining the relevant market too narrowly overstates market power, while
defining the market too broadly would underestimate it. The four-digit SIC category and six-digit NAICS, while
not a perfect delineation, is most often used by industrial organization economists in their studies because, among
publicly available data sources, these industries appear to correspond most closely to economic markets
(Waldman & Jensen, 1997). Therefore, in Table 4A-2 below, industry concentration data is presented for each of
the six-digit NAICS codes that include at least one potentially regulated manufacturing facility for which DQ data
are available.
As shown in Table 4A-2, based on their HHI, 20 six-digit NAICS markets104 can be classified as unconcentrated,
five can be classified as moderately concentrated, and only six can be classified as concentrated. Notably, four out
of six six-digit NAICS categories listed as being concentrated belong to the Chemicals and Allied Products
industry; the other two sectors are in Aluminum and Steel industries. From a market power perspective, in Table
4A-2 seems to suggest that at the six-digit NAICS level, only six NAICS categories are sufficiently concentrated
to argue that firms may possess sufficient market power to pass-through a portion of their compliance-related
costs assuming that competitor firms in the same industry do not incur similar cost increases.
The Herfindahl-Hirschman Index was chosen because it provides a more complete picture of industry concentration compared to other
measures such as the four-firm and eight-firm concentration ratios. In contrast, the four- and eight-firm concentration ratios do not use
the market share of all firms in the industry, and nor do they provide information about the distribution of firm size. For example, if
there were a significant change in the market shares among the firms included in the ratio, the value of the concentration ratio would
not change.
This includes three-digit and four-digit NAICS for Food and Beverage industries, respectively, because every six-digit NAICS sector
covered by these two industries are expected to be affected by the Proposed 316(b) Existing Facilities Regulation.
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4A: Cost Pass-Through Analysis
Table 4A-2: Herfindahl-Hirschman Index for Six-Digit NAICS Sectors
NAICS
NAICS Description
Industry
HHF
Unconcentrated Markets (Hffl < 1,000)
311
325998
322299
325188
325199
331210
331222
325211
331221
3121
325412
322222
324110
33j"i"i"i
331314
322130
322121
322224
322122"
325311
Food Manufacturing
All Other Miscellaneous Chemical
Product and Preparation Manufacturing
All Other Converted Paper Product
Manufacturing
All Other Basic inorganic Chemical
Manufacturing
All Other Basic Organic Chemical
Manufacturing
[ron and Steel Pipe and Tube
Manufacturing from Purchased Steel
Steel Wire Drawing
Plastics Material and Resin
Manufacturing
Rolled Steei Shape Manufacturing
Beverage Manufacturing
Pharmaceutical Preparation
Manufacturing
Coated and Laminated Paper
Manufacturing
Petroleum Refineries
[ron and Steel Mills
Secondary Smelting and Alloying of"
Aluminum
Paperboard Miiis
Paper (except Newsprint) Mills
Uncoated Paper and Multiwall Bag
Manufacturing
Newsprint Mills
Nitrogenous Fertilizer Manufacturing
Food
Chemicals
Paper
Chemicais
Chemicais
Steei
Steei
Chemicais
Steei
Food
Chemicals
Paper
Petroleum
Steei
Aluminum
Paper
Paper
Paper
Paper
Chemicais
119 |
188
192
217
238
279
326 |
443
491 1
512 |
530
569
640
657 1
694 1
749 1
810
864
977 1
977 |
Moderately Concentrated Markets (1,000 < Hffl < 1,800)
322110
325120
325222
32513'j'
325181
Pulp Mills
Industrial Gas Manufacturing
Noncellulosic Organic Fiber
Manufacturing
Inorganic Dye and Pigment
Manufacturing
Alkalies and Chlorine Manufacturing
Paper
Chemicais
Chemicals
Chemicals
Chemicais
1,175 |
17218 1
17262
17704
ij86 1
Concentrated Markets (1,800 < Hffl)
325312
331315
325611
331112
325110
325411
Phosphatic Fertilizer Manufacturing
Aluminum Sheet, Plate, and Foil
Manufacturing
Soap and Other Detergent
Manufacturing
Eiectrometaiiurgicai Ferroalloy Product
Manufacturing
Petrochemical Manufacturing
Medicinai and Botanical Manufacturing
Chemicals
Aluminum
Chemicais
Steei
Chemicais
Chemicais
1,853 |
17856
27666
27196
27662 1
27704 |
a. The 2002 Economic Census is the most recent concentration data available.
b. 2002 Economic Census does not disclose HHI values for three of the analyzed 6-digit NAICS sectors: (1) NAICS 325221: Cellulosic Organic Fiber
Manufacturing (a total of eight companies), (2) NAICS 331311: Alumina Refining (a total of eight companies), and (3) 331312: Primary Aluminum
Production (a total of 26 companies).
Source: Economic Census, 2002
To further examine the level of concentration in each of the analyzed six industry sectors, EPA decided to analyze
HHI at the industry level as well. In general, these estimates understate market power. Nonetheless, industry HHI
should still provide a meaningful insight into the market power of firms in the industry because firms in each
industry still produce similar or related products (for example, paper products, chemicals, etc.). Industry HHIs are
presented below in Table 4A-3
March 28, 2011
4A-5
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4A: Cost Pass-Through Analysis
Table 4A-3: Herfindahl-Hirschman Index by Industry
NAICS
311/3121
322
325
3241
3311/2
3313
Industry
Food and Kindred Products
Paper and Allied Products
Chemicals and Allied Products
Petroleum Refining
Steel
Aluminum
HHT"
190
259
Too
543
547
1,185
a. The 2002 Economic Census is the most recent concentration data available.
b. HHI values are as reported in the 2002 Economic Census for the 3- and 4-digit
NAICS codes and not value of shipments-weighted HHI values for the profiled 6- and
5-digit NAICS codes.
Source: Economic Census, 2002; U.S. EPA Analysis, 2010
Table 4A-3 reveals that, at the industry level, the estimated HHI for five of the six industries are quite small,
implying that they are unconcentrated markets and within these industries, individual firms do not enjoy much
market power. Notably, the Chemicals and Allied Products industry has a low HHI, which suggests that the four
six-digit NAICS categories that were classified as having concentrated markets in reality make up a very small
segment of the Chemicals and Allied Products industry. The same conclusion holds for the Steel industry. Thus,
from the perspective of the Proposed Existing Facilities Rule analysis, the majority of firms in these two
industries have low market power. In addition, EPA notes that only 9 percent of production in the Chemicals and
Allied Products industry would potentially be subject to compliance-related cost increases, which suggests that
the cost pass-through potential of firms from this sector incurring such expenses would be severely curtailed. An
important finding in Table 4A-3 is that the Aluminum industry appears to be moderately concentrated. Thus,
based solely on an analysis of industry concentration, it would appear that firms in the Aluminum industry might
enjoy moderate amounts of market power, which may enable them to pass through costs at a more than negligible
rate. However, as cautioned at the beginning of the market structure analysis, an accurate judgment of the market
power enjoyed by firms in an industry must be reserved until all indicators have been analyzed.
4A.2.2 Import Competition
Theory suggests that imports as a percent of domestic sales are negatively associated with market power because
competition from foreign firms limits domestic firms' ability to exercise such power. Firms belonging to sectors
in which imports make up a relatively large proportion of domestic sales would therefore be at a relative
disadvantage in their ability to pass through costs compared to firms belonging to sectors with lower levels of
import penetration, the measure of import competition used in this analysis. Import penetration, the ratio of
imports in a sector to the total value of domestic consumption in that sector, is particularly relevant because
foreign producers would not incur costs as a result of the proposed 316(b) Existing Facilities Regulation. In this
market structure analysis, EPA assumes that higher import penetration will generally imply that firms are exposed
to greater competition from foreign producers and would thus possess less market power to increase prices in
response to regulation-induced increases in production costs. EPA estimated import penetration ratios for each
industry as total imports in an industry divided by total value of domestic consumption in that industry; where
domestic consumption equals domestic production plus imports minus exports. Import penetration ratios
estimated using 2007 Economic Census data for the six industry sectors considered in this analysis are presented
below in Table 4A-4 below.
4A-6 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4A: Cost Pass-Through Analysis
Table 4A-4: Import Penetration by Industry, 2007
NAICS
311/3121
322
325
3241
337T/2
3313
Industry
Food and Kindred Products
Paper and Allied Products
Chemicals and Allied Products
Petroleum Refining
Steel
Aluminum
Value of Imports
(Millions; S2009)a
$54,655
$257688
$Y69;885
j"i097796
$397680
iji5T254
Implied Domestic
Consumption
(Millions ;$2009)"'a
$708,604
$187^71
$766';Z69
$704^211
$l"53,640
$54-;2"(J3
Import Penetration0'3
7.71%
Y37'72%
2217%
Y5759%
25";44%
287i4%
a. These values are the totals reported for the entire 3- and 4-digit NAICS codes and not just the sum of in-scope 6-digit NAICS codes.
b. Implied Domestic Consumption = Value of Shipments + Value of Imports - Value of Exports.
c. Import Penetration = Value of Imports / Implied Domestic Consumption
Source: Economic Census, 2007; U.S. ITC, 2007; U.S. EPA Analysis, 2010
The estimated import penetration ratios for the six industries range from 8 percent to 28 percent for the year 2007.
The estimated import penetration ratio for the entire U.S. manufacturing sector (NAICS 31-33) is 27 percent.
Considering that the United States is an open economy, EPA believes it is reasonable to assume that in industries
with import penetration ratios close to or above 27- percent domestic firms most likely face stiff competition from
foreign firms. Such competition is likely to curtail the market power enjoyed by domestic firms and, given the
scenario that regulation-induced cost increases are not incurred by foreign producers, would limit the ability of
domestic firms to pass-through such costs. Thus, based on the import penetration ratios presented in Table 4A-4,
firms in all of the industries except Aluminum appear to be in a position to pass-through to consumers a
significant portion of compliance-related costs associated with the Proposed Existing Facilities regulatory options.
However, given the relatively low HHIs for these industries, EPA believes that existing market competition
among domestic firms most likely nullifies any favorable influence the lack of foreign competitors would have on
increasing the market power of firms in this industry. EPA also highlights the above average import penetration
ratio for the Aluminum industry, which suggests low market power for firms in this industry. With respect to the
Aluminum industry, this fact may offset - from a market power perspective - the finding that the industry was
identified above as being moderately concentrated. Thus, even though there are relatively few domestic producers
in the U.S. Aluminum industry, the notable presence of foreign producers in U.S. markets is likely to markedly
reduce their the market power.
4A.2.3 Export Competition
The proposed Existing Facilities Rule would not increase the production costs of foreign producers with which
domestic firms must compete in export markets. As a result, firms in industries that rely to a greater extent on
export sales would have less latitude in increasing prices to recover cost increases resulting from regulation-
induced increases in production costs. They would therefore have a lower CPT potential, all else being equal. This
analysis uses export dependence, defined as the percentage of shipments from an industry that is exported, to
measure the degree to which a sector is exposed to competitive pressures abroad in export sales. Firms in
industries with relatively high export dependence are expected to have lesser market power than those in
industries with relatively low export dependence due to their relatively larger reliance on sales in export markets.
Estimated export dependence ratios for the six industry sectors considered in this analysis are presented below in
Table 4A-5.
March 28, 2011
4A-7
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4A: Cost Pass-Through Analysis
Table 4A-5: Export Dependence by Industry, 2007
NAICS
311/3121
322
325
3241
3311/2
3313
Industry
Food and Kindred Products
Paper and Allied Products
Chemicals and Allied Products
Petroleum Refining
Steel
Aluminum
Value of Export
(Millions; S2009)a
$43,215
$2(X328
jl'52^97
$317878'
$137522
$6364
Value of Shipments
(Millions; S2009)a
$697,164
$18i79i"i
$74^681
$626;293
$128^82
$457513
Export Dependence1"
6.20%
ii7i"7%
2o;'34%
'5'"."6'9%
JO'3'6%
J442%
a. These values are the totals reported for the entire 3- and 4-digit NAICS codes and not just the sum of in-scope 6-digit NAICS codes.
b. Export Dependence = Value of Exports / Value of Shipments.
Source: Economic Census, 2007; U.S. ITC, 2007; U.S. EPA Analysis, 2010
The estimated export dependence ratios for the six industries in 2007 range from 5 percent to 20 percent. The
estimated export dependence ratio for the entire U.S. manufacturing sector for the same year is 17 percent. Thus,
for all but one industry (Chemicals and Allied Products), the export dependence ratio is below the average for the
U.S. manufacturing sector. This finding implies that none of these industries are characterized by strong
competitive pressures from foreign firms/markets, and thus market power and CPT potential are not diminished
by export dependence. However, it is questionable whether this effect works as strongly in the opposite direction,
i.e., firms in an industry will have a comparatively high cost pass-through potential simply because firms in that
industry are not active in export markets. From the standpoint of firms gaining market power, EPA believes that
the finding of low export dependence diminishes the importance of export competition as an indicator of market
power. Thus, the other three indicators must be relied upon to gauge the amount of market power that firms in
each industry are expected to hold. For example, even though the Petroleum Refining and Food and Kindred
Products industries have extremely low export dependence, the low market concentration in these industries leads
EPA to believe that market power held by individual firms is likely to be quite small.
4A.2.4 Long-Term Industry Growth
An industry's competitiveness and the ability of firms to engage in price competition are likely to differ between
declining and growing industries. Most studies have found that recent growth in revenue is positively related to
profitability (Waldman & Jensen, 1997), which suggests a greater ability to recover costs fully. To examine trends
in long-term growth for each of the six industry sectors considered in this analysis, EPA estimated the average
annual growth rate in the constant dollar value of shipments between 1989 and 2007 for each industry using data
available from the U.S. Bureau of Census.105 EPA expects firms in sectors with higher growth rates to be better
positioned to pass through compliance costs rather than being forced to absorb such cost increases in order to
retain market share and revenue. Table 4A-6 shows that of the six industries specifically considered for this
analysis, two industries - Paper and Allied Products and Aluminum - experienced negative growth over the 1989
to 2007 time period. The Petroleum Refining industry experienced the largest growth, displaying an average
annual growth rate of 5.8 percent. In the absence of strong growth performance during the analysis period for all
six industries, it is unlikely that firms in any of these industries possess significant market power based on
growing demand for their products. In effect, the long-term growth performance of all six industries does not
support a conclusion that firms in these industries could be in a strong position to pass on a significant portion of
their compliance costs.
The period from 1989 to 2007 represents the two most recent decades that includes data consistent with the survey period for the 2000
Detailed Industry Questionnaire (1996-1998).
4A-8
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4A: Cost Pass-Through Analysis
Table 4A-6: Average Annual Growth Rate by Industry
NAICS
311/3121
322
325
3241
33TT/2
3313
Industry
Food and Kindred Products
Paper and Allied Products
Chemicals and Allied Products
Petroleum Refining
Steel
Aluminum
Average Annual Grov
Shipm
i989to20o7
1.1%
-0.7%
10%
5"8%
D%
-3.8%
vth Rate in Value of
ents
2000to2007
1.8%
-17%
4.3%
il.6%
5".6%
2.3%
Source: Economic Census, 2007; Annual Survey of Manufacturers. 1989 and 2000; U.S. EPA Analysis, 2010
4A.3 Conclusions
Given that less than 30 percent of the total value of shipments in each of the six industries considered in this
analysis is estimated to be subject to regulation under the regulatory analysis options, and the likelihood that these
percentages represent upper bound estimates, the likelihood that firms incurring such costs would be able to pass
through to consumers a material portion of 316(b) compliance costs is small. To validate this hypothesis, EPA
undertook the market structure analysis presented in the previous section. In general, the weight of evidence from
the market structure analysis suggests that firms in all six industries are unlikely to possess significant amounts of
market power, thereby lending support to EPA's hypothesis that most firms would not be in a position to pass-
through a significant portion of compliance costs. The analysis of individual indicators under the market structure
analysis did reveal a few exceptions to the general finding of low market power in all industries. However,
considering the combined impact of all four indicators of market power together with information on recent
economic trends in these industries suggests that on the whole, firms in each of the six industries hold relatively
low market power and CPT potential. For example, the HHI for the Aluminum industry indicated that this sector
is moderately concentrated, which would potentially allow firms in this industry to pass through a significant
portion of their compliance-related costs. In contrast, however, the market structure analysis also found that the
domestic Aluminum industry witnessed a sustained decline in production during the 1990s and also faces stiff
competition from foreign producers in its U.S. markets. As discussed in the profile of this industry, in the early
1990s the domestic Aluminum industry was affected by reduced U.S. demand and the dissolution of the Soviet
Union, which resulted in substantial increases in Russian exports of aluminum. The recovery that followed was
subsequently affected by the economic crises in Asian markets in the second-half of the 1990s, which along with
growing Russian exports, again resulted in a period of oversupply. Demand for aluminum industry products
declined again in 2000 through 2002, reflecting weakness in both the U.S. and world economies, and again
resulted in oversupply and declining financial performance. In 2003, the U.S. economy began to recover and
continued to do so through 2007, resulting in higher demand for aluminum and improving financial condition for
the aluminum industry. However, the recession that began in 2008 resulted in lower demand for and production of
aluminum, both in the United States and worldwide, and a consequent decline in the financial performance of the
aluminum industry.
In-scope facilities in the Aluminum industry belong to either the Primary Aluminum Production segment (NAICS
331311: Alumina Refining and NAICS 331312: Primary Aluminum Production) or the Secondary Aluminum
Production segment (NAICS 331314: Secondary Smelting and Alloying of Aluminum and NAICS 331315:
Aluminum Sheet, Plate, and Foil Manufacturing) (for more information see Chapter 2: Industry Profiles). The
Secondary Aluminum Production segment on average has been less affected by the economic fluctuations thereby
on average performing better than the Primary Aluminum Production segment. In addition, data reported in the
Aluminum Industry Profile indicate the Secondary Aluminum Production segment is less import dependent and
less concentrated than the Primary Aluminum Production segment. Further, while the Secondary Aluminum
Production segment has grown over the last two decades and especially in the last decade, the Primary Aluminum
Production segment has declined. Consequently, while domestic firms in the Secondary Aluminum Production
March 28, 2011
4A-9
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4A: Cost Pass-Through Analysis
segment may be in a better position to pass some compliance-related costs to consumers than firms in the Primary
Aluminum Production segment, several factors combine to suggest that the Secondary Aluminum Production
O 5 OO J
segment has relatively low cost pass-through potential. Specifically, the general economic condition of the U.S.
Aluminum industry as a whole throughout the last two decades, the moderate-to-high market concentration, and a
rather high degree of import penetration and export dependence, suggest that domestic firms in this industry hold
relatively low market power and are not likely to have the ability to pass through significant portions of their
compliance-related cost increases.
Based on the findings of the market structure analysis, EPA decided to assume a zero CPT rate for all six
industries in the analysis of the Proposed 316(b) Existing Facilities Rule impacts. EPA believes that this
assumption is reasonable given the results of the market structure analysis and is superior to using industry-wide
CPT rates (see Chapter 2: Industry Profiles). In addition, EPA notes that by assuming a CPT rate of zero for all
industries, the analysis of Proposed Existing Facilities Rule impacts is less likely to underestimate facility impacts
in that the analysis assumes that facilities would incur one hundred percent of compliance costs. Thus, whereas an
overstated CPT rate may erroneously underestimate impacts for facilities incurring compliance-related cost
increases, the use of a CPT rate of zero errs on the side of caution, thus potentially overstating impacts to affected
facilities.
4A-10 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Appendix 4B Adjusting Baseline Facility Cash Flow
This appendix documents EPA's development and analysis of adjustment factors for the manufacturing industries
expected to be within the scope of the Proposed Existing Facilities Regulation. This analysis presents an updated
version of the analysis conducted for the previous 316(b) Phase III proposed and final rules. The analysis
incorporates three additional years of data, for 2006, 2007, and 2008, which reflect the time span between the
2006 Phase III Final Rule and the Proposed 316(b) Existing Facilities Rule. In addition to these extra years of
data, EPA also identified and added to the analysis one business sector - Pesticides and Fertilizers - that was not
previously included in the Primary Manufacturing Industries. Further, EPA used a different data source-
Quarterly Financial Report (QFR) published by the U.S. Census Bureau as opposed to the Value Line Investment
Survey firm financial dataset published by the private independent financial research firm Value Line - to develop
these adjustment factors.
To support its analysis of the potential economic impact of the 316(b) regulations - including the previous 316(b)
Phase III regulations and now the Proposed 316(b) Existing Facilities Rule, EPA collected economic/financial
data for the three years 1996-1998 from a sample of facilities in the manufacturing industries primarily expected
to have been subject to the regulation. These facility economic/financial data are used to gauge the potential
economic/financial impact of regulatory compliance: the facilities and their financial data serve as models for
testing the financial effect of regulatory alternatives. For this analysis to provide valid insight into the ability of
the affected industries to meet regulatory requirements without material adverse impact, the sample facility data
should reflect business conditions that might be reasonably anticipated at the time of compliance.
In performing the previous and current impact analyses using these data, EPA was concerned in two ways that the
facility survey data might yield erroneous conclusions:
1. Knowing that U.S. business conditions during the latter half of the 1990s were cyclically strong,
EPA was concerned that business conditions during the 316(b) Survey period (1996-1998) might
be abnormally favorable for some of the six Primary Manufacturing Industries sectors covered in
the 316(b) rule analyses. In this case, the business performance and valuation measures, which
are based on survey data, used to assess the burden of regulatory compliance costs might
overstate industry's ability to bear these costs and therefore understate the potential impact of the
regulatory analysis options considered for the 316(b) regulations.
2. Apart from the issue of short-term deviation from trend caused by a cyclically strong economy,
EPA was also aware from its profile analyses that some of the industries might be experiencing a
longer term trend of deteriorating performance. Using sample facility data that don't reflect such
possible trends would again potentially overstate industry's ability to bear compliance costs and
therefore understate the potential impact of the regulatory analysis options considered for the
Proposed Existing Facilities Rule.
Given these concerns, EPA analyzed for the manufacturing industries (1) whether business conditions were
"abnormally favorable" during the survey period and (2) whether business performance over a longer term might
be following a non-neutral - in particular, negative performance - trend. This analysis validated EPA's concerns
that use of unadjusted survey data might yield erroneous conclusions from the facility impact analysis. From the
findings of this analysis, EPA developed a basis for adjusting survey financial data to account for these effects:
short-term deviation from trend and non-neutral long-term trend.
March 28, 2011
4B-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4B: Adjusting Baseline Facility Cash Flow
4B.1 Background: Review of Overall Business Conditions
As background for its analysis, EPA reviewed general economic data over the past several years to assess whether
business conditions during the survey data collection period of 1996-1998 might be generally perceived as
abnormally favorable for the U.S. economy, as a whole. This review confirmed the concern that business
conditions in 1996-1998 were generally more favorable than the average of conditions over a longer time period.
Figure 4B-1 - Figure 4B-3 present annual and average values for the period 1985-2009 for three measures of
general economic performance:
Figure 4B-1. This exhibit, based on data published by the Department of Commerce, Bureau of Economic
Analysis, focuses on the growth trend of the broad economy, including all sectors. Growth stronger than the
average trend would indicate a strongly expanding economy and would generally indicate strong business
performance.
Figure 4B-2. This exhibit, based on U.S. Federal Reserve Bank data, reports the rate of capital utilization for
all manufacturing sectors. All else equal, when the rate of capital utilization is higher than the average trend,
demand for manufacturing output is strong and manufacturing business performance would be generally
strong.
Figure 4B-3. Like the preceding exhibit, this exhibit is based on data published by the U.S. Federal Reserve
Bank and reports the rate of growth in the Federal Reserve's Industrial Production Index, which is a measure
of the real output of the manufacturing industries. Growth stronger than the average trend would indicate a
strong expansion in the manufacturing industries and would generally indicate strong manufacturing business
performance.
In each case, the annual values in the period 1996-1998 are above the average trend line, indicating stronger
overall economic performance in the survey data collection period than for the longer period presented in the
charts. The data show a consistent year-by-year pattern over the 1996-1998 period:
> 1996: The values for 1996 are above the longer-term average trend but are the lower than the values for
1997, indicating that manufacturing economy was in an upswing from 1996 to 1997.
> 1997: The values for 1997 are the highest of the values for the three years.
> 1998: The values for 1998 are all lower than the values for 1997 and generally appear to be the beginning
of the downswing in economic performance that occurred in the latter part of the 1990s. In the case of
industrial production and capacity utilization in manufacturing industries, 1997 is the peak performance
year over the 1990s decade and is followed by a decline in 1998 and subsequent years leading to the
recession period in 2001. In the case of GDP growth, the fall-off in 1998 (from 1997) is followed by one
more year of strong growth in 1999. GDP growth turns sharply lower during 2000 and the recession year
of 2001. As the U.S. economy began to recover during the first half of the last decade following the
recession of 2001, so did manufacturing production. The second half of the decade, however, once again
experienced economic slowdown leading to the recession of 2008-2009. Even though the U.S.
manufacturing sector experienced some recovery after the recession of 2001, its performance never
achieved the level of performance in 1997.
4B-2 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Figure 4B-1: Growth in Real Domestic Product, 1985-2009
6.0%
5.0%
Year-to-Year Growth in GDP, Percent
Average Annual Growth Rate
h. 001 C>
O
O
CM CM CM CM CM CM CM CM CMICI
00 00 00 00 0> 0> CD CD CD CD CD CD CD CD
CD 0> 0> 0> 0) 0> 0> 0> 0> 0> 0> 0> 0> 0)
-3.0%
March 28, 2011
4B-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Figure 4B-2: Capacity Utilization in Manufacturing Industries, 1985-2009
85.0%
83.0%
81.0%
CO
N
(0
o
75.0%
73.0%
71.0%
69.0%
67.0%
65.0%
•Capacity Utilization
Average Capacity Utilization
A— ,v
A li
•41 in
01010101010101010101
4B-4
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Figure 4B-3: Growth in Industrial Production, 1985-2009
10.0%
Annual Growth Rate in Industrial Production
Average Annual Growth Rate
-10.0%
4B.2 Framing and Executing the Analysi
The objective of this analysis was to understand (1) the extent to which the business conditions and financial
performance of the Manufacturing industries expected to be in scope of the Proposed Existing Facilities Rule
reflected cyclically favorable conditions during the 316(b)Survey period and (2) whether these industries show a
non-neutral longer term trend in economic/financial performance - e.g., deterioration in performance overtime
independent of cyclical variation. If either or both of these conditions were found, then the data used to test for
these conditions would be used to adjust relevant survey data items to a level consistent with normal business
conditions and/or the longer term of performance.
To meet these objectives, EPA set, as its overall approach, identification and analysis of a financial performance
data series for the in-scope Manufacturing industries. This data series would be used to test whether financial
performance at the time of the 316(b) Survey differed from the longer term trend. At the outset, EPA recognized
that, in all likelihood, such a data series would not report financial performance at the level of the individual
facility - which is the unit of analysis for the 316(b) facility impact analysis (see Chapter 4: Cost and Economic
Impact Analysis for Manufacturers} - but would report performance for individual firms or for some industry
aggregate. As a result, EPA would need to infer the trend of performance in facility financial performance from
firm- or industry-level performance and, in turn, apply adjustments, if needed, to facility financial data based on
analysis of the firm- or industry-level performance. Although the use of firm- or industry-level information for
adjusting facility data necessarily represents a limitation in this analysis, EPA judges that the effort is warranted
given: (1) the potential for the facility impact analysis to yield erroneous findings if it is based on data that reflect
cyclically favorable conditions and (2) the absence of facility data to support a more precise analysis.
March 28, 2011
4B-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4B: Adjusting Baseline Facility Cash Flow
Key steps in framing and executing the analysis are described below.
4B.2.1 Identifying the Financial Data Concept to Be Analyzed
EPA determined that the financial data concept to be analyzed should be equivalent, or close in concept, to the
business performance and valuation metrics used in the 316(b) rule impact analyses. For the facility impact
analysis, the key financial metric is after-tax, pre-interest cash flow, calculated as income before interest,
depreciation and amortization, and adjusted to be on an after-tax basis. In the facility impact analysis, this metric
is used to calculate the business value of a sample facility, on both a baseline - i.e., before imposition of
compliance costs - and post-compliance basis. Using this, or a closely related, measure in the analysis of financial
performance at the time of the facility survey would therefore support a direct test of whether and how the survey
financial data - to be subsequently used in the facility impact analysis - might reflect cyclically favorable
conditions or differ from the longer term trend of financial performance in an analysis. If either or both of these
conditions were found, the data would also readily support development of a necessary adjustment to offset these
potential biases in the survey data.
EPA recognized that the after-tax, pre-interest cash flow measure used in the facility impact analysis would very
likely not be directly available from financial datasets that might be practically used in this analysis. However,
reasonable surrogates for this measure that would likely be available include: after-tax cash flow from operations
(net income plus depreciation and amortization); earnings before interest, taxes, depreciation and amortization
(EBITDA); net income; and earnings before interest and taxes (EBIT).
4B.2.2 Selecting Appropriate Data
Other key requirements of the data to be used in the analysis include:
> The financial data need to be a time series, preferably annual, over a sufficiently long period (and
including the survey period) to allow testing of (1) whether survey period business conditions were
cyclically favorable; and (2) whether financial performance in the industries exhibits a longer-term, non-
neutral trend.
> The data need to be at a sufficient level of industry resolution to account for variations in business
conditions and performance not only across the six manufacturing sectors but also within certain sectors,
where there may be substantial variation in performance by important segments. Of particular importance
is the ability to segment the chemicals sector into its segments such as basic chemicals, plastic materials
and resins, and pharmaceuticals.
For the previous 316(b) Phase III rule analyses, EPA considered several data sources, but settled on the Value
Line Investment Survey firm financial dataset as the best choice for this analysis given the requirements above.106
For the current analysis, the Agency again reviewed alternative sources including Value Line and one of the
sources considered for the earlier analyses, the Quarterly Financial Report for Manufacturing, Mining, and Trade
Corporations (QFR) published by the U.S. Census Bureau. For the current analysis, EPA decided to use QFR data
instead of Value Line. In reaching this decision, EPA understood that its earlier reasons for choosing the Value
Line data over the QFR data were still valid, namely:
> While both QFR and Value Line data are reported over the desired analysis period, QFR data are reported
in inconsistent economic classification frameworks over the desired analysis period, thereby creating a
data break (SIC to NAICS) in the time series. Value Line, on the other hand, identifies and groups
companies in a business content classification scheme that approximates 3-digit SIC or 4-digit NAICS
106 For more information see the 2004 Economic Analysis for the Proposed Section 316(b) Rule for Phase III Facilities and the 2006
Economic and Benefits Analysis for the Final Section 316(b) Phase III Existing Facilities Rule.
4B-6 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4B: Adjusting Baseline Facility Cash Flow
classifications. Because the Value Line business classifications are defined by practical business content
instead of in a rigid SIC or NAICS classification scheme, the Value Line dataset would avoid the
challenge of adjusting for changing economic classification schemes.107
> QFR data are reported at the level of the industry while the Value Line data are reported by company. For
the previous 316(b) rule analyses, EPA judged that Value Line's firm-level reporting would provide a
better basis for identifying firms within the in-scope manufacturing industries at a level of sector detail
sufficient for this analysis.
However, the fact that Value Line data are reported at the firm level, presents a problem given the longer time
period (an additional four years since the previous 316(b) Phase III rule analysis in 2006) that would need to be
covered by the financial data for the current analysis. Specifically, in developing adjustment factors for the earlier
Phase III rule analyses, EPA had to closely review the financial data reported for each firm in the Value Line
dataset to exclude firms that had experienced significant business structural changes, such as mergers,
acquisitions, or bankruptcies. Consequently, the set of firms included in the analysis of the 2004 Phase III
Proposed Rule was smaller than the set of firms included in the analysis of the 2006 Phase III Final Rule. After
carefully reviewing Value Line data for the current effort, EPA determined that the set of the remaining firms
without major business structural changes would not be sufficient to develop reliable adjustment factors. In
addition, given the additional years in the analysis period, the Agency was concerned that, even for the small
number of remaining firms with relatively minor business structural changes, it would be increasingly
unreasonable to assume that changes in firm-level cash flow could be attributed to changes in the business
performance of the individual facilities that comprise the firms, as opposed to reflecting changes in the number
and size of facilities in operation. Consequently, EPA decided that for this analysis, the uncertainty and issues of
the Value Line data outweighed that of the QFR data and developed adjustment factors using QFR data.
4B.2.3 Methodology for Development of ATCF Adjustment Factors
As stated above, the overall approach to the analysis was to analyze, for each industry group, the trend of financial
performance over the 21-year analysis period - 1988 through 2008 - and to assess where the industry's financial
performance lay relative to that trend during the 316(b) survey data collection years of 1996-1998. For each
industry group, EPA used as analysis observations an index of constant dollar-adjusted after-tax cash flow for the
relevant industry groups. To analyze the trend, EPA calculated a simple regression of the index values against
time. The estimated regression relationship provides a direct measure of the real (i.e., inflation-adjusted) trend of
financial performance overtime for each industry group. The 1996-1998 average of index values for each
industry group were then compared with the trend values predicted from the estimated regression coefficients -
both for the 1996-1998 years and for 2008, which is the end of the analysis period - to determine the extent to
which 1996-1998 survey values should be adjusted to reflect (1) the deviation from trend at 1996-1998 and (2) the
trend from 1996-1998 to the end of the analysis period.
EPA used the following steps to calculate After-Tax Cash Flow (ATCF) adjustment factors using QFR:
> Choose variables, period of analysis, and industries: EPA used quarterly Income (or Loss) After
Income Taxes (ATI) and Depreciation, Depletion, and Amortization of Property, Plant, and Equipment
(DDA) values as the basis for calculating ATCF, for 21 years - 1988 through 2008 - for all of the
industries in the following table except for Pesticides and Fertilizers and Resins and Synthetics. For the
Pesticides and Fertilizers sector, QFR data are available only starting 1992. Consequently, EPA
developed ATCF adjustment factor for this industry using only 17 years of QFR data. QFR does not
107 As described in the industry profiles, the change from SIC-based to NAICS-based reporting of economic data by federal government
and other data sources at around 1997/98 created difficulties in aligning and ensuring consistency of time series data that are organized
within these frameworks.
March 28, 2011 4B-7
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
provide data specifically for the Resins and Synthetics sector. Instead, these data are a part of the Basic
Chemicals, Resins and Synthetics sector. Therefore, EPA was unable to perform a separate QFR-based
analysis for this industry and used ATCF adjustment factor calculated for the Basic Chemicals sector for
the Resins and Synthetics sector (Table 4B-1).
Table 4B-1: Analysis Sectors and Corresponding Sectors Covered by QFR
Analysis Sector Name
Aluminum
Basic Chemicals; Resins and
Synthetics
Food and Kindred Products
Paper and Allied Products
Pesticides and Fertilizers*
Petroleum Refining
Pharmaceuticals
Resins and Synthetics5
Steel
QFR SIC
Sector
Available f
333-336
281 282 286
22,21
26
____.__.
289
29
283
NA
33l'73327329
SIC Description
Dr 1998 Ql through Tzbbl Q3
Nonferrous Metals
Industrial Chemicals and
Synthetics
Food & Kindred Products (Incl.
Tobacco)
Paper & Allied Products
Residual of Chemicals
Petroleum & Coal Products
Drugs
NA
Iron & Steel
QFR NAICS
Sector
Available
3313,3314
3251 3252
311,312
322
3253 3255
3256, and 3259
324
3254
NA
33TT73312
NAICS Description
for 2000 Q4tnrough2bb8Q4
Nonferrous Metals
Basic Chemicals, Resins, and
Synthetics
Food, Beverage, & Tobacco Products
Paper
Other Chemicals
Petroleum & Coal Products
Pharmaceuticals & Medicines
NA
[ron, Steel, & Ferroalloys
a. QFR does not provide data specifically for the Pesticides and Fertilizers sector. Instead, these data are a part of the Other Chemicals sector (SIC 284, 285,
287, and 289; NAICS 3253, 3255, 3256, and 3259)
b. QFR does not provide data specifically for the Resins and Synthetics sector. Instead, these data are a part of the Basic Chemicals, Resins and Synthetics
sector.
Source: U.S. Census Bureau, 1998-2008 Quarterly Financial Report; U.S. EPA Analysis, 2010
> Adjust ATI and DDA values to constant dollars in 2008: EPA deflated all values to 2008 using the
GDP Deflator series published by the Bureau of Economic Analysis.
> Calculate ATCF: EPA calculated quarterly ATCF values as quarterly ATI plus DDA for each industry,
and summed the resulting quarterly ATCF values to calculate annual ATCF values for 1988 through
2008.
> Generate ATCF index series:
• EPA first adjusted the ATCF series to eliminate negative values for each industry by adding to each
ATCF value in a given industry's 21-year series, the absolute value of the most negative ATCF value
for this industry plus one. This adjustment has the effect of "vertically" shifting the ATCF values for
a given industry so that all values are positive while retaining the mathematical "shape" of the series
as needed for the trend analysis. This adjustment was necessary to prevent the undesirable inversion
of the ATCF index trend - calculated in the next step below - that would occur if a negative index
numerator is combined with a positive series in calculating the ATCF index series.
• EPA calculated ATCF index values for each year and industry by dividing each adjusted ATCF value
by the 21-year average of adjusted ATCF values.
> Calculate the time trend of ATCF index series: EPA regressed ATCF index values against year by
industry, to calculate the time trend of constant dollar ATCF over the period 1988 - 2008 (1992 - 2008
for the Pesticides and Fertilizers sector).
4B.3 Analysis Results
Table 4B-2, below, summarizes the analysis results together with potential adjustments under varying
interpretations of the findings.
4B-8
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Table 4B-2: Statistical Significance of Regression Results and Potential Adjustments
Analysis Sector
Aluminum
Basic Chemicals; Resins and Synthetics
Food and Kindred Products
Paper and Allied Products
Pesticides and Fertilizers
Petroleum Refining
Pharmaceuticals
Resins and Synthetics0
'Steel
P-Value
0.7747
03871
676666
671116
aoi26
676618
aoooo
NA
671742
Statistically
Significant?
no
no
yes
no
yes
yes
yes
NA
no
Difference in Trend-
Predicted ATCF Index and
Actual Index Values -
both at 1996-1998"
-9.6%
:i57o"%
15%
:i2.6%
:2"5;8%
216%
20.0%
NA
i246%
Difference in Trend-Predicted
ATCF Index at 2008 and
Actual Index Value at 1996-
1998"
NA
NA
414%
NA
46%
857i%
1444%
NA
NA
a. For sectors with statistically significant estimated trend factors, the "trend-predicted ATCF values" are the average of 1996-1998 predicted ATCF values
using the estimated non-zero time-trend factor. For sectors for which the estimated trend factor is not statistically significant, the "trend-predicted ATCF
values" are the simple 21-year average of ATCF index values - i.e., the time-trend factor is assumed to be zero.
A negative value indicates that the actual value exceeds the trend-predicted ATCF value; a positive value indicates that the trend-predicted ATCF value
exceeds the actual value. In both instances, the reported percentage value is the adjustment that would be applied to bring the actual index value to the
1996-1998 trend-predicted value.
b. The "trend-predicted ATCF values" are at 2008 and are reported only for sectors for which the estimated time-trend factor is statistically significant. In
all instances, the estimated time-trend factor is positive and the trend-predicted ATCF index values at 2008 are higher than the actual index values at 1996-
1998.
c. QFR does not provide data specifically for the Resins and Synthetics sector. Instead, these data are a part of the Basic Chemicals, Resins and Synthetics
sector.
Source: U.S. Census Bureau, 1998-2008 Quarterly Financial Report; U.S. EPA Analysis, 2010
Several observations are relevant:
> The estimated trend value is not statistically significant for four of the eight analyzed sectors: Aluminum
(Figure 4B-4), Basic Chemicals, Resins and Synthetics (Figure 4B-5), Steel (Figure 4B-11), and Paper
and Allied Products (Figure 4B-7)
• For these sectors, EPA decided not to use the estimated trend value in any adjustments, but to use
simply the average ATCF index values over the 21 years - i.e., a zero slope trend line - as the basis
of any adjustment.
• For each of these four sectors, the indicated direction of adjustment to bring these sectors' ATCF
values to the 1996-1998 trend value is negative - i.e., the ATCF adjustment would lower the
estimated ATCF values for facilities in these sectors.
• Consequently, EPA decided to adjust ATCF values for facilities in these four sectors only to the
1996-1998 trend value. The downward adjustment of the ATCF values avoids overstating the ability
of facilities in these industries to comply with rule requirements.
> The estimated trend value is statistically significant for the other four out of eight sectors: Food and
Kindred Products (Figure 4B-6), Pesticides and Fertilizers (Figure 4B-8), Petroleum Refining (Figure
4B-9), and Pharmaceuticals (Figure 4B-10)
* For these sectors, it would be possible to use the estimated trend line as the basis for adjustment
whether (1) to adjust the survey-based ATCF values to trend at 1996-1998 or (2) to adjust ATCF
values for the trend overtime since the survey.
• For each of these sectors, the calculated ATCF index values for 1996 through 1998 are below the
estimated trend line at 1996-1998 and the estimated trend shows a steep increase in ATCF from
1996-1998 to 2008.
• Although the trend values are statistically significant, EPA decided not to adjust the survey-based
ATCF values along the trend - i.e., from 1996-1998 to 2008 - because the implied change in ATCF
March 28, 2011
4B-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
occurs over too long a period and is too large to reflect unchanging capital in an industry, in terms of
number and/or size of facilities. As a result, the Agency decided to bring survey-based ATCF values
for these sectors only to the estimated trend at 1996-1998 and not adjust along the trend.
Consequently, from the standpoint of adjusting financial statement information from the original
survey, this adjustment avoids overstating the potential of facilities in these industries to comply with
rule requirements.
Table 4B-3, below, summarizes the resulting adjustment factors based on the preceding findings and judgments.
The table also reports the adjustment factors used in the previous 316(b) Phase III cost and economic impact
analyses, for Aluminum, Paper and Allied Products, Basic Chemicals, Resins and Synthetics, and Steel, the
potential adjustment would reduce the survey-based ATCF values by the multiplicative factor. For Food and
Kindred Products, Pesticides and Fertilizers, Petroleum Refining, and Pharmaceuticals the potential adjustment
would increase the survey-based ATCF values by the multiplicative factor. Because QFR does not provide
information for the Resins and Synthetics sector, EPA was not able to calculate an adjustment factor specifically
for this sector. Consequently, for the Resins and Synthetics sector, EPA used the adjustment factor calculated for
the Basic Chemicals, Resins and Synthetics sector.
Table 4B-3: : Potential ATCF Adjustment Factors
Factors
Analysis Sector
Aluminum
Basic Chemicals; Resins and Synthetics
Food and Kindred Products
Beverages
Food
Paper and Allied Products
Pesticides and Fertilizers
Petroleum Refining
Pharmaceuticals
Resins and Synthetics
Steel
Adjustments in P
NA
679228
NA
NA
NA
fo'397
NA
173171
T7T398
6786756
•evious Analyses
''''To''20b5-P3Flr'
NA
NA
i"7'3076
1773835"
i"."6"386
NA
i".'4'"9"i'4"
i".'2'"5"26
i"."i"9"48"
679096
101996-1998
Trend - Current
0.9044
b7'85b''i
170355
NA
NA
678737
1772304
i72bb4
b7'85b''i
0/7539
a. For more information on the development of these adjustments factors see the 2004 Economic Analysis for the
Proposed Section 316(b) Rule for Phase III Facilities
b. For more information on the development of these adjustments factors see the 2006 Economic and Benefits
Analysis for the Final Section 316(b) Phase III Existing Facilities Rule.
Source: U.S. Census Bureau, 1998-2008 Quarterly Financial Report; U.S. EPA Analysis, 2010
The charts below and on the following pages show the calculated ATCF Index Series and Trend-Predicted ATCF
Index series. For sectors for which the estimated time-trend factor is statistically significant, the Trend-Predicted
ATCF Index series is a non-zero slope line and is labeled "Calculated Trend." For sectors for which the estimated
time-trend factor is not significantly significant, the Trend-Predicted Index series is a zero slope line and is
labeled "21-Yr Ave ATCF Index."
4B-10
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
1 Q
1 C
1 A -
1 9
X
0) 1
•o J
_c
LJ_
o
u- n ft
<
OR
04
OO
0_
j
Figure 4B-4: ATCF Index Series and Calculated Trend - Aluminum
ATCF Index Series
— *— 21 Yr Ave ATCF Index
b% <& <& & <& <& <**" * e?^ # ^ e?*
>^>>^>i^>i^>i^>i^>i^>i^>i^>i^>i^>ff>ff>ff>ff>ff>ff>ff>f^ff>
March 28, 2011
4B-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Fi
1 fi
1 4
1 9
X
0)
c
— n ft
u_ "•«»
OR
OA
00
gwre 45-5: ATCF Index Series and Calculated Trend -Basic Chemicals, Resins and Synthetics
ATCF Index Series
-•-21-YrAve ATCF Index
4B-12
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Figure 4B-6: ATCF Index Series and Calculated Trend - Food and Kindred Products
X
01
•c
0.2
ATCF Index Series
Calculated Trend
March 28, 2011
4B-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
1 ft
1 ft
1 A -
1 9
X
0) 1
T3 n
c
LL.
o
C n s
<
Oft
04
00 _
K*
Figure 4B-7: ATCF Index Series and Calculated Trend -Paper and Allied Products
ATCF Index Series
-•-21-Yr Ave ATCF Index
i^<^oi5oN<^<^o^<^oS°<^d?>d>*c^c\scO'cv>c\*tc?3c$3c^ c?>
"» ^ N# N^ N^ N^ N^ & N# N^ N^ N^ ^ ^ ^ ^ ^ ^ ^ ^ ^
4B-14
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Figure 4B-8: ATCFIndex Series and Calculated Trend - Pesticides and Fertilizers
1.6
0.2
ATCF Index Series
Calculated Trend
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
March 28, 2011
4B-15
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Figure 4B-9: ATCF Index Series and Calculated Trend- Petroleum Refining
1.8
1.6
0.8
0.6
0.4
0.2
0
ATCF Index Series
•Calculated Trend
4B-16
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Figure 4B-10: ATCFIndex Series and Calculated Trend - Pharmaceuticals
2.2
2
1.8
1.6
1.4
x
| 1.2
^ 1
<
0.8
0.6
0.4
0.2
0
ATCF Index Series
•Calculated Trend
March 28, 2011
4B-17
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4B: Adjusting Baseline Facility Cash Flow
Figure 4B-11: ATCF Index Series and Calculated Trend- Steel
1 A
1.6
1 A
1 9
1
c
LL. *
O
n ft
Oft
OA
00
ATCF Index Series
— *— 21-Yr Ave ATCF Index
4B-18
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Appendix 4C Estimating Capital Outlays for Section 316(b)
Manufacturing Sectors Discounted Cash Flow Analyses
The analysis of economic impacts to manufacturing facilities expected to be subject to 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 developed from industry questionnaires.108 This appendix presents
the details of the Capital Expenditure analysis, as performed and documented for the previous 316(b) Phase III
Rule analyses. This analysis approach has been carried over into the current analysis of impact on manufacturing
facilities for the proposed 316(b) Existing Facilities rule. EPA did not re-estimate the regression equation for the
current analyses, but did update some of the input data that is used to estimate Capital Expenditure based on the
regression analysis. These updates are described in Section 4C.6. While the estimation of capital outlays relies in
part on data in the SIC framework and uses data from the Value Line dataset, which have been replaced
respectively by the NAICS framework and the Bureau of the Census's Quarterly Financial Reports, EPA judges
that the estimations of capital expenditures remain valid for the analysis of the current proposed rule.109
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.
However, the Section 316(b) Industry Questionnaire did not request information from surveyed facilities on their
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 business value of sample facilities and, as a consequence,
understatement of regulatory impacts in terms of estimated facility closures (EPA, 2003). In response to this
omission, the Office of Management and Budget suggested the adoption of depreciation expense as a surrogate
for cash outlays for capital replacement and additions. However, 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 base. Therefore, depreciation is fundamentally the wrong
concept to use as a surrogate for capital outlays for capital replacement and additions. Second, depreciation is
estimated based on the historical asset cost, which may understate 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 recently acquired assets that are still in the depreciable asset
base, and conversely, understate the real depreciation value 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 growth and financial performance among firms. Businesses with
real growth in revenue will need to expand both their 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 measures such as return on assets or return on invested
capital. As a result, businesses with "strong" asset productivity will attract capital for renewal and expansion of
This analysis is limited to potentially affected facilities in SIC codes 26, 28, 29, and 33.
The prior analysis, and therefore this appendix, relied on classification of businesses in the SIC framework. Although other analyses
and presentations for the 316(b) existing facilities rule have been updated to the NAICS, this appendix continues to the use the SIC
framework as the basis for business classification.
March 28, 2011
4C-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4C: Estimating Capital Outlays
their asset base, while businesses with "weak" asset productivity will have difficulty attracting the capital for
renewal and expansion of the business' asset base. All else being equal, businesses with strong asset productivity
will have higher ongoing outlays for capital assets; businesses 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 316(b)
manufacturers DCF analysis, EPA estimated a regression model of capital outlays using reported capital
expenditures and relevant explanatory financial and business environment information for public-reporting firms
in the original, primary 316(b) manufacturing sectors. The resulting estimated model is used to estimate capital
outlays for facilities in the 316(b) manufacturers sample dataset. The estimated capital outlay values were then
used in the DCF analyses to calculate business value of sample facilities and estimate regulatory impacts in terms
of facility closures.
The approach and regression model described above are based largely on the approach and regression model
developed in support of the analysis of economic impacts 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)
manufacturers analysis are similar to those analyzed in the MP&M analysis: both analyses estimate impacts to
facilities in manufacturing industries only and facilities in SIC 33 are covered under both regulations. In addition,
the Section 316(b) Industry Questionnaire and the MP&M survey instruments are similar; therefore, similar data
are available for the 316(b) manufacturers and MP&M survey facilities. As such, EPA relied heavily on prior
experience from the MP&M final regulation in estimating the regression model used to estimate of capital outlays
for facilities in the 316(b) manufacturers sample dataset.
This appendix reports the results of the effort to estimate capital outlays for 316(b) manufacturing facilities,
including: an overview of the analytic concepts underlying the analysis of capital outlays; specific variables
included in the regression analysis; summary of data selection and preparation; general specification of regression
models to be tested; and the findings from the regression analyses. The model estimation does not include sector
information for the Food and Kindred Products industry, which was added as a primary industry for the Section
316(b) Phase III final rule analysis.110
4C.1 Analytic Concepts Underlying Analysis of Capital Outlays
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 available from the 316(b) manufacturers sample facility dataset.
To aid in identifying the explanatory concepts and variables 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 analysts found to contribute important information in the
Since the estimated regression model for the 316(b) manufacturers facilities 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 lucid discussion of the analytic concepts underlying the model
and the rationale behind specifying variables for the analysis and specification of the regression model, a complete discussion of how
the regression model was developed is presented. During the course 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 current model are
clearly highlighted.
4C-2 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
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 4C-1 to this Appendix 4C .
Table 4C-1, beginning on the subsequent 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, the general explanatory variable(s) that EPA identified to capture the concept in a
regression analysis, and the hypothesized mathematical relationship (sign of estimated coefficients) between the
concept and capital outlays. Table 4C-2 identifies the specific variables included in the analysis, including any
needed manipulations and the correspondence of the variables to 316(b) manufacturers survey information.
Table 4C-1: Summary of Factors 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 Assets of establishment as a indicator of investment
opportunities and management 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 Positive
historical measure of business growth and is a potential indicator of need for
capital expansion. Use of a historical variable implicitly assumes past
performance is indicative of future expectations.
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 Indicators and 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 Intensity of 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.
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
No information is available on the actual useful life of capital equipment by Positive,
business or industry classification. However, the Capital Turnover Rate, as generally, but
calculated by the ratio of book depreciation to net capital assets, provides an with
indicator of the rate at which capital is depleted, according to book recognition of
accounting principles: the higher the turnover rate, the shorter the life of the the potential
capital equipment. However, the measure is imperfect for reasons of both the for counter-
inaccuracies of book reporting as a measure of useful life, and as well the trend effects
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.
Preferably, measures of cost-of-capital would be developed separately for Negative
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 safety
rating (e.g., S&P Debt Rating).
March 28, 2011
4C-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Table 4C-1 : Summary of Factors Influencing Capital Outlays
Explanatory Factor/Concept To Be
Captured in Analysis
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.
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 outlay may 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.
Translation of Concept to Explanatory Variable(s)
The cost of equity capital is more problematic than the cost of debt capital
since it is not directly observable for either public-reporting firms or, in
particular, private firms in the 316(b) manufacturers 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.
Index provides an indicator of the change in capital equipment prices.
Expected
Relationship
Negative
Negative,
generally, but
with
recognition of
the potential
for counter-
trend effects
Source: U.S. EPA analysis, 2004.
4C.2 Specifying Variables for the Analysis
Working from the general concepts of explanatory variables outlined above, EPA defined the specific explanatory
variables to be included in the analysis. A key requirement of the regression analysis is that the firm-specific
explanatory variables included in the regression analysis later be able to be used as the basis for estimating capital
expenditures for facilities in the 316(b) manufacturers dataset. As a result, in defining the firm-specific variables,
it was necessary to ensure that the definition of variables selected for the regression analysis using data on public-
reporting firms be consistent with the data items available for facilities in the 316(b) manufacturers 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 11 years - i.e., 1992-2002. Although
ideally EPA would have preferred a longer time series to include more years 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 analysis (VL provides 37 data items over the 11 reporting years; see
Attachment DB). However, because of the 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.
4C-4
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
The decision to use VL data for the analysis constrained, in some instances, EPA's choice of variables for the
analysis.
Table 4C-2 reports the specific definitions of variables included in the analysis (both the dependent variable and
explanatory variables), including any needed manipulations, the data source, the 316(b) manufacturers estimation
analysis equivalent (either the corresponding variable(s) in the Section 316(b) Industry Questionnaire or other
source outside the questionnaire), and any issues in variable definition.
Table 4C-2: Variables For Capital Expenditure Modeling Analysis
Variables for Regression Analysis
Variable
Source
Calculation
316(b) Manufacturers
Analysis Equivalent
Comment / Issue
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. CAPEX
calculated 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
Revenue:
REV
Capital
Turnover
Rate: CAPT
Value Line
Value Line
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.
REV = Revenues. Revenues
directly available from VL.
CAPT = Depreciation / Total
Assets. Depreciation and
Total Assets directly available
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)
From Survey: Revenue
From Survey:
Depreciation / Total
Assets
Would have preferred an after-tax
concept in numerator and a deployed
production capital concept in
denominator. However, VL provides no
tax value per se and would require
calculation of tax using an estimated tax
rate, which could introduce error. Also
neither VL nor 316(b) manufacturers
survey data provide sufficient
information to get at deployed
production capital.
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 316(b) manufacturers 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 REV it was not included in the final
specification. Based on this previous
finding, Total Assets was not
considered while specifying the 316(b)
manufacturers regression model.
Would have preferred denominator of
net fixed assets instead of total assets.
However, VL provides detailed balance
sheet information for only the four most
recent years. Not possible to separate
current assets and intangibles from total
assets.
March 28, 2011
4C-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Table 4C-2: Variables For Capital Expenditure Modeling Analysis
Variables for Regression Analysis
Variable
Capital
Intensity:
CAPI
Market-to-
Book Ratio:
MV/B
Source
Value Line
Value Line
Calculation
CAPI = Total Assets /
Revenue. Total Assets and
Revenue directly available
fromVL
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.
316(b) Manufacturers
Analysis Equivalent
From Survey:
Total Assets / Revenue
N/A (see
Comment/Issue)
Comment / Issue
As above, would have preferred net
fixed assets instead of total assets, but
needed data are not available from VL
for the full analysis period.
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 316(b)
manufacturers regression model which
eliminated the need to define an
approach to use this variable with
316(b) manufacturers survey data.
General Business Environment Variables
Interest on
10-year, A-
rated
industrial
debt:
DEBTCST
Index of
Leading
Indicators:
ILI
Capacity
Utilization by
Industry:
CAPUTIL
Producer
Price Index
series for
capital
equipment:
CAPPRC
Moody's
Investor
Services
Conference
Board
Federal
Reserve
Board
(Dallas
Federal
Reserve)
Bureau of
Labor
Statistics
(BLS)
DEBTCST = annual average
of rates for each data year
Monthly index series
available from Conference
Board. ILI = geometric mean
of current year values.
Monthly index series
available from Federal
Reserve. CAPUTIL = current
year average value.
Annual average values
available from BLS.
CAPPRC = current year
average value as reported by
BLS.
Use average of
DEBTCST rates at time
of 3 1 6(b) manufacturers
survey.
Use average of ILI
values at time of 3 1 6(b)
manufacturers survey.
Use average of
CAPUTIL values at time
of 3 1 6(b) manufacturers
survey.
Use average of CAPPRC
values at time of 3 1 6(b)
manufacturers survey.
10-year maturity, industry debt selected
as reasonable benchmark for industry
debt costs. 10 years became "standard"
maturity for industrial debt during
1990s.
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 316(b)
manufacturers regression model.
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.
4C.3 Selecting the Regression Analysis Dataset
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. Value Line's SIC code definitions do not match the U.S. Census
Bureau's SIC code definitions; however, in most instances a Value Line SIC code can be reasonably matched to
4C-6
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4C: Estimating Capital Outlays
one or several U.S. Census Bureau defined SIC codes. To build the public firm dataset corresponding to the
original 316(b) Phase III manufacturing sectors (SIC 26: Paper and allied products, SIC 28: Chemicals and allied
products, SIC 29: Petroleum and coal products, and SIC 33 Primary metal industries), 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 (specialty),
> 2830: Biotechnology,
> 2834: Drug,
> 2840: Household products,
> 2844: Toiletries/cosmetics,
> 2900: Petroleum (integrated),
> 3311: Steel (general), and
> 3 312: Steel (integrated).
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4C: Estimating Capital Outlays
This is the same dataset used in the previous 316(b) Phase III rule analyses. Although the Food and Kindred
Products sector was ultimately included as a primary sector for the Section 316(b) Phase III final rule analysis,
EPA judged that it was not necessary to re-estimate the model with data this additional industry because the
model coefficients estimated at Phase III proposal did not vary by industry in a statistically significant way. EPA
continues to rely on this judgment as the basis for carrying forward the previously estimated regression
relationship without inclusion of the Food and Kindred Products sector as an explicitly analyzed sector. The
current model's applicability across industries is detailed further in the next section of this appendix.
In order to derive a dataset of firms whose business activities closely match the activities of firms included in the
316(b) manufacturers 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 that match SIC 331 and SIC 335
(combined together to form the aluminum sector in the original 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 business 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 listed in SIC 3400: Metal fabricating; however, no firms
whose activities matched those described within the profiles of the 316(b) Manufacturing Sectors were
found.111
> EPA reviewed the business activities of firms listed in SIC 2840: Household products and SIC 2844:
Toiletries/cosmetics, and retained only those firms in the dataset whose activities matched those described
within the profiles of the 316(b) 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 unlikely to use cooling water intake structures and
therefore fall outside the 316(b) Manufacturing Sectors.
> EPA reviewed the business activities of firms listed in SIC 2830: Biotechnology and SIC 2834: Drug in
order to exclude firms that are exclusively research and development (R&D) firms and are unlikely to use
cooling water intake structures. However, 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 firms 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 renders earlier year data inconsistent with more recent
data - until after the beginning of the 11-year data period.112 As a result, the omission of observation years for a
firm always starts at the beginning of the data analysis period. This systematic front-end truncation of firm
observations in the dataset could be expected to bias the analysis in favor of the capital expenditure behavior
nearer the end of the 1990s decade. To avoid this problem, EPA removed all firm observations that have fewer
than 11 years of data. As a result, the dataset used in the analysis has a total of 2,244 yearly data observations and
represents 204 firms.
1'' The profiles only focus on 4-digit SIC categories represented in the sample of facilities which received the Section 316(b) detailed
industry questionnaire.
112 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 the length of
time that the firm has been publicly listed and thus subject to SEC public reporting requirements.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Table 4C-3 presents the number of firms by industry classifications.
Table 4C-3: Number of Firms by Industry Classifications
SIC Industry Classification
26: Paper and allied products
28: Chemicals and allied products
29: Petroleum and coal products
33: Primary metal industries
Number of Firms
24
136
20
24
U.S. EPA Analysis, 2004
4C.4 Specification of Models to be Tested
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:
CAPEXU =/(ROAu, REVU, CAPTU, CAPIU, DEBTCSTU, CAPPRQ, CAPUTIL;>() (Equation 4C -1)
Where:
CAPEXU = capital expenditures of firm /', in time period t; 113
t = year (year = 1992, . . . , 2002);
/ = firm /(/ = !,..., 204);
j = industry classification7
ROAU = return on total assets for firm / in year t;
REV,,? = revenue ($ millions) for firm /' in year f,
CAPTU = capital turnover rate for firm / in year t;
CAPIU = capital intensity for firm / in year t;
DEBTCST, = financial cost of capital in year t;
CAPPRQ = price of capital goods in year t;
CAPUTILy^ = the Federal Reserve Board's Index of Capacity utilization for a given industry j in year t.
EPA only tested log-linear model specifications for this analysis.114 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 estimate directly as the coefficients of the model, the
All dollar values were deflated to 2002 using 2-digit PPI values.
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 model. In addition, the log-linear model offered advantages in terms of retention
of early time period observations (by eliminating the need to use percent change variables) and variable specifications, and helped to
reduce outlier effects in the model. As a result, EPA selected a log-linear specification as the final regression model for the MP&M
final regulation. Based on these reasons and the similarity of industry sectors analyzed for the two regulations, EPA decided to test
only log-linear model specifications for the Phase III regression model.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4C: Estimating Capital Outlays
elasticities of capital expenditures with respect to firm financial characteristics and general business environment
factors. The following paragraphs briefly discuss testing 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(CAPEXu) = a + I[(3X ln(Xi()] + ^[jy ln(Y,)] + 8 (Equation 4C -2)
Where:
CAPEXU = capital expenditures of firm /', year t;
fix = elasticity of capital expenditures with respect to firm characteristic X;
Xu, = a vector of financial characteristics of firm /', year t;
yy = elasticity of capital expenditures with respect to economic indicator Y;
Yf = a vector of economic indicators, year t; for CAPUTIL, Y is also differentiated by industry
classification
8 = an error term; and
ln(jt) = 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:
^, n r, v\ d \n(CAPEX] d(CAPEX}/CAPEX
EiCAPEX) = ^ ^ = —^—, £ (Equation 4C -3)
v ' d\n(ROA) d(ROA)/ROA
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 period reporting, EPA set negative revenues for these firms to
$1.
> ROA: the values for return on assets in the public firm 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 transformation on the modeling results, EPA used the following data
transformation approach:115
115 While specifying the MP&M regression model EPA conducted a sensitivity analysis to examine the 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 all ROA values. As a result, the Phase III
regression model utilized the same data transformation procedure.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4C: Estimating Capital Outlays
• EPA excluded 27 firms with any annual ROA values below the 95th percentile of the ROA
distribution (i.e., ROA # - 0.51).
• 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-linear model, including models with the intercept and slope
dummies for different industrial sectors and models with the intercept suppressed.116 Slope dummies were used to
test the influence of industry classification on the elasticity of 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)
manufacturers capital expenditures analysis. The results 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 independent from one period to the next. Heteroscedasticity usually occurs in cross-
sectional data where the scale of the dependent variable and the explanatory power of the model vary across
observations. Not surprisingly, the dataset used in this analysis had both characteristics. Therefore, EPA estimated
the specified model using the generalized least 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 (p);
> Second, EPA used the generalized least squares procedure, where the analysis is applied to transformed
data. The resulting autocorrelation adjustment is as follows:
Zit = Zit - pZi:t_! (Equation 4C-4)
where Zit is either dependent or independent variables.
EPA was unable to correct the estimated model for 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) included 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
that allows forecasting of capital expenditures based on firm and business environment characteristics.
While specifying the MP&M regression model, EPA also tested specifications that included the following structural modifications: (1)
testing contemporary vs. lagged specification of certain explanatory variables: e.g., using prior, instead of current period revenue,
REV, as an explanatory variable; (2) testing scale-normalized specification of the dependent variable: e.g., using CAPEX/REV as the
dependent variable instead of simple CAPEX; (3) testing flexible functional forms that included quadratic terms; and (4) testing
additional explanatory variables including the index of 10 leading economic indicators (ILI) and market-to-book ratio (MV/B).
Because EPA found 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 model.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4C: Estimating Capital Outlays
Table 4C-4 presents model results. The model 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 95th
percentile.
Table 4C-4: Time Series, Cross-Sectional Model Results
Variable
Constant
Ln(ROA)
Ln(REV)
Ln(CAPT)
Ln(CAPI)
Ln(DEBTCST)
Ln(CAPPRC)
Ln(CAPUTIL)
Coefficient
21.880
0.526
1.129
0.687
1.078
-0.789
-5.957
1.716
t-Statistics
2.618
3.964
58.450
11.085
18.491
-1.605
-4.369
2.842
Autocorrelation Coefficient
r
0.385
18.402
Source: U.S. EPA Analysis
The empirical results show that among the firm-specific variables, the output variable (REV) is a dominant
determinant of firms' investment spending. A positive coefficient 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 (ROA) invest more, all else equal: for each one percent increase
in ROA, a firm is expected to increase its capital outlays by 0.53 percent. Other firm-specific characteristics were
also found important and will aid in differentiating the expected capital outlay for 316(b) manufacturers facilities
according to firm-specific characteristics. Firms that require more capital to produce a given level of business
activity (i.e., firms that have high capital intensity, CAPI) tend to invest more: a one percent increase in capital
intensity leads to a 1.08 percent increase in capital spending. Higher capital turnover/shorter capital life (CAPT)
also has a positive effect on investment decisions: 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 conditions 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 also an influential factor: a one percent increase in the
Federal Reserve Index of Capacity Utilization for the relevant industrial sector (CAPUTIL) leads to a 1.7 percent
increase in capital investments. The fact that these systematic variables are significant in the regression analysis
means that EPA will be able to control for economy- and industry-wide conditions in estimating capital outlays
for 316(b) manufacturers facilities.
4C.5 Model Validation
To validate the results of the regression analysis, EPA used 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 variables from the Value Line data as inputs to estimate capital
expenditures and then compared the estimated value to the median reported capital expenditures, and
> EPA used 316(b) manufacturers survey data to estimate capital expenditures and then compared the
estimated values to depreciation reported in the survey.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
First, EPA estimated capital expenditures for a hypothetical firm based on the median values of the four
dependent variables from the Value Line data and the relevant values of the three economic indicators. The
estimated capital expenditures for this hypothetical firm are $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 316(b) manufacturers survey data to confirm that the estimated capital expenditures seem
reasonable. Because the 316(b) manufacturers survey does not provide information on capital 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 facility from each of the 316(b) primary manufacturing sectors for
model validation. The selected facility for 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 facilities, EPA estimated capital expenditures using the estimated regression equation and facility financial
data. Table 4C-5 shows the estimated regression coefficients, financial averages for the primary 316(b) Phase III
sectors, estimated facility capital expenditures, reported facility depreciation, and the comparison of capital
expenditures and depreciation.
As shown in Table 4C-5, the estimated model provides reasonable estimates of capital expenditures.
Table 4C-5: Estimation of Capital Outlays for
Selected by Revenue and ROA Percentiles
Sectors
Coefficient
Intercept
(21.88)
Paper and
allied
products
Chemicals
and allied
products
Petroleum
and coal
products
Primary
metals
industries
Food and
kindred
products
Pre-Tax
Return
on
Assets
(ROA)
0.53
0.16
0.27
0.22
0.09
0.37
Revenue
($2004,
millions)
1.13
252.00
244.59
1516.01
458.46
292.56
Capital
Turnover
Rate
0.69
0.09
0.06
0.05
0.04
0.06
Capital
Intensity
1.08
0.89
1.14
0.59
0.93
0.29
316(b) Manufacturers Sample Facilities: Median
Cost
of
Debt
-0.79
7.71
7.71
7.71
7.71
7.71
Price of
Capital
Goods
-5.96
137.60
137.60
137.60
137.60
137.60
Capacity
Utilization
1.72
86.24
79.36
91.88
88.77
80.46
Estimated
Capital
Expenditures
($2004,
millions)
$19.54
$15.73
$47.03
$16.07
$4.82
Depreciation
($2004,
millions)
$16.73
$14.69
$66.95
$19.21
$4.52
Facilities
Difference
between
Depreciation
and Capital
Expenditures
($2004,
millions)
($2.80)
($1.04)
$19.93
$3.14
($0.30)
Source: U.S. EPA analysis, 2004.
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 extension 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
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4C: Estimating Capital Outlays
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 undertake those investments. EPA examined whether these relationships occur in the 316(b)
sample facilities. Specifically, EPA calculated the predicted capital expenditure for each facility and compared
these values to the facilities' reported depreciation values. To remove the scale effect of revenue, EPA normalized
both the predicted capital expenditure and reported depreciation values by dividing by the three-year average of
revenue for each facility. EPA then estimated the simple linear relationship of the resulting revenue-normalized
capital expenditure and deprecation values against facility ROA. The five graphs on the following pages present,
for each of the five primary 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.117 The graphs indicate the following:
The Paper and Allied Products (SIC 26) graph shows depreciation exceeding predicted capital expenditure at low
ROA values but this relationship 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) graph 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) graph shows predicted capital expenditures exceeding depreciation
over the ROA range analyzed. However, the extent of difference does not materially change as ROA increases.
The Primary Metal Industries (SIC 33) graph also shows predicted capital expenditures exceeding depreciation
over the ROA range analyzed. However, unlike for the Petroleum and Coal Products facilities, the amount by
which predicted capital expenditures exceeds depreciation increases as ROA increases, thus matching the
hypothesized relationship.
The Food and Kindred Products (SIC 20) graph also shows that calculations for these facilities match the
hypothesized relationship, where predicted capital expenditures exceed depreciation over the ROA range
analyzed. The consistency of this result, as well as the CAPEX estimation in Table 4C-5 above, is notable to the
extent that it demonstrates the model's overall applicability across industries, as facility data from SIC 20 were
not used for model specification.
In summary, with the exception of facilities in the Petroleum 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.
117 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 graph for SIC 26: Paper and allied products.
4C-14 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Figure 4C-1: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b)
Manufacturers Survey Facilities in the Paper and Allied Products Sector
-0.30 -0.20
0.10 0.20 0.30
Return on Assets
• Linear (DEPR) •
Linear (CAPEX)
Source: U.S. EPA analysis, 2004.
March 28, 2011
4C-15
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Figure 4C-2: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b)
Manufacturers Survey Facilities in the Chemicals and Allied Products Sector
3
a
O>
•a
01
o
z
a
_0
§•
O
o.
03
u
-0.20
0.00 0.20 0.40 0.60 0.80 I.I
Return on Assets
1.20
1.40
DEPR
CAPEX
• Linear (DEPR).
Linear (CAPEX)
Source: U.S. EPA analysis, 2004.
4C-16
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Figure 4C-3: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b)
Manufacturers Survey Facilities in the Petroleum and Coal Products Sector
1
a
1
*tj
cs
u.
•o
cs
0
a
_0
.5
c.
Q
=8
as
Capital Outli
t,
0 16
0.14
0.12
0.10
0.08
0.06
i
0.04
0.02 -
0 00
*
*
•
' T — — v
» *
• « •
10 0.00 0.10 0.20 0.30 0.40
Return on Assets
• DEPR • CAPEX Linear (DEPR) Linear (C APEX)
^^
r-
0.50 0.60
Source: U.S. EPA analysis, 2004.
March 28, 2011
4C-17
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Figure 4C-4: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b)
Manufacturers Survey Facilities in the Primary Metal Industries Sector
3
a
O>
fi
•a
01
o
z
a
o
O
o.
03
u
-0.10
Q 1Q
«* 0.09 -
0.08 -
0.07 -
0.05 -
0.03 -
'0.02 -
0.01 -
0.10
0.20 0.30 0.40
Return on Assets
0.50
0.60
0.70
DEPR
CAPEX
Linear (DEPR)
Linear (CAPEX)
Source: U.S. EPA analysis, 2004.
4C-18
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4C: Estimating Capital Outlays
Figure 4C-5: Comparison of Estimated Capital Outlays to Reported Depreciation for 316(b)
Manufacturers Survey Facilities in the Food and Kindred Products Sector
.a
o
§
•a
a
'3
v
3.
O
=8
-S
•a
3
0.10
0.06
0.04
0.02
I I
-0.23
0.00
0.23
0.40
0.60
1.00
1.23
1.40
1.60
Source: [7.5. E7M analysis, 2004.
4U.6 Updating inputs to Estimate capital outlays tor tne Proposed Rule Analyses
For the analysis of the 2006 Phase III Final rule, EPA used the 316(b) Survey from 1996-1998, updated to 2004
dollars for the facility-specific explanatory variables. EPA followed the same concept for the current analyses,
updating the facility-specific explanatory values to 2009, on the basis of the GDP deflator.
In the previous analyses, for the "General Business Environment" explanatory variables, EPA used averages of
data from 1996-1998. For the current analysis, EPA updated these "General Business Environment" variables to
the average of values over the period 1999-2008, the period between the end of the survey data and the time
period of the proposed rule analyses. For DEBTCST, EPA took an average of the yield on 10-year BAA-rated
bonds from 1999-2008 from the Federal Reserve; for CAPPRC, EPA averaged the PPI for capital goods from
1999-2008 from the Bureau of Labor Statistics; and for CAPUTIL, EPA averaged, by industry segment, annual
average capacity utilization from the U.S. Census. Using this relatively long-term average for these three business
environment variables is intended to account for changes in facilities' operating environment over the past decade.
March 28, 2011
4C-19
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4C: Estimating Capital Outlays
Attachment A Historical Variables Contained in the Value Line Investment Survey
Dataset
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
4C-20 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4D: Analysis of Other Regulations
Appendix 4D Analysis of Other Regulations
4D.1 Regulations Potentially Affecting 316(b) Manufacturing Facil ties
EPA also accounted for other environmental regulations that were recently or will soon be promulgated
potentially imposing additional costs on 316(b) Manufacturing Industries beyond those reflected in facilities'
baseline financial statements. The after-tax cash flow (ATCF) adjustment analysis, which EPA undertook to bring
the estimates of cash flow forward from the time of the survey (1996-1998) to the time of the regulatory analysis
(2008), accounts implicitly for additional regulatory costs incurred through the end of 2008. However, it does not
capture the impact of new regulations that came into effect during this period and for which costs had not yet been
incurred, or fully incurred, by the end of 2008.
To account for potential costs that had not been fully incurred by the end of 2008, EPA researched additional
regulatory requirements that might apply to facilities in the 316(b) Manufacturing Industries, whose costs were
not likely to have been captured in the ATCF adjustment analysis. This research included searching the Federal
Register and the EPA website for final rules and regulations affecting the relevant NAICS groups and industry
sectors within the timeframe of concern. These searches identified five regulations that apply to the 316(b)
Manufacturing Industries and could result in additional costs to 316(b) manufacturing facilities after 2008.
Table 4D-1 below summarizes these regulations (referred to hereafter as Other Regulations}. The following
discussion uses both the regulation number presented in the first column and the abbreviated regulation name in
bold in the second column.
March 28, 2011
4D-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4D: Analysis of Other Regulations
Table 4D-1: Regulations Potentially Affecting 316(b) Manufacturers
No. Regulation
1
"2
3
4
5
National Air Emission Standards for
Hazardous Air Pollutants:
Halogenated Solvent Cleaning; Final
Rule
Revision of Source Category List for
Standards Under Section 1 12(k) of the
Clean Air Act; and National Emission
Standards for Hazardous Air Pollutants
for Area Sources: Ferroalloys
Production Facilities
National Emission Standards for
Hazardous Air Pollutants: Area Source
Standards for Nine Metal Fabrication
and Finishing Source Categories;
Final Rule
National Emission Standards for
Hazardous Air Pollutants: Paint
Stripping and Miscellaneous Surface
Coating Operations at Area Sources;
Final Rule
National Emission Standards for
Hazardous Air Pollutants from
Petroleum Refineries; Final Rule
Effective
Date
05/07
12768
07/08
oi'/os
10/09
Summary
Revises standards to limit
emissions of methylene chloride
(MC), trichloroethylene (TCE)
and perchloroethylene (PCE) from
facilities engaged in halogenated
solvent cleaning
Revises the area source category
list by changing the name of the
ferroalloys production category to
clarify that it includes all types of
ferroalloys
Issues national emission standards
for control of hazardous air
pollutants for nine metal
fabrication and finishing area
source categories
Promulgates national emission
standards for hazardous air
pollutants (NESHAP) for area
sources engaged in paint stripping,
surface coating of motor vehicles
and mobile equipment, and
miscellaneous surface coating
operations
Amends the national emission
standards for petroleum refineries
to add maximum achievable
control technology standards for
heat exchange systems
3 1 6(b) Industries Affected
Metal manufacturers;
machinery manufacturers;
Ferroalloy product
manufacturers; chemical
manufacturers
Metal products
manufacturing
Chemical manufacturers;
metal and pipe foundries;
alumina refineries; plastics
material and resin
manufacturers
Petroleum refineries
Compliance
Date
Not later than
05/10
Not later than
06/09 for
existing
sources; 12/08
for new sources
Not later than
07/1 1 for
existing
sources; 07/08
for new sources
Not later than
01/11
Not later than
10/12 for
existing
sources; 10/09
for new sources
Source: Rule preambles and supporting materials. See reference section.
In addition to the five regulations listed in the Table 4D-1, EPA identified two regulations that apply to "new
sources," i.e., new and existing but significantly reconstructed or modified facilities. The first of these regulations,
Amendments to the Current Standards of Performance for Petroleum Refineries, applies to petroleum refineries
that are new sources of air pollution.118 EPA estimated the total annualized cost in the fifth year of the rule to be
approximately $31 million ($2006) for new sources, comprised of 40% new processes or facilities, and 60%
modified or reconstructed processes or facilities (U.S. EPA, 2008a). The second regulation, the New Source
Review (NSR) Program for Particulate Matter Less Than 2.5 Micrometers (PM25 NSR), applies to petroleum
refineries, chemical manufacturers, pulp and paper mills, and additional industries expected to be outside the
scope of the Proposed 316(b) Existing Facilities Regulation.119 EPA estimates that facilities within these
industries may incur engineering and permitting costs as a result of the rule, but such costs would only apply to
new sources of pollution. Because it is not possible to predict whether any of the existing 316(b) manufacturing
facilities would undertake sufficient changes to become subject to these new source requirements, EPA judged
that inclusion of the costs of these regulations in the Other Regulations analysis would be highly speculative.
Further, as the potential costs of the PM2 5 NSR are highly variable among facilities' geographical locations and
118 For details see "Standards of Performance for Petroleum Refineries; Final Rule," 73 Federal Register 122 (June 24, 2008), pp. 35838 -
35881.
119 For details see "Implementation of the New Source Review (NSR) Program for Particulate Matter Less Than 2.5 Micrometers
(PM2.5): Final Rule." 73 Federal Register 96 (May 16, 2008). pp. 28321 - 28350.
IEN21 Unknown document property name.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4D: Analysis of Other Regulations
operating capacities, it is not possible to assign these costs to in-scope Manufacturers. Consequently, EPA
decided not to include these compliance costs in this analysis.
To account for the potential impact of the Other Regulations listed in Table 4D-1 on in-scope Manufacturers,
EPA determined which 316(b) industry sectors, based on the NAICS codes reported in the Federal Register
notices for the regulations, that each regulation would be expected to apply to and extracted the reported per
facility costs for facilities in the identified sectors. For each regulation, the per facility compliance costs were
stated on an after-tax basis and subtracted from the estimated baseline free cash flow for each in-scope
Manufacturer that EPA judged potentially affected by the regulation (see also Chapter 4: Cost and Economic
Impact Analysis -Manufacturers, Section 4.3). EPA then determined if the potential costs associated with these
regulations would affect either the baseline or the post-compliance determination of impact for each facility. The
remainder of this appendix discusses the methodology used for this analysis and the findings.
