United States Office of Water EPA-821-R-15-005

Environmental Protection Washington, DC 20460 September 2015

Agency

Benefit and Cost Analysis 

for the Effluent Limitations 

Guidelines and Standards 

for the Steam Electric Power 

Generating Point Source 

Category
Benefit and Cost Analysis for the Effluent 

Limitations Guidelines and Standards for the 

Steam Electric Power Generating Point 

Source Category

EPA-821-R-15-005

September 2015

U.S. Environmental Protection Agency

Office of Water (4303T)

Engineering and Analysis Division

1200 Pennsylvania Avenue, NW

Washington, DC 20460
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Acknowledgments and Disclaimer

This report was prepared by the U.S. Environmental Protection Agency. Neither the United States 

Government nor any of its employees, contractors, subcontractors, or their employees make any warrant, 

expressed or implied, or assume any legal liability or responsibility for any third party’s use of or the results 

of such use of any information, apparatus, product, or process discussed in this report, or represents that its 

use by such party would not infringe on privately owned rights.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Table of Contents

September 29, 2015 i

Table of Contents

1 INTRODUCTION....................................................................................................................................... 1-1

1.1 STEAM ELECTRIC POWER PLANTS ................................................................................................... 1-2

1.2 REGULATORY OPTIONS ANALYZED FOR THE FINAL ELGS ............................................................. 1-2

1.3 ANALYSIS SCENARIOS ..................................................................................................................... 1-3

1.4 LOADING AND WITHDRAWAL REDUCTIONS .................................................................................... 1-4

1.4.1 Loading Reductions............................................................................................................ 1-4

1.4.2 Water Withdrawal Reductions............................................................................................ 1-4

1.4.3 Loading Reductions Used in Estimating Benefits.............................................................. 1-4

1.5 ANALYTIC FRAMEWORK.................................................................................................................. 1-6

1.5.1 Constant Prices................................................................................................................... 1-6

1.5.2 Discount Rate and Year...................................................................................................... 1-7

1.5.3 Period of Analysis .............................................................................................................. 1-7

1.5.4 Population and Income Growth.......................................................................................... 1-7

1.6 ORGANIZATION OF THE BENEFIT AND COST ANALYSIS REPORT..................................................... 1-8

2 BENEFITS OVERVIEW............................................................................................................................. 2-1

2.1 HUMAN HEALTH BENEFITS ASSOCIATED WITH IMPROVEMENTS IN SURFACE WATER 

QUALITY .......................................................................................................................................... 2-3

2.1.1 Fish Consumption............................................................................................................... 2-3

2.1.2 Drinking Water Consumption ............................................................................................ 2-4

2.1.3 Complementary Measure of Human Health Benefits......................................................... 2-5

2.2 ECOLOGICAL BENEFITS ASSOCIATED WITH IMPROVEMENTS IN SURFACE WATER QUALITY......... 2-5

2.2.1 Improved Surface Water Quality........................................................................................ 2-5

2.2.2 Benefits to Threatened and Endangered Species................................................................ 2-7

2.2.3 Reduced Sediment Contamination ..................................................................................... 2-8

2.3 BENEFITS ASSOCIATED WITH IMPROVEMENTS IN GROUNDWATER QUALITY ................................. 2-8

2.4 ECONOMIC PRODUCTIVITY BENEFITS.............................................................................................. 2-9

2.4.1 Reduced Impoundment Releases........................................................................................ 2-9

2.4.2 Enhanced Marketability of Coal Ash for Beneficial Use ................................................... 2-9

2.4.3 Water Supply and Use ...................................................................................................... 2-10

2.4.4 Reduced Sedimentation in Navigational Waterways........................................................ 2-11

2.4.5 Commercial Fisheries....................................................................................................... 2-11

2.4.6 Tourism............................................................................................................................. 2-11

2.4.7 Property Values................................................................................................................ 2-12

2.5 REDUCED AIR POLLUTION ............................................................................................................. 2-12

2.6 REDUCED WATER WITHDRAWALS ................................................................................................ 2-13

2.7 SUMMARY OF BENEFITS CATEGORIES ........................................................................................... 2-13

3 HUMAN HEALTH BENEFITS ................................................................................................................... 3-1

3.1 AFFECTED POPULATION................................................................................................................... 3-2

3.2 POLLUTANT EXPOSURE FROM FISH CONSUMPTION......................................................................... 3-4
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3.2.1 Fish Tissue Pollutant Concentrations................................................................................. 3-4

3.2.2 Average Daily Dose............................................................................................................ 3-4

3.3 BENEFITS TO CHILDREN FROM REDUCED LEAD EXPOSURE ............................................................ 3-6

3.3.1 Methods.............................................................................................................................. 3-6

3.3.2 Results ................................................................................................................................ 3-9

3.4 BENEFITS TO ADULTS FROM REDUCED LEAD EXPOSURE.............................................................. 3-10

3.4.1 Methods............................................................................................................................ 3-10

3.4.2 Results .............................................................................................................................. 3-14

3.5 BENEFITS TO CHILDREN FROM REDUCED MERCURY EXPOSURE .................................................. 3-14

3.5.1 Methods............................................................................................................................ 3-15

3.5.2 Results .............................................................................................................................. 3-16

3.6 REDUCED CANCER CASES FROM ARSENIC EXPOSURE .................................................................. 3-16

3.6.1 Methods............................................................................................................................ 3-16

3.6.2 Results .............................................................................................................................. 3-19

3.7 TOTAL MONETIZED HUMAN HEALTH BENEFITS ........................................................................... 3-19

3.8 ADDITIONAL MEASURES OF HUMAN HEALTH BENEFITS .............................................................. 3-20

3.9 IMPLICATIONS OF REVISED STEAM ELECTRIC PLANT LOADING ESTIMATES ................................ 3-20

3.10 LIMITATIONS AND UNCERTAINTIES ............................................................................................... 3-21

4 NONMARKET BENEFITS FROM WATER QUALITY IMPROVEMENTS.................................................... 4-1

4.1 WATER QUALITY ............................................................................................................................. 4-1

4.1.1 WQI Calculation................................................................................................................. 4-2

4.1.2 Sources of Data on Ambient Water Quality ....................................................................... 4-5

4.1.3 Baseline WQI ..................................................................................................................... 4-8

4.1.4 Estimated Changes in Water Quality (∆WQI) from the ELG Rule.................................... 4-9

4.2 METHODOLOGY FOR ESTIMATING WTP FOR WATER QUALITY IMPROVEMENTS ......................... 4-11

4.3 TOTAL WTP FOR WATER QUALITY IMPROVEMENTS .................................................................... 4-17

4.4 IMPLICATIONS OF REVISED STEAM ELECTRIC PLANT LOADING ESTIMATES ................................ 4-20

4.5 LIMITATIONS AND UNCERTAINTIES ............................................................................................... 4-22

5 IMPACTS AND BENEFITS TO THREATENED AND ENDANGERED SPECIES............................................ 5-1

5.1 INTRODUCTION................................................................................................................................. 5-1

5.2 BASELINE STATUS OF FRESHWATER FISH SPECIES.......................................................................... 5-1

5.3 T&E SPECIES AFFECTED BY THE FINAL ELGS ................................................................................ 5-2

5.3.1 Identifying T&E Species Potentially Affected by the Final ELGs..................................... 5-2

5.3.2 Estimating Benefits of T&E Species Improvements from the Final ELGs........................ 5-4

5.4 ESTIMATING WTP FOR T&E SPECIES POPULATION INCREASES...................................................... 5-5

5.4.1 Economic Valuation Methods ............................................................................................ 5-5

5.4.2 Estimating WTP for Improved Protection of T&E Species ............................................... 5-5

5.5 RESULTS........................................................................................................................................... 5-7

5.6 IMPLICATIONS OF REVISED STEAM ELECTRIC PLANT LOADING ESTIMATES .................................. 5-9

5.7 LIMITATIONS AND UNCERTAINTIES ................................................................................................. 5-9

6 BENEFITS FROM AVOIDED IMPOUNDMENT FAILURES......................................................................... 6-1

6.1 METHODS AND DATA....................................................................................................................... 6-1
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6.1.1 Baseline Failure Probability and Release Quantity ............................................................ 6-2

6.1.2 Effects of the ELGs............................................................................................................ 6-4

6.1.3 Costs of a Release............................................................................................................... 6-6

6.2 RESULTS......................................................................................................................................... 6-10

6.3 IMPLICATIONS OF REVISED STEAM ELECTRIC PLANT LOADING ESTIMATES ................................ 6-13

6.4 LIMITATIONS AND UNCERTAINTIES ............................................................................................... 6-13

7 AIR-RELATED BENEFITS........................................................................................................................ 7-1

7.1 DATA AND METHODOLOGY ............................................................................................................. 7-2

7.1.1 Changes in Air Emissions................................................................................................... 7-2

7.1.2 NOx and SO2 ....................................................................................................................... 7-4

7.1.3 CO2 ..................................................................................................................................... 7-7

7.1.4 Estimating Total Air-Related Benefits ............................................................................... 7-9

7.2 RESULTS......................................................................................................................................... 7-10

7.3 IMPLICATIONS OF REVISED STEAM ELECTRIC PLANT LOADING ESTIMATES ................................ 7-11

7.4 LIMITATIONS AND UNCERTAINTIES ............................................................................................... 7-11

8 BENEFITS FROM REDUCED WATER WITHDRAWALS ........................................................................... 8-1

8.1 GROUNDWATER WITHDRAWALS ..................................................................................................... 8-1

8.1.1 Methods.............................................................................................................................. 8-1

8.1.2 Results ................................................................................................................................ 8-2

8.1.3 Implications of Revised Steam Electric Plant Loading Estimates...................................... 8-2

8.1.4 Limitations and Uncertainties............................................................................................. 8-2

9 BENEFITS FROM AVOIDED DREDGING COSTS...................................................................................... 9-1

10 BENEFITS FROM ENHANCED MARKETABILITY OF COAL COMBUSTION RESIDUALS...................... 10-1

10.1 METHODS ....................................................................................................................................... 10-1

10.1.1 Beneficial Use Applications............................................................................................. 10-1

10.1.2 Marketable CCR by State ................................................................................................. 10-2

10.1.3 Value to Society from Re-Using CCR.............................................................................. 10-5

10.2 RESULTS......................................................................................................................................... 10-8

10.3 IMPLICATIONS OF REVISED STEAM ELECTRIC PLANT LOADING ESTIMATES .............................. 10-10

10.4 LIMITATIONS AND UNCERTAINTIES ............................................................................................. 10-10

11 SUMMARY OF TOTAL MONETIZED BENEFITS .................................................................................... 11-1

11.1 TOTAL ANNUALIZED BENEFITS ..................................................................................................... 11-1

11.2 TIME PROFILE OF BENEFITS ........................................................................................................... 11-6

12 SUMMARY OF TOTAL COSTS ............................................................................................................... 12-1

12.1 OVERVIEW OF COSTS ANALYSIS FRAMEWORK ............................................................................. 12-1

12.2 KEY FINDINGS FOR REGULATORY OPTIONS .................................................................................. 12-2

13 BENEFITS AND COSTS........................................................................................................................... 13-1

13.1 COMPARISON OF BENEFITS AND COSTS BY OPTION ...................................................................... 13-1

13.2 ANALYSIS OF INCREMENTAL BENEFITS AND COSTS...................................................................... 13-2

14 ENVIRONMENTAL JUSTICE .................................................................................................................. 14-1
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14.1 SOCIO-ECONOMIC CHARACTERISTICS OF POPULATIONS RESIDING IN PROXIMITY TO 

STEAM ELECTRIC POWER PLANTS................................................................................................. 14-1

14.2 DISTRIBUTION OF HUMAN HEALTH IMPACTS AND BENEFITS ....................................................... 14-3

14.3 DISTRIBUTION OF BENEFITS ACROSS BENEFITTING POPULATIONS ............................................... 14-4

14.4 SUBSISTENCE FISHERS ................................................................................................................... 14-6

14.5 EJ ANALYSIS FINDINGS.................................................................................................................. 14-7

14.6 LIMITATIONS AND UNCERTAINTIES ............................................................................................... 14-7

15 REFERENCES......................................................................................................................................... 15-1

APPENDIX A. CHANGES TO BENEFITS ANALYSIS SINCE PROPOSAL ..................................................... A-1

APPENDIX B. ANALYSIS FOR SCENARIO WITHOUT CPP RULE ..............................................................B-1

B.1 HUMAN HEALTH BENEFITS..............................................................................................................B-1

B.1.1 Benefits to Children from Reduced Lead Exposure ...........................................................B-1

B.1.2 Benefits to Adults from Reduced Lead Exposure ..............................................................B-2

B.1.3 Benefits to Children from Reduced Mercury Exposure .....................................................B-3

B.1.4 Reduced Cancer Cases from Arsenic Exposure .................................................................B-3

B.1.5 Total Monetized Human Health Benefits...........................................................................B-4

B.1.6 Additional Measures of Human Health Benefits................................................................B-4

B.2 NON-MARKET BENEFITS FOR WATER QUALITY IMPROVEMENTS...................................................B-5

B.3 IMPACTS AND BENEFITS TO THREATENED AND ENDANGERED SPECIES .........................................B-5

B.4 BENEFITS FROM AVOIDED IMPOUNDMENT FAILURES .....................................................................B-7

B.5 AIR-RELATED BENEFITS ..................................................................................................................B-7

B.6 BENEFITS FROM REDUCED WATER WITHDRAWALS......................................................................B-11

B.7 BENEFITS FROM ENHANCED MARKETABILITY OF COAL COMBUSTION RESIDUALS .....................B-11

B.8 TOTAL MONETIZED BENEFITS .......................................................................................................B-14

B.9 TOTAL COSTS.................................................................................................................................B-19

B.10MONETIZED BENEFITS AND COSTS COMPARISON .........................................................................B-21

B.10.1 Comparison of Total Monetized Benefits and Costs........................................................B-21

B.10.2 Analysis of Incremental Monetized Benefits and Costs...................................................B-21

APPENDIX C. ESTIMATION OF EXPOSED POPULATION.......................................................................... C-1

APPENDIX D. DERIVATION OF AMBIENT WATER AND FISH TISSUE CONCENTRATIONS IN 

RECEIVING AND DOWNSTREAM REACHES .................................................................................................. D-1

D.1 METALS........................................................................................................................................... D-1

D.1.1 Estimating Water Concentrations in each Reach............................................................... D-1

D.1.2 Estimating Fish Tissue Concentrations in each Reach ...................................................... D-2

D.2 NUTRIENTS AND SUSPENDED SEDIMENT ........................................................................................ D-3

APPENDIX E. DETAILS ON MODELING OF CARDIOVASCULAR DISEASE INCIDENCE AND 

MORTALITY E-1

E.1 BENEFITS TO ADULTS FROM REDUCED LEAD EXPOSURE................................................................E-1

E.1.1 Hazard Reduction under Final ELGs..................................................................................E-1

E.1.2 Estimating Premature Deaths Avoided Over Multiple Years.............................................E-2

APPENDIX F. HUMAN HEALTH BENEFITS SENSITIVITY ANALYSIS .......................................................F-1
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F.1 ALTERNATIVE FISHING DISTANCE ASSUMPTION............................................................................. F-1

F.2 LOG-LINEAR CONCENTRATION RESPONSE FUNCTION FOR IQ IMPACTS TO CHILDREN 

FROM LEAD EXPOSURE.................................................................................................................... F-2