4D.2 Methodology
4D.2.1 Determination of Applicability to 316(b) Manufacturing Facilities
EPA first identified which of the in-scope Manufacturers would potentially incur costs as a result of each of the
five Other Regulations. This determination was based on the NAICS codes and/or industry description reported in
either the Federal Register preamble or supporting documents of each regulation. All in-scope Manufacturers in
each of the reported 6-digit NAICS sectors and/or industries are assumed to incur costs under that regulation.
EPA's assumption about the applicability of the five regulations - i.e., that an in-scope Manufacturer will incur
costs under the regulation, if it belongs to a NAICS code that is subject to that regulation - is likely to overstate
these regulations' additional cost burden on in-scope Manufacturers. Rules often only affect a specific part of an
industry sector, depending, for example, on specific emission or discharge characteristics, or existing pollution
control technology. It is therefore likely that not all in-scope Manufacturers in a given NAICS code subject to a
rule would actually incur costs under that rule. However, little information is available on those technical
characteristics of in-scope Manufacturers that would determine the applicability of the regulations to these
facilities. To avoid potentially understating the additional cost burden of these regulations, EPA therefore
assumed that all in-scope Manufacturer in the NAICS codes covered by the other regulations would incur costs
under those rules.
4D.2.2 Extraction of Facility-Level Costs
As described in the earlier 316(b) Existing Facilities regulatory analysis documents, EPA considered several
approaches for extracting and applying the costs of the five Other Regulations to 316(b) manufacturing facilities
that might be affected by them. The cost application approach selected for each regulation depended on the level
of detail that was available in the regulatory materials. Regardless of specific approach, EPA calculated the
average annualized per facility cost, on an after-tax basis, for each of the regulations. Except for the Halogenated
Solvent Cleaning rule, the reported implementation costs for each of the other regulations are fairly uniform
across facilities; thus, average facility costs estimated for a given regulation as a whole (as opposed to for each
affected NAICS or for specific facilities within a NAICS sector) should closely approximate the anticipated costs
for all affected facilities. The economic analysis for the Halogenated Solvents Cleaning rule indicates that total
per-facility capital costs are expected to vary between $15,000 and $800,000 ($2007), and that more than 60
percent of facilities would realize cost savings (U.S. EPA, 2008d). Due to data availability constraints, EPA used
a generalized approach and subtracted the average cost savings, weighted by a 60 percent probability of a facility
realizing savings, from the average annualized cost to all facilities. Actual costs incurred by a facility affected by
this rule may deviate from this calculated average. Table 4D-2 summarizes the resulting per facility costs of the
five Other Regulations that were applied to 316(b) manufacturing facilities in the relevant sectors.
March 28, 2011 4D-3
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4D: Analysis of Other Regulations
Table 4D-2: Per Facility Cost of Regulations that Affect 316(b) Industries3
No.
1
2
3
4
5
Regulation
Halogenated Solvent
Cleaning NESHAP
Ferroalloys Production
Facilities NESHAP
Nine Metal Fabrication and
Finishing Source Categories
NESHAP
Paint Stripping and
Miscellaneous Surface
Coating Operations
NESHAP
Petroleum Refineries
NESHAP
Affected 3 16(b)NAICS
Codes
331,332
331112,325188
332618
32511073252T6732513U
325188,325192,325193,
325199,325998,331111,
331221,331942,331311,
325211
324110
Cost Application Method
> Average annualized cost per
facility
> Cost recovery for 60% of facilities
> Average annualized cost per
facility
> Average annualized cost per
facility
> Trie cost analysis estimates a net
savings for plastic parts and
products, and miscellaneous parts
and products manufacturers
> Average annualized cost per
facility
> Includes cost savings to facilities
Per Facility Cost (Pre-
Tax; $2009)a
$1,008
$2,843
$692
$1)
$20,808
a. EPA used GDP Deflator published by the U.S. Bureau of Economic Analysis of the U. S. Department of Commerce to state average cost per facility in
2009 dollars.
Source: U.S. EPA Analysis, 2010
The per facility costs identified in Table 4D-2 were then aggregated for each affected in-scope Manufacturer
(based on NAICS code or individual facility identification), converted to a post-tax value, and subtracted from
baseline adjusted after-tax cash flow (see discussion of the impact analysis method in Chapter 4: Cost and
Economic Impact Analysis -Manufacturers). For all in-scope Manufacturers that were operational in the baseline
under each primary analysis option, EPA determined if the additional cost of complying with the five Other
Regulations would cause the facility to (1) fail the baseline test and become a "baseline closure" or (2) fail the
post-compliance impact test and be considered a "severe impact" of the analysis option.
4D.3 Results
4D.3.1 Baseline Analysis
Of the 203 in-scope sample Manufacturers that are operational in the baseline and have a design intake flow of at
least 2 MGD, EPA found that no additional facilities would become a baseline closure (i.e., before incurring
compliance costs under the Proposed Existing Facilities Rule) due to application of the additional costs from the
Other Regulations.120
4D.3.2 Post-Compliance Analysis
The post-compliance analysis sets aside facilities considered baseline closures. Because the adjusted baseline
analysis found that no additional manufacturing facilities would be assessed as baseline closures due to
application of the additional costs from Other Regulations, no additional facilities were removed from the post-
compliance analysis. Of the 188 sample manufacturing facilities (i.e., those that are not considered a baseline
closure after applying the costs of the Other Regulations), EPA found that no additional facilities would
experience a severe impact as a result of incurring both the Proposed Existing Facilities Rule compliance costs
a«Jthe costs of the Other Regulations.
120 This analysis excludes 15 facilities with insufficient survey-based economic data and 32 facilities determined to be baseline closures
without taking into account the impact of these other regulations.
Ifrefr' Unknown document property name.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4D: Analysis of Other Regulations
Based on this analysis, EPA concluded that consideration of the Other Regulations that might affect 316(b)
Manufacturers would not alter the findings from EPA's facility impact analyses conducted in support of the
Proposed 316(b) Existing Facilities Rule.
March 28, 2011 4D-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4E: Manufacturers Economic Impact Methodology
Appendix 4E Economic Impact Methodology - Manufacturers
EPA conducted an economic impact analysis of each regulatory option for the Manufacturers segment. Measures
of economic impact for this segment include facility closures and associated losses in employment, financial
stress short of closure, and firm-level impacts. For the 316(b) Existing Facilities proposed regulation, the potential
impacts on the Manufacturers segment at the facility-level are defined in two ways:
> Severe impacts are facility closures and the associated losses in jobs at facilities that would close due to
the regulation.
> Moderate impacts are adverse changes in a facility's financial position that are not threatening to its
short-term viability.
In conducting these analyses, EPA closely followed the methodologies used to conduct analyses in support of the
previous 316(b) rule analyses and, to the extent practicable, relied on similar data sources. This appendix details
the calculations of the severe and moderate facility-level impact assessments (See Chapter 4: Cost and Economic
Impact Analysis -Manufacturers for data inputs and analysis approach details).
4E.1 Facility-Level Impacts: Severe Impact Analysis
The assessment of severe impacts for facilities in the Manufacturers segment is based on the change in the
facility's estimated business value, as determined from a discounted present value analysis of baseline cash flow
and the change in cash flow resulting from regulatory compliance.
The cash flow concept used in calculating ongoing business value for the closure analysis is free cashflow
available to all capital. Free cash flow is the cash available to the providers of capital - both equity owners and
creditors - on an after-tax basis from business operations, and takes into account the cash required for ongoing
replacement of the facility's capital equipment. Free cash flow is discounted at an estimated after-tax total cost of
capital to yield the estimated business value of the facility. Details of the calculation office cash flow and the
discounting office cash flow to yield the facility's estimated value are explained in the following sections.
4E.1.1 Calculation of Baseline Free Cash Flow and Performance of Baseline Closure Test
Calculation of baseline free cash flow and performance of the baseline closure test involved the following steps:
Average survey income statement data over response years and convert to mid-year 2004 dollars: EPA first
adjusted facility income statement data for 1996, 1997, and 1998 to the year 1998, using the GDP Deflator. These
data were then averaged over the months and/or years for which survey respondents reported data to develop an
annual average income statement in 1998 constant dollars. For example, if a facility reported income statement
data for 1996, 1997, and 1998, then a simple average was calculated for the three reported years. The annual
average income statement in 1998 was then brought forward from 1998 to 2009, again using the GDP Deflator.
Calculate after-tax income excluding the effects of financial structure: The questionnaire responses include a
calculation of after-tax income in accord with conventional accounting principles. However, this calculation
reflects the financial structure of the business, which may include debt financing and thus interest charges against
income. Because the cash flow concept to be discounted in the business value analysis is cash flow available to all
capital, it is necessary to restate after-tax income to exclude the effects of debt financing, or on a before-interest
basis. This restatement involves: (1) increasing after-tax income by the amount of interest charges and (2)
increasing taxes (and thereby reducing after-tax income) by the amount of tax reduction provided by interest
deductibility. This adjustment amounts to adding tax-adjusted interest expense to after-tax income and yields an
March 28, 2011
4E-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4E: Manufacturers Economic Impact Methodology
estimate of after-tax income independent of capital structure or financing effects. In calculating the tax
adjustment for interest, EPA used a combined federal/state corporate income tax rate. For this calculation, EPA
used a tax rate that integrates the federal corporate income tax rate (35 percent) and state-specific state corporate
income tax rates, based on facility location.
The combined federal/state corporate income tax rate was calculated as follows:
T = TS + TF-(TS*TF) (4E-10)
where:
T = estimated combined federal-state tax rate;
TS = state tax rate; and
IF = federal tax rate (35 percent).
After-tax income, before interest, was calculated as follows:
or (4E-11)
ATI-BI = ATI + (1 -i)I
where:
ATI-5/ = after-tax income before interest,
ATI = after-tax income from baseline financial statement;
I = interest charge from baseline financial statement; and
T = estimated combined federal-state tax rate.
Calculate after-tax cashflow from operations, before interest, by adjusting income for non-cash charges: The
calculation of after-tax income may include a non-cash charge for depreciation (and potentially amortization). To
convert income to after-tax cashflow (ATCF) from operations, it is therefore necessary to add back any
depreciation charge to the calculation of after-tax income, before interest. Cash flow, before interest, was
calculated as follows:
ATCF-5/ = ATI-5/+D (4E-3)
where:
ATCF-B/ = after-tax cash flow before interest,
ATI-5/ = after-tax income before interest, and
D = baseline depreciation.
As a final step in the calculation of after-tax cash flow before interest, EPA eliminated the implied cash flow
benefit of any negative taxes, as reported in the facility's income statement and after adjustment for removal of
interest. That is, in these calculations, negative taxes increase after-tax income and cash flow, and thus appear to
improve the financial performance and value of the facility in terms of cash flow from operations. However,
whether and when the implied cash flow benefit of negative taxes can be realized depends on the overall
profitability and tax circumstances of the total enterprise, including any other facilities owned by the same firm,
4E-2 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4E: Manufacturers Economic Impact Methodology
and the extent of profitability in periods before or after the survey data periods. To ensure this effect is not
overstated, EPA therefore assumed that a facility would not receive the implied cash flow benefit from negative
taxes - negative taxes, after adjustment for interest, were set to zero in the baseline analysis. This assumption is
consistent with a later step in the post-compliance analysis in which EPA limited the cash flow benefit of tax
deductions on compliance outlays not to exceed the amount of taxes paid as reported in the baseline income
statement (and adjusted for interest). In theory, the application of this limit could cause some facilities that would
otherwise pass the baseline closure analysis, instead to fail the analysis if the reported amount of negative tax,
after adjustment for interest, would be sufficient to offset the negative cash flow from operations independent of
taxes. In practice, though, this limitation did not affect the findings of the baseline closure analysis. This limit was
applied as a check and did not cause a different outcome.
Adjust after-tax cashflow to reflect estimated real change in business performance from the time of survey data
collection to the present: EPA adjusted facility baseline cash flow to reflect the estimated real change (i.e.,
independent of inflation) in business performance in the manufacturing industries from the time of the facility
survey, 1996-1998, to the present.
To calculate the adjustment factor, EPA collected data on after-tax cash flow for public firms in the 316(b)
manufacturing industry sectors over a 21-year period and developed adjustment factors by industry and/or key
industry segment (details of this analysis are contained in Appendix 4.B: Adjusting Baseline Facility Cash Flow}.
Adjusted after-tax cash flow is calculated as follows:
ATCF-5/ADj = ATCF-57 * Adj (4E-4)
where:
ATCF-B/ADj = after-tax cash flow before interest adjusted to reflect the real change in business
performance;
ATCF-B/ = after-tax cash flow before interest; and
Adj = adjustment factor to reflect the real change in business performance.
Calculate free cashflow by adjusting after-tax cash flow from operations for ongoing capital equipment outlays:
The measure of after-tax cash flow from the previous step, cash flow from operations, reflects the cash receipts
and outlays from ordinary business operations, but includes no allowance for replacement of the facility's existing
capital equipment. To sustain ongoing operations, however, a business must expend cash for capital replacement.
Accordingly, to understand the true cash flow of a business, it is necessary to reduce cash flow from operations by
an allowance for capital replacement. For the calculation of free cash flow, EPA estimated baseline capital outlays
from a regression analysis of capital expenditures by public firms in the 316(b) industry sectors over an 11-year
period (details of this analysis and estimation framework are contained in Appendix 4.B: Adjusting Baseline
Facility Cash Flow}. Free cash flow is calculated as follows:
FCF = ATCF-5/ADj - CAPEX - OTHREGS (4E-12)
where:
FCF = free cash flow
ATCF-B/ADj = after-tax cash flow before interest adjusted to reflect the real change in business
performance; and
CAPEX = estimated baseline capital outlays; and
OTHREGS = annualized after-tax cost of compliance with Federal environmental regulations
that were recently promulgated and whose costs are not likely to be reflected
March 28, 2011 4E-3
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4E: Manufacturers Economic Impact Methodology
fully in the ATCF adjustment analysis (Other Regulations). This variable and
the associated analysis are not part of the primary case analysis but were dealt
with on an alternative, sensitivity case basis.121
Or on a more detailed accounting statement basis:
FCF = REV - TC - T - T! - CAPEX (4E-6)
where:
FCF = free cash flow
REV = revenue
TC = total operating costs, excluding interest, depreciation, and taxes
T = baseline income tax
T = estimated combined federal-state tax rate;
I = interest charge from baseline financial statement;
il = the increase in tax liability resulting from calculating income on a pre-interest
basis;
CAPEX = estimated annual baseline capital outlays; and
OTHREGS = annualized cost of other compliance with Federal environmental regulations that
were recently promulgated and whose costs are not likely to be reflected fully in
the ATCF adjustment analysis.
This calculation office cash flow is based on a static representation of a facility's business. With the exception of
bringing estimated cash flow forward from the time of the survey, 1996-1998, to approximately the present, 2009,
the facility impact analysis assumes, in effect, that the facility's business will continue in the future - absent the
effects of regulation - exactly as reflected in the baseline financial statements provided in the survey
questionnaire
Calculate baseline facility value as the present value of free cashflow over a 30-year analysis horizon: To
calculate baseline business value, EPA expressed free cash flow over a 30-year period in present value terms
using an estimated real (i.e., excluding the effects of inflation), after-tax cost of capital of 7.0 percent. The use of
30 years as the time horizon reflects the facility-level analysis period for the 316(b) existing facilities rule.
Facility baseline business value is calculated as follows:
^ FCF
VAL UE = y (4E-7)
^^ /i . /~i _ /~i\ t ^ '
where:
VALUE = estimated baseline business value of the facility
FCF = free cash flow
121 EPA also undertook an alternative case analysis in which it further adjusted baseline cash flow to reflect costs that facilities might
incur from compliance with Federal environmental regulations that were recently promulgated and whose costs are not likely to be
reflected fully in the ATCF adjustment analysis. This analysis, which is documented in Appendix 4.D: Analysis of Other Regulations,
found no material effect on the facility impact analysis, as reported in this chapter. The alternative case analysis, which incorporated
estimated compliance costs from the recent Federal environmental regulations, found one additional baseline closure and no change in
post-compliance closures.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4E: Manufacturers Economic Impact Methodology
CoC = after-tax cost-of-capital (7.0 percent); and
t = year index, t = 0-29 (30-year discounting horizon).
In the present value calculation, yearly cash flows accrue at the beginning of the year. As a result, the first year of
cash flows is already in present value terms - i.e., t = 0 for the first year of the analysis - and cash flows in the
tenth and final year of the analysis period are discounted over a 29-year period - i.e., t = 29 in the final year of the
analysis.
As explained above, EPA considered a facility to be a baseline closure if its estimated business value was
negative before incurring regulatory compliance costs. Baseline closures were neither tested for adverse impact in
the post-compliance impact analysis nor were their compliance costs included in the tally of total costs of 316(b)
regulatory compliance.
4E.1.2 Calculation of Post-Compliance Free Cash Flow and Performance of Post-Compliance
Closure Test
For the post-compliance closure analysis, EPA recalculated annual free cash flow, accounting for changes in
revenue, annual expenses and taxes that are estimated to result from compliance-related outlays. EPA combined
the post-compliance free cash flow value and the estimated compliance capital outlay in the present value
framework to calculate business value on a post-compliance basis.
For the post-compliance analysis, EPA considered whether in-scope Manufacturers would be able to pass forward
compliance costs to customers through increased prices. From the analyses presented in Appendix 4A: Cost Pass-
Through Analysis, EPA concluded that an assumption of zero cost pass-through is appropriate for analyzing the
impact of the regulatory analysis options on facilities in the six Primary Manufacturing Industries (this is the same
assumption as applied in the previous analysis conducted in support of the 2006 Final Section 316(b) Phase III
Existing Facilities Rule). Performance of the impact analysis under this assumption means that facilities must
absorb all compliance-related costs and operating effects (e.g., income loss from facility shutdown during
equipment installation) within their baseline cash flow and financial condition. To the extent that facilities would
be able to pass on some of the compliance costs to customers through price increases, the analysis may overstate
the potential impact on complying facilities.
Calculation of post-compliance free cash flow and performance of the post-compliance closure test involved the
following steps:
Adjust baseline annual free cashflow to reflect compliance outlay effects: Compliance-related effects on annual
free cash flow include: annual change in revenue; annually recurring operating and maintenance costs; the annual
equivalent of permitting and re-permitting costs, which recur on other than an annual basis over the life of the
analysis; the annual equivalent of the income loss from installation downtime; and related changes in taxes.122 The
change in taxes includes: (1) the tax effect of these annually recurring and annualized expenses and (2) the tax
effect from depreciation of initial compliance outlays. For calculating the tax effect of depreciation, EPA assumed
122 For the facility cash flow analysis, EPA treated the income loss from installation downtime on an annual equivalent basis even though
this financial event occurs only once, and at the beginning of the assumed analysis period. EPA treated the installation downtime on
an annualized basis for two reasons. First, the installation downtime is assumed to have a useful "financial life" of 30 years to reflect
the total potential business life of the facility with the installed compliance technology (note that reinstallation of the basic capital
equipment other than cooling towers, which is assumed to recur on a 10-, 20-, or 25-year interval depending on the specific
technology, does not require a new round of downtime). Since compliance capital equipment is assumed to have a specific useful life
and the discounted cash flow analysis is accordingly structured around this period, including the income loss from installation
downtime (which is assumed to have a 30-year useful life) as a one-time up-front cost would overstate its impact in the discounted
cash flow calculation. Second, calculation of the downtime cost on an annual basis allows the tax effect from the one-time income loss
to be summed with other annual tax effects for applying the limit to tax offsets, as explained in the next step of the analysis.
March 28, 2011 4E-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4E: Manufacturers Economic Impact Methodology
that compliance capital outlays would be depreciated for tax purposes on a straight-line schedule equal to the
assumed useful life of the technology. Post-compliance free cash flow was calculated as follows:
FCFpc = FCFBL + AR - ATC - T(- ATC - AD) (4E-8)
where:
FCFpC = post-compliance free cash flow;
FCFBL = baseline free cash flow, as calculated above;
AR = change (increase) in revenue from pass through of compliance costs to
customers123
ATC = change in total facility annual costs (excluding interest, depreciation and taxes),
calculated as the cost of operating and maintaining compliance equipment plus
the annual equivalent of certain non-annual costs, as described above;
T = marginal tax rate for calculating compliance-related tax effects (combined
federal-state tax rate); and
AD = change in depreciation expense, calculated as compliance capital outlay (CC)
divided by the useful life of the compliance technology.
Limit tax adjustment to not exceed taxes as reported in baseline financial statement: The tax effect of compliance
outlays is to reduce tax liability. As a result, in the free cash flow calculation, the tax adjustment generally
increases cash flow and business value and, all else equal, reduces the likelihood that a facility will fail the post-
compliance closure test. However, the extent to which a facility would realize this contribution to cash flow
depends on its tax circumstances. In particular, some businesses may not be paying sufficient taxes in the baseline
to take full benefit of the implied tax reduction at the facility level - unless the unused tax loss can be transferred
to other, profitable business units in the firm, these businesses would not be able to use fully the implied tax
reduction on a current basis. Also, the marginal tax rate for businesses with relatively lower pre-tax income may
be less than the combined Federal/State tax rate used in the analysis. While businesses may be able to carry
forward tax losses to reduce taxes in later years, EPA recognizes that the implied cash flow benefit from tax
reduction may not be fully realized, particularly in circumstances involving single-facility firms. To reduce the
risk of over stating this tax offset benefit in its analysis and as a result potentially understating business impact,
EPA therefore limited the amount of tax reduction from compliance outlays to be no greater than the amount of
tax paid by facilities as reported in the baseline financial statement. The analysis effectively assumes that facilities
will not be able to offset an implicit negative tax liability against positive tax liability elsewhere in the owning
firm's operations or to carry forward (or back) the negative income and its implicit negative tax liability to other
positive income/positive tax liability operating periods. Nevertheless, some businesses may be able to benefit
from tax reductions that exceed facility baseline taxes, especially if the facility is owned by a multiple-site firm.
Accordingly, EPA constrained the tax effect term in the free cash flow calculation, [-T( - ATC - AD)] as specified
above, to be no greater than baseline financial statement tax liability, T.
Calculate post-compliance facility value, including post-compliance free cashflow and the compliance capital
outlay: As in the baseline analysis, EPA calculated post-compliance facility value as the present value office cash
flow and accounting for the compliance capital outlay as an undiscounted cash outlay in the first analysis period.
Facility post-compliance business value was calculated as follows:
123 As described above, EPA concluded that in-scope Manufacturers are likely to have little or no potential to pass through compliance
costs to customers through price increases. Accordingly, this variable (AR) is assigned the value of zero in the Manufacturers impact
analysis.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4E: Manufacturers Economic Impact Methodology
29 FCF
VALUEPC = Y ^ - CC (4E-9)
where:
VALUEpc = estimated post-compliance business value of the facility
FCFpC = estimated post-compliance free cash flow
CoC = after-tax cost-of-capital (7.0 percent);
t = year index, t = 0-29 (30-year discounting horizon); and
CC = compliance capital outlay.
EPA considered a facility to be a post-compliance closure if its estimated business value was positive in the
baseline but became negative after adjusting for compliance-related cost, revenue and tax effects. In addition to
tallying closure impacts in terms of the number of estimated facility closures, EPA also measured the significance
of closures in terms of losses in employment and output. Employment losses equal the number of employees
reported by closure facilities in survey responses; output losses equal total revenue reported for regulatory closure
facilities. EPA estimated national results by multiplying facility results by facility sample weights.124
4E.2 Facility-Level Impacts: Moderate Impact Analysis
The analysis of moderate impacts examined two financial measures:
Pre-Tax Return on Assets (PTRA): ratio of pre-tax operating income - earnings before interest and taxes
(EBIT) - to assets. This ratio measures the operating performance and profitability of a business' assets
independent of financial structure and tax circumstances. PTRA is a comprehensive measure of a firm's
economic and financial performance. If a firm cannot sustain a competitive PTRA on a post-compliance
basis, it will likely face difficulty financing its investments, including the outlay for compliance equipment.
Interest Coverage Ratio (ICR): ratio of pre-tax operating cash flow - earnings before interest, taxes, and
depreciation (EBITDA) - to interest expense. This ratio measures the facility's ability to service its debt on
the basis of current, ongoing financial performance and to borrow for capital investments. Investors and
creditors will be concerned about a firm whose operating cash flow does not comfortably exceed its
contractual obligations. As ICR increases, the firm's general ability to meet interest payments and carry
credit also increases. ICR also provides a measure of the amount of cash flow available for equity after
interest payments.
Creditors and equity investors review the above two measures as criteria to determine whether and under what
terms they will finance a business. PTRA and ICR also provide insight into a firm's ability to generate funds for
compliance investments from internally generated equity, i.e., from after-tax cash flow. The following sections
detail the calculation and development of these threshold values.
4E.2.1 Calculation of Moderate Impact Metrics
EPA calculated a firm's PTRA and ICR measures using data collected from the 316(b) industry survey, adjusted
for inflation to 2009. The two measures are defined as follows:
124 For the analysis of options presented in this chapter, none of these impact measures (e.g., employment loss, output loss) were in fact
relevant because none of the three regulatory analysis options resulted in regulatory closures.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4E: Manufacturers Economic Impact Methodology
Pre-Tax Return on Assets
PTRA =
EBIT
TA
(4E-10)
where:
PTRA = pre-tax return on assets,
EBIT = pre-tax operating income, or earnings before interest and taxes, and
TA = total assets.
Or, stated in terms of income statement accounts,
REV-(TC + D}
PTRA=-
TA
(4E-11)
where:
PTRA
REV
TC
D
TA
Interest Coverage Ratio
pre-tax return on assets;
revenue;
total operating costs (excluding interest, taxes, and depreciation/amortization);
depreciation; and
total assets.
ICR =
EBITDA
7
(4E-113)
where:
ICR
EBITDA
I
interest coverage ratio;
pre-tax operating cash flow, or earnings before interest, taxes, and depreciation
(and amortization) and
interest expense.
Or, stated in terms of income statement accounts,
REV-TC
ICR =
I
(4E-13)
where:
ICR
REV
TC
interest coverage ratio;
revenue;
total operating costs (excluding interest, taxes, and depreciation/amortization);
and
4E-8
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Appendix 4E: Manufacturers Economic Impact Methodology
I = interest expenses.
Including the effects of 3 16(b) compliance costs, post-compliance PTRA and ICR are:
(4E.14)
\RET- (TC + ATC)]
- -
(4E-15)
where:
PTRApc
ICRpc
ATC
AD
CC
AI
= pre-tax return on assets, post-compliance;
= interest coverage ratio, post-compliance;
= change in total facility operating costs (excluding interest, depreciation and
taxes), calculated as operating and maintenance costs of compliance;
= change in depreciation expense, calculated as compliance capital outlay (CC)
divided by 10;
= compliance capital outlay (assuming all of the outlay would be capitalized and
reported as an addition to assets on the balance sheet); and
= incremental interest expense from financing of compliance capital outlay. As a
simplifying, conservative assumption, incremental interest expense is calculated
assuming that the compliance capital outlay is fully debt-financed at the overall
real cost-of-capital of 7.0 percent. The annual incremental interest value is
calculated as the annualized value of interest payments over 10 years, assuming
a constant annual payment of principal and interest.
In calculating the baseline values of the PTRA and ICR measures, EPA applied the same cash flow adjustments as
described above for the Manufacturers facility closure analysis, to the numerators of the PTRA and ICR
measures. In the same way as described for the closure analysis, these adjustments are intended to capture the
change in the financial performance of firms in the Primary Manufacturing Industries between the time of the
3 16(b) Phase III survey and 2008 (see Appendix 4.B: Adjusting Baseline Facility Cash Flow}.
4E.2.2 Developing Threshold Values for Pre-Tax Return on Assets (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 that a 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 2Sth Ratio of profit before taxes divided by total assets and multiplied
by 100 for the lowest quartile of values in each 6-digitNAICS
code.
>• Operating Profit
*• Profit Before Taxes
Gross profit minus operating expenses.
Operating profit minus all other expenses (net).
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4E: Manufacturers Economic Impact Methodology
RMA provides a measure of pre-tax return on assets that approximates the measure EPA defined for the moderate
impact analysis. As defined by RMA, this measure is the ratio of pre-tax income to assets, designated ROARMA:
ROARMA = Pre-Tax Income (EBT) / ASSETS25th
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 equity. To derive a pre-tax, total return value, EPA
adjusted RMA's measure of PTRA using the median percentage values of EBIT and EBT available from RMA.
This adjustment yields the PTRA measure that EPA used in the moderate impact analysis, designated ROA3i6(b):
ROA3i6(b) = ROARMA * EBIT / EBT
Negative values are included in the weighted-industry PTRA averages but a different method is used to adjust the
ROA values reported in RMA to the value used in the moderate impact analysis. Specifically, using only those
observations (i.e., 6-digit NAICS code and year combinations) 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 ROA316(b) and ROARMA as follows:
ROA316(b)-ROARMA = adjustment factor.
Those values were consolidated into industry-specific adjustment factors, weighted by 2007 value of shipments
from the Economic Censuses (U.S. DOC, 2002). Each negative PTRA observation from RMA was adjusted by its
industry specific adjustment factor to approximate the measure used in the moderate impact analysis:
+ industry specific adjustment factor = ROA316(b)
The industry specific adjustment factors average 0.41 and range from 0.23 for the Chemicals and Other sectors to
0.61 for the Aluminum industry.
4E.2.3 Developing Threshold Values for Interest Coverage Ration (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 for new debt. 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, as might be required to finance installation of compliance technology.
The following data from Risk Management Association Annual Statement Studies were used to calculate ICR:
*• EBIT /Interest 25th Ratio of earnings (profit) before annual interest expense and
taxes (EBIT) divided by annual interest expense for the lowest
quartile of values in each 6-digit NAICS code.
* % Depr., Dep., Amort./Salesmed 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 the ratio of earnings before interest and taxes to interest,
designated
= EBIT / INTEREST25th
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 (EBITDA) 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
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Appendix 4E: Manufacturers Economic Impact Methodology
payments. To derive the desired ICR value (designated ICR3i6(b)), EPA adjusted the RMA value as outlined
below:
ICR316(b) = EBITDA / INTEREST
Therefore, ICR3i6(b) = ICRnMA * (EBIT + DA) / EBIT
or ICR3i6(b) = ICRRMA * { 1+ [(DA / SALES) / (EBIT / SALES)] }
For consistency of calculation, EPA used the median values available from RMA for the adjusting both the
numerator (DA / SALES) and denominator (EBIT / SALES) terms.125
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 ICR3i6(b) and ICRRMA as follows:
ICR3i6(b)-ICRRMA = adjustment factor.
An industry specific adjustment factor was calculated for 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:
industry specific adjustment factor = ICR3i6(b)
The industry specific adjustment factors average 0.66 and range from 0.51 for the Steel and Petroleum industries
to 1 .2 for the Food industry.
125 Numerator (% Depr., Dep., Amort./Sales) is available for quartile values; denominator (Operating Profit) only for median values.
March 28, 2011 4E-11
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
5 Cost and Economic Impact Analysis - Electric Generators
5.1 Analysis Overview
This chapter assesses the expected economic impact of the Proposed 316(b) Existing Facilities Rule options on
the Electric Generators segment of in-scope facilities. EPA performed this assessment in two parts:
1. A cost and economic impact assessment reflecting baseline operating characteristics of in-scope
facilities and with assignment of estimated compliance costs to those facilities. This analysis
assumes no changes in those baseline operating characteristics - e.g., level of electricity
generation and revenue - as a result of the requirements of the proposed regulatory options. This
analysis, which is documented in this chapter, includes five specific analyses:
• A cost-to-revenue screening analysis to assess the impact of compliance outlays on individual in-
scope facilities (Section 5.2)
* A cost-to-revenue screening analysis to assess the impact of compliance outlays on domestic parent-
entities owning in-scope facilities (Section5.3)n6
• An assessment of the potential electricity rate impact of compliance costs to the residential sector
(Section 5.4)
* An assessment of the potential electricity rate impact of compliance costs, across sectors
(Section 5.5).
• An assessment of reduction in availability of generating capacity due to downtime associated with the
installation of compliance technology and the impact of that capacity reduction on the North
American bulk power system (Section 5.6).
2. A broader electricity market-level analysis based on the Integrated Planning Model (IPM) is
discussed in Chapter 6: Electricity Market Model Analysis (the Market Model Analysis). Unlike
the preceding analysis, the Market Model Analysis accounts for expected changes in the
operating characteristics of facilities from both:
• Estimated changes in electricity markets and operating characteristics of facilities independent of the
considered regulatory options and
• Estimated changes in markets and operating characteristics of facilities as a result of the considered
regulatory options.
In conducting these analyses, EPA closely followed the methodologies used to conduct analyses in support of the
previous 316(b) regulatory analyses, and, to the extent possible, relied on the same data sources.
5.2 Cost-to-Revenue Analysis: Facility-Level Screening Analysis
The cost-to-revenue measure compares the cost of reducing adverse environmental impact from the operation of
the facility's cooling water intake structure (CWIS) with its operating revenue, and provides a screening-level
assessment of the impact of the regulatory options.127
126 The purpose of the cost-to-revenue assessments is to provide an indication of the relative magnitude of the compliance costs
controlling for the size or market share of the firm, and not to predict closures and/or other types of economic impact on facilities
expected to be subject to the Proposed Existing Facilities Rule options.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
5.2.1 Analysis Approach and Data Inputs
As described in Chapter 3: Development of Costs for Regulatory Options, EPA expects in-scope Electric
Generators would comply during a 15-year window, from 2013 through 2027, depending on the regulatory option
analyzed. However, for the facility-level cost-to-revenue analysis, EPA used a single assumed compliance year,
2015, as the basis for the analysis. Specifically, EPA compared annualized after-tax compliance costs, calculated
under the assumption that all facilities would achieve compliance as of 2015 and stated on a present value basis as
of 2015 (see Chapter 3: Development of Costs for Regulatory Options), with estimated 2015 facility revenue.128
EPA selected 2015 as the assumed compliance year for this analysis based on the following considerations. First,
2015 is approximately mid-way through the period in which facilities assigned only IM technology are expected
to achieve compliance (2013-2017) and therefore closely reflects the operating conditions of these in-scope
facilities at the time of compliance (see Chapter 3: Development of Costs for Regulatory Options). Second,
although facilities assigned cooling towers are expected to comply during windows of time that are farther into
the future (2018-2022 for non-nuclear facilities and 2023-2027 for nuclear facilities), EPA was not confident in
the reliability of projecting compliance cost and revenue values beyond 2015 and consequently, used 2015 as the
impact analysis year for facilities assigned cooling towers as well. Thus, the estimated costs of technology
installation and compliance, overall, are calculated as of 2015 for purposes of the cost-to-revenue analysis,
regardless of the specific window of years in which facilities would be expected to achieve compliance and incur
compliance costs.129
To be consistent with the compliance cost estimates, EPA used 2015 as the basis of the revenue estimate to be
used in the facility-level cost-to-revenue comparison. For all Electric Generators modeled in the Integrated
Planning Model (IPM), EPA used the average of facility-specific baseline (i.e. pre-regulation) projections from
IPM analysis for the 2015, 2020, 2025, and 2028.130'131 This revenue estimate captures expected changes in
electricity markets and in the utilization of facilities subject to the regulatory options in the period following rule
promulgation. To estimate facility-level revenue for facilities not modeled in IPM or for which IPM reported no
revenue, EPA used revenue estimates developed from EIA data on electricity generation by prime mover and
utility-level electricity prices.
For this analysis, EPA brought all cost and revenue values to the cost-to-revenue analysis year of 2015, and
expressed them in 2009 dollars to provide cost and revenue comparisons on a consistent analysis-year and dollar-
year basis as described in Chapter 3: Development of Costs for Regulatory Options. EPA performed the following
adjustments:
In conducting this analysis, EPA relied on the cost-to-revenue impact analysis concept as outlined in Guidelines for Preparing
Economic Analyses available online athttp://yosemite.epa.gov/ee/epa/eed.nsf/pages/Guidelines.html/$iile/Guidelines.pdf (U.S. EPA,
2010c).
For private, tax-paying entities, after-tax costs are a more relevant measure of potential cost burden ^han pre-tax costs. For non tax-
paying entities (e.g., State government and municipality owners of in-scope facilities), the estimated costs used in this calculation
include no adjustment for taxes.
Because this analysis relies on a ratio of cost to revenue as opposed to absolute values, a cost to revenue ratio for a given facility will
be the same in years beyond the selected analysis year as long as cost and revenue values are as of the same year and the basis for
projecting cost and revenue values is the same, going forward from the selected analysis year. That is, beyond the selected analysis
year, cost and revenue values are assumed to change at the same rate and thus the ratio of these values will be constant.
The Integrated Planning Model (IPM) is a comprehensive model of the electric power sector. As described above and in the following
chapter, EPA used IPM to assess the market-level impact of the 316(b) Existing Facilities Rule options (See Chapter 6: Market Model
Analysis).
To develop average values for each statistic - i.e., energy revenue and capacity revenue - over the data years, EPA set aside from the
averaging calculation values for years that are anomalously low, i.e., more than 30 percent below the 4-year average values
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
> For facilities for which electricity revenue values were developed from EIA data (i.e., not obtained from
IPM), EPA developed these values as the average of estimated revenue values over the period 2003 to
2007.132 The EIA data are reported in nominal dollars of each year so, EPA's first step in this calculation
was to restate these values in 2009 dollars using the GDP deflator index published by the U.S. Bureau of
Economic Analysis (BEA). These individual yearly values were then averaged and brought forward to
2015 using electricity price projections from the Annual Energy Outlook publication for 2009
(AEO2009).133 Because the AEO2009 electricity price projections are in constant dollars, these
adjustments yield revenue values at 2015 in dollars of the year 2009.
> For facilities where electricity revenue values were obtained from IPM, IPM provided revenue estimates
as of 2015, 2020, 2025, and 2028 but denominated in 2006 dollars. The Agency assumed that these
revenue values are, on average, equivalent to values in 2015. Thus, no adjustment was applied to bring
the values to the 2015 analysis year. However, as described below, a further adjustment was needed to
state the IPM revenue estimates, which are initially stated in 2006 dollars, in 2009 dollars.
> Compliance technology cost values, which were originally estimated as of 2009, were adjusted over time
to the cost-to-revenue analysis year, 2015, using the Construction Cost Index (CCI) from McGraw Hill
Construction. EPA used the average of the year-to-year changes in the CCI over the most recent ten-year
reporting period to bring these values to 2015. Because the CCI is a nominal cost adjustment index, the
resulting technology cost values are as of the assumed year of compliance, 2015, and in 2015 dollars.
> Administrative cost values were also brought forward to the cost-to-revenue analysis year, 2015, using the
Employment Cost Index (ECI) published by the Bureau of Labor Statistics. This adjustment was
performed using the average of the year-to-year changes in the ECI over the most recent ten-year
reporting period. The resulting administrative cost values are as of the assumed year of compliance, 2015,
and in 2015 dollars.
> The above adjustments yield revenue and cost values in dollars of varying years, depending on the
underlying estimation approach and adjustment concept. Because EPA performed the cost and economic
impact analysis in constant dollars of the year 2009, a further adjustment was needed to restate these
projected cost and revenue values in 2009 dollars. To state IPM revenue values in 2009 dollars, EPA used
the BEA GDP deflator. To state compliance cost values in 2009 dollars, the Agency used the average of
the year-to-year changes in the GDP Deflator index over the most recent ten-year reporting period.
> The resulting facility-level compliance cost and revenue values are as of 2015 in 2009 dollars.
In the cost-to-revenue comparisons, EPA used cost-to-revenue thresholds of 1 and 3 percent as markers of
potentially significant impact.134 EPA compared facility-level costs and revenue on a non-weighted basis and
determined the number of instances in which facilities incurred costs in ranges of "less than 1 percent of revenue,"
"between 1 and 3 percent of revenue," and "greater than 3 percent of revenue." EPA applied facility-level sample
weights (see Appendix 3. A: Use of Sample Weights in the Proposed Existing Facilities Rule Analyses for a
132 In using the year-by-year revenue values to develop an average over the data years, EPA set aside from the average calculation,
revenue values that are anomalously low based on very low generating output from a generating unit in a given year. Such low
generating output would likely result from a generating unit being out of service for maintenance.
133 Annual Energy Outlook is published by the Energy Information Administration (EIA). AEO2009 contains projections and analysis of
U.S. energy supply, demand, and prices through 2030; these projections are based on results from the Energy Information
Administration's National Energy Modeling System.
134 The cost and economic impact analysis for the suspended 2004 Phase II Final Rule also used 1 and 3 percent cost-to-revenue
thresholds as markers of potentially significant impact.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
discussion on weights development and application) to the individual facility counts within each impact category
to estimate the number of facilities at the population-level incurring these cost burdens.135
5.2.2 Key Findings for Regulatory Options
Table 5-1 reports facility-level cost-to-revenue results by North American Reliability Corporation (NERC) region
and regulatory option.136 EPA estimates that the majority of Electric Generators subject to the Proposed Existing
Facilities Rule will on average incur annualized costs of less than 1 percent of revenue under Option 1 (86
percent); this finding applies to all NERC regions. The Agency estimates that the majority of in-scope facilities
under Options 2 (60 percent) and 3 (69 percent) will incur annualized costs exceeding 3 percent of revenue. Under
Option 2, this finding applies to all but three NERC regions - MRO, SPP, and WECC - while under Option 3 this
finding applies to all NERC regions except SPP and WECC. Under Options 2 and 3, all in-scope Electric
Generators in HICC are expected to incur costs exceeding 3 percent of revenue.
135 The specific facility-level weights used in this analysis are the facility count-based weights (see Appendix 3.A: Use of Sample Weights
in the Proposed Existing Facilities Rule Analyses).
136 NERC is responsible for the overall reliability, planning, and coordination of the power grids; it is organized into regional councils
that are responsible for the overall coordination of bulk power policies that affect their regions' reliability and quality of service (see
Chapter 2.H: Profile of the Electric Power Industry). As noted previously, NERC region definitions have recently changed.
74 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-1: Facility-Level Cost-to-Revenue Analysis Results by NERC Region and Regulatory Option
NERC Region
Total Number of
Facilities0
No Revenue*
Number of Facilities with a Ratio of
<1% | 1-3% | >3%
Minimum
Ratio
Maximum
Ratio
Option 1: IM Everywhere
ASCC
ERCOT
FRCC
HICC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
0
42
25
3
46
63
164
157
34
23
559
0
5
6
6
o
o
o
6
6
o
5
0 | 0 ! 0
28 [ 7 1 2
18 i 4 ' 4
2" 1 2 0
43 [ 4 1 0
49 i 14 | 0
148 1 13 [ 3
146 i 6 i 5
28 [ 6 I 0
19 ! 0 [ 4
481 1 55 i 18
0.00%
o766%
b7oo%
(134%
o766%
o766%
o766%
b7o6%
b7oo%
o766%
0.00%
0.00%
3728%
3".49%
l704%
1.80%
2764%
3758%
3761%
2.38%
3738%
3.61%
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
ASCC
ERCOT
FRCC
HICC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
0
42
25
3
46
673
164
157
34
23
559
0
5
o
o
o
o
o
o
o
o
5
0 | 0 0
5" i T ' si
5" t 4 16
0" [ 0 1 3
20 i 6 | 20
15 1 10 [ 38
47 [ 15 1 102
44 i 14 | 100
IT 1 6 1 17
19 i 6 1 4
166 I 55 ! 333
0.00%
6766%
6766%
3787%
6766%
6766%
6766%
6766%
6766%
6766%
0.00%
0.00%
4339%
35376%
8.48%
T67%%
37";53%
12750%
2423%
49766%
407T6%
49.66%
Option 3: I&E Mortality Everywhere
ASCC
ERCOT
FRCC
HICC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
0
42
25
3
46
63
164
157
34
23
559
0
5
6
6
6
6
6
6
6
6
5
0 | 0 i 0
5 i T 1 31
5 [ 4 | 16
0 i 0 | 3
6 t 7 [ 33
0191 55
38 i 8 | 119
29 1 22 106
11 i 6 i 17
17 i 6 ' 6
112 1 57 i 386
0.00%
6766%
6766%
3787%
6766%
T722%
6766%
6766%
6766%
6766%
0.00%
0.00%
4339%
3537%
8.48%
18738%
37";53%
5y;38%
2847%
49766%
407T6%
51.38%
a. No explicitly analyzed facilities are located in the ASCC region. For more information on explicitly and implicitly analyzed facilities see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses.
b. Facility counts may not add up due to rounding.
c. Facility counts exclude baseline closures.
d. IPM and EIA report no revenue for 2 facilities (5 on a weighted basis); consequently, the facility-level cost-to-revenue analysis is performed for 257
facilities (559 on a weighted basis).
Source: U.S. EPA Analysis, 2010
5.2.3 Uncertainties and Limitations
Given the large number of implicitly analyzed facilities, it is impossible to develop sample weights that accurately
account for all economic and operating differences of these facilities. Specifically, the facility count-based
weights used for this analysis account only for the number of facilities within each NERC region (Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses}. The actual compliance costs assigned
to each of the explicitly analyzed facilities may differ from the costs that would be assigned to the implicitly
analyzed facilities they represent. Consequently, the facility counts in each impact magnitude group may be over-
or under-estimated.
March 28, 2011
5-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
5.3 Cost-to-Revenue Screening Analysis: Parent Entity-Level Analysis
EPA also assessed the economic impact of the proposed regulatory options at the parent entity (firm) level. The
cost-to-revenue screening analysis at the entity level provides insight on the impact of compliance requirements
on those firms that own multiple facilities.137
5.3.1 Analysis Approach and Data Inputs
To assess the entity-level economic/financial impact of compliance requirements, EPA aggregated compliance
costs to the level of the parent entity identified as the owner of the explicitly analyzed in-scope facilities and
compared these costs to parent entity revenue. As described for the facility-level analysis above, EPA used cost-
to-revenue thresholds of 1 and 3 percent as markers of potentially significant impact for this analysis. This
analysis involved the following steps.
Determining the Parent Entity and Parent Entity Revenue
EPA determined the highest level domestic parent entity for each in-scope Electric Generator (548 facilities).138
EPA performed this determination both for the explicitly and implicitly analyzed Electric Generators (for a
discussion on explicitly and implicitly analyzed facilities and on the use of sample weights, see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses).139 As described below, the
determination for both categories of facilities was needed to support an estimate of entity level impact that reflects
the number of parent entities for not only the explicitly analyzed Electric Generators and associated parent entities
but also for the implicitly analyzed Electric Generators and associated parent entities. EPA determined ownership
using the following data sources:
> EIA-861 databases for 2003 through 2007
> EIA-860 database for 2007
> finance.google.com/finance
> www.hoovers.com
> www.selectory.com
> Corporate websites
EPA developed parent entity-level revenue values using the following sources:
> For publicly owned utilities, for which revenue from corporate websites was not available, EPA used
2003-2007 average revenue (retail plus wholesale) from EIA-861 database
> For revenue of privately owned entities, the Agency used corporate websites, Google Finance, Hoovers,
and Selectory.com (in that order)
For 10 identified parent entities, which are owned ultimately by non-U.S. firms, EPA could not obtain revenue for
a domestic entity but did obtain revenue at the level of the international parent entity; for these 10 entities, the
Agency used the international entity revenue in the cost-to-revenue analysis. EPA developed parent entity-level
137 In conducting this analysis, EPA relied on cost-to-revenue impact analysis as outlined in Guidelines for Preparing Economic Analyses
available online at http://yosemite.epa.gov/ee/epa/eed.nsf/pages/Guidelines.html/$file/Guidelines.pdf (U.S. EPA 2010),.but applied
this analysis at the level of the parent entity.
138 These are non-retired Electric Generators that responded to either the 2000 316(b) Detailed Questionnaire (DQ) or the 2000 316(b)
Short Technical Questionnaire (STQ). EPA found that the remaining 108 facilities that responded to the DQ and the STQ have retired
since the survey (see Chapter 3: Development of Costs for Regulatory Options for more details). This number is not a weighted
estimate.
139 The "explicitly analyzed" facilities are those for which costs were specifically estimated. The "implicitly analyzed" facilities are
accounted for through application of sample weights to the "explicitly analyzed" facilities.
5-6 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
revenue values from 2006 - 2008 financial reports and converted revenue values to dollar year 2009 using the
Electric PPI; therefore, all values are stated in 2009 dollars. Although the entity-level revenue values might
reasonably be expected to change in the future in a way that differs from a general price index - e.g., according to
the Electric PPI or GDP Deflator - EPA was less confident in the reliability of projecting values at the entity level
using the PPI than in the facility-level projections outlined in the preceding section. As a result, for the entity-
level analysis, EPA did not project or further adjust the 2009 entity-level revenue values but used these values as
is for the cost-to-revenue analysis. This non-adjustment assumes in effect that the change overtime in entity-level
revenue will match the change in general inflation.
Estimating Compliance Costs at the Level of the Parent Entity
As described in the preceding section, compliance cost values were brought to the analysis year 2015 and restated,
as necessary, in 2009 dollars.
EPA assessed the potential impact of the regulatory options on parent entities by comparing the estimated entity-
level annualized compliance cost to entity-level revenue for each of the entities identified as owning explicitly and
implicitly analyzed facilities. To calculate entity-level cost, EPA summed the after-tax annualized compliance
cost for the explicitly analyzed facilities owned by these entities. EPA followed two approaches in aggregating
compliance costs to the level of the owning entity:
1. EPA applied facility-level sample weights to the estimated costs for the explicitly analyzed
facilities - these are the facilities for which EPA explicitly estimated costs (for a discussion on
sample weights development and application see Appendix 3.A: Use of Sample Weights in the
Proposed Existing Facilities Rule Analyses)^40 In effect, this analysis assumes that a parent entity
identified as owning one or more explicitly analyzed facilities is assumed to own and incur the
compliance costs for those explicitly analyzed facilities and the implicitly analyzed facilities that
are represented by the sample weights applied to the costs for the explicitly analyzed facilities.
This analysis will likely overstate impacts on the identified parent firms.
2. EPA used only the estimated costs for the explicitly analyzed facilities without application of
sample weights and aggregated costs to the level of the parent firm for only those explicitly
analyzed in-scope facilities. This analysis may understate impacts on the identified parent firms.
To assess whether these parent entity-level costs could constitute a significant impact, EPA assessed whether the
parent-level costs exceed 1 percent or 3 percent of entity-level revenue.
Estimating the Number of Parent Entities Incurring Potentially Significant Impacts
The preceding steps yield the number of parent entities identified as owning explicitly analyzed facilities that
would incur total costs exceeding a given impact threshold: costs exceeding 1 percent of revenue or 3 percent of
revenue. However, the number of parent entities identified as owning explicitly analyzed facilities - and for which
this impact analysis is undertaken - is less than the number of parent entities in the total population of entities
owning both explicitly and implicitly analyzed facilities. Thus, an analysis that does not account for the parent
entities that own only implicitly analyzed facilities may understate the absolute number of parent entities incurring
a given level of cost-to-revenue impact.
To account for those parent entities that own only implicitly analyzed facilities - and thus are not directly
captured in the explicitly analyzed facility-based analysis - EPA developed entity-level weights to extrapolate the
findings from the explicitly analyzed entity analysis to the total population of entities, including those that own at
least one explicitly analyzed facility or own only implicitly analyzed facilities (for a discussion on entity-level
140 The specific facility-level weights used in this analysis are the facility count-based weights (see Appendix 3.A: Use of Sample Weights
in the Proposed Existing Facilities Rule Analyses).
March 28, 2011 sT
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
weights development see Appendix 3. A: Use of Sample Weights in the Proposed Existing Facilities Rule
Analyses). Applying these entity-level weights to the number of parent entities owning explicitly analyzed
facilities assessed in the various cost impact categories yields an estimate of the number of parent entities
including those that own implicitly analyzed facilities. However, as described in the following section, combining
the entity-level weights with the facility-level weights is not straightforward in estimating the number of small
parent entities incurring costs in the specified cost-to-revenue ranges.
Combining the Facility-Level and Entity-Level Weights in the Small Entity Impact Analysis
Estimating the number of parent entities that may incur costs in the specified cost-to-revenue ranges requires
combining the facility-level and entity-level weights outlined above. As outlined above, two weighting
approaches are applicable for each of the two analytic steps:
1. The number of facilities and costs at the level of the entity may be estimated:
• Without application of facility weights to the explicitly analyzed facilities assigned to a given parent
entity
• With application of facility weights to the explicitly analyzed facilities assigned to a given parent
entity.
2. The number of entities incurring costs in the specified cost-to-revenue ranges may be estimated:
• Without application of entity weights
• With application of entity weights.
Given that two weighting approaches are applicable at each of the two analytic steps, four combinations of
facility-level and entity-level weights are possible. None of the four combinations provide a conceptually perfect
estimate of the number of entities incurring costs - from the ownership of in-scope facilities - in the specified
cost-to-revenue ranges. Table 5-2 summarizes the possible combinations and the issues associated with each
possibility.
Table 5-2: Issues in Combining Facility- and Entity-Level Weights in the Small Entity Impact
Analysis
Entity Weighting
Approaches
Without Use of
Entity Weights
With Use of
Entity Weights
Facility Weighting Approaches
Without Use of Facility Weights
Combination 1
> Doesn't account for all estimated
facilities, their compliance costs, or
the entities that own them
May underestimate the number of
facilities owned by a parent entity and
the associated compliance costs
incurred by the parent entity
May underestimate the number of
parent entities falling in a given cost-
to-revenue impact range
Combination 3
> Accounts for all the entities that own
complying facilities
Accounts for all estimated facilities
and their compliance costs, but with
less precision than through use of the
facility weights
May underestimate the number of
facilities owned by a parent entity and
the associated compliance costs
incurred by the parent entity
With Use of Facility Weights
Combination 2
Accounts for all estimated facilities
and their compliance costs
Doesn't account for all of the entities
that own complying facilities
May overestimate the number of
facilities owned by a parent entity and
the associated compliance costs
incurred by the parent entity
May underestimate the number of
parent entities falling in a given cost-
to-revenue impact range
Combination 4
> Accounts for all of the entities that
own complying facilities
Overestimates the number of in-scope
estimated facilities and their
compliance costs
May overestimate the number of
facilities owned by a parent entity and
the associated compliance costs
incurred by the parent entity
5-8
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
Of these four possible combinations, EPA chose not to present results for Combination 1 and Combination 4 for
the following reasons:
> Combination 1 - no facility-level weights or entity-level weights - likely underestimates on both factors
of concern: number of entities and associated compliance costs that may be owned by a parent entity and
the number of parent entities. Given these weaknesses, EPA chose not to present results for this
combination.
> Combination 4 - using facility-level weights and entity-level weights - in effect creates facilities, and
their compliance cost and potential cost impact, that are known not to exist at the population level. This
combination thus may overstate the number of facilities owned by a parent entity and will also
overestimate the total cost expected to be incurred by any parent entity.
EPA chose to present results for Combination 2 and Combination 3 for the entity-level cost-to-revenue analysis as
well as regulatory flexibility analysis (RFA). Combination 2 and Combination 3 each have error in the opposite
directions:
> Combination 2 - using facility-level weights and but not entity-level weights - likely overstates the cost to
the individual parent entity but underestimates the number of parent entities owning in-scope facilities.
> Combination 3 - using entity-level weights and but not facility-level weights - likely underestimates the
number of facilities owned and costs incurred by, individual parent entities, but provides a more accurate
estimate of the number of entities owning in-scope facilities.
Given that Combination 2 and Combination 3 do not systematically overestimate or underestimate on both factors
of concern, EPA judges that both of these estimation options are superior to Combination 1 and Combination 4
and presents the findings from these analyses for the entity-level cost-to revenue analysis (and the RFA analysis,
which is discussed in Chapter 7: Regulatory Flexibility Act Analysis).
EPA presents these estimates of parent entities with costs exceeding, respectively, 1 percent and 3 percent of
entity-level revenue, as the numbers of parent entities that may experience a significant impact as a result of the
Phase II Proposed regulatory options.
5.3.2 Key Findings for Regulatory Options
Using facility weights, EPA estimates that 97 unique parent entities own 559 facilities subject to the Proposed
Existing Facilities Rule (Table 4-5). EPA estimates that the majority of parent entities will incur annualized costs
of less than 1 percent of revenues under Option 1 (88 percent) and Option 2 (56 percent). This finding applies
across all parent entity types under Option 1. Under Option 2, this finding holds true for all parent entity types
except investor-owned utilities and municipalities; the majority of parent entities in these ownership type groups
have revenue exceeding 1 percent of revenue. Under the more expensive Option 3, a nearly equal number of
entities are expected to incur costs above and below 1 percent of revenue, i.e., 46 and 45 out of 91 parent entities,
respectively, not taking into account 6 parent entities with unknown revenue. However, this finding does not hold
for all entity types. EPA estimates that the majority of nonutilities (59 percent) will incur costs below 1 percent of
revenue, while the only federal entity will incur costs exceeding 3 percent of revenue. The agency expects
cooperatives, investor-owned utilities, and municipalities to incur costs exceeding 1 percent of revenue and the
equal number of state-owned entities to incur costs above and below 1 percent of revenue.
EPA estimates that 2 out of 97 parent entities will incur costs exceeding 3 percent under Option 1,15 parent
entities under Option 2, and 20 parent entities under Option 3. As described above, the analysis using only
facility-level weights is likely to overstate the costs to individual parent entities but may under count the number of
parent entities in a given impact range.
March 28, 2011 5-9
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-3: Entity-Level Cost-to-Revenue Analysis Results, Using Facility-Level Weights
Entity Type
Total Number of
Facilities3
Total Number of
Entities
Number of Facilities with a Ratio of
Minimum
Ratio
Maximum
Ratio
Option 1: IM Everywhere
Cooperative
Federal
Investor-owned
Municipality
Nonutility
Other Political
Subdivision
State
Total
25
16
306
25
170
0
17
559
11
1
38
13
30
0
4
97
10 | 0 | 1 | 0
1 i 0 1 0 i 0
38 i 0 1 0 i 0
9 i 4 | 0 i 0
23 | 0 ! 1 | 6
0 | 0 | 0 | 0
4 i 0 | 0 i 0
85 i 4 1 2 i 6
0.00%
0.22%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
4.09%
0.22%
0.65%
2.55%
47.84%
0.00%
0.67%
47.84%
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
Cooperative
Federal
Investor-owned
Municipality
Nonutilitv
Other Political
Subdivision
State
Total
25
16
306
25
170
0
17
559
11
1
38
13
30
0
4
97
71113! 0
o 1 o 1 T 1 o
20 14 1 4 0
6 5 | 2 0
18 1 2 1 4 i 6
0 | 0 | 0 | 0
3 | 0 | 1 | 0
54 i 22 1 15 i 6
0.00%
8.89%
0.00%
0.01%
0.00%
0.00%
0.00%
0.00%
22.16%
8789%
11.18%
17.41%
579;67%
0.00%
13.40%
519.67%
Option 3: I&E Mortality Everywhere
Cooperative
Federal
Investor-owned
Municipality
Nonutility
Other Political
Subdivision
State
Total
25
16
306
25
170
0
17
559
11
1
38
13
30
0
4
97
4 i 3 | 4 i 0
0 0 ! 1 ! 0
20 14 | 4 | 0
21516! 0
18 | 2 | 4 | 6
0 ! 0 | 0 ! 0
2 I 1 1 1 I 0
46 ! 25 | 20 ! 6
0.00%
8.89%
0.00%
0.57%
0.00%
0.00%
0.00%
0.00%
22.16%
8.89%
11.18%
17.41%
519.67%
0.00%
13.40%
519.67%
a. Facility counts exclude baseline closures.
b. EPA was unable to determine revenues for 6 parent entities (
Source: U.S. EPA Analysis, 2010
! weighted).
Using entity weights, EPA estimates that 140 parent entities own 257 explicitly analyzed facilities subject to the
Proposed Existing Facilities Rule (Table 5-4}.141 EPA estimates that the majority of these parent entities will incur
annualized costs of less than 1 percent of revenues under all three options (92 percent of parent entities under
Option 1, 72 percent of parent entities under Option 2, and 61 percent of parent entities under Option 3). Under
Option 1, this finding applies to all ownership categories. Under Option 2, this finding applies to all parent entity
types except federal: only one federal entity is present in this analysis and it is expected to incur costs exceeding 3
percent of revenues. Under Option 3, the finding applies to all parent entity types except cooperatives and federal.
As for Option 2, the only federal entity present in this analysis is expected to incur costs exceeding 3 percent of
revenues under Option 3. In addition, under Option 3, EPA estimates the majority of cooperatives (55 percent) to
incur costs exceeding 1 percent of revenue. EPA estimates 1 entity to incur costs exceeding 3 percent of revenue
under Option 1 (less than 1 percent of all entities), 9 entities under Option 2 (6 percent), and 17 entities under
Option 3 (12 percent). As described above, the analysis using only entity-level weights is likely to understate the
costs to individual parent entities but provides a more comprehensive estimate of the number of parent entities
incurring costs.
141 There are a total of 143 small parent entities on an unweighted basis, 3 of which are Other Political Subdivision entities. These entities
own only implicitly analyzed facilities; consequently, there is no explicitly analyzed Other Political Subdivision parent entity to
represent these implicitly analyzed Other Political Subdivision parent entities. As a result, the weighted entity counts do not include
the 3 known Other Political Subdivision entities even though they are known to be part of the regulated facility and entity universe.
5-10
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-4: Entity-Level Cost-to-Revenue Analysis Results, Using Entity-Level Weights
Entity Type
Total Number of
Facilities3
Total Number of
Entities'"
Number of Facilities with a Ratio of
3% pujjkjjo^jjS"'
Minimum
Ratio
Maximum
Ratio
Option 1: IM Everywhere
Cooperative
Federal
Investor-owned
Municipality
Nonutility
Other Political
Subdivision
State
Total
13
7
138
13
78
0
8
257
20
1
42
35
38
0
4
140
18 | 2 | 0 | 0
1 i 0 1 0 i 0
42 0 1 0 i 0
35 0 | 0 i 0
29 | 0 ! 1 | 8
0 | 0 | 0 | 0
4 i 0 | 0 i 0
129 i 2 1 1 i 8
0.00%
0.09%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
1.78%
0.09%
0.16%
0.82%
22.74%
0.00%
0.29%
22.74%
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
Cooperative
Federal
Investor-owned
Municipality
Nonutilitv
Other Political
Subdivision
State
Total
13
7
138
13
78
0
8
257
20
1
42
35
38
0
4
140
13 ! 5 I 2 ! 0
6 1 o [ i 1 o
35 61110
24 8 | 3 i 0
25 i 4 [ 1 ' 8
0 | 0 | 0 | 0
3 | 0 | 1 | 0
101 i 23 1 9 i 8
0.00%
3783%
0.00%
0.00%
(ioo%
0.00%
0.00%
0.00%
9.63%
183%
5.28%
8.22%
2297i"7%
0.00%
5.86%
229.17%
Option 3: I&E Mortality Everywhere
Cooperative
Federal
Investor-owned
Municipality
Nonutility
Other Political
Subdivision
State
Total
13
7
138
13
78
0
8
257
20
1
42
35
38
0
4
140
9 i 9 | 2 i 0
0 0 ! 1 ! 0
35 6 ! 1 I 0
13 i 11 1 11 i 0
25 | 4 ! 1 8
0 i 0 | 0 i 0
3 I 0 ! 1 I 0
86 ! 29 | 17 ! 8
0.00%
3.83%
0.00%
0.26%
0.00%
0.00%
0.00%
0.00%
9.63%
3.83%
5.28%
8.22%
229.17%
0.00%
5.86%
229.17%
a. Facility counts exclude baseline closures.
c. EPA was unable to determine revenue for 6 parent entities (8 weighted).
b. There are a total of 143 parent entities on an unweighted basis, 3 of which are other political subdivision entities. These entities own only implicitly
analyzed facilities; consequently, there is no explicitly analyzed other political subdivision parent entity to represent these implicitly analyzed parent entities
and total weighted entity counts do not include 3 other political subdivision entities.
Source: U.S. EPA Analysis, 2010
Overall, this analysis shows that the entity-level compliance costs are low in comparison to the entity-level
revenues; consequently, parent entities that own more than one facility subject to the Proposed Existing Facilities
Rule will not be additionally "penalized" as the result of this rule as a result of their ownership of multiple
facilities.
5.3.3 Uncertainties and Limitations
> As described above, the estimates of entity-level impacts and the estimated numbers of entities incurring
costs in given cost-to-revenue impact ranges are based on the application of facility-level and entity-level
weights. The use of these sample weights in the analysis, generally, and the specific use of weights in the
two entity-level estimation approaches outlined above, yield estimates of entity-level impacts that are
subject to estimation error. In particular, each of the two entity-level impact estimation approaches
embeds specific issues of potential over- and under-statement of impact.
• Use of the facility-level weights alone likely overstates the cost-to-revenue impact on identified
parent entities while potentially understating the number of parent entities in a given cost-to-revenue
impact category.
March 28, 2011
5-11
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
• Use of entity-level weights alone may underestimate the number of facilities owned by a parent entity
and the associated compliance costs incurred by the parent entity.
> As described in Section 5.2.3 above, the facility count-based sample weights used for this analysis
account only for the number of facilities within each NERC region (see Appendix 3. A: Use of Sample
Weights in the Proposed Existing Facilities Rule Analyses}. The actual compliance costs assigned to each
of the explicitly analyzed facilities may differ from the costs that would be assigned to the implicitly
analyzed facilities that they represent. Consequently, the cost estimates generated through application of
facility-level weights may be over- or under-stated at the level of a given parent-entity as well as the
entity counts in each of the impact magnitude groups, even if the facility-weights account properly for
facility ownership.
5.4 Impact of Compliance Costs on Household Electricity Costs
As part of its assessment of the cost and economic impact of the Proposed Existing Facilities Rule options, EPA
assessed the burden of compliance cost from potential increases in the cost of electricity to residential consumers.
While the facility-level and parent entity-level cost-to-revenue screening analyses described in Sections 5.2 and
4.3 essentially reflect an assumption that in-scope facilities and their parent entities will absorb 100 percent of the
compliance burden (zero cost pass-through), this household electricity cost analysis and the electricity price
impact analysis described in Section 5.5, assume a simple 100 percent pass-through of compliance costs in
electricity prices. If this full cost pass-through condition were to occur, the screening analyses assessed in
Sections 5.2 and 4.3 would not be relevant.
As discussed in Chapter 2.H: Profile of the Electric Power Industry, the majority of in-scope electric power
generating facilities in the United States operate in states in which electricity prices remain regulated under the
traditional cost-of-service rate regulation framework. In these states, EPA anticipates that facilities will be able to
recover compliance cost-based increases in their production costs through increased electricity rates - as opposed
to states in which electric power generation has been deregulated, and where cost recovery is thus not guaranteed.
While facilities operating within deregulated electricity markets may be able to recover some of their additional
production costs in increased revenue, it is not possible to determine the extent of cost recovery ability for each
facility. Moreover, even though individual complying facilities may not be able to recover all of their compliance
costs through increased revenues, the market-level effect may still be that consumers will see higher overall
electricity prices because of changes in the cost structure of electricity supply and resulting changes in market-
clearing prices in deregulated generation markets. Based on these considerations, for the purpose of the household
electricity cost and electricity price impact analyses, the Agency assumed that 100 percent of compliance costs
would be passed through to consumers; this assumption will avoid understating the potential cost impact to
consumers from 316(b) compliance costs. To the extent that all compliance-related costs are not passed forward to
consumers but are absorbed, at least in part, by electric power generators, this analysis will overstate consumer
impacts.
5.4.1 Analysis Approach and Data Inputs
For this analysis, EPA assumed that compliance costs would be 100 percent passed through as increased
electricity prices and allocated among customer classes in proportion to the baseline quantity of electricity
consumption by customer class. EPA specifically analyzed potential impact on annual electricity costs by
household. The Agency performed this analysis at the level of the NERC region, which is appropriate given the
structure and functioning of sub-national electricity markets, around which NERC regions are defined.142 The
steps in this calculation are as follows:
As noted previously, NERC regions definitions have recently changed.
5-12 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
> To calculate an approximate total estimated annual cost in each NERC region, EPA aggregated weighted
pre-tax, facility-level annualized compliance costs, in 2009 dollars as of a given compliance year, i.e.,
2015, 2020, or 2025, and discounted to 2015 (the assumed analysis year) using a 7 percent discount143
rate, by NERC region.144 This analysis accounts for the different years in which facilities are expected to
achieve compliance in order to reflect the effect of differences in timing of these rate impacts in terms of
cost to household ratepayers and society. Costs and ratepayer effects occurring farther in the future (e.g.,
based on the expected compliance profile for nuclear generating facilities) have a lower present value of
impact than those that occur sooner following rule promulgation. Estimating the cost and ratepayer effect
as of the assumed compliance year (2015, 2020, 2025) and then discounting these effects to a single
analysis year (2015) accounts for this consideration.
> To calculate an approximate average price impact per unit of electricity sales, EPA divided total
compliance costs by the total MWh of sales reported for each NERC region. For all NERC regions except
Alaska System Coordinating Council (ASCC) and Hawaii Coordinating Council (HICC), EPA used
electricity sales (in MWh) for 2015 from AEO2009.145 For ASCC and HICC EPA used the historical
quantity of electricity sales (in MWh) for the year 2007 from the 2007 EIA-861 database; for these two
NERC regions, the Agency assumed that total average electricity sales would remain unchanged through
2015.
> To calculate average annual electricity sales per household, EPA divided the total quantity of residential
sales (in MWh) for 2007 in each NERC region by the number of households in that region; the Agency
obtained both the quantity of residential sales and the number of households for all NERC regions from
the 2007 EIA-861 database. For this analysis, EPA assumed that average electricity sales per household
would remain the same in 2015 as in 2007.
> To assess the potential annual cost impact per household, EPA multiplied the estimated average price
impact by the average quantity of electricity sales per household in 2007 by NERC region.
5.4.2 Key Findings for Regulatory Options
Table 5-5 reports the results of this analysis by NERC region for each option. These results show that for Option
1, the average annual cost per residential household is expected to range from $0.05 in WECC to $3.93 in SPP,
for Option 2 from $0.09 in WECC to $27.11 in SERC, and for Option 3 from $0.11 in WECC to $27.88 in SERC.
On average, for atypical U.S. household, Option 1 is expected to result in the lowest cost of $1.41 per household,
while Option 3 is expected to result in the highest cost of $17.60 per household. Overall, Option 2 is estimated to
result in costs of $17.09 per household.
143
The 7 percent discount rate is intended to reflect the opportunity cost of capital to society, on a real (i.e., without the effects of
inflation) basis, per guidance from the Office of Management and Budget, and thus an approximate basis for estimating potential rate
effects.
For this analysis, EPA brought technology and administrative costs forward to 2015 using the Construction Cost Index and
Employment Cost Index, respectively and assumed that these costs were as of a given compliance year, i.e., 2015 for facilities
installing IM technologies, 2020 for non-nuclear facilities installing cooling towers, and 2025 for nuclear facilities installing cooling
towers. EPA was not confident in the reliability of projecting compliance cost values using the respective Indexes beyond 2015. Not
using explicit index adjustments after 2015 assumes zero real growth (i.e., no difference from general inflation) after 2015. EPA's
choice of the year for which cost and revenue values are used in a particular part of the cost analysis was driven by the concept of a
given analysis and the availability of data for the analysis.
AEO does not provide information for HICC and ASSC.
March 28, 2011 5-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-5: Average Annual Cost per Household in 2015 by NERC Region and Regulatory Option ($2009)a
NERC
Regionb
Total Annual
Compliance Cost
(at 2015; Million;
$2009)
Total Electricity
Sales (at 2015;
MWh)
Compliance Cost
per Unit of Sales
(S2009/MWh)
Residential
Electricity
Sales (at 2015;
MWh)
Number of
Households
(at 2015)
Residential Sales
per Residential
Consumer (MWh)
Compliance Cost
per Household
($2009)
Option 1: IM Everywhere
ASCC
ECAR
ERCOT
FRCC"
nice
MAAC
MAM'
MAPP
NPCC
SERC"
sir
WECC
U.S.
$0
$"62"739"67563
$lJO'7b297i'i''l
PY^'59^03
J47259768
$g]7468746"7
j4j-292^94
$72775657966''
$g]7647^6Y9
$"9"9"736b7633
j53'-gYYj75
j4^0i'5^73
$497,100,012
6,326,610
569,849,487
31373957966
2427326^968"
10,585,038
29473657234
^g^Y^o'og
165,189,056
284,990,412
887,073,303
20477727272"
7QY-g26;043
3,960,424,805
$0.00
$b7"i"i
$673
$o""i"7
$a4'6
Soli
$675
$677
$o7i8
s'o'Ti
$"b73"i
$o"oi
$0.13
2,114,456
I^^^Q"
91,064,812
110,173,004
372o'b7'675
104,073,139
86,988,500
55717278Y5
9575g4773i
33273327257
68,368,566
24o';7575548
1,380,308,173
265,449
16,899,104
676037322
779237249
4077140
10,285,013
8,939,201
5,146,199
1275577410
2277057585"
574397270
2676737T56
123,244,098
7.97
11727
13779
13791
7.86
i'b'"."i"2
'9.73
i'b'".72
7761
14764
12.57
9723
11.20
$0.00
$7723
$776
$2737
$3"T6
$27"i"i
$776
$7779
$7738
$7754
$3793
$67o5
$1.41
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
ASCC
ECAR
ERCOT
FRCC
HICC"
MAAC
MAM
MAPP
NPCC
SERC
SPP"
WECC
U.S.
$0
$l,bl6,9537670
J502J217709
331175997736
$3270747166
$"g5yjY07436
3542jg67Y6o
j246754J7770
3744j387535
$1,673,0597866
j35]T523976"2"T
$67930361
$6,043,455,430
6,326,610
569,8497487
31373957966
242';320,9b8
16,585,038
294;365,234
275j4i55oo9
165,1897056
284,990,412
887,b73,3b3
204,"l72,272
701^267643
3,960,424,805
$0.00
$7777
$7792
$7729
$7103
'$187'
$7797
$779
$2761
$"i""85
$77/2
$"67oi
$1.53
2,114,456
j"9b,477,670
91,064,812
110,173,004
372ob7675
164,073,139
86,988,566
5571727815
95;5g4573"Y
3327332^57
687368,566
24o7757754g
1,380,308,173
265,449
16,899,164
6,603,322
7^9237249
4077140
i6,285,6l3
8,939,261
5,146,199
12;55754ib
227705",585
574-397270
26;0737i''5'6
123,244,098
7.97
Tl".'27
1379
13791
7786
i"b"."i'2"
973
'i"b"."72"
7"6"i
14764
12757
9723
11.20
$0.00
$26766
$26752
117.89
$72182"
$18797
J19J8
$16766
$19789"
$2'7"."i"i'
$21756
$"b"b9
$17.09
Option 3: I&E Mortality Everywhere
ASCC
ECAR
ERCOT
FRCC
HICC"
MAAC
MAM
MAPP
NPCC
SERC"
SPP
WECC
U.S.
$0
jY;b3"57o75775T
j502;72''l7709
$"317,4"l9788"l
J32;o747i66
j5gY7627';43"b
j5yj^33795g
j2623''8''2T596
j79Y^o37354
$"l,6"89",52"67l64
j350^3"97o2i
$"8",64l789""l
$6,222,339,919
6,326,610
5697g4"974g7
3Y^39"g^gg
242732'o,9'b'8''
io;585;o38
294;365^34
27574157009
165,189",656"
28"4",996,412
887,b73,3b3
2045i72;272
70Y;g265043
3,960,424,805
$0.00
$"l'"82
$7792
$7731
$3763
$"i""9"i
'$2707
$159
$2778
$"f9'b
$"i""72
$b"b"i
$1.57
2,114,456
19^477^70
91,664,812"
116,173,664"
Pool's'
164,673, 13 9
86,9"88,566
5571727815
9575g47731
33273327257
68,368,566
24o7757754g
1,380,308,173
265,449
16,8"9"9,T64"
6",603,322
77923^49
4077140
l6,285",6l3
8,93"9,2"6T
5,146,199"
127557;4io
2277"65",585
•^4"39-j27o
2650737i56
123,244,098
7.97
TT727
1379
i"3'"."9"'i'
7'"'8"6
'i"b"."i'2"
973
1072
T61
1764"
T2.57
9723
11.20
$0.00
$2677
$"2"6"752"
$78721
$"2182"
$"i"9'"."3"i'
$2'b"."i"8"
$"i"7"."b'4"
$2i7i3
$72788"
$2i756'
$"b'7i"i
$17.60
a. The rate impact analysis assumes foil pass-through of all compliance costs to electricity consumers.
b. No explicitly analyzed facilities are located in the ASCC region. For more information on explicitly and implicitly analyzed facilities see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2009b; U.S. DOE, 2007c
5.4.3 Uncertainties and Limitations
The assessment of electricity price impact is somewhat simple in its assumption that costs would be passed on in
the form of a flat rate price increase per unit of power, to be distributed in proportion to the current electricity
consumption profile. Within a rate regulation framework, fixed and variable costs would be allocated among
5-14
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
customer classes based on the contribution of each class to consumption during specific electricity production
periods. As a result, the allocation of costs to the residential class could be higher or lower than estimated by this
approach. In addition, this analysis ignores heterogeneous impacts at the household level, which may be more
important for utilities that use block-rate pricing or other price-discrimination rate structures, in which unit
consumption prices vary by consumption level. The analysis also does not account for rate structures - e.g.,
lifeline rates - which could moderate the impact of otherwise increased rates on lower income households.
5.5 Impact of Compliance Costs on Electricity Prices
As an additional measure of the potential cost and economic impact of the Proposed Existing Facilities Rule
beyond the level of the complying entity, EPA also assessed the potential increase in electricity prices to all
consumer groups (residential, commercial, industrial, and transportation), again assuming a simple 100 percent
pass-through of compliance costs in electricity prices.146 As discussed in Section 5.4, above, EPA assumed that
100 percent of compliance costs would be passed through to consumers for the purpose of the electricity price
impact analysis. EPA judges that this assumption is appropriate because the majority of electric power facilities in
the United States operate in states in which electricity generation remains regulated under the cost-of-service
framework, where facilities are able to recover increases in production costs through increased electricity rates.
Although some facilities operate in states in which electric power generation has been deregulated, and, as a
result, compliance costs may not be fully recovered in increased consumer electricity prices, EPA determined that
it would not be possible to estimate this consumer price effect at the state level. Accordingly, EPA judges that the
assumption of 100 percent cost pass-through for the purpose of electricity price impact analysis is appropriate for
this analysis and will avoid understating the potential cost impact to consumers of 316(b) compliance costs. As
stated above, this assumption may overstate the eventual consumer cost impact.
5.5.1 Analysis Approach and Data Inputs
For this analysis, EPA again assumed that compliance costs would be fully passed through as increased electricity
prices and allocated among customer classes in proportion to the baseline quantity of electricity consumption by
customer class. As in the preceding section, EPA performed this analysis at the level of the NERC region, as
follows:147
> EPA summed weighted pre-tax facility-level annualized compliance costs, in 2009 dollars as of a given
compliance year, i.e., 2015, 2020, or 2025, and discounted to 2015 at seven percent, by NERC region
> As done for the analysis of impact of compliance costs on household electricity costs, EPA estimated the
approximate average price impact per unit of electricity consumption by dividing total compliance costs
by the projected total MWh of sales in 2015 by NERC region, from AEO2009. EPA followed this
approach for all NERC regions except ASCC and HICC, for which the Agency used the historical
quantity of electricity sales - total and by consumer group - from the 2007 EIA-861 database.
> EPA compared the estimated average price effect to the projected electricity price by customer class and
NERC region for 2015 from AEO2009 for all NERC regions except, again, for ASCC and HICC. To
estimate average electricity rate by consumer class for ASCC and HICC, EPA divided electricity revenue
by electricity sales (MWh) reported by consumer class in the 2007 EIA-861 database.
146 These consumer groups are defined \nAEO2009.
147 As noted previously, NERC region definitions have recently changed.
March 28, 2011 5-15
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
5.5.2 Key Findings for Regulatory Options
As reported in Table 5-6, annualized compliance costs (in cents per KWh sales) range from 0.0010 in the WECC
region to 0.0400 in the HICC region for Option 1, and from 0.0010 in the WECC region to 0.3030 in the HICC
region for Options 2 and 3. On average, across the United States, Option 1 results in the lowest cost of 0.0130 per
KWh, while Option 3 results in the highest cost of 0.1570 per KWh. Option 2, results in national costs of 0.1530
per KWh.
Table 5-6: Compliance Cost per KWh of Sales by NERC Region and Regulatory Option in 2015 ($2009) a
NERC Region b
Annualized Pre-Tax Compliance
Costs (at 2015; $2009; million)
Total Electricity Sales
(at 2015; KWh)
Costs per Unit of Sales
(2009^/KWh Sales)
Option 1: IM Everywhere
ASCC
ECAR
ERCOT
FRCC
HICC
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
u.s.c
$0
jg2;3905503
Po;o29jii
$4Y;25972b3
P£5£46"g
j6J'46g^467
$4Y;2927594
j27;565;966
-^YfATfiW
$99360,633
j63jgYlj75
j4;OY5^273
$497,100,012
6,326,610,000
jgg-g^-lgy-jQj
jY-p^gg^g
242^2o;9bT393
Y5^g5^3g£bb
294j65^34-375
275^Y5;bo8345
ig^Y897b56396
2g4-99o^Y2;i76
gg7£73^Q^223
204j72^7f;729
7QY--g2^b437b25
3,960,424,804,688
0.000
abTi
abi"3
abi"7
aoio
abii
b"bi"5
abi7
b"oi"8
abi'i
67o3i
abbi
0.013
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
ASCC
ECAR
ERCOT
FRCC
HICC
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
U.S.
$0
3Y£Yb7953767b
J602J2YJ09
$3Yi;6997736
$3270747166
jggYjYblisiS
$542jg6j6b
j246;54Y';77b
j744-73g-535
$Y^43£597866
$35^2397021
$6,930361
$6,043,455,430
6,326,610,000
5"6^g49-4g7^0"g
jY-p^gg^g
242^2b;907393
Y5^5g5^3g;;bbb
294j65^34-375
275^Y5;bb8345
I65^gcrb56396
2g4-99o^Y2;i76
gg7£73^Q^223
204j72^7j;729
7QY--g2^b43;b25
3,960,424,804,688
0.000
bTvv
ai92
b!"l29
0303
O7l87
bT97
ai49"
ale!
ai85
O7l72
abbi
0.153
Option 3: I&E Mortality Everywhere
ASCC
ECAR
ERCOT
FRCC
HICC
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
U.S.
$0
^03^075 J51
j502;72T;709
jyY^T^gSl
J32jb74j66
j55Y;627/i30
j^Y^^g
J262382396
579^^03 J54
jY-;6g9j52oj64
j350^39;b2l
jgj54Y^g9Y
$6,222,339,919
6,326,610,000
jgg-g^-lgy-jQj
jY-p^gg^g
242^2b;9b7393
^^^^.^^^^
294j65^34-375
275^Y5;b08345
Y65jg9;b56;396
2g4-99o^Y27i76
•ggy-Qyj-'-jQj^
204j72^7j;729
7QY--g2^b43;b25
3,960,424,804,688
0.000
ai82
ai92
b'Tsi
0303
bT9i
a'207
b7i'59
Ol78
b7i"90
b7i"72
abbi
0.157
a. The rate impact analysis assumes foil pass-through of all compliance costs to electricity consumers.
b. No explicitly analyzed facilities are located in the ASCC region. For more information on explicitly and implicitly analyzed facilities see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses.
Source: U.S. EPA Analysis, 2010; U.S. DOE 2009b; U.S. DOE 2007c
5-16
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
As discussed above, to determine the potential significance of these compliance costs on electricity prices, EPA
compared the per KWh compliance cost to baseline electricity prices by consuming sector, and for the average of
the sectors. As reported in Table 5-7, across the United States, Option 1 is expected to result in the lowest
electricity price increase, 0.13 percent; Option 3 is expected to yield the highest increase, 1.68 percent; with
Option 2 resulting in a 1.63 percent increase. Looking across the three consumer groups, industrial consumers are
expected to experience the highest price increases: 0.19 percent under Option 1, 2.36 percent under Option 2, and
2.43 percent under Option 3. Residential consumers are expected to experience the lowest price increases: 0.11
percent under Option 1, 1.36 percent under Option 2, and 1.40 percent under Option 3.
Table 5-7
Costs by
NERC
Region b
: Projected
NERC Regi
Compliance
Cost (eYKWh)
2015 Price (Cents
on and Regulator
Residential
Baseline | %
Price | Change
per KWh of Sale
y Option ($2009)
Commercial
Baseline %
Price | Change
s) and Potential F
)
Industrial
Baseline | %
Price | Change
'rice Increase Du
Transportation
Baseline %
Price | Change
e to Compliance
All Sector Average
Baseline | %
Price | Change
Option 1: IM Everywhere
ASCC
ECAR
ERCOT
FRCC
HICC
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
U.S.
0.000
6.011
6.013
0.017
6.040
0.021
0.015
0.017
6.018
6.011
0.031
6.661
0.013
15.69 1 0.00%
9783 | O7il%
13728 | 616%
13.38 1 0.13%
2493 | 0.16%
13769 | 0.16%
10.1 5 | 0.15%
8.02 1 0.21%
18.23 | 0.10%
9.04 | 0.12%
971 1 0.32%
lT.37 | 076T%
11.20 1 0.11%
12.59 i 0.00%
8797 1 612%
937 ' 0.14%
11.28 " 6.15%
22.64 " 6.18%
11.35 ' 6.18%
8.24 " 6.18%
7.45 i 6:22%
14.42 I b7l3%
7.67 " 6.15%
8.25 I 0.38%
9763 I b"01%
9.57 i 0.13%
13.06 1 0.00%
6."15 | 0.18%
747 | O."l7%
9.16 i 0.19%
18.99 | 0.21%
8.14 | 0^26%
5.64 i 0.27%
5.68 1 0.29%
9.69 | 0.19%
5.56 i 0^20%
6.15 1 O."51%
7.00 | 0.01%
6.46 1 0.19%
NA i NA
7790 1 b7i4%
10759 I 612%
8.94 0.19%
NA NA
11.06 ' 0.19%
7.52 0.20%
6784 i 0.24%
15.84 ' 0.11%
5.97 ' 0.19%
7756 i 0.41%
8787 1 b"01%
10.64 i 0.12%
13.73 1 0.00%
8.21 | 0.13%
10.39 | 0.12%
12.19 I 0.14%
22.00 | 0.18%
11.32 | 0.18%
7.99 | 0.19%
7.04 1 0.24%
14.92 | 0.12%
7.60 | 0.15%
8.23 | 0.38%
9.64 | 0.01%
9.35 1 0.13%
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
ASCC
ECAR
ERCOT
FRCC
HICC
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
U.S.
0.000
6177
6192
6129
67303
6187
6197
6.149
67261
6185
6172
6.661
0.153
15.69 1 0.00%
9783 | 1.80%
13728 I 1.45%
13738 1 0.96%
24793 | 1.22%
13709 | 1.43%
l6l5 | 1.94%
8.02 1 1.86%
18723 | 1.43%
9.04 | 2.05%
9771 1 1.77%
J1737 | 0.01%
11.20 1 1.36%
12.59 i 0.00%
8.97 ' 1798%
9.37 " 2705%
11.28 " Il4%
22.64 " L34%
11.35 ' L65%
8.24 " 2739%
7.45 " 2766%
14.42 ; i78i%
7.67 " 2741%
8.25 I 2708%
9.63 ' abl%
9.57 i 1.60%
13.06 1 0.00%
6.'15 | 2.89%
747 | 2.57%
9. 16 i 1.40%
18799 | 1.60%
8.' 14 I 2.30%
5764 i 3.49%
5768 1 2.63%
9769 | 2.70%
5.56 i 3.33%
6. 15 1 2.79%
7.00 | 0.01%
6.46 1 2.36%
NA i NA
7.90 ' 2.25%
16759 ' 1.82%
8794 i 1.44%
NA ' NA
1L06 ' 1.69%
7.52 2.62%
6784 i 2.18%
15784 I 1.65%
5.97 3.10%
756 i 2.27%
8.87 ' 0.01%
10.64 i 1.43%
13.73 1 0.00%
8.21 | 2.16%
10.39 1 1.85%
12.19 I 1.06%
22.00 | 1.38%
11.32 | 1.66%
7.99 | 2.47%
7.04 1 2.12%
14.92 | 1.75%
7.60 [ 2.44%
8.23 | 2.08%
9.64 | 0.01%
9.35 1 1.63%
Option 3: I&E Mortality Everywhere
ASCC
ECAR
ERCOT
FRCC
HICC
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
U.S.
0.000
6182
6192
6.131
67303
6.191
0.207
6159
0.278
6.190
6172
6'.o6"i
0.157
15.69 1 0.00%
9783 | 185%
13728 I 1.45%
13738 1 0.98%
24793 | 1.22%
13709 | 1.46%
l6l5 | 2.04%
8.02 1 1.98%
18723 | 1.52%
9.04 ' 2.11%
9771 1 1.77%
i"O"7 ' o.oi%
11.20 1 1.40%
12.59 i 0.00%
8797 ' 2703%
9.37 " 2705%
11.28 " ll6%
22.64 " L34%
11.35 ' L68%
8.24 " 2752%
7.45 i 2713%
14.42 ' 1792%
7.67 " 248%
8.25 I 2708%
9763 ' 6:'6T%
9.57 i 1.64%
13.06 1 0.00%
6."15 | 2795%
747 i 2.57%
9. 16 i 1.43%
18799 | 1.60%
8.' 14 I 2.34%
5764 i 3.67%
5768 1 2.80%
9769 | 2.87%
5.56 ' 3.42%
6. 15 1 2.79%
7.00 0.02%
6.46 1 2.43%
NA i NA
790 ' 2730%
16759 ' 1.82%
8.94 ' 1.47%
NA ' NA
1L06 ' 1.72%
752 2.76%
6784 i 2.32%
15784 ' 1.75%
5.97 3.19%
756 i 2.27%
8787 ' abi%
10.64 i 1.48%
13.73 1 0.00%
8.21 | 2.21%
10.39 | 1.85%
12.19 I 1.07%
22.00 | 1.38%
11.32 | 1.69%
7.99 | 2.60%
7.04 1 2.26%
14.92 ! 1.86%
7.60 ! 2.51%
8.23 1 2.08%
9764 i b7b"f%"
9.35 1 1.68%
a. The rate impact analysis assumes foil pass-through of all compliance costs to electricity consumers.
b. No explicitly analyzed facilities are located in the ASCC region. For more information on explicitly and implicitly analyzed facilities see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses.c
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2009b; U.S. DOE, 2007c
March 28, 2011
5-17
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
5.5.3 Uncertainties and Limitations
As noted above, the assumptions regarding pass-through of compliance costs to electricity prices are relatively
simple and may overstate or understate the potential impact of 316(b) compliance costs on electricity consumers.
5.6 Assessment of Short-Term Reduction in Capacity Availability Due to Installation
Downtime
EPA assessed the reduction in generating capacity availability due to installation downtime for explicitly and
implicitly analyzed Electric Generators that are estimated to incur downtime during installation of compliance
technology as well as the impact of that capacity reduction on the North American bulk power system:
> For Option 1: IM Everywhere, 224 Electric Generators are estimated to incur net downtime, ranging from
0.3 to 9 weeks
> For Option 2: IM Everywhere and EM for Facilities with DIP > 125 MGD, 343 Electric Generators are
estimated to incur net downtime, ranging from 0.3 to 24 weeks
> For Option 3: I&EMortality Everywhere, 386 Electric Generators are estimated to incur net downtime,
ranging from 0.3 to 24 weeks148
5.6.1 Analysis Approach and Data Inputs
For this assessment, EPA estimated the quantity of generating capacity that would be temporarily out of service
by NERC region over the years in which facilities would be expected to come into compliance with the Proposed
Existing Facilities Rule. This assessment aims to provide an insight into whether the quantity of capacity that
might be out of service at a given time would be substantial in relation to total installed generating capacity by
NERC region, and, as a result, might pose a short-term electricity supply reliability issue.
To perform this assessment, EPA distributed the occurrence of installation downtime by facility, and by NERC
region, over the periods in which facilities are expected to achieve compliance with the regulatory options.
Specifically, EPA distributed downtime occurrence in such way that the total capacity out of service, by NERC
region, would be as uniform as possible over the periods in which facilities would be expected to achieve
compliance and incur downtime.
In implementing this procedure, EPA recognized that the amount of capacity at a facility that would need to be
removed from service at a given time for completion of technology installation, could not be "subdivided" - i.e.,
all of the generating capacity associated with a given intake structure would need to be taken out of service at the
same time to complete compliance technology installation for that intake structure. However, the implementation
of this procedure involved a key simplifying assumption that will tend to overstate the capacity availability impact
during the several year period of technology installation. Compliance technologies and downtime duration for the
installation of these technologies are assigned to individual intake structures. As a result, only generating capacity
associated with a specific intake structure would be expected to be out of service as a result of technology
installation at a given time. For this assessment, EPA was unable to identify the specific steam-generating units
and quantity of generating capacity associated with the individual intake structures. Thus, this assessment
assumes that all steam-generating capacity at a given facility will be out of service at the same time, even though
148 For the purpose of this analysis EPA analyzed DQ and STQ facilities explicitly as is done for the Market Model Analysis (see Chapter
6: Electricity Market Model Analysis) and used the Original 316(b) survey weights (see Appendix 3.A: Use of Sample Weights in the
Proposed Existing Facilities Rule Analyses).
5-18 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
this may overstate the downtime impact on capacity. Therefore, to the extent that some in-scope facilities may
operate several intake structures with different generating units assigned to different intake structures, this
analysis will overstate the impact of downtime on short-term capacity availability.
As described previously in Chapter 3: Development of Costs for Regulatory Options, facilities assigned non-
cooling tower technologies are expected to come into compliance with the Proposed Existing Facilities Rule
during a 5-year time period of 2013 through 2017. Non-nuclear and nuclear facilities assigned cooling towers are
estimated to come into compliance during 5-year windows of 2018 though 2022 and 2023 through 2027,
respectively. For this analysis, EPA assumed that each facility expected to incur installation downtime will do so
in the year of compliance. The Agency also assumed that facilities will incur downtime during the spring or fall
seasons so as not to coincide with either the winter or summer higher demand periods. Consequently, facilities
incurring installation downtime would have 10 time periods in which the downtime might occur (i.e., two time
periods for each of the possible compliance years).
EPA distributed the occurrence of downtime capacity as evenly as possible over these potential downtime
periods, recognizing the limitation described above that all of a facility's reported steam-generating capacity
would need to be taken out of service at once. The resulting assignments of facility capacity to individual
downtime periods were then summed over the facilities, by NERC region, to yield a potential reduction in
capacity availability by downtime period. The resulting downtime capacity values were then sorted from highest
to lowest for each NERC region. EPA intentionally did not assign these capacity estimates to particular years
and/or seasons; the Agency assumed that each NERC region would work with facility owners to coordinate the
occurrence of downtime within a given compliance window in such way as to minimize the potential for adverse
reductions in supply reliability due to the occurrence of installation downtime.
This distribution of downtime occurrence is meant to illustrate how the incremental installation downtime and
capacity availability effects might occur during the available compliance window - based on this specific
approach for distributing the occurrence of downtime. Table 3A-4 presents a summary of the resulting downtime
capacity values by downtime period, for each NERC region and regulatory option. For Options 2 and 3, the
downtime capacity values are presented separately for nuclear and fossil fuel facilities that are assigned cooling
towers and non-cooling tower technologies and facilities that are assigned only non-cooling tower technologies,
because the compliance windows for achieving compliance would not be the same.
To evaluate the reliability impact of technology installation downtime, EPA assessed whether the amount of
generating capacity that would be unavailable could prevent a given NERC region from meeting Reliability
Standards developed and enforced by NERC - i.e., whether a given NERC region will be able to meet its
electricity demand and its reserve margin requirement.149 Reserve margin is the amount of unused available
capacity at peak load in an electric power system, as a percentage of total capacity. EPA made this assessment, by
NERC region, by comparing the Reference Reserve Margin set by NERC with projected actual reserve margin
adjusted for capacity loss as the result of 316(b) technology installation, referred to herein as Compliance
Adjusted Potential Reserve Mar gin}50
EPA calculated Compliance Adjusted Potential Reserve Margin as follows:
(APC-316bNDC-NID)/ (5-1)
= ^ /(APC-316bNDC]
Where:
149 For more information see http://www.nerc.com/files/StandardsBackground.pdf and http://www.nerc.com/page.php?cid=2%7C97.
150 EPA obtained all baseline reserve margin and other information from NERC's 2008 Long-Term Reliability Assessment (LIRA)
report (NERC, 2008), available online at: http://www.nerc.com/files/LTRA2008.pdf
March 28, 2011 5-19
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
CAPRM = Compliance Adjusted Potential Reserve Margin, or baseline NERC region reserve margin
(Reference Reserve Margin) adjusted for the reduction in available capacity due to 316(b)
installation downtime
APC = Adjusted Potential Capacity (MW), an available capacity value published by NERC and defined
as the sum of net capacity resources, existing uncertain resources less all derates, total proposed
resources reduced by a confidence factor and net non-firm transactions. This capacity value
includes future capacity additions and adjusts for the possibility that some of this future capacity
may not be available when estimated to be constructed.
316bNDC = 316(b) Net Downtime Capacity (MW), or estimated capacity reductions due to 316(b)
installation downtime, by NERC region and year; calculated as described above.
NID = Net Internal Demand (MW), a NERC-published region-level electricity demand value, defined
as total internal demand reduced by dispatchable controllable (capacity) demand response.
The result of this calculation is the percentage of available capacity that would be available at peak demand after
adjusting available capacity for capacity reductions due to 316(b) installation downtime.
In performing this calculation, EPA used NERC-reported data for Adjusted Potential Capacity and Net Internal
Demand for the winter season. The LTRA report contains analysis of winter and summer bulk power system
reliability but does not report information for the shoulder season demand periods - fall and spring - which are
the periods when EPA expects that installation downtime would generally occur. EPA used information for the
winter season because, for the United States, winter is generally a lower demand season than summer and
therefore, would provide a better basis for assessing the impact of downtime-based capacity reductions that would
actually be expected to occur during the lower demand shoulder season operating periods. To the extent that
technology installation occurs during the shoulder months of spring and fall, when electricity demand is on
average below that during winter, the reliability impact estimated using winter demand is likely to be over-stated.
In addition, EPA used NERC-reported data for the year 2017, which is the last year covered by the 2008 LTRA
report. EPA judges that the 2017 value, which lies within the period (2013-2027) during which installation
downtime under the Proposed Existing Facilities Rule is expected to occur, provides a reasonable basis for
assessing the reliability impact of downtime.
To assess whether the reduction in available capacity due to 316(b) installation downtime could pose a bulk
power reliability concern, EPA compared Compliance Adjusted Potential Reserve Margin with Reference Reserve
Margin, as reported by NERC. Reference Reserve Margin (percent MW) represents either the target reserve
margin provided by the region/subregion or the target reserve margin assigned by NERC based on capacity mix
(i.e., thermal vs. hydro).
The results of these calculations are reported in Table 3A-4. Table 3A-4 reports for eight NERC regions (ERCOT,
FRCC, MRO, NPCC, RFC, SERC, SPP, WECC ):151
> NERC Reference Reserve Margin (percentage) level
> Net Internal Demand (MW) for the 2017/2018 winter season
> Adjusted Potential Capacity (MW) for the 2017/2018 winter season
> EPA's estimate of Downtime Capacity (MW) for each of the analyzed downtime periods
151 This analysis was undertaken for all NERC regions except ASCC (Alaska) and HICC (Hawaii). Energy concerns in the States of
Alaska and Hawaii (and the Dominion of Puerto Rico, and the Territories of American Samoa, Guam, and the Virgin Islands, as well)
are not under reliability oversight by NERC. As described below, because the 2008 LTRA report does not include information on
ASCC and HICC, EPA undertook a more summary analysis for these regions
5-20 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
> Compliance Adjusted Potential Reserve Margin (percentage), as calculated above, by downtime period ,
based on the 2017/2018 winter Net Internal Demand and Adjusted Potential Capacity.
In any downtime period, the higher the percentage of total capacity that would potentially be out of service due to
316(b) regulatory compliance, the greater the potential for electricity supply reliability effects.
It is important to note that this assessment of downtime effects does not account for the duration of downtime. As
noted above, the analysis assumes that all of the downtime across facilities in a region occurs at the same time
within an analysis period. This assumption may lead to significant overstatement of the potential impact of
downtime on electricity supply reliability.
Because energy reliability in Alaska and Hawaii is not under NERC's oversight, the 2008 LTRA report does not
include any information on these states. To assess reliability impact for Alaska and Hawaii, EPA performed an
additional analysis where the Agency looked at downtime capacity as a percentage of total regional capacity for
Alaska and Hawaii (Table 5-9).
For non-cooling tower technologies, which is the minimum technology standard required nationally under Option
1, the required duration of net downtime is between 2 and 9 weeks; only 2 explicitly analyzed facilities are
estimated to incur 9 weeks of downtime while for the majority of explicitly analyzed facilities, this duration is on
average 0.3 weeks. For cooling towers, most fossil fuel facilities, which are the vast majority of 316(b) Electric
Generators, are assigned 4 weeks of downtime, while some fossil facilities are assigned no net downtime; only 8
nuclear facilities are assigned 24 weeks of downtime, while the other 33 nuclear facilities are assigned no net
downtime. Thus, incremental downtime is quite low for nearly all facilities under Option 1, and for the non-
cooling tower facilities under Options 2 and 3. For the installation of cooling towers, which are required for all
facilities under Option 3 and most facilities under Option 2, net downtime is estimated at 4 weeks for non-nuclear
facilities; as stated above, while some nuclear facilities are expected to incur 24 weeks of net downtime, most are
expected to incur none.
5.6.2 Key Findings for Regulatory Options
For the 8 NERC regions that were able to be analyzed under the downtime assignment concept outlined above,
capacity loss due to 316(b) compliance technology installation is not expected to prevent any of these regions
from meeting either the expected electricity demand or required reserve capacity margin under any of the three
proposed regulatory options. Table 3A-4, which summarizes the results from this analysis, reports, for each NERC
region, the Reference Margin (second column from left), the baseline Demand and Capacity values derived from
the LTRA report, and the estimated Downtime Capacity and Compliance Adjusted Potential Capacity Margin for
each of the 10 periods in which downtime might be taken within the 5-year compliance windows for each
compliance technology. As shown in Table 3A-4, the Compliance Adjusted Potential Capacity Margin remains
substantially greater than the target Reference Margin for all regulatory options and NERC regions in all of the
potential downtime periods.
To assess reliability impact for ASCC and HICC, EPA performed an additional analysis where the Agency looked
at downtime capacity as a percentage of total regional capacity in each of these two regions (Table 5-9). Only 1
in-scope Electric Generator is located in ASCC (a non-nuclear facility with relatively low capacity, 28 MW). This
facility is expected to install IM technology under Options 1 and 2 and a cooling tower under Option 3, which
would require 0.3 weeks and 4 weeks of net downtime, respectively. Given the small facility size and relatively
short net downtime duration that would be required to install 316(b) compliance technology, EPA does not expect
significant reliability effects in the ASCC region as a result of this Rule.
The HICC region includes 3 in-scope Electric Generators, all of which are non-nuclear. These facilities are
relatively large with 610 MW, 372 MW, and 104 MW of capacity. Under Option I, only one facility (610 MW) is
estimated to incur additional downtime (0.3 weeks); this facility represents approximately 23 percent of the
March 28, 2011 5-21
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 5: Electric Generators Impact Analyses
region's total electric generating capacity. Under Options 2 and 3, all three facilities are expected to incur net
downtime of 4 weeks for cooling tower installation; these facilities represent 23 percent, 14 percent, and 4 percent
of the total regional capacity. Given the relatively large size of these facilities, it is quite likely for them to operate
multiple intake structures, in which case they would not need to be completely out of service to complete
technology installation.
In conclusion, EPA does not expect the short-term loss of capacity as the result of compliance with the Proposed
Existing Facilities Rule in any of the NERC regions to cause significant reliability effects in any of the NERC
regions.
5-22 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-8: Summary of Downtime Impact Analysis by NERC Region, Downtime Period, and Option
NERC
Region
Reference
Margin3
Demandb
Capacity0
Measure*1'6'*
Downtime Periods
1 | 2 | 3 [ 4 | 5 | 6 [ 7 | 8 | 9 [ 10
Option 1: IM Everywhere
ERCOT
FRCC
MRO
NPCC
RFC
SERC
SPP
WECC
11.1%
13.0%
13.0%
13.0%
12.8%
13.0%
13.0%
12.1%
54,085
55,516
40,067
50,760
157,900
23,698
37,592
131,752
88,965
72,725
54,436
73,549
231,959
266,885
63,650
171,152
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
2,460
37.5
%
3,140
20.2
%
1,365
24.5
%
1,216
29.8
%
5,526
30.3
%
5,053
22.2
%
1,623
39.4
%
1,103
22.5
%
2,250
3776
%
1,441
22"7T
%
950
2"5'7"l
%
946
3"b7"l
%
5,537
303
%
4,999
2272
%
1,584
3974
%
665
2277
%
1,094
3874
%
1,252
2273
%
959
25'7"i
%
1,099
2979
%
5,521
3073
%
5,041
2272
%
1,547
3975
%
2,080
227"i
%
936
38.6
%
924
22.7
%
956
257'i'
%
1,550
29.5
%
5,531
30.3
%
5,017
22.2"
%
1,406
39.6
%
1,392
22.4
%
1,335
3873
%
1,025
2276
%
936
2"5'7"i
%
1,213
2978
%
5,516
303
%
4,974
2272
%
1,331
3977
%
762
2277
%
818
3876
%
960
2276
%
923
25'7'i
%
1,187
2979
%
5,539
3"67'3
%
4,916
2272
%
1,311
3977
%
700
2277
%
1,150
38.4
%
779
22.8
%
939
25"'."i
%
1,669
29.4"
%
5,537
30.3
%
4,982
22.2"
%
1,241
39.8
%
2,213
22.0
%
0
3972
%
0
2377
%
932
2"'5'7"i
%
835
3072
%
5,529
3"b'7'3
%
4,914
2272
%
1,282
3977
%
1,320
2274
%
0
3972
%
0
2377
%
801
2573
%
1,418
2976
%
5,525
3"67'3
%
4,817
2273
%
825
4"b72
%
0
2376
%
0
3972"
%
0
23"77
%
0
2674
%
0
31.0
%
598
31.8
%
1,337
2"3"."3
%
0
40.9
%
0
2376
%
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
IM Technology
ERCOT
FRCC
MRO
NPCC
RFC
SERC
SPP
WECC
11.1%
13.0%
13.0%
13.0%
12.8%
13.0%
13.0%
12.1%
54,085
55,516
40,067
50,760
157,900
23,698
37,592
131,752
88,965
72,725
54,436
73,549
231,959
266,885
63,650
171,152
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
0
39.2
%
1,252
2273"
%
670
2575
%
388
30.6
%
2,900
31.1
%
880
23'".'4"
%
315
46.6
%
359
2279
%
0
3972
%
65
237(5
%
135
2672
%
39
3679
%
2,600
3172"
%
852
2374"
%
171
4678
%
2,080
22"7"i
%
0
3972
%
0
2377
%
122
2672
%
28
3176
%
1,933
3174
%
878
2374
%
0
4679
%
1,392
2274
%
0
39.2
%
0
2377
%
133
26.2"
%
0
31.6
%
1,700
3174
%
859
23'".'4"
%
0
4b'"."9"
%
700
22.7
%
0
3972
%
0
2377"
%
120
2672
%
0
3176
%
1,477
3175
%
1,635
2372"
%
0
4679
%
2,213
2276
%
0
3972
%
0
2377
%
49
2673
%
0
3176
%
1,464
3175
%
757
23775
%
0
4679
%
1,320
2274
%
0
39.2
%
0
2377
%
0
26.4"
%
0
31.6
%
1,398
3175
%
1,678
23'".'2"
%
0
40"."9"
%
0
2376
%
0
3972
%
0
2377
%
0
2674
%
0
3176
%
0
3179
%
515
2375
%
0
4679
%
0
2376
%
0
3972
%
0
2377
%
0
2674
%
0
3176
%
0
3179"
%
0
2377
%
0
4679
%
0
23"7'b
%
0
3972"
%
0
23"".7
%
0
2674
%
0
31.6
%
0
3179
%
0
23"77
%
0
4"b"."9"
%
0
2376
%
Cooling Towers - Fossil Fuel Facilities
ERCOT
FRCC
11.1%
13.0%
54,085
55,516
88,965
72,725
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
2,460
37.5
%
3,352
2,250
37.6
%
3,140
1,568
38.1
%
1,889
1,641
38.1
%
1,673
1,595
38.1
%
1,826
1,280
38.3
%
1,763
1,880
37.9
%
1,819
1,150
38.4
%
1,154
1,133
38.4
%
0
0
39.2
%
0
March 28, 2011
5-23
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-8: Summary of Downtime Impact Analysis by NERC Region, Downtime Period, and Option
NERC
Region
MRO
NPCC
RFC
SERC
SPP
WECC
Reference
Margin3
13.0%
13.0%
12.8%
13.0%
13.0%
12.1%
Demandb
40,067
50,760
157,900
23,698
37,592
131,752
Capacity0
54,436
73,549
231,959
266,885
63,650
171,152
Measure*1'6'*
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
i
20.0
%
1,618
24.1
%
1,849
29.2
%
7,620
29.6
%
8,345
2l".2
%
1,623
39.4
%
1,025
22.6
%
2
20.2
%
1,365
245
%
2,360
287
%
7,613
2976
%
8,349
2L2
%
1,584
394
%
199
2279
%
3
21.6
%
1,299
246
%
1,818
2972
%
7,623
2976
%
8,336
2L2
%
1,547
3975
%
184
2279
%
D
4
21.9
%
1,305
24.6
%
1,691
29.4
%
7,624
29.6
%
8,290
2l".2
%
1,406
39.6
%
762
22.7
%
owntim
5
21.7
%
1,315
246
%
1,726
2973
%
7,624
2976
%
8,390
2L2
%
1,396
3976
%
0
2376
%
e Periot
6
21.8
%
1,332
246
%
1,796
2973
%
7,620
2976
%
8,320
2l"2
%
1,307
3977
%
0
2376
%
Is
7
21.7
%
1,290
24.6
%
1,650
29.4
%
7,599
29.6
%
8,253
2l".2
%
1,326
39.7
%
0
23.0
%
8
22.4
%
1,323
246
%
1,669
294
%
7,624
2976
%
8,237
2L2
%
1,344
397
%
0
2376
%
9
23.7
%
1,307
246
%
835
3672
%
7,575
2976
%
7,891
214
%
1,038
4"67o
%
0
2376
%
io
23.7
%
0
26.4
%
1,418
29.6
%
822
3l".7
%
2,230
23.0
%
508
40.5
%
0
23.0
%
Cooling Towers - Nuclear Facilities
ERCOT
FRCC
MRO
NPCC
RFC
SERC
SPP
WECC
11.1%
13.0%
13.0%
13.0%
12.8%
13.0%
13.0%
12.1%
54,085
55,516
40,067
50,760
157,900
23,698
37,592
131,752
88,965
72,725
54,436
73,549
231,959
266,885
63,650
171,152
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
0
39.2
%
0
23.7
%
572
25.6
%
620
30.4
%
1,734
314
%
3,700
22.6
%
0
40.9
%
0
23.0
%
0
3972
%
0
2377
%
0
264
%
0
3176
%
1,734
3774
%
1,152
2373
%
0
4"6"9
%
0
2376
%
0
3972
%
0
2377
%
0
2674
%
0
3l76
%
0
3l"9
%
2,057
23"7l
%
0
40"9
%
0
2376
%
0
39.2
%
0
23.7
%
0
26.4
%
0
31.6
%
0
31.9
%
1,824
23.2
%
0
40.9
%
0
23.0
%
0
3972
%
0
2377
%
0
264
%
0
3176
%
0
3L9
%
0
2377
%
0
4"6"9
%
0
2376
%
0
3972
%
0
2377
%
0
264
%
0
3l76
%
0
3l"9
%
0
2377
%
0
40"9
%
0
2376
%
0
39.2
%
0
23.7
%
0
264
%
0
31.6
%
0
31.9
%
0
23.7
%
0
40.9
%
0
23.0
%
0
3972
%
0
2377
%
0
264
%
0
3l76
%
0
3L9
%
0
2377
%
0
4"6"9
%
0
2376
%
0
3972
%
0
2377
%
0
264
%
0
3l76
%
0
3l"9
%
0
2377
%
0
40"9
%
0
2376
%
0
39.2
%
0
2377
%
0
264
%
0
31.6
%
0
31.9
%
0
2377
%
0
40.9
%
0
23.0
%
Option 3: I&E Mortality Everywhere
IM Technology
ERCOT
FRCC
MRO
11.1%
13.0%
13.0%
54,085
55,516
40,067
88,965
72,725
54,436
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
0
39.2
%
1,252
22.3
%
670
25.5
%
0
3972
%
65
2376
%
0
264
%
0
3972
%
0
2377
%
0
264
%
0
39.2
%
0
23.7
%
0
264
%
0
3972
%
0
2377
%
0
264
%
0
3972
%
0
2377
%
0
264
%
0
39.2
%
0
23.7
%
0
264
%
0
3972
%
0
2377
%
0
264
%
0
3972
%
0
2377
%
0
264
%
0
3972
%
0
23.7
%
0
264
%
5-24
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-8: Summary of Downtime Impact Analysis by NERC Region, Downtime Period, and Option
NERC
Region
NPCC
RFC
SERC
SPP
WECC
Reference
Margin3
13.0%
12.8%
13.0%
13.0%
12.1%
Demandb
50,760
157,900
23,698
37,592
131,752
Capacity0
73,549
231,959
266,885
63,650
171,152
Measure*1'6'*
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
i
39
30.9
%
2,900
31.1
%
679
23.5
%
315
40.6
%
281
22.9
%
2
28
3LO
%
2,600
3L2
%
676
2375
%
0
4(19
%
1,392
224
%
3
0
3176
%
1,933
314
%
1,635
2372
%
0
4(19
%
700
2277
%
D
4
0
31.0
%
1,700
314
%
639
23.5
%
0
40.9
%
2,213
22.0
%
owntim
5
0
3176
%
1,388
3775
%
1,678
2372
%
0
4'6"9
%
1,320
2274
%
e Periot
6
0
3176
%
1,387
3L5
%
668
2375
%
0
4ll9
%
0
2376
%
Is
7
0
31.6
%
1,306
3T.5
%
237
23.6
%
0
4679
%
0
23.0
%
8
0
3i76
%
0
3L9
%
0
2377
%
0
4"6"9
%
0
2376
%
9
0
3176
%
0
3L9
%
0
2377
%
0
4ll9
%
0
2376
%
io
0
3T6
%
0
31.9
%
0
23.7
%
0
4"6"."9
%
0
23.0
%
Cooling Towers - Fossil Fuel Facilities
ERCOT
FRCC
MRO
NPCC
RFC
SERC
SPP
WECC
11.1%
13.0%
13.0%
13.0%
12.8%
13.0%
13.0%
12.1%
54,085
55,516
40,067
50,760
157,900
23,698
37,592
131,752
88,965
72,725
54,436
73,549
231,959
266,885
63,650
171,152
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
2,460
37.5
%
3,352
20.0
%
1,618
241
%
1,936
29.1
%
8,042
29.5
%
9,276
20.9
%
1,623
39.4
%
1,025
22.6
%
2,250
3776
%
3,140
2(12
%
1,463
2474
%
2,360
2877
%
8,038
2975
%
9,274
2"6"9
%
1,584
3974
%
320
2279
%
1,625
381
%
1,910
2L6
%
1,463
2474
%
1,913
291
%
8,039
2975
%
9,258
20"9
%
1,547
3975
%
184
2279
%
1,641
38.1
%
1,889
21.6
%
1,461
24.4
%
1,931
29.1
%
8,039
29.5
%
9,273
20.9
%
1,406
39.6
%
2,080
221
%
1,595
381
%
1,945
2176
%
1,458
2474
%
1,919
297!
%
8,041
2975
%
9,174
2l76
%
1,396
3976
%
762
2277
%
1,406
3872
%
1,991
2l"5
%
1,461
2474
%
1,907
291
%
8,042
2975
%
9,174
276
%
1,307
3977
%
0
2376
%
1,880
37.9
%
1,852
2l".7
%
1,466
24.4
%
1,936
29.1
%
8,037
29.5
%
9,270
20.9
%
1,326
39.7
%
0
23.0
%
1,150
384
%
1,959
2175
%
1,464
244
%
1,931
291
%
8,039
2975
%
9,140
2176
%
1,344
3977
%
0
2376
%
1,133
3874
%
488
231
%
1,445
2474
%
609
3674
%
8,026
2975
%
8,305
272
%
1,209
3978
%
0
2376
%
0
3972
%
0
23.7
%
29
26.4
%
1,418
29.6
%
111
31.7
%
2,230
23.0
%
508
40'.5
%
0
23.0
%
Cooling Towers - Nuclear Facilities
ERCOT
FRCC
MRO
NPCC
RFC
11.1%
13.0%
13.0%
13.0%
12.8%
54,085
55,516
40,067
50,760
157,900
88,965
72,725
54,436
73,549
231,959
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
0
39.2
%
0
23.7
%
572
25.6
%
620
304
%
1,734
3l".4
%
0
3972
%
0
2377
%
0
264
%
0
3176
%
1,734
3774
%
0
3972
%
0
2377
%
0
2674
%
0
3176
%
0
3L9
%
0
39.2
%
0
23.7
%
0
264
%
0
31.6
%
0
31.9
%
0
3972
%
0
2377
%
0
264
%
0
3176
%
0
3L9
%
0
3972
%
0
2377
%
0
2674
%
0
3176
%
0
3L9
%
0
39.2
%
0
23.7
%
0
264
%
0
31.6
%
0
31.9
%
0
3972
%
0
2377
%
0
264
%
0
3176
%
0
3L9
%
0
3972
%
0
2377
%
0
2674
%
0
3176
%
0
3L9
%
0
39.2
%
0
2377
%
0
264
%
0
31.6
%
0
31.9
%
March 28, 2011
5-25
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-8: Summary of Downtime Impact Analysis by NERC Region, Downtime Period, and Option
NERC
Region
SERC
SPP
WECC
Reference
Margin3
13.0%
13.0%
12.1%
Demandb
23,698
37,592
131,752
Capacity0
266,885
63,650
171,152
Measure*1'6'*
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
Downtime Capacity (MW)
Compliance Adj Potential Cap
Margin
i
3,700
22.6
%
0
40.9
%
0
23.0
%
2
1,152
23!
%
0
40"9
%
0
2376
%
3
2,057
231
%
0
4(19
%
0
2376
%
D
4
1,824
23.2
%
0
40.9
%
0
23.0
%
owntim
5
0
2377
%
0
4679
%
0
2376
%
e Periot
6
0
2377
%
0
4(19
%
0
2376
%
Is
7
0
23.7
%
0
40.9
%
0
23.0
%
8
0
2377
%
0
4"6"9
%
0
2376
%
9
0
2377
%
0
4(19
%
0
2376
%
io
0
2377
%
0
40.9
%
0
23.0
%
Source: U.S. EPA Analysis, 2010; NERC 2008
Notes to Table 5-8
a. Reference reserve margin: either the target reserve margin provided by the region/subregion or NERC assigned based on capacity mix (i.e., thermal/hydro).
b. The expected 2017/2018 winter net internal demand. Net internal demand is atotal internal demand reduced by dispatchable controllable (capacity) demand response.
c. The expected 2017/2018 winter adjusted potential capacity. Adjusted potential capacity is the sum of net capacity resources, existing uncertain resources less all derates, total proposed
resources reduced by a confidence factor and net non-firm transactions
d. Compliance Adjusted Potential Capacity Margin is calculated as (Adjusted Potential Capacity - Downtime Capacity - Net Internal Demand) / (Adjusted Potential Capacity - Downtime
Capacity)
e. 316(b) Facility-Level Downtime Capacity values for most facilities were developed using the average of baseline projections from IPM for 2015, 2020, 2025, and 2028. For the other
facilities, capacity values are from the 2007 EIA-860 database. Facility-level capacity used for the downtime assessment includes steam capacity only.
f In most instances when downtime capacity in a given time period exceeds 2 percent of the total capacity in the region, this downtime capacity belongs to an individual facility and,
therefore, could not be subdivided to ensure a more uniform downtime capacity distribution across time periods. To the extent that the entire steam generating capacity of these individual
facilities would not need to be out of service at the same time to complete technology installation, the assessment of capacity availability impact is likely overstated in these instances.
5-26
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 5: Electric Generators Impact Analyses
Table 5-9: Downtime Capacity for the ASCC and HICC NERC
Regions, by Region, Compliance Year, and Option
NERC
Region
Total
Regional
Capacity
(MW)a
Measure1"'0
1
2
3
Option 1: IM Everywhere
ASCC
HICC
2,049
2,648
Downtime Capacity
(MW)
% of Region Total
Downtime Capacity
(MW)
% of Region Total
28
f.37%
610
23;o2%
0
6.66%
0
6.66%
0
ao'6%
0
a66%
Option 2: IM Everywhere and EM for Facilities with DIF>125MGD
IM Technology
ASCC
HICC
2,049
2,648
Downtime Capacity
(MW)
% of Region Total
Downtime Capacity
(MW)
% of Region Total
28
07%
0
6:66%
0
6:66%
0
6:66%
0
6:66%
0
6:66%
Cooling Towers - Fossil Fuel Facilities
ASCC
HICC
2,049
2,648
Downtime Capacity
;MW)
% of Region Total
Downtime Capacity
(MW)
% of Region Total
o
0.00%
610
23.02%
o
0.00%
372
14.05%
o
0.00%
104
3.94%
Option 3: I&E Mortality Everywhere
Cooling Towers - Nuclear
ASCC
HICC
2,049
2,648
Downtime Capacity
(MW)
% of Region Total
Downtime Capacity
(MW)
% of Region Total
28
1.37%
610
23.02%
o
0.00%
372
14.05%
o
0.00%
104
3.94%
a. Regional capacity values for HICC and ASCC are from the 2007 EIA-860 database. Regional
capacity is a total of steam and non-steam capacity.
b. Facility-level downtime capacity values are from the 2007 EIA-860 database. Facility-level
capacity used for the downtime assessment includes steam capacity only.
c. There is only 1 in-scope Electric Generator in the ASCC NERC region and 3 in-scope Electric
Generators in the HICC NERC region.
d. When downtime capacity in a given time period exceeds 2 percent of the total capacity in the
region, this downtime capacity belongs to an individual facility and, therefore, could not be
subdivided to ensure a more uniform downtime capacity distribution across time periods. To the
extent that the entire steam generating capacity of these individual facilities would not need to be out
of service at the same time to complete technology installation, the assessment of capacity availability
impact is likely overstated in these instances.
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2007b
March 28, 2011
5-27
-------
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
6 Assessing the Impact of the Existing Facilities Regulatory Options in
the Context of National Electricity Markets
In the previous analyses for the 316(b) Phase II Regulations, EPA used ICF International's Integrated Planning
Model (IPM®)152, a comprehensive electricity market optimization model, to assess the economic impact of
regulatory options within the context of regional and national electricity markets. For its market model analysis of
the 316(b) existing facilities rule options, EPA used an updated version of this same analytic system, Integrated
Planning Model Version 3.02 EISA (IPM V3.02), to assess facility and market-level effects.153
Use of a comprehensive, market model analysis system is important in assessing the potential impact of the
options because of the interdependence of electricity generating units in supplying power to the electric
transmission grid. Increases in electricity production costs and potential reductions in electricity output at directly
affected facilities - whether due to the temporary shutdown of electric generating units during technology
installation and/or the energy production penalties resulting from compliance system operation - can have a range
of broader market impacts that extend beyond the effect on complying facilities. In addition, the impact of
compliance requirements on directly affected facilities may be seen differently when the analysis considers the
impact on those facilities in the context of the broader electricity market instead of looking at the impact on a
standalone, single-facility basis.
This chapter is organized as follows:
> Section 6.1 provides an overview of IPM V3.02, which is the basis of the Market Model Analysis for the
proposed regulatory options.
• Within Section 6.1, Section 6.1.1 reviews changes in the model since the time of the suspended 2004
Phase II Rule analyses.
• Section 6.1.2 reviews specifications and changes to the standard IPM configuration to meet the
requirements of the Proposed Existing Facilities Rule analyses.
> Section 6.2 summarizes the key inputs to IPM for performing the Proposed Existing Facilities Rule
analyses and the key outputs reviewed as indicators of the effect of the regulatory options.
> Section 6.3 describes the specific regulatory options considered in the market model analysis and how
these options map to the broader set of regulatory options that EPA considered in developing the
Proposed Existing Facilities Rule options for presentation in this report.
> Section 6.4 provides the findings from the market model analysis.
> Section 6.5 identifies key uncertainties and limitations in the market model analysis.
Specifically, IPM Version 2.1.
EPA reviewed a number of electricity market models for potential use in assessing the impact of the 316(b) Phase II regulation in its
analyses for the suspended 2004 Phase II Rule. At that time, EPA concluded that IPM represented the best choice for 316(b) rule
analyses considering a number of factors: ability to receive and account for as inputs, the cost and operating effect specifications of the
316(b) regulation; ability to assess the impact of 316(b) regulatory requirements on capacity dispatch and utilization, capacity
planning and management (i.e., capacity expansion, modifications, and retirements), and electricity production costs and prices; level
of documentation and acceptance of the models for use in assessing electricity market impacts of environmental regulations; ability to
incorporate other environmental regulatory actions in the baseline analysis; ability to incorporate EPA preferences in terms of
adjustments to the baseline electricity demand forecasts built into the model; and cost of model usage. On the basis of this prior model
review and selection process, EPA decided to rely again on IPM for the analyses of the existing facilities rule.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
6.1 Overview of the IPM Model and Its Use for the Market Model Analysis of the
Existing Facilities Rule Options
IPM V3.02 is an engineering-economic optimization model of the electric power industry, which generates least-
cost resource dispatch decisions based on user-specified constraints such as environmental, demand, and other
operational constraints. The model can be used to analyze a wide range of electric power market questions at the
plant, regional, and national levels. In the past, applications of IPM have included capacity planning,
environmental policy analysis and compliance planning, wholesale price forecasting, and asset valuation.
IPM uses a long-term dynamic linear programming framework that simulates the dispatch of generating capacity
to achieve a demand-supply equilibrium on a seasonal basis and by region. The model seeks the optimal solution
to an "objective function," which is the summation of all the costs incurred by the electric power sector, i.e.,
capital costs, fixed and variable operation and maintenance (O&M) costs, and fuel costs, over the entire evaluated
time horizon; the result is expressed as the net present value of all cost components. 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 constraints. 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-wide electricity demand on a seasonal basis by region. In
addition to existing capacity, the model also considers new resource investment options, including capacity
expansion at existing facilities, 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, system reliability considerations, and other constraints. The resulting system
dispatch is optimized given the resource mix, unit operating characteristics, and fuel and other costs, to achieve
the most efficient use of existing and new resources available to meet demand. The model is dynamic in that it is
capable of using forecasts of future conditions to make decisions for the present. For a detailed discussion on
adjustments made to the IPM framework to analyze the impact of the regulatory options on electricity generation
market, see Section 6.1.2 below.
6.1.1 Key Specifications of the IPM V3.02 Update
For the current analysis, EPA used IPM Version 3.02, which is an update from the version (Version 2.1) used for
the suspended 2004 Phase II regulatory analysis. Key specifications of the updated Version 3.02 that are relevant
to the existing facilities rule analyses are summarized in the following sections.
Power Plant Universe
IPM V3.02 is based on an inventory of all U.S. utility- and non-utility-owned boilers and generation facilities154
that provide power to the integrated electric transmission grid, as recorded in the Department of Energy's EIA
databases as of 2005.155 The modeling system includes 533, or nearly all, of the 559 explicitly and implicitly
analyzed electric generating facilities that EPA estimates will be within the scope of Proposed Existing Facilities
Rule. The exclusions of facilities from the IPM analysis include 4 facilities that are located in Alaska or Hawaii
(and thus not included in IPM), 4 "lower-48" facilities that are not connected to the integrated electric
transmission grid, 7 facilities excluded from the IPM baseline as the result of custom adjustments made by ICF,
and 11 facilities that are not explicitly present in the 316(b) facility dataset for this analysis.156
154 With the exception that IPM does not include units based in Hawaii or Alaska.
155 In some instances, facility information has been updated to reflect known material changes in a plant's generating capacity since 2005.
156 EPA's analysis of electricity market impacts is based on the total of "lower-48"/grid-connected facilities that responded to the
Detailed Questionnaire (DQ) and Short Technical Questionnaire (STQ) to the 316(b) survey. A small number of facilities did not
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Electricity Demand Baseline
IPM Version 3.02 embeds a baseline energy demand forecast that is derived from the Department of Energy's
Annual Energy Outlook 2008 (AEO2008), with adjustments by EPA to account for the effect of certain voluntary
energy efficiency programs. This electricity demand baseline is the same as that used by EPA in IPM-based
analyses for air program regulations.
Regional Analysis Framework
IPM V3.02 divides the U.S. electric power market into 32 regions in the contiguous 48 states. It does not include
generators located in Alaska or Hawaii. The 32 regions map to North American Reliability Corporation (NERC)
regions and sub-regions. IPM models electricity demand, generation, transmission, and distribution within each
region and across the transmission grid that connects regions. For the analyses presented in this chapter, IPM
regions were aggregated back into NERC regions. Figure 4C-5 provides a map of the 2009 NERC regions and
Table 6-1 lists the regions included in IPM V3.02 and a crosswalk between these NERC regions and the IPM
regions.
Figure 6-1: 2009 North American Electric Reliability Corporation (NERC) Regions
FRCC
a. The ASCC and HICC are not shown.
Source: U.S. DOE, 2009c
respond to either the DQ or STQ. In the analyses described elsewhere in this report, these non-respondents are accounted in the
facility sample weights. However, use of sample weights would not be appropriate in the IPM framework, and thus these "sample
weight-represented" facilities cannot be analyzed in the IPM-based electricity market analysis.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Table 6-1: Crosswalk between NERC Regions and IPM Regions3
NERC Region
Corresponding IPM Region(s)
ASCC Alaska Systems Coordinating Council
TRE5 Texas Regional Entity
FRCC Florida Reliability Coordinating Council
F£I Flawaii
MRO Midwest Reliability Organization
NPCC Northeast Power Coordination Council
RFC ReliabiHtyFirst Council
SERC Southeastern Electricity Reliability Council
!i!EIIlJ3o^^
WECC Western Electricity Coordinating Council
Alaska plants are not included in IPM
|RCT [[[
FRCC [[[
Hawaii plants are not included in IPM
[[[
' ................
a. The definition and configurations of NERC regions have changed several times over the past few years. This report uses different NERC
region configurations in different analyses, depending on the NERC region definition in which the data underlying a given analysis were
reported. The NERC region framework used in the IPM Version 3.02 and underlying the Market Model Analysis is based on NERC region
definitions as of 2009.
b. TRE replaced ERGOT (Electric Reliability Council of Texas). For the purpose of this analysis, to identify this geographic region EPA used
ERCOT instead of TRE.
Source: U.S. EPA, 2010
Regulations Accounted for in the IPM Analysis Baseline
An important reason for using IPM for the 316(b) regulatory analyses is that EPA uses the model to support
analysis of air regulations and the model thus incorporates in its analytic baseline, the expected compliance
response for air regulations affecting the power sector: Title IV of the Clean Air Act (the Acid Raid Program); the
NOx SIP Call; various New Source Review (NSR) settlements157; and several state rules158 affecting emissions of
SO2 and NOx that were finalized through February 3, 2009. IPM also includes state rules that have been finalized
and/or approved by a state's legislature or environmental agency, and in certain instances, facility-level
compliance technology installations that have already been undertaken because of CAIR requirements.
Earlier versions of the IPM baseline included the Clean Air Visibility Act (CAVR) and the Clean Air Mercury
Rule (CAMR); however, these regulations were removed from the Version 3.02 baseline. CAMR was vacated by
the B.C. Circuit Court in February 2008 along with EPA's rule removing power plants from the Clean Air Act list
of sources of hazardous air pollutants. The specific CAVR electric power sector assumptions formerly modeled in
IPM have been removed because of uncertainties associated with the measures and requirements States will adopt
to satisfy CAVR requirements.159
As described in the preceding paragraph, the B.C. Circuit vacated EPA's Clean Air Interstate Rule (CAIR) on
July 11, 2008. However, on Becember 23, 2008, the U.S. Court of Appeals issued a new ruling that repealed the
vacatur and instead, remanded CAIR, noting that: "allowing CAIR to remain in effect until it is replaced by a rule
consistent with our opinion would at least temporarily preserve the environmental values."160 At the time that
EPA began analyzing the Proposed Existing Facilities Rule, the Agency was still in the process of developing the
regulatory standards to replace CAIR requirements. The Transport Rule, which replaces CAIR, was proposed on
July 6, 2010, i.e., after EPA began developing the baseline for the current analyses. Consequently, the IPM
Including agreements between EPA and Southern Indiana Gas and Electric Company (Vectren), Public Service Enterprise Group,
Tampa Electric Company, We Energies (WEPCO), Virginia Electric & Power Company (Dominion), Santee Cooper, Minnkota
Power Coop, American Electric Power (AEP), East Kentucky Power Cooperative (EKPC), Nevada Power Company, Illinois Power,
Mirant, Ohio Edison, and Kentucky.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
baseline used for the analysis of the Proposed Existing Facilities Rule does not reflect requirements under the
Transport Rule.161 However, because EPA used the v3.02 EISA, i.e., the same IPM version used for the market
model analysis of 316(b) regulatory options, to assess the impact of the proposed Transport Rule on the U.S.
electric power sector, the 316(b) baseline includes other important existing regulations currently affecting this
industry sector. Consequently, on balance, EPA judges that the performance of the market model analyses against
the v3.02 EISA constitutes a reasonable cost and economic impact analysis for the Proposed Existing Facilities
Rule - in particular, given the uncertainties regarding ^Q final standards promulgated, and the specific
requirements that States will adopt in implementing the Transport Rule.162
6.1.2 Key Specifications for Analysis of the Proposed Existing Facilities Regulatory Options
In the same way as in the analysis for the suspended 2004 Phase II rule, EPA specified certain adjustments to the
IPM framework to meet the objectives of the electricity market analysis for the 316(b) Existing Facilities Rule.
Treatment of Individual Plants and Generating Units
IPM is supported by a database of boilers and electric generation units that includes all existing utility-owned
generation units as well as those located at plants owned by non-utility power generators that contribute capacity
to the electric transmission grid.163 Individual generators are aggregated into model plants with similar O&M
costs and specific operating characteristics including seasonal capacities, heat rates, maintenance schedules,
outage rates, fuels, and transmission and distribution loss characteristics.
In the analyses for EPA air regulations, 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 analyses, this configuration was not appropriate for the 316(b) analysis. As a result, the steam electric
generators at facilities subject to the existing facilities rule were unbundled from the existing IPM model plants
and analyzed as individual generating units along with the other existing model plants. Consequently, the IPM
baseline for the existing facilities rule market analysis consists of 14,903 individually modeled generating units.
Model Run Years
For the Proposed Existing Facilities Rule analyses, IPM V3.02 modeled the electric power market over the 25-
year period from 2012 to 2035. Due to the highly data- and calculation-intensive computational procedures
required for the IPM dynamic optimization algorithm, IPM is run only for a limited number of years. Run years
are selected based on analytical requirements and the necessity to maintain a balanced choice of run years
throughout the modeled time horizon. Further, depending on the analytical needs, in the IPM analysis, these
individual run years are assigned to represent other adjacent years in addition to the run year, itself. In the
standard IPM baseline analysis (i.e., before any adjustments to accommodate the 316(b) analysis requirements),
the IPM run years are set up on a simple interval: 2015, 2020, and 2025. IPM run years represent the 5-year
period beginning two years before and ending two years after the run year - e.g., 2015 would represent the
interval 2013-2017. In specifying the run years for the current 316(b) analyses, EPA used the standard 5-year
interval-based run years (e.g., 2015, 2020, and 2025) and identified an additional run year to meet specific 316(b)
regulatory analysis requirements.
In specifying the additional run years EPA accounted for two key analytic requirements:
> The need to assess the effect of the Proposed Existing Facilities Rule on electricity markets during the
years in which facilities would be expected to incur downtime to install compliance technology. This
161 For more information on the Transport Rule see http://www.epa.gov/airtransport/actions.htmWjullO.
162 For more information on IPM v3.02 EISA see http://www.epa.gov/airmarkets/progsregs/epa-ipm/transport.html.
163 See Chapter 2 for a review of the operating structure of the national electric power sector.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
compliance window is expected to occur over the period 2013-2027: for IM-only technology installations,
the currently expected compliance window is from 2013 to 2017, and for cooling tower installations from
2018 to 2022 for fossil fuel facilities and 2023 to 2027 for nuclear facilities leading to a "total"
compliance window of 2013 to 2027 for all facilities (see Chapter 3: Development of Costs for
Regulatory Options). The incurrence of downtime may lead to increased cost of electricity generation as
in-scope generating units are taken out of service to complete technology installation and other,
presumably higher production cost generating units are dispatched to meet electricity demand. Because of
the potential resulting increase in electricity production costs, it is important to examine market-level
effects during the period in which downtime would occur.
> The need to assess the effect of the proposed existing facilities rule on electricity markets during the
period after achievement of compliance by all 316(b) facilities, which, under the regulatory option
specifications considered for this analysis, is expected to occur in 2028 and subsequent years. Effects that
may occur during the post-compliance "steady state" include potential permanent losses in generating
capacity from early retirement (closure) of generating units, increases in electricity production costs due
to higher operating expenses and permanent reduction in electric generating capability and production
efficiency at 316(b) facilities (in particular, from energy penalty effects), and, as described for the
assessment of downtime effects, the need to dispatch other, potentially higher production cost, generating
units to offset losses in electric generating capacity. The increase in electricity production costs observed
during the steady state post-compliance period would be expected to be less than the increase in costs
during the period of installation downtime: during the period of installation downtime, full generating
units could be out of service for technology installation and for early retirement; during the post-
compliance period, capacity losses and increased overall production costs would result only from the
early retirements, energy penalty effects, and other increased expenses and would not include the effect of
the temporary unit closures for completing of technology installation.
Based on these considerations, EPA designated the following run years and assigned representation years as
follows:
> To capture the effect of 316(b)-related installation downtime, EPA designated the years:
• 2015, which was assigned to the years 2013 through 2017
• 2020, which was assigned to the years 2018 through 2022
• 2025, which was assigned to the years 2023 through 2027
> To capture the effect of 316(b) increased electricity production costs at the market level resulting from
capacity closures, and increased operating and maintenance expenses and energy penalty effects at 316(b)
facilities, during the period following achievement of compliance by all facilities and completion of all
installation downtime, EPA designated 2028, which was assigned only to one year - 2028.
In the analyses presented later in this chapter, EPA reports results for the 2028 model run year, which is the first
year after promulgation in which all in-scope facilities would have achieved compliance, to provide insight on the
effect of the proposed rule during the steady state period of post-compliance operations.
EPA also reports results for the 2015, 2020, and 2025 model run years. These results provide insight into potential
market-level effects for the years during which facilities would be expected to shut down operations temporarily
to complete technology installation (2015 for installation of IM-only technologies and 2020 and 2025 for
installation of cooling towers for nuclear and fossil fuel facilities, respectively).
Selection of Compliance Responses
In the same way as was done in the earlier IPM-based analyses for the suspended 2004 Phase II Rule, EPA did
not apply a feature available in the IPM framework in which modeled facilities select their compliance response
to a regulation that is being analyzed. This capability is used regularly in analyses of air regulations and permits
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
facilities to be analyzed assuming selection of a compliance response from a menu of options, based on the most
advantageous economic outcome to the facility. For the analysis of the Proposed Existing Facilities Rule options,
EPA determined the compliance response to regulatory options external to IPM, from an evaluation of baseline
engineering factors for facilities (e.g., design flow of existing cooling water intake structures, presence of
equipment for reducing impingement and entrainment of aquatic organisms, etc.) in relation to the requirements
of a given regulatory option. As described below, the compliance specifications were input to IPM via several
cost factors, adjustments to the energy production efficiency of affected generating units, and potential downtime
of generating units for installation of compliance equipment.
6.2 Model Analysis Inputs and Outputs
In performing the analysis based on IPM V3.02, EPA first developed a baseline - i.e., without regulation -
projection of electricity markets and facility operations over the period from the expected promulgation date,
2012, through 2028 (pre-regulation baseline case). EPA then overlaid this analysis with the estimated compliance
costs and other operating effects - downtime for installation of compliance technology and energy penalty - for
in-scope facilities under selected regulatory options (post-compliance cases).
6.2.1 Key Inputs to IPM V3.0 for the Proposed Phase II Rule Analyses
The inputs for the electricity market analysis include compliance costs, compliance installation downtime, and
technology-based reductions in plant generating efficiency (energy penalty) by affected generating unit, and the
assigned year of compliance. EPA developed the cost and compliance-related capacity reduction input values for
each of the 533 facilities subject to the Proposed Existing Facilities Rule and modeled by IPM, based on the
costing methodologies described in the Section 316(b) Technical Development Document (U.S. EPA, 2010).
These input categories are as follows:
> Capital cost inputs, which reflect the cost of compliance technology equipment, construction, and other
upfront, non annually recurring outlays associated with compliance with the proposed regulatory options.
Capital costs are specified in terms of the expected useful service life for the specific capital outlay. The
categories of outlay and associated service life used in the Proposed Existing Facilities Rule analysis are
as follows:
• Cooling tower equipment: 30 years
• All other compliance technology outlays: 20, 25, and 30 years
In the IPM analysis, these outlays are converted into a constant annual charge using IPM's conventional
frameworks for recognition of capital outlays over the useful life of the technology.
> Fixed O&M cost inputs are expressed in dollars per KW of capacity per year.
> Variable O&M cost inputs are expressed in dollars per megawatt hour (MWh) of generation. As discussed
in Chapter 3: Development of Costs for Regulatory Options, variable O&M costs also include initial
entrainment study over a three-year period and follow-up entrainment studies of one year duration every
third year after completion of the initial entrainment study. For the purpose of Market Model Analysis,
these entrainment study costs are expressed as follows: initial entrainment study is necessary for the on-
going operation of the plant and costs associated with this initial study are therefore added to the capital
costs for cooling towers and non-cooling tower technologies with useful life of 30 years; recurring
entrainment study costs (annualized over 3 years) are added to fixed O&M costs.
> Permitting costs consist of initial permitting costs, annual monitoring costs, and repermitting costs
(occurring every five years after issuance of the initial permit). For the purpose of Market Model
Analysis, permitting cost inputs are expressed as follows: initial permitting activities are necessary for the
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
on-going operation of the plant and are therefore added to the capital costs for cooling towers and non-
cooling tower technologies with useful life of 30 years; annual monitoring and repermitting costs
(annualized over 5 years) are added to fixed O&M costs.
> Energy penalty consists of a reduction in the energy production capability and energy efficiency of an
affected generating unit based on (1) reduced net saleable generating output from the affected unit due to
energy required for operating compliance technology and (2) for cooling towers only, a reduction in
energy conversion efficiency due to increased turbine back pressure. These values were combined into a
single percentage reduction in the energy production efficiency of the affected generating unit following
the methodology outlined in Chapter 3. In IPM, the percentage reduction is applied as a permanent
decrease in the affected generating unit's energy conversion efficiency - i.e., a reduction in the saleable
electricity derived from a given quantity of energy input to the generating unit. The energy penalty affects
the electricity production cost of the affected unit in the same way as a change in variable O&M.
> Installation downtime capacity reductions enter the analysis as a designated time period in which affected
generating units are taken out of service for installation of compliance technology. Installation downtime
values are expressed in weeks, and are estimated and applied in the analysis, as the additional downtime
beyond normally scheduled downtime for affected generating units (see Chapter 3). That is, plant
operators are assumed to schedule downtime for 316(b) compliance in conjunction with ordinary
scheduled downtime; the effect of 316(b) compliance downtime on electricity markets thus results only
from the extension of downtime beyond the ordinary scheduled downtime. Installation downtime is
assumed to occur in the year in which a facility complies with a regulatory option. For the electricity
market analysis, downtime therefore occurs during the IPM analysis compliance windows of 2013-2017,
2018-2022, and 2023-2027, depending on assigned technology and facility fuel type. In the IPM analysis,
the total downtime for each affected generating unit is spread uniformly over a given five-year period.164
Thus, the analysis assumes that installation downtime occurs uniformly over the five-year compliance
window both for individual generating units and for the aggregate of affected generating units. As
discussed in Chapter 3, nuclear facilities are not expected to incur any additional downtime as the result
of either IM technology or cooling tower installation.
Because the market model analysis is performed at the level of the individual boiler and/or generating unit, it was
necessary to distribute facility-level costs across affected boilers/generating units in developing the above inputs.
EPA followed a similar approach to that used in the previous 316(b) rule analyses. Specifically, EPA allocated
facility-level costs across all affected steam generating units (boilers and generators) using allocation factors
based either on steam generating capacity from IPM or on boiler-level water flow data from 2005 EIA-767.165 For
facilities with available design intake flow data, this distribution was based on each affected generating unit's
proportion of total design intake flow; for facilities without available design intake flow, this distribution was
based on each generating unit's proportion of total steam electric capacity. Generator-level compliance costs were
aggregated to the boiler level (for use in IPM) based on the boiler-generator crosswalk contained in the IPM
baseline datasets.
In addition to specifying these cost elements and the duration of installation downtime, it was necessary to assign
compliance years for each facility. To develop this compliance schedule, the Agency closely followed the
approach of the suspended Phase II Final Rule for fossil fuel facilities, and of the original 2002 Phase II Proposed
Rule for nuclear facilities. For this analysis, EPA assumed that non-nuclear and nuclear Electric Generators
This required treatment is an artifact of the way in which IPM performs analysis using single model run-years to represent the effect
over a specified period of years. One-fifth of the downtime value is assigned to each of the two 5-year analysis periods of 2013-2017,
for IM technologies, 2018-2022, for cooling towers at fossil fuel facilities, and 2023-2027 for cooling towers at nuclear facilities with
each of the years receiving one-fifth of the relevant downtime value.
The latest year for which EIA flow data are available is 2005.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
assigned either only IM technology or no compliance technology166 would comply in the year of their first post-
promulgation permit and first post-promulgation In-Service Inspection (ISI), respectively, resulting in a
compliance window of 2013 through 2017. Further, the Agency assumed that fossil fuel and nuclear Electric
Generators assigned cooling towers regardless of whether they were also assigned IM technologies would comply
in the year of their second post-promulgation permit and third post-promulgation ISI, respectively, resulting in a
compliance window of 2018 through 2022 for fossil fuel facilities and 2023 through 2027 for nuclear facilities.
For more information on compliance schedule see Chapter 3: Development of Costs for Regulatory Options^61
6.2.2 Key Outputs of the Market Model Analysis Used in Assessing the Effects of the Proposed
Phase II Regulatory Options
IPM V3.02 provides outputs for the NERC regions that lie within the continental United States. As described
above, IPM V3.02 does not analyze electric power operations in Alaska and Hawaii because these states' electric
power operations are not interconnected to the continental U.S. power grid.
IPM V3.02 generates a series of outputs at different levels of aggregation (boiler, model plant, region, and
nation). The economic analysis for the Proposed Existing Facilities Rule used a subset of the available IPM
output. For each model run (baseline case and each analyzed regulatory option) and for the analysis years
indicated above, the 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.
> Early Retirements - IPM models two types of plant closures: closures of nuclear plants as a result of
license expiration and economic closures as a result of negative net present value of future operation. This
analysis considers only economic closures in assessing the impacts of the Proposed Existing Facilities
Rule. However, cases where a nuclear facility decides to renew its license in the baseline case but does
not renew its license in the post-compliance case for a given policy option, are also considered economic
closures and an impact of that policy option.
> 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 during 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 plant that is available for dispatch to the
transmission grid ("net generation"). IPM provides summer, winter, and total annual generation.
> Energy Revenue - Revenue from the sale of electricity to the grid. IPM provides summer, winter, and
total annual energy revenue.
> Capacity Revenue - Revenue 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
166 These facilities still incur permitting costs.
167 EPA obtained information on NPDES permit renewals from the Water Permit Compliance System (PCS) or the Integrated Container
Information Systems - National Pollutant Discharge Elimination System (ICIS-NPDES) and information on ISI schedules for nuclear
facilities from the 2007 EIA-860 database.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
premium which reflects the cost of the required reserve margin 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. IPM provides summer, winter,
and total annual fuel costs.
> Variable Operation and Maintenance (VOM) Costs - Non-fuel O&M costs that vary with the level of
generation, e.g., cost of consumables, including water, lubricants, and electricity. IPM provides summer,
winter, and total annual VOM costs.
> Fixed Operation and Maintenance (FOM) Costs - O&M costs that do not vary with the level of
generation, e.g., labor costs and capital expenditures for maintenance. In the post-compliance cases, fixed
O&M costs also include annualized capital costs 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.
Comparison of these outputs for the baseline and post-compliance cases provides insight into the effect of the
Proposed Existing Facilities Rule options on affected facilities and broader electric power markets.168
6.3 Regulatory Options Analyzed
For this analysis, EPA analyzed three regulatory options that closely align with the three options discussed
elsewhere in this EA report:169
1. Option 1: Impingement Mortality at All Existing Facilities and Entrainment Controls for All New
Units at Existing Facilities; Determined Entrainment Controls for Facilities Greater than 2 MGD
DIP On a Site-Specific Basis (IM Everywhere170'171)
2. Option 2: Impingement Mortality Everywhere Plus Entrainment Mortality for Facilities with DIP
>125 MGD (IM Everywhere, EM for Facilities with DIF>125 MGD)
3. Option 3: Impingement and Entrainment Mortality Everywhere (I&E Mortality Everywhere).
The fourth option - Option 4: Non-Cooling Tower-Based Impingement and Entrainment requirements at all In-
Scope Facilities with DIP of 50 MGD or more - which is addressed in Chapter 13 of this report, was not analyzed
in IPM due to time constraints. Since this option mimics the requirements of Option 1, but only applies them to a
subset of in-scope facilities, the findings for this option in the IPM analysis would be lower than those estimated
for Option 1.
168 IPM output also includes NOx, SO2, CO2 and Mercury emissions and total fuel usage - for the full year and by winter and summer
season. These metrics are not a part of the analysis discussed in this Chapter.
169 As the case for the Regulatory Options presented in other chapters of this document, these options do not include new unit costs; new
unit costs are not a part of IPM analysis.
170 The shorthand notation for this and the other option refers to the minimum direct requirements of the regulatory options. For example,
for Option 1, in addition to this minimum requirement (e.g., IM technology for all in-scope facilities), additional requirements for EM
technology may be determined on a case-by-case basis and all new units at existing facilities would be required to meet EM
technology standards.
171 As analyzed in IPM, Option 1 differs slightly from the Regulatory Option presented in other chapters of this document. In particular,
as analyzed in IPM, Option 1 assumes lower costs from the performance of pre-compliance entrainment studies. In the IPM analysis,
the duration of these studies is shorter (and the costs lower) than as assessed in the rest of the EA report.
6-10 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
6.4 Findings from the Market Model Analysis
The impacts of the analysis options are assessed as the difference between key economic and operational impact
metrics that compare the post-compliance cases to the pre-regulation baseline case.
The main analysis presented in this chapter uses output from the analysis run year 2028, which is the first year
after promulgation in which all in-scope facilities would have achieved compliance under either of the three
analyzed regulatory options.172 These results provide insight on the effect of the Proposed Existing Facilities Rule
during the steady state period of post-compliance operations.
EPA also presents a subset of results for model run years 2015, 2020, and 2025, which are years during which
some in-scope facilities would experience installation downtime. This secondary analysis provides insight into
potential market-level effects for a year during which facilities would be expected to shut down operations
temporarily to complete technology installation.
6.4.1 Analysis Results for the Year 2028 - To Reflect Steady State, Post-Compliance
Operations
In these results, all facilities are assumed to have reached compliance with the analyzed regulatory options and no
facilities would be incurring downtime for installation of compliance technology. EPA considered impact metrics
of interest at three levels of aggregation:
1. Impact on national and regional electricity markets
2. Impact on the group of in-scope power generating facilities (i.e., facilities that are expected to be
within the scope of today's proposed regulation)
3. Impact on individual in-scope facilities.
Impact on National and Regional Electricity Markets
The market-level analysis assesses national and regional changes as a result of the regulatory options. Seven
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 result from partial or full closures
of plants subject to the rule. For this impact measure, EPA distinguished between existing
capacity and new capacity additions. Under this measure, EPA also analyzed capacity closures.
Only capacity that is projected to remain operational in the baseline case but is closed in the post-
compliance case is considered a closure as the result of the Rule. The Market Model Analysis
may result in partial (i.e., unit) or full plant early retirements (closures) for a given regulatory
option; it may also result in avoided closures if a facility's compliance costs are low relative to
other affected facilities. An avoided closure is a unit or plant that would is estimate to close in the
baseline, but estimated to continue operation in the post-compliance case.
2. Changes in the price of electricity: This measure considers changes in regional prices as a result
of the regulatory options. In the long term, electricity prices may change as a result of increased
production costs at the affected facilities. In the short-term, price increases may be higher if large
power plants have to shut down temporarily to construct and/or install compliance technologies.
For this analysis, EPA combined both components of the estimated electricity price - i.e., energy
price and capacity price - into a single energy-unit equivalent price (i.e., $ per MWh of energy).
172 The first year of full compliance is 2028 for Options 2 and 3, and 2018 for Option 1. To facilitate comparison of market-level impacts
across options, this presentation focuses on 2028 as the steady state comparison year.
March 28, 2011 6-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
3. Changes in generation: This measure considers the amount of electricity generated. At a regional
level, long-term changes in generation may result from plant closures or a change in the amount
of electricity traded between regions. At the national level, the demand for electricity does not
change between the baseline and the 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 revenue: This measure considers the revenue realized by all facilities in the market
and includes both energy revenue and capacity revenue (see definition in Section 6.2.2 above). A
change in revenue could be the result of a change in generation, the price of electricity, or both.
5. Changes in costs: This measure considers changes in the 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 revenue and/or
increases in costs.
7. Changes in variable production costs per MWh: This measure considers the change in average
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. Production cost per MWh is a
primary determinant of how often a power plant's units are dispatched. This measure presents
similar information to total fuel and variable O&M costs, but normalized for changes in
generation.
Table 6-2 reports results for regulatory options at the level of the national market and also for regional electricity
markets defined on the basis of NERC regions (Table 6-1). All of the impact metrics described above are reported
at both the national and NERC regional level except electricity prices, which are calculated in IPM only at the
regional level.
Table 6-2: Impact of Regulatory Options on National and Regional Markets at the Year 2028
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Diff
%Diff
Option 2
Value
Diff
%Diff
Option 3
Value
Diff
%Diff
National Totals
Total Capacity (GW)
Existing
New Additions
Early Retirements
Electricity Prices (S/MWhij
Generation (TWh)
Revenue (SMillionsj
Costs (SMillionsj
Fuel Cost
Variable 6"&M
Fixed O&M
Capital Cost
Pre-Tax Income (SMillions)
Variable Production" Cost ($MWnj
1,148
'p*p"p*
I 9 '''9, 9
NA
4,895
$315,010
$187,914
$967903
$147623
$48,578
$3378l6
$1277696
$2"l"."56
1,148
p"p*pB,
*m 9 *m
NA
4,895
$3"l57l86
$188,413
""$"9"678"6"6
$147656
$49,021
""$"33,877
$1267773
$2"i""56
0
-1
1
1
NA
0
$177
$499
-$44
$33
$443
$67
-$323
'$"6766'
0.0%
-61%
0.1%
o7i%
NA
0.0%
67i'%"
0.3%
'676'%"
672%
0.9%
672%
-673%
676%
1,148
'p'VV
• *m 9 \
NA
4,894
$"315743"7
$197,971
'"$9'i"38'7'
$"l4,939
$54,474
"$367976
$"ll7,466
'$21777
0
-24
24
14
NA
-1
$427
$10,057
$6"8"4"
$"316
$5,897
$3"7l60
-$9,630
$"6'."2i
0.0%
-2"7i%"
2.1%
13%
NA
0.0%
0.1%
5.4%
678%
272%
12.1%
973%
-776%
1.6%
1,148
p*p"p*p
9 *m 9
NA
4,894
$315,518
$198,215
""$"'9"l75'87
$"14,941
$54,601
""$377687
$"117,363
$21777
0
-24
24
15
NA
-1
$5"6'8
$10,301
$6"83
$"318
$6,023
$3"7276
-$9,793
$"6'."2i
0.0%
-2. 1%
2.1%
13%
NA
0.0%
6".2%
5.5%
678%
2.2%
12.4%
9.7%
-7.7%
1.6%
6-12
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Table 6-2: Impact of Regulatory Options on National and Regional Markets at the Year 2028
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Diff
%Diff
Option 2
Value
Diff
%Diff
Option 3
Value
Diff
% Diff
Electric Reliability Council of Texas (ERGOT)
Total Capacity (GW)
Existing
New Additions
Early Retirements
ElectticEyPricei'($/MWh)
______.;..
Revenue (SMillions)
Costs"($Millions)
Fuel Cost
Variable O&M
pj^ed'o&M
CapitalCost
Pre-Tax Income (SMillions)
Variable Production ^^^^^^j^y
99
99
'•"•"•™|i™H™B~l
$59.65
392
$26,284
$17,476
$8,739
$1,361
$3,207
$4,169
$8,808
$25.74
$59.64
392
$26,282
$17,513
$8,725
$1,363
$3,237
$4,187
$8,769
$25.71
0
0
0
0
-$0.01
0
-$2
$37
-$14
IIII$lI
$30
$18
-$39
-$0.03
0.0%
-0.2%
0.2%
0.2%
0.0%
0.0%
0.0%
0.2%
-0.2%
0.2%
0.9%
0.4%
-0.4%
-0.1%
99
^•"•"•*
$59.75
392
$26,307
$18,335
$8,554
$1,434
$3,593
$4,754
$7,972
$25.45
0
-5
5
4
$0.10
0
$23
$858
-$186
$73
$386
$585
-$836
-$0.29
0.0%
-5.3%
5.3%
4.5%
0.2%
0.0%
0.1%
4.9%
-2.1%
5.4%
12.0%
14.0%
-9.5%
-1.1%
99
•"•"•~!