F.3 ALTERNATIVE CANCER SLOPE FACTOR AND CASE VALUATION FOR ARSENIC ANALYSIS............. F-3

APPENDIX G. WQI REGIONAL SUBINDICES.................................................................................................1

APPENDIX H. DEVELOPMENT OF META-REGRESSION MODELS OF WILLINGNESS TO PAY FOR 

WATER QUALITY IMPROVEMENTS .............................................................................................................. H-1

H.1 LITERATURE REVIEW TO IDENTIFY ADDITIONAL STUDIES .............................................................. H-1

H.2 VARIABLE DEVELOPMENT AND CODING ........................................................................................ H-5

H.2.1 Study Methodology and Year............................................................................................ H-9

H.2.2 Region and Surveyed Populations..................................................................................... H-9

H.2.3 Reconciliation of Water Quality Baseline and Change ................................................... H-10

H.2.4 Delineation of the Affected Resource and Substitutes.................................................... H-12

H.3 MODEL SPECIFICATION..................................................................................................................H-15

H.3.1 Marginal Willingness to Pay (Model 1) .......................................................................... H-15

H.3.2 Marginal Willingness to Pay (Model 2) .......................................................................... H-16

H.4 REGRESSION RESULTS ...................................................................................................................H-17

H.5 LIMITATIONS AND UNCERTAINTY..................................................................................................H-20

APPENDIX I. IMPACTS OF STEAM ELECTRIC POLLUTANTS ON AQUATIC SPECIES ..............................I-1

APPENDIX J. SUPPORTING DATA FOR IMPOUNDMENT RELEASE ANALYSIS ........................................ J-1

APPENDIX K. METHODOLOGY FOR BENEFITS FROM AVOIDED DREDGING COSTS............................. K-1

K.1 REVIEW AND ANALYSIS OF HISTORICAL DREDGING DATA............................................................ K-1

K.1.1 Dredging Location, Recurrence Interval, and Dredging Volumes.................................... K-2

K.1.2 Determining Affected Navigational Dredging Jobs and Unit Costs.................................. K-3

K.1.3 Determining Reservoir Dredging Locations...................................................................... K-4

K.2 SEDIMENT DEPOSITION IN NAVIGABLE WATERWAYS AND RESERVOIRS....................................... K-4

K.3 ESTIMATING DREDGING COSTS UNDER BASELINE AND REGULATORY OPTIONS........................... K-4

K.3.1 Estimating Annualized Dredging Costs for Navigational Waterbodies............................ K-4

K.3.2 Estimating Annualized Dredging Costs for Reservoirs..................................................... K-6

K.4 RESULTS.......................................................................................................................................... K-7

K.4.1 Navigational Dredging Benefits........................................................................................ K-7

K.4.2 Reservoir Dredging Benefits ............................................................................................. K-7

K.5 LIMITATIONS AND UNCERTAINTIES ................................................................................................ K-8
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List of Figures

Figure 2-1: Summary of Benefits Resulting from the ELGs. .......................................................................... 2-2

Figure 3-1: Example changes in PbB through time under baseline, option B, and option D for cohorts in 

the 20s and 60s (age as of 2014). Differences between cohorts are driven by differences in 

body mass. Example data represent modeled PbBs of male subsistence fishers residing in 

CBG 560419753004. The majority of cohorts nationwide experience a smaller reduction in 

PbB due to the rule. .................................................................................................................... 3-12

Figure 4-1: Comparison between the NHD and E2RF1 Network in a Single Watershed. .............................. 4-7

Figure 6-1: Baseline Release Probability and Capacity Factor Assumptions.................................................. 6-6

Figure 10-1: Cost Accounting Framework for the Beneficial Reuse Analysis.............................................. 10-5

Figure E-1: Illustration of a Hypothetical Policy Effect on a Survival Curve. ................................................E-3

Figure E-2: A Recursive Illustration of a Policy Effect on Survival Rates......................................................E-4

List of Tables

Table 1-1: Steam Electric ELG Regulatory Options........................................................................................ 1-3

Table 1-2: Pollutant Removal for Final ELGs Regulatory Options................................................................. 1-4

Table 1-3: Pollutant Removal for Final ELGs Regulatory Options Used in Estimating Benefits................... 1-5

Table 1-4: Impacts of Loading Reduction Revisions on Benefit Estimates .................................................... 1-5

Table 2-1: Estimated Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants ........... 2-13

Table 3-1: Summary of Potentially Affected Population Living within 50 Miles of Affected Reaches 

(baseline, as of 2010).................................................................................................................... 3-4

Table 3-2: Summary of group-specific assumptions for human health benefit analysisa................................ 3-5

Table 3-3: Value of an IQ Pointa(2013$)........................................................................................................ 3-8

Table 3-4: Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead................................. 3-9

Table 3-5: Estimated Avoided Cost of Compensatory Education for Children with Blood Lead 

Concentrations above 20 g/dL and IQ Less than 70 ................................................................ 3-10

Table 3-6: Parameters Used to Apply the Leggett+ Model ........................................................................... 3-11

Table 3-7: Sample of Inputs for Age- and Sex-Specific Hazard Functions................................................... 3-13

Table 3-8: Summary of Estimated Health Benefits due to Decreased Risk of CVD Mortality during 

2019-2042 based on the Economic Value of Avoided Premature Mortality (VSL) .................. 3-14

Table 3-9: Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure ...................... 3-16

Table 3-10: Total Costs of Illness for Skin Cancera....................................................................................... 3-18

Table 3-11: Weighted Average Skin Cancer Cost of Illnessa........................................................................ 3-18

Table 3-12: Annual Benefits from Reduced Cancer Cases due to Arsenic Exposure ................................... 3-19

Table 3-13: Total Monetized Human Health Benefits for ELG Options (millions of 2013$)....................... 3-19

Table 3-14: Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric 

Pollutants.................................................................................................................................... 3-20

Table 3-15: Estimated Aggregate Changes in Pollutant Loadings for Lead, Mercury and Arsenic 

(Pounds per Year)....................................................................................................................... 3-21

Table 3-16: Limitations and Uncertainties in the Analysis of Human Health Benefits................................. 3-22

Table 4-1: Freshwater Water Quality Subindices............................................................................................ 4-3

Table 4-2: Freshwater Water Quality Subindex for Heavy Metals.................................................................. 4-4

Table 4-3: Water Quality Classifications......................................................................................................... 4-5
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Table 4-4: Water Quality Modeling Data used in Calculating the Baseline and Policy Metal, Nutrient 

and Sediment Concentrations....................................................................................................... 4-6

Table 4-5: Water Quality Data used in Calculating the Baseline and Policy WQI.......................................... 4-8

Table 4-6: Percentage of Potentially Affected Inland Reach Miles by WQI Classification: Baseline 

Scenario........................................................................................................................................ 4-9

Table 4-7: Water Quality Improvements from Final ELGs in All Benefiting Reaches................................. 4-10

Table 4-8: Independent Variable Assignments for Surface Water Quality Meta-Analysis........................... 4-15

Table 4-9: Household Willingness-to-Pay for Water Quality Improvements................................................ 4-18

Table 4-10: Total Willingness-to-Pay for Water Quality Improvements ...................................................... 4-19

Table 4-11: Estimated Aggregate Changes in Pollutant Loadings for Metals, Nutrients, and Suspended 

Solids.......................................................................................................................................... 4-21

Table 4-12: Number of Reaches with Baseline AWQC Exceedances Based on Initial Loadings................. 4-21

Table 4-13: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits .............. 4-22

Table 5-1: T&E Species with Habitat Occurring within Waterbodies Affected by Steam Electric Power 

Plants............................................................................................................................................ 5-3

Table 5-2: T&E Species Whose Recovery May Benefit from the Final ELGs ............................................... 5-4

Table 5-3: Independent Variable Assignments for the T&E Meta-Regression ............................................... 5-6

Table 5-4: Estimated Annualized Benefits to T&E Species from WQ Improvements (Millions 2013$)a,b..... 5-8

Table 5-5: Limitations and Uncertainties in the Analysis of T&E Species Benefits....................................... 5-9

Table 6-1: Probabilities of CCR impoundment releases, based on analysis of 49 historical release events 

1995–2008.................................................................................................................................... 6-3

Table 6-2: Release Probability and Capacity Factor Assumptions for the ELGs............................................ 6-4

Table 6-3: Steam Electric Impoundments by Size in ELG Baseline ............................................................... 6-5

Table 6-4: Documented Cleanup Costs from Impoundment Releases ............................................................ 6-7

Table 6-5: NRD Settlements Summary Statistics............................................................................................ 6-9

Table 6-6: Studies Summarizing Transaction Costs as a Share of Superfund Spending (for potentially 

responsible parties)....................................................................................................................... 6-9

Table 6-7: Unit Costs for Impoundment Releases (2013$) ........................................................................... 6-10

Table 6-8: Total Expected Number of Releases in 2019 through 2042, by Failure Type.............................. 6-11

Table 6-9: Expected Total Coal Combustion Residuals Volume Released Annually, by Failure Type 

(Million Gallons)........................................................................................................................ 6-12

Table 6-10: Estimated Annualized Benefits of Avoided Impoundment Failures by Release Type 

(Millions; 2013$)a....................................................................................................................... 6-13

Table 6-11: Limitations and Uncertainties in Analysis of Avoided Risk of Impoundment Failure 

Benefits....................................................................................................................................... 6-14

Table 7-1: Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to Increase 

in Auxiliary Service at Steam Electric Power Plants, Relative to Baseline ................................. 7-2

Table 7-2: Estimated Changes in Annual Air Pollutant Emissions due to Increased Trucking at Steam 

Electric Power Plants, Relative to Baseline ................................................................................. 7-3

Table 7-3: Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity 

Generation Profile, Relative to Baseline ...................................................................................... 7-3

Table 7-4: Estimated Net Changes in Air Pollutant Emissions due to Increase in Auxiliary Service at 

Steam Electric Power Plants, Increased Trucking at Steam Electric Power Plants, and 

Changes in Electricity Generation Profile, Relative to Baseline.................................................. 7-4

Table 7-5: National Benefits per Ton Estimates for NOx and SO2 Emissions (2013$/ton) from the 

Benefits per Ton Analysis Reported by Fann et al. (2012) a, b, c.................................................... 7-7

Table 7-6: Social Cost of Carbon Values (2013$/metric ton CO2).................................................................. 7-9
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September 29, 2015 viii

Table 7-7: Estimated Benefits from Reduced Air Emissions for Selected Years (millions; 2013$)a............ 7-10

Table 7-8: Estimated Annualized Benefits from Reduced Air Emissions (Millions; 2013$)........................ 7-11

Table 7-9: Limitations and Uncertainties in Analysis of Air-related Benefits .............................................. 7-12

Table 8-1: Estimated Annualized Benefits from Reduced Groundwater Withdrawals (Millions; 2013$)...... 8-2

Table 8-2: Limitations and Uncertainties in Analysis of Reduced Groundwater Withdrawals....................... 8-2

Table 10-1: Applicable Beneficial Use Applications of CCRs, by CCR Category ....................................... 10-1

Table 10-2: State-level Market Approximation (Short Tons)........................................................................ 10-4

Table 10-3: Economic Value of CCR Handling Costs per Unit (2013$)....................................................... 10-7

Table 10-4: Avoided Resource and Environmental Impacts per Ton of Virgin Material Produced.............. 10-7

Table 10-5: Economic Value of Avoided Resource and Environmental Impacts per Unit of Impact 

(2013$). ...................................................................................................................................... 10-8

Table 10-6: Estimated Beneficial Use Applications of CCRs, by CCR Category......................................... 10-8

Table 10-7: Annual Avoided Resource and Environmental Impacts Given CCR Reuse in Concrete and 

Fill Applications......................................................................................................................... 10-9

Table 10-8: Annualized Economic Value of Estimated Changes in Beneficial Use (Million 2013$) a,b..... 10-10

Table 11-1: Summary of Total Annualized Benefits at 3 Percent (Millions; 2013$).................................... 11-2

Table 11-2: Summary of Total Annualized Benefits at 7 Percent (Millions; 2013$).................................... 11-4

Table 11-3: Time Profile of Benefits at 3 Percent (Millions; 2013$) (Including Air-Related Benefits for 

Options B and D)........................................................................................................................ 11-7

Table 11-4: Time Profile of Benefits at 7 Percent (Millions; 2013$) (Including Air-Related Benefits for 

Options B and D)........................................................................................................................ 11-8

Table 12-1: Summary of Annualized Costs (Millions; $2013)...................................................................... 12-2

Table 12-2: Time Profile of Costs to Society (Millions; $2013) ................................................................... 12-3

Table 13-1: Total Annualized Benefits and Costs by Regulatory Option and Discount Rate (Millions; 

2013$)......................................................................................................................................... 13-1

Table 13-2: Incremental Net Benefit Analysis (Millions; 2013$) ................................................................. 13-3

Table 14-1: Socio-economic Characteristics of Communities Living in Proximity to Receiving Reaches .. 14-2

Table 14-2: Socio-economic Characteristics of Affected Communities, Compared to State Average.......... 14-3

Table 14-3: Characteristics of Population Potentially Exposed to Steam Electric Pollutants via 

Consumption of Self-caught Fish............................................................................................... 14-4

Table 14-4: Distribution of Baseline IQ Point Decrements by Pollutant (2021 to 2042).............................. 14-5

Table 14-5: Distribution of Avoided IQ Point Decrements Relative to the Baseline, by Pollutant (2021 

to 2042) ...................................................................................................................................... 14-5

Table 14-6: Distribution of Baseline IQ Point Decrements by Pollutant and Fishing Mode (2021 to 

2042)........................................................................................................................................... 14-6

Table 14-7: Distribution of Avoided IQ Point Decrements Relative to the Baseline by Fishing Mode, 

and Pollutant (2021 to 2042)...................................................................................................... 14-6
Benefit and Cost Analysis for Steam Electric Power Generating ELGs List of Abbreviations

September 29, 2015 ix

List of Abbreviations

ADD Average daily dose

ASMFC Atlantic States Marine Fisheries Commission

As Arsenic

AFSC Alaskan Fisheries Science Center

AWQC Ambient water quality criteria

BAT Best available technology economically achievable

BCA Benefit cost analysis

BEA Bureau of Economic Analysis

BenMAP Environmental Benefits Mapping and Analysis Program

BLS Bureau of Labor Services

BOD Biochemical oxygen demand

BPJ Best professional judgment

BPT Best practicable control technology currently available

C&D Construction and development

Cal OEHHA California Office of Environmental Health Hazard Assessment

CBG Census Block Group

CBI Confidential Business Information

CCI Construction Cost Index

CCME Canadian Council of Ministers of the Environment

CCR Coal combustion residuals

CMAQ Community Multiscale Air Quality

CO2 Carbon dioxide

COD Chemical oxygen demand

COI Cost of illness

COPD Chronic obstructive pulmonary disease

CPI Consumer Price Index

CSF Cancer slope factor

CVD Cardiovascular disease

DCN Document Control Number

DHHS Department of Health and Human Services

DO Dissolved oxygen

DOJ Department of Justice

EA Environmental Assessment

ECI Employment Cost Index

EGU Electric Generating Unit

EJ Environmental justice

ELGs Effluent limitations guidelines and standards

EPA United States Environmental Protection Agency

ESA Endangered Species Act

FC Fecal coliform

FCA Fish consumption advisories

FGD Flue gas desulfurization

FGMC Flue gas mercury control

FR Federal Register

GDP Gross domestic product

GIS Geographic Information System
Benefit and Cost Analysis for Steam Electric Power Generating ELGs List of Abbreviations