$59.75
392
$26,307
$18,337
$8,538
$1,436
$3,596
$4,767
$7,970
$25.42
0
-5
5
4
$0.10
0
$23
$861
-$201
$76
$389
$598
-$838
-$0.32
0.0%
-5.3%
5.3%
4.6%
0.2%
0.0%
0.1%
4.9%
-2.3%
5.6%
12.1%
14.3%
-9.5%
-1.2%
Florida Reliability Coordinating Council (FRCC)
Total Capacity (GW)
Existing
New Additions
Early Retirements
ElecnicEyPricei($/MWn)
Generation (TWh)
Revenue (SMillkms)
Costs (SMillions)
FuefCo<£
Variable O&M
FJ^Q&M
Capital Cost
Pre-Tax Income (SMillions)
Variable Production ••••^•^•^•^
79 [ 79
t. 'm*m
$63.56
310
$22,432
$16,745
$9,236
$1,111
$2,311
$4,087
$5,687
$33.33
$63.56
311
$22,438
$16,775
$9,276
$1,107
$2,324
$4,069
$5,662
$33.43
0
0
0
0
-$0.01
0
$6
$31
$40
-$5
$13
-$18
-$25
$0.10
0.0%
-0.1%
0.1%
0.1%
0.0%
0.0%
0.0%
0.2%
0.4%
-0.4%
0.6%
-0.4%
-0.4%
0.3%
79
"n\
$63.62
311
$22,526
$17,105
$9,387
$1,099
$2,522
$4,098
$5,422
$33.75
0
-1
1
0
$0.06
0
$95
$360
$151
-$12
$211
$11
-$266
$0.41
0.0%
-0.7%
0.7%
0.0%
0.1%
0.1%
0.4%
2.2%
1.6%
-1.1%
9.1%
0.3%
-4.7%
1.2%
79
'm*m "wi.
$63.62
311
$22,518
$17,101
$9,395
$1,095
$2,522
$4,090
$5,417
$33.78
0
-1
1
0
$0.06
0
$86
$357
$159
-$17
$211
$3
-$271
$0.45
0.0%
-0.8%
0.8%
0.0%
0.1%
0.0%
0.4%
2.1%
1.7%
-1.5%
9.1%
0.1%
-4.8%
1.3%
Midwest Reliability Organization (MRO)
T.0M.Capacity (GW)
Existing
New Additions
Early Retirements
ElectticEyPricei'($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs"($Millions)
FugfO,"^
Variable O&M
F^Q&M
CapitalCost
Pre-Tax Income (SMuTions)
Variable Production ^^^^^^y^^
71 1 71
^"'m^'9^"W9^""J^'9
*mr 'i/i, %fmr 'i/i
$52.16
292
$17,779
$9,392
$4,158
$743
$2,797
$1,694
$8,387
$16.81
$52.16
292
$17,783
$9,479
$4,143
$743
$2,850
$1,743
$8,305
$16.75
0
0
0
0
$0.00
0
$5
$87
-$15
:*L
$53
$49
-$82
-$0.06
0.2%
0.0%
0.3%
0.0%
0.0%
0.0%
0.0%
0.9%
-0.4%
-0. 1%
1.9%
2.9%
-1.0%
-0.4%
71
/i/i, tf
$52.22
290
$17,705
$10,024
$4,171
$770
$3,103
$1,980
$7,682
$17.06
0
-1
1
1
$0.06
-2
-$73
$632
$14
$26
$306
$286
-$705
$0.25
0.0%
-2.0%
2.0%
1.1%
0.1%
-0.7%
-0.4%
6.7%
0.3%
3.6%
10.9%
16.9%
-8.4%
1.5%
71
i, 'mfmr 1
$52.20
290
$17,712
$10,090
$4,174
$772
$3,143
$2,001
$7,622
$17.05
0
-1
1
1
$0.04
-2
-$66
$699
$16
$29
$347
$307
-$765
$0.25
0.0%
-2.0%
2.1%
1.1%
0.1%
-0.5%
-0.4%
7.4%
0.4%
3.9%
12.4%
18.1%
-9.1%
1.5%
March 28, 2011
6-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Table 6-2: Impact of Regulatory Options on National and Regional Markets at the Year 2028
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Diff
%Diff
Option 2
Value
Diff
%Diff
Option 3
Value
Diff
% Diff
Northeast Power Coordinating Council (NPCC)
Total Capacity (GW)
Existing
New Additions
Early Retirements
E!ectricity'Prices'($7NMhj
.____.__„
Revenue (SMillions)
Costs"($Millionsj
Fuel Cost
VariabifiO&M
pjxedo&M
Capital Cost
Pre-Tax Income (SMillions)
Variable Production ^^^^^y
80
79
'•"•"•^Ti™B™H™i
$74.51
317
$26,624
$15,829
$7,941
$1,064
$4,117
$2,708
$10,795
$28.36
$74.60
317
$26,686
$15,861
$7,900
$1,071
$4,150
$2,741
$10,825
$28.26
-1
-1
0
1
$0.09
0
$62
$32
-$41
$7
$33
$33
$30
-$0.10
-0.7%
-0.9%
0.2%
0.9%
0.1%
0.0%
0.2%
0.2%
-0.5%
0.6%
0.8%
1.2%
0.3%
-0.4%
78
*•"•"•*
$73.29
317
$26,446
$16,771
$7,684
$1,068
$4,703
$3,316
$9,676
$27.62
-2
-5
3
4
-$1.22
-1
-$178
$941
-$256
$3
$586
$608
-$1,119
-$0.75
-2.3%
-6.1%
3.8%
4.8%
-1.6%
-0.2%
-0.7%
5.9%
-3.2%
0.3%
14.2%
22.5%
-10.4%
-2.6%
78
•"•"•~l
$73.27
317
$26,413
$16,801
$7,680
$1,066
$4,725
$3,330
$9,613
$27.60
-2
-5
3
4
-$1.25
-1
-$211
$971
-$261
$2
$608
$622
-$1,182
-$0.77
-2.3%
-6.1%
3.8%
4.8%
-1.7%
-0.2%
-0.8%
6.1%
-3.3%
0.2%
14.8%
23.0%
-11.0%
-2.7%
ReliabilityFirst Corporation (RFC)
Total Capacity (GW)
Existing
New Additions
Early Retirements
Electricity Prices ($7MWhj
Generation (TWh)
Revenue (SMillionsj
Costs (SMillionsj
pueJCost
Variable O&M
Fixed O&M
Capital Cost
Pre-Tax Income (SMillions)
Variable production Cost ($MWnj
245 J 245
i jm '• Jai '• JB '•'
^*lm^w***'^m'm*m^m
$53.23
1,144
$69,701
$41,944
$19,555
$2,723
$13,818
$5,848
$27,757
$19.48
$53.26
1,144
$69,833
$42,099
$19,581
$2,737
$13,964
$5,817
$27,734
$19.52
0
0
0
0
$0.03
0
$132
$155
$26
$14
$146
-$31
-$23
$0.03
0.1%
0.1%
0.0%
-0.1%
0.1%
0.0%
0.2%
0.4%
0.1%
0.5%
1.1%
-0.5%
-0.1%
0.2%
246
• 'BjH 'l
"•j_jssl~*
$53.42
1,143
$70,051
$45,155
$20,065
$2,788
$15,641
$6,661
$24,896
$20.00
1
-6
7
3
$0.19
-1
$350
$3,211
$510
$65
$1,823
$813
-$2,861
$0.52
0.6%
-2.4%
3.0%
1.3%
0.3%
-0. 1%
0.5%
7.7%
2.6%
2.4%
13.2%
13.9%
-10.3%
2.7%
246
^•/•J*!
"•*!»**«?
$53.50
1,143
$70,130
$45,213
$20,082
$2,787
$15,669
$6,675
$24,918
$20.01
1
-6
7
3
$0.27
-1
$429
$3,269
$527
$64
$1,851
$827
-$2,839
$0.53
0.6%
-2.4%
3.0%
1.3%
0.5%
-0.1%
0.6%
7.8%
2.7%
2.3%
13.4%
14.1%
-10.2%
2.7%
Southeast Electric Reliability Council (SERC)
1°M ...Capacity (GW)
Existing
New Additions
Early Retirements
Electricity'Prices'($/NMhj
Generation (TWh)
Revenue (SMillions)
Costs"($Millionsj
pugJQost
VariabifiO&M
fixed O&M
Capital Cost
Pre-Tax Income (SMiilions)
Yariabk; pro^^on Qosf ($/Mwhj'
286 I 287
W, 9, 9,^, 9, 9, I
li/if, 'm^%r 1
$54.82
1,263
$79,803
$43,976
$22,087
$3,319
$13,555
$5,015
$35,828
$20.11
$54.82
1,263
$79,792
$44,101
$22,056
$3,331
$13,688
$5,026
$35,691
$20.10
0
0
0
0
-$0.01
0
-$11
$126
-$31
$12
$134
$11
-$137
-$0.01
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
-0.1%
0.4%
1.0%
0.2%
-0.4%
-0.1%
287
• • ,
^1, '•/•*
$54.79
1,266
$80,145
$47,488
$22,504
$3,457
$15,806
$5,721
$32,657
$20.50
0
-4
4
1
-$0.04
3
$341
$3,512
$417
$138
$2,251
$706
-$3,171
$0.39
0.1%
-1.4%
1.6%
0.3%
-0. 1%
0.2%
0.4%
8.0%
1.9%
4.2%
16.6%
14.1%
-8.9%
2.0%
287
• • Bj
i/i, 'i/i
$54.82
1,266
$80,202
$47,565
$22,506
$3,460
$15,831
$5,768
$32,637
$20.51
0
-4
5
1
$0.00
3
$398
$3,589
$419
$141
$2,276
$754
-$3,191
$0.40
0.1%
-1.5%
1.6%
0.3%
0.0%
0.2%
0.5%
8.2%
1.9%
4.2%
16.8%
15.0%
-8.9%
2.0%
6-14
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Table 6-2: Impact of Regulatory Options on National and Regional Markets at the Year 2028
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Diff
%Diff
Option 2
Value
Diff
%Diff
Option 3
Value
Diff
% Diff
Southwest Power Pool (SPP)
Total Capacity (GW)
Existing
New Additions
Early Retirements
ElectticEyPricei'($/MWh)
______.;..
Revenue (SMillions)
Costs"($Millions)
Fuel Cost
Variable O&M
pj^ed'o&M
CapitalCost
Pre-f ax Income (SMillions)
Variable Production ^^^^^^j^y
68
68
'•"•"•™|i™H™B~l
$49.81
282
$15,589
$9,916
$4,238
$922
$1,916
$2,840
$5,673
$18.33
$49.80
282
$15,588
$9,945
$4,236
$924
$1,943
$2,842
$5,643
$18.33
0
0
0
0
-$0.01
0
-$1
$29
-$2
IIII$lI
$28
$2
-$31
$0.00
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.3%
-0.1%
0.3%
1.5%
0.1%
-0.5%
0.0%
68
^•"•"•*
$49.77
281
$15,580
$10,395
$4,253
$939
$2,228
$2,975
$5,185
$18.49
0
-1
1
1
-$0.04
-1
-$9
$480
$15
$18
$312
$135
-$489
$0.16
0.0%
-2.1%
2.1%
1.4%
-0. 1%
-0.3%
-0.1%
4.8%
0.3%
1.9%
16.3%
4.8%
-8.6%
0.9%
68
•"•"•~!
$49.80
281
$15,577
$10,400
$4,251
$940
$2,230
$2,979
$5,177
$18.49
0
-1
1
1
-$0.01
-1
-$12
$484
$13
$18
$314
$139
-$496
$0.16
0.0%
-2.2%
2.2%
1.5%
0.0%
-0.3%
-0.1%
4.9%
0.3%
1.9%
16.4%
4.9%
-8.7%
0.9%
Western Electricity Coordinating Council (WECC)
Total Capacity (GW)
Existing
New Additions
Early Retirements
ElecnicEyPricei($/MWn)
Generation (TWh)
Revenue (SMillkms)
Costs (SMillions)
FuefCo<£
Variable O&M
FJ^Q&M
Capital Cost
Pre-Tax Income (SMillions)
Variable Production ••••^•^•^•^
220 [ 220
t. 'm*m
$56.08
894
$56,798
$32,637
$14,950
$3,379
$6,858
$7,450
$24,161
$20.49
$56.06
894
$56,785
$32,639
$14,943
$3,380
$6,864
$7,453
$24,145
$20.49
0
0
0
0
-$0.01
0
-$13
$3
:*Z
$1
$6
IIIIKI
-$16
-$0.01
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.1%
0.0%
-0.1%
0.0%
220
"n\
$55.92
895
$56,677
$32,700
$14,970
$3,384
$6,879
$7,466
$23,978
$20.52
0
0
0
0
-$0.16
0
-$121
$63
$21
$5
$21
$16
-$184
$0.02
0.0%
-0.1%
0.1%
0.1%
-0.3%
0.0%
-0.2%
0.2%
0.1%
0.2%
0.3%
0.2%
-0.8%
0.1%
220
'm*m "wi.
$55.92
894
$56,658
$32,708
$14,961
$3,385
$6,886
$7,477
$23,950
$20.52
0
0
0
0
-$0.16
0
-$139
$72
$11
$6
$28
$27
-$211
$0.02
0.0%
-0.1%
0.1%
0.1%
-0.3%
0.0%
-0.2%
0.2%
0.1%
0.2%
0.4%
0.4%
-0.9%
0.1%
Findings for Option 1 (IM Everywhere)
As reported in Table 6-2, the market model analysis indicates that Option 1, the preferred Option, would have
very small effects in overall electricity markets, on both a national and regional sub-market basis, in the year
2028.
Overall at the national level, the net change in total capacity, including reductions in capacity (which includes
early retirements) and capacity additions in new plants/units, is essentially zero. Consequently, Option 1 (IM
Everywhere) would not be expected to have a material ongoing effect on capacity availability and supply
reliability at the national level. At the NERC region level, 5 of the 8 analyzed NERC regions record nearly no
change in capacity, 2 record modest capacity increases of no more than 0.2 percent of baseline, and only 1 region
records a non-consequential loss in capacity of 0.7 percent of baseline.
At the national level, the analysis indicates a total reduction in capacity from closures of approximately 1 GW, or
less than 0.1 percent of the total capacity baseline in 2028 (capacity closures are discussed in greater detail in the
next section (Impact on In-Scope Facilities as a Group)). At the regional level, the greatest capacity reduction of
approximately 1 GW occurs in the NPCC region; this reduction would be less than 1 percent of total baseline
capacity in that region. Two NERC regions - RFC and SERC - are estimated to experience avoided capacity
closures - i.e., one or more generating units that are otherwise projected to cease operations in the baseline
become more economically attractive sources of electricity in the post-compliance case, because of relative
changes in the economics of electricity production across the full market, and thus avoid closure.
March 28, 2011
6-15
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
Overall, Option 1 has a slight impact on electricity prices. For 5 out of 8 NERC regions, electricity prices are
projected to decline very slightly - by no more than $0.01 per MWh resulting in nearly zero percent change - in
the post-compliance case. For the remaining three NERC regions, electricity price does not increase by more than
$0.09 per MWh or 0.1 percent (NPCC). These very small estimated changes in electricity prices are essentially
within the analytic "noise" of the market model analysis system.
At the national level, in a similar way as described for net changes in capacity, the net change in generation is
essentially zero, as electricity demand is assumed not to change as a result of the regulation. No region records a
consequential change in total generation.
Total revenue in the electric power sector increases by a very small amount (less than 0.1 percent), potentially
reflecting modest increases in prices to customers. No region records a material change, with the largest change -
an increase of $132 million or 0.2 percent - occurring in the RFC region.
At the national level, total costs increase by less than 0.3 percent of the baseline value - again, a very modest
amount. None of the cost components changes in a material way. Across regions, no NERC region records an
increase in power sector total costs exceeding 1 percent.
As a specific measure of the effect of Option 1 on the short-term cost of electricity production, the change in
variable production cost of electricity from the pre-regulation baseline value is nearly zero. Again, this effect
varies by region, with the greatest increase occurring in the FRCC region (0.3 percent) and the most significant
drop occurring in the NPCC region (-0.4 percent).
With the additional costs from compliance and resulting increases in overall electricity production costs, national
sector-level pre-tax income is projected to decline very slightly, by approximately 0.3 percent. All regions except
NPCC experience a decrease in pre-tax income; the greatest drop, approximately 1 percent, occurs in the MRO
region.
Findings for Regulatory Option 2 (IM Everywhere and EM for Facilities with DIF>125MGD)
Option 2 (IM Everywhere and EM for Facilities with DIF>125MGD) blends the requirements of regulatory
Options 1 and 3, with some facilities being required to meet non-cooling tower-based impingement mortality
requirements (i.e., Regulatory Option 1 specifications) and others required to install cooling towers (i.e.,
Regulatory Option 3 specifications). As a result, the findings for Option 2 overall lie within the range of results
found from the analysis of Options 1 and 3, although very close to Option 3 analysis results.
Similarly to Option 1, under Option 2, the net change in total capacity is essentially zero, indicating that this
option would not be expected to have a material ongoing effect on capacity availability and supply reliability, at
the national level. This is also the case for 5 of the 8 NERC regions. Of the remaining 3 NERC regions, one -
NPCC - records a decrease in total capacity of 2.3 percent, while the other two - RFC and SERC - record a
modest increase of no more than 0.6 percent of the projected baseline capacity in 2028.
The change in existing capacity at the national level, a reduction of approximately 24 GW, represents
approximately 2.1 percent of total baseline generating capacity. Of this reduction, approximately 14 GW occurs as
early retirements, or approximately 1.3 percent of total baseline capacity; the residual results from energy penalty
reductions in capacity. At the regional level, the largest drop in existing capacity and the largest increase in retired
capacity of approximately 5 GW (6.1 percent) and 4 GW (4.8 percent), respectively, occur in the NPCC region.
No NERC region records a reduction in capacity retirements.
At the national level, the offsetting increase in capacity at new plants/units of approximately 24 GW is
approximately 2 percent of total baseline generating capacity. At the regional level, the largest increase in new
capacity of 5.3 percent occurs in the ERCOT region.
6-16 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
Option 2 also has a slight impact on electricity prices across all NERC regions. In 4 out of 8 NERC regions
electricity prices increase slightly by no more than 0.3 percent. The other four NERC regions record a slight drop
in electricity prices of no more than 0.3 percent.
As is the case for Option 1, for Option 2, the net change in generation at the national level is essentially zero. At
the regional level, the change in net generation is also non-consequential. The largest decline of approximately 2
TWh (0.7 percent) occurs in the MRO region.
Total revenue in the electric power sector increases by a very small amount (0.1 percent), reflecting very modest
overall increases in prices to customers. At the regional level, 4 of the 8 NERC regions record a slight increase in
revenue of no more than 0.5 percent; the remaining 4 NERC regions record a slight drop in revenue of no more
than 0.7 percent.
As expected for Option 2, which is more expensive than Option 1, the increase in total annual costs for the
electric power sector is greater than under Option 1. At the national level, total annual costs increase by $ 10.1
billion, which is approximately a 5.4 percent increase from baseline power sector costs. The larger parts of this
increase occur in fixed O&M ($5.9 billion or 12.1 percent) and annual recognition of capital costs ($3.2 billion or
9.3 percent), reflecting the cost requirements of cooling tower installation. Fuel costs increase by a much smaller
amount ($0.7 billion) reflecting the improved energy efficiency and lower fuel cost of new capacity. All NERC
regions show an increase in total costs, ranging from 0.2 percent in the WECC region to 8.0 percent in the SERC
region; this increase in total costs is largely due to an increase in fixed O&M and capital costs.
At the national level, variable production costs, which is a measure of the short-run production cost of
electricity - fuel and variable O&M - increase by a comparatively small amount ($0.21 per MWh) or less than 1
percent of the baseline value. The impact on production costs varies across NERC regions, however, increasing in
some and decreasing in others; the largest increase of 2.7 percent occurs in the RFC region and the largest drop of
2.6 percent occurs in the NPCC region.
As would be expected with higher compliance outlays, and increase in capacity reduction as the result of more
facilities incurring energy penalty under Option 2, total sector pre-tax income is more materially affected than
under Option 1. At the national level, pre-tax income declines by $9.6 billion or 7.6 percent. All NERC regions
incur a reduction in pre-tax income, with the largest reduction of 10.4 percent occurring in the NPCC region.
Findings for Regulatory Option 3 (I&E Mortality Everywhere)
The market model analysis projects that the most expensive Option 3 (I&E Mortality Everywhere) would have a
slightly greater impact on national and regional electricity markets than Option 2, as more in-scope facilities
install cooling towers (nearly all) to meet compliance requirements.
As observed for Options 1 and 2, the net change in total capacity under the Option 3 is essentially zero, indicating
that this option would not be expected to have a material ongoing effect on capacity availability and supply
reliability, at the national level. This is also the case of 5 of the 8 NERC regions. The 2 of the remaining 3 NERC
regions record a slight increase in total capacity of no more than 0.6 percent; for the remaining NERC region -
NPCC - records a 2.3 percent in total capacity loss.
At the national level, the change in existing capacity, a reduction of approximately 24 GW (approximately the
same as under Option 2), represents approximately 2.1 percent of total baseline generating capacity in 2028. Of
this reduction, approximately 14 GW occurs as early retirements, or approximately 1.3 percent of total baseline
capacity. The residual results from energy penalty reductions in capacity. As is the case under Option 2, at the
regional level, the largest drop in existing capacity and the largest increase in retired capacity of approximately 5
GW (6.1 percent) and 4 GW (4.8 percent), respectively, occur in the NPCC region. No NERC region records a
reduction in capacity retirements.
March 28, 2011 6-17
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
At the national level, the offsetting increase in capacity at new plants/units of approximately 24 GW is
approximately 2 percent of total baseline generating capacity. At the regional level, the largest increase in new
capacity of 5.3 percent occurs in the ERCOT region.
Overall, Option 3 has a slightly larger impact on electricity prices than Option 2. Of the 8 NERC regions, 5 record
a slight price increase of no more than 0.5 percent, occurring in RFC; the remaining 3 NERC regions record a
slight price drop of no more than 1.7 percent, occurring in ERCOT.
At the national level, as is the case under Options 1 and 2, the net change in generation is essentially zero. No
NERC region records a consequential change in total generation - the largest decline is 0.5 percent, which occurs
in the MRO region.
Total revenue in the national electric power sector increases by a very small amount (0.2 percent), again reflecting
overall very modest increases in prices to customers. Of the 8 NERC regions, 4 record a modest increase in
revenue of no more than 0.6 percent, with the largest increase occurring in RFC; in the remaining 4 NERC
regions, revenue drops slightly by no more than 0.8 percent, with the largest decline occurring in NPCC.
The overall increase in total annual costs under Option 3 is slightly higher than under Option 2. At the market
level, total costs increase by approximately $10 billion or 5.5 percent. As for Option 2, the larger parts of this
increase occur in fixed O&M ($6.0 billion) and annual recognition of capital costs ($3.3 billion), reflecting the
cost requirements of cooling tower installations. Fuel costs increase by a much smaller amount ($0.7 billion)
reflecting again the improved energy efficiency and lower fuel cost of new capacity. All NERC regions show an
increase in total costs for electric power sector, ranging from 0.2 percent in the WECC region to 8.2 percent in the
SERC region; this increase in total costs is largely due to an increase in fixed O&M and capital costs.
For Option 3, variable production costs increase by the same amount as that under Option 2 ($0.21 per MWh), or
less than 1 percent of the baseline 2028 value. For all but 2 NERC regions, variable production costs increase by
no more than 2.7 percent, which occurs in RFC; in ERCOT and NPCC variable production costs decrease by 1.2
percent and 2.7 percent, respectively.
Consistent with the policy impacts observed for annual costs and revenue, the impact on pre-tax income is slightly
more material under Option 3 than under Option 2. At the national level, pre-tax income declines by $9.9 billion
or 7.7 percent. All regions experience a loss in pre-tax income, with the largest loss occurring in the NPCC
region, at 11.0 percent.
Impact on In-Scope Facilities as a Group
For the analysis of impact on in-scope facilities as a group, EPA used the same IPM V3.02 results for 2028 that
were used to analyze the impact on national and regional electricity markets described above; however, this
analysis considers the effect of the market impact analysis options on only the subset of electric generating
facilities that are estimated to be within the scope of this proposed regulation's compliance requirements. The
purpose of the market-level analysis is to assess the impact of the Proposed Rule on the entire electric power
sector, i.e., including facilities that would not be directly affected by this Rule. The purpose of the analysis for in-
scope facilities as a group is to assess the impact of Proposed Existing Facilities Rule on only those facilities that
are directly in the scope of this regulation. The analysis results for the group of in-scope facilities (Table 6-3)
overall show a greater degree of impact than that observed over all generating units (i.e., market-level analysis
discussed in the preceding section (Impact on National and Regional Electricity Markets)): at the national level,
more substantial impacts at the level of the directly affected in-scope units are offset by changes in capacity and
energy production in the non in-scope units. This difference in impact magnitude is most prominent under Option
3.
The metrics of interest are largely the same as those presented above in assessing the effect of the regulatory
options for the aggregate of electric generating facilities. However, in this assessment, the impact measures reflect
6-18 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
only the economic activities of the 533 in-scope facilities analyzed in IPM. In addition, a few measures differ: (1)
new capacity additions and prices are not relevant at the facility level, and (2) the number of in-scope facilities
that experience closure of all their steam electric capacity is presented.
The following six measures are reported in the analysis of in-scope facilities as a group. In all instances, the
measures are tabulated only for the 533 in-scope facilities that are analyzed in the Market Model Analysis:
1. Changes in available capacity: As defined in the preceding section (Impact on National and
Regional Electricity Markets).
2. Changes in generation: Long-term changes in generation may result from a reduction in available
capacity (see discussion above) or less frequent dispatch of a plant due to higher production cost
resulting from compliance response. At the same time, for some in-scope facilities, the Proposed
Existing Facilities Rule options may lead to an increase in generation if their compliance costs are
low relative to other affected facilities.
3. Changes in revenue: This measure includes both energy revenue and capacity revenue (see
definitions in the beginning of Section 6.2.2). A change in revenue could result from a change in
generation, a change in the price of electricity, or both. For some modeled facilities, a regulatory
option may lead to an increase in revenue, particularly for facilities that are more competitive in a
post-regulation world.
4. Changes in costs: As defined in the preceding section (Impact on National and Regional
Electricity Markets).
5. Changes in pre-tax income: As defined in the preceding section (Impact on National and
Regional Electricity Markets).
6. Changes in variable production costs per MWh: As defined in the preceding section (Impact on
National and Regional Electricity Markets).
Table 6-3 reports results for the Market Impact Analysis Options for in-scope facilities, as a group.
The impacts of the regulatory options on in-scope facilities differ from the total market impacts as these facilities
become less competitive compared to facilities that do not incur compliance costs as a result of the regulatory
options. As a result, capacity and generation impacts are greater in this set of facilities than for the entire
electricity market, as summarized above. However, in the same way as described above, the impacts of Option 1
are considerably smaller than for Options 2 and 3.
Table 6-3: Impact of Market Impact Analysis Options on In-Scope Facilities, as a Group, at the Year 2028
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Dif
%Diff
Option 2
Value
Dif
%Diff
Option 3
Value
Dif
%Diff
National Totals
Total Capacity (GW)
Early Retirements —
Number of Facilities
Full and Partial Retirements -
Capacity (GW)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable" O&M
P^e^Q£jy[
Capital Cost
Pre-Tax Income (SMillions)
Variable Production Cost ($/MWh)
479
39
33
2"775'6
"$1597029"
$86,130
$"45j'3"4"
$"5,123
$"347566
'$"'773'
$72,899
$18.49
478
45
34
27747'
$"1587868
$86,403
$"45'7582"
$5353
$"3"4,'9"6'5
'$"'763'
$72,465
$"18.47'
-1
6
1
-4
-$161
$273
""-$"l"5"2"
$30
'$"'406
-$"l6
-$434
"-$"6"."6'2
-0.2%
15.4%
3.2%
-61%
-67i%"
0.3%
-'61'%"
0.6%
i"'2%
-Tl'%"
-0.6%
-61%
453
54
50
'27667'
"$153'2i6
$90,244
'$'4474"26
$5,115
$"3"97996
$"707'
$62,972
$"18758
-26
15
17
-84
"'-'$"'5",'8"l"3
$4,114
"'-$"T16'8
-$8
$5",'4"9"6
'-$"66
-$9,927
$6'."6'9"
-5.4%
38.5%
51.3%
-3".'6"%
-3.7%
4.8%
-2".'9"%
-6.2%
i'5".'9"%
-8".5%
-13.6%
6.5%
452
58
50
'27662"
$"l"5"37'6'3"6
$90,224
""$4473"2'"l
$5,164
""$"4'6';'6'9"2"
'$"708"
$62,812
'$"'l"8"j7
-27
19
17
-88
•":$"5';993
$4,094
-$1,414
'-'$"'i"9
$"5^"5"9"2
-'$"64"
-$10,087
$"6'."6'8
-5.6%
48.7%
52.3%
-372%
-378%
4.8%
-3.1%
-6.4%
16"."2%'
-8"'."3"%
-13.8%
6^4%
March 28, 2011
6-19
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Table 6-3: Impact of Market Impact Analysis Options on In-Scope Facilities, as a Group, at the Year 2028
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Dif
% Diff
Option 2
Value
Dif
% Diff
Option 3
Value
Dif
% Diff
Electric Reliability Council of Texas (ERGOT)
Total Capacity (GW)
Early Retirements —
Number of Facilities
Full and Partial Retirements -
Capacity (GW)
Gmeratiori (TWii)
Revenue (SMillions)
Costs (SMillkms)
Fuel Cost
Variable 6&M
pj^Q^JyJ
Capital Cost
Pre-Tax Income (SMillions)
:^^jepr0(juc^0"11 "cost (j/M^y
36
6
8
158
$107126
$5"374
$3,162
$267
$1,935
$11
$7,751
$21.66
36
6
8
158
$10,"ll8
$5,399
$3,150
$269
$17976
$11
$4,719
$21.62
0
o
o
0
-$8
$25
-$12
$2
$35
$o
-$33
-$0.04
0.3%
0.0%
-1.2%
-6.1%
-671%
0.5%
-0.4%
0.7%
1.8%
-0.6%
-O."T%"
-0.2%
30
H
14
147
$97114
$57262
$2,791
$264
$2,196
$71
$37852
$20.73
-6
5
5
-11
"-$1,012
-$112
-$371
-$3
$261
$6
-$899
-$0.93
-17.4%
83.3%
68.2%
-679%
""-i"676%
-271%
-11.7%
-i7T%"
1375%
374%
""-1879%
-4.3%
30
H
14
147
$97678
$57230
$2,761
$261
$2,197
$"TT
$37848
$20.61
-6
5
5
-12
""-$l7647
-$144
-$401
-$6
$262
$6
-$"963"
-$1.05
-17.5%
83.3%
68.7%
-774%
:io3%
-277%
-12.7%
-272%
1375%
374%
:i'9";o%'
-4.9%
Florida Reliability Coordinating Council (FRCC)
Total Capacity (GW)
Early Retirements —
Number of Facilities
Full and Partial Retirements -
Capacity (GW)
Generation (xwh)
Revenue (SMillions)
Costs (SMillkms)
fugfCogt
:^ableO&M
FJ^Q&M
Capital Cost
Pre-Tax Income (SMillions)
T^^lgp^^^ "cost '($/MWh)'
27
2
4
93
$6,832
$3,714
$27247
$267
$17261
$6
$3,118
$27713
27
4
4
93
$67827
$3,729
$2,241
$269
$"1,219
$6
$3,098
$27."l2
0
2
0
6
-$5
$15
-$6
$2
$18
$6
-$20
"-$6761
0.0%
100.0%
-0.3%
-61%
-6.1%
0.4%
-673%
0.9%
F.5%'
NA
-0.6%
676%
27
3
3
89
$"6,665
$3,849
$"27l64"
$261
$17424"
$6
$2,816
$27715
0
1
0
-3
-$167
$135
-$82
-$6
$"223
$6
-$302
$6762
-0.7%
50.0%
-9.3%
-376%
-274%
3.6%
-377%
-2.1%
1876%
NA
-9.7%
671%
27
3
3
89
$6,658
$3,848
$27l62
$261
$1,425
$6
$2,811
$27713
0
1
0
-3
-$174
$134
-$85
-$6
$224
$6
-$307
$"6766
-0.9%
50.0%
-9.3%
-376%
-275%
3.6%
-378%
-2.2%
1877%
NA
-9.9%
676%
Midwest Reliability Organization (MRO)
Total Capacity (GW)
Early Retirements —
Number of Facilities
Full and Partial Retirements -
Capacity (GW)
Gmeratio"n (TWii)
Revenue (SMillions)
Costs (SMiilkms)
Fuel Cost
Arable O&M
PJ^Q^jyj
Capital Cost
Pre-Tax Income (SMillions)
:^^jepr0(^c^0"][1 c"0"^ "(j/Mwh)
29
4
3
188
"$767698
$5,190
$2,868
$387
$1,883
$52
$47967
$17.36
29
4
3
186
$16,024
$57170
$2,837
$386
$17965
$42
$47854
$17.31
0
0
o
-1
-$73
-$20
-$31
-$"T
$23
-$16
-$53
-$0.04
-1.0%
0.0%
10.5%
-6.8%
-677%
-6.4%
-1.1%
-674%
1.2%
""-1972%
-1.1%
-0.3%
28
8
4
178
$97604"
$"5,357
$2,797
$38"6
$"2,139
$42
$47247
$17.82
-2
4
\
-9
-$"493"
$167
-$71
-$7
$256
-$11
-$660
$0.46
-5.4%
100.0%
34.2%
-479%
-479%
372%
-2.5%
-19%"
1376%
""-2676%
""-l"3"75%"
2.6%
27
9
4
178
$97572"
$5,372
$2,787
$379
$2,164
$42
$47266
$17.82
-2
5
\
76
-$525
$182
-$81
-$8
$282
-$"l"l
-$"767
$0.47
-5.7%
125.0%
35.8%
-573%
-572%"
375%
-2.8%
-276%
1576%
:2"oi%
:i44%
2.7%
6-20
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Table 6-3: Impact of Market Impact Analysis Options on In-Scope Facilities, as a Group, at the Year 2028
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Dif
% Diff
Option 2
Value
Dif
% Diff
Option 3
Value
Dif
% Diff
Northeast Power Coordinating Council (NPCC)
Total Capacity (GW)
Early Retirements —
Number of Facilities
Full and Partial Retirements -
Capacity (GW)
G"eneration(TWh")
Revenue (SMiliions)
Costs"($Millio"n"s7i
Fuel Cost
Variable" O&M
PJ^Q^jyj
Capital Cost
Pre-Tax Income (SMiilions)
Variable Production Cost ($/MWh)
34
3
2
143
$11,749
$67651
$3,203
$281
$"3,121
'$46
$57"69"8"
$24.36
33
3
3
142
$"'i"i"7'663
$67666
$3,130
$28"3
$37148
$46'
$5,056
$24.08
-1
o
1
-1
'-'$"'87
-$45
-$73
$2"
$26
'$"6
-$42
-$0.29
-2.6%
0.0%
38.3%
-'679%
-677%'
-677%'
-2.3%
677%
678"%
-'672%
-678%
-1.2%
28
4
1
127
'$"'l"6'73"3"5
$"67482"
$2,564
$"2"63"
$3,612
$"43"
$3'78"53
$22.23
-5
I
4
"-16
'"-$"l",'4"l"4"
-$169
-$639
-$18
$"491
'-$"3
'"-'$"l",24"6'
-$2.13
-15.9%
33.3%
197.0%
7T7%
-12.6%
-2"'."5"%
-20.0%
-673%
15.7%
-6".'7"%
-24.4%
-8.8%
28
4
7
127
"$'"i'67'2"9"6
$"67488
$2,551
'$"261
$"3,632
'$"'43
$3"7'8"6'9"
$22.18
-5
I
4
-16
-$1,453
-$"'l63
-$652
-$19
$"511
-$3
-$1,290
-$2.19
-16.0%
33.3%
197.0%
77.3%
-12.4%
-275%
-20.4%
-678%
16.4%
-6"."7%
-25.3%
-9.0%
ReliabilityFirst Corporation (RFC)
Total Capacity (GW)
Early Retirements -
Number of Facilities
Full and Partial Retirements -
Capacity (GW)
Generation (TWh)
Revenue (SMillions)
Costs($Millions)
fueled
Variable O&M
F^Q&jyf
Capital Cost
Pre-Tax Income (SMillions)
Variable Production Cost ($/MWhj
139
3
1
907
$49,318
$27,370
$137747
$1,568
$Tij36
$319
$21,947
$1688
139
3
1
907
$497371
$27,528
$13,748
$1,580
$ll",886
$319
$21,843
$1696
0
o
0
6
$53
$157
$1
$12
$144
$6
-$104
$"6'."6T
0.1%
0.0%
-6.9%
676%
67%
0.6%
676%
0.8%
r'2%"
676%
-0.5%
671%
133
4
5
885
$48,667
$28,989
$137663
$1,570
$137456
$300
$19,018
$17721
-6
\
3
-22
-$77311
$1,619
-$84
$2
$77726
-$19
-$2,930
$6733
-4.3%
33.3%
239.4%
-2.4%
-2.7%
5.9%
-676%
0.1%
147%
-6.6%
-13.3%
'f.9%
132
4
5
884
"$487642
$29,015
"$13,662
$1,568
"$137483
$361
$19,027
$17723
-6
\
3
-23
-$1,276
$1,645
-$85
$1
$"l7747
-$18
-$2,921
$6734
-4.4%
33.3%
239.4%
-2.5%
-2.6%
6.0%
-676%
0.1%
149%
-5.7%
-13.3%
276%'
Southeast Electric Reliability Council (SERC)
Total Capacity (GW)
Early Retirements —
Number of Facilities
Full and Partial Retirements -
Capacity (GW)
Generation (TWh)
Revenue (SMiliions)
Costs($Mulio"n"s7>
Fuel Cost
Variable" O&M
PJ^Q^jyj
Capital Cost
Pre-Tax Income (^Millions)
Variable Production Cost ($/MWh)
152
8
8
966
$"53'7"8"5"i
$28,976
$15,662
$"'l7759"
$"11,268
'$"'34"'l
$24,881
$18.16
152
12
g
959
$53782'6
$297686
$15,645
$77776
$17336
$341
$"2"4,73"3'
$18.16
0
4
o
6
-$3"i
$"ll6
-$17
$i'"i
$"l22
'$"6
-$147
$0.00
-0. 1%
50.0%
2.4%
676%
-67%"
'674%
-0. 1%
'676%
i7%
'676%
-676%
0.0%
148
10
9
943
'$5"2'7'8"8"6
$"31,257
$15,690
'$"'l'77'8"4
$"13,475
$"3"6"8
$"'2"l"762"3"
$18.54
-4
2
o
"-17
-$9"7"l
$2"7'28"7
$28
$2"4"
$"27268
-$"3"3
'"-$"3"',25"8"
$0.38
-2.4%
25.0%
5.3%
7.8%
-"i"."8"%
7.9%
0.2%
l".'4%
26.2%
-'9"'."7%
-13.1%
2.1%
148
13
9
941
"'$52"7'8"3"3"
$"3l725"'l
$15,675
$"l",'7'8"6
$"l3748"9
$"3"6'8"
$21,582
$18.54
-4
5
\
-18
-$"i,6i'7
$2,281
$12
$21
$2,281
'-'$33
-$3,298
$0.39
-2.7%
62.5%
8.6%
7.9%
-1.9%
7.9%
0.1%
i72%
26.4%
-'9"'."7%
7'3"73%'
2.1%
March 28, 2011
6-21
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Table 6-3: Impact of Market Impact Analysis Options on In-Scope Facilities, as a Group, at the Year 2028
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Dif
% Diff
Option 2
Value
Dif
% Diff
Option 3
Value
Dif
% Diff
Southwest Power Pool (SPP)
Total Capacity (GW)
Early Retirements —
Number of Facilities
Full and Partial Retirements -
Capacity (GW)
Gmeratio"n (TWii)
Revenue (SMillions)
Costs (SMillkms)
Fuel Cost
^ableO&M
pj^Q^jyj
Capital Cost
Pre-f ax Income (SMillions)
Variable Production Cost ($/MWh)
24
10
4
122
$57765
$27939
$1,795
$216
$924
$3
$27826
$16.50
24
10
4
122
$57763
$27963
$1,787
$217
$955
$3
$"2",'8"66
$16.47
0
0
o
6
-$2
$24
-$8
$1
$31
$6
-$26
-$0.03
0.4%
0.0%
-2.4%
-6.1%
6'."6'%"
6.8%
-0.5%
6'."7%"
3.4%
a'6'%"
-0.9%
-0.2%
21
9
7
118
$57409
$"37105
$1,704
$216
$1,183
$3
$27363
$16.30
-3
-1
2
-4
-$356
$167
-$91
$6
$258
$6
-$523
-$0.20
-11.4%
-10.0%
54.0%
-374%
-672"%"
577%"
-5.1%
-6"2%"
2779%
a'6'%"
-18.5%
-1.2%
21
9
5
117
$5378
$37682
$1,677
$2"l5
$l7l86
$3
$27297
$16.13
-3
-1
2
-5
-$387
'$'"143"
-$117
-$"l
$261
$"6
-$536
-$0.37
-11.4%
-10.0%
53.3%
-377%
'-677%"
'479"%"
-6.5%
-0.4%
2873%
a'6'%"
-18.7%
-2.3%
Western Electricity Coordinating Council (WECC)
Total Capacity (GW)
Early Retirements - Number of
Facilities
Full and Partial Retirements -
Capacity (GW)
Generation (xwh)
Revenue (SMillions)
Costs (SMillkms)
Fuel Cost
Variable O&M
pj^Q^jyj
Capital Cost
Pre-Tax Income (SMillions)
Variable Production Cost ($/MWh)
39
3
2
180
$ll,291
$5,921
$3,050
$379
'$"27492"
$6
$5,369
$19766
39
3
2
180
$"Tl7283
$5,922
'$"3",044
$380
$27498
$6
$5,361
$18798"
0
0
0
6
-$8
$1
-$6
$0
$6
$6
-$9
"-$a'62
0.0%
0.0%
0.4%
6.0%
-6.1%
0.0%
-0.2%
0.1%
63%
NA
-0.2%
-61%
39
5
2
179
$11,262
$5,942
$37652"
$379
$27511
$6
$5,260
$19.14
0
2
0
-1
-$89
$20
$2
$0
$19
$6
-$109
$"67i4
-0.7%
66.7%
10.4%
'-a'7%"
-6"8%"
0.3%
61%
-0. 1%
a'7%"
NA
-2.0%
a'7%"
39
5
2
179
$"ll7l78
$5,939
$37645
$378
$27516
$6
$5,239
$"l97l5
0
2
0
-2
-$113
$17
-$5
-$"i
$24
$6
-$131
'$"67i5
-0.8%
66.7%
10.4%
'176'%"
-16%
0.3%
-a'2%"
-0.3%
i""6%"
NA
-2.4%
a'8%"
Findings for Regulatory Option 1 (IM Everywhere)
For Option 1, the degree of policy impact in terms of capacity and electricity generation on the group of in-scope
facilities is greater than on the electric power sector as a whole; however, the magnitude of this difference is small
(see Table 6-2 and Table 6-3). For instance, at the market level, there is essentially no change in either total
available capacity or electricity generation, while for the group of in-scope facilities at the national level total
available capacity and electricity generation fall by only 0.2 percent and 0.1 percent, respectively.
Looking over all in-scope facilities, under Option 1, the total capacity loss from early retirements (closures) is
approximately 1 GW at the national level, or 0.2 percent of total baseline capacity in the in-scope units. Out of 8
NERC regions, 3 - MRO, NPCC, and SERC - record a total capacity loss. The largest percentage loss (2.6
percent) and absolute loss (approximately 1 GW) occur in the NPCC region.
The 1 GW of capacity losses in in-scope facilities reflect a combination of closures and avoided closures in the
universe of in-scope facilities. Some closures (or avoided closures) are full facility closures (i.e., all generating
units at the facility close or avoid closure), while others are partial closures (i.e., at least one generating unit at the
facility is assessed as closing, or avoiding closure, in the post-compliance case). Overall, 39 generating units close
(approximately 9.8 GW) and 30 generating units avoid closure (approximately 8.8 GW) in the post-compliance
case, resulting in net closure of 9 generating units (approximately 1 GW). The 39 generating unit closures reflect
full closure of 20 units in 13 facilities (5.6 MW) and partial closure of 19 units in 16 facilities (4.2 GW).
Other assessed effects of Option 1 for the group of in-scope facilities are of less consequence. At the national
level, total generation in in-scope facilities declines by less than 4 TWh, which represents approximately 0.1
6-22
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
percent of baseline generation in these facilities. At the regional level, MRO and NPCC record the largest decline
in generation of approximately 1 TWh each or 0.8 percent and 0.9 percent of the baseline generation, respectively,
with all other regions experiencing slight decreases not exceeding 0.1 percent.
Over all in-scope facilities, total costs increase by less than 0.3 percent of the baseline value - again, a modest
amount. None of the cost components changes in a material way. Most regions record slight increases within the
in-scope facility group with the SPP region recording the largest increase, at 0.8 percent. Variable production
costs decline over all in-scope facilities by approximately 0.1 percent, with slight decreases recorded for most
NERC regions. MRO records the largest decrease of approximately 0.3 percent. These findings of very small
national and regional effects in these impact metrics confirm EPA's assessment, stated in the preceding
paragraph, that the assessed capacity closures among in-scope facilities are of little economic consequence.
Finally, the losses in pre-tax income are greater among in-scope facilities than among the aggregate of generating
facilities. Nationally, the group of in-scope facilities records a 0.6 percent reduction in pre-tax income, with the
large st impact occurring in the MRO region (reduction of approximately 1.1%).
Findings for Regulatory Option 2 (IM Everywhere and EM for Facilities with DIF>125MGD)
As is the case for Option 1, for Option 2, the results for the group of in-scope facilities also show a greater degree
of impact on capacity and electricity generation compared to the degree of impact observed at the market level.
While there is almost no change in either total generating capacity or electricity generation for the electric power
sector, for the group of in-scope facilities generating capacity and electricity generation fall by 5.4 percent and 3.0
percent, respectively (Table 6-2 and Table 6-3).
As expected, the findings for the group of in-scope facilities for Option 2 are of greater consequence than those
observed for Option 1 as some facilities are now required to install cooling towers. The total loss in capacity at in-
scope facilities at the national level is approximately 26 GW. The percentage loss in certain regions - the ERCOT
(17.4 percent), NPCC (16.0 percent), and SPP (11.4 percent) - would be substantial in relation to the baseline
capacity of the total of in-scope units in these regions.
This capacity reduction includes early retirement and avoided retirement of generating units with net capacity
losses of 17 GW; the residual capacity loss results from energy penalty. Under this option, 149 generating units
close (36 GW) and 86 generating units avoid closure (19 GW), leading to an estimated net closure of 63
generating units (17 GW). Out of the 149 closed units, 72 units (23 GW) are in 35 fully closed facilities and 77
units (13 GW) are in 46 partially closed facilities.
Findings for the change in total costs, variable production costs, and generation under this Option also
significantly exceed those under Option 1. As stated above, at the national level, generation falls by 3.0 percent,
with the largest decline of 11.1 percent occurring in NPCC. Further, there is a 4.8 percent increase in total costs at
the national level, with SERC recording the largest increase of 7.9 percent. For 2 of the 8 NERC regions -
ERCOT and NPCC - total costs decline by 2.1 percent and 2.5 percent, respectively. At the national level, the
increase in total costs occurs only in fixed O&M ($5.5 billion or approximately 16 percent); all other cost
components decrease, reflecting the lower generation from these units. For some NERC regions, fixed O&M is
not the only cost increasing; however, across all NERC regions fixed O&M increases by the largest amount.
At the national level, variable production costs increase by 0.5 percent, with MRO recording the highest increase
of 2.7 percent. For 3 out of 8 NERC regions - ERCOT, NPCC, and SPP - variable production costs decline by
4.3 percent, 8.8 percent, and 1.2 percent, respectively.
Findings for Regulatory Option 3 (I&E Mortality Everywhere)
As is the case for Options 1 and 2, for Option 3, the results for the group of in-scope facilities also show a greater
degree of impact on capacity and electricity generation compared to the degree of impact observed at the market
March 28, 2011 6-23
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
level. At the national market level, there is almost no change in either generating capacity or electricity
generation; however, for the group of in-scope facilities, generating capacity and electricity generation fall by 5.6
percent and 3.2 percent, respectively, at the national level (Table 6-2 and Table 6-3).
In-scope facility impacts in the steady state year for Option 3 are slightly higher than those observed for Option 2,
as additional facilities install cooling towers to meet compliance requirements. The total loss in capacity in 2028
is assessed at 27 GW compared to 26 GW under Option 2. The percentage loss in certain regions - ERCOT (17.5
percent), NPCC (16.0 percent), and SPP (11.4 percent) - would be substantial in relation to the baseline capacity
of the total of in-scope units in these regions.
As is the case under Option 2, this reduction includes early retirement and avoided retirement of generating units
with net capacity losses of approximately 17 GW; the residual capacity loss results from energy penalty. Under
this option, 162 generating units close (37 GW) and 88 generating units avoid closure (20 GW), leading to an
estimated net closure of 74 generating units (17 GW). Out of the 162 closed units, 79 units (23 GW) are in 39
fully closed facilities and 83 units (14 GW) are in 50 partially closed facilities.
For Option 3, the changes in total cost, variable production cost, and generation for in-scope facilities at the
national level are nearly the same as those estimated for Option 2-4.8 percent increase in total cost, 0.4 percent
increase in variable production cost, and 3.2 percent fall in generation. While for some NERC regions the changes
in these metrics are on average more prominent under Option 3 than under Option 2, the general pattern of
changes is very similar. The largest fall in generation occurs in NPCC (11.3 percent), while the largest increase in
total costs and variable production costs occurs in SERC (7.9 percent) and MRO (2.7 percent), respectively.
Impact on Individual In-Scope Facilities
Results for the group of in-scope facilities as a whole may mask shifts in economic performance among individual
facilities subject to the Proposed Existing Facilities Regulatory options. To assess potential facility-level effects,
EPA analyzed the distribution of facility-specific changes between the baseline and the post-compliance cases for
the following five metrics:
1. Capacity Utilization, defined as generation divided by capacity times 8,760 hours
2. Electricity Generation, as defined above
3. Revenue, as defined above
4. Variable Production Costs per MWh, defined as variable O&M cost plus fuel cost divided by net
generation
5. Pre-Tax Income, defined as total revenues minus the sum of fixed and variable O&M costs, fuel
costs, and capital costs.
Table 6-4 presents the estimated number of in-scope facilities with specific degrees of change in operations and
financial performance as a result of regulatory options. This table excludes in-scope facilities with estimated
significant status changes in 2028 that render these metrics of change not meaningful - i.e., under the analyzed
Option, a facility is assessed as either a full closure between the base case and the post-compliance case. On this
basis, 118, 159, and 165 facilities are excluded from assessment under Options 1, 2, and 3, respectively. As a
result, the measures presented in Table 6-3, such as change in revenue, are not meaningful for these facilities. For
example, for a facility that is projected to close in the post-compliance case, the change in revenue is 100 percent.
In addition, the change in variable production cost per MWh of generation could not be developed for 28, 21, and
18 facilities with zero generation in either baseline or post-compliance cases under Options 1, 2, and 3,
respectively. For these facilities, variable production cost per MWh cannot be calculated for one or other of the
two cases (because the divisor, MWh, is zero), and therefore the change in variable production cost per MWh
6-24 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
cannot be meaningfully determined. For change in variable production cost per MWh, these facilities are recorded
in the "N/A" column.
Table 6-4: Impact of Market Impact Analysis Options on Individual In-Scope Facilities at the Year 2028
(number of in-scope facilities with indicated effect)
Economic Measures
Reduction
>3%
1-3%
3%
N/Ab
Option 1: IM Everywhere
Change in Capacity Utilization0
Change in Generation
Change in Revenue
Change in Variable Production
Costs/MWh
Change in Pre-Tax Income
0
6
5
0
40
1
7
3
2
126
23
39
164
91
243
398
391
4
22
0
41
26
282
319
55
5
0
13
6
4
3
2
0
3
3
118
118
118
146
118
Option 2: IM Everywhere and EM for Facilities with DIF>125MGD
Change in Capacity Utilization0
Change in Generation
Change in Revenue
Change in Variable Production
Costs/MWh
Change in Pre-Tax Income
13
154
139
3
267
18
89
103
5
33
102
6
51
24
55
147
146
0
14
0
104
8
73
107
28
24
12
54
55
23
22
15
10
201
24
159
159
159
180
159
Option 3: I&E Mortality Everywhere
Change in Capacity Utilization0
Change in Generation
Change in Revenue
Change in Variable Production
Costs/MWh
Change in Pre-Tax Income
10
184
158
4
315
16
110
127
8
12
132
6
44
15
41
96
95
0
9
0
118
9
49
74
24
25
10
38
63
11
27
10
8
233
21
165
165
165
183
165
a. The change in capacity utilization is the difference between the capacity utilization percentages in the base case and post-compliance cases. For all
other measures, the change is expressed as the percentage change between the base case and post-compliance values.
b. Facilities with status changes in either base case or post-compliance scenario have been excluded from these calculations. In addition, the change in
variable production cost per MWh could not be developed for 28, 21, and 18 facilities with zero generation in either base case or Options 1, 2, or 3 post-
compliance scenarios, respectively.
Source: U.S. EPA analysis, 2010
For Option 1, which corresponds to EPA's proposed option, the analysis of changes in individual facilities
indicates that most facilities experience very slight effects - no change, or less than a 1 percent reduction or 1
percent increase - in all of the impact metrics except Change in Pre-Tax Income. Only 1 facility is estimated to
incur a reduction in capacity utilization exceeding 1 percent; 13 facilities incur a reduction in generation
exceeding 1 percent; and 8 facilities incur a reduction in revenue exceeding 1 percent. Only 9 facilities incur an
increase in variable production costs exceeding 1 percent. The estimated change in pre-tax income is more
consequential as 126 facilities are projected to incur reductions in pre-tax income of 1-3 percent and 40 facilities
are projected to incur reductions in pre-tax income exceeding 3 percent of the baseline value.
The findings for Option 2 are significantly more consequential compared to those estimated for Option 1. For 243
facilities, the reduction in generation is estimated to exceed 1 percent; for 242 facilities, the reduction in revenue
is estimated to exceed 1 percent; for 256 facilities, the increase in variable production costs is estimated to exceed
1 percent. Again, the change in pre-tax income is more substantial, with 33 facilities expected to incur reductions
in pre-tax income of 1-3 percent and 267 facilities, greater than 3 percent.
As in the preceding discussions, the findings for Option 3 are slightly more consequential than those estimated for
Option 2. For 294 facilities, the reduction in generation is estimated to exceed 1 percent; for 285 facilities, the
reduction in revenue is estimated to exceed 1 percent; for 296 facilities, the increase in variable production costs
is estimated to exceed 1 percent. The change in pre-tax income is more substantial, with 12 facilities expected to
incur reductions in pre-tax income of 1-3 percent and 315 facilities, greater than 3 percent.
March 28, 2011
6-25
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
6.4.2 Analysis Results for the Years 2015, 2020, and 2025 - To Capture the Effect of Installation
Downtime
This section presents market-level results for the Proposed Existing Facilities Rule options for model run years
2015, 2020, and 2025. As discussed above, run year 2015 captures the period when in-scope facilities install IM
technologies, while run years 2020 and 2025 capture the period when fossil fuel and nuclear facilities install
cooling towers, respectively, and may incur installation downtime. Of particular importance as a potential impact,
the additional unit downtime from installation of compliance technology would manifest as increased electricity
production costs resulting from the dispatch of higher production cost generating units during the periods when
units are taken offline to install compliance technologies. Because these effects are of most concern in terms of
potential impact on national and regional electricity markets, this section presents results only for the total set of
facilities analyzed in IPM and does not present results for the subset of only in-scope facilities.
Table 6-5 presents the following national market-level impacts for 2015, 2020, and 2025, respectively (for
regional impacts see Appendix 6.A):
1. Electricity price changes, including changes in energy prices and capacity prices
2. Generation changes
3. Revenue changes
4. Cost changes, including changes in fuel costs, variable O&M costs, fixed O&M costs, and capital
costs
5. Changes in pre-tax income
6. Changes in variable production costs per MWh.
For each measure, Table 6-5 presents the results for the base case and the existing facilities rule options for each
downtime year, i.e., 2015, 2020, and 2025, the absolute difference between the two cases, and the percentage
difference. The following discussion of the impact findings for the three regulatory options focuses on these
differences.
6-26 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Chapter 6: National Electricity Market Analysis
Table 6-5: Impact of Regulatory Options on National Electricity Market During Periods of Technology
Installation Downtime
Economic Measures
(all dollar values in $2009)
Baseline
Value
Option 1
Value
Diff
%
Change
Option 2
Value
Diff
%
Change
Option 3
Value
Diff
%
Change
2015 (2013-2017)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
P^e^Q£jy[
Capital C"o"st
Pre-Tax Income (SMillions)
Variable Production Cost
($/MWh)
4,320
$212,857
$144,212
$81,076
$12,034
$437697
$7,405
$68,646
$21.55
4,320
$212,883
$144,764
$81,080
""$12,080
$447140
$7,463"
$68,119
$21.57
0
$26
$552
$5
$46
$443
$59
-$527
$0.01
0.0%
0.0%
0.4%
0.0%
b""4%"
i""6"%"
a'8%"
-a'8%"
0.1%
4,320
$214712"
4
$"144725
1
$80,896
$12,056
$437683
$77616
$69,873
$21.52
0
$1,267
$39
-$180
$22
-$14
$211
$"l",228
-$0.04
0.0%
0.6%
0.0%
-0.2%
a'2%"
ao%
278%
i"8%
-0.2%
4,320
$214,201
$144;244
$80,895
$12,054
$"43,680
$7,614
""$"69'957
$21.52
0
$1,343
$33
-$181
$20
-$17
$209
$17311
-$0.04
0.0%
0.6%
0.0%
-0.2%
0.2%
0.6%
2."8%
L9%
-0.2%
2020 (2018-2022)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable'O&M
Fixed O&M
Capital Cost
Pre-Tax Income (SMillions)
Variable Production Cost
($/MWh)
4,530
$261,531
$160,340
$83,418
........ jl"3™349""
$467l60
$101,191
$21.36
4,530
$276756'"
7
'$"'l6774"5""
0
$82,295
———
$20,605
$103765""
7
$21.18
0
$8,976
$7,110
-$1,122
$312"'
$47728
""$3492"
$1,866
-$0.18
0.0%
3.4%
4.4%
-1.3%
2"3%""
10.2%
'"183%""
4,530
$270,709
$167,719
$82,295
...^._...__.
$51^6l6
"$2'6j36"'"
$102,990
$21.18
0
$9,179
$7,380
-$1,123
$324""
$47856'""
$37323"""
$1,799
-$0.18
0.0%
3.5%
4.6%
-1.3%
2"4%""
Tas%
1971%
1.8%
-0.8%
2025 (2023-2027)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
Fixed O&M
Capital Cost
Pre-Tax Income (SMillions)
Variable Production Cost
($/MWh)
4,746
$280,613
$174,856
$86,633
$137907""
$47,561
J26J55""
$1657757
$21.18
4,746
$2"823'6""
3
$184796
0
$86,812
'$147295'"
$53,500
••--
$97,463
$21.30
0
$1,750
$10,044
$179
$388"
$5,938
$3"753"8"'"
-$87294
$0.12
0.0%
0.6%
5.7%
0.2%
278%""
12.5%
4,746
$282,381
$185,148
$86,834
$147299""
$53,625
$367396""
$97,233
$21.31
0
$1,768
$10,291
$201
$392""
$6,064
$37635'""
-$8^523
$0.13
0.0%
0.6%
5.9%
0.2%
....... 278%""
12.7%
0.6%
Findings for Regulatory Option 1 (IM Everywhere)
Because in-scope facilities would be required to meet compliance requirements no later than 5 years following
rule promulgation, Option 1, the preferred Option, has downtime effects during only the five year period of 2013-
2017. Results for the year 2015 are indicative of annual effects during each of these years.
With few facilities having an increase in net downtime under Option 1, the estimated effects of downtime are
relatively minor. Variable production costs increase by less than 0.1 percent. While the effect on energy
production costs varies at the regional level (see Appendix 6. A), this effect is overall very small. Of the 8 NERC
regions, 2 - ERCOT and MRO - record a slight reduction in variable production costs of $0.03 per MWh and
March 28, 2011
6-27
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
$0.01 per MWh, respectively. For the remaining 5 NERC regions, the increase in variable production cost is no
more than $0.03 per MWh or 0.2 percent, occurring in RFC.
Another potential market level impact due to the incurrence of downtime is the possible increase in electricity
prices and, consequently, revenue. At the market level, the change in total revenue is nearly zero, indicating very
small overall effects on consumer prices. The largest increase in revenue of $35 million or 0.2 percent occurs in
ERCOT. The changes in electricity prices are not consequential either - no more than 0.1 percent - across all
NERC regions.
Findings for Regulatory Option 2 (IM Everywhere and EM for Facilities with DIF>125MGD)
Option 2 would be expected to have downtime effects during each of the three five-year periods, as IM-only
facilities comply during the first five years (2012-2017) following rule promulgation, fossil fuel facilities
installing cooling tower technology comply during the second five years (2018-2022), and nuclear facilities
installing cooling tower technology comply during the third five years (2023-2027).
2015
During the first five-year period (2012-2017), downtime effects under Option 2, although more significant than
those under Option 1, remain small. Variable production costs decline by a very minor amount, 0.2 percent, as the
market begins to adjust overall in anticipation of the larger effects on capacity availability as the result of cooling
tower installation in later years. The effect on energy production costs varies at the regional level (see Appendix
6.A). Of the 8 NERC regions, 5 record a slight reduction in variable production costs of up to $0.49 per MWh or
1.8 percent. For the remaining 3 NERC regions, variable production costs increase by no more than $0.12 per
MWh or 0.3 percent (FRCC).
Total market-level revenue increases by $1.2 billion, or 0.6 percent, indicating small effects on consumer prices.
The largest increase in revenue of $524 million or 2.7 percent occurs in NPCC. Of the 8 NERC regions, 2 -
FRCC and MRO - record a slight reduction in revenue of $14 million (0.1 percent) and 21 million (0.2 percent),
respectively. The largest increase in electricity prices of $0.86 per MWh or 1.3 percent occurs in NPCC with the
second largest increase of $0.29 per MWh or 0.6 percent occurring in SERC.
2020
During the second five-year period (2018-2022), downtime effects are more pronounced. At the market level,
variable production costs decline again, by 0.8 percent, but revenue increases by nearly $9.0 billion, or 3.4
percent. Thus, the impact on consumer prices is greater during this period than during the preceding five years.
Again, of the 8 NERC regions, 5 record a reduction in variable production costs of up to $2.41 per MWh or 9.3
percent (see Appendix 6.A). For the remaining 3 NERC regions, variable production costs increase by no more
than $0.22 per MWh or 1.2 percent (RFC).
The largest increases in revenue of $3.1 billion (5.2 percent) and in electricity prices of $2.33 per MWh (4.7
percent) occur in RFC. Of the 8 NERC regions, only 1 - WECC - records a slight reduction in revenue of $49
million (0.7 percent). All but two NERC regions - FRCC and WECC - record an electricity price decrease of
$1.32 per MWh (2.0 percent) and $0.53 per MWh (0.9 percent). Again, the reduction in variable production costs
and revenue reflect replacement of generation from older, less efficient and higher fuel cost capacity, with
generation from more energy efficient, lower production cost capacity.
2025
The greatest impact on variable production cost under this option occurs during the third five-year period (2023-
2027), when nuclear facilities incur downtime during technology installation. Net downtime for cooling tower
installation at nuclear facilities is estimated at 24 weeks compared to 0.3 - 4 weeks for installations at fossil fuel
facilities. During this period, variable production costs increase by $0.12 per MWh or approximately 0.6 percent.
6-28 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
Although variable production cost increases during this period (while declining during the preceding two five-
year periods), annual revenue increases by a smaller amount, $1.8 billion, or a 0.6 percent increase above
baseline. The smaller increase in revenue, and by inference in consumer prices, results from the ongoing market
adjustment with replacement of less efficient, higher fuel cost generation with more efficient, lower fuel cost
capacity.
Again, the impact of downtime varies across NERC regions (see Appendix 6. A). Out of 8 NERC regions, 2 -
ERCOT and NPCC - record a reduction in variable production costs of $0.60 per MWh (2.4 percent) and $0.69
per MWh (2.5 percent), respectively. The same two NERC regions and WECC also record a slight reduction in
electricity prices - no more than $0.54 per MWh (0.8 percent). The largest increases in variable production cost of
$0.46 per MWh (2.5 percent), revenue of $1.2 billion, and electricity prices of $0.77 per MWh (2.0 percent) occur
in the RFC region. ERCOT, FRCC, and WECC record a modest reduction in revenue - no more than $205
million (0.9 percent).
Findings for Regulatory Option 3 (I&E Mortality- Everywhere)
Like Option 2, Option 3 would be expected to have downtime effects during each of the three five-year periods.
2015
During the first five-year period (2012-2017), impacts are nearly identical to those of Option 2 at the national and
regional level. At the national level, variable production costs decline by 0.2 percent, and total revenue increases
by $1.2 billion, or 0.6 percent, indicating small effects on consumer prices.
At the regional level (see Appendix 6.A), the only NERC region with slightly different impacts is MRO. While
under Option 2, revenue declines by 0.2 percent, under Option 3 it increases by 0.5 percent. Further, under Option
3, the decline in variable production costs as well as the drop in electricity prices are slightly more significant.
2020
During the second five-year period (2018-2022), downtime effects are again very similar to, but slightly higher
than, those of Option 2. At the national level, variable production costs decline by 0.8 percent, while revenue
increases by $9.2 billion, or 3.4 percent. Again, the impact on consumer prices is greater during this period than
during the preceding five years.
At the regional level (see Appendix 6. A), the direction of the change in variable production costs, revenue, and
electricity prices under Option 3 is the same as that under Option 2 for all NERC regions; the difference in the
magnitude of change is not very pronounced either.
2025
As with Option 2, the greatest impact on variable production cost occurs during the third five-year period (2023-
2027). During this period, market-level variable production costs increase by $0.13 per MWh or approximately
0.6 percent. Although variable production cost increases during this period (while declining during the preceding
two five-year periods), annual revenue increases by a smaller amount, $1.8 billion, or a 0.6 percent increase above
baseline.
At the regional level (see Appendix 6. A), as is the case in the preceding two five-year periods, the direction of the
change in variable production costs, revenue, and electricity prices under Option 3 is the same as that under
Option 2 for all NERC regions; the difference in the magnitude of change is not very pronounced either.
6.5 Uncertainties and Limitations
EPA's analysis of the electric power market and the economic impacts of the final rule involves several
uncertainties:
March 28, 2011 6-29
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation Chapter 6: National Electricity Market Analysis
> Demand for electricity: IPM assumes that electricity demand at the national level would not change
between the base case and the analyzed post-compliance options (generation within the regions is allowed
to vary). IPM Version 3.02 embeds a baseline energy demand forecast that is derived from the
Department of Energy's Annual Energy Outlook 2008 (AEO 2008), with certain adjustments by EPA to
account for the effect of certain voluntary energy efficiency programs. As specified for this analysis, IPM
does not capture changes in demand that may result from electricity price increases associated with the
rule. 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 analyzed in support of the final rule. As described in Section 0 above, the price increases
associated with the proposed option rule in most NERC regions are small. EPA therefore concludes that
the assumption of inelastic demand-responses to changes in prices is reasonable.
> International imports: IPM also assumes that imports from Canada and Mexico would not change
between the base case and the analyzed policy options. Holding international imports fixed would
potentially overstate production costs and electricity prices, because imports are not subject to the rule
and may therefore become more competitive relative to domestic capacity, displacing some of the more
expensive domestic generating units. On the other hand, holding imports fixed may understate effects on
marginal domestic units, which may be displaced by increased imports. However, EPA concludes that
fixed imports do not materially affect the results of the analyses. In 2020, only three of the eight NERC
regions are projected to import electricity (MRO, NPCC, and WECC), and the level of imports compared
to domestic generation in each of these regions is very small (1.6 percent in MRO, 5.5 percent in NPCC,
and 0.1 percent in WECC).
> Repowering: For this analysis, EPA did not use 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 existing oil/gas or coal capacity to new combined-cycle capacity.
The repowering analysis also increases the electric generating capacity of the repowered unit. 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 coal facilities of equivalent capacity, the effect of repowering is likely to be a reduction in
cooling water requirements (even considering the increase in the plant's capacity). As a result, not
allowing the model to adjust the compliance response or cost is likely to overstate compliance costs and
potential economic impacts from the analyzed regulatory options.
> Downtime associated with installation 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. Downtime is estimated to range between two and 28 weeks,
depending on the technology and type of generating unit. Generator downtime is estimated to occur
during the year when a facility complies with the final rule. Since the years that are mapped to 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. 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 effect; this approach does not model potential more adverse
effects of above-average amounts of capacity being down in any one NERC region during any one year.
6-30 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Appendix 6A Market Model Analysis Results for the Years 2015, 2020,
and 2025 - To Capture the Effect of Installation Downtime by NERC
Region
This Appendix presents electricity market-level results for the Proposed Existing Facilities Rule options for model
run years 2015 (Table 6A-1), 2020 (Table 6A-2), and 2025 (Table 6A-3) at the national and regional level. As
discussed in Chapter 6, run year 2015 captures the period when in-scope facilities install IM technologies, while
run years 2020 and 2025 capture the period when fossil fuel and nuclear facilities install cooling towers,
respectively, and may incur installation downtime. Table 6A-1, Table 6A-2, and Table 6A-3 present the following
national and regional impacts for 2015, 2020, and 2025, respectively:
1. Electricity price changes, including changes in energy prices and capacity prices
2. Generation changes
3. Revenue changes
4. Cost changes, including changes in fuel costs, variable O&M costs, fixed O&M costs, and capital
costs
5. Changes in pre-tax income
6. Changes in variable production costs per MWh.
For each measure, Table 6A-1, Table 6A-2, and Table 6A-3 present the results for the base case and the existing
facilities rule options for each downtime year, i.e., 2015, 2020, and 2025, respectively, the absolute difference
between the two cases, and the percentage difference.
March 28, 2011
6A-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-1: Impact of Market Impact Analysis Options on National and Regional Markets at the Year 2015
Economic Measures
(all dollar values in
$2009)
Baseline
Value
MMA Option 1 -
Value
Diff
% Change
2015
2028
MMA Option 2 -
Value
Diff
% Change
2015
2028
MMA Option 3 -
Value
Diff
% Change
2015
2028
National Totals
Electricity Prices
($MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
NA
4326
$212^85
7
$144,21
2
$81,076
$12,034
$43,697
$7,405
$68,646
$21.55
NA
4,320
"$21278
83
"$144,7
64
"$8l7o8
0
"$12768
0
"$44,l"4
0
$7,463
$68,11
9
$21.57
NA
6
$26
$552
$5
$46
$443
$59
-$527
$0.01
NA
b""6"%"
0.0%
0.4%
0.0%
0.4%
1.0%
0.8%
-0.8%
0.1%
NA
6".o%
0.1%
0.3%
0.0%
0.2%
0.9%
0.2%
-0.3%
0.0%
NA
4,320
$214712"
4
$"144725
1
$80,896
$12,056
$43,683
$7,616
$69,873
$21.52
NA
o
$1,267
$39
-$180
$22
-$14
$211
$1,228
-$0.04
NA
b""6"%"
0.6%
0.0%
-0.2%
0.2%
0.0%
2.8%
1.8%
-0.2%
NA
b"b%"
0.1%
5.4%
0.8%
2.2%
12.1%
9.3%
-7.6%
1.0%
NA
'47326
$214,26
1
$144"724
4
$80,895
$12,054
$43,680
$7,614
$69,957
$21.52
NA
6
$1,343
$33
-$181
$20
-$17
$209
$1,311
-$0.04
NA
b"b%"
0.6%
0.0%
-0.2%
0.2%
0.0%
2.8%
1.9%
-0.2%
NA
""ao%"
0.2%
5.5%
0.8%
2.2%
12.4%
9.7%
-7.7%
1.0%
Electric Reliability Council of Texas (ERGOT)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
FugJQost
Variabie O&M
P^ej-Q£j^
CaplaiCost
Pre-Tcix Income
(SMillions)
Variable Production
Cost ($/MWh)
$58.43
338
$20,299
$13,216
""$87298
'""$'"i7665
""$27625
$1,228
$7,083
$27.71
$58.39
338
"$2"b7'33
4
'"$"'i'3'"725'
8
"$87284
'"$'"i'7669
"$27655
$T7249
$7,076
$27.68
-$0.04
6
$35
$42
-$14
$4
$30
$21
-$7
-$0.03
-0.1%
a'6%"
0.2%
0.3%
""-a'2%"
'674%
i'7i%
i""7%"
-0.1%
-0.1%
0.0%
6.6%
0.0%
0.2%
""-'6"."2%
0.2%
6".9%
6.4%
-0.4%
-0. 1%
$58.66
338
$20,523
$13,242
$"'8",'l'"l4"
$17081
$27644'
$"17463
$7,281
$27.22
$0.23
6
$224
$26
-$184
'$"l6
$19
$175
$198
-$0.49
0.4%
676%"
1.1%
0.2%
-272%
i"75%
677%"
1473%
2.8%
-1.8%
0.2%
676%"
0.1%
4.9%
"-2"7i'%"
574"%"
"i2'76%"
1476%
-9.5%
-1.1%
$58.63
338
$20,526
$13,243
""$87113
'""$'"i768i
""'$"2"7645'
$1,463
$7,283
$27.22
$0.20
6
$227
$27
-$184
'$"l6
$20
$175
$201
-$0.49
0.3%
676%"
1.1%
0.2%
"-272%
i"75%
678"%"
1473%
2.8%
-1.8%
0.2%
""676%"
0.1%
4.9%
"-273%
""'5"76%
i"2'7i%
1473%
-9.5%
-1.2%
Florida Reliability Coordinating Council (FRCC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
FuelCost
VariabifiO&M
FixedO&M
CaplaiCost
Pre-Tcix Income
(SMillions)
Variable Production
Cost ($/MWh)
$66.75
23"6
$16,115
$11,625
$8,131
$868
$1,893
$733"
$4,490
$38.16
$66.83
236
"$16'J6
7
$"ll,66
3
$8,130
$873
$1,911
$"749
$4,445
$38.17
$0.08
6
-$8
$37
-$'l
$5
$18
$16
-$45
$0.02
0.1%
a'6'%"
0.0%
0.3%
0.0%
a'6%"
0.9%
2'7i'%"
-1.0%
0.0%
0.0%
6".'6%
0.0%
0.2%
0.4%
-0.4%
0.6%
-6.4%
-0.4%
0.3%
$66.93
236
$16,101
$11,646
$8,154
$873
$1,897
$721
$4,456
$38.28
$0.18
6
-$14
$21
$24
$5
'$"4"
-$12
-$34
$0.12
0.3%
a'6%"
-0.1%
0.2%
0.3%
a'6%"
0.2%
-i"6%"
-0.8%
0.3%
0.1%
67i'%"
0.4%
2.2%
1.6%
'"-i'7i'%"
9.1%
a'3%"
-4.7%
1.2%
$66.93
236
$16,102
$11,646
$8,155
$"8"73"
$1,896
$721
$4,456
$38.28
$0.18
6
-$14
$21
$24
$5
$3
-$12
-$34
$0.12
0.3%
a'6%"
-0.1%
0.2%
0.3%
a'6%"
0.2%
"-i76%"
-0.8%
0.3%
0.1%
""a'6%"
0.4%
2.1%
1.7%
"-i""5%"
9.1%
""67i'%"
-4.8%
1.3%
6A-2
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-1: Impact of Market Impact Analysis Options on National and Regional Markets at the Year 2015
Economic Measures
(all dollar values in
$2009)
Baseline
Value
MMA Option 1 -
Value
Diff
% Change
2015
2028
MMA Option 2 -
Value
Diff
% Change
2015
2028
MMA Option 3 -
Value
Diff
% Change
2015
2028
Midwest Reliability Organization (MRO)
Electricity Prices
($MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMilrionsj
pugJQost
VariableO&M
f^edO&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($MWh)
$37.68
256
$97554
$6,751
$37496
$618
$2,563
$73
$2,803
$16.08
$37.70
255
$97544
"$6,783
$37487
$619
"$2,609
$68
$2,761
$16.08
$0.02
o
-$9
$33
-$10
$1
$46
-$5
-$42
-$0.01
0.1%
""-672%
-67i%
675%
-673%
0.2%
i""8%"
-7.1%
-1.5%
0.0%
0.0%
676%
6.6%
6".9%
-6.4%
-0. 1%
l".9%
2.9%
-1.0%
-0.4%
$37.49
255
$"97533
$"6,717
$"37478
$615
$2,561
$63
$2,816
$16.07
-$0.19
-1
-$21
-$33
-$18
-$4
-$2
-$10
$13
-$0.02
-0.5%
-674%
-672%
-675%
-675%
-0.6%
-67i'%"
-13.3%
0.5%
-0.1%
0.1%
"-677%
-674%
677%
673%
3.6%
1679%
16.9%
-8.4%
1.5%
$37.40
255
$9,606
""$67720
$3,475
$614
""$27568
$64
$2,886
$16.04
-$0.27
-1
$52
-$30
-$22
-$4
$5
-$9
$83
-$0.04
-0.7%
-0.4%
675%
"-675%
-676%
-0.7%
672%
13.0%
3.0%
-0.3%
0.1%
"-675%
-674%
""774%
674%
3.9%
1274%"
18.1%
-9.1%
1.5%
Northeast Power Coordinating Council (NPCC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
FueiCost
VariableO&M
p^g^Q^jyj
Caplal Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$64.91
296
$19,195
$13,554
$"87364"
$947
$"3",69"6
$553
$5,641
$31.48
$64.90
296
$19,18
8
"$13758
3
"$87356
$953
"$37720
$554
$5,605
$31.48
-$0.01
6
-$7
$29
-$8
$5
$30
$1
-$36
$0.00
0.0%
6.6%
0.0%
0.2%
-6.1%
0.6%
678%
6.2%
-0.6%
0.0%
0.1%
6.6%
0.2%
0.2%
-675%
0.6%
678%
1.2%
0.3%
-0.4%
$65.78
296
$19,719
$13,470
$"87347'
$949
$"37615
$559
$6,248
$31.42
$0.86
6
$524
-$84
-$16
$2
-$75
$5
$608
-$0.06
1.3%
676%
2.7%
-0.6%
-672"%"
0.2%
-276%"
i76%
10.8%
-0.2%
-1.6%
-672%
-0.7%
5.9%
"-372%
0.3%
14.2%
2275%
10.4%
-2.6%
$65.78
296
$19,723
$13,468
""$87347
$949
""$37613
$559
$6,255
$31.42
$0.87
6
$528
-$86
-$17
$2"
-$77
$5
$614
-$0.06
1.3%
676%
2.8%
-0.6%
"-672"%"
0.2%
"-2"7i'%"
i76%
10.9%
-0.2%
-1.7%
-672%
-0.8%
6.1%
'-33%
0.2%
14.8%
2376%
11.0%
-2.7%
ReliabilityFirst Corporation (RFC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
Fixed O&M
Caplal Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$39.22
1,042
$41,259
$32,794
$16,181
$27196
$12,782
$l7634
$8,465
$17.64
$39.22
1,042
"$4l724
8
"$32,96
4
"$16,26
2
$27264
"$12,93
1
$l7626
$8,284
$17.66
$0.00
6
-$11
$170
$20
$8
$149
-$7
-$181
$0.03
0.0%
676%
0.0%
0.5%
0.1%
6.4%
1.2%
-6.5%
-2.1%
0.2%
0.1%
676%
0.2%
0.4%
0.1%
6.5%
1.1%
-6.5%
-0.1%
0.2%
$39.24
1,639
$41,223
$32,678
$16,114
$27185
$12,740
$17638'
$8,545
$17.61
$0.03
-3
-$36
-$116
-$67
-$12
-$42
$5
$80
-$0.02
0.1%
-673"%"
-0.1%
-0.4%
-0.4%
-675%
-0.3%
673%
0.9%
-0.1%
0.3%
"-6'7i'%"
0.5%
7.7%
2.6%
274%
13.2%
1379%
10.3%
2.7%
$39.25
17639"
$41,230
$32,682
$16,118
$2,185
$12,738
$1,641
$8,548
$17.62
$0.03
-3
-$29
-$111
-$63
-$11
-$44
$7
$82
-$0.02
0.1%
"-673"%"
-0.1%
-0.3%
-0.4%
-675%
-0.3%
674%
1.0%
-0. 1%
0.5%
"-6'7i'%"
0.6%
7.8%
2.7%
273%
13.4%
141%
10.2%
2.7%
March 28, 2011
6A-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-1: Impact of Market Impact Analysis Options on National and Regional Markets at the Year 2015
Economic Measures
(all dollar values in
$2009)
Baseline
Value
MMA Option 1 -
Value
Diff
% Change
2015
2028
MMA Option 2 -
Value
Diff
% Change
2015
2028
MMA Option 3 -
Value
Diff
% Change
2015
2028
Southeast Electric Reliability Council (SERC)
Electricity Prices
($MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
VariabieO&M
Fixed O&M
CapEaiCost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$45.62
U21
$53,084
$34,454
$18,813
$'£'636
$12,566
$438
$18,630
$19.14
$45.66
1,121
"$53769
9
"$34,63
5
'"$78783
1
$27653
"$T2,'6"9
9
$452
$18,46
4
$19.16
$0.04
6
$15
$181
$18
$17
$133
$14
-$166
$0.02
0.1%
""6".'6%"
0.0%
0.5%
0.1%
""a'6%
1.1%
3.1%
-0.9%
0.1%
0.0%
6".'6%
0.0%
0.3%
-0.1%
6.4%
1.0%
6.2%
-0.4%
-0.1%
$45.91
U25
$53,485
$34,645
$18,908
$"27647
$12,642
$448
$18,840
$19.16
$0.29
4
$401
$191
$95
$l6
$76
$9
$210
$0.02
0.6%
61'%"
0.8%
0.6%
0.5%
0.4%
0.6%
27%
1.1%
0.1%
-0.1%
a'2%"
0.4%
8.0%
1.9%
42%
16.6%
1471%
-8.9%
2.0%
$45.91
i'Tis
$53,478
$34,635
$18,909
$2,646
$12,637
$443
$18,842
$19.17
$0.29
4"
$393
$181
$96
$9
$71
$5
$212
$0.03
0.6%
03%"
0.7%
0.5%
0.5%
0.4%
0.6%
l7l%
1.1%
0.1%
0.0%
""a'2%"
0.5%
8.2%
1.9%
42%
16.8%
1576%
-8.9%
2.0%
Southwest Power Pool (SPP)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillionsj
FuelCost
VariabifiO&M
pj£ed"o&M
CaplalCost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($MWh)
$46.14
249
$11,775
$"7',946'
$4,239
$"724"
$7,698
$"U84
$3,829
$19.96
$46.17
249'
"'$7i78'6
0
"'$"7',99"5'
$4,237
$"729'
$7728
"'$7,302
$3,805
$19.98
$0.03
6
$25
$50
-$"2"
$5
$36
$"l"8"
-$25
$0.01
0.1%
a'6'%"
0.2%
a'6%"
-0.1%
a'7%"
i""8%"
i""4%"
-0.6%
0.1%
0.0%
a'6%"
0.0%
63%"
-0.1%
a'3%"
i""5%"
67%
-0.5%
0.0%
$46.36
249"
$11,907
""$7^956
$4,224
'$"726
$1,702
'""$"'i"3b4
$3,951
$19.91
$0.22
6
$132
'$"'16
-$15
$2
$4"
'$"2"6
$122
-$0.05
0.5%
a'6%"
1.1%
67%
-0.4%
a'2%"
0.2%
L6%"
3.2%
-0.3%
-0.1%
"'-a'3%"
-0.1%
48"%"
0.3%
L9%"
1673%
48"%"
-8.6%
0.9%
$46.34
249
$11,907
""$77954"
$4,224
$"72"6
$1,761
'""$"'1364
$3,953
$19.91
$0.19
6
$132
$9"
-$15
$2"
$2"
$20
$123
-$0.05
0.4%
a'6%"
1.1%
67%
-0.4%
a'2%"
67%
L6%"
3.2%
-0.3%
0.0%
"-a'3%"
-0.1%
'""479"%"
0.3%
L9%"
1674%
'""479"%"
-8.7%
0.9%
Western Electricity Coordinating Council (WECC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
VariabifiO&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$52.60
783
$41,577
$23,872
$13,553
$"27979
$57880
$1,461
$17,705
$21.12
$52.60
783
4
"'$"23788
3
4
"'$"27980
$"57886
$1,463
$17,68
0
$21.12
$0.00
0
-$13
$11
$1
$"'l
'$"?'