September 29, 2015 x

Hg Mercury

HR Hazard ratio

HUC Hydrologic unit code

IEUBK Integrated Exposure, Uptake, and Biokinetics

IPM Integrated Planning Model

IRIS Integrated Risk Information System

IQ Intelligence quotient

LADD Lifetime average daily dose

MCL Maximum contaminant level

MCLG Maximum contaminant level goal

MDNR Minnesota Department of Natural Resources

MEPS Medical Expenditure Panel Survey

NAAQS National Ambient Air Quality Standards

NEFSC Northeast Fisheries Science Center

NERC North American Electric Reliability Corporation

NESHAP National Emissions Standards for Hazardous Air Pollutants

NHD National hydrography dataset

NMFS National Marine Fisheries Service

NOAA National Oceanic and Atmospheric Administration

NOX Nitrogen oxides

NPDES National Pollutant Discharge Elimination System

NRC National Research Council

NRD Natural resource damages

NSPS New Source Performance Standards

NWIS National Water Information System

OAQPS Office of Air Quality Planning and Standards

O&M Operation and maintenance

OMB Office of Management and Budget

OPPT Office of Pollution Prevention and Toxics

ORCR Office of Resource Conservation and Recovery

OSC On-scene coordinator

PbB Blood lead concentration

PIFSC Pacific Islands Fisheries Science Center

ppm parts per million

PSES Pretreatment Standards for Existing Sources

PSNS Pretreatment Standards for New Sources

QA Quality assurance

QC Quality control

RIA Regulatory Impact Analysis

RSEI Risk-Screening Environmental Indicators

SCC Social cost of carbon

Se Selenium

SEFSC Southeast Fisheries Science Center

SO2 Sulfur dioxide

SPARROW SPAtially Referenced Regressions On Watershed attributes

STORET STOrage and RETrieval Data Warehouse

SWFSC Southwest Fisheries Science Center

T&E Threatened and endangered
Benefit and Cost Analysis for Steam Electric Power Generating ELGs List of Abbreviations

September 29, 2015 xi

TDD Technical Development Document

TDS Total dissolved solids

THMs Trihalomethanes

TN Total nitrogen

TP Total phosphorus

TRI Toxic Release Inventory

TSD Technical Support Document

TSS Total suspended solids

TVA Tennessee Valley Authority

U.S. FWS United States Fish and Wildlife Service

USGS United States Geological Survey

VSL Value of a statistical life

WQI Water quality index

WQL Water quality ladder

WTP Willingness to pay
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-1

1 Introduction

The U.S. Environmental Protection Agency (EPA) is promulgating a regulation that would strengthen the 

existing controls on discharges from steam electric power plants by revising technology-based effluent 

limitations guidelines and standards (ELGs) for the steam electric power generating point source category, 40 

CFR part 423. 

The analyses supporting the final ELGs for the Steam Electric Power Generating Point Source Category are 

based on data generated or obtained in accordance with EPA’s Quality Policy and Information Quality 

Guidelines. EPA’s quality assurance (QA) and quality control (QC) activities for this rulemaking include the 

development, approval and implementation of Quality Assurance Project Plans for the use of environmental 

data generated or collected from all sampling and analyses, existing databases and literature searches, and for 

the development of any models which used environmental data. Unless otherwise stated within this document, 

the data used and associated data analyses were evaluated as described in these quality assurance documents 

to ensure they are of known and documented quality, meet EPA's requirements for objectivity, integrity and 

utility, and are appropriate for the intended use.

This document presents an analysis of the social benefits and social costs of the final rule and complements 

other analyses EPA conducted in support of the ELGs, described in separate documents:

 Environmental Assessment for the Effluent Limitations Guidelines and Standards for the Steam 

Electric Power Generating Point Source Category (EA) (U.S. EPA, 2015a; DCN SE04527). The EA

summarizes the environmental and human health improvements that are expected to result from 

implementation of the ELGs.

 Technical Development Document for the Effluent Limitations Guidelines and Standards for the 

Steam Electric Power Generating Point Source Category (TDD) (U.S. EPA, 2015b; DCN SE05904). 

The TDD provides background on the final ELGs; applicability and summary of the ELGs; industry 

description; wastewater characterization and identification of pollutants of concern; and treatment 

technologies and pollution prevention techniques. It also documents EPA’s engineering analyses to 

support the final rule including facility specific compliance cost estimates, pollutant loadings, and 

non-water quality impact assessment.

 Regulatory Impact Analysis for the Effluent Limitations Guidelines and Standards for the Steam 

Electric Power Generating Point Source Category (RIA) (U.S. EPA, 2015c; DCN SE05976). The 

RIA describes EPA’s analysis of the costs and economic impacts of the final rule. This analysis 

provides the basis for social cost estimates presented in this document. The RIA also provides 

information pertinent to meeting several legislative and administrative requirements, including the 

Regulatory Flexibility Act of 1980 (as amended by the Small Business Regulatory Enforcement 

Fairness Act (SBREFA) of 1996), the Unfunded Mandates Reform Act of 1995, Executive Order 

13211 on Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or 

Use, and others. 

The rest of this chapter discusses aspects of the final ELGs that are salient to EPA’s analysis of the social 

benefits and social costs of the rule and summarizes key analytic assumptions used throughout this document. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-2

1.1 Steam Electric Power Plants

The final rule establishes new limitations and standards for plants subject to the previously established ELGs 

for the Steam Electric Power Generating Point Source Category The ELGs apply to a subset of the electric 

power industry, namely those plants with discharges resulting from the operation of a generating unit 

“primarily engaged in the generation of electricity for distribution and/or sale, which results primarily from a 

process utilizing fossil-type fuels (coal, petroleum coke, oil, gas) or nuclear fuel in conjunction with a thermal 

cycle employing the steam water system as the thermodynamic medium.”

1

Based on data EPA obtained from the 2010 Questionnaire for the Steam Electric Power Generating Effluent 

Guidelines (industry survey; U.S. EPA, 2010c) and other sources (see TDD), EPA estimates that there were 

1,080 steam electric power plants in 2009.

2 EPA projects that a subset of these plants will implement changes 

to meet the final limitations (refer to the TDD and RIA for details). 

1.2 Regulatory Options Analyzed for the Final ELGs

EPA presents six regulatory options for the final rule (see Table 1-1). These options differ in the wastestreams 

controlled by the regulation, the size of the units controlled, and the stringency of controls (see TDD for a 

detailed discussion of the options and the associated treatment technology bases). Thus, EPA evaluated 

revising or establishing Best Available Technology Economically Achievable (BAT), New Source 

Performance Standards (NSPS), Pretreatment Standards for Existing Sources (PSES), and Pretreatment 

Standards for New Sources (PSNS) that apply to discharges of up to seven wastestreams: FGD wastewater, 

fly ash transport water, bottom ash transport water, combustion residual leachate from landfills and surface 

impoundments, wastewater from FGMC systems, wastewater from gasification systems, and nonchemical 

metal cleaning wastes.

EPA is establishing limitations and standards for existing sources (BAT/PSES) based on the technologies in 

Option D. For new sources, EPA selected the technologies in Option F as the basis for the NSPS and PSNS. 

The preamble that accompanies the final rule explains the rationale for EPA’s decision.

 

1 The final rule contains three minor modifications to the wording of the previously established applicability 

provision in the steam electric power generating ELGs to reflect EPA’s longstanding interpretation and 

implementation of the rule. These revisions do not alter the universe of generating units regulated by the ELGs, 

nor do they impose compliance costs on the industry. Instead, they remove potential ambiguity in the 

regulations by revising the text to more clearly reflect EPA’s longstanding interpretation. See Section VIII of 

the preamble for more details.

2 The industry survey EPA conducted in 2010 requested data for several years of operation, up to the most recent 

complete calendar year at the time the survey was conducted: 2009. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-3

Table 1-1: Steam Electric ELG Regulatory Options

Wastestreams

Technology Basis for BAT/NSPS/PSES/PSNS

Regulatory Options

A B C D E F

FGD

Wastewater

Chemical 

Precipitation

Chemical 

Precipitation + 

Biological 

Treatment

Chemical 

Precipitation + 

Biological 

Treatment

Chemical 

Precipitation + 

Biological 

Treatment

Chemical 

Precipitation + 

Biological 

Treatment

Evaporation

Fly Ash Transport 

Water

Dry Handling Dry Handling Dry Handling Dry Handling Dry Handling Dry handling

Bottom Ash 

Transport Water

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Dry handling /

Closed loop (for 

units >400 

MW); 

Impoundment 

(Equal to 

BPT)(for units 

≤400 MW)

Dry Handling /

Closed loop

Dry Handling /

Closed loop

Dry handling / 

Closed loop

FGMC

Wastewater

Dry Handling Dry Handling Dry Handling Dry Handling Dry Handling Dry handling

Gasification

Wastewater

Evaporation Evaporation Evaporation Evaporation Evaporation Evaporation

Combustion 

Residual Leachate

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Chemical 

Precipitation

Chemical 

Precipitation

Nonchemical 

Metal Cleaning 

Wastes

[Reserved] [Reserved] [Reserved] [Reserved] [Reserved] [Reserved]

Source: U.S. EPA, 2015

In the remainder of this document, EPA presents the analytical results only for Options A through E for 

existing sources. During development of the final rule, EPA decided not to base the final rule on Option F for 

existing sources due primarily to the high cost of that option, particularly in light of the costs associated with 

other rulemakings expected to impact the steam electric industry (see Section VIII.C.1 of the preamble). As a 

result, EPA chose not to conduct particular analyses for Option F to the same extent that it did for some of the 

other options considered.

While EPA calculated the cost impacts of New Sources Performance Standards (NSPS) and Pretreatment 

Standards for New Sources (PSNS) for the final ELGs, no new coal steam plants have been announced nor 

are projected (see RIA Section 3.2; U.S. EPA 2015c). The lack of any new coal steam plant in the near future 

and the site-specific nature of environmental effects and benefits make the assessment of load reductions and 

benefits associated with new source requirements hypothetical and speculative. Accordingly, EPA focused the 

analysis of the benefits of the final ELGs on the BAT/PSES requirements for existing sources.

1.3 Analysis Scenarios

EPA made every effort to appropriately account for other rules in its many analyses for this rule. Since 

proposal, EPA has promulgated several other rules affecting the steam electric industry: the Cooling Water 

Intake Structures (CWIS) rule for existing facilities (79 FR 48300), the CCR rule (80 FR 21302), the CPP rule 

(FR publication forthcoming). At the time it conducted these analyses, the CPP rule had not yet been 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-4

finalized, and thus EPA used the proposed CPP rule for its analyses as a proxy for the final CPP rule 

requirements. In some cases, EPA performed two sets of parallel analyses to demonstrate how the other rules 

affected the final ELGs. For example, EPA conducted an assessment of the final ELGs both with and without 

accounting for the CPP rule. EPA approached analyses associated with each rulemaking carefully. EPA also 

recognizes that the steam electric industry complying with three regulations cumulatively in a very short 

period time may choose different compliance path than assumed in the analyses. The cumulative effect 

introduces uncertainty on the compliance path, and thereby on the benefits and costs associated with these 

rules.

The results presented in the main body of this document are based on this scenario with the CPP rule. The 

results of EPA’s analyses without accounting for the CPP rule are presented in Appendix B.

1.4 Loading and Withdrawal Reductions

1.4.1 Loading Reductions

EPA expects that final rule will reduce discharge loads of various categories of pollutants including 

conventional (such as total suspended solids (TSS), biochemical oxygen demand (BOD), and oil and grease), 

priority (such as mercury (Hg), arsenic (As), and selenium (Se)), and non-conventional pollutants (such as 

phosphorus (TP), chemical oxygen demand (COD) and total dissolved solids (TDS)). Table 1-2 summarizes 

the estimated pollutant reductions under each of the five regulatory options for existing sources.

Table 1-2: Pollutant Removal for Final ELGs Regulatory Options

Regulatory Option

Pollutant Load Reduction 

(pounds per year)

Option A 123,814,202

Option B 132,342,281

Option C 306,198,515

Option D 371,152,958

Option E 382,032,630

Source: U.S. EPA Analysis, 2015

1.4.2 Water Withdrawal Reductions

The regulatory options are also expected to eliminate or reduce water withdrawals associated with wet ash 

transport and wet FGD scrubbers. EPA estimates that the final BAT/PSES option (Option D) will reduce 

surface water withdrawals at steam electric power plants by 57 billion gallons per year (155 million gallons 

per day), and reduce withdrawals of 8 million gallons of groundwater per year (21,971 gallons per day). 

1.4.3 Loading Reductions Used in Estimating Benefits

EPA revised the estimated steam electric power generating plant discharge loads to incorporate data 

submitted via public comments and following additional review conducted after completing the benefit 

analyses described in this report.

3 The revisions affect baseline loadings as well as loading reductions 

 

3 EPA reevaluated the bottom ash dataset for the final rule, including the addition of new data submitted via 

public comments. EPA subjected all data to its revised data editing criteria and as such, removed or replaced 

some of the data included in the 1982 TDD. See TDD for details (U.S. EPA, 2015b).
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-5

estimated for each of the regulatory options. The load reductions presented in Table 1-2 above reflect these 

revisions. 

Table 1-3 shows loading reductions calculated from the original loading data used in estimating the benefits 

of the regulatory options; these are 0.01 percent to 0.17 percent greater than the reductions shown Table 1-2. 

The changes affect most pollutants of concern, including arsenic, cadmium, chromium, lead, mercury, nickel, 

total nitrogen, total phosphorus, selenium, total suspended solids, and zinc. See TDD for details (U.S. EPA, 

2015b).

Table 1-3: Pollutant Removal for Final ELGs Regulatory Options Used 

in Estimating Benefits

Regulatory Option

Pollutant Load Reduction 

(pounds per year)

Option A 124,030,659

Option B 132,558,737

Option C 306,236,745

Option D 371,220,336

Option E 382,110,008

Source: U.S. EPA Analysis, 2015

Revisions to the plant loadings may affect estimated benefits in several categories. Implication of the changes 

are summarized in Table 1-4, and discussed at greater length in each of the relevant chapters. 

Table 1-4: Impacts of Loading Reduction Revisions on Benefit Estimates

Benefit Category Impact of Revised Loading Reduction on Benefit 

Estimate

Report 

Chapter

Human health benefits (from fish 

consumption)

Revisions may reduce benefits, relative to estimates 

presented in Chapter 3. 

3

Nonmarket benefits from water quality 

improvements

Revisions may reduce benefits, relative to estimates 

presented in Chapter 4. 

4

Benefits to threatened and endangered 

species

Revisions may reduce benefits, relative to estimates 

presented in Chapter 5. 

5

Benefits from avoided impoundment 

failures

No impact. BCA estimates are unchanged. 6

Air-related benefits No impact. BCA estimates are unchanged. 7

Benefits from reduced water withdrawals No impact. BCA estimates are unchanged. 8

Benefits from avoided dredging costs Revisions may reduce benefits, relative to estimates 

presented in Chapter 9. 