$2"
-$24
$0.00
0.0%
0.0%
0.0%
0.0%
0.0%
a'6%"
67%
0.1%
-0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
a'6%"
67%
0.0%
-0.1%
0.0%
$52.65
783
$41,634
$23,898
$13,555
""$2'798"l
$5,882
$1,479
$17,736
$21.12
$0.05
0
$57
$25
$2
$2"
$2"
$18
$31
$0.00
0.1%
0.0%
0.1%
0.1%
0.0%
67%
b"b%"
1.3%
0.2%
0.0%
-0.3%
0.0%
-0.2%
0.2%
0.1%
a'2%"
a'3%"
0.2%
-0.8%
0.1%
$52.65
783
$41,629
$23,895
$13,554
""$27981
$5,881
$1,479
$17,734
$21.12
$0.05
0
$52
$23
$1
$2"
$1
$18
$29
$0.00
0.1%
0.0%
0.1%
0.1%
0.0%
67%
b"b%"
1.3%
0.2%
0.0%
-0.3%
0.0%
-0.2%
0.2%
0.1%
""a'2%"
""67%
0.4%
-0.9%
0.1%
6A-4
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-2: Impact of Market Impact Analysis Options on National and Regional Markets at the Year
2020
Economic Measures
(all dollar values in
$2009)
Baselin
e Value
MMA Option 1 -
Value
Diff
% Change
2020
2028
MMA Option 2 -
Value
Diff
% Change
2020
2028
MMA Option 3 -
Value
Diff
% Change
2020
2028
National Totals
Electricity Prices
($MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
NA
4,530
"$26l",5
31
"$160,3
40
"$83741
8
"$13,34"
9
"$4636
0
"$17741
3
$101,1
91
$21.36
NA
4,530
$26176
48
$16076
68
$83715'
5
$13,39
4
$46760
8
$17751
1
$100,9
81
$21.31
NA
0
$118
$328
-$263
$45
$449
$98
-$210
-$0.05
NA
0.0%
0.0%
0.2%
-0.3%
0.3%
1.0%
0.6%
-0.2%
-0.2%
NA
0.0%
0.1%
0.3%
0.0%
0.2%
0.9%
0.2%
-0.3%
0.0%
NA
4,530
"$27675
07
"$16774
50
"$82729'
5
"$13,66
1
'"$"56788
8
"$20760
5
$103,0
57
$21.18
NA
0
$8,976
$7,110
-$1,122
$312
$4,728
$3,192
$1,866
-$0.18
NA
0.0%
3.4%
4.4%
-1.3%
2.3%
10.2%
18.3%
1.8%
-0.8%
NA
0.0%
0.1%
5.4%
0.8%
2.2%
12.1%
9.3%
-7.6%
1.0%
NA
4,530
"$270,7
09
"$167,7
19
"$82,29"
5
"$13767
3
"$51,01
6
"$20,73
6
$102,9
90
$21.18
NA
0
$9,179
$7,380
-$1,123
$324
$4,856
$3,323
$1,799
-$0.18
NA
0.0%
3.5%
4.6%
-1.3%
2.4%
10.5%
19.1%
1.8%
-0.8%
NA
0.0%
0.2%
5.5%
0.8%
2.2%
1274
%
9.7%
-7.7%
1.0%
Electric Reliability Council of Texas (ERGOT)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
VariableO&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$59.05
358
"$"21793
8
"$14768
1
$8,097
$1,221
$2,937
$2,426
$7,256
$26.01
$59.00
358
$21798"
1
$14768"
6
$8,062
$1,224
$2,965
$2,435
$7,295
$25.93
-$0.05
6"
$44
$5
-$35
$3
$29
$8
$39
-$0.08
-0. 1%
6.6%
0.2%
0.0%
-0.4%
0.2%
1.0%
0.3%
0.5%
-0.3%
0.0%
6.6%
0.0%
0.2%
-0.2%
0.2%
0.9%
0.4%
-0.4%
-0.1%
$59.63
359
'"$"22796
2
"'$"'i"5"72"5
0
$7,169
$1,306
$3,264
$3,512
$7,652
$23.60
$0.58
1
$965
$569
-$929
$85
$328
$1,085
$396
-$2.41
1.0%
6.2%
4.4%
3.9%
-11.5%
6.9%
11.2%
44.7%
5.5%
-9.3%
0.2%
6.6%
0.1%
4.9%
-2.1%
5.4%
12.0%
14.0%
-9.5%
-1.1%
$59.65
359
"$"22791
4
"'$"'i"5",'2"5
4
$7,151
$1,307
$3,267
$3,530
$7,660
$23.56
$0.60
1
$977
$573
-$947
$86
$331
$1,103
$403
-$2.45
1.0%
6.2%
4.5%
3.9%
11.7%
7.0%
11.3%
45.5%
5.6%
-9.4%
0.2%
6.6%
0.1%
4.9%
-2.3%
5.6%
12%
143
-9.5%
-1.2%
Florida Reliability Coordinating Council (FRCC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fu-ej-£ost-
VariableO&M
FixedO&M
Capital CoTst
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$66.56
266
"'$"'l"8"73"4
9
'"$"'l"3",86
1
$"87412"
$1,027
"$"27692'
$"27336
$4,488
$35.49
$66.31
266
$"l"8",32"
2
$"13787'
1
$8,399
$1,030
$"2"7l08
$2,333
$4,451
$35.42
-$0.24
0
-$27
$10
-$13
$3
$16
$3"
-$37
-$0.06
-0.4%
0.1%
-0. 1%
0.1%
-6.2%
0.3%
""678%
6.1%
-0.8%
-0.2%
0.0%
0.0%
0.0%
0.2%
6.4%
-0.4%
67e'%
-6.4%
-0.4%
0.3%
$65.24
272
$"18798
8
'"$"'14751
2
$87574
$1,031
"$27294"
$27613
$4,476
$35.28
-$1.32
6
$639
$651
$162
$3
$2"63
$283
-$12
-$0.20
-2.0%
2.3%
3.5%
4.7%
1.9%
0.3%
9.7%
12.1%
-0.3%
-0.6%
0.1%
0.1%
0.4%
2.2%
1.6%
-1.1%
9.1%
6.3%
-4.7%
1.2%
$65.18
272
"$18797
1
1
$8,566
$1,030
"$"27296
$2,618
$4,460
$35.26
-$1.38
6
$622
$649
$154
$3
$"264"
$"288
-$27
-$0.23
-2.1%
2.3%
3.4%
4.7%
1.8%
0.3%
"978%
12.3%
-0.6%
-0.6%
0.1%
0.0%
0.4%
2.1%
1.7%
-1.5%
9.1%
6.1%
-4.8%
1.3%
March 28, 2011
6A-5
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-2: Impact of Market Impact Analysis Options on National and Regional Markets at the Year
2020
Economic Measures
(all dollar values in
$2009)
Baselin
e Value
MMA Option 1 -
Value
Diff
% Change
2020
2028
MMA Option 2 -
Value
Diff
% Change
2020
2028
MMA Option 3 -
Value
Diff
% Change
2020
2028
Midwest Reliability Organization (MRO)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMiilions)
Fuel Cost
VariableO&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$47.25
269
"'$T4726
4
"$7,595
"$37861
$680
$2,622
$432
$6,668
$16.86
Northeast Power Coordinatin
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Pue|-^os^
VariableO&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$70.55
303
"$2165
3
"$14,67
1
$87255
$1,063
$3,880
$1,472
$7,982
$30.74
$47.42
269
$14,28
8
$77611
$"37852"
$680
$2,667
$412
$6,677
$16.86
$0.17
-1
$24
$16
-$9
$0
$45
-$20
$8
$0.00
0.4%
-0.2%
0.2%
""672%
"-672%"
0.0%
1.7%
-4.6%
0.1%
0.0%
0.0%
0.0%
0.0%
679%
-0.4%
-0.1%
1.9%
2.9%
-1.0%
-0.4%
$48.64
268
"$14,59
6
"$8,"l26
"$3,874
$704
$2,977
$571
$6,471
$17.06
$1.39
-1
$332
$536
$13
$23
$355
$139
-$198
$0.20
2.9%
-0.4%
2.3%
776%
03%"
3.4%
13.5%
32.2%
-3.0%
1.2%
0.1%
-0.7%
-0.4%
""677%
""6'3%"
3.6%
10.9%
16.9%
-8.4%
1.5%
$48.68
268
"$14760
2
"$87"l70
"$"37874
$704
$3,016
$576
$6,432
$17.08
$1.43
-1
$338
$575
$13
$24
$394
$145
-$237
$0.22
3.0%
-0.5%
2.4%
"776%
""6'3%"
3.5%
15.0%
?3.5%
-3.5%
1.3%
0.1%
-0.5%
-0.4%
"774%
"674%
3.9%
114
^
-9.1%
1.5%
g Council (NPCC)
$70.38
303
$2165
7
$"14766
7
$8,136
$1,074
$3,917
$1,539
$7,990
$30.40
-$0.17
0
$4
-$4
-$119
$11
$37
$67
$8
-$0.34
-0.2%
-0. 1%
0.0%
0.0%
-l".4%
1.0%
1.0%
4.6%
0.1%
-1.1%
0.1%
0.0%
0.2%
0.2%
763%
0.6%
0.8%
1.2%
0.3%
-0.4%
$70.81
303
' 9
'"$"l57l2
8
$77943"
$1,101
$4,275
$1,809
$7,991
$29.86
$0.26
0
$466
$457
-$"313
$38
$394
$337
$9
-$0.88
0.4%
-0. 1%
2.1%
3.1%
-378%
3.5%
10.2%
22.9%
0.1%
-2.9%
-1.6%
-0.2%
-0.7%
5.9%
-372%
0.3%
14.2%
22.5%
10.4%
-2.6%
$70.77
303
1
"$15,15
8
$"7,956
$1,100
$4,294
$1,808
$7,954
$29.90
$0.22
0
$458
$487
-$299
$36
$414
$336
-$29
-$0.84
0.3%
-0.1%
2.0%
3.3%
-376%
3.4%
10.7%
22.8%
-0.4%
-2.7%
-1.7%
-0.2%
-0.8%
6.1%
-373%
0.2%
148
23.6
11.0
-2.7%
ReliabilityFirst Corporation (RFC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
VariableO&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$50.00
17676
"$60,04"
6
"$36,04
8
"$"l7'7l2
6
$2,440
"$13740
0
$3,082
$23,99
8
$18.19
$49.97
1,076
$667l9
1
$36724
5
$17,"l4
0
$2,452
$13755
2
$3,101
$23,94
6
$18.21
-$0.03
1
$145
$197
$14
$12
$153
$19
-$52
$0.01
-0. 1%
6.1%
0.2%
0.5%
0.1%
0.5%
1.1%
0.6%
-0.2%
0.1%
0.1%
'676'%
0.2%
0.4%
0.1%
0.5%
1.1%
-0.5%
-0. 1%
0.2%
$52.32
l7678
"$637l4
3
"$38758
2
"$17,32
9
$2,521
"$14788
9
$3,843
$24,56
1
$18.42
$2.33
2
$3,097
$2,534
$203
$81
$1,489
$761
$563
$0.22
4.7%
672%
5.2%
7.0%
1.2%
3.3%
11.1%
24.7%
2.3%
1.2%
0.3%
-61%
0.5%
7.7%
2.6%
2.4%
13.2%
13.9%
10.3%
2.7%
$52.37
1,079
"$63,23
3
"$38,66
6
"$173"4
2
$2,529
"$"l479"T
9
$3,877
$24,56
7
$18.42
$2.37
3
$3,187
$2,618
$215
$90
$1,519
$794
$569
$0.22
4.7%
63%
5.3%
7.3%
1.3%
3.7%
11.3%
25.8%
2.4%
1.2%
0.5%
-6'7i'%"
0.6%
7.8%
2.7%
2.3%
1374
147T
10.2
2.7%
6A-6
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-2: Impact of Market Impact Analysis Options on National and Regional Markets at the Year
2020
Economic Measures
(all dollar values in
$2009)
Baselin
e Value
MMA Option 1 -
Value
Diff
% Change
2020
2028
MMA Option 2 -
Value
Diff
% Change
2020
2028
MMA Option 3 -
Value
Diff
% Change
2020
2028
Southeast Electric Reliability Council (SERC)
Electricity Prices
($MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$50.83
1,170
"$63773
7
"$37745
4
"$"207l2
8
"$2,871
"$13766
0
$1,454
$26,28
3
$19.65
$50.77
1,170
$"63,72"
0
$37756
4
$20769
8
$27882
$13713
3
$1,451
$26,15
6
$19.64
-$0.06
0
-$17
$110
-$30
$11
$133
-$3
-$127
-$0.01
-
0.1%
0.0%
0.0%
0.3%
0.1%
"674%'
1.0%
0.2%
0.5%
0.1%
0.0%
0.0%
0.0%
0.3%
-0.1%
0.4%
1.0%
0.2%
-0.4%
-0. 1%
$52.49
1,162
$"66,61
5
"$39,37
9
'''$26717
2
"$2,9l"7
"$14757
7
$1,713
$27,23
7
$19.86
$1.66
-8
$2,879
$1,925
$44
$46
$1,576
$259
$954
$0.21
3.3%
-0.7%
4.5%
5.1%
0.2%
l".6%
12.1%
17.8%
3.6%
1.1%
-0.1%
0.2%
0.4%
8.0%
1.9%
'"4"."2%"
16.6%
14.1%
-8.9%
2.0%
$52.62
1,162
"$66772
6
"$39745
3
"$26,"i9
4
"$"27918
"$"l"4",5"9"
9
$1,741
$27,27
3
$19.90
$1.78
-9
$2,989
$1,999
$66
$47
$1,599
$286
$990
$0.24
3.5%
-0.7%
4.7%
5.3%
0.3%
"T.6%
12.3%
19.7%
3.8%
1.2%
0.0%
0.2%
0.5%
8.2%
1.9%
"4'.2%
1678
%
1576
%
-8.9%
2.0%
Southwest Power Pool (SPP)
Electricity Prices
($/MWh)
Generation (Tm)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variabie" O&M
Fixed O&M
Capital Cost
Pre-T
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-3: Impact of Market Impact Analysis Options on National and Regional Markets at the Year
2025
Economic Measures
(all dollar values in
$2009)
Baselin
e Value
MMA Option 1 -
Value
Diff
% Change
2025
2028
MMA Option 2 -
Value
Diff
% Change
2025
2028
MMA Option 3 -
Value
Diff
% Change
2025
2028
National Totals
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
Fixed O&M
Capital Cost
Pre-fax Income
(SMillions)
Variable Production
Cost ($/MWh)
NA
4,746
"'$"2"8"6','6
13
"$'l"74",'8"
56
'"$"8"67'63"
3
'"$"l"3",'9"6
7
"$47^56
1
"'$26775'
5
$105,7
57
$21.18
NA
4,746
$"2"8"67"5
98
$17572
48
$"8"6'75"i'
9
'$"'l3",'9"4
2
'$"4"8'7'66
2
'$"2"67'7"8
6
$105,3
50
$21.17
NA
0
-$15
$391
-$114
$35
$441
$30
-$406
-$0.02
NA
0.0%
0.0%
0.2%
-0.1%
0.2%
0.9%
0.1%
-0.4%
-0. 1%
NA
0.0%
0.1%
0.3%
0.0%
0.2%
0.9%
0.2%
-0.3%
0.0%
NA
4,746
"$"2"8"2'7'3"
63
$18479
00
$86781
2
"$"l4",2"9"
5
'"$"53756
0
"'$'3"b7'2"9
4
$97,46
3
$21.30
NA
0
$1,750
""$"'i"67'6'4
4
$179
$388
$5,938
$3,538
-$8,294
$0.12
NA
0.0%
0.6%
5.7%
0.2%
2.8%
12.5%
13.2%
-7.8%
0.6%
NA
0.0%
0.1%
5.4%
0.8%
2.2%
12.1%
9.3%
-7.6%
1.0%
NA
4,746
'"$"2"8"2"',3
81
48
"'$"8"6',"83"
4
"$14729
9
'"$"53,62
5
"'$"3"6,'3'"9'
0
$97,23
3
$21.31
NA
0
$1,768
1
$201
$392
$6,064
$3,635
-$8,523
$0.13
NA
0.0%
0.6%
5.9%
0.2%
2.8%
12.7%
13.6%
-8.1%
0.6%
NA
0.0%
0.2%
5.5%
0.8%
2.2%
1274
9.7%
-7.7%
1.0%
Electric Reliability Council of Texas (ERGOT)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
P^gj-Q^j:
Variable O&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$56.69
379
'"$"23776
7
"$16765
1
"$8","l9"3"
$77268
$3,103
$3,487
$7,656
$24.96
$56.53
379
'$"23776
6
$16768"
3
$8"72"6"i
$"1,270
$3,131
$3,481
$7,623
$24.96
-$0.16
6
-$1
$32
$8
$2
$28
-$6
-$33
$0.00
-0.3%
67i%
0.0%
0.2%
""67i%
67i%
0.9%
-0.2%
-0.4%
0.0%
0.0%
676%
0.0%
0.2%
"-672"%"
672%
0.9%
0.4%
-0.4%
-0.1%
$56.21
380
'"$"23756
2
"'$"'l679"4'
0
"$"77967'
$"17353"
$3,499
$4,181
$6,562
$24.36
-$0.47
1
-$205
$889
-$286'
$85
$396
$694
-$1,094
-$0.60
-0.8%
673%
-0.9%
5.5%
"'-3'7"5%"
677%
12.8%
19.9%
-14.3%
-2.4%
0.2%
676%
0.1%
4.9%
-2"7T%"
574%
12.0%
14.0%
-9.5%
-1.1%
$56.22
380
'"$"2'3",49
3
'"$'"i'6','9"4"
5
"$"77894"
$7,355
$3,502
$4,194
$6,548
$24.33
-$0.47
1
-$214
$894
-$"2"9"9"
$87
$399
$707
-$1,108
-$0.63
-0.8%
673%
-0.9%
5.6%
-3"77%
679%
12.9%
20.3%
14.5%
-2.5%
0.2%
676%
0.1%
4.9%
-2"73"%"
576%
127T
143
-9.5%
-1.2%
Florida Reliability Coordinating Council (FRCC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
fuefCost
Variable O&M
F^edQ&M
Capital Cost
Pre-Tcix Income
(SMillions)
Variable Production
Cost ($/MWh)
$60.15
298
"'$"19765
7
"'$"'i'5','4"7'
0
"'$"976'l7
$17614
'"$"27262"
'"$"3",'2"3"7
$4,187
$33.61
$60.18
298
$19767
9
$15747
0
$8"79"9"2"
$"T76l5
$27218
$37244
$4,210
$33.53
$0.04
6
$22
$0
-$25
$7
$17'
$7
$22
-$0.08
0.1%
""'676%
0.1%
0.0%
"-673"%"
67i%
""678%"
'""672%
0.5%
-0.2%
0.0%
'""676%
0.0%
0.2%
0.4%
-674%
"'"676%"
-0.4%
-0.4%
0.3%
$60.19
299
$19763
4
"'$"'i'5"779
4
"$9",'6'2"8"
$"l76l9
"$27424
"$"37324"
$3,840
$33.64
$0.05
6
-$23
$324
$"l"l
$5
'$"222"
'$"87
-$347
$0.03
0.1%
67T%
-0. 1%
2.1%
67"i%
675%
16.1%
277%"
-8.3%
0.1%
0.1%
""67T%
0.4%
2.2%
""i7'6%
-"i7"i%
""9'7"i%
""673"%
-4.7%
1.2%
$60.19
299
$19763
4
'"$'"i'5',"8"6
0
"$9'7b"2"7
$776l8
"$27425
"$"3"73"29"
$3,834
$33.63
$0.05
6
-$23
$330
$"T6
$4
$2"24"
$"92"
-$353
$0.03
0.1%
""67T%"
-0. 1%
2.1%
""67"i%"
674%
i"67'2"%
'"278%"
-8.4%
0.1%
0.1%
"'676%
0.4%
2.1%
"'i'7'7%"
775%
"9"7'i'%"
"67"i'%"
-4.8%
1.3%
6A-8
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-3: Impact of Market Impact Analysis Options on National and Regional Markets at the Year
2025
Economic Measures
(all dollar values in
$2009)
Baselin
e Value
MMA Option 1 -
Value
Diff
% Change
2025
2028
MMA Option 2 -
Value
Diff
% Change
2025
2028
MMA Option 3 -
Value
Diff
% Change
2025
2028
Midwest Reliability Organization (MRO)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMiliions)
fue]Ccist
VariableO&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$48.67
282
' 2
"'$"'8"','6"8"6
"HO'SI
$711
$2,726
$1,199
$7,016
$16.88
Northeast Power Coordinatin
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Pu-ej-£ost-
VariableO&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$68.16
313
' 9
"$l"5,29
5
$"77516
$1,075
$4,106
$2,598
$7,935
$27.41
$48.64
282
$15776
0
$"8"j71
Hose
$710
$2,778
$1,247
$6,929
$16.81
-$0.03
0
-$2
$85
-$14
:$L
$53
$47
-$87
-$0.06
-0.1%
0.1%
0.0%
""'i".'6%
-6".4%
-0. 1%
1.9%
4.0%
-1.2%
-0.4%
0.0%
0.0%
0.0%
6"."9"%"
-0.4%
-0.1%
1.9%
2.9%
-1.0%
-0.4%
$49.43
282
'"$"l"5",'9"7
6
"$97374
"HOS'S
$749
$3,033
$1,506
$6,602
$17.17
$0.76
-1
$274
$688
$"35
$38
$308
$307
-$413
$0.29
1.6%
-0.2%
1.7%
7"."9%"
6"."9"%"
5.4%
11.3%
25.6%
-5.9%
1.7%
0.1%
-0.7%
-0.4%
'"6"."7%"
0.3%
3.6%
10.9%
16.9%
-8.4%
1.5%
$49.43
282
"'$"'l"5'79"7
4
"$"97426'
"$4,088
$750
$3,072
$1,515
$6,548
$17.18
$0.76
-1
$272
$"74"6
$38'
$39
$347
$316
-$467
$0.31
1.6%
-0.2%
1.7%
""8."5"%"
0.9%
5.5%
12.7%
26.4%
-6.7%
1.8%
0.1%
-0.5%
-0.4%
"7.4%
0.4%
3.9%
12%
1871
-9.1%
1.5%
g Council (NPCC)
$68.15
313
$"2"3"',3'6
9
$15731
4
$7,448
$1,081
$4,140
$2,644
$7,995
$27.22
-$0.01
0
$79
$19
-$69
$7
$35
$46
$60
-$0.19
0.0%
0.0%
0.3%
0.1%
-679%
0.6%
0.8%
1.8%
0.8%
-0.7%
0.1%
0.0%
0.2%
0.2%
-6.5%
0.6%
0.8%
1.2%
0.3%
-0.4%
$67.62
313
"$23734
2
"$16721
5
$77274
$1,080
$4,689
$3,172
$7,126
$26.73
-$0.54
-1
$112
$920
-$242
$5
$583
$574
-$808
-$0.69
-0.8%
-0.3%
0.5%
6.0%
-3.2%
0.5%
14.2%
22.1%
-10.2%
-2.5%
-1.6%
-0.2%
-0.7%
5.9%
-3.2%
0.3%
14.2%
22.5%
10.4%
-2.6%
$67.62
313
"$23734
2
'"$"'l"6',24
6
$7,271
$1,079
$4,711
$3,185
$7,096
$26.71
-$0.54
-1
$113
$952
-$246
$5
$606
$587
-$839
-$0.70
-0.8%
-0.3%
0.5%
6.2%
-3.3%
0.4%
14.7%
22.6%
10.6%
-2.6%
-1.7%
-0.2%
-0.8%
6.1%
-3.3%
0.2%
148
'2376
11.0
-2.7%
ReliabilityFirst Corporation (RFC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
VariableO&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$49.82
i"Ji3
'"$"'62,64
5
"'$"39",'3"8"
0
"$18738
7
$2,609
"$13766
9
$4,714
$22,66
6
$18.87
$49.73
1,113
$62765
7
$"39"75'6
6
$"'i"8"',4"6
2
$2,621
$"l3"','8'"l
3
$4,669
$22,55
1
$18.90
-$0.08
6
$11
$126
$15
$12
$145
-$45
-$115
$0.03
-0.2%
'"676%
0.0%
0.3%
0.1%
0.5%
1.1%
-1.0%
-0.5%
0.2%
0.1%
6.6%
0.2%
0.4%
0.1%
0.5%
1.1%
-0.5%
-0. 1%
0.2%
$50.59
i'7l2
'"$"63727
8
"'$"42"75"3'
7
"$l"8"','8"6
6
$2,687
$"l5,49
3
$5,551
$20,74
1
$19.33
$0.77
-1
$1,233
$3,157
$419
$78
$1,824
$837
-$1,924
$0.46
1.5%
-6.1%
2.0%
8.0%
2.3%
3.0%
13.3%
17.8%
-8.5%
2.5%
0.3%
-6.1%
0.5%
7.7%
2.6%
2.4%
13.2%
13.9%
10.3%
2.7%
$50.59
1,112
"'$"'63,28
8
"$42760
3
"$18782"
8
$2,688
"$"l"5'75"2"
0
$5,566
$20,68
5
$19.35
$0.77
-1
$1,243
$3,224
$442
$79
$1,852
$852
-$1,981
$0.48
1.5%
-6.1%
2.0%
8.2%
2.4%
3.0%
13.5%
18.1%
-8.7%
2.5%
0.5%
-6.1%
0.6%
7.8%
2.7%
2.3%
1374"
141
10.2
2.7%
March 28, 2011
6A-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Regulation
Appendix 6A: IPM Assessment of Downtime
Table 6A-3: Impact of Market Impact Analysis Options on National and Regional Markets at the Year
2025
Economic Measures
(all dollar values in
$2009)
Baselin
e Value
MMA Option 1 -
Value
Diff
% Change
2025
2028
MMA Option 2 -
Value
Diff
% Change
2025
2028
MMA Option 3 -
Value
Diff
% Change
2025
2028
Southeast Electric Reliability Council (SERC)
Electricity Prices
($MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable 6"&M
Fixed O&M
Capital Cost
Pre-Tax Income
(SMillipns)
Variable Production
Cost ($/MWh)
$51.95
1,221
"$71,63
1
"$46772
3
"$21,"l2
6
"$3,091
"$13,27
4
$3,232
$30,90
7
$19.83
$51.91
1,221
$7136
5
$46,85
0
$2"U2
0
$37163"
$"13746
5
$3,222
$30,71
5
$19.84
-$0.04
0
-$65
$127
-$7
$12
$131
-$10
-$192
$0.01
-
0.1%
0.0%
0.1%
0.3%
0.0%
"6".4%
1.0%
0.3%
0.6%
0.0%
0.0%
0.0%
0.0%
0.3%
-0.1%
0.4%
1.0%
0.2%
-0.4%
-0.1%
$51.96
1,223
"$7"l,86
8
"$44,25
4
"$2137
3
"$3,243"
"$15754
3
$4,094
$27,61
4
$20.13
$0.01
1
$238
$3,531
$247
$152
$2,270
$862
-$3,293
$0.30
0.0%
0.1%
0.3%
8.7%
1.2%
49%
17.1%
26.7%
-10.7%
1.5%
-0.1%
0.2%
0.4%
8.0%
1.9%
"42%
16.6%
14.1%
-8.9%
2.0%
$51.96
1,222
"$71788
6
"$44731
7
"$2"T,38
7
"$3"344
"$15756
6
$4,120
$27,56
9
$20.15
$0.02
1
$255
$3,593
$261
$153
$2,292
$888
-$3,338
$0.32
0.0%
0.1%
0.4%
8.8%
1.2%
"49%
17.3%
27.5%
10.8%
1.6%
0.0%
0.2%
0.5%
8.2%
1.9%
"42%
16.8
%
15.0
%
-8.9%
2.0%
Southwest Power Pool (SPP)
Electricity Prices
($/MWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
Fixed O&M
Capital C"ost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$46.67
273
"'$"13790
4
"$9,291
$47698
$876
$1,869
"$2,454
$4,613
$18.23
$46.52
273
$"i379"6
7
$97316
$"4,691
$873
$1,897
$27456
$4,591
$18.21
-$0.15
6
$3
$25
-$7
$3
$28
$2
-$22
-$0.02
-0.3%
0.0%
""03%"
-02%
""03%"
1.5%
""o"T%"
-0.5%
-0.1%
0.0%
b".b%
0.0%
0.3%
-6.1%
0.3%
1.5%
01%
-0.5%
0.0%
$47.31
272
9
"$97777
$4,699
$"889
$2,183
"$27606
$4,372
$18.33
$0.64
b
$245
$486
$1
$19
$314
$152
-$241
$0.11
1.4%
1.8%
5""2%
ob%
272%
16.8%
6"2%
-5.2%
0.6%
-0.1%
-03%
-0.1%
"48%
03%
""i"9%
16.3%
"48%"
-8.6%
0.9%
$47.36
272
"$14715
3
"$97783
$4,096
$890
$2,185
"$27613"
$4,369
$18.32
$0.69
6
$249
$492
-$2
$20
$316
$159
-$244
$0.10
1.5%
1.8%
53%
0.0%
""23%
16.9%
""63%
-5.3%
0.5%
0.0%
-03%
-0.1%
"49%
"O3%"
"I9%
16.4
"49%
-8.7%
0.9%
Western Electricity Coordinating Council (WECC)
Electricity Prices
($/MWh)
Generation (TWh)
Revenue (SMillions)
Costs (SMillions)
Fuel Cost
Variable O&M
p-^-Q^
Capital C"ost
Pre-Tax Income
(SMillions)
Variable Production
Cost ($/MWh)
$52.97
866
"$56773
8
"$29,96
1
"$14,24
5
"$3,269
$6^614
"$5,834
$20,77
7
$20.21
$52.88
867
$5"6,67
4
$29793
8
$14723"
0
$37268
$6,618
$"57822"
$20,73
6
$20.19
-$0.09
0
-$63
-$23
-$15
-$"l
$5
-$12
-$40
-$0.02
-0.2%
0.0%
-0.1%
-0.1%
-0.1%
""b"b%
67i%
"-b"2%"
-0.2%
-0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
b".b%
6.1%
b".b%
-0.1%
0.0%
$52.81
866
"$50,61
3
"$36,66
8
"$14,24
0
"$37273
$6,636
"$57859
$20,60
4
$20.21
-$0.15
0
-$125
$48
-$5
$5
$22
$26
-$172
$0.00
-0.3%
0.0%
-0.2%
0.2%
0.0%
oT%"
03%
b.4%
-0.8%
0.0%
-0.3%
0.0%
-0.2%
0.2%
0.1%
"02%"
03%
"02%
-0.8%
0.1%
$52.81
866
"$50,61
1
"$36,62
7
"$14,24
3
"$"37274"
$"6,642
"$57868
$20,58
4
$20.22
-$0.16
0
-$127
$66
-$2
$5
$29
$34
-$193
$0.01
-0.3%
0.0%
-0.2%
0.2%
0.0%
""02%
""o"4%
""o6%
-0.9%
0.0%
-0.3%
0.0%
-0.2%
0.2%
0.1%
"02%
O4%
"04%
-0.9%
0.1%
6A-10
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
Assessing the Potential Impact of the Proposed Existing Facilities Rule
on Small Entities - Regulatory Flexibility Act (RFA) Analysis
In accordance with requirements of the Regulatory Flexibility Act (RFA), EPA assessed whether the Proposed
Rule regulatory options would have "a significant impact on a substantial number of small entities" (SISNOSE).
Small entities include small businesses, small organizations, and small governmental jurisdictions. This
assessment followed the same concepts and methods as applied for the previous 316(b) rule analyses, and
involved the following steps:
> Determining the domestic parent entities of in-scope facilities
> Determining which of those domestic parent entities are small entities, based on Small Business
Administration (SBA) entity size criteria
> Assessing the potential impact of the regulatory options on those small entities by comparing the
estimated entity-level annualized compliance cost to entity-level revenue. Small entities with compliance
costs estimated to exceed 1 percent or 3 percent of entity-level revenue were assessed as potentially
incurring significant impacts.
> Assessing whether those small entities incurring potentially significant impacts represent a substantial
number of small entities based on (1) the estimated absolute numbers of small entities incurring
potentially significant impacts according to the two cost impact criteria, and (2) the percentage of small
in-scope entities in the relevant entity categories that are estimated to incur these impacts.
EPA undertook the assessment of small entity impacts separately, and using somewhat different population-level
estimation methods, for Manufacturers and Electric Generators. The separate analyses reflect the different levels
of information available for Manufacturers and Electric Generators from the 316(b) facility surveys. In particular,
the 316(b) surveys provide facility-level information for essentially the universe of Electric Generators that rely
on cooling water in their operations. In contrast, the sample of Manufacturers facilities for which the survey
provides information is much smaller than the universe of manufacturing facilities potentially affected by the
proposed rule. As a result, a more precise analysis of potential entity-level impacts is possible for Electric
Generators than for Manufacturers, and the different analytic methods reflect this difference.
The following sections of this chapter describe the analytic approach and findings first for Manufacturers and then
for Electric Generators. The final section of the chapter reviews uncertainties and limitations in the analysis.
The discussion immediately below presents a consolidated summary of findings of small entity impact for
Manufacturers and Electric Generators.
For Electric Generators, the number of small entities potentially incurring a significant impact are small for all
three regulatory options: no more than 21 small parent entities are expected to incur costs exceeding 1 percent of
revenue and no more than 14 small entities are expected to incur costs exceeding 3 percent of revenue. For
Options 1 and 2, the percentages of small in-scope entities incurring an impact at either the 1 or 3 percent of
revenue threshold are no higher than 18 percent, and therefore remain below a key threshold of concern for
determining - 20 percent of small in-scope entities - as specified in EPA's Final Guidance for EPA Rulewriters:
Regulatory Flexibility Act,173 Accordingly, on the basis of both the small number of small entities potentially
incurring a significant impact and percentage that those small entities represent in the total of small in-scope
entities in the Electric Generators regulated industry segment, EPA concluded that Options 1 and 2 would not
173 U.S. EPA, Final Guidance for EPA Rulewriters: Regulatory Flexibility Act, November 2006, see pages 23-26.
March 28, 2011
7-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
have "a significant impact on a substantial number of small entities" (SISNOSE) for the Electric Generators
regulated industry segment. EPA did not reach a SISNOSE finding for Option 3 for the Electric Generators
segment, since, at the time of completing the RFA analysis, the Agency did not anticipate selecting Option 3 as
the proposed option for the 316(b) existing facilities rule.
The comparable findings for Manufacturers point to an even lower expected impact on small entities: across the
three regulatory options, EPA estimated that no only one small entity would incur costs exceeding either the 1 or
3 percent of revenue threshold. EPA estimated that this single small entity would represent no more than 5 percent
of the estimated number of small in-scope entities in the Manufacturers regulated industry segment. Accordingly,
on the basis of both the small number of small entities potentially incurring a significant impact and percentage of
small in-scope entities in the Manufacturers regulated industry segment, EPA concluded that none of the
regulatory options would have "a significant impact on a substantial number of small entities" (SISNOSE) for the
Manufacturers regulated industry segment.
Table 7-1, below, presents the overall findings from the small entity impact analysis on a combined regulated
industry segment basis. Given (1) the small absolute number of small parent entities estimated to incur a
potentially significant cost impact in both regulated industry segments and (2) the small percentage of total small
in-scope parent entities that these entities represent, EPA concluded that regulatory Options 1 and 2 would not
have "a significant impact on a substantial number of small entities" (no SISNOSE). As stated above, EPA did
not reach an overall finding for Option 3.
Table 7-1: Summary of Small Entity Impact Analysis Findings for 316(b) Existing Facilities Rule
Regulatory Options
Cost Impact Category
Regulatory Option
Option 1 : IM Everywhere
Option 2: IM Everywhere and EM for
Facilities with DIF>125 MOD
Option 3: I&E Mortality Everywhere
Cost >1 % of Revenue
Number of Small
Entities
5-7
5-7
10-22
% of Small
In-Scope Entities3
5% -13%
5% -13%
10% -3 9%
Cost >3% of Revenue
Number of Small
Entities'"
3C
3-7
7-15
% of Small
In-Scope Entities
3% - 5%
3% -13%
7% - 27%
a. Percentage of small in-scope entities incurring a cost-to-revenue impact involves range estimates in both the numerator (number of
affected entities) and
b. The number of entities with cost-to-revenue ratios exceeding 3 percent is a subset of the number of entities with such ratios exceeding 1
percent.
c. The estimated number of small entities exceeding the impact threshold is the same under both estimation approaches.
Source: EPA analyses
7.1 Analysis of Manufacturers
7.1.1 Analysis Approach and Data Inputs
The small entity determination for Manufacturers facilities was conducted in two steps:
1. For each analysis option, identify the domestic parent entity of the sample Manufacturers
facilities.
2. For each analysis option, determine the size of the entities owning the sample Manufacturers
facilities.
7-2
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 7: Regulatory Flexibility Act Analysis
Identification of Domestic Parent Entities
The RFA analysis is conducted at the highest level of domestic ownership, referred to as the "domestic parent
entity" or "domestic parent firm." EPA gathered information on the domestic parent firm in the Detailed Industry
Questionnaire. In instances where a response was not provided, EPA used several other data sources to determine
the domestic parent firm including the Screener Questionnaire, corporate websites, and Dun & Bradstreet data
(D&B, 2009). For the current analysis, EPA verified and updated the ownership determination made for the
previous Phase III rule analyses using the current Dun & Bradstreet database and updated domestic parent entity
data, where appropriate. This update included information on parent entity NAICS code, revenues, and
employment, when available. If either parent revenue or employment were not available in the D&B database,
EPA summed the revenue and/or employment information for all facilities owned by the firm as a lower-bound
estimate of these metrics. This has the potential to understate the size of the parent entity and thus overstate the
impact on small entities.
Size Determination of Domestic Parent Entities
EPA identified the size of each entity owning a potentially regulated Manufacturers facility using the most recent
Small Business Administration (SBA) size threshold guidelines at the time of the analysis.174 These thresholds
define the minimum firm-level employment or revenue size, by industry (by 6-digit NAICS code), below which a
business qualifies as a small business under SBA guidelines. To determine the entity size, EPA used data from the
2000 Detailed Industry Questionnaire, as well as the 7999 Industry Screener Questionnaire, and Dun &
Bradstreet data (D&B, 2009).
EPA started with the unique firm-level, 6-digit NAICS codes for firms that own existing facilities potentially
subject to the proposed regulation under the regulatory analysis options. Table 7-2, following page, presents the
unique firm-level 6-digit NAICS codes and corresponding SBA size standards used to determine the size of
entities that own Manufacturers facilities determined to be potentially subject to proposed regulation.
174 Since the time of the analysis, SBA issued a more recent set of small business size guidelines available online at
http://www.sba.gov/site s/default/files/Size_Standards_Table.pdf.
March 28, 2011 7-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
Table 7-2: Unique 6-Digit Firm-Level NAICS Codes and SBA Size Standards for Manufacturers
Firm NAICS
NAICS Description
SBA Size Threshold
111930
113110
""212210""
""212391""
""311221""
"iniir
""311312""
""311313""
""311942""
""313210""
""322121""
""322122""
""322130""
""322211""
""322222""
""322291""
""324110""
""324191""
""325120""
""325181""
""325188""
""325199""
""325211""
""325311""
""325320""
""325412""
""325510""
""325992"
""325998""
331111"
331112""
331210""
331221""
331222""
331312""
331315""
332312""
337910""
339999
""423310""
""423930""
""424510""
""424690"
""424710""
""447190""
""522220"
""523910""
""523930""
""524126""
""525990""
531110
""551112""
561110
Sugarcane Farming
Timber Tract Operations
Crude Petroleum and Natural Gas Extraction
Iron Ore Mining
...
Other Electric Power Generation
Wet Com MJlling
Sugarcane Mills
Cane Sugar Refining
Beet Sugar Manufacturing
Spice and Extract Manufacturing
Broadwoven Fabric Mills
Sawmills
Paper (except Newsprint) Miiis
Paperboard Mills
Corrugated and Solid Fiber Box Manufacturing
Coated and Laminated Paper Manufacturing
Sanitary Paper Product Manufacturing
P?troje.um Refineries
Petroleum Lubricating Oil and Grease Manufacturing
industrial Gas Manufacturing
Alkalis and Chlorine Manufacturing
All Other Basic inorganic Chemical Manufacturing
All Other Basic Organic Chemical Manufacturing
Plastics Material and Resin Manufacturing
Nitrogenous Fertilizer Manufacturirig
Pesticide and Other Agricultural Chemical Manufacturing
Pharmaceutical Preparation Manufacturing
Paint and Coating Manufacturing
Photographic Film, Paper, Plate and Chemical Manufacturing
All Other Miscellaneous Chemical Product and Preparation Manufacturing
Iron and Steel Mills
Electrometallurgical Ferroalloy Product Manufacturing
. Pipe. Ei Tube Manufacturirig from Purchased Steel
Rolled Steel Shape Manufacturing
Steel Wire Drawing
Primary Aluminum Production
Aluminum Sheet, Plate and Foil Manufacturing
Imbricated Structural Metal Manufacturing
Mattress Manufacturing
All Other Miscellaneous Manufacturing
Lumber, Plywood, Millwork, and Wood Panel Merchant Wholesalers
Recyclable Material Merchant Wholesalers
Grain and Field Bean Merchant Wholesalers
Other Chemical and Allied Products Merchant Wholesalers
Petroleum Bulk Stations and Terminals
Other Gasoline Stations
Sales Financing
^
Investment Advice
Direct Property and Casualty insurance Carriers
Other Financial Vehicles
Lessors of Residential Buildings and Dwellings
Offices of Other Holding Companies
Office Administrative Services
.$150,000 in Revenue
IZiOOO=000 jn Revenue [[[
500 Employees
500 Employees
50.0 Employees
1=000^000 MWh 'ofElecSc"^eration'"
750 Employees
500 Employees
750 Employees
Z10 Employees
500 Employees
I'OOO 'Employees [[[
500 Employees
750 Employees
Z10 Employees
750 Employees
500 Employees
500 Employees
500 Employees
1>5 00 Employ ees
500 Employees
I'OOO "Employees [[[
1 ,000 Employees
I>000 "Employees [[[
750 Employees
Milil^^
500 Employees
750 Employees
500 Employ ees
500 Employees
500 Employees
1 ,000 Employees
750 Employees
''ijMi:!!^^
1 ,000 Employees
I'OOO Employees [[[
1 ,000 Employees
750 Employees
500 Employees
500 Employees
500 Employees
100 Employees
100 Employees
10.0 Employees
100 Employees
lOO Employees
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 7: Regulatory Flexibility Act Analysis
As discussed in Chapter 4, EPA estimated the number of small entities owning facilities in the manufacturing
industries as a range, based on alternative assumptions about the possible ownership of potentially regulated
manufacturing facilities by small entities. EPA considered two cases based on the sample weights developed from
the facility survey. These cases provide a range of estimates for (1) the number of firms incurring compliance
costs and (2) the costs incurred by any firm owning a regulated facility. Chapter 4: Cost Impact Analysis -
Manufacturers provides a more detailed description of these cases.
Case 1: Lower bound estimate of number of firms owning facilities that face requirements under each
primary analysis option; upper bound estimate of total compliance costs that a firm may incur.
For this case, EPA assumed that any firm owning a regulated sample facility(ies) owns the known sample
facility(ies) and all of the sample weight associated with the sample facility(ies). This case minimizes the
count of affected firms, while tending to maximize the potential cost burden to any single firm.
Case 2: Upper bound estimate of number of firms owning facilities that face requirements under each
primary analysis option; lower bound estimate of total compliance costs that a firm may incur.
For this case, EPA assumed (1) that a firm owns only the regulated sample facility(ies) that it is known to
own from the sample analysis and (2) that this pattern of ownership, observed for sampled facilities and
their owning firms, extends over the facility population represented by the sample facilities. This case
minimizes the possibility of multi-facility ownership by a single firm and thus maximizes the count of
affected firms, but also minimizes the potential cost burden to any single firm.
Data in the rest of this section are presented by the industry sector of the firm. EPA determined a firm's sector
based on the sample facilities owned by the firm, and their industry sector(s). If all of the sampled facilities owned
by the firm are in the same industry sector, then that industry sector was assigned to the firm. If sample facilities
owned by the firm are in more than one industry sector, then the firm was assigned to the "multiple industries"
firm sector. One known facility in the Other Industries group was found to be owned by a firm that owns facilities
in the Primary Manufacturing Industries. This firm is included in the data reported for multiple industries. The
remaining entities that were found to own facilities in Other Industries are presented separately.
The number of Manufacturers entities that would be required to install recirculating systems varies by option
based on the DIP applicability threshold specified in the option, while all other facilities will be required to meet
impingement reduction requirements. Table 7-3 on the following page, presents the total number of firms with
facilities potentially subject to the Existing Facilities rule as well as the number and percentage of those firms
determined to be small. The data are shown for the three regulatory options under the two ownership cases
described above.
March 28, 2011 7-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
Table 7-3: Number of Firms by Sector and Size (assuming two different ownership cases)
Firm Sector
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Multiple Industries
Firms that own facilities in Primary
Manufacturing Industries3'1"
Additional firms that own known
facilities in Other Industries3
Case 1: Lowe
of firms o
requiremt
Total
Number of
Firms
42
26
17
16
5
8
3
117
9
r bound estim
wning facilitk
;nts under the
analysis
Number of
Small
Firms
9
4
4
3
2
1
0
23
4
ate of number
s that face
regulatory
Percentage
of Firms that
are Small
21.4%
15.4%
23.5%
18.8%
40.0%
12.5%
0.0%
19.7%
44.4%
Case 2: Uppe
of firms o
requiremi
Total
Number of
Firms
126
116
24
43
14
24
13
359
9
r bound estim
wning facilitk
;nts under the
analysis
Number of
Small
Firms
29
18
4
8
5
1
0
64
4
ate of number
s that face
regulatory
Percentage
of Firms that
are Small
23.0%
15.5%
16.7%
18.6%
35.7%
0.0%
0.0%
17.5%
44.4%
a. Excludes firms whose only sample facilities close in the baseline.
b. Individual numbers may not sum to reported totals due to independent rounding.
Source: U.S. EPA Analysis, 2010.
7.1.2 Key Findings for Regulatory Options
Number and Percentage of Small Manufacturers Entities Under the Regulatory Analysis Options
As part of its assessment of the small entity impact of the regulatory analysis options on Manufacturers, EPA
estimated the percentage of all small entities in the Primary Manufacturing Industries that would be expected to
be subject to the national requirements for the proposed options and additionally those small entities owning
facilities required to install recirculating systems. Because the analysis of facilities in Other Industries is not based
on a statistically valid sample, EPA could not estimate the number of entities in Other Industries that would be
subject to the regulatory requirements of the regulatory analysis options, or the percentage that are small entities.
From its prior analysis of the use of cooling water in industries other than the electric power industry, EPA judges
the overall effect and coverage of the 316(b) existing facilities rule in the Other Industries to be minor in relation
to the effect and coverage in the six Primary Manufacturing Industries.
EPA used the Statistics of U.S. Businesses (SUSB) published by the Small Business Administration, to estimate
the total number of manufacturing establishments owned by small firms in each of the six Primary Manufacturing
Industries. EPA included all of the NAICS industry groups with a sample facility in the six Primary
Manufacturing Industries. Based on the SUSB reporting framework, EPA considered all establishments owned by
a firm with 500 or fewer employees to be a small entity-owned establishment. This assumption will tend to
underestimate the number of small entity-owned establishments in these industry groups because the SBA small
entity size criterion is greater than 500 employees for some NAICS codes. Underestimating the total number of
small entities would result in an overestimate of the percentage of small entities in these industries that are
potentially subject to the proposed regulation under each option.
As shown in Table 7-4 EPA estimated that 27,834 entities within the six Primary Manufacturing Industries are
small. The proposed rule is estimated to affect 23-64 small entities in these industries, or 0.1-0.2 percent.
7-6
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
Table 7-4: Number and Percentage of Small Manufacturers Firms Subject to the Proposed Regulation, by
Industry Sector
Firm Sector
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Firms that own facilities in
Primary Manufacturing
Industries
Total Sector
Small Firms3
218
2,506
188
1,149
227
23,546
27,834
Case 1: Lower bound estimate of number of
firms owning facilities that face
requirements under the regulatory analysis
In-Scope Small
Firms
9
4
4
3
2
1
23
Percent of Small Firms
Subject to Regulation
4.1%
0.2%
2.1%
0.3%
0.9%
0.0%
0.1%
Case 2: Upper bound estimate of number of
firms owning facilities that face
requirements under the regulatory analysis
In-Scope Small
Firms
29
18
4
8
5
1
64
Percent of Small Firms
Subject to Regulation
13.2%
0.7%
2.2%
0.7%
2.0%
0.0%
0.2%
Individual values may not sum to reported totals due to rounding.
a Includes all firms with less than 500 employees from 2006 Statistics of U.S. Businesses (SUSB) of the U.S. Department of Commerce
(U.S. DOC). The Small Business Administration defines firms in nearly all profiled NAICS codes according to the firm's number of
employees; however, for some in-scope manufacturing NAICS codes this threshold is 500 employees while for others this threshold is 750,
1,100, or 1,500 employees. Because the SUSB employment size categories do not correspond to the SBA entity size classifications EPA
used the 500 employee threshold for all in-scope NAICS.
Sources: U.S. EPA Analysis, 2010; D&B, 2009; U.S. EPA, 2000; U.S. DOC, 2006; SBA, 2009
Sales Test for Small Entities
In addition to considering the fraction of small entities in each of the affected Manufacturers industries that would
be potentially subject to the proposed existing facilities rule, EPA also assessed the extent of economic/financial
impact on small entities by comparing estimated compliance costs to estimated entity revenue (also referred to as
the "sales test"). The analysis is based on the ratio of estimated annualized after-tax compliance costs to annual
revenue of the entity. For this analysis, EPA judges that entities for which annualized compliance costs exceed 1
percent or 3 percent of revenue, might experience a significant economic/financial impact as a result of the
regulatory requirements under the three regulatory analysis options.
EPA included the following compliance cost categories in this analysis: one-time technology costs of complying
with the regulatory requirements; one-time costs of installation downtime; annual operating and maintenance
costs; annual energy penalty for operating a recirculating system (where applicable) and permitting costs (initial
permit costs, annual monitoring costs, and permit re-issuance costs). A detailed summary of how these costs were
developed is presented in Chapter 3: Development of Costs for Regulatory Options and Chapter 4: Cost Impact
Analysis -Manufacturers. EPA collected revenue data for the small entities in EPA's Detailed Industry
Questionnaire.
As reported in Table 7-5, EPA's findings for the numbers of small entities incurring costs exceeding 1 or 3
percent of revenue were the same under both of the Weighting Cases, and, as well, are the same across all three
regulatory options. Specifically, EPA estimated that one small entity within the Primary Manufacturing
Industries, in the Petroleum Refining Sector, would incur costs exceeding 1 and 3 percent of revenue. EPA
estimated that no entities owning facilities in the Other Industries would incur costs exceeding 1 or 3 percent of
revenue.
March 28, 2011
7-7
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
Table 7-5: Estimated Cost-To-Revenue Impact on Small Manufacturers Entities, by Industry
Firm Sector
Case 1: Lo
owning fac
Total In-
Scope
Firms
wer bound
ilities that 1
regula
Small In-
Scope
Firms
estimate of number of firms
ace requirements under the
:ory analysis
Small Firms with Costs
Exceeding
l%of
Revenue
3% of
Revenue
Case 2: Up
owning fac
Total In-
Scope
Firms
per bound
ilities that 1
regula
Small In-
Scope
Firms
estimate of number of firms
'ace requirements under the
;ory analysis
Small Firms with Costs
Exceeding
l%of
Revenue
3% of
Revenue
Option 1: EM Everywhere
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Multiple
Firms that own facilities
in Primary
Manufacturing
Industries
Additional firms that own
known facilities in Other
Industries
42
26
17
16
5
8
3
117
9
9
4
4
o
J
2
1
o
23
4
0
o
1
o
o
o
o
1
0
0
o
1
o
o
o
o
1
0
126
116
24
43
14
24
13
359
9
29
18
4
8
5
1
o
64
4
0
o
1
o
o
o
o
1
0
0
o
1
o
o
o
o
1
0
Option 2: EM Everywhere and EM for Facilities with DIF > 125 MGD
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Multiple
Firms that own facilities
in Primary
Manufacturing
Industries
Additional firms that own
known facilities in Other
Industries
42
26
17
16
5
8
3
117
9
9
4
4
o
J
2
1
o
23
4
0
o
1
o
o
o
o
1
0
0
o
1
o
o
o
o
1
0
126
116
24
43
14
24
13
359
9
29
18
4
8
5
1
o
64
4
0
o
1
o
o
o
o
1
0
0
o
1
o
o
o
o
1
0
Option 3: I&E Mortality Everywhere
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Multiple
Firms that own facilities
in Primary
Manufacturing
Industries
Additional firms that own
known facilities in Other
Industries
42
26
17
16
5
8
3
117
9
9
4
4
o
J
2
1
o
23
4
0
o
1
o
o
o
o
1
0
0
o
1
o
o
o
o
1
0
126
116
24
43
14
24
13
359
9
29
18
4
8
5
1
o
64
4
0
1
1
o
o
o
o
1
0
0
o
1
o
o
o
o
1
0
a. Includes all firms with less than 500 employees from 2006 Statistics of U.S. Businesses (SUSB) of the U.S. Department of Commerce (U.S. DOC).
The Small Business Administration defines firms in nearly all profiled NAICS codes according to the firm's number of employees; however, for some in-
scope manufacturing NAICS codes this threshold is 500 employees while for others this threshold is 750, 1,100, or 1,500 employees. Because the SUSB
employment size categories do not correspond to the SBA entity size classifications, EPA used the 500 employee threshold for all in-scope NAICS
sectors.
Sources: U.S. EPA Analysis, 2010; D&B, 2009; U.S. EPA, 2000; U.S. DOC, 2006; SBA, 2009
7-8
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 7: Regulatory Flexibility Act Analysis
For the Primary Manufacturing Industries, the single small entity estimated to incur a significant impact under
either of the impact thresholds represents 4.3 percent of the estimated 23 small in-scope entities under the Case 1
weighting approach and 1.6 percent of the estimated 64 small in-scope entities under the Case 2 weighting
approach.
7.2 Analysis of Electric Generators
7.2.1 Analysis Approach and Data Inputs
EPA used the following methodology and assumptions in performing the RFA analysis for Electric Generators.
Determining Parent Entity of In-Scope Facilities
EPA determined the highest level domestic parent entity for each in-scope facility (565 facilities) using the
approach outlined in Chapter 3: Development of Regulation Costs.175 EPA performed this determination both for
the explicitly and implicitly analyzed facilities (for a discussion on explicitly and implicitly analyzed facilities see
Appendix 3.A: Use of Sample Weights in the Proposed Existing Facilities Rule Analyses}^16 As described below,
the determination for both categories of facilities was needed to support an estimate of entity level impact that
reflects the number of small parent entities for not only the explicitly analyzed facilities and associated parent
entities but also for the implicitly analyzed facilities and associated parent entities.
Determining Whether Parent Entities Are Small Entities
For each of these identified parent entities, EPA assessed entity size based on the appropriate Small Business
Administration (SBA) entity size criterion. The criteria for entity size determination vary by the organization/
operation category of the parent entity, as follows:
> Private entities
• Include investor-owned utilities, non-utility entities, and entities with a primary business other than
electric power generation.
• For entities with electric power generation as a primary business, small entities are those with total
annual electric output less than 4 million MWh.
• For entities with a primary business other than electric power generation, the relevant size criteria are
based on revenue or number of employees by NAICS sector (see Table 7-6):177'178
175 These are non-retired Electric Generators that responded to either the 2000 316(b) Detailed Questionnaire (DQ) or the 2000 316(b)
Short Technical Questionnaire (STQ). EPA found that the remaining 91 of the total of 284 and 372 facilities responded to the DQ and
the STQ, respectively (see Chapter 3: Development of Costs for Regulatory Options for more details). This number is not a weighted
estimate.
176 The "explicitly analyzed" facilities are those for which costs were specifically estimated. The "implicitly analyzed" facilities are
accounted for through application of sample weights to the "explicitly analyzed" facilities.
177 Certain in-scope facilities are owned by entities whose primary business is not electric power generation.
178 For 9 identified parent entities, which are owned ultimately by non-U.S. firms, EPA could not obtain revenue for a domestic entity but
did obtain revenue at the level of the international parent entity; for these 9 entities, EPA used this international entity revenue in the
cost-to-revenue analysis.
March 28, 2011 ?T
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
Table 7-6: NAICS Codes and SBA Size Standards for Entities Owning Electric Generators, With a Primary
Business Other Than Electric Power Generation
NAICS Code
NAICS Description
SBA Size Standard
221112
221113
221119
221122"
221210"
238210"
3311J1
""331315"
""523910"
486210
""523920"
""523930"
""524126"
""525990"
""525910"
541990
5511J2'"
""561499"
""562212"'
562219"
""562920"
611316"
...... .
Nuclear Electric Power Generation
Other Electric Power Generation
Electric Power Distribution
Natural Gas Distribution
Electrical Contractors
[ron and Steel Mills
Aluminum Sheet, Plate, and Foil Manufacturing
Miscellaneous Intermediation
Pipeline Transportation of Natural Gas
4,000,000 MWh
^000=000 MWh
"
500 Employees
..................................................
OO Employees
750 Employees
[[[
. ......
Investment Advice
Direct Property and Casualty insurance Carriers
Other Financial Vehicles
Open-End investment Funds
1 (^^Professional, Scientific, and Technical Services
Offices of Other Holding Companies
. ^EeI..lHs..!Be..s..s. Support S£r..Y!c.e..s.
Solid Waste Landfill
Other Nonhazardous Waste Treatment and Disposal
Materials Recovery Facilities
olleges, Universities, and Professional Schools
11=000=000 Revenue
1,500 Employees
lZ'000'000 Revenue
IZ'000'000 Revenue
$7,6o6,OOC I Revenue
Source: SBA, 2008
> Public entities
• Include federal, state, municipal, and political subdivision entities
• Facilities owned by Federal and State governments were considered to be large; facilities owned by
municipalities and other political units with population less than 50,000 were considered to be small
> Not-for-profit enterprises
• Include rural electric cooperatives
• Small entities are those with total annual electric output less than 4 million MWh.
To determine whether a parent entity is a small entity according to these criteria, EPA compared the relevant
measure for the identified parent entities to the appropriate SBA size criterion. EPA obtained these values for each
parent entity from the following sources:
> For size determination based on electricity output, EPA used average utility-level electricity sales over
the period 2003-2007 as reported in the EIA-861 database. For facilities not listed in the EIA-861
database, the Agency used 2003-2007 average facility-level generation values from the EIA-906/920/923
database.
> For size determination based on revenue and employment, EPA obtained revenue and employment
values from SEC filings, Google Finance, Dun & Bradstreet (D&B), Hoovers, and/or corporate websites
for 2006 through 2009. Values were brought forward to 2009 using the Producer Price Index for
industrial electric power (Electric PPI) obtained from BLS (http://data.bls.gov/cgi-bin/srgate).
> Population data for municipalities and other non-state political subdivisions were obtained from the U.S.
Census Bureau (estimated population for 2008).
Parent entities for which the relevant measure is less than the SBA size criterion were identified as small entities
and carried forward in the RFA analysis.
7-10
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
As reported in Table 7-7 when looking only at explicitly analyzed facilities, EPA identified 97 entities owning
only explicitly analyzed facilities - i.e., 261 facilities. Using this approach, a typical parent entity on average is
estimated to own three 316(b) Electric Generators. Only 12 percent of these entities are small; these small entities
own 5 percent of 316(b) Electric Generators. When looking at the combination of explicitly and implicitly
analyzed facilities, the Agency identified 143 entities owning 548 implicitly and explicitly analyzed facilities.
Using this approach, atypical parent entity is estimated on average to own four 316(b) Electric Generators. Of
these 143 parent entities, 23 percent are small; these entities own 7 percent of 316(b) Electric Generators.
Table 7-7: Unique Parent Entities and Facilities for Electric Generators (by Entity Type and Size)
Parent Entity Type
Small Entity Size
Standard
Number of Parent Entitiesa>b
Large | Small | Total
Number of Facilities
Large | Small | Total
Parent Entities Owning at Least One Explicitly Analyzed Facility
Rural Electric Cooperative
Federal
Investor-Owned Utilities
Municipality
Nonutility
Other Political Subdivision
State
4,000 MWh output
assumed large
4,000 MWh output
50,000 population served
4,000 MWh output
50,000 population served
assumed large
Total
9 1
1 |
37 |
7 |
27 !
0 1
4 1
85 1
2 !
0
1
6
3
0
0
12 i
11
1
38
13
30
0
4
97
11 1
7 |
137 |
7 1
73 !
0 1
8 1
243 1
2 1
0 1
1 |
6 1
5 !
0 1
0 1
14 1
13
7
138
13
82
0
8
261
Parent Entities Owning Only Implicitly Analyzed Facilities or Owning at Least One Explicitly Analyzed Facility0'
Rural Electric Cooperative
Federal
Investor-Owned Utilities
Municipality
Nonutility
Other Political Subdivision
State
4,000 MWh output
assumed large
4,000 MWh output
50,000 population served
4,000 MWh output
50,000 population served
assumed large
Total
12 |
1 |
40 |
18 |
33 |
2 1
4 1
110 |
8 |
0
2
17
5
1
0
33 1
20
1
42
35
38
3
4
143
23 |
14 |
280 |
26 |
153 |
6 1
9 1
511 |
8 |
0 1
3 |
17 I
8 1
1 |
0 1
37 |
31
14
283
43
161
7
9
548
a. For 8 entities EPA was unable to find entity revenue necessary to determine the size of these entities; consequently, EPA used the total revenue for all
facilities owned by these entities to determine entity size.
b. In three instances, a facility is owned by a joint venture of two entities.
c. 548 facilities include (1) explicitly analyzed facilities and (2) implicitly analyzed facilities that responded to the 316(b) 2000 Surveys. For a discussion of
explicitly and implicitly analyzed facilities refer to Appendix 3.A: Use of Sample Weights in the Proposed Existing Facilities Rule Analyses.
d. These counts are unweighted and reflect the known universe of facilities and their parent entities expected to be in scope of Proposed Existing Facilities
Rule.
Source: U.S. EPA Analysis, 2010
Assessing Parent Entity Impact for Electric Generators
EPA assessed the potential impact of the Proposed Existing Facilities Rule options on these small entities by
comparing the estimated entity-level annualized compliance cost to entity-level revenue for each small entity
identified as owning an explicitly analyzed facility. To calculate entity-level cost, EPA summed the after-tax
annualized compliance cost for the explicitly analyzed facilities owned by these entities. In the same way as
described in Chapter 5: Cost and Economic Impact Analyses-Electric Generators for the general firm-level
impact assessment, EPA followed two approaches in aggregating compliance costs to the level of the owning
entity:
1. EPA applied facility-level sample weights to the estimated costs for the explicitly analyzed
electric power generating facilities - these are the facilities for which EPA explicitly estimated
costs (see Appendix 3.A: Use of Sample Weights in the Proposed Existing Facilities Rule
Analyses).179 In effect, this analysis assumes that a parent entity identified as owning one or more
179 The specific facility-level weights used in this analysis are the facility count-based weights (see Appendix 3.A: Use of Sample Weights
in the Proposed Existing Facilities Rule Analyses).
March 28, 2011
7-11
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 7: Regulatory Flexibility Act Analysis
explicitly analyzed facilities is assumed to own and incur the compliance costs for those explicitly
analyzed facilities and the implicitly analyzed facilities that are represented by the sample
weights applied to the costs for the explicitly analyzed facilities. This analysis will likely
overstate impacts on the identified parent firms.
2. EPA used only the estimated costs for the explicitly analyzed facilities without application of
sample weights and aggregated costs to the level of the parent firm for only those explicitly
analyzed in-scope facilities. This analysis may understate impacts on the identified parent firms.
To assess whether these parent entity-level costs could constitute a significant impact, EPA assessed whether the
parent-level costs exceed one percent or three percent of entity-level revenue.
Estimating the Number of Electric Generators Parent Entities Incurring Potentially Significant
Impacts
The preceding steps yield the number of small entities identified as owning explicitly analyzed facilities that
would incur total costs exceeding a given significant impact threshold: costs exceeding one percent of revenue or
three percent of revenue. However, the number of small parent entities identified as owning explicitly analyzed
facilities - and for which this impact analysis is undertaken - is less than the number of small parent entities in the
total population of entities owning both explicitly and implicitly analyzed facilities, as shown in Table 7-7.
However, the small entity analysis may rely on a finding of the absolute number of small entities incurring a
potentially significant impact. As a result, accounting for only the entities identified through their ownership of at
least one explicitly analyzed facility could understate the absolute number of small entities incurring this impact //
small entities owning only implicitly analyzed facilities would also incur a significant impact.
To account for those small entities that own only implicitly analyzed facilities - and thus are not directly captured
in the explicitly analyzed facility-based analysis - EPA developed entity-level weights to extrapolate the findings
from the analysis of small entities owning only explicitly analyzed facilities, to the total population of entities,
including those that own only implicitly analyzed facilities (for a discussion on entity-level weights development
see to Appendix 3. A: Use of Sample Weights in the Proposed Existing Facilities Rule Analyses).180 Applying these
entity-level weights to the numbers of small parent entities owning only explicitly analyzed facilities assessed in
the various cost impact categories yields an estimate of the number of small parent entities including both entities
owning at least one explicitly analyzed facilities and entities owning only implicitly analyzed facilities.
Combining the Facility-Level and Entity-Level Weights in the Small Entity Impact Analysis
As described in Chapter 5: Cost and Economic Impact Analyses - Electric Generators for the general entity-level
impact assessment, EPA defined two cases which utilize entity-level sample weights and facility-level weights to
yield approximate estimates of the numbers of parent entities incurring costs in specific cost-to-revenue ranges:
> Using facility-level weights: For this case, facility-level weights were applied to the estimated
compliance costs for Electric Generators identified as being owned by a given parent entity.181 This
calculation may overstate the number of facilities and compliance costs at the level of any given parent
entity, but will also likely underestimates the number of affected parent entities. This analysis indicates
that 12 small unique domestic parent entities own 26 facilities subject to the Proposed Existing Facilities
Rule.
180 The development of entity-level weights was possible for Electric Generators because of the near universal coverage of potential in-
scope Electric Generators by the 316(b) survey, including bothDQ facilities and STQ facilities. The development of entity-level
weights was not possible for the Manufacturers entity impact analysis because the Manufacturers survey does not provide the needed
level of understanding of all entities potentially subject to the Proposed Existing Facilities Rule.
181 Parent entity weights were not used in this calculation because the combination of facility weights and entity weights would overstate,
perhaps substantially, the estimate of in-scope facilities and compliance costs assigned to parent entities.
7-12 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 7: Regulatory Flexibility Act Analysis
> Using entity-level weights: For this case, entity-level weights were applied to the calculated number of
parent entities estimated to incur costs in each cost-to-revenue range.182 This calculation may understate
the number of facilities and compliance costs at the level of any given parent entity, but accounts more
comprehensively for the number of parent entities owning in-scope facilities. This analysis found that 32
small unique domestic parent entities own 14 in-scope Electric Generators.183
EPA presents these estimates of small entities with costs exceeding one and three percent of entity-level revenue
as the numbers of small entities that may experience a significant impact as a result of the regulatory options.
These estimates of the numbers of small entities incurring a potentially significant impact represent one of the key
factors EPA considered in determining that the Proposed Existing Facilities Rule would qualify for a no-
SISNOSE finding.
Estimating the Total Number of Electric Generators Small Entities by Entity Type and the
Fraction of Those Entities Incurring Potentially Significant Impacts
As outlined in the introduction to this chapter, two criteria are assessed in determining whether the Proposed
316(b) Existing Facilities Rule would qualify for a no-SISNOSE finding:
1. Is the absolute number of small entities estimated to incur a potentially significant impact, as
described above, substantial!
and
2. Do these significant impact entities represent a substantial fraction of small entities in the electric
power industry that could potentially be within the scope of a regulation?
Insight on the second factor requires information on the total number of small entities in the electric power sector
and by entity type. The fraction of small entity totals represented by the significant impact entities is an important
measure of the potential small entity impact of the proposed regulation. For example, if a high percentage of
potentially in-scope small entities incur significant impacts even though the absolute number of significant impact
entities is low, then the regulation could constitute a substantial burden on small entities.
In the same way as found in the analysis for the suspended 2004 Phase II Final Regulation, EPA determined that
data are not readily available to support a. parent entity-level determination of the number of small entities in the
affected electric power generating segments. The best data found by EPA to support this analysis are from the
2007 EIA-861 database, which reports total electric power generation for regulated utilities at the level of the
electric power utility, 2007 EIA-860, which reports facility regulatory status and lists operator, and 2007 EIA-
906/920/923, which reports net generation for all facilities regardless of regulatory status.184 These data thus
provide a basis for estimating the total number of regulated utilities at the level of the utility and non-regulated
facilities at the operator level that would qualify as small based on the 4,000 MWh production quantity threshold
of electric power generation. The EIA-861 data indicate that a total of 3,098 utilities and 1,737 non-utilities
182 In the same way as stated above, facility weights were not used in conjunction with entity weights because the combination of facility
weights and entity weights would overstate, perhaps, the estimate of in-scope facilities and compliance costs assigned to parent
entities.
183 As shown in Table 7-7 and Table 7-8, there are a total of 33 small parent entities on an unweighted basis, 1 of which is an Other
Political Subdivision entity. This entity owns only implicitly analyzed facilities; consequently, there is no explicitly analyzed other
political Subdivision parent entity to represent this implicitly analyzed parent entity. As a result, the weighted entity counts do not
include 1 small Other Political Subdivision entity even though this entity is known to exist in the regulated facility and entity universe.
184 For the analysis in support of the suspended 2004 Final Section 316(b) Phase II Existing Facilities Rule, EPA used only the EIA-861
database for the analysis, and the total industry number of small nonutilities was not estimated.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 7: Regulatory Flexibility Act Analysis
operated industry-wide in the United States in 2007 and that 2,900 of these utilities and 1,547 of non-utilities
would qualify as small according to the 4,000 MWh SBA size criterion.185'186
However, this estimate will diverge from an appropriate estimate of small entities for two reasons (error factors):
1. The desired count of small entities for the small entity analysis is not at the level of the
utility/operator but at the level of the highest domestic parent, which in the electric power
industry is often a holding company that may own several utilities operating in different state
jurisdictions. In addition, these holding companies may also own non-regulated electric power
generating businesses. As a result, the count of utilities that appear to be small entities on the
basis of their utility-level and/or operator-level electric power generation as reported in EIA-861
and EIA-906/920/923, respectively, is likely to overstate the count of parent-level entities that
would qualify as small entities if the determination were made on the basis of electric power
generation aggregated over regulated utilities and non-regulated businesses at the parent level.
This factor will cause the EIA-861-based and EIA-906/920/923-based determination to overstate
the number of small entities in the affected electric power generating segments.
2. According to the SBA entity-size criteria, the determination of small entities for electric power
facilities owned by municipalities and other political subdivisions should be based on population
served instead of electric power generated.187 There is no a priori basis for knowing how a
determination based on electric power generated would differ from a determination based on
population served.
Again, in the same way as done previously for the suspended 2004 Phase II Final Regulation analysis, to the
extent possible, EPA adjusted the estimates based on electric power generated from the EIA-861 database to
account for these error factors and to provide a more appropriate estimate of small entities.188 The adjustments
vary by entity type, as follows:
> Investor-Owned Utilities: Based on the observed relationship between utility-based and entity-based
determinations of "small entity" for privately owned facilities and entities in the 316(b) database, EPA
reduced the utility-based count of "small entities" for private entities by 80 percent189 to yield a more
appropriate estimate of small entities at the true entity level. This adjustment addresses the first error
factor described above.
> Municipalities: Based on the observed relationship between electric power generated- and population
served-based determinations of small entity for municipality-owned facilities in the 316(b) database,
EPA reduced the electric power generated-based count by 47 percent to yield a more appropriate
estimate of small entities at the true entity level. This adjustment addresses the second error factor
described above.
185 These are industry-wide counts of utilities and non-utility electric power facility operators. These numbers are larger than the number
of in-scope utilities and non-utility operators because they include, for instance, businesses that may not have steam generating
operations or may have DIF below the 2 MOD threshold.
186 Number of utilities is the sum of State, federal, investor-owned, municipal, other political subdivision, and cooperative operators from
the 2007 EIA-861 database. Number of nonutilities is the number of non-regulated operators from the 2007 EIA-860 database.
187 Except the federal and State government owners of electric power generating facilities, which were all assumed to be large entities.
188 For all entity types except nonutilities, EPA used the same adjustment factors used for the RFA analysis in support of the suspended
2004 Phase II Final Rule.
189 This adjustment value and those applied for municipalities and nonutilities are based on analyses undertaken for the previous 316(b)
Phase II regulations.
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Chapter 7: Regulatory Flexibility Act Analysis
> Other Political Subdivisions: EPA found no difference in the numbers of small political subdivision
entities estimated using power generation-based and population-based SBA criteria. Consequently, no
adjustment was needed for this entity-type category.
> Rural Electric Cooperatives: Because power generation is an appropriate basis for size determination for
this entity type and because cooperatives are not owned by a holding company, EPA determined that no
adjustment was needed for this entity-type category.
> Nonutilities: Based on the observed relationship between electric power generated-, revenue-, and
employment-based determinations of small entity for nonutility-owned facilities in the 316(b) database,
EPA reduced the electric power generated-based count by 98 percent to yield a more appropriate
estimate of small entities at the true entity level. 19° This adjustment addresses the second error factor
described above.
As a result, the adjusted estimates of total small entities in the affected electric power generation segments may
understate the true value to the extent that some non-regulated private entities would qualify as a small entity.
EPA's adjustments assume that the profile of entities and facilities and the determinations of whether they qualify
as "small entities" as observed in the 316(b) facility dataset are also applicable to the total universe of electric
power generating facilities.
Table 7-8 summarizes the estimated numbers of small entities by parent entity type, based on the EIA-861 data
and adjustments, as described above. Table 7-8 also reports the estimated number of small entities owning in-
scope facilities, from Table 7-7 and as a percentage of total small entities.
Table 7-8: Number of Small Electric Generators Parent Entities (Industry Total and Entities with In-
Scope Facilities)
Parent Entity Type
Rural Electric Cooperative
Federal
Investor-Owned Utilities
Municipality
Nonutility
Other Political Subdivision
State
All Entity Types
Total Small Entities3
848
6
18
968
130
113
0
2,078
Small Entities '
Number*
8
o
2
17
5
1
0
33
3wning In-Scope Facilities
Percentage of Total Small Entities
0.94%
67oo%
10.87%
1.76%
3784%
0.88%
0.00%
1.59%
a. Form EIA-861 does not provide data on nonutilities. The reported number of total small nonutilities is the total of non-regulated operators
from the 2007 EIA-860 database; the number of small parent entities owning 316(b) facilities for non-regulated operators was determined using
the 2007 EIA-906/920/923 database. State and federal entities are considered large.
b. Entity counts include entities owning explicitly and implicitly analyzed facilities and are not weighted estimates. For a discussion on explicitly
and implicitly owned facilities see Appendix 3.A: Use of Sample Weights in the Proposed Existing Facilities Rule Analyses.
c. Industry non-regulated operators with no electricity sales/generation data were excluded from this analysis.
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2007b; U.S. DOE, 2007c; U.S. DOE, 2007d
As shown in Table 7-8, very small percentages of total small entities, by entity type, are estimated to own in-
scope facilities and thus be directly affected by the Proposed Existing Facilities Rule.
In assessing the small entity impact of the Proposed Rule, EPA calculated the fraction of estimated small in-scope
entities, in aggregate and by parent entity type, that are estimated to incur potentially significant impacts, as
described in the preceding sections.
190 Electricity generation, revenue, and employment are SBA criteria used to determine parent entity size for in-scope nonutilities.
Industry non-regulated operators with no electricity sales/generation data were excluded from this analysis.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 7: Regulatory Flexibility Act Analysis
7.2.2 Key Findings for Regulatory Options
Table 7-9 and Table 7-10 summarize the findings from the analyses outlined above in terms of numbers of small
Electric Generators entities incurring costs exceeding the significant cost impact thresholds of one percent and
three percent.
As described above, EPA developed estimates of the number of small Electric Generators entities incurring costs
in the specified cost-to-revenue impact ranges using two weighting concepts:
> Using Facility-Level Weights, EPA estimates that 12 small unique domestic parent entities own 26
facilities subject the existing facilities rule options (Table 7-9). As described above , this assessment may
overstate the number of facilities and compliance costs at the level of the small parent entity, but may
also understate the number of small parent entities.
> Using Entity-Level Weights, EPA estimates that 32 small unique domestic parent entities own 14
facilities subject the existing facilities rule options (Table 7-10).191 As described above, this assessment
may understate the number of facilities and compliance costs at the level of any given small parent
entity, but accounts more comprehensively for the number of small parent entities owning 316(b) electric
power generating facilities.
Using Facility-Level Weights
Under the facility-level sample-weighting approach, EPA estimates that between 4 and 9 small entities owning
Electric Generators will incur costs exceeding 1 percent of revenue, and that between 2 and 6 small entities will
incur costs exceeding 3 percent of revenue, depending on the regulatory option, (see Table 7-9).
Options 1 and 2 yield the same results. Under these options, EPA estimates that 4 small entities, or 12.1 percent of
the estimated 33 small in-scope entities, will incur costs exceeding 1 percent of revenue, and 2 small entities, or
6.1 percent of small in-scope entities, will incur costs exceeding 3 percent of revenue. Under Option 3, the most
costly option, EPA estimates that 9 small entities, or 27.3 percent of small in-scope entities, would incur costs
exceeding 1 percent of revenue and 6 small entities, or 18.2 percent of small in-scope entities, would incur costs
exceeding 3 percent of revenue.
The impact findings in terms of numbers of affected entities are consistently low across ownership categories,
with the largest numbers of small entities incurring costs exceeding an impact threshold in the Municipality
category, at 6 entities exceeding the 1 percent threshold, and 4 exceeding the 3 percent threshold under Option 3.
On the basis of percentage of small in-scope entities by ownership category, the largest percentage under Options
1 and 2 occurs among nonutilities, at 20 percent. For Option 3, the largest percentages occur among
municipalities at 35 percent forthe 1 percent of revenue threshold and 24 percent for the 3 percent of revenue
threshold; rural electric cooperatives.
191 As shown in Table 7-7, above, there are a total of 33 small parent entities on an unweighted basis, 1 of which is another political
subdivision entity. This entity owns only implicitly analyzed facilities; consequently, there is no explicitly analyzed other political
subdivision parent entity to represent this implicitly analyzed parent entity and weighted entity counts do not include 1 small other
political subdivision entity.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
Table 7-9: Estimated Cost-to-Revenue Impact on Small Electric Generators Entities, by
Entity Type - Using Facility-Level Weights3
Parent Entity Typeb
Cost Impact Category
Cost > 1% of Revenue
Number of j % of Small
Small Entities jln-scope Entities
Cost > 3% of Revenue
Number of j % of Small
Small Entities jln-scope Entities
Option 1: IM Everywhere
Rural Electric Cooperative
Investor-Owner Utility
Municipality
Nonutility
Other Political Subdivision
Total
1
0
2
1
0
4
1 12.5%
| 0.0%
11.8%
20.0%
0.0%
I 12.1%
1
0
0
1
0
2
1 12.5%
| 0.0%
0.0%
20.0%
0.0%
I 6.1%
Option 2: IM Everywhere and EM for Facilities with DIF>125 MOD
Rural Electric Cooperative
Investor-Owner Utility
Municipality
Nonutility
Other Political Subdivision
Total
1
0
2
1
0
4
I 12.5%
0.0%
11.8%
20.0%
| 0.0%
| 12.1%
1
0
0
1
0
2
I 12.5%
0.0%
0.0%
20.0%
| 0.0%
| 6.1%
Option 3: I&E Mortality Everywhere
Rural Electric Cooperative
Investor-Owner Utility
Municipality
Nonutility
Other Political Subdivision
Total
2
0
6
1
0
9
25.0%
0.0%
35.3%
| 20.0%
1 0.0%
1 27.3%
1
0
4
1
0
6
12.5%
0.0%
23.5%
| 20.0%
1 0.0%
1 18.2%
a. The number of entities with cost-to-revenue impact exceeding 3 percent is a subset of the number of entities with such ratios
exceeding 1 percent.
Source: U.S. EPA Analysis, 2010
Using Entity-Level Weights
Under the entity-level sample-weighting approach, EPA's findings of potential small entity impacts differ from
those under the facility-level sample-weighting approach, with more entities generally estimated to incur costs
exceeding an impact threshold under all of the regulatory options. However, the profile of difference varies across
options and by ownership category.
Overall, EPA estimates that between 6 and 21 small entities will incur costs exceeding 1 percent of revenue, and
that between 2 and 14 small entities will incur costs exceeding 3 percent of revenue, depending on the regulatory
option (see Table 7-10).
Under Option 1, and Option 2, EPA estimates that 6 small entities, or 18.2 percent of the 33 small in-scope
Electric Generators entities, will incur costs exceeding 1 percent of revenue. Under Option 1, 2 small parent
entities, or 6.1 percent of small in-scope entities, are expected to incur costs exceeding 3 percent of revenue, while
under Option 2, this number is 6, or 18.2 percent. Under Option 3, the most costly option, EPA estimates that 21
small entities, or approximately 63.6 percent of small in-scope entities, would incur costs exceeding 1 percent of
revenue, and that 14 small entities, or 42.4 percent of all small entities, would incur costs exceeding 3 percent of
revenue.
Only two ownership categories record small entities exceeding either of the impact thresholds - rural electric
cooperatives and nonutilities - under Options 1 and 2. Under Option 1, 4 rural electric cooperatives entities incur
costs in excess of the 1 percent of revenue threshold, representing 50 percent of estimated small in-scope entities
in this category; 2 nonutility entities incur an impact, representing 40 percent of this category. At the 3 percent of
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
revenue threshold, only the 2 nonutility entities incur an impact, representing 40 percent of this category. For
Option 2, the impact profile is the same at the 1 percent and 3 percent of revenue thresholds (and also the same as
for Option 1): 4 rural electric cooperatives entities incur costs in excess of the 1 percent of revenue threshold,
representing 50 percent of estimated small in-scope entities in this category; 2 nonutility entities incur an impact,
representing 40 percent of this category. For Option 3, the largest number of entities incurring impacts occurs
among municipalities, with 11 entities (65 percent of small in-scope entities in this category) incurring costs in
excess of the 1 percent of revenue threshold and 9 entities (53 percent of small in-scope entities in this category)
incurring costs in excess of the 3 percent of revenue threshold. In the rural electric cooperative category, 8
entities (or all of small in-scope entities) incur costs in excess of the 1 percent of revenue threshold, and 4 rural
electric cooperatives entities (50 percent of small in-scope entities) incur costs in excess of the 3 percent of
revenue threshold. Finally, 2 nonutility entities incur an impact at both the 1 and 3 percent of revenue thresholds,
representing 40 percent of this category.
Table 7-10: Estimated Cost-to-Revenue Impact on Small Electric Generators Entities, by
Entity Type - Using Entity-Level Weights3
Parent Entity Typeb
Cost Impact Category
Cost > 1% of Revenue
Number of | % of Small
Small Entities |In-scope Entities
Cost > 3% of Revenue
Number of | % of Small
Small Entities |In-scope Entities
Option 1: IM Everywhere
Rural Electric Cooperative
Investor-Owner Utility
Municipality
Nonutility
Other Political Subdivision
Total
4
0
0
2
0
6
I 50.0%
! 0.0%
| 0.0%
I 40.0%
1 0.0%
1 18.2%
0
0
0
2
0
2
I 0.0%
! 0.0%
| 0.0%
I 40.0%
1 0.0%
1 6.1%
Option 2: IM Everywhere and EM for Facilities with DIF>125 MOD
Rural Electric Cooperative
Investor-Owner Utility
Municipality
Nonutility
Other Political Subdivision
Total
4
0
0
2
0
6
1 50.0%
I o"6%
I o"o%
| 4(10%
| (10%
I 18.2%
4
o
o
2
o
6
1 50.0%
[ 0.0%
1 0.0%
i 40.0%
[ 0.0%
I 18.2%
Option 3: I&E Mortality Everywhere
Rural Electric Cooperative
Investor-Owner Utility
Municipality
Nonutility
Other Political Subdivision
Total
8
0
11
2
0
21
1 100.0%
1 0.0%
I 64.7%
1 40.0%
| 0.0%
1 63.6%
4
0
9
2
0
14
1 50.0%
1 0.0%
I 52.9%
1 40.0%
| 0.0%
1 42.4%
a. The number of entities with cost-to-revenue impact exceeding 3 percent is a subset of the number of entities with such ratios
exceeding 1 percent.
Source: U.S. EPA Analysis, 2010
Comparing the Findings from the Facility-Level Weights and Entity-Level Weights Approaches
In comparison to the facility-level sample-weighting approach, under the entity-level sample-weighting approach,
EPA finds that a larger number of small entities will incur costs exceeding 1 percent of revenue for all three
regulatory options. For both Options 1 and 2, 6 small entities are estimated to incur costs exceeding 1 percent of
revenue using the entity-level weights, compared to 4 small entities for each Options 1 and 2 using facility-level
weights (see Table 7-11). For Option 3, 21 small entities are estimated to incur costs exceeding the 1 percent of
revenue threshold using the facility-level weights, compared to 9 small entities using entity-level weights.
However, at the 3 percent cost-to-revenue threshold, the difference in findings between the facility-level sample-
weighting approach and the entity-level sample-weighting approach varies by regulatory option, with the same
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March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 7: Regulatory Flexibility Act Analysis
number of small entities estimated to incur costs exceeding the 3 percent of revenue threshold under Option 1 (2
entities). However, for Option 2, more entities (6 entities) are estimated to incur costs exceeding the 3 percent of
revenue threshold under the entity-level weights than under the facility-level weights (2 entities). A similar pattern
occurs for Option 3 with 6 small entities exceeding the 3 percent of revenue threshold under the facility-level
weights and 14 entities under the entity-level weights.
The observed profile of difference between the two weighting approaches - greater number of affected entities at
the 1 percent of revenue threshold for all regulatory options, but a mixed finding at the 3 percent of revenue
threshold over the regulatory options - is reasonable given the known potential biases in each of the two
approaches:
> The facility-level sample-weighting approach, discussed in the previous section, is likely to overstate
impact in terms of number of in-scope facilities owned and associated costs for any given parent entity,
but may understate the number of parent entities in a given impact category.
> The entity-level sample-weighting approach, discussed in this section, may understate the impact in
terms of number of in-scope facilities owned and associated costs for any given parent entity, but will
tend to account more accurately for the number of parent entities, overall, in the analysis.
Table 7-11: Estimated Cost-to-Revenue Impact on Small Entities - Comparing Findings from
the Facility-Level Weights and Entity-Level Weights Analyses3
Cost Impact Category
Parent Entity Type
Number of Small
Entities
In-scope Entities
Cost > 3% of Revenue
Number of Smalll % of Small
Entities | In-scope Entities
Using Facility-Level Weights
Option 1: IM Every where
Option 2: IM Everywhere and EM for
Facilities with DIF>125 MOD
Option 3: I&E Mortality Everywhere
4
4
9
12.1%
12.1%
27.3%
2
2
6
6.1%
6.1%
18.2%
Using Entity-Level Weights
Option 1 : IM Everywhere
Option 2: IM Everywhere and EM for
Facilities with DIF>125 MOD
Option 3: I&E Mortality Everywhere
6
6
21
18.2%
18.2%
63.6%
2
6
14
6.1%
18.2%
42.4%
a. The number of entities with cost-to-revenue impact exceeding 3 percent is a subset of the number of entities with such ratios exceeding
1 percent.
Source: U.S. EPA analysis, 2010
Summary of Findings for Electric Generators
Regardless of the weighting approach applied, the estimated numbers of small entities incurring potentially
significant cost impacts are small and, in particular, for Option 1, represent small percentages of the total of small
in-scope entities.
As shown in Table 7-11 the estimated numbers of small entities incurring a potentially significant impact at the 1
percent of revenue threshold are small: 4-6 entities under the least costly Option 1 and the mid-cost Option 2;
and 9-21 entities under the most costly Option 3.192 At the 3 percent of revenue threshold, the findings of
potentially significant impact are smaller: 2 entities under the least costly Option 1; 2 - 6 entities under the mid-
cost Option 2; and 6-14 entities under the most costly Option 3. For Options 1 and 2, the percentages of small in-
scope entities incurring an impact at either the 1 or 3 percent of revenue threshold, remain below a key threshold
The number ranges result from use of the two estimation methods: (1) using facility-level weights and (2) using entity-level weights.
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 7: Regulatory Flexibility Act Analysis
of concern - 20 percent of small in-scope entities - as specified in EPA's Final Guidance for EPA Rulewriters:
Regulatory Flexibility Act.193
Given (1) the small absolute number of small entities estimated to incur a potentially significant cost impact and
(2) the low percentage of total in-scope small entities in the Electric Generators regulated industry segment, EPA
concluded that Options 1 and 2 would not have "a significant impact on a substantial number of small entities."
EPA did not reach a finding for Option 3 since, at the time of completing the RFA analysis, the Agency did not
anticipate selecting Option 3 as the proposed option for the 316(b) existing facilities rule.
7.3 Uncertainties and Limitations
> None of the sample-weighting approaches used for this analysis accounts precisely for the number of
parent-entities and compliance costs assigned to those entities simultaneously for either Manufacturers
or Electric Generators. EPA assesses the values presented in this chapter as reasonable estimates of the
numbers of small entities that could incur a significant impact according to the impact concepts.
> To the extent that EPA used the 2004 Phase II Final adjustment factors to estimate the total number of
small Electric Generators entities, the uncertainties associated with those factors still apply to the
analysis in support of this proposed regulation. In particular, these adjustments are based on the
assumption that the originally analyzed Phase II utilities are representative of the EIA universe of
electric utilities (for private entities in terms of their respective sizes at the utility level and the holding
company level; for municipalities and political subdivisions in terms of their respective sizes based on
electric output and population). If this is not the case, the industry-wide numbers of small entities may be
over- or under-estimated. Further, these adjustment factors may not be representative of the current
electric power generating industry.
> To the extent that information reported in the 316(b) Surveys for Manufacturers and used in this analysis
is not reflective of the present conditions, the number of small parent entities of manufacturing in-scope
facilities may be over- or under-stated.
> To estimate the total industry-wide number of small entities for each Manufacturing sector subject to the
Proposed Existing Facilities Rule, EPA used counts of 2006 SUSB firms with less than 500 employees.
SBA defines firms in nearly all profiled NAICS codes according to the firm's number of employees. For
some in-scope NAICS codes this threshold is 500 employees while for others it is 750, 1,100, or 1,500
employees. Because the SUSB employment size categories do not correspond to the SBA size EPA has
to use the 500 employee threshold for all in-scope NAICS codes. Consequently, total industry-wide
counts of small entities for Manufacturing sectors are likely understated, thereby potentially overstating
the percent of small in-scope entities and percent of small in-scope entities with compliance costs
exceeding one and three percent impact thresholds.
193 U.S. EPA, Final Guidance for EPA Rulewriters: Regulatory Flexibility Act, November 2006, see pages 23-26.
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Economic and Benefits Analysis for Proposed Existing Facilities Rule
Chapter 8: UMRA Analysis
8 Unfunded Mandates Reform Act (UMRA) Analysis
Title II of the Unfunded Mandates Reform Act of 1995, Pub. L. 104-4, establishes requirements for Federal
agencies to assess the effects of their regulatory actions on State, local, and Tribal governments and the private
sector. Under UMRA section 202, EPA generally must prepare a written statement, including a cost-benefit
analysis, for proposed and final rules with "Federal mandates" that might result in expenditures by State, local,
and Tribal governments, in the aggregate, or by the private sector, of $100 million or more in any one year.
Before promulgating a regulation for which a written statement is needed, UMRA section 205 generally requires
EPA to identify and consider a reasonable number of regulatory alternatives and adopt the least costly, most cost-
effective, or least burdensome alternative that achieves the objectives of the rule. The provisions of section 205 do
not apply when they are inconsistent with applicable law. Moreover, section 205 allows EPA to adopt an
alternative other than the least costly, most cost-effective, or least burdensome alternative if the Administrator
publishes with the rule an explanation why that alternative was not adopted. Before EPA establishes any
regulatory requirements that might significantly or uniquely affect small governments, including Tribal
governments, it must have developed under section 203 of the UMRA a small government agency plan. The plan
must provide for notifying potentially affected small governments, enabling officials of affected small
governments to have meaningful and timely input in the development of EPA regulatory proposals with
significant intergovernmental mandates, and informing, educating, and advising small governments on
compliance with regulatory requirements.
For the Electric Generators segment of the Proposed Existing Facilities Regulation, EPA estimates that the
maximum cost in any one year for compliance with, and administration of, the regulatory options to governments
(excluding federal government) is $23 million under Option 1, $336 million under Option 2, and $390 million
under Option 3.194 The maximum cost in any given year to the private sector for the Electric Generators segment
(compliance cost only) is $756 million, $10,749 million, and $11,124 million, respectively, for Options 1, 2, and
3. For the Manufacturers segment of the Proposed Rule, EPA estimates that the maximum cost in any one year to
governments (administrative cost only, as no Manufacturers facilities are government-owned) of the regulatory
options in any given year is $1.6 million, $1.4 million, and $1.0 million under Options 1, 2, and 3, respectively.
The maximum cost in any one year to the private sector for the Manufacturers segment (compliance cost only) is
$373 million, $833 million, and $1,439 million, respectively, for Options 1, 2, and 3. Thus, EPA has determined
that the Proposed Existing Facilities Rule contains a Federal mandate that may result in expenditures of $100
million or more for State, local, and Tribal governments, in the aggregate, or the private sector in any one year.
Accordingly, under §202 of the UMRA, EPA has prepared a written statement, presented in the preamble to the
proposed rule, that includes (1) a cost-benefit analysis; (2) a summary of State, local, and Tribal input; (3) a
discussion related to the least burdensome option requirement; and (3) an analysis of small government burden.
This chapter contains additional information to support that statement, including information on compliance and
administrative costs, and on impacts to small governments.
In performing this analysis, EPA closely followed the methodology and assumptions used for the analysis in
support of previous CWA 316(b) regulatory analyses, with the following modifications:195
> Costs to private facilities reflect pre-tax cost values.
Maximum costs are undiscounted costs incurred by the entire universe of in-scope facilities in a given year of occurrence under a
given regulatory option.
For more details on these analyses, see Chapter Bl: Summary of Compliance Costs in the suspended 2004 Economic and Benefits
Analysis for the Final Section 316(b) Phase II Existing Facilities Rule report (U.S. EPA, 2004a) and Chapter Cl: Summary of Cost
Categories and Key Analysis Elements for Existing Facilities in the 2006 Economic and Benefits Analysis for the Final Section 316(b)
Phase III Existing Facilities Rule report (U.S. EPA, 2006a).
March 28, 2011
8-1
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Economic and Benefits Analysis for Proposed Existing Facilities Rule Chapter 8: UMRA Analysis
> Annualized costs presented in this UMRA analysis are calculated using the social cost framework
presented in Chapter 11: Social Cost Analysis, being assigned on a year-explicit basis and discounted
from each year of occurrence to the promulgation year (2012) using a 7 percent discount rate and a 3
percent discount rate, then annualized over a period of 50 years. For more details, see Section 1 of
Chapterll. All costs reflect weighted values unless otherwise noted.
> Unlike the total cost of compliance presented in Chapter 77, the costs to the private sector include the
private concept of net downtime, being the difference between forgone revenue and reduced operating
costs, rather than the social cost of downtime which is calculated as the total increase in variable costs of
electricity generation for the entire industry while units are down.
> All costs are in 2009 dollars. Annualized values are derived from present values that were discounted to
the promulgation year (expected to be 2012 for the Proposed Existing Facilities Rule).
The Agency analyzed the impact of the regulatory options on government entities, small government entities, and
the private sector, the details of which are presented in the three sections below, followed by a summary.
8,1 UMRA; Analysis of Impact on Government Entities
This part of the UMRA analysis assesses the burden of the existing facilities regulatory options on State, local,
and Tribal governments. The use of the phrase "government entities" in this section does not include the federal
government, which owns 16 of the 559 (weighted) in-scope Electric Generators and is expected to incur both
compliance and administrative costs under the regulatory options. In evaluating the magnitude of the impact of
the options on government entities, EPA considered two burden concepts:
> Compliance costs incurred by government entities owning facilities required to meet the standards of the
regulation. Because no Manufacturer facilities are government owned, EPA conducted this assessment for
Electric Generators only.
> Administrative costs incurred to implement the rule option. This assessment applies to both Electric
Generators and Manufacturers.
> Administrative costs to government entities were estimated based on the administrative costs for facilities
that require State review, which are detailed in Section 8.1.2 below.
The determination of owning entities, their type, and their size is detailed in Chapter 7: Regulatory Flexibility Act
Analysis.
8.1.1 Compliance Costs
Table 8-1 summarizes the number of State and local government entities and the number of in-scope Electric
Generators they own.
Table 8-1: Government-Owned Electric Generators and Their Parent
Entities
Entity Type
Municipality
State
Other Political Subdivision
Total
Parent Entities3
35
4
3
42
Electric Generators'"
43
9
7
59
a. Counts of entities owning explicitly and implicitly analyzed Electric Generators; these are not weighted entity
counts.
b. Counts of explicitly and implicitly analyzed Electric Generators; these are not weighted estimates.
Source: U.S. EPA analysis, 2010
8-2 March 28, 2011
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Economic and Benefits Analysis for Proposed Existing Facilities Rule Chapter 8: UMRA Analysis
Out of 548 in-scope Electric Generators (known universe; unweighted counts), 59 are owned by 42 government
entities.196 The majority (73 percent) of these government-owned Electric Generators are owned by
municipalities, followed by State governments (15 percent). The remaining 12 percent are owned by other
political subdivisions.
EPA identified government entities that are expected to incur compliance, and calculated their compliance costs
as part of the analyses described in Chapter 3: Development of Costs for Regulatory Options. These costs were
assigned and discounted on a year-explicit basis as described in Chapter 11: Assessment of Total Social Costs.
Under Option 1: IM Everywhere, compliance costs for government-owned Electric Generators are approximately
$10.8 million in the aggregate (total weighted compliance cost annualized at 7 %), an average of about $0.3
million per facility. Municipally-owned Electric Generators account for approximately $4.7 million of this cost,
and State-owned Electric Generators account for the remaining $6.1 million. The average cost to a State-owned
Electric Generator is $0.4 million, compared to about $0.2 million for the average municipal Electric Generator.
The maximum annualized compliance costs expected to be incurred by any single government-owned Electric
Generator is $1.0 million for a State-owned Electric Generator and $0.5 million for a municipal Electric
Generator.197
Under Option 2: IM Everywhere and EM for Facilities with DIP > 125 MGD, government-owned Electric
Generators incur annualized total cost of just over $102.3 million to comply with regulatory requirements, with
$29.9 million in compliance costs borne by municipally-owned entities and $72.4 million by State-owned entities.
Overall, government-owned Electric Generators incur average costs of $2.5 million per facility, with municipal
Electric Generators incurring costs of $1.2 million per facility, and State-owned Electric Generator incurring costs
of $4.3 million per facility. The largest annualized compliance cost to any government-owned Electric Generator
is $17.8 million, incurred by a State-owned Electric Generator.
Under Option 3: I&E Mortality Everywhere, total annualized compliance costs for government-owned Electric
Generators are expected to be about $120.1 million, an average of just over $2.9 million per facility. The highest
annualized cost is $17.8 million, which is incurred by a State-owned Electric Generator. The maximum cost to a
municipally-owned Electric Generator is $5.6 million.
196 Excluding federal government-owned facilities.
197 Maximum per facility values are reported on an unweighted basis.
March 28, 2011 8-3
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Economic and Benefits Analysis for Proposed Existing Facilities Rule
Chapter 8: UMRA Analysis
Table 8-2: Compliance Costs to Government-Owned Electric Generators (Millions; $2009)
Ownership Type
Number of In-scope
Facilities (weighted) a
Total Weighted,
Annualized Pre-tax
Compliance Cost
Average Annual
Compliance Cost
(per facility)
Maximum
Annualized Facility
Compliance Costb
Option 1: IM Everywhere
Municipality
State
Other Political Subdivision0
Total
25
17
o
42
$4.7
$6."l
$0.0
$10.8
$0.2
$0.4
$676
$0.3
$0.5
$To
$o
$1.0
Option 2: IM Everywhere and EM for Facilities with DIP > 125 MGD
Municipality
State
Other Political Subdivision0
Total
25
17
o
42
$29.9
$72\4
sixo
$102.3
$1.2
$4.3
$6'.o
$2.5
$5.6
$T7\8
$ao
$17.8
Option 3: I&E Mortality Everywhere
Municipality
State
Other Political Subdivision0
Total
25
17
0
42
$42.5
$77.6
$0.0
$120.1
$1.7
$4.6
$0.0
$2.9
$5.6
$17.8
$0.0
$17.8
a. Facility counts are weighted estimates and differ from the values reported in Table 8-1 and Table 8-4, which are un-weighted values. This
table presents sample weighted facility counts because costs were developed only for the explicitly analyzed Electric Generators facilities.
See Appendix A 3: Used of Sample Weights in the Proposed Existing Facilities Rule Analysis for discussion on explicitly and implicitly
analyzed facilities and facility sample weights.
b. Reflects maximum of un-weighted costs to explicitly analyzed facilities only.
c. EPA's analysis indicates there are 3 Other Political Subdivision entities (Table 8-1). These entities own only implicitly analyzed facilities;
consequently, there is no explicitly analyzed Other Political Subdivision parent entity to represent these implicitly analyzed Other Political
Subdivision parent entities. As a result, the weighted entity counts do not include the 3 known Other Political Subdivision entities even
though they are known to be part of the regulated facility and entity universe.
Source: U.S. EPA analysis, 2010
8.1.2 Administrative Costs
Forty-five States and 1 territory with NPDES permitting authority are expected to incur costs to administer the
Proposed Existing Facilities Rule in their jurisdictions.198 The Federal government is also expected to incur costs
to oversee the initial post-promulgation permitting process. Details of the development of costs to NPDES
authorities and the Federal government can be found in Section 2 of Chapter 3: Development of Costs for
Regulatory Options. These costs were assigned and discounted on a year-explicit basis as described in Chapter
11: Assessment of Total Social Costs.
As shown in Table 8-3, government entities are expected to incur annualized costs of $5.31 million to administer
Option 1, $2.19 million to administer Option 2, and $1.28 million to administer Option 3. Administrative costs are
lower for Options 2 and 3 than for Option 1 because they require cooling tower technology to be installed at some
or all facilities, and facilities with cooling towers incur no monitoring costs and no entrainment study costs,
reducing the administrative burden on the permitting authority. As described above, this tabulation includes
government administrative costs associated with Electric Generators and Manufacturers facilities.
Annual monitoring activities are expected to account for the largest portion of administrative costs under
Option 1 - $2.29 of the $5.31 million in annualized administrative costs incurred for this option. Under Option 2,
EPA expects governments to spend about $2.19 million to administer the rule, of which the largest share is again
annual monitoring activities, at $1.37 million. Under Option 3, in-scope facilities are expected to incur $ 0.72
million in monitoring costs, with other administrative costs of approximately $0.04 for start-up activities and
$0.52 for permit issuance and reissuance.
198 Since the time of this analysis, Alaska was also granted NPDES permitting authority. Only one in-scope generator is located in
Alaska.
8-4
March 28, 2011
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Economic and Benefits Analysis for Proposed Existing Facilities Rule
Chapter 8: UMRA Analysis
Table 8-3: Annualized Government Administrative Costs (Millions; $2009)
Activity
Annualized Cost,
Electric Generators
Annualized Cost,
Manufacturers
Total Annualized Cost
Option 1: IM Everywhere
Start-Up Activities
First Permit Issuance Activities
Annual Monitoring Activities
Entrainment Study
Permit Reissuance Activities
Total
$0.02
$0.23
$1.17
$1.19
$0.18
$2.79
$0.02
$0.24
$1.12
$0.97
$0.18
$2.52
$0.04
$0.45
$2.29
$2.16
$0.36
$5.31
Option 2: IM Everywhere and EM for Facilities with DIF > 125 MOD
Start-Up Activities
First Permit Issuance Activities
Annual Monitoring Activities
Entrainment Study
Permit Reissuance Activities
Total
$0.02
$0.17
$0.36
$0.00
$0.14
$0.69
$0.02
$0.23
$1.07
$0.00
$0.17
$1.48
$0.04
$0.35
$1.37
$0.00
$0.31
$2.19
Option 3: IM&EM Everywhere
Start-Up Activities
First Permit Issuance Activities
Annual Monitoring Activities
Entrainment Study
Permit Reissuance Activities
Total
$0.02
$0.16
$0.20
$0.00
$0.13
$0.51
$0.02
$0.13
$0.52
$0.00
$0.10
$0.77
$0.04
$0.29
$0.72
$0.00
$0.23
$1.28
a. These costs reflect the assumption that all facilities will comply in one year, and were discounted accordingly because individual
components of costs were not distributed over the 5-year compliance window, as described in Chapter 11, Section 1.
Source: U.S. EPA analysis, 2010
8,2 UMRA: Analysis of Impact on Small Governments
As part of the UMRA analysis, EPA also assessed whether the regulatory options would significantly and
uniquely affect small governments. To assess whether the proposed regulatory options would affect small
governments in a way that is disproportionately burdensome in comparison to the effect on large governments,
EPA compared total costs and costs per facility as estimated to be incurred by small governments with those
values as estimated to be incurred by large governments. EPA also compared the per facility costs incurred for
small government-owned facilities with those incurred by non-government-owned facilities. The Agency
evaluated costs per facility on the basis of both average and maximum annualized cost per facility. Because no
Manufacturers facilities are government-owned, EPA conducted this analysis for Electric Generators only.
Costs to government-owned facilities were determined as part of EPA's analysis of the impact on facilities, as
described in Chapter 3: Development of Costs for Regulatory Options. Facility ownership type and size were
determined for the RFA analysis described in Chapter 7: Regulatory Flexibility Act Analysis.
Out of 59 government-owned in-scope Electric Generator facilities, EPA identified 18 facilities that are owned by
18 small government entities. These 18 facilities constitute approximately 31 percent of the total number of
government-owned Electric Generators.199
Excluding federal government-owned facilities.
March 28, 2011
8-5
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Economic and Benefits Analysis for Proposed Existing Facilities Rule
Chapter 8: UMRA Analysis
Table 8-4: Government-Owned Electric Generators and Their Parent Entities, by Size
Entity Type
Municipality
State
Other Political
Subdivision
Total
Large
18
4
2
24
Entities3
Small
17
o
1
18
Total
35
4
3
42
Ek
Large
26
9
6
41
ctric Generate
Small
17
o
1
18
rs"
Total
43
9
7
59
a. Counts of entities owning explicitly and implicitly analyzed Electric Generators; these are not weighted entity counts.
b. Counts of explicitly and implicitly analyzed Electric Generators; these are not weighted estimates.
Source: U.S. EPA analysis, 2010
As presented in Table 8-5, costs are lower for small governments in comparison to large governments in the
aggregate, and approximately the same on a per facility basis. Under Option 1, the 10 facilities owned by small
government entities, all of which are municipalities, incur total annualized costs of $1.5 million, which is
substantially less than the total of $9.2 million in costs incurred by the 31 facilities owned by large governments.
Small and large governments incur costs of approximately $0.1 million and $0.3 million per facility,
respectively.200 Small government-owned facilities are also expected to incur lower cost per facility than the 16
small privately owned facilities, for which compliance costs are estimated to be $7.7 million in the aggregate, or
about $0.5 million per facility. Moreover, the largest annualized cost to any individual facility owned by a small
government is about $0.2 million, significantly lower than the maximum facility costs of around $1.0 million for
large government-owned facilities and $2.5 million for small privately-owned facilities.
Under Option 2, impacts to facilities owned by small governments remain lower than those to other categories of
facilities. Total annualized compliance costs are $1.5 million for facilities owned by small governments, which
represents an average of $0.1 million per facility, with a maximum of $0.2 million for a single facility. This is
substantially less than the total cost of $100.7 million, with an average cost of $3.2 million per facility, and
maximum cost of $17.8 million to a single facility for facilities owned by large governments. Facilities owned by
small private entities are expected to incur an average annualized compliance cost of $2.0 million and a maximum
of $10.9 million for a single facility. These values are again substantially higher than the comparable values for
small government-owned facilities.
Under Option 3, impacts to facilities owned by small governments are also less substantial than those to other
categories of facilities. Total annualized compliance costs are $12.5 million for facilities owned by small
governments compared to $107.6 million for facilities owned by large governments and $34.0 millions for
facilities owned by small private entities. EPA estimates that a small government-owned facility would on
average incur $1.2 million (maximum of $2.1 million to a single facility) in compliance costs compared to $3.4
million per facility (maximum of $17.8 million to a single facility) and $2.2 million per facility (maximum of
$10.9 million to a single facility) for facilities owned by large governments and small private entities.
As described in the preceding paragraphs, under all of the regulatory options, EPA expects costs to facilities
owned by small governments to be lower than costs to facilities owned by large-governments or by small private
entities, and that this finding applies for both the average cost to facilities and the maximum cost to any one
facility. Consequently, EPA assesses that small government will not be significantly or uniquely affected by the
Proposed Existing Facilities Regulation.
Excluding federal government-owned facilities.
8-6
March 28, 2011
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Economic and Benefits Analysis for Proposed Existing Facilities Rule
Chapter 8: UMRA Analysis
Table 8-5: Compliance Costs for Electric Generators by Ownership Type and Size (Millions;
$2009)
Ownership Type
Entity
Size
Number of
Facilities
(weighted)3
Total Annualized Pre-
Tax Compliance Costs
Average Annualized
Pre-tax Compliance
Cost per Facility
Maximum Facility
Annualized Pre-tax
Compliance Cost0
Option 1: IM Everywhere
Government (excluding
Federal)
Private
Small
Large
Small
Large
All Facilities15
10
31
16
485
559
$1.5
$972
$7.7
$354;4
$394.2
$0.1
$03
$0.5
$'677
$0.7
$0.2
$176
$2.5
$772
$7.2
Option 2: IM Everywhere and EM for Facilities with DIF>125 MOD
Government (excluding
Federal)
Private
Small
Large
Small
Large
All Facilities15
10
31
16
485
559
$1.5
$16677''
$32.3
$4j7ij
$4,811.3
$0.1
$372
$2.0
$876
$8.6
$0.2
$1778
$10.9
$5979
$59.9
Option 3: I&E Mortality Everywhere
Government (excluding
Federal)
Private
Small
Large
Small
Large
All Facilities15
10
31
16
485
559
$12.5
$Yo"7'76"
$34.0
$4736673
$4,959.4
$1.2
$374
$2.2
$879
$8.9
$2.1
$1778
$10.9
$5979
$59.9
a. Facility counts are weighted estimates and differ from the values reported in Table 8-1 and Table 8-4, which are un-weighted values. This
table presents sample weighted facility counts because costs were developed only for the explicitly analyzed Electric Generators facilities.
See Appendix A. 3: Used of Sample Weights in the Proposed Existing Facilities Rule Analysis for discussion on explicitly and implicitly
analyzed facilities and facility sample weights.
b. Facility counts and cost estimates reported for All Facilities include 15 federal government-owned facilities and costs estimated for these
facilities.
c. Reflects maximum of un-weighted costs to explicitly analyzed facilities only.
Source: U.S. EPA analysis, 2010
8,3 UMRA; Analysis of Impact on the Private Sector
As a final part of the UMRA analysis, EPA reports the compliance costs expected to be incurred by private
entities according to the methods described in Chapter 3: Development of Costs for Regulatory Options and
annualized based on the year-explicit framework presented in Chapter 11: Assessment of Total Social Costs.
EPA estimates total annualized pre-tax compliance costs for 1,003 privately owned in-scope facilities - Electric
Generators and Manufacturers - to be $0.4 billion under Option 1, $4.3 billion under Option 2, and $4.5 billion
under Option 3. The highest undiscounted pre-tax compliance cost for privately owned in-scope facilities in any
single year is expected to be $0.8 billion in 2016 for Electric Generators and $0.4 billion in 2015 for
Manufacturers under Option 1, $10.8 billion in 2021 for Electric Generators and $0.8 billion in 2025 for
Manufacturers under Option 2, and $11.0 billion in 2021 for Electric Generators and $1.4 billion in 2025 for
Manufacturers under Option 3.
8,4 UMRA: Analysis Summary
EPA estimates that the Proposed 316(b) Existing Facilities regulatory options will result in expenditures of at
least $100 million for State and local government entities, in the aggregate, or for the private sector in any one
year. Table 8-6 presents a summary of compliance costs for publicly- and privately-owned facilities, along with
government administrative costs, for each regulatory option.
March 28, 2011
8-7
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Economic and Benefits Analysis for Proposed Existing Facilities Rule Chapter 8: UMRA Analysis
For Option 1, EPA estimates total annual compliance costs for government-owned Electric Generators to be about
$10.8 million. NPDES permitting authorities are expected to incur another $3.7 million per year to implement this
option for both Electric Generators and Manufacturers, resulting in a total annualized cost of approximately $14.5
million for State and local governments. The maximum compliance cost for government-owned Electric
Generators in any one year under Option 1 is $18.9 million in 2036. The maximum administrative cost to NPDES
authorities for administering this option is $4.1 million in 2018 and $1.6 million in 2015 for the Electric
Generators and Manufacturers rule segments, respectively. Privately owned Electric Generators and
Manufacturers are expected to incur annualized compliance costs of $427.4 million under this option, with a
maximum of $0.8 billion in 2015 for Electric Generators and $0.4 billion in 2015 for Manufacturers.
Under Option 2, EPA estimates total annualized costs to State and local governments of $104 million, of which
$102.3 million is the compliance cost to government-owned Electric Generators and $1.7 million is the cost of
implementation of this option for Electric Generators and Manufacturers. The maximum compliance cost for
government-owned Electric Generators in any one year is $334.9 million in 2021 and the maximum costs of
administration to governments of this option for the Electric Generators and Manufacturers segments are $0.8
million in 2022 and $1.4 million in 2025, respectively. EPA estimates total annualized compliance costs of $4.3
billion to the private sector, with maximum compliance costs in any one year of $10.7 billion in 2021 for Electric
Generators, and $0.8 billion in 2025 for Manufacturers.
Option 3 is expected to result in a total of $120.9 million in annualized costs to State and local governments, of
which $120.1 million is the compliance cost to government-owned Electric Generators and $0.8 million is the
cost of implementation of this option for Electric Generators and Manufacturers. The maximum compliance cost
for government-owned Electric Generators in any one year is $389.0 million in 2021, and the maximum costs of
administration of this option for the Electric Generators and Manufacturers segments are $0.7 million in 2022 and
$1.0 million in 2025, respectively. The private sector is expected to incur total annualized compliance costs of
$4.5 billion under Option 3, with a maximum one-year cost of $ 11.1 billion in 2021 for Electric Generators, and
$1.4 billion in 2025 for Manufacturers.
8-8 March 28, 2011
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Economic and Benefits Analysis for Proposed Existing Facilities Rule
Chapter 8: UMRA Analysis
Table 8-6: Summary of UMRA Costs (Millions; $2009)
Sector Incurring
Costs
To
Facility
Compliance
Costs3
tal Annualized Cos
Government
Administrative
Costs"
:
Total
Mas
Facility
Compliance
Costs
imum One- Year C
Government
Administrative
Costs
ast
Total
Option 1: EVI Everywhere
Electric Generators
Government
(excluding Federal)
Private
$10.8
$362"l
$2.5
N/A
$13.3
$36271
$18.9
$75674
$4.1
N/A
$23.0
$75674
Manufacturers
Government
(excluding Federal)
Private
$0.0
$653
$1.2
N/A
$1.2
$653
$0.0
$3713
$1.6
N/A
$1.6
$3713
Option 2: EVI Everywhere and EM for Facilities with DIF>125 MGD
Electric Generators
Government
(excluding Federal)
Private
$102.3
$4^041
$0.6
N/A
$102.9
$47720471
$334.9
$Jo7j4878
$0.8
N/A
$335.7
$Yo;74878
Manufacturers
Government
(excluding Federal)
Private
$0.0
$1393
$1.1
N/A
$1.1
$1391
$0.0
$8323
$1.4
N/A
$1.4
$8323
Option 3: I&E Mortality Everywhere
Electric Generators
Government
(excluding Federal)
Private
$120.1
$473343
$0.4
N/A
$120.5
$47334!
$389.0
$il7l244
$0.7
N/A
$389.7
$11712474
Manufacturers
Government
(excluding Federal)
Private
$0.0
$1648
$0.4
N/A
$0.4
$16478
$0.0
$1743878
$1.0
N/A
$1.0
$J74"387g
a. Cost values for Electric Generators are lower than those presented in Table 8-5 because they reflect a distribution over the compliance window, as
described in Chapter 11: Assessment of Total Social Costs, thus changing the amount discounted in each year.
b. These values are slightly lower than those presented in Table 8-3 because they reflect a distribution over the compliance window, as described in
Chapter 11: Assessment of Total Social Costs, thus changing the amount discounted in each year.
Source: U.S. EPA analysis, 2010
March 28, 2011
8-9
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 9: Other Administrative Requirements
9 Other Administrative Requirements
This chapter presents several other analyses in support of the Proposed Section 316(b) Existing Facilities Rule.
These analyses address the requirements of Executive Orders and Acts applicable to this rule.
9.1 Executive Order 12866: Regulatory Planning and Review
Under Executive Order 12866 (58 FR 51735, October 4, 1993), the Agency must determine whether the
regulatory action is "significant" and therefore subject to OMB review and the requirements of the Executive
Order. The order defines a "significant regulatory action" as one that is likely to result in a rule that may:
> Have an annual effect on the economy of $ 100 million or more or adversely affect in a material way the
economy, a sector of the economy, productivity, competition, jobs, the environment, public health or
safety, or State, local, or Tribal governments or communities; or
> Create a serious inconsistency or otherwise interfere with an action taken or planned by another agency;
or
> Materially alter the budgetary impact of entitlements, grants, user fees, or loan programs or the rights and
obligations of recipients thereof; or
> Raise novel legal or policy issues arising out of legal mandates, the President's priorities, or the principles
set forth in the Executive Order.
Pursuant to the terms of Executive Order 12866, EPA determined that proposed 316(b) Existing Facilities Rule is
an "economically significant regulatory action" because it is likely to have an annual effect on the economy of
$100 million or more. As such, this action was submitted to the Office of Management and Budget (OMB) for
review. Changes made in response to OMB suggestions or recommendations are documented in the docket for
this action.
In addition, EPA prepared an analysis of the potential costs and benefits associated with this action. This analysis
is also described in Chapter 12: Comparison of Social Cost and Monetized Benefits.
9.2 Executive Order 12898: Federal Actions to Address Environmental Justice in
Minority Populations and Low-Income Populations
Executive Order 12898 (59 FR 7629, February 11, 1994) requires that, to the greatest extent practicable and
permitted by law, each Federal agency must make the achievement of environmental justice part of its mission.
E.O. 12898 provides that each Federal agency must conduct its programs, policies, and activities that substantially
affect human health or the environment in a manner that ensures such programs, policies, and activities do not
have the effect of (1) excluding persons (including populations) from participation in, or (2) denying persons
(including populations) the benefits of, or (3) subjecting persons (including populations) to discrimination under
such programs, policies, and activities because of their race, color, or national origin.
The Proposed Existing Facilities Rule options require that the location, design, construction, and capacity of
cooling water intake structures (CWIS) at 316(b) existing facilities reflect the best technology available for
minimizing adverse environmental impact. For several reasons, EPA does not expect that this proposed rule will
have an exclusionary effect, deny persons the benefits of the participation in a program, or subject persons to
discrimination because of their race, color, or national origin. In fact, because EPA expects that this proposed rule
will help to preserve the health of aquatic ecosystems located in reasonable proximity to 316(b) existing facilities,
March 28, 2011
9-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
it believes that all populations, including minority and low-income populations, will benefit from improved
environmental conditions as a result of this rule.
To meet the objectives of Executive Order 12898, EPA assessed whether the Proposed Existing Facilities Rule
could distribute benefits among population sub-groups in a way that is significantly unfavorable to low income
and minority populations. For this analysis, EPA reviewed the profile of populations that would be expected to
benefit (the "benefit populations") from reduced impingement mortality and entrainment mortality of aquatic
organisms as a result of the Proposed Existing Facilities Rule options. The analysis considered the benefit
populations associated with all 700 facilities - 509 Electric Generators and 191 Manufacturers -that could
potentially implement technology improvements as a result of the Proposed Existing Facilities Rule options.201
The majority of these facilities are located inland, and in the eastern half of the United States. For this analysis,
EPA defined the benefit population as (1) all individuals who live within a 50-mile radius of the facilities and (2)
any additional anglers who live outside of the 50-mile facility buffer and within a 50-mile radius of the reaches
nearest to the facilities. Individuals who live within a 50-mile radius of a facility may receive both use (e.g.,
recreational fishing or wildlife viewing) and non-use benefits from the improved aquatic ecosystem health of the
area (e.g., satisfaction from knowing that the overall ecosystem health has improved). Anglers who live within the
50-mile buffer zone are likely to fish the affected water bodies and thus benefit from improved catch rates as a
result of the proposed rule.202
For the assessment of the distribution of benefits among population sub-groups, EPA compared on a state-by-state
basis, key demographic characteristics of the sub-state populations that are expected to benefit from the Proposed
Rule with those demographic characteristics at the level of the state. If the demographic profile of the sub-state
benefit population was found to be statistically similar to the demographic profile of the state and not
exclusionary of minority and low income populations specifically, then the Proposed Rule would be assessed as
not yielding an unfavorable distribution of benefits, from the perspective of the public policy principles of
Executive Order 12898.
EPA completed the analysis of the socio-economic characteristics of the populations affected by the 316(b)
Existing Facilities using the Fish Consumption Pathway (FCP) Module, which reports population estimates by
socio-economic characteristics (U.S. EPA, 2004c).203 The two demographic variables of interest for this EJ
analysis are those within the FCP Module that best capture the low-income and minority aspects of the
populations affected, which are:
> Annual household income: less than $20,000 (low-income) and at least $20,000 (not low-income);204 and
> Race: white, black or African American, Asian or Native Hawaiian or Other Pacific Islander, American
Indian and Alaska Native, and some other race.
201 These are un-weighted explicitly and implicitly analyzed Electricity Generators and un-weighted explicitly analyzed Manufacturers
with a compliance technology - Cooling Tower and/or IM technology - assigned under either of the three proposed options; these
facility counts exclude baseline closures.
202 According to the US Fish and Wildlife Service, over 65% of anglers travel less than 50 miles one-way on a typical fishing trip (U.S.
DOI, 2006).
203 The FCP Module is part of the Risk-Screening Environmental Indicators (RSEI) Model (U.S. EPA, 2004c).
204 Household data in the FCP Module are available for the following household income categories: less than $10,000; $10,000 to
$19,999; $20,000 to $24,999; $25,000 to $29,999; $30,000 to $34,999; $35,000 to $39,999; $40,000 to $49,999; $50,000 to $74,999;
$75,000 to $99,999; and more than $100,000. For this analysis as well as previous 316(b) rule analyses, these categories were
combined into low- and not low-income groups based on the U.S. Department of Health and Human Services' poverty guidelines for a
family of four living in the contiguous United States or D.C. The current (2009) poverty guideline is $22,050, which falls within the
$20,000 to $24,999 income range (U.S. HHS, 2009). For the current analysis, EPA used $20,000 as the threshold for separating
populations into low- and not low-income groups.
9-2 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
As described above, EPA assumed that the primary groups that benefit from the Proposed Rule are (1) all
individuals who live within a 50-mile radius of the facilities and (2) any additional anglers who live outside of the
50-mile facility buffer and within a 50-mile radius of the reaches nearest to in-scope facilities.205 To assess
whether a lower income or minority group would experience a disproportionately low share of this Rule's use and
non-use benefits in relation to the general population, the income and ethnicity of the affected populations were
calculated in the FCP Module and analyzed statistically, using the following procedures:
1. The coordinate locations of each of the 316(b) sample facilities - Existing Generators and
Manufacturers - were imported into the FCP Module.
2. The FCP module estimated the number of individuals residing within 50 miles of each facility.
3. The FCP module calculated the number of additional anglers that fish in the affected reaches but
do not live within a 50-mile radius of the facility on the affected reach by estimating the number
of anglers within a 50-mile radius of the affected reach and then subtracting the number of
anglers within 50 miles of the facility that overlap with the 50-mile radius surrounding the
affected reach.
4. Areas affected by the 316(b) Existing Facilities were spatially defined. They were then
superimposed on the FCP Module's grid, and cell-level population data were used to define a
demographic profile for the affected populations.
5. Once these population estimates were made, the data were exported and examined on a state-by-
state basis.
6. To assess the presence of Environmental Justice concerns for the regulatory options, EPA
compared the composition of the affected populations' income and race with the demographic
composition of the state population as follows:
• Calculating a ratio of low- to not low-income individuals in the vicinity of in-scope facilities and
comparing it to the averages within each affected state.
• Calculating a ratio of minority to white individuals in the vicinity of in-scope facilities and comparing
it to the averages within each affected state.
• Testing the statistical significance of any adverse differences in these observed state-by-state
relationships. That is, the differences are only of concern ("adverse") in the context of the
Environmental Justice analysis when a calculated ratio for the benefit population is lower than the
ratio for the general population. In effect, the analysis uses the observed relationships in individual
states as a set of observations for testing the statistical significance of differences across all states.
If the demographic profiles of the benefit populations and general state populations are not statistically different
and not exclusionary of low-income and minority populations specifically, then the proposed rule would be
assessed as not yielding an unfavorable distribution of benefits, from the perspective of the public policy
principles of Executive Order 12898.
9.2.1 Presence of Low Income Populations in the Benefit Population
Facilities in 48 states are expected to install technologies in response to the rule. Table 9-1 on the following page
reports the ratio of low- to not low-income individuals for the benefit population and the overall state population,
Users of the resources were identified as the group that receives the largest benefits (including their receipt of non-use benefits) from
the Proposed Rule. Non-users of the resource receive smaller benefits (non-use benefits only). Further, non-users could potentially
include all individuals in a given state or other defined non-use benefit region, which could be larger than a state. Where a state is used
as the defined non-use benefit region, the benefit population's characteristics would not differ from the state's overall population.
March 28, 2011 9-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
by state. Instances in which the ratio of low- to not low-income individuals for the benefit population is lower
than this ratio for the overall state population indicate a lower rate of participation in the proposed regulation's
expected benefits in the low income population group than in the general population.
As reported in Table 9-1, following page, the ratio of low income to not low-income populations in the benefit
populations is lower than the ratio for the states' general populations in 38 of the 48 states, with an average
difference of-0.03, which indicates a lower rate of participation in the proposed regulation's expected benefits in
the low income population group than in the general population. The greatest negative difference, -0.14, occurs in
West Virginia, followed by Maine and Kentucky, at -0.11. All other negative differences (35 of the 38 instances
of negative difference) are less than 0.10 (as an absolute value). In no state would the low income population be
excluded or denied participation in the benefits of the proposed regulation - that is, in all states the ratio is greater
than zero for the benefit population. Although the ratio of low income to not low-income populations in the
benefit populations is lower than the ratio for the general populations in a substantial number of states, the
difference across states may not be statistically significant. The following paragraphs review the statistical
analysis of these observed relationships.
9-4 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 9: Other Administrative Requirements
Table 9-1: Low-Income Population Participation in 316(b) Existing Facilities
Rule Benefits by State3
States
Alabama
Alaska6
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii8
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
New Hampshire
New Jersey
New Mexico
NewYork
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Total
Mean
P-valuec
Ratio of Low-I
(<$20
Affected by Facilities
0.40
6723
636
042
6723
b"."i"9
6725
0.24
636
631
6724
0.24
6".27
6725
63s
046
6723"
b".T7
6".23
0.24
046
6".27
636
0.24
6722
0.26
640
'0.27
631
632
0.29
637
0.22
6726
6724
633
638
637
636
6.16
6728
6".23
6723"
042
b".2"b
0.28
6.27
OJ19
ticome to Not ]
,ppp/year) / (>=
State Total
0.45
67i9
031
045
6727
6720
6721
6723
033
031
6722
6725
6727
030
b""2"8
045
6751
034
67i9
6725
6727
6721
6754
034
040
6729
67i9
6720
045
633"
034
037
030
6.40
6728
032
034
039
038
037
035
67i9
6"27
6724
6724
6756
6724
032
636
63i
jow-lncome Individuals
= S2p,ppp/year)
Difference (Affected minus State)
-0.04
b"."b"4
67i9
-6m
-6.04
-b"b'2
6.64
b"."b"i
-6m
6.66
-61)4"
-b"b"i
-6m
-6m
-6m
-oil
-6.06
-oil
-61)2
-6m
-6m
-om
-b"'b"8
-61)7
-6.04
-61)4
b"."b'3"
b"."b"6"
-6.04
-6.06
-b"b'2
-61)5
-b"'b"i
-6m
-61)6
-6"66
-6.09
-61)6
b"."b"i
b"."b"i
-61)5
-6m
b"."bi
-b"b"i
:0^
-654
-61)5
lab's
-6ib3
0.07
a. The "Affected Population" includes all individuals within 50 miles of an in-scope facility and any anglers within
50 miles of the reach nearest to these facilities.
b. Additional angler populations were not counted for Alaska and Hawaii facilities due to lack of RF1 network
coverage in those states.
c. A p-value of 0.05 or less would support the hypothesis that the ratio of low-income to high-income individuals in
areas affected by facilities is statistically different from the overall low-income to high-income ratios in states with
facilities based on a 95% confidence interval.
March 28, 2011
9-5
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
To test the statistical significance of these observed state-by-state relationships, EPA compared the ratios of low-
to high-income individuals affected by the 316(b) Existing Facilities to the ratios of low- to high-income
individuals on a state-by-state basis using a one-tail t-test. This analysis tests whether the mean of the ratios for
the affected populations is lower, in a statistically significant way, than that of the ratios for the states' general
populations. The analysis is based on the following equation:
(9-1)
n.
Where:
t = t-statistic
Xa = Mean ratio of low-income to other income (i.e., not low-income) individuals
within the affected populations sample
Xs = Mean ratio of low-income to other income individuals within the state
populations sample
sa = Variance of ratios of low-income to other income individuals within the affected
populations sample
Ss = Variance of ratios of low-income to other income individuals within the state
populations sample
na = Sample size of affected populations
ns = Sample size of state populations.
From this t-test, the ratio of low-income to not low-income individuals in areas affected by 316(b) Existing
Facilities is not significantly lower than the overall low-income to high-income ratios based on a p-value, or
observed significance level of 0.07.206 This finding indicates that lower income populations are not significantly
underrepresented in the regulation's estimated "benefit population" as compared to the states' general
populations. The proposed regulation thus does not systematically discriminate against, or exclude or deny
participation of, the lower income population group in a way that would be contrary to the intent of E.O. 12898.
In particular, EPA observes that the lower income population group is materially present in the benefit population
for all states and, in all but three states, the amount by which the lower income population group is less present
than in the overall population is very small. Indeed, in these states, the finding that low income populations are
observed to be less present in the potential benefit population, would mean that this population group has
systematically incurred less damage from the ongoing operation of cooling water intake structures at 316(b)
Existing Facilities than the general population of these states. Finally, because all 316(b) Existing Facilities are
subject to the proposed Existing Facilities regulation, there can be no systematic discrimination or exclusion of
206 A p-value of 0.05 or less would support the hypothesis that the ratio of low-income to not low-income individuals in areas affected by
316(b) Existing Facilities is significantly different from the overall low-income to high-income ratios in states with 316(b) facilities
based on a 95% confidence interval.
9-6 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
low income populations from participation in the rule's benefits, based, for example, on selection of only specific
facilities to which the regulation would apply.
9.2.2 Assessment of Presence of Minority Populations in the Benefit Population
Table 9-2, following page, summarizes the ratio of non-white to white individuals affected by the 316(b) Existing
Facilities by state. The state with the highest ratio of non-white to white individuals that are affected by the 316(b)
Existing Facilities Rule is Hawaii, with a ratio of 2.08, while Wyoming has the lowest ratio of 0.05.
As reported in Table 9-2, non-white populations are, on average, more present in the estimated benefit population
than in the states' general populations. On average, the ratio of Non-White to White individuals in the benefit
population exceeds the ratio of Non-White to White individuals in the general populations by 0.04. Thus, on
average, minority populations would be expected to participate by a somewhat greater extent than states' general
populations in the proposed rule's expected benefits. Of the 48 states with in-scope facilities, the difference in the
ratio is negative in 15 states (less presence by minority populations) and is positive in 26 states (greater presence
by minority populations). In only one state (Alaska), does the negative difference exceed 0.1 (as an absolute
value).
The ratios of non-white to white individuals in areas affected by the 316(b) Existing Facilities versus the ratios of
non-white to white individuals on a state-by-state basis were compared to one another using, again, a one-tail t-
test. Based on this t-test, the ratio of Total Non-White to White individuals in areas affected by 316(b) Existing
Facilities is not significantly lower than the overall Total Non-White to White ratios in states with Existing
Facilities based on a p-value of 0.28.20?
9.2.3 Overall Finding
Based on this comparison of socio-economic characteristics of individuals affected by the 316(b) Existing
Facilities to the affected states' overall populations, neither the low-income population nor minority populations
are significantly less present in the estimated benefit population than in the states' general populations. As
described in the preceding discussion, EPA's findings on these questions is modestly stronger for the participation
of minority populations in the rule's benefits than for the participation of low income populations in the rule's
benefits. However, in both instances, any findings of lower participation by the low-income population or
minority populations are not statistically significant.
Thus, from this analysis, neither population group participates to a lower extent, in a statistically significant way,
in the benefits of the proposed regulation than the general population in states with in-scope facilities. EPA judges
that the proposed regulation does not systematically discriminate against, or exclude or deny participation of, the
lower income population group or minority populations in a way that would be contrary to the intent of E.O.
12898. EPA thus concludes, overall, that the proposed regulation is consistent with the policy intent of E.O.
12898.
207 A p-value of 0.05 or less would support the hypothesis that the ratio of non-white to white individuals in areas affected by 316(b)
Existing Facilities is significantly different from the overall non-white to white ratios in states with Existing Facilities based on a 95%
confidence interval.
March 28, 2011 9-7
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 9: Other Administrative Requirements
Table 9-2: Minority Population Participation in 316(b) Existing Facilities Rule Benefits by State3
States0
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusett
s
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
New
Hampshire
New Jersey
New Mexico
NewYork
North
Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South
Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
Black
0.29
6.62
6.02
6.17
6.14
"b".05
6.20
"6724
6.19
6.50
6.11
6.18
6.18
6.64
6.11
6.09
6.46
6.62
"6732
0.06
6.18
6.03
"6755
6.14
6.66
6.05
0.06
6.26
6.6T
6.20
0.31
6.6T
6.13
6.11
"b".03
6.15
6.07
0.38
6.66
6.18
6.18
6.6T
6.04
"6735
6.64
Afftct
Asian &
Pacific
Islander
0.01
b"."bl
6.61
6.6!
6725
b".03
6.68
6.04
6.62
b".03
1.71
6.04
6.64
6.6T
6.62
6.61
6.02
b".03
6767
0.04
6.02
6.03
6.6T
b"."bl
6.61
6.6!
0.05
6.69
6.6T
6.68
0.02
6.6T
6.6!
6762
b".05
b".03
b".05
0.02
6.6!
6.61
6.64
b".03
6762
b".05
6.66
ed by Faci
American
Indian
0.01
6.14
6767'
6.03
6762
6.6T
6.6T
6.66
6.6T
6.6T
676s
6.66
6.66
6.6T
6762
6.66
6.6T
6.66
6.6T
0.01
6.6T
6.6T
6.6T
6.6T
676s
6.6T
0.00
6.6T
6745
6.6T
0.02
6763
6.66
6.T6
6.6T
6.66
6.6T
0.01
6.04
6.66
6.6T
6.6T
6.66
6.6T
6762
lities
Other
0.01
6763
6767
6762
""6732
6769
67i3
6764
6765
6764
6721
6766
6766
6762
6763
6761
6762
6763
6764
0.05
6762
6762
6761
6762
6762
6763
0.04
67ii
67T6
67T6
0.03
6761
6761
6764
6766
6762
6765
0.02
6761
6761
67i6
6765
6765
6763
6767
Total
Non-
White
0.32
""bl2
"0777
""bl4
""b"73
67l9
""b"42
""6732
""bl'7
""6757
2768
""bis"
6l9
6767
67i8
""b7i"i
6750
b767
6744
0.15
""bis"
"o7b9
6758
67i8
""b7b'7
67i6
0.16
""6747
6756
6738
0.37
6766
67i6
""bl'7
67i5
616
67i7
0.43
Hoe
oil
6739
67T6
67ii
""b"45
67i9
Ratio
Black
0.36
"b".05
6.04
6.18
6.11
6.64
6.11
6.26
6.19
6743
6.69
"bib
6.69
6.62
6.66
6.68
6.51
6.6T
6.41
0.06
6.18
6.03"
6.58
6.12
6.66
6.04
0.01
6.18
6.63
"bis"
0.30
6.6T
6.14
6.69
6.62
6.11
"b"."b"5
0.43
6.6!
"bib
6.15
6.6T
6.61
6.26
6.64
ofNon-V
J8
Asian &
Pacific
Islander
0.01
6766
6762
b"b"i
67i7
6763
6763
6763
6762
6763
F.44
6764
b"b"i
b"b"i
6762
b"b"i
6762
b"b"i
b"b'6
0.04
6762
6763
b"b"i
b"b"i
b"b"i
b"b"i
0.01
6768
6762
6767
0.02
b"b"i
b"b"i
6762
6763
6762
6763
0.01
b"b"i
b"b"i
b"b3
b"02
b"b"i
6765
6767
/hite to W
tate Total
American
Indian
0.01
bl'2
6767
b"b"i
b"b'2
b"b"i
6766
b"b"i
b"b"i
b"bb
b"b'5
b"bb
b"bb
b"bb
b"b"i
b"bb
b"b"i
b"b"i
b"b"i
0.00
b"b"i
b"b"i
b"b"i
b"b"i
b"b'7
b"b"i
0.00
b"bb
67i5
b"b"i
0.02
b"b'6
6766
67T6
b"b2
6766
b"b"i
0.00
67i6
b"bb
b"b"i
b"b'2
b"bb
b"b"i
b"b'2
liite Inc
Other
0.01
6.65
6.17
6.02
6.30
6.69
6.66
6.04
6.05
6.64
""bl2
6.08
6.62
6.62
6.04
6.6T
6.6T
6.66
6.03
0.05
6.62
6.62
6.6T
6.6T
6.6T
6.03
0.01
6.08
6.27
6.11
0.03
6.6T
6.6T
6.04
6.66
6.62
6.67
0.01
6.6T
6.6T
6.16
6.65
6.6T
6.03
6.66
ividual
fotal
Non-
White
0.39
""6737
6736
""6722
6760
67i7
6720
""6733
""6727
6756
Ion
. O\J
""6733
67i3
6765
67i4
67i6
""6755
6762
6751
0.16
""6723
6769
6760
67i5
6769
67io
0.03
""6734
""6747
""6742
0.37
6768
67i6
6726
67i3
67i6
67i6
0.46
67i2
""6723
6736
6769
6762
""6735
67i9
s
IIIjM;
Black
-0.07
-6763
-6763
-6761
6763
6761
6769
-6763
6766
6767
6762
-6762
6769
6762
6765
6761
-6766
6761
-6769
0.00
6766
6766
-6763
6762
6766
6761
0.05
6768
-6762
-6763
0.01
6766
-6761
6761
6766
6763
6762
-0.05
6766
-6761
6763
6766
6764
6769
6766
ference (=
Asian &
Pacific
Islander
0.00
-6763
-b"b"i
6766
6768
b"b"i
6765
b"b"i
6766
6766
bis
-b"b"i
b"b3
6766
b766
b766
6766
b"b'2
b"b"i
0.00
b766
b766
b"b"i
b"bb
b"bb
b"bb
0.04
b"b"i
-6.61
b"bb
0.00
b"bb
b"bb
b"b"i
b"b2
b"b"i
b"b2
0.00
b"bb
b"bb
b"b"i
b"b"i
b"b"i
b"bb
:0:bi
Affected r
American
Indian
0.00
-6767
6760
6762
6766
6766
6766
6766
6766
6766
6766
6766
6766
6766
6761
6766
6766
6766
6766
0.00
6766
6766
6766
6766
-6762
6766
0.00
6766
6729
6766
0.00
-6762
6766
6766
6766
6766
6766
0.01
-6765
6766
6766
-6761
6766
6766
6766
tinus S
Other
0.00
-6.62
-6.T6
6.66
6.03
6.66
6.07
6.66
6.66
6.66
-6.6T
-6.62
6.04
6.66
-6.6T
6.66
6.66
6.62
6.6T
0.00
6.66
6.66
6.66
6.66
6.6T
6.66
0.03
6.04
-6.17
-6.6T
0.00
6.66
6.66
6.66
6.66
6.66
-6.62
0.01
6.66
6.66
-6.6T
6.66
6.04
6.66
6.6T
ate)
Total
Non-
White
-0.06
-6.15
047
a62"
6.13
6.6T
Oil
-b"."bi
6.66
b"."b7
bis"
-6^65
6.16
6762
6.64
6^62
-6765
b"."b"5
-6^67
-0.01
6.66
6.66
-6762
6.63
-b"."bi
6.66
0.13
6.13
6.69
-6.64
0.00
-6762
6.66
6.61
6762
6.04
6.61
-0.03
-aoe"
-b"."bi
6.63
6.6T
aos"
b".Tb
6.66
9-8
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 9: Other Administrative Requirements
Table 9-2: Minority Population Participation in 316(b) Existing Facilities Rule Benefits by State3
Ratio of Non-White to White Individuals"
States0
West
Virginia
Wisconsin
Wyoming
Total
Mean
P-valuesd
Black
0.06
6.10
"o"."6"i
"bis
"b."i5
0.39
Affect
Asian &
Pacific
Islander
0.01
0.05
676T
0.05
0.07
0.40
ed by Faci
American
Indian
0.00
6.01
b"."6"i
b7bi
b."b4
0.20
lities
Other
0.00
6767
""b"o3
""o7b7
6.05
0.50
Total
Non-
White
0.07
""6722
"aos
b73o
""6.3i
0.28
Black
0.03
"0.06
"6"."bl
b7l6
6.14
s
Asian &
Pacific
Islander
0.00
6762
676"i
b7bs
6.06
tate Total
American
Indian
0.00
b"7b""i
6762
o7bi
6.02"
Other
0.00
""0.02
""b".o3
"o7b8
0.05
fotal
Non-
White
0.04
oil
""o"o7
o73"b
""b.27
Di
Black
0.03
6764
'""6766
""b7b2"
b.bi
ference (=
Asian &
Pacific
Islander
0.00
6763
6766
b7bb
b.bi
Affected r
American
Indian
0.00
6766
-6761
b7bb
0.02"
tinus S
Other
0.00
6.05
""6766
-b7bi
b.bb
ate)
Total
Non-
White
0.03
6.11
-676T
b7bi
ab<4
a. The "Affected Population" includes all individuals within 50 miles of an in-scope facility and any anglers within 50 miles of the reach nearest these facilities.
b. The U.S. Census Bureau (U.S. Census Bureau, 2001) defines these racial categories as follows: "'White' refers to people having origins in any of the original peoples
of Europe, the Middle East, or North Africa. It includes people who indicated their race or races as 'White' or wrote in entries such as Irish, German, Italian, Lebanese,
Near Easterner, Arab, or Polish. 'Black or African American' refers to people having origins in any of the Black racial groups of Africa. It includes people who
indicated their race or races as 'Black, African Am., or Negro,' or wrote in entries such as African American, Afro American, Nigerian, or Haitian. 'American Indian
and Alaska Native' refers to people having origins in any of the original peoples of North and South America (including Central America), and who maintain tribal
affiliation or community attachment. It includes people who indicated their race or races by marking this category or writing in their principal or enrolled tribe, such as
Rosebud Sioux, Chippewa, or Navajo. 'Asian' refers to people having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent.
It includes people who indicated their race or races as 'Asian Indian,' 'Chinese,' 'Filipino,' 'Korean,' 'Japanese,' 'Vietnamese,' or 'Other Asian,' or wrote in entries
such as Burmese, Hmong, Pakistani, or Thai. 'Native Hawaiian and Other Pacific Islander' refers to people having origins in any of the original peoples of Hawaii,
Guam, Samoa, or other Pacific Islands. It includes people who indicated their race or races as 'Native Hawaiian,' 'Guamanian or Chamorro,' 'Samoan,' or 'Other
Pacific Islander,' or wrote in entries such as Tahitian, Mariana Islander, or Chuukese. 'Some other race' was included in Census 2000 for respondents who were unable
to identify with the five Office of Management and Budget race categories. Respondents who provided write-in entries such as Moroccan, South African, Belizean, or a
Hispanic origin (for example, Mexican, Puerto Rican, or Cuban) are included in the Some other race category."
c. Additional angler populations were not counted for Alaska and Hawaii facilities due to lack of RF1 network coverage in those states.
d. A p-value of 0.05 or less would support the hypothesis that the ratio of non-white to white individuals in areas affected by facilities is statistically different from the
overall non-white to white ratios in states with facilities based on a 95% confidence interval.
March 28, 2011
9-9
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
9.3 Executive Order 13045: Protection of Children from Environmental Health Risks
and Safety Risks
Executive Order 13045 (62 FR 19885, April 23, 1997) applies to any rule that (1) is determined to be
"economically significant" as defined under Executive Order 12866 and (2) concerns an environmental health or
safety risk that EPA has reason to believe might have a disproportionate effect on children. If the regulatory
action meets both criteria, the Agency must evaluate the environmental health and safety effects of the planned
rule on children and explain why the planned regulation is preferable to other potentially effective and reasonably
feasible alternatives considered by the Agency. This proposed rule is an economically significant rule as defined
under Executive Order 12866. However, it does not concern an environmental health or safety risk that would
have a disproportionate effect on children. This regulation establishes requirements for cooling water intake
structures to protect aquatic organisms. Therefore, EPA determined that the Proposed 316(b) Existing Facilities
Rule is not subject to Executive Order 13045.
9.4 Executive Order 13132: Federalism
Executive Order 13132 (64 FR 43255, August 10, 1999) requires EPA to develop an accountable process to
ensure "meaningful and timely input by State and local officials in the development of regulatory policies that
have federalism implications." "Policies that have federalism implications" are defined in the Executive Order to
include regulations that have "substantial direct effects on the States, on the relationship between the national
government and the States, or on the distribution of power and responsibilities among the various levels of
government."
Under section 6 of Executive Order 13132, EPA may not issue a regulation that has federalism implications, that
imposes substantial direct compliance costs, and that is not required by statute unless the Federal government
provides the funds necessary to pay the direct compliance costs incurred by State and local governments or unless
EPA consults with State and local officials early in the process of developing the regulation. EPA also may not
issue a regulation that has federalism implications and that preempts State law, unless the Agency consults with
State and local officials early in the process of developing the regulation.
This proposed rule does not have federalism implications. It will not have substantial direct effects on the States,
on the relationship between the national government and the States, or on the distribution of power and
responsibilities among the various levels of government, as specified in Executive Order 13132. EPA expects an
average annual burden of 21,785 hours with total average annual cost of $1.1 million under Option 1, 6,538 hours
and $353,000 under Option 2, and 20,395 hours and $1.0 million under Option 3, for States to collectively
administer this rule during the compliance period. After the initial compliance period, EPA expects an average
annual burden of 23,550 hours with an average annual cost of $1.2 million for Option 1, 2,528 hours and
$179,000 for Option 2, and 16,988 hours and $848,000 for Option 3. EPA has identified 47 Existing Facilities
generators that are owned by State or local government entities. The estimated average annual compliance cost
incurred by these facilities is approximately $452,000 per facility under Option 1, $4.5 million under Option 2,
and $1.1 million under Option 3. Based on EPA's preferred Option - Option 2 - the Proposed Rule does not have
federalism implications.
The national cooling water intake structure requirements will be implemented through permits issued under the
NPDES program. Forty-six States and territories are currently authorized pursuant to section 402(b) of the CWA,
to implement the NPDES program. In States not authorized to implement the NPDES program, EPA issues
NPDES permits. Under the CWA, States are not required to become authorized to administer the NPDES
program. Rather, such authorization is available to States if they operate their programs in a manner consistent
with section 402(b) and applicable regulations. Generally, these provisions require that State NPDES programs
include requirements that are as stringent as Federal program requirements. States retain the ability to implement
9-10 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
requirements that are broader in scope or more stringent than Federal requirements. (See Section 510 of the
CWA.)
EPA does not expect the proposed rule to have substantial direct effects on either authorized or nonauthorized
States or on local governments because it will not change how EPA and the States and local governments interact
or their respective authority or responsibilities for implementing the NPDES program. This rule establishes
national requirements for 316(b) Existing Facilities with cooling water intake structures. NPDES-authorized
States that currently do not comply with the proposed regulations based on this rule might need to amend their
regulations or statutes to ensure that their NPDES programs are consistent with Federal section 316(b)
requirements. (See 40 CFR 123.62(e).) For purposes of this rule, the relationship and distribution of power and
responsibilities between the Federal government and the State and local governments are established under the
CWA (e.g., sections 402(b) and 510); nothing in this rule alters that. Thus, the requirements of section 6 of the
Executive Order do not apply to this rule.
9.5 Executive Order 13158: Marine Protected Areas
A Marine Protected Area (MPA) is "any area of the marine environment that has been reserved by federal, state,
tribal, territorial, or local laws or regulations to provide lasting protection for part or all of the natural and cultural
resources therein" (Executive Order No. 13158, 2001). Because MPAs focus on the preservation and maintenance
of cultural and natural resources, and/or sustainable production, the ecological importance of MPAs varies widely
(NMPAC, 2006). In some states, the majority of coastal waters are found within MPAs (e.g., Massachusetts,
Hawaii). Consequently, measuring the impact of CWISs on the entire universe of MPAs is unlikely to provide a
realistic estimate of the nonuse values associated with reducing I&E losses. For this reason, EPA focused on
MPAs within the National Estuary Program (NEP) to examine public spending on the conservation of natural
resources and sustainable production. The NEP was established in the 1987 amendments to the Clean Water Act
(CWA) because the "Nation's estuaries are of great importance to fish and wildlife resources and recreation and
economic opportunity [and because maintaining] the health and ecological integrity of these estuaries is in the
national interest" (Water Quality Act, 1987). In addition to the 28 estuaries designated under the NEP (U.S. EPA,
2008c), EPA included facilities found in Chesapeake Bay (itself protected by the Chesapeake Bay Program
[CBP]).
Substantial federal and state resources have been directed to the NEP and Chesapeake Bay Program to enhance
conservation of and knowledge about estuaries. From 2005 to 2007, NEP budgeted $965 million to protect and
restore aquatic habitat, conduct outreach and research, upgrade stormwater infrastructure, and implement other
priority actions to benefit the health of the 28 constituent estuaries. Approximately $130 million (13.5 percent) of
the funding was designated for restoration programs (EPA, 2008a). Between fiscal years 1995 and 2004, federal
and state governments spent an estimated $3.7 billion in direct funding to restore the Chesapeake Bay (GAO,
2005), with an additional $131 million in direct spending in fiscal year 2005 (CBP, 2007). Moreover, recent
governmental action is likely to increase federal spending on restoration efforts in the future (Executive Order No.
13508, 2009). All told, these expenditures reflect high public values for restoring (or protecting) the biological
integrity of these ecosystems.
A total of 116 section 316(b) facilities exist on 75 waterbodies within MPAs designed to preserve natural
resources and/or to ensure sustainable production (NOAA, 2010; Figure 9-3; Table 9-6). Although these facilities
are found in fresh, brackish, and marine waters, and in all regions of the country except California, the vast
majority of 316(b) facilities occurring within MPAs occur in coastal waters, and are most highly concentrated in
the Northeastern United States (i.e. both coastal and inland facilities) (Figure 9-4; Table 9-6). Under Option 1, 87
percent of in-scope facilities found within MPAs obtain reductions in impingement mortality, while entrainment
mortality is not necessarily reduced at any facilities (Table 9-6). Under Options 2 and 3, impingement mortality is
reduced at 92 and 97 percent of 316(b) facilities in MPAs, while the addition of closed-cycle cooling towers
March 28, 2011 9-11
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
results in reduced entrainment mortality at 72 and 92 percent of in-scope facilities found in MPAs, respectively
(Table 9-6).
9-12 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 9: Other Administrative Requirements
Figure 9-5: In-scope Facilities with CWISs Located In Marine Protected Areas
Regi
March 28, 2011
9-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 9: Other Administrative Requirements
Table 9-6: 316(b) Facilities in Marine Protected Areas, and Improvements in IM&EM Technologies by
Policy Option
Benefits Region
California
North Atlantic
Mid-Atlantic
South Atlantic
GulfofMexico
Great Lakes
Inland
Total
Affected
Waterbodies
0
18
24
5
9
3
14
75
Baseline 316(b)
Facilities
0
9
10
23
44
20
10
118
Number of
Option 1
IM
0
17
40
10
8
8
18
103
EM
0
0
0
0
0
0
0
0
facilities with Improved T
Option 2
IM
0
19
41
10
10
9
18
109
EM
0
16
31
9
10
8
9
85
echnologies
Option 3
IM
0
20
43
10
10
9
20
114
EM
0
20
40
9
10
9
18
108
Source: U.S. EPA Analysis, 2010
9.6 Executive Order 13175: Consultation and Coordination With Indian Tribal
Governments
Executive Order 13175 (65 FR 67249, November 6, 2000) requires EPA to develop an accountable process to
ensure "meaningful and timely input by tribal officials in the development of regulatory policies that have tribal
implications." "Policies that have tribal implications" is defined in the Executive Order to include regulations that
have "substantial direct effects on one or more Indian Tribes, on the relationship between the Federal government
and the Indian Tribes, or on the distribution of power and responsibilities between the Federal government and
Indian Tribes."
The Proposed Existing Facilities Rule does not have tribal implications. It will not have substantial direct effects
on tribal governments, on the relationship between the Federal government and Indian Tribes, or on the
distribution of power and responsibilities between the Federal government and Indian Tribes, as specified in
Executive Order 13175. The national cooling water intake structure requirements would be implemented through
permits issued under the NPDES program. No tribal governments are currently authorized pursuant to section
402(b) of the CWA to implement the NPDES program. In addition, EPA's analyses show that no facility subject
to the proposed regulation is owned by tribal governments and thus this regulation does not affect Tribes in any
way in the foreseeable future. Consequently, Executive Order 13175 does not apply to this regulation.