9

Benefits from enhanced marketability of 

coal combustion residuals

No impact. BCA estimates are unchanged. 10

Source: U.S. EPA Analysis, 2015
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-6

1.5 Analytic Framework 

The analytic framework of this benefit-cost analysis (BCA) includes four basic components used consistently 

throughout the analysis of social benefits and social costs4of the final ELGs: 

1. All values are presented in 2013 dollars; 

2. Future benefits and costs are discounted using rates of 3 percent and 7 percent back to 2015;

3. Benefits and costs are analyzed over a 24-year period (2019 to 2042); and

4. Future values account for annual U.S. population and income growth. 

These components are discussed in the sections below.

EPA’s analysis of the final ELGs generally follows the methodology the Agency used previously to analyze 

the proposed ELGs (see Benefit and Cost Analysis for the Proposed Effluent Limitations Guidelines and 

Standards for the Steam Electric Power Generating Point Source Category (U.S. EPA, 2013a; DCN 

SE03172). In analyzing the final ELGs, however, EPA made several important changes relative to the 

analysis of the proposed rule:

 EPA used revised inputs that reflect the costs and loads estimated for the final regulatory options (see 

TDD and RIA for details).

 EPA updated the universe and characteristics of steam electric power plants to reflect generating units 

that have been announced to retire or convert (e.g., to natural gas) during the period of analysis as 

well as the anticipated effects of other regulations affecting the power sector and which may change 

operations and wastestreams of steam electric power plants.

 EPA updated the baseline scenario to reflect the projected effects of the CCR rule, including 

regarding the residual environmental risks from CCR impoundments.

 EPA revised assumptions to use more recent data (e.g., analysis year, compliance period, dollar year 

adjustments). 

 Finally, EPA made certain changes to the methodologies to address comments EPA received on the 

proposed rule (e.g., Environmental Justice analysis), to be consistent with approaches used by the 

Agency for other rules, and to incorporate recent advances in the health risk and resource valuation 

research. 

These changes are described in the relevant sections of this document, and summarized in Appendix A.

The benefits and social cost analyses presented in this document are generally based on loading reductions 

and other inputs generated for individual steam electric power plants (for all plants that were surveyed). These 

inputs are used to determine surface waters and other resources affected by steam electric power plant 

discharges, estimate changes in pollutant levels, identify populations exposed to steam electric pollutants, etc. 

1.5.1 Constant Prices

This BCA applies a year 2013 constant price level to all future annual monetary values of costs and benefits. 

Some monetary values of benefits and costs are based on actual past market price data (i.e., prior to 2013), 

and in those instances, EPA updated the prices to 2013 by multiplying them by appropriate indexes, or 

specific sub-components of these general indexes (index-updated prices). However, not all dollar-monetized 

 

4 Unless otherwise noted, costs represented in this document are social costs.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-7

benefits and costs in this BCA are based on actual market prices of goods or services. Several categories of 

benefits presented in this report are estimated based on household willingness-to-pay (WTP) surveys, such as 

WTP for surface water quality improvements for monetizing ecological benefits of the final ELGs. This BCA 

updates these non-market prices as needed using appropriate indexes (e.g., Consumer Price Index (CPI)).

1.5.2 Discount Rate and Year

This BCA estimates the annualized value of future benefits using two discount rates: 3 percent and 7 percent. 

The 3 percent discount rate reflects society’s valuation of differences in the timing of consumption; the 

7 percent discount rate reflects the opportunity cost of capital to society. In Circular A-4, the Office of 

Management and Budget (OMB) recommends that 3 percent be used when a regulation affects private 

consumption, and 7 percent in evaluating a regulation that will mainly displace or alter the use of capital in 

the private sector (U.S. OMB, 2003; updated 2009). The same discount rates are used for both benefits and

costs.

All future cost and benefit values are discounted back to 2015. 

1.5.3 Period of Analysis

Benefits are expected to begin accruing when each plant implements the control technologies needed to 

comply with any applicable new effluent limits or standards. As discussed in the RIA (in Chapter 3: 

Compliance Costs), for the purpose of the economic impact and benefit analysis, EPA assumes that plants 

will implement control technologies to meet the final rule limitations and standards as their permits are 

renewed over the period of 2019 through 2023. This schedule recognizes that control technology 

implementation is likely to be staggered over time across the universe of steam electric power plants.

As discussed in the relevant sections of this document, for several benefit categories where environmental 

changes are not provided on a plant-specific basis (e.g., reduced air emissions or changes in surface water 

quality attributed to multiple plants), EPA was not able to use plant-specific assumed compliance years in the 

benefits analysis but instead used the mid-point of the compliance period (2021) as the assumed starting year 

when benefits begin accruing. As presented in the RIA (Table 3-1; U.S. EPA 2015c), over half of the plants 

potentially incurring costs for the final ELGs (under Option E) have their technology implementation year in 

2021 or earlier. 

The period of analysis extends to 2042 to capture the life of the longest-lived compliance technology at any 

steam electric power plant (20 years), and the last year of technology implementation (2023).

1.5.4 Population and Income Growth

To account for future population growth or decline, EPA used the population forecasts in Woods & Poole 

(2012), which developed county-level forecasts for each year from 2000 through 2040, by age and gender for 

non-Hispanic White, African-American, Asian-American, and Native-American and for all Hispanics.5EPA 

aggregated the population forecasts across all ages, genders, races, and ethnicities for the entire U.S. and used 

the aggregated growth projections to adjust affected population estimates for future years (i.e., from 2019 to 

2040). EPA used a linear extrapolation approach to forecast population values between 2040 and 2042.

Also, since WTP is expected to increase as income increases, EPA took into account income growth for 

estimating the value of avoided premature mortality based on the value of a statistical life (VSL) and WTP for 

 

5 Woods and Poole (2012); the detailed documentation can be found at 

http://www.woodsandpoole.com/pdfs/CED12.pdf.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-8

water quality (WQ) improvements. To develop adjustment factors for VSL, EPA first used income growth 

factors in the Environmental Benefits Mapping and Analysis Program (BenMAP) database between 1990 and 

2024 to estimate a linear regression model. Using coefficient estimates from the linear regression, EPA 

extrapolated the income growth factors for years 2025-2042. EPA applied the projected income data along 

with the income elasticity for the respective models (VSL and meta-regression) to adjust the VSL and WQ

meta-analysis estimates of WTP in future years.

6

1.6 Organization of the Benefit and Cost Analysis Report

This BCA report presents EPA’s analysis of the benefits of the ELGs, assessment of the total costs, and 

comparison of the costs and monetized benefits. 

The remainder of this report is organized as follows:

 Chapter 2: Benefits Overview provides an overview of the main benefits expected to result from the 

implementation of the ELGs. 

 Chapter 3: Human Health Benefits details the methods and results of EPA’s analysis of the human 

health benefits. 

 Chapter 4: Nonmarket Benefits from Water Quality Improvements discusses EPA’s analysis of the

surface water quality improvements resulting from the ELGs.

 Chapter 5: Impacts and Benefits to Threatened and Endangered Species discusses expected benefits

to threatened and endangered (T&E) species.

 Chapter 6: Benefits from Avoided Impoundment Failures assesses the benefits of reducing the 

impacts of any future CCR releases from impoundments used by some steam electric power plants to 

manage their CCR waste.

 Chapter 7: Air-Related Benefits describes EPA’s analysis of benefits associated with changes in

emissions of air pollutants due to increased electricity consumption, transportation, and changes in the 

profile of electricity generation.

 Chapter 8: Benefits from Reduced Water Withdrawals discusses benefits arising from reduced surface 

water intake and groundwater use.

 Chapter 9: Benefits from Avoided Dredging Costs describes benefits from reduced maintenance 

dredging of navigational channels and reservoirs.

 Chapter 10: Benefits from Enhanced Marketability of Coal Combustion Residuals discusses benefits 

arising from the enhanced ability by plants to market dry coal combustion ashes.

 Chapter 11: Summary of Total Monetized Benefits summarizes results across benefit categories.

 Chapter 12: Summary of Total Costs summarizes costs of the ELGs.

 Chapter 13: Benefits and addresses the requirements of Executive Orders that EPA is required to 

satisfy for this proposal, notably Executive Order 12866, which requires EPA to compare the benefits 

and costs of its actions.

 

6 These extrapolated income growth factors were originally developed for EPA’s COBRA tool 

(http://epa.gov/statelocalclimate/resources/cobra.html). The latest public version is 2.613 released in September 

2014.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 1: Introduction

September 29, 2015 1-9

 Chapter 14: Environmental Justice details EPA’s analysis of the distribution of benefits across 

socioeconomic groups to fulfill requirements under Executive Order (E.O.) 12898.

 Chapter 15 provides references cited in the text.

Several appendices provide additional details on selected aspects of analyses described in the main text of the 

report. In particular, Appendix B presents the results of EPA’s analysis of the benefits for an alternate scenario 

using a baseline that excludes the incremental conversions, retirements, and other changes projected to occur 

in response to the CPP rule.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2-1

2 Benefits Overview

This chapter provides an overview of the potential benefits to society resulting from implementation of the

ELGs. EPA expects that benefits will accrue to society in several broad categories, including enhanced 

surface water quality, reduced health risks, and increased productivity in economic activities that are 

adversely affected by steam electric discharges. These effects follow directly from changes in effluent limits 

and standards, which will reduce pollutant loadings to receiving waters. Benefits of the ELGs also include 

other effects of the implementation of control technologies or other changes in plant operations, such as 

reduction in emissions of air pollutants (e.g., carbon dioxide (CO2), nitrogen oxides (NOX), and sulfur dioxide 

(SO2)) which provide benefits in the form of reduced mortality and CO2 impacts on environmental quality and 

economic activities; reduction in water use, which provide benefits in the form of increased availability of 

surface water and groundwater; and reduction in the use of surface impoundment to manage CCR wastes, 

with benefits in the form of avoided cleanup and other costs associated with impoundment releases.

This chapter also provides a brief discussion of the steam electric pollutants, their human health and 

ecological effects, and a framework for understanding the benefits expected to be achieved by the steam 

electric ELGs. For a more detailed description of steam electric pollutants, their fate, transport, and impacts 

on human health and environment, see the Environmental Assessment document (U.S. EPA, 2015a).

Figure 2-1 summarizes the potential effects of the ELGs, the expected environmental changes, and categories 

of benefits, as well as EPA’s approach to analyzing those benefits. EPA was not able to bring the same depth 

of analysis to all categories of benefits because of imperfect understanding of the link between discharge 

reductions or other environmental effects of the ELGs and benefit categories, and how society values some of 

the benefits. EPA was able to quantify and monetize some benefits, quantify but not monetize other benefits, 

and assess still other benefits only qualitatively. The remainder of this chapter provides a qualitative 

discussion of the benefit categories applicable to this rule, including human health benefits, ecological

benefits, improved groundwater quality, economic productivity, and reduced air pollution and water 

withdrawals. Some benefits estimates presented in this document rely on complex models that embed a 

variety of assumptions, limitations and uncertainties discussed in more details in chapters 3 through 10 for the 

relevant benefit categories.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview 

September 29, 2015 2-2 

Figure 2-1: Summary of Benefits Resulting from the ELGs.

--------------------------------------------------------------------------------------------------, 

! 1 .. ·1. "' ::J 

Reduced toxic, 

bioaccumulative, 

and other harmful 

waters 

a 

... 

Qj 

.... 

s "' 

Reduced reliance on 

impoundments to 

manageCCR 

Change in: 

Auxiliary power 

use 

Transportation 

Electricity 

generation 

Conversion to dry 

systems 

Reduced water use 

Reduced fish tissue 

contamination 

Improved surface 

water quality 

Reduced CCR waste 

managed in 

impoundments 

Reduced air 

emissions of C02, 

NOx:, and SOx 

Enhanced ability to 

market ash for 

beneficial use 

Reduced 

groundwater 

withdrawals 

Reduced surface 

water withdrawals 

Improved Human Health COl 

Avoided cardiovascular disease from lead and arsenic exposure VSL 

Avoided cancer cases from arsenic exposure Value of an IQ point 

Avoided IQ losses in children from mercury and lead exposure Count of human health criteria 

Reduced cases of other cancer and non-cancer health effects exceedances (non-monetized) 

Improved Ecological Conditions 

Improved recreational and non-use values 

Threatened and endangered (T&E) species protection 

Reduced sediment deposition in channels and reservoirs 

Improved Economic Productivity 

Improved tourism 

Increased commercial fishery yields 

• Reduced need for water treatment 

• Enhanced property values 

Fewer and less consequential impoundment failures 

Reduced premature mortality, non-fatal heart attacks, hospital 

admissions, emergency department visits, upper and lower 

respiratory symptoms, acute bronchitis, aggravated asthma, lost 

work days and acute respiratory symptoms 

Reduced C02 impacts 

Avoided disposal costs 

Avoided life-cycle impacts and costs of virgin raw materials 

Increased groundwater availability 

Reduced vulnerability to drought 

Reduced impingement and entrainment mortality 

WTP for use and non-use values of 

surface water quality improvements 

WTP for T&E population increases 

Avoided maintenance dredging costs 

Avoided cleanup costs, natural resource 

damage, and transaction costs 

Benefits-per-ton 

Social cost of carbon 

Avoided disposal costs 

Avoided raw material costs and lifecycle production impacts (various) 

Avoided cost of water purchase 

Qualitative discussion 

COl = Cost of illness; VSL = Value of Statistical life; WTP =Willingness to Pay; CCR = Coal Combustion Residuals 
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2.1 Human Health Benefits Associated with Improvements in Surface Water Quality

Pollutants present in steam electric plant discharges can cause a wide variety of adverse human health effects 

arising, for example, from consuming contaminated fish tissue. Toxic bioaccumulative pollutants are of 

particular concern because they do not volatilize, do not biodegrade, can be toxic to plants, invertebrates and 

fish, adsorb to sediments, and bio-concentrate in fish tissues (U.S. EPA, 2003). More details on the fate, 

transport, and exposure risks of steam electric pollutants are provided in the EA (U.S. EPA, 2015a).

Reducing pollutant discharges to the nation’s waterways provides human health benefits by several 

mechanisms. The most important and readily analyzed benefits stem from reduced risk of illness associated 

with the consumption of water, fish, shellfish, and other aquatic organisms that are taken from waterways 

affected by steam electric discharges. Human health benefits are typically analyzed by estimating the change 

in the expected number of adverse human health events in the exposed population resulting from a reduction 

in effluent discharges. While some health effects (e.g., cancer or mortality from cardiovascular disease 

(CVD)) are relatively well understood and can be quantified in a benefits analysis, others are less well 

characterized and cannot be assessed with the same rigor, or at all.

The ELGs provide human health benefits by reducing exposure to pollutants in water via two principal 

exposure pathways discussed below: (1) consumption of fish and shellfish taken from waterways affected by 

steam electric discharges, and (2) consumption of water from surface waters affected by steam electric power 

plant discharges. The ELGs also provide human health benefits by reducing air emissions of pollutants via 

changes in the profile of electricity generation; these benefits are discussed separately in Section 2.5.

2.1.1 Fish Consumption

Recreational anglers and subsistence fishers (and their household members) who consume fish caught in the 

reaches receiving steam electric power plant discharges are expected to benefit from reduced pollutant 

concentrations in fish tissue. EPA analyzed the following five direct measures of change in risk to human 

health from exposure to contaminated fish tissue: 

1. Neurological effects to children ages 0 to 7 and incidence of cardiovascular disease in adults from 

exposure to lead; 

2. Neurological effects to infants from in-utero exposure to mercury;

3. Incidence of skin cancer and cardiovascular disease from exposure to arsenic; and

4. Reduced risk of other cancer and non-cancer toxic effects. 

EPA was able to monetize only the first three of these four measures. The Agency evaluated lead and mercury 

impacts to children in terms of potential intellectual impairment as measured by estimated changes in 

intelligence quotient (IQ). Incidence of cardiovascular diseases was translated into an expected level of 

avoided early mortality and, on that basis, monetized. Incidence of cancer was translated into an expected 

number of avoided cases and monetized based on avoided costs. Chapter 3 of this report provides details on 

these analyses.