9.7 Executive Order 13211: Actions Concerning Regulations That Significantly Affect
Energy Supply, Distribution, or Use
The OMB implementation memorandum for E.O. 13211 outlines specific criteria for assessing whether a
regulation constitutes a "significant energy action" and would have a "significant adverse effect on the supply,
distribution or use of energy."208 Those criteria include:
> Reductions in crude oil supply in excess of 10,000 barrels per day;
> Reductions in fuel production in excess of 4,000 barrels per day;
> Reductions in coal production in excess of 5 million tons per year;
> Reductions in natural gas production in excess of 25 million mcf per year;
208 Executive Order 13211 was issued May 18, 2002. The Office of Management and Budget later released an Implementation Guidance
memorandum on July 13, 2002.
9-14
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
> Reductions in electricity production in excess of 1 billion kilowatt-hours per year, or in excess of 500
megawatts of installed capacity;
> Increases in the cost of energy production in excess of 1 percent;
> Increases in the cost of energy distribution in excess of 1 percent;
> Significant increases in dependence on foreign supplies of energy; or
> Having other similar adverse outcomes, particularly unintended ones.
Of the potential significant adverse effects on the supply, distribution, or use of energy (listed above) only four
apply to the 316(b) Existing Facilities Rule. Through increases in the cost of generating electricity and shifts in
the types of generators employed, the Existing Facilities Rule might affect (1) the production of electricity, (2) the
amount of installed capacity, (3) the cost of energy production, and (4) the dependence on foreign supplies of
energy. EPA used the results from the electricity Market Model Analysis (see Chapter 6: Market Model Analysis)
to analyze this rule for each of these potential effects.
9.7.1 Impact on Electricity Generation
The Market Model Analysis (Chapter 6) predicts in the aggregate, that the electricity market will generate
29,304,900 KWh less electricity in 2028 (the steady-state post-compliance year) under the preferred regulatory
case (IM Everywhere) than it would in the base case. This is significantly less than the 1 billion KWh reduction
required for the regulation to be considered a significant energy action. EPA does recognize that generation from
the affected in-scope facilities may be reduced more substantially, though this reduction is offset by increased
production from other facilities, resulting in a small net decrease in overall production.
9.7.2 Impact on Electric Generating Capacity
From the Market Model Analysis, EPA identified 39 in-scope generating units, representing 9,874 MW of
generating capacity that are forecast by IPM to retire by 2028 under the preferred option (IM Everywhere). The
Market Model Analysis predicts that another 30 in-scope generating units representing 8,819 MW of capacity will
remain open in the policy case, after being predicted to retire in the base case. This results in a net loss of only
1,055 MW of generating capacity from in-scope units, which constitutes only 0.2 percent of total baseline in-
scope capacity; consequently, EPA does not believe that the Proposed Existing Facilities Rule constitutes a
"significant energy action" in terms of estimated potential effects on electric generating capacity.
Moreover, looking at the total capacity of the electric power industry as forecast by the Market Model Analysis
for the year 2028, the policy case for the preferred option actually shows 585 MW more capacity than in the base
case. Therefore, although the analysis predicts a loss of capacity, however small, for units subject to the Proposed
Existing Facilities Rule, it projects an overall increase in the total generating capacity of the industry.
EPA does not consider the loss of capacity to technology installation downtime to fall within the scope of E.O.
13211 because this loss is temporary. However, even if it did, it would be non-consequential. EPA estimates
temporary loss of capacity due to technology installation downtime to be 318 MW each year during the five-year
compliance window of 2013 though 2017 under the preferred option (see Chapter 3: Development of Costs for
Regulatory Options). A more detailed analysis of the impact of downtime on electricity reliability at any given
point during the compliance period can be found in Appendix 3B: Downtime Impact of Capacity Availability.
From this assessment, EPA judges that the preferred regulatory option does not constitute a "significant energy
action" and would not cause a "significant adverse effect" based on the criterion of reduced electricity generating
capacity.
March 28, 2011 9-15
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
9.7.3 Cost of Energy Production
This Proposed Existing Facilities Rule will not significantly affect the cost of electricity production in either the
short or the long run. In the short run (2015), changes in energy production costs are expected to vary between a
drop of 0.003 percent in WECC (due to lower fuel costs) and an increase of 0.12 percent in RFC. EPA estimates
that two regions - ERCOT and MRO - will experience decreases in their energy production costs in the short run,
while the other six regions will experience increases. In the long-run (2028), sixNERC regions are expected to
experience a slight drop in electricity prices of no more than 0.03 percent occurring in WECC; the remaining two
NERC regions - NPCC and RFC - are expected t o experience a slight increase in electricity prices of 0.12
percent and 0.06 percent, respectively. Consequently, no region will experience energy price increases of more
than 1 percent as a result of the Proposed Existing Facilities Rule in either the short or the long run.
9.7.4 Dependence on Foreign Supply of Energy
EPA's electricity market analysis did not allow for an explicit consideration of the effects of the Proposed Rule on
foreign imports of energy. However, this Rule only affects electricity generators, which are generally not subject
to significant foreign competition. Only Canada and Mexico are connected to the U.S. electricity grid, and
transmission losses are substantial when electricity is transmitted over long distances. In addition, the effects on
installed capacity and electricity prices are estimated to be small. EPA therefore concludes that this Proposed Rule
will not significantly increase dependence on foreign supplies of energy.
9.7.5 Overall E.G. 13211 Finding
From these analyses, EPA concludes that the Proposed Existing Facilities Regulation will not have a significant
adverse effect at a national or regional level. As a result, EPA did not prepare a Statement of Energy Effects. For
more detail on effects of this proposed rule on electricity markets, see Chapter 6: Electricity Market Model
Analysis.
9.8 Paperwork Reduction Act of 1995
The Paperwork Reduction Act of 1995 (PRA) (superseding the PRA of 1980) is implemented by the Office of
Management and Budget (OMB) and requires that agencies submit a supporting statement to OMB for any
information collection that solicits the same data from more than nine parties. The PRA seeks to ensure that
Federal agencies balance their need to collect information with the paperwork burden imposed on the public by
the collection.
The definition of "information collection" includes activities required by regulations, such as permit development,
monitoring, record keeping, and reporting. The term "burden" refers to the "time, effort, or financial resources"
the public expends to provide information to or for a Federal agency, or to otherwise fulfill statutory or regulatory
requirements. PRA paperwork burden is measured in terms of annual time and financial resources the public
devotes to meet one-time and recurring information requests (44 U.S.C. 3502(2); 5 C.F.R. 1320.3(b)).
Information collection activities may include:
> reviewing instructions;
> using technology to collect, process, and disclose information;
> adjusting existing practices to comply with requirements;
> searching data sources;
> completing and reviewing the response;
> and transmitting or disclosing information.
Agencies must provide information to OMB on the parties affected, the annual reporting burden, the annualized
cost of responding to the information collection, and whether the request significantly impacts a substantial
"JM6 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 9: Other Administrative Requirements
number of small entities. An agency may not conduct or sponsor, and a person is not required to respond to, an
information collection unless it displays a currently valid OMB control number.
The OMB previously approved information collection requirements contained in the now suspended Phase II
Rule and the Final Phase III Rule under the provisions of the Paperwork Reduction Act, 44 U.S.C. 3501 et seq.
and assigned OMB control numbers DCN 6-0001 and DCN 9-0002, respectively. These Rules required applicable
facilities to perform several data-gathering activities as part of the permit renewal application process. The
information collection requirements included a one-time burden associated with the initial permit application and
those activities associated with monitoring and reporting after the permit is issued. The total average annual
burden of the information collection requirements associated with the suspended Phase II Final Rule was
estimated at 1,700,392 hours. The annual average reporting and record keeping burden for the collection of
information by facilities responding to the section 316(b) Phase II Existing Facility Final Rule was estimated to be
5,428 hours per respondent (i.e., an annual average of 1,595,786 hours of burden divided among an anticipated
annual average of 294 facilities). The Director reporting and record keeping burden for the review, oversight, and
administration of the rule was estimated to average 2,615 hours per respondent (i.e., an annual average of 104,606
hours of burden divided among an anticipated 40 States on average per year).
The Proposed Existing Facilities Rule would also require affected facilities to perform information collection.
Like the previous 316(b) rules, the proposed rule would subject facilities to both one-time and continuing
requirements. The proposed rule would only require all facilities to monitor for and comply with impingement
mortality and entrainment limits. The proposed rule would impose different and more onerous information
collection requirements on facilities that would elect to comply with one of the compliance alternatives. EPA
notes that no facility is required to use these compliance alternatives and their requirements unless the facility
chooses. The proposed information reporting requirements under these compliance alternatives include
submission of an initial certification statement and annual certification statements thereafter, and maintenance of
on-site compliance paperwork.
A comparison of the proposed requirements and those previously approved by OMB for the suspended Phase II
rule and the Phase III Final rule demonstrates that the proposed requirements (whether a facility would elect to
comply with one of the compliance alternatives or not) are much less burdensome than those associated with the
existing OMB approval. As a result, EPA judges that the existing OMB approved ICR is sufficient to address any
of the information collection requirements proposed.
9.9 National Technology Transfer and Advancement Act
Section 12(d) of the National Technology Transfer and Advancement Act (NTTAA) of 1995, Pub L. No. 104-
113, Sec. 12(d) directs EPA to use voluntary consensus standards in its regulatory activities unless doing so would
be inconsistent with applicable law or otherwise impractical. Voluntary consensus standards are technical
standards (e.g., materials specifications, test methods, sampling procedures, and business practices) that are
developed or adopted by voluntary consensus standard bodies. The NTTAA directs EPA to provide Congress,
through the Office of Management and Budget (OMB), explanations when the Agency decides not to use
available and applicable voluntary consensus standards.
This proposed rule does not involve such technical standards. Therefore, EPA is not considering the use of any
voluntary consensus standards.
March 28, 2011 9-17
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-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
10 Economy-Wide Output and Employment Effects
EPA estimated the economy-wide output and employment effects of the proposed regulation, accounting for inter-
industry linkages at the national level. These economic effects are classified into categories of direct, indirect, and
induced effects:
> Direct effects include changes in jobs, income, and economic activity associated with the expenditures to
achieve compliance with the regulation - for example, installation and operation of compliance
technology, and the economic activity effects associated with compliance cost-related price increases.
> Indirect effects refer to changes in jobs, income, and economic activity in upstream-linked sectors in the
economy that supply materials and services to the directly affected sectors.
> Induced effects refer to changes in jobs, income, and economic activity that are induced by the spending
of those persons directly and indirectly affected by the project. These effects occur when the income
generated by the direct and indirect effects is re-spent in the local economy.
Total economy-wide effects refer to the sum of the direct, indirect, and induced effects. For example, cost outlays
to comply with the regulation lead to increased production from those sectors that provide compliance services.
This constitutes a direct effect (i.e., a change in demand) due to the regulation. Those sectors, in turn, require
inputs from other sectors to produce any given unit of output. The total change in economic activity, accounting
for industry linkages, derived from the outlays required for compliance may be thought of as the total increase in
the demand for society's resources necessary for compliance to occur, where the value-added components of all
the increased activity would be equal to the total cost of compliance.
The key direct effect concepts modeled in this analysis include:
> Compliance technology and other initial/one-time resource cost outlays, which include capital outlays for
compliance technology, initial permitting costs, and the cost of downtime. These cost outlays primarily
occur during the initial compliance period for a given regulatory option. However, some of the
compliance technologies have shorter useful lives than the overall analysis period, and these otherwise
initial/one-time type outlays may be repeated during the analysis period.
> Recurring resource cost outlays for compliance, which may include permit renewal costs, O&M costs,
monitoring costs, and energy penalties - depending on the regulatory option under consideration.
Recurring costs include all costs that, once initiated, continue with some frequency for the duration of the
cost analysis period.
The initial/one-time and recurring resource costs outlays for compliance are taken directly from the
analysis of total social cost, which accounts for costs on an explicit year-by-year basis.
> Cost recovery in the electricity sector, which refers to the expectation that in-scope Electric Generators
will attempt to recover the cost of the regulation through increased electricity rates. Increased electricity
rates for residential and business consumers constitute a direct effect on these entities. Cost recovery in
the electricity sector is assumed to occur annually over the entire cost analysis period for a given
regulatory option.
> Cost absorption in manufacturing sectors, which refers to the expectation that increased costs incurred by
in-scope Manufacturers will be absorbed by those entities, manifesting as a reduction in lost profit and
ultimately household income. EPA judges that it is reasonable to assume that Manufacturers will absorb
all (or a substantial portion) of their costs since these entities constitute a small fraction of the total value
March 28, 2011
10-1
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 10: Total Output and Employment Effects
of economic activity in their respective sectors.209 The effect of total costs absorbed by manufacturing
entities is modeled on an annualized basis over the entire cost analysis period for a given regulatory
option (i.e., a concept analogous to treatment of electricity sector rate recovery).
EPA performed the analysis of total economy-wide effects for the three regulatory options discussed elsewhere in
this document:
> Option 1: IM Everywhere
> Option 2: IM Everywhere and EM for Facilities with DIP > 125 MOD
> Option 3: I&E Mortality Everywhere.
For this analysis, EPA assessed economic effects over the same cost analysis period as used in the social cost
analysis (see Chapter 11: Assessment of Total Social Costs): that is, from rule promulgation at 2012 through
2056, or 45 years of compliance costs. As described more fully in Chapter 77, the 45 years of compliance costs
reflects the following:
> Rule promulgation and first incurrence of compliance-related costs at 2012
> Achievement of compliance for IM-only facilities, beginning year 2013 and ending 2017
> Achievement of compliance for non-nuclear Electric Generators installing cooling towers, assumed for
analysis purposes to begin in year 2018, and ending not later than 2022
> Achievement of compliance for Manufacturers and nuclear Electric Generators installing cooling towers,
assumed for analysis purposes to begin in year 2023, and ending not later than 2027
> Beginning of steady state compliance for all facilities in 2027 (the last year in which any facility is
expected to achieve compliance) and continuing for 30 compliance years, to 2056. The 30 years of
compliance reflects the estimated useful life of the longest-lived compliance technology equipment
expected to be implemented in response to any of the regulatory options. Note that the first year of steady
state compliance for all facilities overlaps with the last year in which any facility would be expected to
achieve compliance under any of the regulatory options.
Because the initial/one-time resource cost outlays are concentrated in the nearer term years following rule
promulgation while the recurring cost outlays and cost recovery/cost absorption effects occur on a more steady
state basis over the life of the analysis, the profile of effect for these broad categories of outlay/cost recovery
event varies substantially over time. For this reason, EPA reviews the cost and effect concepts profiles for these
two categories of outlay/cost recovery event separately below.
EPA used an input-output-based multiplier modeling approach to estimate total output and employment effects
from the direct effects for each option. In this approach, the value of each direct effect is estimated on an average
annual basis for the time period over which the given effect is expected to occur under the regulatory option being
considered.210 All dollar values presented in the analysis are in units of millions of 2009 dollars. After estimating
the annual direct effect values, EPA then used the 60-sector input-output multiplier framework from the U.S.
Bureau of Economic Analysis' 2006 Regional Input-Output Modeling System (RIMS II) to estimate the total
economy effects - also on an annual basis - due to a given direct effect. Multiplying the RIMS multiplier for a
directly affected industry sector by its assigned direct effect value yields an estimate of the total economic effect.
209 An alternative case would model this direct effect by assuming these costs are passed-through by Manufacturers to their customers,
causing a demand elasticity response, and ultimately, a reduction in Manufacturers output (assuming some degree of elasticity in
consumers' response to increased prices). EPA did not make this assumption for this primary analysis, since the expectation for cost
pass-through for affected manufacturing entities is low (see Appendix 4A: Cost Pass-Through Analysis).
210 Average annual effects are calculated simply as the aggregate value of a given effect divided by the number of years in the time period
applicable to that effect.
10-2 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 10: Total Output and Employment Effects
Lastly, annual effects are summed for each year of each time period. The RIMS II multipliers for a given direct
effect sector are defined as follows:
> Economic output multiplier. The output multiplier for a given direct effect sector represents the total
dollar output effect that occurs in all sectors for each dollar of final demand effect in the direct effect
sector.
> Employment multiplier. The employment multiplier for a given direct effect sector represents the total
potential job effect that occurs in all sectors for each $1 million of output delivery to final demand in the
direct effect sector.
For reasons outlined below, this analysis likely overstates economic impacts. The extent of likely overstatement
increases as the estimates are extended farther into the future - i.e., during the long-term steady-state periods of
rule compliance and analysis described above. The reasons for overstatement include:
> The input-output based multiplier analysis methodology - by definition - embeds a Leontief, fixed-
coefficients model of economic activity. Within this framework, the mix of inputs required to produce a
unit of economic activity in a given sector is fixed. In this way, an input-output multiplier approach does
not account for potential adjustments to the production framework, such as input substitutions or
productivity changes (e.g., technological change overtime), which may reasonably occur in response to
the rule's cost and price effects. Such changes would potentially moderate the cost and economic impacts
estimated in this analysis. In addition, the opportunity for, and likelihood of, these changes increase over
time.
> Similarly, on the demand side, this analysis approach does not account for potential elasticity and
dynamic behavioral responses overtime by affected entities. For example, the analysis of impacts due to
cost recovery through electricity rates is based on a level of cost recovery assuming that the profile of
electricity consumption and demand characteristics do not change overtime. Again, such changes could
moderate the cost and economic impacts estimated in this analysis, and the opportunity for these changes
increases overtime.
> With respect to both points above, the degree to which these responses would potentially affect analysis
results increases as one looks farther into the future. The multipliers used in this analysis reflect the
structure, composition, and activity profile of the U.S. economy during 2006. And, elasticity/behavior
responses are generally greater in the long-run than in the short-run. The combination of these factors
means that the accuracy of the results of this analysis declines as the time horizon lengthens. In addition,
the likelihood that the results are over-estimates increases overtime.
Overall, the preceding observations point to a likelihood that the analysis findings of longer term economic and
employment effects - for example, resulting from increased electricity production costs, associated electricity
price effects and impacts on consumers and the total economy - overstate, perhaps substantially, the impact of the
regulation on economic output and employment.211
Table 10-1 presents the output and employment multipliers used in this analysis; the multipliers vary by industry
defined in the North American Industry Classification (NAICS) framework at the 3-digit NAICS level. The
following Sections 10.1-10.4 of this chapter detail the analysis for each direct effect category. Section 10.5
presents the analysis results.
211 Despite these limitations, this analysis approach provides important insights and value in this analysis. Notably, the accessibility of the
main data sources required for the analysis makes it possible to produce a conservative estimate of economic impacts without
conducting relatively high level-of-effort modeling exercises. In addition, the level of industrial detail desired for this analysis can be
captured by the RIMS multipliers, whereas other general equilibrium modeling approaches generally do not provide a sufficient level
of industry disaggregation. Additional uncertainties and caveats to this analysis are summarized in Section 10.6.
March 28, 2011 10-3
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
Table 10-1: Key RIMS 2006 Economic Impact Multipliers
RIMS Economic Sector
Utilities (NAICS 221)
Construction (NAICS 231)
Paper manufacturing (NAICS 322)
Chemical manufacturing (NAICS 325)
Petroleum and coal products manufacturing (NAICS 324)
Primary metal manufacturing (NAICS 331)
Food, beverage, and tobacco product manufacturing (NAICS 311, 312)
Households (NAICS 814)
Output Multiplier
2.21
3.23
3.08
2.98
2.29
2.67
3.45
2.32
Employment Multiplier
8.45
25.06
16.40
13.27
7.94
12.45
19.97
17.58
Source: U.S. EPA Analysis, 2010
10.1 Economic Effects Due to Initial/One-Time Compliance Outlays
As described above, the initial/one-time compliance outlays include capital outlays for compliance technology,
initial permitting costs, and the cost of downtime. These outlays occur initially during the post-promulgation
period based on the compliance schedule requirements of a given regulatory option and the compliance
technology being installed to meet compliance requirements. In addition, these outlays may recur during the
analysis period as specific compliance technology installations reach the end of their useful life and are assumed
to be reinstalled by facilities. EPA assumed that all technologies except cooling towers would be reinstalled upon
reaching the end of the technology's useful life, with additional technology outlays occurring in the year of
reinstallation. In those instances in which the end of the analysis period would be reached before the reinstalled
technology had run its full useful life, EPA prorated the technology outlay based on the number of years
remaining the analysis period relative to the estimated useful life of the technology.
These initial/one-time cost outlays occur initially during the years following rule promulgation and the economic
effects of these outlays are therefore concentrated during these initial compliance periods. The intervals of years
over which the initial compliance outlays occur vary by regulatory option based on the compliance schedule
specified for in-scope facilities under the option. In addition, within a given regulatory option, the cost outlay
values vary over time based on the compliance schedule requirements applicable to specific categories of
facilities. Finally, the specific cost items incurred - e.g., technology installation, initial permitting, installation
downtime - also vary by option and by facility type. For this analysis, EPA assumed the following profile of
outlays for the initial compliance outlays:
> Under Option 1: IM Everywhere, all Electric Generators and Manufacturers are expected to incur initial
technology installation costs, downtime costs, and initial permitting costs during the period 2012-2017.
> Under Option 2: IM Everywhere and EM for Facilities with DIP > 125 MGD, facilities installing only IM
technology incur initial costs during the period 2012 - 2017 (like Option 1), while facilities that install
cooling towers incur initial costs as follows: non-nuclear Electric Generators incur costs during the period
2017 - 2022; Manufacturers and nuclear Electric Generators incur initial costs during the period 2022 -
2027.
> Under Option 3: I&E Mortality Everywhere, non-nuclear Electric Generators incur initial costs during the
period 2017 - 2022, while Manufacturers and nuclear Electric Generators incur initial costs during the
period 2022 - 2027.
For all options, technology installation-related outlays, whether during the initial compliance period or during
reinstallation, are modeled as an increase in demand for goods and services from the Construction sector. Other
initial outlays (i.e., initial permitting and downtime) are modeled as an increase in demand in the directly affected
sector. For example, annual permitting and downtime costs for Electric Generators are modeled as an increase in
demand in the Utilities sector, while permitting and downtime costs for Paper Manufacturers are modeled as an
10-4
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
increase in demand in the Paper Manufacturing sector.212 The total annual economic effect - calculated as the
product of a given multiplier and the associated direct effect - is assumed to occur during each year over which
the direct effect is applicable.
Table 10-2 summarizes the annual average direct values by regulatory option, for each of the categories of outlay,
during the initial technology installation/compliance achievement periods.
Table 10-2: Average Annual Initial/One-Time Compliance Costs during Initial Compliance
Achievement Periods (Millions; $2009)a
Facility and
Direct Effect Category
Applicable
Time Period
Option 1
Annual Direct Effect Value
| Option 2 1 Option 3
Electric Generators
Technology installation
Initial permitting costs
Cost of downtime
2012-2017
2018-2022
2023 - 2027
2012-2017
2018-2022
2023 - 2027
2012-2017
2018-2022
2023 - 2027
$308.2
$0.0
$0.0
$3.3
$0.0
$0.0
$24.0
$0.0
$0.0
$774.2
$8,443.1
$2,775.5
$1.5
$1.9
$0.2
$43.6
$1,063.3
$358.6
$783.2
$8,649.0
$2,775.5
$1.1
$2.4
$0.2
$45.5
$1,192.7
$377.7
Manufacturers
a. Facilities may also incur these one-time costs at the time of technology re-installation.
Source: U.S. EPA Analysis, 2010
Technology installation
Initial permitting costs
Cost of downtime
2012-2017
2018-2022
2023 - 2027
2012-2017
2018-2022
2023 - 2027
2012-2017
2018-2022
2023 - 2027
$54.4
$0.0
$0.0
$3.4
$0.0
$0.0
$3.4
$0.0
$0.0
$29.8
$25.3
$240.8
$3.0
$0.1
$0.3
$3.3
$0.0
$0.1
$2.7
$48.8
$457.0
$0.6
$0.7
$2.7
$0.2
$0.0
$3.9
10J Economic Effects Due to Recurring Compliance Costs
As previously described, recurring compliance outlays include permit renewal costs, O&M costs, monitoring
costs, and the energy penalty cost, depending on the regulatory option under consideration. As described above
for initial/one-time compliance outlays, the years in which different categories of facilities incur a particular
category of costs vary by regulatory option, as follows:
> Under Option 1: IM Everywhere, Electric Generators and Manufacturers incur O&M, permit renewal, and
monitoring costs. O&M and monitoring costs begin in 2013 and are incurred annually for the remainder
of the analysis period, while permit renewal costs are incurred on a 5-year interval with the first set of
facilities incurring these costs in 2018.
> Under Option 2: IM Everywhere and EM for Facilities with DIP > 125 MGD, facilities installing only IM
technology, incur recurring costs beginning in 2013, following the same schedule as outlined for Option
1, above. Facilities installing either only a cooling tower or a combination of a cooling tower and IM
As described here, economic effects are modeled individually for each regulated manufacturing sector, and reported as an aggregate
across all manufacturers. This is significant since compliance costs are not distributed uniformly across regulated manufacturing
sectors and multiplier effects differ by sector. If manufacturing sector effects were modeled for an aggregate manufacturing sector, the
results would be more crude than in the current analysis.
March 28, 2011
10-5
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
technology incur the categories of costs over the specified time intervals as summarized for Option 3 in
Table 10-3.
> Under Option 3: I&E Mortality Everywhere, non-nuclear Electric Generators incur O&M, energy penalty,
and permit renewal costs beginning in 2018, while Manufacturers and nuclear Electric Generators incur
these costs beginning in 2023. Permit renewal costs begin 5 years later than these start dates for each
category of facility. Facilities installing IM technology under this option incur recurring costs beginning
in 2013, following the same schedule as outlined for Option 1, above.
The economic effect of recurring cost outlays is modeled as an increase in demand in the directly affected sector.
In the same way as described above for the initial/one-time compliance outlays analysis, the total effect of these
outlays is calculated as the product of the RIMS multiplier for the direct effect sector and the direct outlay value.
Table 10-3 summarizes the occurrence of these costs over illustrative time periods. The first three periods
reported correspond to the periods in which facilities are required to achieve compliance under the regulatory
options. As described above, these "compliance achievement" periods vary by regulatory option, but, looking
over all options, all facilities achieve compliance no later than 2027. The fourth period reported (2028-2056)
reflects the steady state compliance period, in which all facilities will be in compliance regardless of regulatory
option.
Table 10-3: Average Annual Recurring Compliance Costs by (Millions; $2009)
Facility and
Direct Effect Category
Applicable
Time Period
Annual Direct Effect Value
Option 1
Option 2
Option 3
Electric Generators
O&M
Permit Renewal
Monitoring
Energy Penalty
2013-2017
2018-2022
2023 - 2027
2028 - 2056
2013-2017
2018-2022
2023 - 2027
2028 - 2056
2013-2017
2018-2022
2023 - 2027
2028 - 2056
2013-2017
2018-2022
2023 - 2027
2028 - 2056
$68.8
$115.0
$115.0
$107.1
$0.0
$1.8
$1.8
$1.5
$18.2
$30.4
$30.4
$28.3
$0.0
$0.0
$0.0
$0.0
$4.0
$248.1
$490.0
$506.5
$0.0
$0.6
$1.6
$1.5
$5.2
$20.3
$29.5
$28.3
$0.0
$1,165.0
$2,607.4
$2,840.7
$1.9
$250.1
$495.8
$511.9
$0.0
$0.3
$1.6
$1.5
$2.7
$18.6
$29.5
$28.3
$0.0
$1,246.8
$2,743.7
$2,967.7
Manufacturers
O&M
Permit Renewal
Monitoring
Energy Penalty
2013-2017
2018-2022
2023 - 2027
2028 - 2056
2013-2017
2018-2022
2023 - 2027
2028 - 2056
2013-2017
2018-2022
2023 - 2027
2028 - 2056
2013-2017
2018-2022
2023 - 2027
2028 - 2056
$13.2
$22.1
$22.1
$20.5
$0.4
$1.8
$1.8
$1.5
$16.7
$27.9
$27.9
$26.0
$4.1
$0.0
$0.0
$0.0
$6.9
$11.6
$20.5
$24.7
$0.3
$1.6
$1.7
$1.5
$14.7
$24.6
$25.9
$24.9
$3.9
$0.0
$0.1
$0.0
$0.7
$1.2
$16.4
$24.7
$0.1
$0.3
$0.6
$1.5
$2.2
$3.6
$9.3
$12.1
$0.2
$0.0
$3.9
$0.0
Source: U.S. EPA Analysis, 2010
10-6
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
10.3 Economic Effects Due to Changes in Electricity Rates
This section presents EPA's assessment of economy-wide output and employment effects due to increased
electricity rates for residential and business electricity consumers. This assessment is based on the assumption that
electricity sector generators will pass through the cost of the regulation as an increase in electricity rates.
> For residential electricity customers, the analysis of total economic effects is based on the expected
change in the profile of household-level personal consumption expenditures (PCE) for goods and services
in the overall economy in response to higher rates.
> For business electricity customers, the framework for modeling the effects of increases electricity rates is
slightly more complex. For these customers, the analysis is based on the assumption that business
customers will attempt to pass through to their customers as increased prices, the electricity rate increases
that they see as a result of this Regulation. The analysis then evaluates the change in consumer demand
for the products of these affected business sectors (the electricity customers) due to increased prices using
demand and supply elasticity values for the products of these sectors. The resulting change in the value of
output for affected electricity consuming sectors (due to the supply-demand response to higher production
costs and higher prices) then becomes the direct effect input into the multiplier analysis for each affected
business sector (the electricity customers).
These analyses are described more fully below. However, as a first step in this analysis, EPA initially allocated
the aggregate annual change in electricity rates to these categories of residential and business electricity
consumers. The aggregate annual change in electricity rates is based on the total annualized, pre-tax compliance
cost for Electric Generators facilities as of a given compliance year discounted to the analysis year of 2015. This
is the same value that is used in estimating potential rate effects to residential, commercial, industrial, and
transportation customers in other parts of this analysis, as described in Chapter 5: Cost and Economic Impact
Analysis - Electric Generators.
EPA allocated this aggregate annual rate effect across four electricity consuming sectors - residential,
commercial, industrial, and transportation - in proportion to each sector's share of national electricity
consumption as reported in the 2009 Annual Energy Outlook (EIA). Table 10-4 presents the annual rate recovery
value for each regulatory option and electricity consuming sector, along with the time period over which this
annual value is applicable. For this analysis, EPA calculated the rate recovery values as the annual average value
over the full time period of potential rate recovery effect and carried these values forward for the analysis on a
constant annual effect basis (see Table 10-4, below). For example, under Option 3, the electricity sector is
expected to recover approximately $3.7 billion on a constant annual equivalent value basis, annually from 2013 -
2056, from residential customers. To the extent that the rate increase from compliance costs would phase in
before reaching the "steady state" constant value, this analysis will overstate the economic impact from the
electricity rate increase.
Table 10-4: Average Annual Electricity Rate Recovery for Generators (Millions; $2009)
Sector Impact Category
Option 1
Applicable | Annual Direct
Time Period | Effect Value
Option 2
Applicable | Annual Direct
Time Period | Effect Value
Option 3
Applicable | Annual Direct
Time Period | Effect Value
Residential Consumers
All Residential
2013-2056
$179
2013-2056
$2,171
2013-2056
$2,235
Non-Residential Consumers
Commercial
Industrial
Transportation
2013-2056
2013-2056
2013-2056
$189
$129
$1
2013-2056
2013-2056
2013-2056
$2,297
$1,564
$12
2013-2056
2013-2056
2013-2056
$2,365
$1,610
$12
Source: U.S. EPA Analysis, 2010
March 28, 2011
10-7
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 10: Total Output and Employment Effects
10.3.1 Residential Electricity Consumer Effects
The assessment of economic effects arising from rate changes to residential electricity customers begins with the
residential rate impact values reported for each regulatory option in Table 10-4. EPA assumed that increased
household expenditures on electricity due to this Regulation cause an offsetting equivalent reduction in household
expenditures on other goods and services. This reduction in household expenditures, in turn, leads to a reduction
in activity in the economic sectors linked to those sectors that are directly affected by the change in household
expenditures. EPA estimated the effect of the residential rate recovery value using multipliers from the RIMS
Households sector, which reflects the overall personal consumption expenditure pattern of U.S. households (see
Table 10-1 for Household sector multiplier values).
10.3.2 Business Electricity Consumer Effects
As summarized above, assessing the economy-wide effect of rate changes to business electricity customers
involves three principal steps:
1. Allocate the total commercial, industrial, and transportation rate effect (from Table 10-4) over the
affected business subsectors;
2. Estimate the change in economic activity in directly affected business subsectors; and,
3. Estimate the total economic effect of the activity changes in directly affected subsectors.
Allocating the Business Customer Rate Effect over Directly Affected Business Sub-Sectors
Table 10-4 reports the aggregate annual rate recovery value allocated to the broad commercial, industrial, and
transportation business categories, nationally. Using these values as a starting point, EPA then undertook an
analysis to allocate the rate effect in each broad business category to individual business sectors (at approximately
the 3-digit NAICS level). The rate effect in each broad business category is allocated to related subsectors in
proportion to each subsector's estimated electricity consumption out of the total of electricity consumption in the
affected business sectors.
The electricity consumption profile by economic subsector is based on (1) estimated electricity consumption
intensity by sector (national electricity consumption in relation to economic value-added), and (2) 2007 national-
level value-added in these sectors as reported by the Bureau of Economic Analysis of the U.S. Department of
Commerce. EPA developed the estimates of electricity consumption intensity using electricity consumption data
at the state and national levels by economic sector from several sources, as follows:
> The Manufacturing Energy Consumption Survey (MECS), compiled by the U.S. Department of Energy,
Energy Information Administration, which reports energy consumption, including consumption of
purchased electricity, at approximately the level of 3-digit NAICS manufacturing sectors. These data are
used to estimate electricity consumption intensity by sector, and, in conjunction with value-added by
sector, total electricity consumption by manufacturing sector.
> The State Energy Data System (SEDS), also compiled by the U.S. Department of Energy, Energy
Information Administration, which reports electricity and other energy consumption by state for
aggregate sectors - commercial sector, industrial sector, transportation sector, and electric power sector.
SEDS data were used to estimate electricity consumption intensity for the commercial and transportation
sectors.
> The Economic Census, which reports electricity consumption data for the construction and mining
sectors.
10-8 March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
EPA combined the electricity consumption data with value-added data from the BEA to calculate electricity
consumption intensity (MWh per million dollars of value added) for key sectors. All electricity consumption
intensity values are at the national level and vary by sector and subsector. To estimate electricity consumption by
economic sector, EPA multiplied the sector-level electricity intensity values by sector-level value-added. As the
final step of this part of the analysis, EPA allocated the total business customer rate effect over all sub-sectors in
proportion to the estimated electricity consumption values, by sector. Key results of this part of the analysis are
presented in Table 10-5, which shows the estimated allocation factors for each broad business category that are
used to distribute the annual electricity rate effect across economic subsectors.
Table 10-5:
Allocation Factors for Distributing Business Customer Rate Effects Across Economic Sectors
Economic Subsector
Electricity
Consumption Intensity
(kWh/SMln; $2009)
2007 Value
Added,
(Mm; $2009)
Electricity
Consumption
(Mm kWh)
Rate Effect
Allocation
Factors
Industrial Sector
Oil and gas extraction
Mining, except oil and gas
Support activities for mining
Construction
Wood product manufacturing
Nonmetailic mineral product manufacturing
Primary metal manufacturing
Fabricated metal product manufacturing
Machinery manufacturing
Computer and electronic product manufacturing
Electrical equipment and appliance manuf
Motor vehicle, body, trailer, and parts manuf
Other transportation equipment manufacturing
Furniture and related product manufacturing
Miscellaneous manufacturing
Food, beverage, and tobacco product manuf
Textile and textile product mills
Apparel, leather, and allied product manuf
Paper manufacturing
Printing and related support activities
Petroleum and coal products manufacturing
Chemical manufacturing
Plastics and rubber products manufacturing
504,624
564,624
5047624
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568744"6
747396
25674;979
3697266
2157536
168,836
230393
3227936
3227936
f96782i
1487614
3987795
fj53;638
1587698
f;65o52i
2757642
9357765
733,036
689726!
168,273
46583
667410
6247649
327681
557580
637754
1437445
f287427
1497473
597667
166337
98485
367168
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1787490
26"5'29
167449
517866
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84,914
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637944
T'8,236
417207
176339
527969
27380
25536
137747
327499
317707
77119
11,684
717181
23338
27610
547401
137460
67363
1867635
46315
7.7%
2.T%
376%
578%
F.7%
377%
153%
478%
2.5%
273%
F.2%
279%
279%
676%
176%
6.5%
2.T%
672%
479%
f.2%
671%
1679%
472%
Commercial Sector
Wholesale trade
Retail trade
Publishing including software
Motion picture and sound recording industries
Broadcasting and telecommunications
Information and data processing services
Federal Reserve banks, credit intermediation
Securities, commodity contracts, investments
insurance carriers and related activities
Funds, trusts, and other financial vehicles
Real estate
Rental and leasing services
Professional, scientific, and technical services
Management of companies and enterprises
Administrative and support services
Waste management and remediation services
Educational services
Ambulatory health care services
Hospitals and nursing and residential care
Social assistance
Performing arts, museums, and related activities
Amusements, gambling, and recreation
123,978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
1237978
822,768
9117860
1417811
44341
3497827
637638
5757-341
2427652
3397712
17569
17626,914
1337191
1,029,661
2777185
3897469
34335
1327309
5087965
3817866
877559
637141
73362
102,005
1137651
177581
5'7497
43571
77'8"l"5
63591
3"6",6"8"4
427TT7
27F53
2667958
167885
347365
48586
4"594
167403
637693
47536
16555
77828
97126
9.3%
i'65%
i'3%
63%
379%
677%
578%
277%
378%
65%
i'85%
i'3%
FF.6%
371%
474%
674%
i'3%
577%
4.3%
F6%
677%
678%
March 28, 2011
10-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
Table 10-5: Allocation Factors for Distributing Business Customer Rate Effects Across Economic Sectors
Economic Subsector
Accommodation
Food services and drinking places
Other services
Electricity
Consumption Intensity
(kWh/SMln; $2009)
123,978
123;978
1237978
2007 Value
Added,
(Mm; $2009)
123,523
2647209
322/146
Electricity
Consumption
(Mm kWh)
15,314
3727756"
397975
Rate Effect
Allocation
Factors
1.4%
16%
37(5%
Transportation Sector
Air transportation
Rail transportation
Water transportation
Truck transportation
Transit and ground passenger transportation
Pipeline transportation
Other transportation and support activities
Warehousing and storage
18,210
187210
187210
187210
T'8,210
187210
T'8,210
TsTiTo
56,397
4J";379
io'^932"
130368
19J19
127260
103J02
4074
1,027
754
199
2,374"
359
223
1,888
750
13.6%
979%
"2.6%
3773%"
477%
279%
2479%
979%"
Source: U.S. EPA Analysis, 2010
Estimating the Change in Economic Activity in Affected Business Sub-Sectors
EPA assessed the economic effects of the business customer rate increases in a partial equilibrium impact analysis
framework, in which directly affected business customers - i.e., those customers incurring an electricity rate
increase - will attempt to pass that increase along to their customers as price increases. Markets respond to these
cost-induced price increases by adjusting to a new supply-demand equilibrium in which prices are typically
increased, and total sales and production quantities are typically decreased. The analysis is described as being
performed in a partial equilibrium framework because these market responses are assessed only in the context of
single affected market(s) and do not consider the interactions of these individual markets with other economically
linked markets (e.g., markets for the supplier goods of the directly affected markets, or markets of competitor
goods of the directly affected markets), which may also adjust in response to the production cost effects in the
individually analyzed markets. Overall, EPA expects the business customer production cost and price effects of
increased electricity prices from the Proposed Existing Facilities Rule to be sufficiently small that a partial
equilibrium analysis will provide appropriate insight into the potential total market effects of the proposed
regulation.
Within a given market, the increase in production costs and resulting potential increase in product prices will
likely lead to contractions in the total sales and production (i.e., economic output) in the affected markets. The
extent of contraction in output will depend on the expected character of the market response, which is captured by
the concepts of price elasticity of demand and supply. Price elasticity of demand captures the quantity response of
customers of those goods and services for which prices increase, while price elasticity of supply captures the
quantity response of the suppliers of the affected goods and services to the changes in production cost resulting
from increased electricity rates and related price effects. Both of these factors interact to lead to the new market
equilibrium.
On the demand side, for markets in which demand is relatively elastic - i.e., markets for goods and services for
which consumers are relatively sensitive to changes in price - the contraction in output will be more substantial,
all else equal. For markets in which demand is relatively inelastic - i.e., markets for goods and services for which
consumers are relatively insensitive to changes in price - the contraction in output will be less substantial.
A similar response occurs on the supply side, with supply also responding to change in production cost (resulting
from increased electricity prices) and related product price effects. The supply-side response may be thought of as
potentially reducing the demand-side response - if the demand-side response is considered independent of the
supply-side response. Specifically, for markets in which supply is relatively elastic - i.e., production quantity
responds more substantially to changes in product prices, or stated inversely, prices change little in response to
10-10
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
changes in market quantity - the contraction in quantity resulting from the demand response will lead to a smaller
supply-side price response, with relatively less reduction in the demand response-based quantity effect.
Conversely, for markets in which supply is relatively inelastic - i.e., production quantity responds little to changes
in product prices, or stated inversely, prices change more substantially in response to changes in market quantity -
the contraction in quantity resulting from the demand response will lead to a relatively larger supply-side price
response, which in turn will typically lead to a larger reduction in the demand response-based quantity effect.
For its analyses of supply-demand response in affected markets, EPA developed two analysis cases:
> Case 1: Accounting for demand elasticity response only, with no supply elasticity effect
> Case 2: Accounting for both demand and supply elasticity response.
EPA developed and analyzed these two alternative cases in part reflecting that relatively less information is
available on supply elasticity and expected supply response than is typically available for demand elasticity and
expected demand response. Because Case 1 does not account for potential supply-side response effect, it provides
a higher impact value, in terms of potential output contraction in affected markets, than Case 2.
Case 1: Accounting for demand elasticity response only, with no supply elasticity effect
For Case 1, to assess the contraction in output among the directly affected business sectors, EPA compiled a set of
demand elasticity values, which are summarized in Table 10-6. Mathematically, the elasticity value indicates the
percentage change in the quantity demanded of a given product for a percent change in product price. Because the
normal demand response to a price increase is a reduction in quantity demanded, elasticity values have a negative
sign. For example, the indicated value of-0.50 for wholesale trade means that for a one percent increase in
product prices, the quantity of wholesale trade activity would contract by one-half percent. Elasticity values that
are closer to zero (e.g., Food, Beverage, and Tobacco at -0.30) are indicative of inelastic demand - demand that is
less sensitive to changes in product prices. Elasticity values that are farther from zero (e.g., Accommodation, Food
Services at -2.27) are indicative of elastic demand - demand that is more sensitive to changes in product prices.
Table 10-6: Price Elasticity of Demand, By Rate Impact Sector
Sector
Elasticity
Commercial Sectors
Wholesale trade (m)
Retail trade (m)
Information (e)
Finance and insurance (g)
Real estate and rental and leasing (g)
Professional and technical services (e)
Management of companies and enterprises (m)
Administrative and waste services (j)
Educational services (a)
Health care and social assistance (a, e, h)
Arts, entertainment, and recreation (d)
Accommodation and food services (e)
Other services, except government (e)
Government (m)
-0.50
-636
-'67"i"8"
'-6756"
-035
-(137
-(150
-LOO
-LIO
-(136
-6769'
-2"27
-67'4'6
-616
Other Sectors
Construction (f, p)
Mining (c )
Transportation (g)
-0.45
-636
-L03
Sector | Elasticity
Manufacturing Sectors
Food & Beverage and Tobacco Products (c, e, 1)
futile Mills ^ j^j- pro^-M^s ^ -
Apparel & Leather and Allied Products (e)
Wood Products (m)
Paper (k, o)
Printing and Related Support (m)
Petroleum and Coal Products (e)
Chemicals (g, i)
Plastics and Rubber Products (a)
Nonmetallic Mineral Products (m)
Primary Metals (c )
Fabricated Metal Products (g)
Machinery (m)
Computer and Electronic Products (a, b)
Electrical Equip., Appliances, and Components (e, g, n)
Transportation Equipment (c, e)
Furniture and Related Products (g)
Miscellaneous (m)
-0.30
-040
-6770
-6778
-6"6"3
-6776
-6760
-0"89
'-'Eos'
-636
-LOO
'-"E'52
'-"E'6'8
'-'E'43"
-6"6"4
'-'E"i'7
'-"E'26
-0"85
Sources: (a) Anderson et al. (1997), (b) Crandall and Jackson. (2001), (c) DeMilo and Tarr (1988), (d) Heilbrun and Gray (2001), (e)
McConnell andBrue (2005), (f) Benjamin et al. (1998), (g) Parkin (1998), (h) Ringel et al. (2005), (i) Santerre and Vernon (2004), (j) U.S.
EPA (2000), (k) U.S. EPA (1997), (I) You etal. (1998), (m) Professional judgment, (n) Dale (2008), (o) U.S. Congress (1984), (p) U.S.
Department of Housing and Urban Development (HUD) (2006)
March 28, 2011
10-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 10: Total Output and Employment Effects
To apply the demand elasticity concept to estimate the change in output, EPA assumed, in Case 1, that the directly
affected business customers would attempt to pass on the entire direct rate effect as a price increase. Holding
constant the quantity of goods and services produced/sold in the sector - at the outset, before calculating the
demand elasticity response from the price increase - this means that the attempted percentage increase in price of
the sector's goods and services will equal the total rate effect to the sector as a percentage of the total value of
goods and services sold/produced in the sector (nationally) before the rate effect. This percentage change can then
be multiplied by the sector's elasticity value to calculate the percentage change in total sector output. Further,
multiplying that value by the initial total value of goods and services produced/sold in the sector (again,
nationally) yields the direct economic output loss for the sector. This change in economic output by sector is the
direct effect of the rate increase for each business sector. The calculation of direct economic impact is as follows:
(Rate Effect^/ ^ ^ .. .. „ (10-1)
•» /„ \xElaStlCltysector, demzndXReVemiesector
^ /Revenuesector j J
Or, simply
tor = Rate Effectsectoi x Elasticity'sector, demand (10-2)
As described below, this direct effect is then used in combination with the RIMS multipliers for each sector to
estimate the total economic effect, including direct, indirect, and induced effects.
Case 2: Accounting for both demand and supply elasticity response
For Case 2, EPA researched supply elasticity values for the rate impact sectors listed above. In general, less
information is available on supply elasticity values for the sectors of interest than for demand elasticity. In
addition, the assessment of supply response and elasticity effects is quite difficult to reduce to a simple concept
such as supply elasticity, given the likelihood that supply at any time in an industry is composed of output from
multiple producers who may have diverse production characteristics and who may not be uniformly affected by
an increase in electricity rates due to the proposed 316(b) regulation. For these reasons, EPA views the Case 2
analysis as more uncertain in terms of market-level effects in the affected business sectors than the analysis under
Case I.213
The primary source of supply elasticity values used in this analysis is the Elasticity Databank compiled by the
EPA Office of Air Quality Planning and Standards. This database contains elasticity values across a range of
sectors and according to different conceptual definitions - for example, supply and demand elasticities,
substitution elasticities, trade elasticities. Supply elasticity values compiled from this dataset for this analysis are
listed in Table 10-7. Where several elasticity values were available within the dataset for a given sector, EPA used
the simple arithmetic average of the reported values for this analysis. As stated above, supply elasticity values
were not available for most of the sectors of interest in this analysis - for these sectors, EPA used the arithmetic
average of the supply elasticity values developed from the EPA dataset for the sectors of interest in this analysis.
This value, 2.15, is reported for most of the sectors in Table 10-7.
213 Even acknowledging that the Case 1 analysis is likely an overstatement of market-level effects, given that the Case 1 analysis does not
account for supply-side/elasticity response effects.
10-12 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
Table 10-7: Price Elasticity of Supply, By Rate Impact Sector
Sector
Elasticity
Commercial Sectors
Wholesale trade (b) 2.15
'''RetaiFtr;5e'(b)
information (b)
Finance and insurance (b)
Real estate and rental and leasing (b)
Professional and technical services (b)
2.15
2."l5
2."l5
2.T5
2."l5
Management of companies and enterprises (b) 2.15
Administrative and waste services (b)
Educational services (b)
Health care and social assistance (a)
Arts, entertainment, and recreation (b)
Accommodation and food services (b)
Other services, except government (b)
Government (b)
2.15
2."l5
2."15
2."l5
2.T5
2."l5
2."l5
Other Sectors
Construction (b)
Mining (b)
Transportation (b)
2.15
2."15
2.T5
Sector Elasticity
Manufacturing Sectors
Food & Beverage and Tobacco Products (b) 2.15
Textile'Mnis&Textue'ProductMms(b) 2.T5
Apparel & Leather and Allied Products (b) 2.15
Wood Products (a) 6.77
"Paper (a) 6.80
Printing and Related Support (b) 2.15
Petroleum and Coal products (bj F.30
''®!*^^
Plastics and Rubber Products (a) 4.01
Nonmetailic Mineral Products (b) 2.15
Primary Metals (a) 1 .44
Fabricated Metal Products (b) 2.T5
Machinery (b) 2.15
Computer and Electronic Products (b) 2.15
Electrical Equip., Appliances, and Components (b) 2.15
Transportation Equipment (b) 2.15
..^^^^..^.^^^.^^^^..^ _._
Miscellaneous (b) 2.15
Sources: (a) U.S. Environmental Protection Agency, Elasticity Databank, http://www.epa.gov/ttnecasl/Elasticily.htm (b) default supply
elasticity value calculated as average of elasticity values developed from EPA Elasticity Databank.
To apply the combination of demand elasticity values (Table 10-6) and supply values (Table 10-7) in calculating
output effects, EPA used the following relationship, which is a generalized variant of equation (10-2):
^Outputs,
_ Rate EffectsectorX Elasticity-.
(10-3)
i _ ElaStidtysectoi, demand /
/Elasticitysecioi, supply
As the supply elasticity value increases towards infinity (i.e., highly elastic supply), the value of the denominator
goes to one, and this relationship becomes the same as equation (10-2).
Estimating the Total Economic Effects of Output Changes in Business Sub-Sectors
In the same way as described above, the last step in the business customer electricity rate impact analysis
estimates the total output and employment effect from the electricity rate increase, by sector, as the product of
each sector's RIMS multipliers and direct output loss (i.e., using multipliers specific to each sector reported in
Table 10-5). These calculations were performed for both of the business impact cases outlined above:
> Case 1: Accounting for demand elasticity response only, with no supply elasticity effect
> Case 2: Accounting for both demand and supply elasticity response.
10.4 Economic Effects Due to Manufacturer's Compliance Costs
EPA also considered economic output and employment effects due to the compliance costs that are incurred by
in-scope Manufacturers. In this part of the analysis, EPA assumes that compliance costs incurred by in-scope
Manufacturers will be absorbed by those entities, manifesting as a reduction in lost after-tax profit and ultimately
lost household income via the income reduction effect on the owners of the manufacturing facilities.214 As noted
214 This effect could occur through several mechanisms: reduced profits and owners' income in privately owned businesses; reduced
dividends from publicly traded companies; and/or reduced equity market value in publicly traded companies. Ultimately, these effects
are expected to reach to individuals and manifest as a reduction in household income and/or wealth, with attendant impacts on
household consumption. For this analysis, EPA assumed that the entire reduction in after-tax profit would result in reduced household
consumption in the United States in the relevant time periods. Other analysis concepts are possible - e.g., reduced income reduces
March 28, 2011 10-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
previously, EPA judges that it is reasonable to assume that in-scope Manufacturers will absorb all (or a substantial
portion) of their costs since these entities constitute a small fraction of the overall activity in their respective
sectors. EPA used the estimate of total, after-tax annualized compliance costs for all regulated manufacturers as
the measure of this annual cost effect. In the same way as described for the analysis of electricity rate effects, EPA
calculated the Manufacturers' compliance cost effect as the annual average value over the full time period of
compliance cost incurrence and carried these values forward for the analysis on a constant annual effect basis.
These annual values vary by regulatory option and are presented in Table 10-8 along with the applicable time
periods. EPA again used RIMS Household sector multipliers to estimate the total economic effects from the
annual cost effect, assuming that the cost effect ultimately manifests as lost household income (see Table 10-1 for
Household sector multiplier values).
Table 10-8: Manufacturer's Average Annual Compliance Cost (Millions; $2009)
Direct Effect
Total annualized, after-tax cost
Opti
Applicable
Time Period
2013-2056
on 1
Annual Direct
Effect Value
$42
Opti
Applicable
Time Period
2013-2056
on 2
Annual Direct
Effect Value
$110
Opti
Applicable
Time Period
2023-2056
on 3
Annual Direct
Effect Value
$150
Source: U.S. EPA Analysis, 2010
10.5 Results of the Economy-Wide Output and Employment Effects Analysis
Table 10-9 and Table 10-10, following pages, summarize the annual output and employment effects by
cost/outlay category and regulatory option, over specified multiple year intervals (rows in the table). The first
three intervals reflect, respectively, the initial compliance periods for:
> IM technology-only installations - 2012-2017, or six years, which includes the first year of cost
incurrence following rule promulgation, and the five years of compliance for this compliance technology
> Cooling tower technology installations at non-nuclear Electric Generators - 2018-2022, or five years
> Cooling tower technology installations at nuclear Electric Generators and Manufacturers - 2023-2027, or
five years.
The next six intervals report output and employment effects in 5-year intervals over the "steady state" compliance
period, with the exception that the first of these intervals (2028-2031) is only four years, reflecting the fact that
the first year of full compliance, 2027, looking over all regulatory options, overlaps with the last year of
compliance technology installation. The last row for each option reports the annual average values over the full
cost analysis period: 2012-2056.
The results reported below capture the economic effects resulting, on the one hand, from increased demand in
some parts of the economy from initial and recurring compliance outlays, and on the other hand, from decreased
demand resulting from increased electricity rates and reductions in household income and business activity. These
values do not represent "benefits" or "costs" (depending on the sign of each value) of the Rule, but rather are a
measure of the sum of inter-industry transfers that arise from the regulation.
Table 10-9 and Table 10-10 indicate that all three regulatory options cause an increase in overall economic
activity during the periods in which the larger outlays occur for technology installation and other initial
compliance activities. As expected, these effects are larger for the options that would require installation of
cooling tower technology - Option 2 and Option 3 - than for Option 1, which would not require cooling tower
installation. However, the analysis also show that, during other periods, the regulation's impact on electricity rates
household savings but not household consumption, which might in turn affect the flow of savings into, and/or the cost of capital for,
U.S. capital formation, which would lead, over the longer term, to reduced domestic economic product.
10-14
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
and manufacturers' compliance costs results in a reduction in overall economic activity. As described in this
chapter, the estimated reductions in economic activity from increased electricity rates are higher when only the
demand elasticity response is accounted for in the analysis, and conversely, lower when the supply elasticity
response in electricity customer markets is also taken into account. The reductions in economic activity due to
increased electricity rates and manufacturers' compliance costs are greater in Options 2 and 3 than in Option 1.
Table 10-9: Output Effect, Reported as Average Annual Values by Effect Category for Indicated Time
Periods
(Millions; $2009)
Electricity Price Effects in
Dependent Product Markets
Compliance Outlay Effects Household
Year Interval
Initial/
One-Time
Recurring
Sector
Effects
Option 1: IM
2012-2017
2018 -2022
2023 - 2027
2028-2031
2032 - 2036
2037-2041
2042 - 2046
2047-2051
2052 - 2056
2012 - 2056
2012-2017
2018-2022
2023 - 2027
2028-2031
2032 - 2036
2037-2041
2042 - 2046
2047-2051
2052 - 2056
2012 - 2056
2012-2017
2018-2022
2023 - 2027
2028-2031
2032 - 2036
2037-2041
2042 - 2046
2047-2051
2052 - 2056
2012 - 2056
$1,253
$0
$0
$0
$815
$373
$55
$3
$0
$305
Option
$2,719
$29,742
$10,550
$0
$96
$58
$40
$9
$0
$4,862
$2,647
$30,773
$11,310
$0
$24
$23
$41
$10
$0
$5,040
$245
$478
$478
$478
$478
$478
$478
$478
$282
$425
2: IM Everywhere
$81
$3,287
$7,070
$8,194
$8,194
$8,194
$8,194
$8,194
$4,912
$6,078
Option 3:
$16
$3,371
$7,331
$8,468
$8,468
$8,468
$8,468
$8,468
$5,076
$6,272
-$426
-$512
-$512
-$512
-$512
-$512
-$512
-$512
-$512
-$500
Demand
Elasticity
only
Everywhere
-$500
-$600
-$600
-$600
-$600
-$600
-$600
-$600
-$600
-$586
with
Supply
Elasticity
-$361
-$433
-$433
-$433
-$433
-$433
-$433
-$433
-$433
-$424
Total Output Effect
Demand
Elasticity
only
$572
-$633
-$633
-$633
$181
-$261
-$579
-$630
-$829
-$356
With
Supply
Elasticity
$711
-$467
-$467
-$467
$348
-$95
-$413
-$464
-$663
-$194
and EM for Facilities with DIP > 125 MGD
-$4,279
-$5,134
-$5,134
-$5,134
-$5,134
-$5,134
-$5,134
-$5,134
-$5,134
-$5,020
-$6,075
-$7,291
-$7,291
-$7,291
-$7,291
-$7,291
-$7,291
-$7,291
-$7,291
-$7,129
-$4,391
-$5,269
-$5,269
-$5,269
-$5,269
-$5,269
-$5,269
-$5,269
-$5,269
-$5,152
-$7,554
$20,605
$5,195
-$4,230
-$4,134
-$4,173
-$4,190
-$4,222
-$7,513
-$1,209
-$5,869
$22,626
$7,216
-$2,209
-$2,112
-$2,151
-$2,169
-$2,200
-$5,491
$768
I&E Mortality Everywhere
-$4,403
-$5,283
-$5,283
-$5,283
-$5,283
-$5,283
-$5,283
-$5,283
-$5,283
-$5,166
-$4,521
-$5,425
-$5,425
-$5,425
-$5,425
-$5,425
-$5,425
-$5,425
-$5,425
-$5,304
-$1,609
-$1,931
-$1,931
-$1,931
-$1,931
-$1,931
-$1,931
-$1,931
-$1,931
-$1,888
-$6,261
$23,436
$7,932
-$2,240
-$2,216
-$2,217
-$2,199
-$2,230
-$5,632
$841
-$3,349
$26,931
$11,426
$1,254
$1,278
$1,277
$1,295
$1,264
-$2,137
$4,258
Source: U.S. EPA Analysis, 2010
March 28, 2011
10-15
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
Table 10-10: Employment Effect, Reported as Average Annual Values by Effect Category for
Indicated Time Periods
(full-time-equivalent jobs)
Compliance Outlay Effects Household
Year Interval
Initial/
One-Time
Recurring
Sector
Effects
Option 1: IM
2012-2017
2018-2022
2023 - 2027
2028-2031
2032 - 2036
2037-2041
2042 - 2046
2047-2051
2052 - 2056
2012 - 2056
2012-2017
2018-2022
2023 - 2027
2028-2031
2032 - 2036
2037-2041
2042 - 2046
2047-2051
2052 - 2056
2012 - 2056
2012-2017
2018-2022
2023 - 2027
2028-2031
2032 - 2036
2037-2041
2042 - 2046
2047-2051
2052 - 2056
2012 - 2056
9,421
0
0
0
6,313
2,887
423
24
0
2,328
Option
20,625
221,193
78,619
0
748
449
312
69
2
36,238
20,096
228,047
84,294
0
187
179
318
79
2
37,469
1,029
1,985
1,985
1,985
1,985
1,985
1,985
1,985
1,170
1,767
2: IM Everywhere
385
12,672
27,143
31,456
31,456
31,456
31,456
31,456
18,852
23,346
Option 3:
66
12,879
28,081
32,454
32,454
32,454
32,454
32,454
19,451
24,030
-3,229
-3,875
-3,875
-3,875
-3,875
-3,875
-3,875
-3,875
-3,875
-3,789
and EM
-32,411
-38,894
-38,894
-38,894
-38,894
-38,894
-38,894
-38,894
-38,894
-38,029
Electricity Price Effects in
Dependent Product Markets Total Employment Effect
Demand
Elasticity
only
Everywhere
-3,319
-3,983
-3,983
-3,983
-3,983
-3,983
-3,983
-3,983
-3,983
-3,894
with
Supply
Elasticity
-2,370
-2,844
-2,844
-2,844
-2,844
-2,844
-2,844
-2,844
-2,844
-2,781
Demand
Elasticity
only
3,902
-5,873
-5,873
-5,873
440
-2,986
-5,450
-5,849
-6,688
-3,588
with
Supply
Elasticity
4,851
-4,734
-4,734
-4,734
1,579
-1,847
-4,311
-4,710
-5,550
-2,475
for Facilities with DIP > 125 MGD
-40,349
-48,419
-48,419
-48,419
-48,419
-48,419
-48,419
-48,419
-48,419
-47,343
-28,812
-34,574
-34,574
-34,574
-34,574
-34,574
-34,574
-34,574
-34,574
-33,806
-51,751
146,553
18,449
-55,856
-55,109
-55,408
-55,544
-55,788
-68,459
-25,788
-40,214
160,397
32,294
-42,012
-41,264
-41,563
-41,700
-41,943
-54,614
-12,251
I&E Mortality Everywhere
-33,353
-40,023
-40,023
-40,023
-40,023
-40,023
-40,023
-40,023
-40,023
-39,134
-41,543
-49,852
-49,852
-49,852
-49,852
-49,852
-49,852
-49,852
-49,852
-48,744
-29,664
-35,597
-35,597
-35,597
-35,597
-35,597
-35,597
-35,597
-35,597
-34,806
-54,734
151,051
22,499
-57,421
-57,235
-57,242
-57,103
-57,343
-70,422
-26,379
-42,855
165,306
36,754
-43,167
-42,980
-42,987
-42,848
-43,088
-56,168
-12,441
Source: U.S. EPA Analysis, 2010
Table 10-11, following page, reports these effects on a present value and annualized (constant annual equivalent
effect) basis, using the 7 and 3 percent discount rates used in the social cost analysis. Reporting these values on a
constant annual equivalent effect basis is appropriate for understanding the economic effect, given that the
estimated effects do not occur uniformly over time. As shown in Table 10-11, on a constant annual equivalent
effect basis, EPA estimates that Option 1: IM Everywhere would cause an overall modest effect on economic
activity, ranging from an annual reduction of approximately $260 million to an annual increase of approximately
$15 million, depending on whether supply elasticity is accounted for in estimating the impact of increased
electricity rates and discount rate. EPA estimates that Options 2 and 3 would both lead to annualized increases in
economic activity, due to the total economy effect of the outlays for technology installation. For Option 2, the
estimated annual effect ranges from $33 million to $2.8 billion, and for Option 3, from $2.1 billion to $6.2 billion.
A key reason that the analysis indicates estimated increases in economic activity, on an constant annual equivalent
effect basis, for Options 2 and 3 is that the large outlays for cooling tower technology installation occur relatively
early in the total analysis period, and thus see less "reduction" from present value discounting, while the impacts
10-16
March 28, 2011
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 10: Total Output and Employment Effects
from increased electricity rates are spread over the analysis period. These effects are thus subject to a greater
discounting effect in this analysis.
The estimated employment effects follow a similar profile, with Option 1 estimated to yield a more modest effect
than Options 2 and 3. On an equivalent annual effect basis, Option 1 yields job losses ranging from approximately
800 to 2,800; both Options 2 and 3 yield employment effects ranging from a loss of approximately 14,000 jobs to
an increase of approximately 10,000 jobs.
Table 10-11: Total Present Value and Annualized Values of Output and Employment Effects
Direct Effect
Option 1
Option 2
Option 3
Present Value [4nnualized Value\ Present Value \Annualized Value\\ Present Value \Annualized Value
Total Output Effect (Smillions, S2009)
-without accounting for elasticity of supply response in industrial sectors affected by electricity price increase
1 percent discount rate
3 percent discount rate
E-$ 1,900
-$6,354
-$140
-$259
1 $12,339
$821
$907
$33
1 $39,466
$51,017
$2,901
$2,081
with accounting for elasticity of supply response in industrial sectors affected by electricity price increase
7 percent discount rate
3 percent discount rate
$207
-$2,438
$15
-$99
I! $37,955
1 $48,427
$2,790
$1,975
I! $83,742
1 $133,300
$6,155
$5,437
Total Employment Effect (full-time-equivalent jobs)
-without accounting for elasticity of supply response in industrial sectors affected by electricity price increase
1 percent discount rate
3 percent discount rate
E -25,361
-69,464
-1,864
-2,833
1-42,037
-336,916
-3,090
-13,741
1-43,169
-343,494
-3,173
-14,009
with accounting for elasticity of supply response in industrial sectors affected by electricity price increase
7 percent discount rate
3 percent discount rate
-10,931
-42,648
-803
-1,739
I! 133,389
1 -10,903
9,804
-445
1 137,449
| -7,830
10,102
-319
Source: U.S. EPA Analysis, 2010
10.6 Key Uncertainties and Limitations
Key uncertainties and limitations to consider for this analysis include:
> RIMS Multipliers. EPA used 2006 economic impact multipliers from the BEA's RIMS system to support
this analysis. These multipliers reflect the structure, composition, and activity profile of the U.S. economy
during 2006. Using these multipliers to estimate economic effects for any year other than 2006 introduces
uncertainty into the analysis because it implicitly assumes that the economy is similarly structured in
those years.
> Measure of Annual Electricity Rate Effect. The aggregate annual change in electricity rates is based on the
total annualized, pre-tax compliance cost for all regulated generators as of a single benchmark year, 2015,
and is assumed to be a constant value for each regulatory option, over the analysis period. The estimates
of increase in electricity rates are subject to considerable uncertainty due to a number of factors,
including, in particular, the assumed recovery of cost increases through electricity rates. The analysis in
effect assumes that all Electric Generators' compliance costs will be passed forward to ratepayers through
the conventional utility rate regulation framework. To the extent that generators operate in a deregulated
wholesale generation environment, the electricity rate effect may differ substantially from the effect
estimated in this analysis. If in-scope generators are typically not price setters, or cannot otherwise
increase rates (e.g., via contract renegotiation) in deregulated markets, then the implicitly estimated rate
effect would be less than estimated here. Alternatively, if in-scope generators typically are price setters in
deregulated markets, then the overall rate impact in those markets could be greater in those markets than
estimated in this analysis.215 The assumption of a constant annual rate effect is also subject to
215 Because the increased prices in the deregulated market would apply not only to the in-scope generators, but would also apply to the
electricity sold by other generators that are producing electricity at the same time in those markets.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 10: Total Output and Employment Effects
considerable error as the recovery profile for capital outlays in a regulated utility framework is not likely
to be constant over time.
> Annual Effect Concept. The direct effect inputs and economic impact results are representative of the
estimated average annual effect over specified periods of time. These average annual effects are
computed simply as the sum of a given direct effect over a given time period divided by the number of
years in the applicable time period. The actual economic effects that occur during each specific year over
that same time period will vary from this average.
> Cost Pass-Through. The degree of cost pass-through is a critical, but uncertain, assumption in these
estimates of total economic output and employment effects. For instance, EPA's assumption that
compliance costs incurred by Manufacturers will be 100% absorbed by those entities is uncertain. As
noted earlier, EPA judges that it is reasonable to assume that manufacturing entities will absorb a
substantial portion of their costs since these entities constitute a small fraction of the overall activity in
their respective sectors; however, whether that fraction is 100% is uncertain.
> Estimates of Electricity Consumption Intensity, by Economic Sector. EPA relied on estimates of
electricity consumption, by economic sector, to allocate the regulation's non-residential customer rate
effect to affected sectors. The estimates of electricity consumption, in turn, are based on estimates of
electricity consumption intensity, by sector, which measures the quantity of electricity consumed per unit
of sector-level value-added. Due to data limitations, electricity consumption intensity estimated for the
commercial and transportation sectors is based on the 2006 relationship between electricity consumption
and value-added. These estimates are based on 2002 data for the manufacturing, mining, and construction
sectors. To the degree that electricity consumption intensity has since changed, EPA's estimates of
electricity consumption by sector are subject to error.
> Estimates of Price Elasticity of Supply and Demand. EPA estimated the direct impact on business sector
output from electricity rate increases using estimates of each sector's price elasticities of supply and
demand. These elasticity values are used to estimate the change in the quantity supplied and/or
demanded in a given market in response to a change in the price of the outputs of that market. These
elasticity values were drawn from a range of publically available sources and are subject to considerable
uncertainty in terms of the quality of the analysis underlying estimates. Moreover, elasticity values/
estimates may vary substantially over time, and geographically, according to supply and demand
conditions in specific product segments, which in turn may further reduce the robustness of the estimates
of change in output for the current analysis. In addition, elasticity values may vary substantially based on
the duration of the response period to which the estimates apply: in general, both supply and demand
elasticity would be expected to become more elastic as the response period lengthens. For these analyses,
EPA used available elasticity estimates regardless of the response period to which the estimates are
assumed to apply. Finally, as described above, fewer supply elasticity values were available to support
this analysis, and the estimates of supply elasticity are generally regarded as being subject to greater
uncertainty than the estimates of demand elasticity. All of these factors point to considerable uncertainty
in the estimates of quantity response in the affected markets based on changes in electricity prices and the
resulting changes in prices of goods and services that depend on electricity in their production.
> Reliance on a Static Partial Equilibrium/Fixed Relationships Analysis Framework. As described in this
chapter, EPA's analysis is based on a static partial equilibrium/fixed production relationships analysis,
which does not account for inter-sectoral feedback and production adjustment effects (see discussion at
pages 10-3 and 10-10, and following pages). In general, as described at page 10-3, EPA expects that use
of the static partial equilibrium/fixed production relationships framework leads to larger estimated output
and employment effects, in particular when those effects are negative, than would likely occur in the real
economy. EPA assesses that the static partial equilibrium framework may tend to overstate effects
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 10: Total Output and Employment Effects
because the framework does not account for inter-sectoral feedbacks, and because the response
relationships are assumed to be constant over time and do not reflect the likely adjustments in the
economy and affected sectors, that would result from increased production costs and product prices that
would likely result from the regulation. In general, these adjustments would tend to moderate the impacts
that are estimated to occur in the partial equilibrium, rigid production relationships framework.
March 28, 2011 10-19
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Economic Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 11: Assessment of Total Social Costs
11 Assessment of Total Social Costs
This chapter develops EPA's estimates of the costs to society resulting from the proposed rule. As analyzed in this
chapter, the social costs of regulatory actions are the opportunity costs to society of employing scarce resources to
prevent the environmental damage otherwise occurring from the design and operation of cooling water intake
structures.
11.1 Overview of Social Costs
Chapter 3: Development of Costs for Regulatory Options presents EPA's development of costs to complying
facilities and governments, which are also used as the basis of the social cost analysis. The social cost analysis
considers costs on an as-incurred, year-by-year basis - that is, this analysis takes the components of costs before
escalation to compliance year, discounting, or annualizing and assigns them to the years in which they are
assumed to occur relative to the assumed promulgation and compliance years.216 For this analysis, EPA assumed
that facilities, in the aggregate, would achieve compliance as follows. For facilities already in compliance or
installing technologies other than cooling towers, the compliance period is assumed to be a 5-year period from
2013 to 2017; for facilities required to install cooling towers, the compliance period is assumed to be a 5-year
period from 2018 to 2022 for non-nuclear electric power facilities, and from 2023 to 2027 for manufacturing and
nuclear facilities. As described further below, all costs and activities associated with the achievement of
compliance are estimated to occur uniformly within these periods for the relevant facilities. Following the
achievement of compliance, all costs and other operational effects of compliance are also assumed to occur as
though they originated from a compliance schedule that is uniformly spread over these compliance windows.
The year-explicit treatment of compliance costs for the social cost analysis differs from the analysis of facility and
firm impacts described in Chapters 3, 4, and 5. In those chapters, all facilities within a given compliance
requirement specification (e.g., nuclear facilities installing closed cycle cooling system) were assumed to achieve
compliance at the mid-point of the respective 5-year compliance periods. This assignment to an approximate
compliance year is sufficient for the cost impact analysis, which looks at the potential impact on individual
facilities or firms. However, for the social cost analysis, which looks at the aggregate of costs across complying
facilities and for which costs are needed on a year-explicit basis for developing total present value and annual
equivalent values of cost to society, it was necessary to "spread" the costs out within the respective compliance
windows. For the social cost analysis, EPA developed a year-explicit schedule of compliance outlays, assuming
that compliance would be achieved uniformly over the respective 5-year compliance windows for each category
of complying facility.
To develop the year-explicit schedule of compliance costs and operational effects, EPA first assigned a. pro forma
compliance year of 2015 for facilities not installing cooling towers, 2020 for non-nuclear Electric Generators
installing cooling towers, and 2025 for Manufacturers and nuclear Electric Generators installing cooling towers.
EPA then determined the last year of initial compliance for these facilities based on the year of technology
installation, and the technology life of the longest-lived compliance technology (cooling tower) installed at any
facility (30 years). This step yields an initial compliance analysis period of 2012 through the end of 2044 for
facilities not installing cooling towers, and through the end of 2054 for facilities installing cooling towers (due to
the later compliance year for manufacturing and nuclear facilities). After creating a cost incurrence schedule for
each cost component, EPA summed the costs expected to be incurred in each year for each facility, then
216 The specific assumptions of when each cost component is incurred can be found in Chapter 3: Development of Costs for Regulatory
Options
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 11: Assessment of Total Social Costs
aggregated these costs to estimate the total cost to the industry for each year in the analysis period, still using
compliance years of 2015, 2020, and 2025.
To account for the specific compliance windows of 2013 to 2017, 2017 to 2022, and 2023 to 2027, during which
facilities are actually expected to achieve compliance, EPA distributed the total costs calculated for each year
evenly over the surrounding years of the compliance window. For example, costs for facilities assigned the
compliance year 2015 were distributed evenly over 5 years: the 2 years prior to 2015, 2015 itself, and the 2 years
after 2015. After being distributed, costs were adjusted for real change between their stated year and the year(s) of
their incurrence using as follows: technology costs were adjusted to their incurrence year(s) using the
Construction Cost Index (CCI) and GDP deflator; administrative costs were adjusted to their incurrence year(s)
using the Employment Cost Index (ECI) and GDP deflator; energy penalty and downtime costs were adjusted to
2030 based on Energy Outlook (AEO) 2009 electricity price projections (which do not require adjustment for
inflation).217 Note that costs must be aggregated in three separate groups according to the index used to adjust
them, and are then summed to estimate total industry cost. CCI and ECI adjustment factors were only available
through 2015 and AEO adjustment factors through 2030; after these years, the real change in prices is assumed to
be zero - that is, costs are expected to change in line with general inflation. EPA judges this to be a reasonable
assumption, given the uncertainty of long-term future price projections. This distribution of costs represents the
overall burden to society in each year, assuming that the costs to facilities coming into compliance in each year
will be equal over the relevant distribution periods, which is consistent with the assignment of costs in the market
model analysis. This analysis accounts for technology costs associated with repowering and new generating units
starting in the first year after promulgation, i.e., 2013 (for more information on repowering and new units see
Chapter 3: Development of Costs for Regulatory Options).
After developing the year-explicit schedule of total social costs and adjusting them for predicted real change to
the year of their incurrence, EPA calculated the present value of these cost outlays as of the promulgation year by
discounting the cost in each year back to 2012, using both 3 percent and 7 percent discount rates. These discount
rate values reflect guidance from the Office of Management and Budget (OMB) regulatory analysis guidance
document, Circular A-4 (OMB, 2003). EPA also calculated the constant annual equivalent value (annualized
value) of these present values, again using the two values of the discount rate, 3 percent and 7 percent, over a 50-
year analysis period. The 50-year analysis period results from several considerations: the compliance periods
specified in the rule for different categories of facilities, the expected useful life of compliance technology
installed at these facilities, and the duration of benefits achieved by the compliance technology. The analysis is
structured to account for the longest life of compliance technology at the latest complying facility, and the
duration of benefits from that "last" compliance installation. This analysis period thus reflects the "first round" of
full compliance by existing in-scope facilities, and does not mean that the rule's compliance requirements and
resulting costs and benefits would end at that point. More specifically, the 50-year analysis period reflects the
following:
> Rule promulgation and first incurrence of compliance-related costs at 2012
> Achievement of compliance for IM-only facilities, beginning year 2013 and ending 2017
> Achievement of compliance for non-nuclear Electric Generators installing cooling towers, assumed for
analysis purposes to begin in year 2018, and ending not later than 2022
> Achievement of compliance for Manufacturers and nuclear Electric Generators installing cooling towers,
assumed for analysis purposes to begin in year 2023, and ending not later than 2027
217 The stated year for technology and labor costs is 2009, in 2009 dollars. Energy penalty costs are estimated using average revenue from
the market model analysis over a period of 4 years, expressed in 2009 dollars, so their stated year is effectively an average of the AEO
projected electricity prices in these 4 years. Future cost escalations were calculated in terms of change from this average.
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Economic Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 11: Assessment of Total Social Costs
> Beginning of steady state compliance for all facilities in 2027 (the last year in which any facility is
expected to achieve compliance) and continuing for 30 compliance years, to 2056. The 30 years of
compliance reflects the estimated useful life of the longest-lived compliance technology equipment
expected to be implemented in response to any of the regulatory options. Note that the first year of steady
state compliance for all facilities overlaps with the last year in which any facility would be expected to
achieve compliance under any of the regulatory options.
> Beginning of the 5-year period after the last year of compliance technology operation, during which
benefits continue to decline to zero. That is, benefits are estimated to continue for 5 years after the end of
the useful life of a compliance technology, but to decline to zero over this period.218
In summary, under this framework, the last year for which costs were tallied in the analysis is 2056, with benefits
continuing on a diminishing basis through 2061. Because the estimated useful life for some IM compliance
technology installations would cease before the end of the compliance period (i.e., before 2056), the social cost
analysis accounts for re-installation of IM compliance technologies after the end of their initial useful life periods.
EPA does not expect in-scope facilities to re-install cooling towers. In those instances in which the estimated
useful life a technology reinstallation would extend beyond the remaining number of compliance years in the
overall analysis period, EPA prorated the initial capital value based on the remaining number of compliance years
in the analysis.
11.1.1 Costs of Regulatory Compliance
The compliance costs used to estimate total social costs differ in their consideration of taxes from those in
Section 1 of Chapter 3: Development of Costs for Regulatory Options, which were calculated for the purpose of
estimating the private costs and impacts of the rule, with the exception of the cost of downtime for generators,
which is discussed in the following section. The cost of downtime for manufacturers used in the social cost
analysis continues to be based on the cost estimated to be incurred by complying facilities.
For the impact analyses, compliance costs are measured as they affect the financial performance of the regulated
facilities and firms. The economic impact analyses therefore explicitly consider the tax deductibility of
compliance expenditures as appropriate, depending on the tax status of the complying entity. In the analysis of
costs to society, however, these compliance costs are considered without accounting for any tax effects. The costs
to society are the full value of the resources used, whether they are paid for by the regulated facilities or by all
taxpayers in the form of lost tax revenues. Thus pre-tax costs are used in calculating social costs.