The fifth effect (reduced risk of other cancer and non-cancer toxic effects from fish consumption) is addressed 

indirectly in EPA’s assessment of changes in exceedances of ambient water quality criteria (see Section 3.8). 

The value of health benefits is the monetary value that society is willing to pay to avoid the adverse health 

effects. WTP to avoid morbidity or mortality is generally considered to be a comprehensive measure of the 

costs of health care, losses in income, and pain and suffering of affected individuals and their caregivers. For 
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example, the value of a statistical life (VSL) (see Section 3.4) is based on estimates of society’s WTP to avoid 

the risk of premature mortality. Alternatively, the cost-of-illness (COI) approach, which is used to estimate 

the value of avoided skin cancer cases (see Section 3.6), is a less comprehensive measure of cost: it allows 

valuation of a particular type of non-fatal illness by placing monetary values on metrics, such as lost 

productivity and the cost of health care and medications that can be monetized. 

Some health benefits of reduced exposure to steam electric pollutants, such as neurological effects to children 

and infants exposed to lead and mercury, are measured based on avoided IQ losses. Changes in IQ cannot be 

valued based on WTP approaches since available economic research provides little empirical data on 

society’s WTP to avoid IQ losses. Instead, EPA calculated monetary values for avoided neurological and 

cognitive damages based on the impact of an additional IQ point on an individual’s future earnings and the 

cost of compensatory education for children with learning disabilities. These estimates represent only one 

component of society's WTP to avoid adverse neurological effects and therefore produce a partial measure of 

benefits from reduced exposure to lead and mercury. Employed alone, these monetized benefits will 

underestimate society's WTP, and perhaps significantly so. See Sections 3.3 and 3.5 for applications of this 

method to valuing benefits to children and infants from reduced exposure to lead and mercury.

EPA expects that there could also be material health benefits via the fish consumption pathway arising from 

reduced discharges of other steam electric pollutants, such as cadmium, selenium, and zinc. Analyses of these 

health benefits are not possible due to lack of data on a quantitative relationship between ingestion rate and 

potential adverse health effects. 

Despite numerous studies conducted by EPA and other researchers, dose-response functions are available 

only for a handful of health endpoints associated with steam electric pollutants. In addition, the available 

research does not always allow complete economic evaluation, even for quantifiable health effects. For 

example, EPA’s analysis of health benefits omits the following health effects: low birth weight and neonatal 

mortality from in-utero exposure to lead (U.S. EPA, 2009d); additional effects to adults from exposure to lead 

(e.g., nervous system disorders, anemia and blood disorders) (U.S. EPA, 2009d; 2013a); effects to adults from 

exposure to mercury, including vision defects, hand-eye coordination, hearing loss, tremors, cerebellar 

changes, and others (Mergler, et al., 2007; CDC, 2009); and other cancer and non-cancer effects from 

exposure to other steam electric pollutants. Therefore, the total monetized human health benefits included in 

this analysis represent only a subset of the potential health benefits that are expected to result from the ELGs. 

2.1.2 Drinking Water Consumption

Steam electric pollutants discharged to surface waters may affect the quality of water used for public drinking 

supplies. However, public drinking water supplies are subject to legally enforceable maximum contaminant 

levels (MCLs) established by EPA (U.S. EPA, 2012a). As the term implies, an MCL for drinking water 

specifies the highest level of a contaminant that is allowed in drinking water. The MCL is based on the MCL 

Goal (MCLG), which is the level of a contaminant in drinking water below which there is no known or 

expected risk to human health. EPA sets the MCL as close to the MCLG as possible, with consideration for 

the best available treatment technologies and costs. 

Pursuant to MCLs, public drinking water supplies are already treated for pollutants that pose human health 

risks. Detection of the pollutants is subject to imperfect monitoring and treatment may not remove all 

contaminants from the drinking water supplies, as evidenced by reported MCL violations for inorganic and 

other contaminants at community water systems (U.S. EPA, 2013d). There may therefore be some 

incremental health-related benefits associated with reduced concentrations arising from the final ELGs. 

However, EPA’s screening level analysis suggests that these benefits would not be substantial. As a first step 

in assessing the potential benefits, EPA determined that 27 directly receiving reaches have metal 
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concentrations above the MCL in the baseline. None of those reaches, however, has an associated drinking 

water intake. Based on the analysis of the larger set of reaches (including downstream reaches) there are 817

reaches with MCL exceedances in the baseline; 250 of these reaches improve under the final ELGs. However, 

again, none of these improving reaches has an associated drinking water intake. Accordingly, EPA restricted 

the analysis of monetized health benefits from improved surface water quality to benefits arising from the 

consumption of contaminated fish tissue.

2.1.3 Complementary Measure of Human Health Benefits

EPA quantified but did not monetize the expected reduction of pollutant concentrations in excess of human 

health-based ambient water quality criteria (AWQC) limits. This analysis provides a measure of the change in 

cancer and non-cancer health risk by comparing the number of receiving reaches exceeding health-based 

AWQC for steam electric pollutants in the baseline to the number exceeding AWQC under the regulatory 

options (Section 3.8).

Because AWQC are set at levels to protect human health through ingestion of water and aquatic organisms, 

reducing the frequency at which human health-based AWQC are exceeded should translate into reduced risk 

to human health. This measure should be viewed as an indirect indicator of reduced risk to human health 

because it does not reflect the size of the exposed population and does not quantify changes in human health 

risk per se.

2.2 Ecological Benefits Associated with Improvements in Surface Water Quality

The composition of steam electric power plant wastewater depends on a variety of factors, such as fuel 

composition, air pollution control technologies used, and waste management techniques used; wastewater

often contains metals such as aluminum, arsenic, boron, cadmium, chromium, copper, iron, lead, manganese, 

mercury, nickel, selenium, thallium, vanadium, and zinc (U.S. EPA, 2015a). Discharges of these pollutants to 

surface water has a wide variety of environmental effects, including fish kills, reduction in the survival and 

growth of aquatic organisms, behavioral and physiological effects in wildlife, and degradation of aquatic 

habitat in the vicinity of steam electric power plant discharges (U.S. EPA, 2015a). The adverse effects 

associated with releases of steam electric pollutants depend on many factors such as the chemical-specific 

properties of the effluent, the mechanism, medium, and timing of releases, and site-specific environmental 

conditions. 

EPA expects the ecological benefits from the ELGs to include enhanced habitat for fresh- and saltwater 

plants, invertebrates, fish, and amphibians, as well as terrestrial wildlife and birds that prey on aquatic 

organisms exposed to steam electric pollutants. The reduction in pollutant loadings is expected to reestablish 

productive ecosystems in damaged waterways and to protect resident species, including threatened and 

endangered species. EPA expects the regulation to enhance the general health of fish and invertebrate 

populations, increase their propagation to waters currently impaired, and expand fisheries for both 

commercial and recreational purposes. Improvements in water quality will also favor recreational activities 

such as swimming, boating, fishing, and water skiing. Finally, the Agency expects the regulation to augment 

nonuse values (e.g., option, existence, and bequest values) of the affected water resources. 

2.2.1 Improved Surface Water Quality

The steam electric ELGs are expected to provide ecological benefits through improvements in the habitats or 

ecosystems (aquatic and terrestrial) that are affected by steam electric power plant discharges. Society values

such ecological improvements by a number of mechanisms, including increased frequency and value of use of 
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the improved habitat for recreational activities. In addition, individuals also value the protection of habitats 

and species that are adversely affected by effluent discharges, even when those individuals do not use or 

anticipate future use of the affected waterways for recreational or other purposes, resulting in nonuse values.

Recreational activities that may be enhanced by reducing steam electric discharges to surface waters include:

 Recreational Fishing. Degraded water can reduce fish populations by inhibiting reproduction, growth,

and survival of an aquatic species (Friedman et al. 1996; Niimi and Kissoon 1994; U.S. EPA, 2009d; 

U.S. EPA, 2011a) resulting in fewer and smaller fish and thereby reducing the value of a fishing trip.

Reducing pollutant loads in steam electric power plant discharges is expected to improve aquatic 

habitat and thus increase the number, size, diversity, and health of recreational fish species and, as a 

result, the value of recreational fishing. Studies have shown that the value of water resources for 

recreational fishing increases with declining level of toxic contamination in fish tissue (Phaneuf et al., 

1998; and Jakus et al., 1997). In addition, improved aesthetic qualities of the waterbody (e.g., from 

reduced nutrient loadings) and knowledge that the water is cleaner and does not contain any or 

contains fewer pollutants that harm humans and aquatic life, increases individuals’ enjoyment of their 

recreational experience.

 Outings. Participants in other recreational activities such as hiking, jogging, picnicking, and wildlife 

viewing also benefit from improved abundance and diversity of aquatic and terrestrial species. For 

example, wildlife viewers benefit from improved abundance of piscivorous birds (e.g., osprey, eagle) 

and waterfowl whose populations are likely to increase due to a reduction of mercury and other heavy 

metals in the food web and an increase in the forage fish populations (Schoch et al., 2011; U.S. EPA, 

2011a). In addition, improved aesthetic quality of surface waters (e.g., clarity and odors) enhances the 

recreational experience of wildlife viewers and other recreational users. (Schoch et al., 2011; U.S. 

EPA, 2011a). 

 Boating. Boaters benefit from enhanced opportunities for companion activities, such as fishing and 

wildlife viewing (e.g., piscivorous birds), and from improved aesthetic quality.

 Swimming. Swimmers benefit from improved aesthetic quality of surface waters including water 

clarity and odor thereby enhancing swimmer’s aesthetic enjoyment of a waterbody. 

 Hunting. Waterfowl hunters benefit from improved aesthetic enjoyment of a water resource, an 

increase in the number and quality of game available, and the removal of waterfowl consumption 

advisories. Reducing nutrient loadings from steam electric power plants is likely to benefit diving 

ducks populations by reducing eutrophication and turbidity in the affected waters and improving their 

food sources. Diving ducks rely upon undisturbed and abundant plant and invertebrate sources to 

prepare for migration. Excessive nutrient loadings can lead to eutrophic and turbid waters, with few 

plants and invertebrates food sources (Minnesota Department of Natural Resources (MDNR), 2010). 

Waterfowl populations are adversely affected by consuming contaminated fish or invertebrates; zebra 

mussels are an attractive food source for ducks and have been found to have high concentrations of 

methyl mercury (MDNR, 2010). High mercury levels have led to duck consumption advisories (Utah 

Department of Natural Resources, 2005). Reduction in metal loading to surface waters and of their 

presence in the food web may benefit waterfowl reproduction and lead to removal of duck 

consumption advisories.

EPA quantified potential ecological impacts from the final ELG options by estimating in-waterway 

concentrations of nutrients and other harmful pollutants discharged by steam electric power plants and 

translating water quality measurements into a single numerical indicator (water quality index (WQI)). EPA 

used the estimated change in WQI as a quantitative measure of ecological benefit for this regulatory analysis. 
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Section 4.1 of this report provides detail on the parameters used in formulating the WQI and the WQI

methodology and calculations. 

A variety of primary methods exist for estimating recreational use values, including both revealed and stated 

preference methods (Freeman, 2003). Where appropriate data are available or can be collected, revealed 

preference methods can represent a preferred set of methods for estimating use values. These methods use 

observed behavior to infer users’ values for environmental goods and services. Examples of revealed 

preference methods include travel cost, hedonic pricing, and random utility (or site choice) models. 

In contrast to direct use values, nonuse values are considered more difficult to estimate. Stated preference 

methods, or benefit transfer based on stated preference studies, are the generally accepted techniques for 

estimating these values (U.S. EPA, 2010a; U.S. OMB, 2003). Stated preference methods rely on carefully 

designed surveys, which either (1) ask people about their WTP for particular ecological improvements, such 

as increased protection of aquatic species or habitats with particular attributes, or (2) ask people to choose 

between competing hypothetical “packages” of ecological improvements and household cost (Bateman et al.,

2006). In either case, values are estimated by statistical analysis of survey responses. 

Although the use of primary research to estimate values is generally preferred because it affords the 

opportunity for the valuation questions to closely match the policy scenario, the realities of the regulatory

process often dictate that benefit transfer is the only option for assessing certain types of non-market values 

(Rosenberger and Johnston, 2007). Thus, EPA developed a benefit transfer approach based on a meta-analysis 

of surface water valuation studies to evaluate the use and non-use benefits of improved surface water quality 

resulting from the final rule. This analysis is presented in Chapter 4. Benefit transfer is described as the 

“practice of taking and adapting value estimates from past research ... and using them ... to assess the value 

of a similar, but separate, change in a different resource” (Smith et al. 2002, p. 134). It involves adapting 

research conducted for another purpose to estimate values within a particular policy context (Bergstrom and 

De Civita, 1999). In the benefit transfer used for analyzing non-market benefits associated with water quality 

improvements, EPA used a regression-based meta-analysis of 140 estimates of total WTP (including both use 

and nonuse values) for water quality improvements, provided by 51 original studies conducted between 1981 

and 2011.

7 The estimated econometric model allows calculation of total WTP for improvements in a variety 

of environmental services affected by water quality and valued by humans, including enhanced recreational 

fishing, other water-based recreation, and existence services such as aquatic life, wildlife, and habitat 

designated uses. The model also allows EPA to adjust WTP values based on the core geospatial factors 

predicted by theory to influence WTP, including: scale (the size of affected resources or areas), market extent 

(the size of the market area over which WTP is estimated) and the availability of substitutes. 

2.2.2 Benefits to Threatened and Endangered Species

For threatened and endangered (T&E) species vulnerable to future extinction, even minor changes to 

reproductive rates and small levels of mortality may represent a substantial portion of annual population 

growth. Consequently, steam electric power plant discharges may either lengthen recovery time, or hasten the 

demise of these species. By reducing the discharge of steam electric pollutants to aquatic habitats, the ELGs

are expected to enhance the survivability of some T&E species living in these habitats. These T&E species 

may have both use and nonuse values. However, given the protected nature of T&E species and the fact that 

 

7 Although the potential limitations and challenges of benefit transfer are well established (Desvousges et al.,

1998), benefit transfers are a nearly universal component of benefit cost analyses conducted by and for 

government agencies. As noted by Smith et al. (2002; p. 134), “nearly all benefit cost analyses rely on benefit 

transfers, whether they acknowledge it or not.”
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use activities general constitute take, which is illegal unless permitted, the majority of the economic value for 

T&E species comes from nonuse values.

Species-specific estimates of nonuse values held for the protection of T&E species can be derived only by 

primary research using stated preference techniques. As a second-best alternative,8 EPA used a benefit 

transfer approach that relies on information from existing studies (U.S. EPA, 2010a). This benefit transfer 

approach is based on a meta-analysis of 31 stated preference studies valuing threatened, rare, or endangered 

fish, bird or mammal species (Richardson and Loomis, 2009). EPA used the estimated WTP equation 

provided in this meta-analysis to estimate the monetary value of the potential increases in T&E populations 

resulting from the ELGs. This analysis and results are presented in Chapter 5. WTP values for improvements 

in water quality discussed in the preceding section may inherently include benefits to T&E species. Although 

there may be some overlap between WTP estimates for T&E species and the WTP estimates for 

improvements in water quality, this overlap is likely to be minimal, however, since none of the studies in 

EPA’s meta-analysis of WTP for water quality improvements specifically mentioned or otherwise prompted 

respondents to include benefits to T&E species populations (see Chapter 4).

2.2.3 Reduced Sediment Contamination

Effluent discharges from steam electric power plants can also contaminate waterbody sediments. For 

example, adsorption of arsenic, selenium, and other pollutants found in steam electric power plant discharges 

can result in accumulation of contaminated sediment on stream and lake beds (Ruhl, et al., 2012), posing a 

particular threat to benthic (i.e., bottom-dwelling) organisms. These pollutants can later be re-released into the 

water column and enter organisms at different trophic levels; concentrations of selenium and other steam 

electric pollutants in fish tissue of organisms of lower trophic levels can bio-magnify through higher trophic 

levels, posing a threat to the food chain at large (Ruhl, et al., 2012). 

By reducing discharges of pollutants to receiving reaches, the ELGs are expected to reduce the future 

contamination of waterbody sediments, thereby mitigating impacts to benthic organisms and reducing the 

probability that the pollutants would later be released into the water column and affect surface water quality 

and the waterbody food chain. Due to data limitations, EPA did not quantify or monetize this benefit. 

2.3 Benefits Associated with Improvements in Groundwater Quality

Impoundments used by steam electric power plants to manage their wastewater can leach pollutants into 

groundwater aquifers, degrading water quality and potentially creating health hazards to households drawing 

drinking water from affected aquifers. The operational changes prompted by the ELGs are expected to result 

in plants ceasing or significantly reducing their use of impoundments to manage coal combustion residuals 

(CCR). EPA estimated benefits from reducing the risk of groundwater contamination as part of its analysis of 

the proposed ELG options (U.S. EPA, 2013a). In December 2014, EPA promulgated the CCR rule which 

specifically addresses risks to groundwater quality from leaking impoundments. The CCR rule establishes 

technical requirements for CCR surface impoundments, including composite liners, groundwater monitoring, 

corrective action, and closure/post closure care, among others. For example, the rule requires any existing 

unlined CCR surface impoundment that is contaminating groundwater above a regulated constituent’s 

groundwater protection standard to stop receiving CCR and either retrofit or close, and that corrective action 

be taken to address contamination from leaking clay- or composite-lined impoundments. The final ELGs may 

still provide groundwater protection benefits, however, by reducing the potential impacts of future 

 

8 The cost, administrative burden, and time required to develop primary research estimates to value effects of the 

regulation on T&E species were beyond the schedule and resources available for this rulemaking.
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impoundment leaks, thereby avoiding future corrective action costs. EPA does not have sufficient data to 

model the avoidance of future leaks of clay- or composite-lined impoundments that may no longer receive 

CCR as a result of the final ELGs. EPA’s analysis of the CCR rule showed significantly lower lifetime risk of 

groundwater contamination from lined impoundments than from unlined impoundments (4 and 300 times 

smaller risk for clay- and composite-lined impoundments respectively), which suggests that corrective actions 

associated with lined impoundments should be infrequent. Accordingly, EPA estimates that the residual risk 

to aquifers after implementation of the CCR rule is small and did not estimate incremental benefits for the 

ELGs.

2.4 Economic Productivity Benefits

The economic productivity benefits expected to result from the ELGs include reduced impacts of 

impoundment releases of CCR and the reduction in the costs associated with the resulting cleanup, 

environmental damages, and transaction costs. Conversion to dry handling systems to comply with the ELG 

are expected to provide economic benefits by enhancing the ability of steam electric power plants to market 

the ash for beneficial use (e.g., in concrete or fill), thereby reducing the disposal costs otherwise incurred by 

steam electric power plants and displacing resource intensive virgin materials. Other economic productivity 

benefits may stem from reduced contamination of public drinking water supplies and irrigation water; 

increased tourism; increased commercial fish harvests; and increased property values.

2.4.1 Reduced Impoundment Releases

Steam electric power plants manage CCR such as fly ash and bottom ash through either wet or dry handling. 

For plants that use wet handling, the waste is typically sluiced to one or more surface impoundments (e.g., 

settling ponds), where the solids settle out of the water. Many plants also use surface impoundments to 

manage their flue gas desulfurization (FGD) wastewater. In addition to solids associated with the ash and 

FGD wastes, these impoundments typically contain water with high concentrations of steam electric 

pollutants, including dissolved metals. 

The operational changes prompted by the ELGs, such as conversion to dry handling, are expected to cause 

some plant owners to reduce their reliance on impoundments to handle CCR. These changes could affect the 

volume of CCR released in the event of a failure and/or the future probability of impoundment releases. 

Benefits arising from the reduced risk of impoundment releases include avoided cleanup costs, environmental 

damage, and transaction costs. 

EPA quantified and monetized these benefits based on expected future impoundment release rates, the 

volumes of CCR that would be released in an incident, and the costs of cleanup, natural resource damages, 

and transaction costs. Chapter 6 describes this analysis.

2.4.2 Enhanced Marketability of Coal Ash for Beneficial Use

EPA anticipates that the final ELGs will prompt certain plants to convert from wet handling of fly ash, bottom 

ash, and/or FGD waste to dry handling. This change would in turn allow plants to more readily market the 

CCR to beneficial uses. EPA quantified and monetized changes in the marketability of two CCR 

wastestreams, and two end uses: (1) fly ash as a substitute for Portland cement in concrete production and (2) 

fly- and bottom ashes as substitutes for sand and gravel in fill applications. The changes are based on the 

tonnage of fly and bottom ash handled dry instead of wet, with benefits derived from plants avoiding certain 

costs associated with disposing of the ashes as waste and society or users of the ash avoiding the cost and lifecycle effects associated with the displaced virgin material. Chapter 10 describes this analysis.
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2.4.3 Water Supply and Use

The ELGs are expected to reduce loading of steam electric pollutants to surface waters and thus enhance uses 

of these waters for drinking water supply and agriculture:

 Drinking water treatment costs. The ELGs have the potential to reduce costs of drinking water 

treatment (e.g., filtration and chemical treatment) by reducing metal concentrations and 

eutrophication in source waters. Eutrophication is one of the main causes of taste and odor 

impairment in drinking water, which has a major negative impact on public perceptions of drinking 

water safety. Additional treatment to address foul tastes and odors can significantly increase the cost 

of public water supply. The Agency conducted screening-level assessment to evaluate the potential 

for cost saving to public drinking water systems and concluded that such savings, while they exist, 

may not be significant. The assessment involved identifying the pollutants for which treatment costs 

may vary depending on source water quality, and using data from EPA’s ELG analysis and the 

location of drinking water intakes to determine whether modeled water quality improvements have 

the potential to reduce drinking water treatment costs. During the first step in the assessment, EPA 

determined that water utilities may see reduced costs for the removal of arsenic, cadmium, copper, 

lead, mercury, selenium, thallium, nitrogen, and total suspended solids. During the second step in the 

assessment, EPA determined that few drinking water systems are currently drawing water at levels 

that exceed one or more MCLs and would improve under the policy options. And similarly, few 

reaches with elevated total nitrogen (TN) levels will see those levels decline significantly under the 

regulatory options to result in substantive cost savings. Accordingly, EPA did not conduct detailed 

analysis of cost savings to publicly operated treatment systems.

 Reduction in bromide concentrations. Public drinking water sources do not always effectively remove 

bromides (a steam electric pollutant) from raw surface waters. While bromide itself is not thought to 

be toxic at levels present in the environment, lab studies and case reports show that bromide found in 

source water can react during routine drinking water treatment to generate harmful disinfection 

byproducts (DBPs) (Richardson, et al., 2007; U.S. EPA, 2012a). For example, McTigue et al. (2014) 

estimate that 96 drinking water treatment plants using surface water are downstream of 57 coal-fired 

power plants using wet scrubbers. If existing water treatment is not sufficient, an alternate water 

source needs to be substituted or developed, or alternate disinfection technologies need to be adopted

(Watson, et al., 2012). Long-term solutions might require the development of new raw water supplies, 

which would involve costs for the acquisition of land (if available), regulatory review and permitting, 

development of infrastructure (dams, pumps, pipes), and watershed protection. Thus, increased 

bromide levels in raw source water could translate into additional drinking water treatment costs at 

some plants, and potentially pose human health risk. In this Final Rule, EPA is not proposing 

technology-based effluent limits for bromide for steam electric industry. However, NPDES permit 

developers could specify effluent limits for bromide on a plant-by-plant basis based on site-specific 

considerations that include the potential to affect public drinking water sources. Benefits of any plantspecific limits in terms of avoided treatment costs or human health risk would need to be determined 

based on more detailed information about the receiving waters and drinking water system.

 Irrigation and other agricultural uses: Reducing steam electric pollutants discharges can improve 

agricultural productivity by improving water quality used for irrigation and livestock watering (Clark 

et al., 1985). Although elevated nutrient concentrations in irrigation water would not adversely affect 

its usefulness for plants, concerns exist for potential residual effects due to steam electric pollutants

entering the food chain. Further, eutrophication promotes cyanobacteria blooms that can kill livestock 
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and wildlife that drink the contaminated surface water. EPA did not quantify or monetize benefits 

from enhanced quality agricultural water sources arising from the ELGs due to data limitations.

 Reservoir Capacity. Reservoirs serve many functions, including storage of drinking and irrigation 

water supplies, flood control, hydropower supply, and recreation. Streams can carry sediment into 

reservoirs, where it can settle and cause buildup of silt layers over time, at a recorded average rate of 

1.2 billion kilograms per reservoir every year (USGS, 2007b). Sedimentation reduces reservoir 

capacity (Graf et al., 2010) and the useful life of reservoirs unless measures such as dredging are 

taken to reclaim capacity (Clark et al., 1985). EPA expects that by reducing total suspended solids 

(TSS) concentrations, the ELGs will provide modest cost savings by reducing dredging activity to 

reclaim capacity at existing reservoirs.

2.4.4 Reduced Sedimentation in Navigational Waterways

Navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are an integral part of the 

United States’ transportation network. Navigable channels are prone to reduced functionality due to sediment 

build-up, which can reduce the navigable depth and width of the waterway (Clark et al., 1985). For many 

navigable waters, periodic dredging is necessary to remove sediment and keep them passable. Dredging of 

navigable waterways can be costly. 

EPA expects that the ELGs will reduce sediment loadings to surface waters and reduce dredging of

navigational waterways and reservoirs. EPA quantified and monetized these benefits based on the avoided 

cost for expected future dredging volumes. Chapter 9 describes this analysis.

2.4.5 Commercial Fisheries

Pollutants in steam electric power plant discharges can reduce fish populations by inhibiting reproduction and 

survival of aquatic species. These changes may negatively affect commercial fishing industries as well as 

consumers of fish, shellfish, and fish and seafood products. Estuaries are particularly important breeding and 

nursery areas for commercial fish and shellfish species. In some cases, excessive pollutant loadings can lead 

to the closures of shellfish beds, thereby reducing shellfish harvests. Improved water quality due to reduced 

discharges of steam electric pollutants would enhance aquatic life habitat and, as a result, contribute to 

reproduction and survival of commercially harvested species and larger fish and shellfish harvest, which in 

turn lead to an increase in producer and consumer surplus. 

EPA did not quantify or monetize benefits to commercial fisheries from the ELGs. EPA’s EA (see U.S. EPA, 

2015a) shows that a small number of steam electric power plants discharge to estuaries or marine waters. 

Although benefits to local fish populations and commercial harvest may be positive, the overall benefits to 

commercial fisheries arising from the ELGs are likely to be negligible. Most species of fish have numerous 

close substitutes. The literature suggests that when there are plentiful substitute fish products, numerous 

fishers, and a strong ex-vessel market, individual fishers are generally price takers. Therefore, the measure of 

consumers welfare (consumer surplus) is unlikely to change as a result of small changes in fish landings, such 

as those EPA expects under the final rule. 

2.4.6 Tourism

The ELGs may also benefit local economies by contributing to the tourism industries (e.g., sales of fishing 

equipment) in the areas surrounding affected waters due to improved recreational opportunities. The effects of 

water quality on tourism are likely to be highly localized. Moreover, since substitute tourism locations may be 

available, increased tourism in the vicinity of steam electric power plants may lead to a reduction in tourism 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2-12

in other locations. Due to these factors EPA believes that benefit from an increase in tourism would be limited 

to communities in the vicinity of steam electric power plants; although tourism revenue is potentially 

important to these communities, the overall societal benefits are likely to be small. Therefore, EPA did not 

quantify or monetize this benefit category. 

2.4.7 Property Values

The ELGs are expected to improve the aesthetic quality of land and water resources by reducing pollutant 

discharges and thus enhancing water clarity, odor, and color in the receiving and downstream reaches. Several 

studies (Boyle et al., 1999; Poor et al., 2001; Leggett and Bockstael, 2000; Bin and Czakowski, 2013; Walsh 

et al., 2011; Tuttle and Heintzelman, 2014) suggest that waterfront property is more desirable when located 

near unpolluted water. Therefore, the value of properties located in proximity to waters contaminated with 

steam electric pollutants may increase due to reduced steam electric discharges. Although this benefit would

accrue to the current property owners only, it represents an overall increase in societal wealth. 

Due to data limitations, EPA was not able to quantify or monetize the potential increase in property values 

associated with the ELGs. The magnitude of the potential increase depends on many factors, including the 

number of housing units located in the vicinity of the affected waterbodies, community (e.g., residential 

density) and housing stock (e.g., single family or multiple family) and the effects of steam electric pollutants 

on aesthetic quality of surface water. Given that the main benefit of the final ELG is reduction in metal 

concentrations that do not affect aesthetic quality of surface water, changes in property values are expected to 

be small. In addition, there may be overlap between changes in property values and the estimated total WTP 

for surface water quality improvements summarized in Section 2.2.1.

2.5 Reduced Air Pollution

The ELGs are expected to affect air pollution through three main mechanisms: 1) additional auxiliary 

electricity use by steam electric power plants to operate wastewater treatment, ash handling, and other 

systems needed to comply with the new effluent limits and standards; 2) additional transportation-related 

emissions due to the increased trucking of CCR waste to on-site or off-site landfills; and 3) the change in the 

profile of electricity generation due to relatively higher cost to generate electricity at plants incurring 

compliance costs for the ELGs. The different profile of generation can result in lower or higher air pollutant 

emissions due to differences in emission factors for coal or natural gas combustion, or nuclear or 

hydroelectric power generation.

Of the three mechanisms above, the change in the emissions profile of electricity generation at the market 

level is the most significant. Small reductions in coal-based electricity generation as a result of the final ELGs

are compensated by increases in generation using other fuels or energy sources – biomass, landfill gas, natural 

gas, nuclear power, oil, and wind power. The changes in air emissions reflect the differences in emissions 

factors for these other fuels, as compared to coal-fueled generation. Overall for the three mechanisms 

(auxiliary services, transportation, and market-level generation), EPA estimates a net reduction in CO2 and 

SO2, and a slight increase in NOX emissions. 