Cost of Installation Downtime for Electric Generators
For the assessment of impacts to private firms, the cost of downtime was calculated as the lost net income to
facilities required to suspend operation in order to install new equipment. However, this approach does not
accurately capture the cost to society of downtime at electric generating stations. Specifically, when generating
units are taken out of service to install compliance technology, other generating units provide the electricity that
would otherwise have been generated by the out-of-service units. In this case, the opportunity cost to society from
installation downtime is the increase in energy production costs from using the alternative generating units to
supply electricity compared to the cost that would have been incurred if the 316(b) compliance units remained in
service - and not the loss in net income to the individual generating units that are temporarily out of service.
Under the principles of economic dispatch (i.e., at any point of time, electricity is supplied by the combination of
available electric generating units, which in the aggregate, can provide electricity at the lowest total cost), the
alternative generating units are presumed to provide the replacement electricity at a somewhat higher production
218 See Chapter 10, Section 1 for a summary of benefits methodology and the phase-down of benefits following termination of
compliance activities.
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Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 11: Assessment of Total Social Costs
cost than would otherwise be incurred.219 This increase in short-term energy production cost is then the
appropriate concept for social cost of downtime. The Market Model Analysis, described in Chapter 6: Electricity
Market Model Analysis of this document, provides an estimate of the increase in energy production costs resulting
from installation downtime. EPA used this estimated increase in energy production costs as the social cost of
installation downtime.
Specifically, EPA calculated the aggregate increase, from baseline to post-compliance case, in the annual variable
O&M and annual fuel costs from the Market Model Analysis output for the 2015, 2020, and 2025 model run-
years.220'221'222 Each model run-year represents a five-year period beginning two years before and ending two years
after the indicated run-year. These single year cost values were applied to each of the five years in the respective
run-year windows as a measure of the social cost of installation downtime for electric power generators during
those run-year windows.
Cost of Installation Downtime for Manufacturing Facilities
EPA does not expect installation downtime to interrupt the production of goods at Manufacturer facilities, but
only to interrupt their ability to produce their own power, requiring them to purchase power from the grid. As a
result, EPA did not perform an analogous market-level analysis for Manufacturers. The cost of installation
downtime at Manufacturers facilities for the purposes of the social costs analysis is thus the same as that stated in
Chapter 3: Development of Costs for Regulatory Options. Downtime requires Manufacturers to temporarily
curtail electricity-generation and purchase power from the grid in order to continue operation. The cost of this
temporary suspension of power generation is calculated as the cost of purchasing replacement power plus the loss
of any revenues received from selling power but minus the variable costs of generating electricity. If a
Manufacturer does not sell power to the grid, then its cost of downtime is simply the cost of purchasing
replacement power minus the variable cost of generation.
Other Considerations in Estimating the Social Costs of Regulatory Compliance
To assess the economic costs to society of the Proposed Rule, EPA relied first on the estimated costs to facilities
for the labor, equipment, material, and other economic resources needed to comply with the proposed rule. In this
analysis, EPA assumed that the market prices for labor, equipment, material, and other compliance resources
represent the opportunity costs to society for use of those resources in regulatory compliance. Finally, EPA
assumed in its social cost analysis that the regulation does not affect the aggregate quantity of electricity that
would be sold to consumers and, thus, that the regulation's social cost will include no loss in consumer and
219 This is a considerable simplification of the economic dispatch concept, in that it doesn't account for a range of factors - for example,
"must run" requirements for certain generating units. However, overall, the least-cost-solution concept is the applicable governing
concept in the management of electric power generation throughout the country.
220 EPA assumed that facilities will incur technology installation downtime during the spring or fall seasons so as not to coincide with
either the winter or summer higher demand periods. The IPM modeling framework is built around winter and summer seasons, which
last 7 and 5 months, respectively. Within this framework, in-scope facilities are expected to incur downtime during the lower
electricity demand season, which lasts 9 months, and includes the entire winter season (7 months) and 2 of the 5 summer months. To
the extent that using total annual variable costs to estimate the social cost of downtime includes changes in variable costs outside of
the 9 months of lower electricity demand period (i.e., 3 months of the summer period), the downtime impact of the Proposed Rule may
be overestimated.
221 Updated from 2006 to 2009 dollars using the GDP deflator.
222 Under Option 1, total market-level variable costs are less than those reported in the base case (i.e., pre-policy) run for IPM run-years
2020 and 2025. Under Options 2 and 3, total market-level variable costs are less than those reported in the base case run for IPM run
years 2015 and 2020. For all three Options, this change is the result of reduced total fuel costs due to changes in the energy input mix
for electricity generation (decreased use of higher cost fuels and higher use of lower cost fuels). In these instances - i.e., in 2020 and
2025 for Option 1 and for 2015 and 2020 for Options 2 and 3 - EPA assumed no change in variable costs between the base case and a
given policy run. That is, variable costs were assumed not to decline for the calculation of installation downtime.
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Economic Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 11: Assessment of Total Social Costs
producer surplus from lost electricity sales by the electricity industry in aggregate. Given the small impact of the
regulation on electricity production cost for the total industry, EPA believes this assumption is reasonable for the
social cost analysis.
EPA's estimates include compliance costs for facilities estimated to close because of the rule. This approach may
overstate the social costs of compliance, to the extent that the net economic loss to society in facility closures is
less than the estimated cost to society of compliance.223
11.1.2 Government Administrative Costs
Administrative costs to NPDES permitting authorities and the Federal government are taken from Section 2 of
Chapter 3: Development of Costs for Regulatory Options, again on an as-incurred, year-explicit basis and before
adjustment to incurrence year, discounting, or annualizing. For the social cost analysis, government administrative
costs reflect the opportunity cost of expending taxpayer dollars to administer this regulation in lieu of other public
projects.
11.2 Key Findings for Regulatory Options
The following sections present EPA's estimates of the components of, and total values for, social costs of the
regulatory options.
11.2.1 Costs of Regulatory Compliance
Table 11-1 presents annualized compliance costs for each of the regulatory options. At a 3 percent discount rate,
EPA estimates annualized costs of compliance of $380.1 million under Option 1, $4,461.3 million under Option
2, and $4,630.7 million under Option 3; at a 7 percent discount rate, these costs are $454.6 million, $4,697.6
million, and $4,861.1 million, respectively. These costs include: the direct costs of compliance, the cost of
installation downtime as described above, and the administrative costs incurred by complying facilities.
Table 11-1: Summary of Annualized Costs of Compliance (Millions; $2009)
Discount
Rate
3%
7%
Option 1: IM Everywhere
$380.08
$45458
Option 2: IM Everywhere and EM for
Facilities with DIP > 125 MGD
$4,461.28
j4^97";62
Option 3: I&E Mortality Everywhere
$4,630.71
J45g5fj4
Source: U.S. EPA Analysis, 2010.
11.2.2 Costs of Government Administration of the Proposed Existing Facilities Rule
Table 11-2 summarizes government administrative costs under each of the regulatory options. EPA estimates that
Option 1 will result in $3.7 and $4.2 million (3 and 7 percent discount rates, respectively) in government
administrative costs. Option 2 is expected to result in $1.6 and $1.7 million (3 and 7 percent discount rates,
respectively) in administrative costs to governments. For Option 3, EPA estimates that about $0.9 and $0.9
million (3 and 7 percent discount rates, respectively) will be incurred by governments administering the rule.
Under all options, State and Territory governments bear almost all administrative costs, with Federal
administrative costs ranging between $30,000 and $60,000.
223 Including costs for regulatory closures yields an estimate of social costs assuming that all facilities, except those assessed as baseline
closures, would incur the costs of regulatory compliance and continue to operate post-regulation. Calculating costs as if all facilities
continue operating will overstate social costs if the social cost of compliance is greater than the net economic loss to society from
facility closure. Whether this result will hold depends, in part, on the difference between social and private discount rates, and the
marginal cost to society to replace the lost production of goods and services in closing facilities.
March 28, 2011 11-5
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 11: Assessment of Total Social Costs
Table 11-2: Summary of Annualized Government Administrative Costs (Millions; $2009)
Discount
Rate
3%
7%
Government
Level
State/Territory
Federal
Total3
State/Territory
Federal
Total3
Option 1: IM Everywhere
$3.68
$6763
$3.71
$4.18
$0.06
$4.23
Option 2: IM Everywhere and
EM for Facilities with DIP > 125
MGD
$1.59
$6763
$1.62
$1.68
$(105
$1.72
Option 3: I&E Mortality
Everywhere
$0.89
$0.03
$0.92
$0.87
$0.04
$0.91
a. Values may not sum to the reported total to due independent rounding.
Source: U.S. EPA Analysis, 2010.
11.2.3 Total Social Cost
Table 11-3 combines the information presented above for each regulatory option and reports the total social costs
discounted at both 3 and 7 percent rates. At a 3 percent discount rate, total social costs are approximately $383.8
million under Option 1, $4,462.9 million under Option 2, and $4,631.6 million under Option 3. Using a 7 percent
discount rate, these costs are $458.8 million, $4,699.4 million, and $4,862.1 million for Options 1, 2, and 3,
respectively. Under all options, compliance costs account for the larger share of total social costs, with
government administrative costs accounting for approximately 1 percent of costs (under Option 1 at a 3 percent
rate) and as little as 0.02 percent (under Option 3 at a 3 percent rate).
Table 11-3: Summary of Total Social Costs (Millions; $2009)
Discount
Rate
3%
7%
Cost Category
Compliance Cost
Gov. Admin.
Total3
Compliance Cost
Gov. Admin.
Total3
Option 1: TM Everywhere
$380.08
$371
$383.80
$454.58
$4.23
$458.81
Option 2: IM Everywhere and
EM for Facilities with DIP > 125
MGD
$4,461.28
$7.62
$4,462.90
$4,697.62
$7.72
$4,699.35
Option 3: I&E Mortality
Everywhere
$4,630.71
$(192
$4,631.62
$4,861.14
$(191
$4,862.05
a. Values may not add up to total to due independent rounding.
Source: U.S. EPA Analysis, 2010.
Table 11-4, Table 11-5 and Table 11-6 provide additional detail on the social cost calculations. The tables
compile, for each of the three options, the time profiles of costs incurred in the broad cost categories: compliance
costs, administrative costs, and total costs. The costs presented for each year are in undiscounted 2009 dollars.
The tables also report the calculated present and annualized values of costs at 3 percent and 7 percent discount
rates. The maximum compliance outlays are incurred over the years 2017 (Option 1) and 2021 (Options 2 and 3),
when compliance is being achieved and complying facilities are making outlays for compliance technology and
incurring installation downtime. As stated above, EPA does not expect in-scope facilities to re-install cooling
towers. Replacement of IM capital equipment and consequent additional capital outlays are required at years 20,
25, or 30 for all facilities under Option 1 and some facilities under Options 2 and 3, reflected in the higher costs in
years 2033-2047. As a note of clarification in interpreting the cost profiles, under Options 2 and 3, although no
facilities installing cooling towers would be expected to incur permitting or other costs before 2017, compliance
costs still start in 2012, because facilities with cooling towers already in place are assumed to incur permitting
costs beginning in 2012.
11-6
March 28, 2011
-------
Economic Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 11: Assessment of Total Social Costs
Table 11-4: Time Profile of Costs to Society for Option
Everywhere (Millions; $2009)
1: IM
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
Present Value, 3%
Annualized, 3%
Present Value, 7%
Annualized, 7%
Compliance Costs a
$7.49
$799792"
$858725
$906"24
$776:98
$799:03"
$370:95
$213780
$201:37
$370:95
$213780
$201:37
$370795
$213780
$201737
$376795
$"213786
$261:37
$37035
$"213780
$261:37
$682723
$525768"
$512765
$"68272"3"
$525768"
$"2"6"6765"
$"4"36723"
$279767
$"26676"5"
$"4"3"6723"
$218731
$"2"6"578"8
$"375746"
$"218731
$"2"0578"8"
$"376:95"
$"213786
$7201:37
$"376:95"
$2i27l7
$"l"5"8":39
$"175732"
$7979
$"3"9760
$6:66
$6:66
$6:66
$6:66"
$6:66
$6:66
$9,779.49
$380.08
$6,273.49
$454.58
Administrative Costs
$0.48
$4751
$7.09
$7"."59
$"5734
$3.64
$"5730
$475
$2.62"
$"5730
$"2762
$2.62"
$"5730
$2.62"
$"2762
$"5730
$2.62"
$"2762
$"5730
$"2762
$"2762
$"5730
$"2762
$2.62"
$"5730
$"2762
$2.62"
$"5730
$2.62"
$"2762
$"5730
$2.62"
$"2762
$"5730
$"2762
$"2762
$"5730
$2.62"
$"2.62
$"5730
$"2762
$7.73
$279"
$6.86
$673
$"6".oo
$6.66
$"6".oo
$6.66
$"6".oo
$"6".oo
$95.48
$3.71
$58.44
$4.23
Total
$7.96
$80443
$865:34"
$913784
$"782733"
$802:67
$376:25
$218:54"
$203799
$"376:25
$"2""i674"i
$263799"
$376^5
$2""i674i
$203799"
$376^5
$2""i674i
$263799
$"376:25
$2"i674i
$263799
$"68X54
$527770
$5"l5727
$"68X54
$527770
$269727
$441753"
$281769
$269727
$441753"
$226:92"
$208756
$386:76
$226:92"
$"208.56
$376:25
$2"i674i
$"20379"9"
$"376:25
$"2"i4":79
$i6"6:i2
$17"7751
$86:66
$46:63"
$"6:66
$"6:66
$6:66
$"6:66
$"6:66
$6:66
$9,874.98
$383.80
$6,331.93
$458.81
Source: U.S. EPA Analysis, 2010.
March 28, 2011
11-7
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 11: Assessment of Total Social Costs
Table 11-5: Time Profile of Costs to Society for Option 2: IM
Everywhere and EM for Facilities with DIP > 125 MGD (Millions;
$2009)
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
Present Value, 3%
Annualized, 3%
Present Value, 7%
Annualized, 7%
Compliance Costs a
$4.55
$79.92
$90.23
$100.77
$fl0.42
$4';633".83
j9;959;g7
$TO,430".62
$"T679"63758"
jlYj54"j7"
$8;982.36
$6,139.80
$6/i5f86
$6J8"6765
$7"j08.56
$57775732
$3,996.90
$45023.75
$47554726
$4;054.26
$47054726
$47688731
$47688731
$4;088.31
$47688731
$47688731
$47)67.32
$4',067.32
$47667732
$47)6732"
$47)6732"
$47666"1T
$4;066.Ti
$47666"1T
$45066.Tl
$47666"1T
$47554762
$45054.62
$47554762
$47554762"
$47652799
$3"72"4"6770
$27430.53
$17620.35
$810."l8
$6766
$0.06
$6.66
$6766
$b".'b"o
$0.06
$114,901.39
$4,465.70
$64,921.00
$4,704.17
Administrative Costs
$0.48
$7i2
$736"
$762
$786"
$"2"7i"6"
$T"90
$1791
$1791
$792"
$792"
$177
$178
$1786"
$781
$783
$774
$774
$174"
$774
$774
$774
$774
$174"
$774
$774
$774
$774
$174"
$774
$774
$174"
$774
$174"
$774
$1774
$174"
$1774"
$174"
$1774
$1774
$7b"4
$6778
$6752
$"6726"
$6766
$6"66
$6"66
$6"66
$"6"66
moo
$41.72
$1.62
$23.80
$1.72
Total
$5.03
$8i76"4
$9159
$102739
$112728
$4,635!93
$9;96l777
$107432753
J107905749
$ii;36676"9
$8,98429"
$67141757
$67453764
$6,782745
$77iio;37
$57777714
$"379"9"8764
$47025749
$4,056.00
$74;b"5676"6"
$"4,056.06'
$4b"9"676"5
$4,09"6;05
$4,096.05
$469^65
$4,696.05
$47o697o"7
$47o697o"7
^o^o^"
$47o697o"7
^o^o^"
$4,667.86
$4,067;86
$4,667.86
$406X86
$4,067.86
$4,056737
$4056737"
$747656737
$747656737"
$47054774
^-41774
$27431731
$l"762o7'8"7"
$810744
$"676"6"
$a66
$a66
$6.ob
$a66
$"676"6"
$114,943.11
$4,467.32
$64,944.79
$4,705.89
Source: U.S. EPA Analysis, 2010.
11-8
March 28, 2011
-------
Economic Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 11: Assessment of Total Social Costs
Table 11-6: Time Profile of Costs to Society for Option 3: I&E
Mortality Everywhere (Millions; $2009)
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
Present Value, 3%
Annualized, 3%
Present Value, 7%
Annualized, 7%
Compliance Costs
$1.31
$25.48
$28.17
$30.95
$3348
$47666789
$lQ72o5i5
$io7705764
$ll72075o
$il7696737
$97537734
$67479728
$6305.73
$77149741
$77491778
$67065751
$4178779
$4,206.84
$47258771
$47258771
$47258771
$47247797
$47247797
$47247797
$47247797
$47247797'
$4724553
$4724553
$4724553
$4724553
$4724553
$47252761
$47252761
$47252761
$47252761
$47252761
$47239765
$47239767
$47239767
$47239767
$47237745
$3,388.26
$27541720
$1769413
$847767
$0.00
$0.00
$0.00
$0.06
$0.00
$636
$119,147.01
$4,630.71
$67,087.36
$4,861.14
Administrative Costs
$0.48
$65!
$65s
$0.44
$656
$0.56
$0.91
$6792
$0.92
$6793
$6793
$7.24"
$7731
$158
$145
$152
$77i
$11!
$17iT
$11!
$11!
$77i
$11!
$17iT
$11!
$11!
$77i
$11!
$17iT
$11!
$11!
$77i
$11!
$17iT
$11!
$11!
$77i
$11!
$771
$11!
$11!
$"0.55
$0.41
$"o.27
$6714
$0.06
$"b"."6b"
$"b"."bb"
$"b"."bb"
$"b"."bb"
$"b"."6"b"
$23.57
$0.92
$12.55
$0.91
Total
$1.79
$"25779
$28755
$"37739"
$33798
$4667745
$i672067o6
$10,705795
$yi7208723
$yi769773o
$97338728
$67480752
$6,807764
$77l5o779
$77493723
$6,067763
$47179789
$47207795
$47239782
$47239782
$47239782
$4^49708
$472497"o8
$472497"08
$47249708
$472497"o8
$472467"43
$472467"43
p-4'6743
$472467"43
$472467"43
$4^53772
$47253772
p-53772
$47253772
$47253772
$4724o7i8
$4724o7i8
$472407i8
$4724o7i8
$47238755
$73,388781
$27541761
$17694741
$847720
$6766
$6766
$6766
$6766
$6766
$6766
$119,170.58
$4,631.62
$67,099.91
$4,862.05
Source: U.S. EPA Analysis, 2010.
March 28, 2011
11-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 11: Assessment of Total Social Costs
11.2.4 Compliance Costs for New Generating Units
Table 11-7 presents annualized compliance costs for new units (for details see Chapter 3: Development of Costs
for Regulatory Options). As discussed in Chapter 3 of this document, compliance costs for new generating units
are not included in total compliance cost estimates because benefits associated with reduced I&E mortality at
these new units has not been estimated.
Table 11-7: Annualized Costs of
Compliance for New Generating
Units (Millions; $2009)
Discount
Rate
3%
7%
Annualized Costs
$14.66
$10.94
Source: U.S. EPA Analysis, 2010.
11-10 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 12: Social Costs and Benefits
12 Social Costs and Benefits of the Proposed Rule
This chapter compares total monetized benefits and social costs for the three options considered for the proposed
316(b) Existing Facilities rule. Benefits and costs are compared on two bases: (1) for each of the options analyzed
and (2) incrementally across options. For more information on the analysis of social costs and benefits, see
Chapter 11: Social Costs in this document and the Environmental and Economic Benefits Assessment (EEBA)
report. This chapter also satisfies the requirements of Executive Order 12866: Regulatory Planning and Review.
Table 12-1 summarizes compliance requirements for the proposed options based on the performance standard
each facility would need to meet and its baseline technologies in-place. The cooling tower installation values
listed in Table 12-1 are the minimum number of cooling tower installations anticipated by EPA under the direct
requirements of regulatory Options 2 and 3. Additional cooling tower installations may occur under Options 1 and
2 as the result of case by case determinations of the permitting authority. Additional cooling tower installations
may occur under all options as the result of units increasing their generating capacity and becoming subject to the
cooling tower requirement under the new units provisions of the regulatory options. The cost of these potential
cooling tower installations for new units has not been accounted for in the social cost and benefits analysis.
Table 12-1: Weighted Number of In-Scope Facilities by Technology Standard3
Facility Compliance Action
Total Facilities Estimated Subject to Regulation
Facilities Required to install IM Technology Only
Facilities Required to Install Cooling Towers
Facilities Required to Install IM Technology and Cooling Towers
No Upgrade Required6
Option 1
1,077
855
o
o
222
Option 2
1,077
477
382
40
177
Option 3
1,077
122
740
149
66
a. These numbers reflect facility-count based weighting, see Appendix 3A for details.
b. These facilities meet compliance requirements in the baseline and thus would require no action to comply with the regulation.
Source: U.S. EPA Analysis, 2010
12.1 Summary of Benefits Estimation for the Proposed Regulation
Benefits from the proposed existing facilities rule occur due to the reduction in impingement and entrainment at
cooling water intake structures affected by the rule. Impingement and entrainment kills or injures large numbers
of aquatic organisms at all life stages. By reducing the levels of impingement and entrainment, the proposed
options would increase the number offish, shellfish, and other aquatic organisms in the affected water bodies.
This in turn would directly and indirectly improve use benefits such as those associated with recreational and
commercial fisheries. Other types of benefits, including nonuse values of the affected resources, would also be
enhanced. Chapter 4: Economic Benefit Categories Associated with I&E Mortality Reduction of the EEBA report
provides an overview of the types and sources of benefits anticipated and how these benefits are estimated (i.e.,
monetized, quantified but not monetized, or assessed qualitatively) (U.S. EPA 2010). Chapters 5 through 8 of the
EEBA provide detailed descriptions of the methodologies used in analyzing benefits and the estimated benefits of
the proposed options.
Economic benefits of the proposed options can be broadly defined according to categories of goods and services
provided by the species that are affected by impingement and entrainment from cooling water intake structures.
The first category includes benefits that pertain to the use (direct or indirect) of the affected fishery resources. The
"direct use" benefits of the options include both "market" commodities (e.g., commercial fisheries) and
"nonmarket" goods (e.g., recreational angling). Indirect use benefits also can be linked to either market or
nonmarket goods and services. An example of an indirect use benefit would be the manner in which reduced
impingement and entrainment related losses of forage species leads through the aquatic ecosystem food web to
enhance the biomass of species targeted for commercial (market) and recreational (nonmarket) uses.
March 28, 2011
12-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule Chapter 12: Social Costs and Benefits
The second category includes benefits that are independent of any current or anticipated use of the resource; these
are known as "nonuse" or "passive use" values. Nonuse benefits reflect human values associated with existence
and bequest motives, or willingness to pay for the knowledge that an ecosystem is functioning as if it were not
affected by human activity, or to pass such ecosystem function on to future generations.
EPA estimated the economic benefits from the regulatory options using a range of valuation methods, depending
on the benefit category, data availability, and other suitable factors. Commercial fishery benefits are valued using
market data. Recreational angling benefits are valued using a benefits transfer approach. Nonuse values were
estimated for two of the seven benefits regions using a separate benefits transfer approach. Agency benefits
estimates are based on projected numbers of age-one equivalent fish saved and changes in harvest under proposed
regulatory options.
EPA derived national benefit estimates for the proposed options from a series of regional studies across the
country representing a range of water body types and aquatic resources. National benefit estimates are obtained by
summing regional benefits. EPA calculated the monetary value of benefits of the national categorical regulatory
options for existing facilities using two discount rate values: 3 percent and 7 percent. All dollar values presented
are in 2009 dollars (average or mid-year). Because avoided fish deaths occur mainly in fish that are younger than
harvestable age (eggs, larvae, and juveniles), the benefits from avoided impingement and entrainment would be
realized typically 3-4 years after their avoided death. Appendix C of the EEBA report provides detail on the time
profile of expected benefits.
12.2 Comparison of Benefits and Social Costs by Option
Chapter 11: Assessment of Total Social Costs in this document and Chapter 10: National Benefits in the EEBA,
present estimates of social cost and benefit for the three regulatory options evaluated in developing the proposed
316(b) Existing Facilities regulation.
As documented in the EEBA, the monetized benefit values developed by EPA for the regulatory options
presented in this chapter, include estimated use values for commercial and recreational fishing (including
recreational use value of threatened and endangered species) for all benefits regions, and estimated nonuse values
for two of the seven benefit regions. EPA was unable, at this time, to estimate a monetized value of non-use
benefits from reduced impingement and entrainment (I&E) mortality in all of the seven benefits regions. As
Chapter 3 of the EEBA reports, the harvested commercial and recreational fish species that have direct use values
comprise between 1 and 9 percent of baseline IM&EM losses in each region, with a national average of 3 percent.
The remaining 97 percent of I&E mortality losses include unharvested recreational and commercial fish and
forage fish which do not have direct use values. EPA's nonuse analysis was limited to two of the seven benefit
regions and values were not estimated for unharvested fish in the remaining five benefits regions. The total
estimated benefits are likely to be significantly understated due to the regional limitations of EPA's nonuse
analysis and the relatively large fraction of I&E mortality reductions which are not commercially or recreationally
harvested. EPA did not assess use values other than commercial and recreational fishing, such as improved
recreation opportunities for non-fishing activities such as diving or wildlife viewing, in this analysis. EPA notes,
however, that recreational users other than fishers (e.g., divers) are likely to have positive use values for all fish
and shellfish species including commercially and recreationally targeted species as well as for forage species.
Although the analysis omits some categories of use benefits (i.e., benefits for recreational users other than
fishers), EPA judges that the largest use-value categories (i.e., commercial and recreational fishing) have been
captured.
As stated above, EPA was unable to use benefit transfer to generate national estimates of non-use benefits for the
proposed regulatory options. EPA's nonuse analysis generated estimates of nonuse values for resource changes
expected to result in the North Atlantic and Mid-Atlantic benefit regions from the proposed options, but EPA was
unable to estimate reliable nonuse valuations for changes expected to result in other study regions. EPA is in the
12-2 March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 12: Social Costs and Benefits
process of developing a stated preference survey to estimated total willingness to pay for improvements to fishery
resources affected by I&E mortality from in-scope 316(b) facilities (75 FR 42,438). However, EPA did not have
sufficient time to fully develop and implement this survey for the proposed regulation. Thus, the monetized
benefit values that are compared with the estimated total social cost values in this chapter represent a partial
estimate of the total social benefits of the given option.
Table 12-2 presents EPA's estimates of use benefits and social costs for the regulatory options for existing
facilities, at 3 percent and 7 percent discount rates, and annualized over 50 years. At a 3 percent discount rate,
EPA estimates that social costs exceed mean monetized benefits by $366 million for Option 1, by $4.3 billion for
Option 2, and by $4.5 billion for Option 3. At a 7 percent discount rate, social costs exceed mean monetized
benefits by $443 million for Option 1, by $4.6 billion for Option 2, and by $4.8 billion for Option 3. These values
are all in 2009 dollars and are based on the discounting of costs and benefits to the beginning of the year 2012, the
assumed date when the rule would take effect.
Table 12-2: Total Benefits and Social Costs by Option (Millions; $2009)
Option
Option 1
Option!
Option 3
Total M
Bent
3%
$17.63
$120.79
$125.65
onetized
rfits"
7%
$16.04
$9120
$"95771
Total Soc
3%
$383.80
$47462790
$4^63L62
ial Costsb
7%
$458.81
$47699735
$"4"78"6"2765
a. The benefit values presented in this table are the estimated "mean" values. Additional "low" and "high"
value estimates are presented in Chapter 11 of the EEBA.
b. Total Social Costs include compliance costs to facilities and government administrative costs.
Source: U.S. EPA Analysis, 2010.
Table 12-3, following page, provides additional detail on net benefits. Table 12-3 compiles for the three options,
the time profiles of benefits and costs as presented in the preceding chapters. The table also reports the calculated
present and annualized values of benefits and costs at 3 percent and 7 percent discount rates. Benefits were
estimated assuming the same compliance years as costs (see Chapter 3, Section 1.5: Development of Compliance
Years). Table 12-3 distributes these benefits over the assumed compliance window for each option, according to
the methodology described in Chapter 9, Section 1.
Table 12-3: Time Profile of Benefits and Social Costs (Millions; $2009)
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
Option 1
Monetized
Benefits Total Social Costs
$0.00
$048
$139
$435
$840
$1248
$16729
$19770
$720.86
$2133
$2i757
$2i759
$727.59
$21759
$2i759
$2i759
$27.59
$27.59
$7.96
$804.43
$865734
$913784
$782733
$802.67'
$376725
$218754
$7203799
$376725
$2i674"i
$203799
$376725
$216741
$7203799
$376725
$216741
$7203799
Option 2
Monetized
Benefits Total
$0.00
$0!03
"^^^^^^^..
$0.29
$0.52
$0.76
$1725
$34767
$56.26
$80.22
$104.80
$J19708
$133.20
$144.72
$156.68
$168.55
$174.76
$180.39
Social Costs
$5.03
$81.04
$91.59
$102739
Slip's
$4^635.93
$9^961.77
$10,432.53
$107965.49
$7l366.09
$87.984.29
$^41757
$6,453.64
$"677872.45
$"77li6.37
S5J77714
$37.998.64
$4^025749
Option 3
Monetized
Benefits Total
$0.00
$o7oT
7$o7o2
$0.08
$0.14
$0.21
$17.21
$357l9
$57.56
$82.40
$107.88
$J22782
$137.63
$149.94
$162772
$17543
$182.09
$188.12
Social Costs
$1.79
$72579
$7278755
$3L39
$3198
""$4766745
$167206766
$"107765795
$"TT,268.23
$"Tl769736
-J97J38728
""$6748"6"."52"
$6,867.64
""$745079
""$7749123
""$6',067'."6"3"
$"4,179.89
""$4720795
March 28, 2011
12-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 12: Social Costs and Benefits
Table 12-3: Time Profile of Benefits and Social Costs (Millions; $2009)
Year
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
PV3%
Annualized at 3%
PV 7%
Annualized at 7%
Option 1
Monetized
Benefits Total
$21.59
$2i:59
$2i:59
$2T'59
$2159
$2i:59
$2i:59
$2T'59
$2T'59
$2i:59
$2i:59
$2T'59
$2T'59
$2i:59
$2i:59
$2T'59
$2T'59
$2i:59
$2i:59
$2i:59
$2T'59
$2T'59
$2i:59
$2i:59
$2T'59
$2T'59
$2i:59
$19739
$1720
$449
$2729'
$"i""'i"5"
$libb
$453.68
$17.63
$221.34
$16.04
Social Costs
$376.25
$2i6;41
$203799
$687:54
$527:70
$515:27
$687:54
$527:70
$269727
$441:53
$28T:69
$269727
$441:53
$22b:92
$208750
$'38"b7'7"6'
$220:92
$208750
$376:25
$216:41
$203799
$376:25
$214:79
$i6b:"i"2
$177:51
'$'"8"b:b'6
'$"40:03"
$7o7bb
$7o7bb
$7o7bb
$7o7bb
$"'b:bb"
$7o7bb
$9,874.98
$383.80
$6,331.93
$458.81
Option 2
Monetized
Benefits Total Social Costs
$184.33
$185.42
1185-96
$186.18
$186.18
$186'"."i"8
$J867l8
$186.18
$186.18
$186'"."i"8
$J867l8
$186.18
$186.18
$186'"."i"8
$J867l8
$186.18
$186.18
$186'"."i"8
$186'"."i"8
$J867l8
$186.18
$186.18
$186'"."i"8
$J867l8
$186.18
$186.18
$186'"."i"8
$160l4
$135.35
$68.61
$4167
$2L86
$0.00
$3,108.23
$120.79
$1,272.59
$92.20
$4,056.00
$4,056.66
$4,656.66
Homos
$4,696.65
$4,69665
$4,696."65
$4,696.65
$4;069;07
$4,069.67
$4;069;07
$4;069;07
$4;069;07
$4,067.86
$4,667.'86
$4,067.'86
$4,067.86
$4,067.86
$4,056.37'
H'656.37
K0507
$4,056.37
$4,054.74
$3^24174
^43L31
§i£2Q&7
$816.44
$¥66'
$¥"66
$6.66
$6.66
$¥"66
$¥66
$114,943.11
$4,467.32
$64,944.79
$4,705.89
Option 3
Monetized
Benefits Total Social Costs
$192.23
$19138
$193!95
$194.18
$194.18
$"l947i"8"
$19438
$194.18
$194.18
$194J8"
$19438
$194.18
$194.18
$194:18
$19438
$194.18
$194.18
$194J8"
$"l947i"8"
$19438
$194.18
$194.18
$194J8"
$19438
$194.18
$194.18
$194:18
$16709
$141.09
$71.71
$4462
"^^^^?^^^..
$0.00
$3,232.89
$125.65
$1,320.89
$95.71
$4,239.82
$4;2397'82
$47239782
$4^49708
$47249708
$4"^4"9708"
$4;2"4"9708
$452"4"9708
$4^46:43
$4^46';43
$4:245:43
$4^246';43
$4^46:43
$4^2537'72
$4:253;'72
$4^2537'72
$4"^53:72
$4^537'72
$452"4"b"l"8
$4;2"4"b"l"8
$452"4"6;i8
$47240'I'8
$4^238755
$37388781
$2";54'i':6"i
$l"-69"4""4'i
$84"7""20
'$"'b'"b'6
$"o"'6'6
$o'"6'6
$'b"6'6
'$"'6'"6'6
$b"6'6
$119,170.58
$4,631.62
$67,099.91
$4,862.05
Source: U.S. EPA Analysis, 2010
12.3 Incremental Analysis of Benefits and Social Costs
In addition to comparing benefits and costs for each primary analysis option, as presented in the preceding
section, EPA also analyzed the benefits and costs of the options on an incremental basis. The comparison in the
preceding section addresses the simple quantitative relationship between estimated benefits and costs for each
option by itself: for a given option, which is greater - costs or benefits - and by how much in relative terms? In
contrast, incremental analysis looks at the differential relationship of benefits and costs across options and poses a
different question: as increasingly more costly options are considered, by what amount do benefits, costs, and net
benefits (i.e., benefits minus costs) change from option to option? Incremental net benefit analysis provides
insight into the net gain to society from imposing increasingly more costly requirements and may aid regulatory
12-4
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 12: Social Costs and Benefits
decision-makers in choosing among a set of regulatory proposals that otherwise have a similar quantitative
relationship between benefits and costs based on a one-option-at-a-time comparison.
The Agency conducted the incremental net benefit analysis by calculating, for each option, the change in net
benefits, from option to option, in moving from the least stringent option to successively more stringent options.
As described previously, the regulatory options differ in the technology standard required of facilities and the
criteria by which these technologies are assigned to facilities. Thus, the difference in benefits and costs across the
options derives from the effectiveness of the installed technology in reducing impingement mortality and
entrainment, and in the case of Option 2, from the number of facilities installing a re-circulating system. As
reported in Table 12-4, at a 3 percent discount rate, the incremental change in mean net benefits in moving from
Option 1 to Option 2 is -$4.1 billion, and from Option 2 to Option 3 is another -$74 million. Thus, for both
incremental steps, calculated net benefits become increasingly more negative but the step from Option 1 to Option
2 is more costly to society, on a net benefit basis, than the step from Option 2 to Option 3. The same pattern of
change occurs for the analysis under a 7 percent discount rate: the incremental change in mean net benefits in
moving from Option 1 to Option 2 is -$4.2 billion, and from Option 2 to Option 3 is -$159 million.
Table 12-4: Incremental Net Benefit Analysis (Millions; $2009)
Option3
Option 1
Option 2
Option 3
Net Be
3%
-366.17
^432'Ji
I47505.97
tieflts"
7%
-442.77
^607."i5
:4j6673"4
Incremental
3%
-366.17
^065.94
-73.86
Vet Benefits0
7%
-442.77
I4j6473"g
:f59j9
a. Options are presented in order of increasing stringency.
b. Net benefits are calculated by subtracting total annualized costs from total annual benefits.
c. Incremental net benefits are equal to the difference between net benefits of an option and net benefits of the previous, less
stringent option.
Source: U.S. EPA Analysis, 2010
March 28, 2011
12-5
-------
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
13 Cost and Economic Impact of Additional Regulatory Option (Option 4 -
IM for Facilities with Design Intake Flow Greater than 50 MGD)
In addition to the three regulatory options presented in the preceding chapters of this report, i.e., Options 1, 2, and
3, EPA analyzed an additional regulatory option - Option 4: IMfor Facilities with a Design Intake Flow Greater
than 50 MGD - in developing the Proposed 316(b) Existing Facilities Regulation. Option 4 is the same as Option
1: IM Everywhere, in all respects except that Option 4 requires only in-scope existing facilities with a design
intake flow greater that 50 million gallons per day to achieve the uniform impingement mortality design and
performance standards; existing in-scope facilities between 2 and 50 MGD would continue to receive permits
based on best professional judgment (BPJ).
Because EPA analyzed Option 4 after completing the analysis and documentation of the three regulatory options
presented elsewhere in this EA report, the analysis results for Option 4 are presented separately in this chapter.
The same analyses that were conducted in the preceding chapters for Options 1, 2, and 3 were conducted for
Option 4; findings from these analyses are presented in this chapter.
This chapter includes:
> Private cost estimates (Section 13.1)
> Social costs and benefits estimates (Section 13.2)
> Unfunded Mandates Reform Act (Section 13.3)
> Cost and Economic Impact for Manufacturers (Section 13.4)
> Cost and Economic Impact for Electric Generators (Section 13.5)
> Regulatory Flexibility Act (Section 13.6)
The cost and economic impact analyses for Option 4 were conducted using the same methodology and
assumptions, and relied on the same data sources, as the analyses conducted for Options 1, 2, and 3 (for details
see Chapter 3: Development of Costs for Regulatory Options, Chapter 5: Cost and Economic Impact Analysis -
Electric Generators, Chapter 11: Assessment of Total Social Costs, and Chapter 12: Social Costs and Benefits of
the Proposed Rule)
13.1 Annualized Compliance Costs to Complying Facilities
Table 13-1 and Table 13-2 on the following pages present pre-tax and after-tax compliance costs for
Manufacturers and Electric Generators under Option 4. Table 13-3 presents the total national annualized
compliance costs for Manufacturers and Electric Generators.
March 28, 2011
13-1
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
Table 13-1: Option 4 - Annualized Compliance Costs for Manufacturers by Industry Sector (millions,
$2009, at 2012)
Sector
One-Time Costs
Capital | Connection I Initial Permit
Technology | Outage | Application
Recurring
! Monitoring, |
i Record I
I Keeping, and |
O&M i Reporting I
Costs
Energy
Penalty
Permit
Renewal
Total
Option 4: IM for Facilities with DIF>50 MOD
Pre-Tax Compliance Costs
Aluminum
Chemicals and Allied
Products
Food and Kindred
Products
Paper and Allied
Products
Petroleum Refining
Steel
Other(Miscj
Total
$0.16
$6.98
$0.99
$0.26
$3.o"i
$7.53
$0.26
$16.11
$0.04 |
$o"oo i
$o"oo I
$0"03 1
$675
$o"ob
$0"03
$0.23 !
$0.01
$(ii3
$lib3
$6761
$6768
$0"03
$libi
$0.35
$0.42 |
$467 |
$077 |
$(155 |
$168
$2.03 '
$6.55
$16.62 !
$0.12 |
$2.34 1
$678 1
$677 1
$7755 f
$6.56 |
$6.17 i
$5.93 1
$0.00
$6.66
$6.66
$6.66
$6.66
$6.66
$6766
$0.00
$0.01
$67i2
$0.02
$6.61
$0.08
$0.03
$6.61
$0.33
$0.77
$1425
$2110
$1763
$8757
$418
$To3
$39.58
After-Tax Compliance Costs
Aluminum
Chemicals and Allied
Products
Food and Kindred
Products
Paper and Allied
Products
Petroleum Refining
Steel
Other (Misc)
Total
$0.10
$4.30
$0.57
$0.15
$1.82
$0.91
$0.15
$9.78
$0.03 !
$0.00 i
$0.00 i
$0.02 1
$0.09 |
$6"oo 1
$0.02 |
$0.14 !
$0.01
$0.08
$0.02
$0.01
$0.05
$0"02
$0.01
$0.21
$0.27 !
$2.89 i
$0.44 i
$0.32 1
$2.22 |
$121 |
$0.32 |
$10.05 !
$0.08 I
$1.44 1
$0.10 1
$0.10 |
$0.94 |
$0.34 1
$0.10 I
$3.60 |
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.00
$0.01
$0.07
$0.01
$0.01
$0.05
$0.02
$0.01
$0.20
$0.49
$8.79
$1.14
$0.60
$5.17
$249
$0.60
$23.98
Source: U.S. EPA analysis, 2010
13-2
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
Table 13-2: Option 4 - Annualized Compliance Costs for Electric Generators by NERC Region (millions,
$2009, at 2012)a
NERC
Region
One-Time Costs
Capital
Technology
Connection
Outage
Initial Permit
Application
Recurring Costs
O&M
Monitoring,
Record
Keeping, and
Reporting
Energy
Penalty
Permit
Renewal
Total
Option 4: IM for Facilities with DIF>50 MOD
Pre-Tax Compliance Costs
ASCC
ERCOT
FRCC
fflCC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
$0.00
sTEso
$15784
$138
$5784
$1421
$45798
$35"03
$5796
$0"26
$136.30
$0.00
$3.59
$6.21
$0.00
$5.82
$o."6b"
$3."l7
$6.28
$31.03
$o.ob"
$56.09
$0.00
$"'b"."i'i'
$0.06
$0.01
$0."12
$0."l6
$0.41
$0.40
$0.09
$0.06
$1.41
$0.00
$15702
$"io'"4"6"
$""i'"'9"'i
$8760
$2484"
$56""b"l
$4747
$12760
$b7i6
$177.07
$0.00
$""i""6"8"
$105
$o7i7
$""f'6"5"
$2""51
$7722
$6""i"6
$144
$6""21
$22.03
$0.00
$"o"ob"
$"'b"'b'b
$o7bb
$'b"'bb"
$o"ob"
$"o"bb
$"'b"'b'b
$o"ob"
$b"bb
$0.00
$0.00
$"'b"'7b'
$"'b"'"b"6"
$"b""b"i
$"b"'7i
$o75
$"b"39
$"b"38
$"b"08
$"b"06
$1.33
$0.00
$32.29
$33768
$3748
$22715
$41787'
$113717
$"95.65
$5i72"o"
$075
$394.23
After-Tax Compliance Costs
ASCC
ERCOT
FRCC
FflCC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
$0.00
$8725
$9775
$"0"84
$4748
$8746
$27759
$24785
$3786
$0"23
$88.31
$0.00
$2733
$3781
$6.66
$5.76
$6.66
$7.87'
$3781
$18.96
$o."6b
$36.54
$0.00
$0.08
$"b"."b'4
$6.66
$"b"."b"8"
$616
$"b".'2"6"
$6728
$"b"."b'6"
$"'b"."b'4"
$0.94
$0.00
'$"'i"'i'7i"i
$6744
$776
$5781
$1478
$33785
$34713
$87"i"3"
$674
$115.54
$0.00
$""i""26"
$ll69
$"'b7"i"i
$772
$149
$"449"
$4719
$"0"95"
$677
$14.47
$0.00
$'b"bb"
$b"'bb"
$"o"bb
$"'b"'b'b
$"'b"bb
$'b"bb
$b"bb
$"'b"bb
'$"'b'"bb
$0.00
$0.00
$"b"b'7'
$"b""b"4"
$"'b"bb
$"b'"b'7
$"b"b'9"
$"'b'"2"5
$"'b'"26"
$"b"b'5"
$"b"b4"
$0.89
$0.00
$23^36
$'2b"."7'7'
$"2'"."l2"
$""i"7"."9"6"
$25716
$71737
$768770
$32755
$27l7
$264.16
a. EPA data indicate that no DQ in-scope facilities are located in the ASCC NERC region; an STQ facility in ASCC facility was grouped with STQ facilities
in the WECC region (see Appendix 3.A: Use of Sample Weights in the Proposed Existing Facilities Rule Analyses).
Source: U.S. EPA analysis, 2010
Table 13-3: Option 4 - Annualized Compliance Costs For Manufacturers and Electric Generators (millions,
$2009, at 2012)a
Facility
Group
Capital
Technology
One-Time Costs
| Connection | Initial Permit
! Outage ! Application
Recurring Costs
| Monitoring, |
| Record |
| Keeping, and | Energy
O&M | Reporting | Penalty
Permit
Renewal
Total
Option 4: IM for Facilities with DIF>50 MOD
Pre-Tax Compliance Costs
Manufacturers
Generators
Total
$16.11 |
$136730 1
$152.41 i
$0.23 |
$5"6""b'9" 1
$56.32 i
$0.35
$141
$1.76
$16.62 |
$177707 1
$193.69 1
$5.93 |
$22^03 1
$27.96 1
$0.00 !
$b7bb 1
$0.00 i
$0.33
$7733
$1.66
$39.58
$39423
$433.80
After-Tax Compliance Costs
Manufacturers
Generators
Total
$9.78 |
$88731 1
$98.09 |
$0.14 |
$36754 |
$36.68 |
$0.21
$"'b""94"
$1.15
$10.05 |
$115754 1
$125.59 |
$3.60 |
$14747 |
$18.07 |
$0.00 |
$"b7bb 1
$0.00 |
$0.20
$0789
$1.09
$23.98
$264"i'6"
$288.14
Source: U.S. EPA analysis, 2010
13.2 Total Social Costs and Benefits
Table 13-4 reports total social costs for Option 4 discounted at 3 and 1 percent rates. At a 3 percent discount rate,
total annual social costs are $326.55 million; at a 7 percent discount rate, these costs are $383.10 million.
March 28, 2011
13-3
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
Table 13-4: Option 4-Total Social Costs (Millions; $2009)
Cost Category
Compliance Cost
Gov. Admin.
Total3
Discounted anc
3%
$323.77
$2779
$326.55
Annualized at
7%
$379.84
$3726'
$383.10
a. Values may not add up to total to due independent rounding.
b. Values include costs for Electric Generators and Manufacturers.
c. Social cost values do not include costs potentially incurred for installation of entrainment
mortality (cooling tower) technology at new generating units.
Source: U.S. EPA Analysis, 2010.
Table 13-5 presents EPA's estimates of benefits and social costs for Option 4 for existing facilities, at 3 percent
and 7 percent discount rates, and annualized over 50 years. At a 3 percent discount rate, EPA estimates that social
costs exceed mean monetized benefits by $309.2 million; at a 7 percent discount rate, social costs exceed mean
monetized benefits by $367.3 million. These values are all in 2009 dollars and are based on discounting of costs
and benefits to the beginning of the year 2012, the assumed date when the rule would take effect.
Table 13-5: Option 4 - Total Benefits and Social Costs (Millions; $2009)
Option
Option 4
Total M
Bent
3%
$17.33
onetized
rfits"
7%
$15.76
Total Social Costsb
3% [ 7%
$326.55 | $383.10
a. The benefit values presented in this table are the estimated "mean" values. Additional "low" and "high"
value estimates are presented in Chapter 11 of the EEBA.
b. Total Social Costs include compliance costs to facilities and government administrative costs; reported
social costs do not include costs potentially incurred for installation of entrainment mortality (cooling tower)
technology at new generating units.
Source: U.S. EPA Analysis, 2010.
Table 13-6 provides additional detail on net benefits; this Table compiles the time profiles of benefits and social
costs for Option 4. Table 13-6 also reports the discounted present value and annualized value of benefits and costs
at 3 percent and 7 percent discount rates. The time profiles of social costs and benefits reflect the same
compliance schedules as described previously (see Chapter 3, Section 1.5: Development of Compliance Years).
Table 13-6 distributes these benefits over the assumed compliance window for each option, according to the
methodology described in Chapter 9, Section 1.
Table 13-6: Option 4 - Time Profile of Benefits and Social
Costs (Millions; $2009)
Year
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
Opti
Monetized Benefits
$0.00
$048
$7737
$4747
$8725
$12726
$i67oo
$19735
$26749
$26796
$2O9
$21722
$21722
$2L22
'$"2"'i'"22
$21722
$21722
on 4
Total Social Costs
$5.33
$639782
$694749
$735792
$597773
$612773
$344730
$186759
$172703
$344730
$184746
$172703
$344730
$184746
$172703"
$344730
$184746
13-4
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
Table 13-6: Option 4 - Time Profile of Benefits and Social
Costs (Millions; $2009)
Year
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
PV3%
Annualized at 3%
PV 7%
Annualized at 7%
Option 4
Monetized Benefits
$21.22
$2'i".'2"2"
$2'i".'2"2"
$2'i".'2"2"
$2L22
$2L22
$2'i".'2"2"
$2'i".'2"2"
$2'i".'2"2"
$2L22
$2L22
$2'i".'2"2"
$2'i".'2"2"
$2'i".'2"2"
$2L22
$2L22
$2'i".'2"2"
$2i".22
$2L22
$2L22
$2L22
$2'i".'2"2"
$2'i".'2"2"
$2L22
$2L22
$2L22
$2'i".'2"2"
$2'i".'2"2"
J19706
$"i"6'"."9"b
$442
$2'".'2"6"
$"'i"."i"3
$6.00
$445.83
$17.33
$217.50
$15.76
Total Social Costs
$172.03
$34430
$18446
$172763
$641 87
$482'"."b'4"
$469.61
$64i787
$482704
$79:32752
$404'"."7"8"
$244794
$232752
$404:78
$186.96"
$174753
$346.79
$78"6":%
$174753
$34430
$184.46"
$"i"72"."o"3
$34430
$184o"2
$135.6"3"
$J597i5
$6"7"."8"2"
$33791
$"abb
$"abb
$"abb
$0^00
$"abb
$b:bb
$8,402.17
$326.55
$5,287.03
$383.10
Source: U.S. EPA Analysis, 2010
13.3 Unfunded Mandates Reform Act (UMRA) Analysis
13.3.1 Administrative Costs
Table 13-7 shows that for Option 4: IM for Facilities with DIP > 50 MGD, compliance costs for government-
owned Electric Generators are approximately $9.5 million annually in the aggregate (total weighted compliance
cost annualized at 7 %), with an average annual cost per facility of about $0.2 million. Municipally-owned
Electric Generators account for approximately $3.8 million of this cost, and State-owned Electric Generators
account for the remaining $5.7 million. The average cost to a State-owned Electric Generator is $0.3 million,
compared to about $0.2 million for the average municipal Electric Generator. The maximum annualized
March 28, 2011
13-5
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
compliance costs expected to be incurred by any single government-owned Electric Generator is $1.0 million for
a State-owned Electric Generator and $0.5 million for a municipal Electric Generator.224
Table 13-7: Option 4- Compliance Costs to Government-Owned Electric Generators (Millions;
$2009)
Ownership Type
Number of In-scope
Facilities (weighted)
Total Weighted,
Annualized Pre-tax
Compliance Cost
Average Annual
Compliance Cost
(per facility)
Maximum
Annualized Facility
Compliance Cost3
Option 4: IM for Facilities with DIF>50 MOD
Municipality
State
Other Political Subdivision1"
Total
25
17
0
42
$3.8
$5.7
$0.0
$9.5
$0.2
$0.3
$0.0
$0.2
$0.5
$To
$0.0
$1.0
a. Reflects maximum of un-weighted costs to explicitly analyzed facilities only.
b. EPA's analysis indicates there are 3 Other Political Subdivision entities. These entities own only implicitly analyzed facilities;
consequently, there is no explicitly analyzed Other Political Subdivision parent entity to represent these implicitly analyzed Other Political
Subdivision parent entities. As a result, the weighted entity counts do not include the 3 known Other Political Subdivision entities even
though they are known to be part of the regulated facility and entity universe.
Source: U.S. EPA analysis, 2010
13.3.2 Compliance Costs
As shown in Table 13-8, government entities are expected to incur annualized costs of $4.06 million to administer
Option 4. Administrative costs are higher for this Option than for Options 2 and 3 because, like Option 1, they do
not require cooling tower technology to be installed. As discussed in Chapter 8: UMRA Analysis, facilities with
cooling towers incur no monitoring costs and no entrainment study costs, thereby reducing the administrative
burden on the permitting authority.
Unlike Options 1, 2, and 3, entrainment study activities are expected to account for the largest portion of
administrative costs under Option 4 - $2.16 of the $4.06 million in annualized administrative costs incurred for
this option. Other administrative costs of approximately $0.04 for start-up activities and $0.52 for permit issuance
and reissuance.
Table 13-8: Option 4
- Annualized Government Administrative Costs (Millions; $2009)
Activity
Annualized Cost,
Electric Generators
Annualized Cost,
Manufacturers
Total Annualized Cost
Option 4: IM for Facilities with DIF>50 MOD
Start-Up Activities
First Permit Issuance Activities
Annual Monitoring Activities
Entrainment Study
Permit Reissuance Activities
Total
$0.02
$0.23
$1.04
$1.19
$0.18
$2.65
$0.02
$0.06
$0.31
$0.97
$0.05
$1.41
$0.04
$0.29
$1.35
$2.16
$0.23
$4.06
a. These costs reflect the assumption that all facilities will comply in one year, and were discounted accordingly because individual
components of costs were not distributed over the 5-year compliance window, as described in Chapter 11, Section 1.
Source: U.S. EPA analysis, 2010
13.3.3 Analysis Impact on Small Governments
As presented in Table 13-9, costs are lower for small governments in comparison to large governments in the
aggregate, and approximately the same on a per facility basis. Under Option 1, the 10 facilities owned by small
Maximum per facility values are reported on an unweighted basis.
13-6
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
government entities, all of which are municipalities, incur total annualized costs of $1.4 million, which is
substantially less than the total of $8.1 million in costs incurred by the 31 facilities owned by large governments.
Small and large governments incur costs of approximately $0.1 million and $0.3 million per facility,
respectively.225 Small government-owned facilities are also expected to incur lower costs per facility than the 16
small privately owned facilities, for which compliance costs are estimated to be $6.0 million in the aggregate, or
about $0.4 million per facility. Moreover, the largest annualized cost to any individual facility owned by a small
government is about $0.2 million, significantly lower than the maximum facility costs of around $1.0 million for
large government-owned facilities and $2.5 million for small privately-owned facilities.
Table 13-9: Option 4 - Compliance Costs for Electric Generators by Ownership Type and Size
(Millions; $2009)
Ownership Type
Entity
Size
Number of
Facilities
(weighted)
Total Annualized Pre-
Tax Compliance Costs
Average Annualized
Pre-tax Compliance
Cost per Facility
Maximum Facility
Annualized Pre-tax
Compliance Costb
Option 4: IM for Facilities with DIF>50 MOD
Government (excluding
Federal)
Private
Small
Large
Small
Large
All Facilities3
10
31
16
485
559
$1.4
$sl
$6.0
$3467];
$383.0
$0.1
$03
$0.4
$'677
$0.7
$0.2
$"i"."6
$2.5
$7"."2
$7.2
a. Facility counts and cost estimates reported for All Facilities include 15 federal government-owned facilities and costs estimated for these
facilities.
b. Reflects maximum of un-weighted costs to explicitly analyzed facilities only.
Source: U.S. EPA analysis, 2010
13.3.4 Analysis Summary
Table 13-10 presents a summary of compliance costs under Option 4 for publicly- and privately-owned facilities,
along with government administrative costs, for each regulatory option.
For Option 4, EPA estimates total annual compliance costs for government-owned Electric Generators at $9.5
million. NPDES permitting authorities are expected to incur another $3.2 million per year to implement this
option for both Electric Generators and Manufacturers, resulting in a total annual cost of approximately $12.7
million for State and local governments. The maximum compliance cost for government-owned Electric
Generators in any one year under Option 4 is $17.6 million in 2033. The maximum administrative cost to NPDES
authorities in any single year for administering this option is $4.0 million in 2015 and $2.5 million in 2016 for the
Electric Generators and Manufacturers rule segments, respectively. Privately owned Electric Generators and
Manufacturers are expected to incur annualized compliance costs of $387.0 million under this option, with a
maximum yearly value of $0.7 billion in 2015 for Electric Generators and $0.2 billion in 2015 for Manufacturers.
Excluding federal government-owned facilities.
March 28, 2011
13-7
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
Table 13-10: Option 4 - Summary of UMRA Costs (Millions; $2009)
Sector Incurring
Costs
To
Facility
Compliance
Costs3
tal Annualized Cos
Government
Administrative
Costs"
:
Total
Mas
Facility
Compliance
Costs
imum One- Year C
Government
Administrative
Costs
ast
Total
Option 4: EVI for Facilities with DIF>50 MGD
Electric Generators
Government
(excluding Federal)
Private
$9.5
$352^2
$2.4
N/A
$11.9
$17.6
$7343
$4.0
N/A
$21.6
$7345
Manufacturers
Government
(excluding Federal)
Private
$0.00
$34.8
$0.8
N/A
$0.8
$34.8
$0.0
$239.7
$2.5
N/A
$2.5
$239.7
a. Cost values for Electric Generators are lower than those presented in Table 13-9 because they reflect a distribution over the compliance window, as
described in Chapter 11: Assessment of Total Social Costs, thus changing the amount discounted in each year.
b. These values are slightly lower than those presented in Table 13-8 because they reflect a distribution over the compliance window, as described in
Chapter 11: Assessment of Total Social Costs, thus changing the amount discounted in each year.
Source: U.S. EPA analysis, 2010
13.4 Cost and Economic Impact Analysis ~ Manufacturers
Table 13-11 summarizes the results from the firm impact analysis for Option 4 assuming that facilities
represented by sample weights are owned by the same firm that owns the sample facility (Case 1). The following
table, Table 13-12, reports the results from the firm impact analysis assuming that the facilities presented by
sample weights are owned by different firms than that owning the sample facility (Case 2). Both tables show the
number of firms that incur costs in three ranges: less than 1 percent of a firm's revenue, within 1 and 3 percent of
revenue, and greater than 3 percent of revenue.
Under Option 4/Case 1, of the 123 entities estimated to own facilities subject to the proposed regulation; all are
estimated to incur costs less than 1 percent of revenue with none incurring costs greater than 1 or 3 percent of
revenue. Under Option 4/Case 2, 356 entities are estimated to own facilities subject to regulation; all are estimated
to incur costs less than 1 percent of revenue, while zero incur costs greater than 1 or 3 percent of revenue.
Table 13-11: Option 4- Entity-Level Cost-to-Revenue Analysis Results, Assuming that Facilities
Represented by Sample Weights are Owned by the Same Firm that Owns the Sample Facility (Case 1)
Entity Type
Total Number of
Facilities
Total Number of
Entities
Number of Firms with a Ratio of
<1% | 1-3% | >3% (Unknown*
Minimum
Ratio
Maximum
Ratio
Option 4: IM for Facilities with DIF>50 MGD
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Total
31
198
30
167
46
24
7
504
8
42
17
26
16
5
9
123
81 0 I 0 | 0
42 | 0| 0 | 0
17 1 0 i 0 1 0
26 | 0 I 0 i 0
16 i 0 i 0 i 0
5 i 0 i 0 i 0
9| 0 I 0 i 0
123 ! 0 ! 010
0
0
0
0
0
0
0
0
0.00
0.01
0.00
0.00
0.03
0.02
0.01
0.03
a. EPA was unable to determine revenues for 3 parent entities.
Source: U.S. EPA Analysis, 2010
13-8
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
Table 13-12: Option 4- Entity-Level Cost-to-Revenue Analysis Results, Assuming that Facilities
Represented by Sample Weights are Owned by Different Firms than those Owning the Sample Facility
(Case 2)
Entity Type
Total Number of
Facilities
Total Number of
Entities
Number of Firms with a Ratio of
<1% I 1-3% I >3% | Unknown"
Minimum
Ratio
Maximum
Ratio
Option 4: IM for Facilities with DIF>50 MOD
Food
Paper
Petroleum
Chemicals
Steel
Aluminum
Other
Total
31
198
30
167
46
24
7
504
24
126
24
116
43
14
9
356
24 | 01 01 0
126 i 0 i 0 i 0
24 1 0 I 0 1 0
116 | 0 | 0 | 0
43 i 0 i 0 i 0
14 i 0 i 0 i 0
9 | 0 | 0 | 0
356 i 0 i 0 i 0
0
0
0
0
0
0
0
0
0.00
0.00
0.00
0.00
0.01
0.02
0.01
0.02
a. EPA was unable to determine revenues for 9 parent entities.
Source: U.S. EPA Analysis, 2010
13,5 Cost and Economic Impact Analysis ~ Electric Generators
13.5.1 Cost-to-Revenue Analysis: Facility-Level Screening Analysis
Table 13-13 reports facility-level cost-to-revenue results for Option 4 by North American Reliability Corporation
(NERC) region.226 EPA estimates that 488 Electric Generators facilities subject to the Proposed Existing Facilities
Rule will on incur annualized costs of less than 1 percent of revenue under Option 4 (87 percent); this finding
applies to all NERC regions.
Table 13-13: Facility-Level Cost-to-Revenue Analysis Results by NERC Region and Regulatory Option
NERC Region
Total Number of
Facilities0
No Revenue
Number of Facilities with a Ratio of
<1% I 1-3% i >3%
Minimum
Ratio
Maximum
Ratio
Option 4: IM for Facilities DIF>50 MOD
ASCC
ERCOT
FRCC
FflCC
MRO
NPCC
RFC
SERC
SPP
WECC
Total
0
42
25
3
46
63
164
157
34
23
559
0
5
o
6
6
6
o
6
6
6
5
0 | 0 ! 0
28 1 7 1 2
18 [ 4 1 4
2" i 2 ' 0
43 1 4 0
52 i 11 i 0
151 i 12 ' 2
148 1 5 ' 5
28 i 6 i 0
19 [ 0 I 4
488 1 49 i 17
0.00%
o7oo%
o7o6%
(134%
(io6%
(ioo%
o7oo%
cT6o%
(ioo%
o"oo%
0.00%
0.00%
3.28%
3749%
1.04%
f.80%
2764%
3754%
161%
2738%
3.38%
3.61%
a. No explicitly analyzed facilities are located in the ASCC region. For more information on explicitly and implicitly analyzed facilities see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses.
b. Facility counts may not add up due to rounding.
c. Facility counts exclude baseline closures.
d. IPM and EIA report no revenue for 2 facilities (5 on a weighted basis); consequently, the facility-level cost-to-revenue analysis is performed for 257
facilities (559 on a weighted basis).
Source: U.S. EPA Analysis, 2010
226 NERC is responsible for the overall reliability, planning, and coordination of the power grids; it is organized into regional councils
that are responsible for the overall coordination of bulk power policies that affect their regions' reliability and quality of service (see
Chapter 2.H: Profile of the Electric Power Industry). As noted previously, NERC region definitions have recently changed.
March 28, 2011
13-9
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
13.5.2 Cost-to-Revenue Analysis: Entity-Level Screening Analysis
Using facility weights, EPA estimates that 97 unique parent entities own 559 facilities subject to the Proposed
Existing Facilities Rule (Table 13-14). EPA estimates that 86 parent entities will incur annualized costs of less
than 1 percent of revenues under Option 4 (89 percent). This finding applies across all parent entity types under
Option 4. EPA estimates that 2 out of 97 parent entities will incur costs exceeding 3 percent under Option 4. As
described previously, the analysis using only facility-level weights is likely to overstate the costs to individual
parent entities but may undercount the number of parent entities in a given impact range.
Using entity weights, EPA estimates that 141 parent entities own 257 explicitly analyzed facilities subject to the
Proposed Existing Facilities Rule (Table 13-14)221 EPA estimates that 130 of these parent entities will incur
annualized costs of less than 1 percent of revenues under Option 4 (92 percent). This finding applies to all
ownership categories. As described above, the analysis using only entity-level weights is likely to understate the
costs to individual parent entities but provides a more comprehensive estimate of the number of parent entities
incurring costs.
Table 13-14: Entity-Level Cost-to-Revenue Analysis Results for Option 4: IM for Facilities with DIF>50
MGD
Entity Type
Total Number of
Facilities3
Total Number of
Entities1"
Number of Facilities with a Ratio of
<1% [ 1-3% 1 >3% pijnikiiown1''"
Minimum
Ratio
Maximum
Ratio
Using Facility-Level Weights
Cooperative
Federal
Investor-owned
Municipality
Nonutility
Other Political
Subdivision
State
Total
25
16
306
25
170
0
17
559
11
1
38
13
30
0
4
97
10 | 0 | 1 | 0
1 i 0 1 0 i 0
38 i 0 1 0 i 0
10 | 3 | 0 | 0
23 ! 0 | 1 ! 6
0 | 0 | 0 | 0
4 i 0 | 0 i 0
86 i 3 1 2 i 6
0.00%
0.22%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
4.09%
0.22%
0.65%
1.46%
22.44%
0.00%
0.67%
22.44%
Using Entity-Level Weights
Cooperative
Federal
Investor-owned
Municipality
Nonutility
Other Political
Subdivision
State
Total
13
7
138
13
78
0
8
257
20
1
42
36
38
0
4
141
18 i 2 1 0 i 0
1 i 0 1 6 i 0
42 i 0 1 0 i 0
36 0 | 0 0
29 1 0 1 T 8
0 i 0 | 0 i 0
4 0 | 0 0
130 i 2 1 1 i 8
0.00%
0.09%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
1.78%
0"09%
0.16%
0.76%
10.04%
0.00%
0.29%
10.04%
a. Facility counts exclude baseline closures.
b. EPA was unable to determine revenues for 6 parent entities (8 weighted).
b. There are a total of 143 parent entities on an unweighted basis, 3 of which are other political subdivision entities. These entities own only implicitly
analyzed facilities; consequently, there is no explicitly analyzed other political subdivision parent entity to represent these implicitly analyzed parent entities
and total weighted entity counts do not include 3 other political subdivision entities.
Source: U.S. EPA Analysis, 2010
13.5.3 Impact of Compliance Costs on Household Electricity Costs
Table 13-15 reports the results of this analysis by NERC region for Option 4. These results for this option show
that the average annual cost per residential household is expected to range from $0.01 in WECC to $3.86 in SPP.
There are a total of 143 small parent entities on an unweighted basis, 3 of which are Other Political Subdivision entities. These entities
own only implicitly analyzed facilities; consequently, there is no explicitly analyzed Other Political Subdivision parent entity to
represent these implicitly analyzed Other Political Subdivision parent entities. As a result, the weighted entity counts do not include
the 3 known Other Political Subdivision entities even though they are known to be part of the regulated facility and entity universe.
13-10
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
On average, for atypical U.S. household, Option 4 is expected to result in a cost of $1.37 per household, which is
the lowest cost of all regulatory options (comparing to the 3 regulatory options presented previously in this EA
report).
Table 13-15: Option 4 - Average Annual Cost per Household in 2015 by NERC Region ($2009)a
NERC
Regionb
Total Annual
Compliance Cost
(at 2015; Million;
$2009)
Total Electricity
Sales (at 2015;
MWh)
Compliance Cost
per Unit of Sales
(S2009/MWh)
Residential
Electricity
Sales (at 2015;
MWh)
Number of
Households
(at 2015)
Residential Sales
per Residential
Consumer (MWh)
Compliance Cost
per Household
($2009)
Option 4: IM for Facilities DIF>50 MOD
ASCC
ECAR
ERCOT
FRCC
HICC
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
U.S.
$0
••^651,375
j3^560;948
$41^59^203
j^gcT^g
$5gj49"j32
•pQ-Qjg-375
J26J447938
$51^96^663
j97jgg^54
$g2772l7433
$913^556
$482,954,744
6,326,610
569^49^7
313^95^66
242j320;908
10,585,038
294;365^34
^g^Jg^Qg
165,189,056
284,990,412
887,073,303
204^72^272
701,826,043
3,960,424,805
$0.00
$6".Ti
$"b"."i"3
$0.'l7
$0.40
$0.19
$"b"."i"5
$(116
$"b"."i"8"
$6".Ti
$6731
$o."6b"
$0.12
2,114,456
19^477^70
91,064,812
110,173,004
£2ob7675
104,073,139
86,988,506
55j72~gT5
9555g4;731
3327332^57
68,368,566
24bj57;54g
1,380,308,173
265,449
16,899,104
6,66'3,322
7^92p49
407J40
10,285,613
8,939,261
5,146,199
127557;4ib
22;7b57585
57439^70
265073j56
123,244,098
7.97
1L27
13:79
13791
7.86
I6T2
9.73
1072
7.61
14764
12757
'9"'."23
11.20
$0.00
$7.22'
$174
$2737
$73.76"
$1795
$141
$174
$"'i"."3"7"
$1761
$7.86
$"'6'"."6'"i'
$1.37
a. The rate impact analysis assumes foil pass-through of all compliance costs to electricity consumers.
b. No explicitly analyzed facilities are located in the ASCC region. For more information on explicitly and implicitly analyzed facilities see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2009b; U.S. DOE, 2007c
13.5.4 Impact of Compliance Costs on Electricity Prices
As reported in Table 13-16, annualized compliance costs (in cents per KWh sales) range from 0.0110 in the
ECAR and SERC regions to 0.0400 in the HICC region for Option 4. On average, across the United States,
Option 4 results in the lowest cost, 0.0120 per kWh, across all regulatory options.
Table 13-16: Compliance Cost per KWh of Sales by NERC Region for Option 4 in 2015 ($2009)'
NERC Region1
Annualized Pre-Tax Compliance
Costs (at 2015; $2009; million)
Total Electricity Sales
(at 2015; KWh)
Costs per Unit of Sales
(2009^/KWh Sales)
Option 4: IM Everywhere Without New Unit Requirements
ASCC
ECAR
ERCOT
FRCC
HICC
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
U.S.
$0
$6^65^375
j39^94g
$4^2597203
j^2597468
$ggj49j32
Po;'o'i''8'3'7''5'
J26J47938
$5^27967563
j97jgg^54
$g2;7277433
$"913;556"
$482,954,744
6,326,610,000
5^49^305
3YP^9g^5"7g
24p2"b796"'7"'3"93
10^03^000
294^g^234^7g
275^Y'57b"6"8"3'4"5'
jg5jg9-05^9-6-
^^Q^^
gg7^ypQ3^23
204j725'27f;72"9"
jQY£26fl43f)25
3,960,424,804,688
0.000
b"."b"'i"'i
b"."b"'i"3
b"."b"'i"7"
b"."b4'b
b"."b'i"9
b"."b'i"5'
b"."b'i"6
b"."b'i"8
'b"."b'i'"i
b"."b'3'"i
aboo
0.012
a. The rate impact analysis assumes full pass-through of all compliance costs to electricity consumers.
b. No explicitly analyzed facilities are located in the ASCC region. For more information on explicitly and implicitly analyzed facilities see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses.
Source: U.S. EPA Analysis, 2010; U.S. DOE 2009b; U.S. DOE 2007c
March 28, 2011
13-11
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
To determine the potential significance of these compliance costs on electricity prices, EPA compared the per
KWh compliance cost to baseline electricity prices by consuming sector, and for the average of the sectors. As
reported in Table 13-17 across the United States, Option 4 is expected to result in the lowest electricity price
increase and is tied with Option 1 at 0.13 percent. Looking across the three consumer groups, industrial
consumers are expected to experience the highest price increases: 0.19 percent under Option 4, and residential
consumers are expected to experience the lowest price increases: 0.11 percent.
Table 13-17: Option 4 - Projected 2015 Price (Cents per KWh of Sales) and Potential Price Increase Due to
Compliance Costs by NERC Region ($2009)a
NERC
Region b
Compliance
Cost (eYKWh)
Residential
Baseline | %
Price | Change
Commercial
Baseline | %
Price | Change
Industrial
Baseline | %
Price | Change
Transportation
Baseline I %
Price | Change
All Sector Average
Baseline | %
Price | Change
Option 4: IM Everywhere Without New Unit Requirements
ASCC
ECAR
ERCOT
FRCC
HIC'C
MAAC
MAIN
MAPP
NPCC
SERC
SPP
WECC
u.s.c
0.000
(loii
0"013
(1017
(1040
(1619
(1015
(1016
(1018
o"oi"i
(1031
(io66
0.012
15.69 | 0.00%
9" 83 1 0.11%
13".28 | 0.10%
1338 | 0.13%
2493 | 0.16%
1109 | 0.15%
1(115 | 0.14%
8"02 1 0.20%
18T23 | 0.10%
9"04 I 0.12%
9""7"i" | 0.32%
i"i"."37 1 0.00%
11.20 I 0.11%
12.59 0.00%
8.97 i 6"."i'2"%
9.37 0.13%
11.28 0.15%
22.64 0.18%
11.35 0.17%
8.24 0.18%
7.45 6"."22"%
14.42 0.12%
7.67 0.14%
8.25 6"."3"7%
9.63 I 0^00%
9.57 | 0.13%
13.06 | 0.00%
6""i5 [ 0.18%
747 i 0.17%
9.16 \ 0.19%
18799 i 0.21%
8""i4 i 0.24%
5.64 ! 0.26%
5^68 t 0.29%
9.69 I 0.19%
5""56 i 0.20%
6""i5 t 0.50%
TOO [ 0.00%
6.46 I 0.19%
NA NA
7""9"0 1 0.14%
10.59 ' 0.12%
8""94 i 0.19%
NA" i NA
Ti.06 ' 0.17%
7""52 ' 0.19%
6""84 ' 0.24%
15.84 ' 0.11%
5 "97 ' 0.18%
736 | 0.41%
8"""8"7' 1 0.00%
10.64 ! 0.11%
13.73 | 0.00%
8.21 1 6""l"3"%
10.39 | 6""l2"%
12.19 | 6""l4"%
22.00 1 6""l"8"%
11.32 | 6""i'7%
7.99 | 6""l"8"%
7.04 | 6:23%
14.92 | 6""l'2"%
7.60 | 6""l"5%
8.23 | 6"'3"'7%
9.64 1 6"0'6%
9.35 I 0.13%
a. The rate impact analysis assumes foil pass-through of all compliance costs to electricity consumers.
b. No explicitly analyzed facilities are located in the ASCC region. For more information on explicitly and implicitly analyzed facilities see Appendix 3.A:
Use of Sample Weights in the Proposed Existing Facilities Rule Analyses.
Source: U.S. EPA Analysis, 2010; U.S. DOE, 2009b; U.S. DOE, 2007c
13.6 Assessing the Potential Impact of the Proposed Existing Facilities Rule on Small
Entities ~ Regulatory Flexibility Act f RFA) Analysis
13.6.1 Analysis of Manufacturers
As reported in Table 13-18, for Option 4, EPA estimated that no small entities within the Primary Manufacturing
Industries or entities owning facilities in Other Industries would receive costs exceeding 1 or 3 percent of
revenue, under either of the sample-weighting approaches used for this analysis. Thus, this option would have no
material impact on small Manufacturers entities.
13-12
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
Table 13-18: Option 4- Estimated Cost-To-Revenue Impact on Small Manufacturers Entities, by Industry
Firm Sector
Case 1: Lo
owning fat
Total In-
Scope
Firms
wer bound
ilities that 1
regula
Small In-
Scope
Firms
estimate of number of firms
ace requirements under the
ory analysis
Small Firms with Costs
Exceeding
l%of
Revenue
3% of
Revenue
Case 2: U]
owning fa
Total In-
Scope
Firms
jper bound
cilities that
regula
Small In-
Scope
Firms
estimate of number of firms
'ace requirements under the
tory analysis
Small Firms with Costs
Exceeding
l%of
Revenue
3% of
Revenue
Option 4: IM Everywhere Without New Unit Requirements
Paper
Chemicals
Petroleum
Steel
Aluminum
Food
Multiple
Firms that own facilities
in Primary
Manufacturing
Industries
Additional firms that own
known facilities in Other
Industries
42
26
17
16
5
8
3
117
9
9
4
4
3
2
1
o
23
4
0
o
o
o
o
o
o
0
0
0
o
o
o
o
o
o
0
0
126
116
24
43
14
24
13
359
9
29
18
4
8
5
1
o
64
4
0
o
o
o
o
o
o
0
0
0
o
o
o
o
o
o
0
0
a. Includes all firms with less than 500 employees from 2006 Statistics of U.S. Businesses (SUSB) of the U.S. Department of Commerce (U.S. DOC).
mall Business Administration defines firms in nearly all profiled NAICS codes according to the firm's number of employees; however, for some in-
manufacturing NAICS codes this threshold is 500 employees while for others this threshold is 750, 1,100, or 1,500 employees. Because the SUSB
'yment size categories do not correspond to the SBA entity size classifications, EPA was had to used the 500 employee threshold for all in-scope NAICS
Sources: U.S. EPA Analysis, 2010; D&B, 2009; U.S. EPA, 2000; U.S. DOC, 2006; SBA, 2009
13.6.2 Analysis of Electric Generators
Table 13-19 summarizes the findings for Option 4 from the analyses outlined in Chapter 7: Regulatory Flexibility
Act Analysis in terms of numbers of small Electric Generators entities incurring costs exceeding the significant
impact thresholds of 1 percent and 3 percent of revenue.
Under the facility-level sample-weighting approach, EPA estimates that 4 small entities owning Electric
Generators, or 12.1 percent of all small in-scope entities, will incur costs exceeding 1 percent of revenue, and that
2 small entities, or 6.1 percent of all the estimated 33 small in-scope entities, will incur costs exceeding 3 percent
of revenue under Option 4. In all instances, the numbers of small entities incurring costs exceeding either the 1 or
3 percent impact threshold are small percentages of the total estimated number of small entities in the affected
industry segments.
Under the entity-level sample-weighting approach, EPA estimates that 6 small entities or 18.2 percent of the 33
estimated small in-scope Electric Generators entities, will incur costs exceeding 1 percent of revenue, and that 2
small entities, or 6.1 percent of estimated small in-scope entities, will incur costs exceeding 3 percent of revenue.
EPA assesses these impacts - both in number of entities incurring costs in excess of the 1 or 3 percent of revenue
thresholds and as percentage of estimated small in-scope entities - as supportive of a finding of "No Significant
Impact on a Substantial Number of Small Entities" (No SISNOSE) for the Electric Generators regulated industry
segment of Option 4.
March 28, 2011
13-13
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule
Chapter 13: Cost and Economic Impact of Additional Option
Table 13-19: Option 4- Estimated Cost-to-Revenue Impact on Small Electric
Generators Entities, by Entity Type3
Parent Entity Typeb
Cost Impact Category
Cost > 1% of Revenue
Number of j% of All Small
Small Entities i Entities
Cost > 3% of Revenue
Number of |% of All Small
Small Entities i Entities
Using Facility-Level Weights
Rural Electric Cooperative
Investor-Owner Utility
Municipality
Nonutility
Other Political Subdivision
Total
1
0
2
1
0
4
\ 12.5%
0.0%
11.8%
20.0%
! 0.0%
! 12.1%
1
0
0
1
0
2
\ 12.5%
0.0%
0.0%
20.0%
! 0.0%
! 6.1%
Using Entity-Level Weights
Rural Electric Cooperative
Investor-Owner Utility
Municipality
Nonutility
Other Political Subdivision
Total
4
0
0
2
0
6
! 50.0%
i 0.0%
! 0.0%
! 40.0%
! 0.0%
! 18.2%
0
0
0
2
0
2
I 0.0%
i 0.0%
i 0.0%
i 40.0%
! 0.0%
! 6.1%
a. The number of entities with cost-to-revenue impact exceeding 3 percent is a subset of the number of entities with such
ratios exceeding 1 percent.
Source: U.S. EPA Analysis, 2010
13.6.3 Overall Small Entity Impact Assessment for Option 4
Given that no Manufacturers small entities are estimated to incur a significant impact and no more than 6 Electric
Generators small entities (18 percent of the estimated total of Electric Generators small in-scope entities) are
estimated to incur a significant impact, EPA concludes that Option 4 would qualify overall for a finding of "No
Significant Impact on a Substantial Number of Small Entities."
13-14
March 28, 2011
-------
Economic and Benefits Analysis for Proposed 316(b) Existing Facilities Rule References
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