NOX, and SO2 are known precursors to PM2.5, a criteria air pollutant that has been associated with a variety 

of adverse health effects, including premature mortality and hospitalization for cardiovascular and respiratory 

diseases (e.g., asthma, chronic obstructive pulmonary disease (COPD), and shortness of breath). To estimate 

benefits of reducing NOX, and SO2 emissions, EPA used estimates of national monetized benefits per ton of 

emissions avoided. CO2 is an important greenhouse gas that is linked to climate change effects including 

global warming, sea level rise, increased frequency of extreme weather events, ocean acidification, etc. EPA 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2-13

used estimates of the social cost of carbon (SCC) obtained from the Interagency Working Group on Social 

Cost of Carbon (see Interagency Working Group on Social Cost of Carbon (IWGSCC), 2013b) to derive 

benefits per ton for CO2. The SCC reflects a broad range of climate change impacts, including changes in 

agricultural productivity, human health risks, property damage from increased flood frequencies, the loss of

ecosystem services, and others. Chapter 7 details this analysis.

2.6 Reduced Water Withdrawals

Steam electric power plants use water for wet ash transport and for operating wet FGD scrubbers. By 

eliminating or reducing water used in sluicing operations or prompting the recycling of water in FGD 

wastewater treatment systems, the ELGs are expected to reduce demand on aquifers by plants that rely on 

groundwater sources. 

Additionally, reduced surface water intake would reduce impingement and entrainment mortality. Due to data 

limitations, EPA did not quantify and monetize these benefits as part of this analysis. For more details on the 

impacts of surface water withdrawals, see paper titled “Water Withdrawals in Water Stressed Areas: Impacts 

of the Steam Electric Effluent Limitations Guidelines” (DCN SE05943) in the record for the final steam 

electric ELG rule.

Reduced water use from groundwater sources by steam electric power plants would result in greater

availability of groundwater supplies for alternative uses. EPA used the state specific prices for bulk drinking 

water supplies to value the increased quantity of groundwater. This analysis is presented in Chapter 8.

2.7 Summary of Benefits Categories

Table 2-1 summarizes the benefits of the ELGs and the level of analysis applied to each category. As 

indicated in the table, only a subset of anticipated benefits can be quantified and monetized (in which case the 

table identifies the section of the report that discusses the analysis). The monetized benefits include reductions 

in some human health risks, use and non-use values from improved surface water quality, benefits to T&E 

species, reduced impacts from impoundment releases, increase in the amount of ash marketed for beneficial 

uses, reduced costs for dredging navigational waterways, reduced air pollution, and reduced water 

withdrawals. Other benefit categories, including expected reduction of pollutant concentrations in excess of 

human health-based AWQC limits, can be quantified but not monetized. Finally, EPA was not able to 

quantify or monetize other benefits, including drinking water treatment costs and benefits to commercial 

fisheries; EPA evaluated these benefits qualitatively as discussed above in Sections 2.1 through 2.6. 

Table 2-1: Estimated Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants

Category Effect of ELGs

Benefits Analysis

Quantified Monetized

Methods (Report 

Chapter or Section

where Analysis is 

Detailed)

Human Health Benefits from Surface Water Quality Improvements

Reduced IQ losses to 

children ages 0 to 7

Reduced childhood exposure to lead 

from fish consumption

 

IQ point valuation

(Section 3.3)

Reduced need for 

specialized education

Reduced childhood exposure to lead 

from fish consumption

 

Avoided cost 

(Section 3.3)

Reduced incidence of 

cardiovascular disease

Reduced exposure to lead from fish 

consumption

 

VSL (Section 3.4)
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Table 2-1: Estimated Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants

Category Effect of ELGs

Benefits Analysis

Quantified Monetized

Methods (Report 

Chapter or Section

where Analysis is 

Detailed)

Reduced IQ losses to 

infants

Reduced in-utero mercury exposure 

from maternal fish consumption

 

IQ point valuation

(Section 3.5)

Reduced incidence of 

cancer 

Reduced exposure to arsenic from 

fish consumption

 

COI (Section 3.6)

Reduced other adverse 

health effects (cancer 

and non-cancer)

Reduced exposure to other 

pollutants (arsenic, lead, etc.) via 

fish consumption



Human health 

criteria exceedances

(Section 3.8)

Reduced adverse health 

effects

Reduced exposure to pollutants from 

recreational water uses

Qualitative 

discussion

Ecological Conditions and Recreational use Benefits from Surface Water Quality Improvements

Improved aquatic and 

wildlife habitata

Improved ambient water quality in 

receiving reaches

 

Benefit transfer

(Chapter 4)

Water-based recreationa Enhanced swimming, fishing, 

boating, and near-water activities 

from improved water quality

AestheticsaIncreased aesthetics from improved 

water clarity, color, odor, including 

nearby site amenities (residing, 

working, traveling)

Non-use valuesa Enhanced existence, option, and 

bequest values from improved 

ecosystem health

Aquatic and wildlife Reduced risks to aquatic life from 

exposure to steam electric pollutants

Improved protection of 

T&E species

Improved T&E habitat and thus 

potential increase in T&E population

 

Benefit transfer

(Chapter 5)

Reduced sediment 

contamination 

Reduced deposition of toxic 

pollutants to sediment 

Qualitative 

discussion

Groundwater Quality Benefits

Groundwater quality Reduced groundwater contamination Qualitative 

discussion

Market and Productivity Benefits

Impoundment releases Reduced risk of impoundment 

releases due to changes in the use of 

impoundment  

Avoided cost of 

cleanup, natural 

resource damages, 

and transaction 

costs (Chapter 6)

Reduced dredging costs Reduced costs for maintaining 

navigational waterways and 

reservoir capacity

 

Avoided dredging 

costs (Chapter 9)
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 2: Benefits Overview

September 29, 2015 2-15

Table 2-1: Estimated Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants

Category Effect of ELGs

Benefits Analysis

Quantified Monetized

Methods (Report 

Chapter or Section

where Analysis is 

Detailed)

Beneficial use of ash Reduced disposal costs and avoided 

life-cycle impacts from displaced 

virgin material  

Avoided disposal 

cost and avoided 

resource use and 

environmental 

impacts (Chapter 

10)

Reduced water treatment 

costs for drinking water 

and irrigation water 

Improved quality of source water 

used for drinking and irrigation 

Qualitative 

discussion

Commercial fisheries Improved fisheries yield and harvest 

quality due to aquatic habitat 

improvement 

Qualitative 

discussion

Benefits to tourism 

industries 

Increased participation in waterbased recreationQualitative 

discussion

Property values Increased property values from 

water quality improvements 

Qualitative 

discussion

Air-Related Benefits

Reduced air emissions of 

NOx, SO2

Reduced mortality and morbidity 

from exposure to NOx, SO2 and 

particulate matter (PM2.5)

 

Benefit per ton of 

air pollutant 

removed (Chapter 

7)

Reduced air emissions of 

CO2

Avoided climate change /global 

warming impacts  

Social cost of 

carbon (SCC)

(Chapter 7)

Reduced Water Withdrawal Benefits

Reduced groundwater 

withdrawals

Increased availability of 

groundwater resources

 

Avoided cost

(Chapter 8)

Reduced surface water

withdrawals

Reduced vulnerability to drought 

and reduced impingement and 

entrainment mortality

Qualitative 

discussion

a. These values are implicit in the total WTP for water quality improvements.
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3 Human Health Benefits

EPA expects that the ELGs will yield a range of human health benefits by reducing effluent discharges to 

surface waters and, as a result, ambient pollutant concentrations in the receiving reaches. EPA’s EA (U.S. 

EPA, 2015a) provides details on the health effects of steam electric pollutants. Recreational anglers and 

subsistence fishers (and their household members) who consume fish caught in the reaches receiving steam 

electric discharges are expected to benefit from reduced pollutant concentrations in fish tissue. This chapter 

presents EPA’s analysis of human health benefits from reduced exposure to steam electric pollutants via the 

fish consumption pathway.9 The analyzed health benefits include: 

 Reduced exposure to lead:

‒ Avoided neurological and cognitive damages in children (ages 0-7) based on the impact of an 

additional IQ point on an individual’s future earnings and the cost of compensatory education 

for children with learning delays

‒ Reduced incidence of CVD in adults

 Reduced exposure to mercury:

‒ Reduced neurological and cognitive damages in infants from exposure to mercury in-utero

 Reduced exposure to arsenic:

‒ Reduced incidence of cancer cases. 

The total quantified human health benefits included in this analysis represent only a subset of the potential 

health benefits expected to result from the final ELGs. While additional adverse health effects are also 

associated with steam electric pollutants (such as kidney damage from cadmium or selenium exposure, 

gastrointestinal problems from zinc, thallium, or boron exposure, and others), the lack of data on doseresponse relationships10 between ingestion rates and these effects precluded EPA from quantifying the 

associated benefits.

EPA’s analysis of the monetary value of human health benefits is based on data and methodologies described 

in the EA (U.S. EPA, 2015a). The relevant data include COMIDs11 for receiving waters, baseline and postcompliance annual plant-level loadings of each discharged pollutant, ambient pollutant concentrations in 

receiving reaches and downstream reaches, pollutant concentrations in fish tissue, fish consumption rates 

among different racial and ethnic cohorts for affected recreational anglers and subsistence fishers, and the 

average daily dose (ADD) or lifetime average daily dose (LADD) of pollutants for each age cohort for 

recreational anglers and subsistence fishers.

Section 3.1 describes how EPA identified the population potentially exposed to pollutants from steam electric 

discharges via fish consumption. Section 3.2 describes the methods for determining fish tissue pollutant 

concentrations and exposure via fish consumption in the affected population. Sections 3.3 to 3.7 describe 

 

9 The analysis of human health benefits focuses on the fish consumption pathway only, since EPA assumed that 

drinking water is treated to reduce pollutant concentrations below MCLs. See Chapter 2 for further discussion.

10 A dose response relationship is an increase in incidences of an adverse health outcome per unit increase in 

exposure to a toxin.

11 A COMID is a unique numeric identifier for a given waterbody, assigned by a joint effort of the United States 

Geological Survey, EPA, and Horizon Systems, Inc.
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EPA’s analysis of benefits of various human health endpoints affected by the final ELGs. The human health 

benefits estimates rely on models of concentration-response and exposure pathways that embed assumptions 

and involve limitations and uncertainties. Section 3.9 describes the limitations and uncertainties in the health 

benefit analyses.

3.1 Affected Population

The affected population (i.e., individuals potentially exposed to steam electric pollutants via consumption of 

contaminated fish tissue) includes recreational anglers and subsistence fishers who fish reaches affected by 

steam electric discharges (including receiving and downstream reaches), as well as their household members. 

EPA estimated the number of people who are likely to fish affected reaches based on typical travel distances 

to a fishing site, presence of substitute fishing locations, data on the locations and status of fish consumption 

advisories (FCAs) for affected reaches, and information on anglers’ awareness and adherence to FCAs. Since 

fish consumption rates vary across different racial and ethnic groups and across fishing mode (recreational 

versus subsistence fishing), EPA estimated benefits separately for a number of age-, ethnicity-, and modespecific cohorts. 

First, for each Census Block Group (CBG) within 50 miles of an affected reach, EPA pulled 2010 Census 

data on the number of people in 7 age categories (0 to 1, 2, 3 to 5, 6 to 10, 11 to 15, 16 to 21, and 21 or 

higher), and then subdivided each group according to 7 racial/ethnic categories:12 1) White non-Hispanic; 2) 

African-American non-Hispanic; 3) Tribal/Native Alaskan non-Hispanic; 4) Asian/Pacific Islander nonHispanic; 5) Other non-Hispanic (including multiple races), 6) Mexican Hispanic, and 7) Other Hispanic13

. 

Within each racial/ethnic group, EPA further subdivided the population according to recreational and 

subsistence groups, assuming that 5 percent of the population practices subsistence fishing.14 Finally, EPA 

also subdivided the affected population by income into poverty and non-poverty groups, based on the share of 

people below the federal poverty line.15 After subdividing population groups by age, race, fishing mode, and 

the poverty indicator, each CBG has 196 unique population cohorts (7 age groups × 7 ethnic/racial groups × 2 

exposure cohorts [recreational vs. subsistence fishing] × 2 poverty status designations).

EPA distinguished the exposed population by racial/ethnic group and poverty status to support analysis of 

potential Environmental Justice (EJ) considerations in baseline exposure to pollutants in steam electric power 

plant discharges, and to allow evaluation of the effects of the regulatory options on mitigating any EJ 

concerns. See Chapter 14 for details of the EJ analysis. As noted below, distinguishing the exposed 

population in this manner also allows the Agency to account for differences in exposure among demographic 

groups, where supported by data.

Equation 3-1 shows how EPA estimated the affected population, ExPop(i)(s)(c), for CBG i in state s for 

cohort c.

 

12 The racial/ethnic categories are based on available fish consumption data as well as the breakout of ethnic/racial 

populations in Census data, which distinguishes racial groups within Hispanic and non-Hispanic categories.

13 The Mexican Hispanic and Hispanic block group populations were calculated by applying the Census tract 

percent Mexican Hispanic and Hispanic to the underlying block-group populations, since these data were not 

available at the block-group level.

14 Data is not available on the share of the fishing population that practices subsistence fishing. EPA assumed that 

5% of people who fish practice subsistence fishing, based on the assumed 95th percentile fish consumption rate 

for this population (see U.S. EPA, 2011b).

15 Poverty status is based on data from the Census Bureau’s American Community Survey which determines 

poverty status by comparing annual income to a set of dollar values called poverty thresholds that vary by 

family size, number of children, and the age of the householder.
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Equation 3-1. ExPop(i)(s)(c) = Pop(i)(c) × %Fish(s) × A(i) × CaR(c)

Where:

Pop(i)(c) = Total CBG population in cohort c. Age and racial/ethnicity-specific populations in each 

CBG are based on data from the U.S. Census Bureau’s 2010 Census. The Census data provides 

population numbers for each CBG broken out by age and racial/ethnic group separately. To estimate the 

population in each age- and ethnicity/race-specific group, EPA calculated the share of the population in 

each racial/ethnic group and applied the percentages to the population in each age group.

%Fish(s) = Fraction of people who live in households with anglers. To determine what percentage of the 

total population participates in fishing, EPA used region-specific U.S. Fish and Wildlife Service (U.S. 

FWS, 2011) estimates of the population 16 and older who fish.16 EPA assumed that the share of 

households that includes anglers is equal to the fraction of people over 16 who are anglers.

A(i) = Adjustment for fish consumption advisories. EPA further adjusted the affected angler population 

to reflect the presence of FCAs, where applicable for a given reach. Based on EPA’s review of studies 

documenting anglers’ awareness of FCA and their behavioral responses to FCAs, 57.0 percent to 

61.2 percent of anglers are aware of FCAs, and 71.6 percent to 76.1 percent of those who are aware 

ignore FCAs. Conservatively assuming that 61.2 percent of anglers are aware of applicable FCAs and 

that 71.6 percent of aware anglers ignore them, the number of anglers exposed to steam electric 

pollutants would be 17.4 percent lower for reaches with FCAs.17 Therefore, for receiving reaches with 

FCAs, EPA reduced the affected populations by 17.4 percent. 

CaR(c) = Adjustment for catch-and-release practices. According to U.S. FWS (2006) data, 

approximately 23.3 percent of anglers release all the fish they catch (“catch-and-release” anglers). 

Anglers practicing “catch-and-release” would not be exposed to steam electric pollutants via 

consumption of contaminated fish. For all recreational anglers, EPA reduced the affected population by 

23.3 percent. EPA assumed that subsistence fishers do not practice “catch-and-release” fishing.

Table 3-1 summarizes the population living within 50 miles of reaches affected by steam electric power plant

discharges (see Section 3.2.1 for a discussion of this distance buffer) and EPA’s estimate of the population 

potentially exposed to the pollutants via consumption of recreationally-caught fish (based on 2010 population 

data and not adjusted for population growth during the analysis period). Of the total population, 14 percent 

live within 50 miles of an affected reach and participate in recreational and/or subsistence fishing, and 

10 percent are potentially exposed to fish contaminated by steam electric pollutants.

 

16 The share of the population who fishes ranges from 9 percent in the Pacific region to 23 percent in the West 

North Central region. Other regions include the Middle Atlantic (11 percent), New England (12 percent), South 

Atlantic (13 percent), Mountain (15 percent), West South Central (16 percent), East North Central (16 percent), 

and East South Central (17 percent).

17 This is calculated as 61.2 percent aware of advisories times 28.4 percent (100%-71.6%) who choose not to fish 

or otherwise don’t eat fish caught in waterbodies affected by advisories.
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Table 3-1: Summary of Potentially Affected Population Living within 50 Miles of Affected 

Reaches (baseline, as of 2010)

Total population 306,707,864

Total angler populationa

42,710,492

Population potentially exposed to contaminated fishb, c 29,655,404

Source: U.S. EPA Analysis, 2015

a. Total population living within 50 miles of an affected reach times the state-specific share of the population who 

fishes based on U.S. FWS (2011; between 9% and 23%).

b. Total angler population adjusted to reflect lower fishing/consumption rates for reaches with fish consumption 

advisories and catch-and-release practices. 

c. Analysis accounts for projected population growth so that the average affected population over the period of 2019 

through 2042 is 21.3 percent higher than population in 2010 presented in the table, or 36 million people.

3.2 Pollutant Exposure from Fish Consumption

EPA calculated an average fish tissue concentration for each pollutant for each CBG based on a lengthweighted average concentration for all reaches within 50 miles. For each combination of pollutant, cohort and 

CBG, EPA calculated ADD and LADD consumed via the fish consumption pathway. 

3.2.1 Fish Tissue Pollutant Concentrations

The set of reaches that may represent a source of contaminated fish for recreational anglers and subsistence 

fishers in each CBG depends on the typical travel distance anglers travel to fish. EPA assumed that anglers 

typically travel up to 50 miles to fish, using this distance to estimate the relevant fishing sites for the 

population of anglers in each CBG. See Appendix F for a sensitivity analysis using a travel distance of 

100 miles. Based on data from the National Survey on Recreation and the Environment, about 80 percent of 

all water-based recreation occurs with 100 miles of the users’ homes (Viscusi et al., 2008).

Anglers may have several fishable sites to choose from within 50 miles of travel. To account for the effect of 

substitute sites, EPA assumed that anglers are uniformly distributed among all the available fishing sites 

within 50 miles from the CBG (travel zone) and alternate their travels across all the sites. For each CBG, EPA 

identified all fishable COMIDs within 50 miles (where distance was determined based on the Pythagorean 

distance between the centroid of the CBG and the midpoint of the COMID) and the COMID length in miles 

EPA then calculated, for each CBG, the reach-length weighted average fish tissue concentration of arsenic 

(As), mercury (Hg), and lead (Pb) based on all fishable sites within the 50 mile buffer. Appendix D describes 

the approach used to derive ambient water and fish tissue concentrations of steam electric pollutants in the 

baseline and under each of the regulatory options. 

For each CBG, EPA then calculating the reach length (Lengthi) weighted fish filet concentration (C Fish_Filet

(CBG)) based on all fishable COMIDS within the 50 mile radius according to Equation 3-2:

Equation 3-2. CFish_Filete(CBG) =

∑ CFish_Filet(i)∗Lengthi

n

i=1

∑ Lengthi

n

i=1

3.2.2 Average Daily Dose

Exposure to steam electric pollutants via fish consumption depends on the cohort-specific fish consumption 

rates. Table 3-2 summarizes the fish consumption rates, expressed in daily grams per kilogram of body 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3-5

weight, according to the race/ethnicity and fishing mode. For more details on these fish consumption rates, 

see U.S. EPA (2015a).

Table 3-2: Summary of group-specific assumptions for human health benefit analysisa

Race/ Ethnicity EA Cohortb

Consumption Rate (g/ kg BW/day)

Recreational Subsistence

White (non-Hispanic) Non-Hispanic White 0.67 1.9

African American (non-Hispanic) Non-Hispanic Black 0.77 2.1

Asian/Pacific Islander (non-Hispanic) Other, including Multiple Races 0.96 3.6

Tribal/Native Alaskan (non-Hispanic) Other, including Multiple Races 0.96 3.6

Other non-Hispanic Other, including Multiple Races 0.96 3.6

Mexican Hispanic Mexican Hispanic 0.93 2.8

Other Hispanic Other Hispanic 0.82 2.7

a. Each group is also subdivided into seven age groups (0-1, 2, 3-5, 6-10, 11-15, 16-20, Adult (21 or higher) and two income groups 

(above and below the poverty threshold).

b. U.S. EPA (2015a).

Equation 3-3 and Equation 3-4 show the cohort- and CBG-specific calculation of the ADD and LADD based 

on fish tissue concentrations, consumption rates, and other assumptions about exposure duration and 

averaging periods, as shown below.

Equation 3-3. ADD(c)(i) =

CFish_Filet (i) ×CRFish(c)×FFish

1000

Where:

ADD(c)(i) = Average daily dose of pollutant from fish consumption for cohort c in CBG i

(milligrams[mg] per kilogram [kg] body weight [BW] per day)

Cfish_filet(i) = average fish filet pollutant concentration consumed by humans for CBG i (mg per kg)

CRfish(c) = Consumption rate of fish for cohort c (grams per kg BW per day); see Table 3-2.

Ffish = fraction of fish from contaminated source (percent; assumed value of 100%)

Equation 3-4. LADD =

ADD ×ED ×EF

AT ×365

Where: 

LADD(i)(c) = Lifetime average daily dose (mg per kg BW per day) for cohort c in CBG i

ADD(i)(c) = Average daily dose (mg per kg BW per day) for cohort c in CBG i

ED(c) = exposure duration (years) for cohort c

EF = exposure frequency (days; assumed value of 350)

AT = averaging time (years; assumed value of 70)

EPA used the doses of steam electric pollutants from recreational caught fish thus obtained in its analysis of 

benefits associated with the various human health endpoints described below.
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3.3 Benefits to Children from Reduced Lead Exposure

Lead is a highly toxic pollutant that can damage neurological, cardiovascular, and other major organ systems.

In particular, elevated lead exposure may induce a number of adverse neurological effects in children, 

including hyperactivity, behavioral and attentional difficulties, delayed mental development, and motor and 

perceptual skill deficits (see U.S. EPA, 2015a). Elevated blood lead (PbB) concentrations in children may 

also result in metabolic effects such as impaired heme synthesis, anemia, and slowed growth (U.S. EPA, 

1990; National Academy of Sciences, 1993; Kim et al. 1995). Severe lead poisoning may result in renal 

effects, seizures, impaired coordination, recurrent vomiting, coma, and acute lead encephalopathy, a 

potentially fatal condition (Piomelli et al., 1984; National Academy of Sciences, 1993; CDC, 2005). Studies 

have also found a relationship between lead exposure in expectant mothers and lower birth weight in 

newborns (Zhu et al., 2010; Bornschein et al., 1989; and Dietrich et al. 1987). Because of data limitations, 

EPA estimated only the benefits from reducing neurological and cognitive damages to pre-school (ages 0 to 

7) children using the dose-response relationship for IQ decrements (Schwartz 1994). 

EPA estimated benefits from reduced exposure to lead to preschool children using PbB as a biomarker of lead 

exposure. EPA first modeled PbB under the baseline and post-compliance scenarios, and then used a 

concentration-response relationship between PbB and IQ loss to estimate avoided IQ losses in the affected 

population of children and reduced incidences of extremely low IQ scores (less than 70, or two standard 

deviations below the mean). EPA calculated the monetary value of benefits to children based on the impact of 

an additional IQ point on an individual’s future earnings and the cost of compensatory education for children 

with learning disabilities (including children with IQ less than 70 and PbB levels above 20 g/dL). 

EPA used the methodology described in Section 3.1 to estimate the population of pre-school children who

live in recreational angler and subsistence fisher households and are potentially exposed to lead via 

consumption of contaminated fish tissue. Since this benefit category applies to children up to the seventh 

birthday only, EPA restricted the analysis to the relevant age cohorts of angler household members.

3.3.1 Methods 

This analysis considers children who are born after implementation of the ELGs and live in recreational 

angler and subsistence fisher households. It relies on EPA’s Integrated Exposure, Uptake, and Biokinetics 

(IEUBK) Model for Lead in Children (U.S. EPA, 2009c), which uses lead concentrations in a variety of 

media – including soil, dust, air, water, and diet – to estimate total exposure to lead for children in seven oneyear age cohorts from birth through the seventh birthday. Based on this total exposure, the model generates a 

predicted geometric mean PbB for a population of children exposed to similar lead levels. The BCA report for 

the proposed ELG provides a more detailed description of the IEUBK model and describes EPA’s application 

of the model to estimating benefits to pre-school children from reduced exposure to lead contaminated fish

(U.S. EPA, 2013a).

For each CBG, EPA used the cohort-specific ADD based on Equation 3-3. Lead bioavailability and uptake 

after consumption varies for different chemical forms. Many factors complicate the estimation of 

bioavailability, including nutritional status and timing of meals relative to lead intake. For this analysis, EPA 

used the default media-specific bioavailability factor provided in the IEUBK model, which is 50 percent for 

oral ingestion. EPA used the IEUBK model to generate the geometric mean PbB for each cohort in each CBG

under the baseline and post-compliance scenarios. Note the IEUBK model processes daily intake to two

decimal places (ug/day). For this analysis, this means that some of the change between the baseline and 

regulatory options is missed by using the model (i.e., it does not capture very small changes), since the 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3-7

benefits are driven by very small changes across large populations.

18 As such, the benefits shown in this 

section are likely to underestimate the actual lead-related benefits to children arising from the ELG.

3.3.1.1 Estimating of Avoided IQ Point Losses

In a pooled data analysis, Lanphear et al. (2005; as cited in Agency for Toxic Substances and Disease 

Registry (ATSDR), 2007) found that the greatest IQ losses per 1 g/dL occur at the lowest ranges of PbB. 

When the authors grouped IQ losses data for children with PbB below and above 7.5 g/dL, they found that 

the IQ losses were 2.94 points per g/dL for children with PbB concentrations below 7.5 g/dL and 

0.16 points per g/d for children with PbB concentrations above 7.5 g/dL. 

Given the baseline PbB levels estimated using the IEUBK model (mean PbB of approximately 2.7 μg/dL), 

EPA used the dose-response factor of 2.94 points per g/dL from Lanphear et al. to estimate changes in IQ 

losses between the baseline and post-compliance scenarios.

19 Comparing the baseline and post-compliance 

results provides the avoided IQ loss per child. Multiplying the result by the number of affected pre-school 

children yields the total increase in the number of IQ points for the affected population of children for the 

baseline and each regulatory option. 

The IEUBK model estimates the mean of the PbB distribution in children, assuming a continuous exposure 

pattern for children from birth through the seventh birthday. The U.S. Census Bureau (2010) indicates that 

children ages 0 to 7 are approximately evenly distributed by age. To get an annual estimate of the number of 

children that would benefit from implementation of the final rule, EPA divided the estimated number of 

affected pre-school children by 7. This division adjusts the equation to apply only to children age 0 to 1. The 

estimated avoided IQ loss is thus an annual value (i.e., it would apply to the cohort of children born each year 

after implementation).20 Equation 3-5 shows this calculation for the annual increase in total IQ points.

Equation 3-5. ∆IQ(i)(c) = (∆GM(i)(c) ∗ CRF ∗ (

ExCh(i)

7

))

Where:

∆IQ(i)(c) = the difference in total IQ points between the baseline and regulatory option scenarios for 

cohort c in CBG i

∆GM(i)(c) = the change in the average PbB in affected population of children (μg/dL) in cohort c in 

CBG i

CRF = concentration response function (2.94 IQ points lost per ug/dL increase in PbB)

ExCh(i) = the number of affected children aged 0 to 7 for CBG i

The available economic literature provides little empirical data on society’s overall WTP to avoid a decrease 

in children’s IQ. To determine the value of avoided IQ losses, EPA used estimates of the changes in a child’s 

future expected lifetime earnings per one IQ point reduction and the cost of compensatory education for 

children with learning disabilities.

 

18 For example, the average intake across all affected children is reduced from 0.333 ug/kg BW/day under the 

baseline to 0.332 under Option D.

19 See Appendix F for a sensitivity analysis using a log-linear concentration-response function.

20 Dividing by seven undercounts overall benefits. Children from ages 1 to 7 are not accounted for in the base year 

of the analysis, although they are presumably affected by lead exposure.
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Salkever (1995) and Schwartz (1994) estimate that a one point IQ reduction reduces expected lifetime 

earnings by 2.38 percent and 1.76 percent, respectively. Data from the U.S. Census Bureau for 2009 indicate 

that lifetime earnings are approximately $147,462 when discounting future earnings at 7 percent, and 

$656,737 when discounting future earnings at 3 percent.21 The resulting estimated values of an IQ point are 

summarized in Table 3-3. 

Table 3-3: Value of an IQ Pointa(2013$)

Discount Rate

Assumed Reduction in Expected Lifetime Earnings (percent per IQ point)

1.76 percent/IQ point (Schwartz, 1994 ) 2.38 percent/IQ point (Salkever, 1995)

3 percent $11,559 $15,630

7 percent $2,559 $3,510

a. Values are not adjusted for the cost of education.

Decreased IQ also results in less education and, therefore, reduced education costs. Data from the National 

Center for Education Statistics (2014) indicate that the average expenditure per student in 2010/2011 was 

$12,600 (in 2013$). Schwartz (1994) and Salkever (1995) estimate that a one IQ point reduction results in 

0.131 and 0.101 fewer education years, respectively; this represents lifetime cost savings between $377 and 

$970, discounting the avoided costs over 18 years. EPA subtracted these education costs from the value of 

lifetime earnings per IQ point in Table 3-3. Subtracting education costs is done for accounting purposes only 

and does not suggest that this is a desirable, positive outcome.

The value of an IQ point reduction adjusted for the avoided cost of education ranges between $2,107 

(following Schwarz (1994) and discounting at 7 percent) to $14,883 (following Salkever (1995) and 

discounting at 3 percent). This effect represents only one component of society’s WTP to avoid IQ decreases, 

and thus underestimates the total value of benefits to children from reduced exposure to lead.

3.3.1.2 Reduced Expenditures on Compensatory Education

Children whose PbB exceeds 20 g/dL are more likely to have IQs less than 70, which means that they would 

require compensatory education tailored to their specific needs. EPA’s IEUBK model can generate 

probabilities that a child would have a PbB in excess of a specific threshold. 

EPA estimated the number of children that would have PbB above 20 g/dL for each CBG under the baseline 

and each analyzed regulatory option. EPA assumed that 20 percent of children with PbB above 20 ug/dL 

would have IQs less than 70 and require compensatory education.22 Equation 3-6 shows the calculation of the 

number of children requiring compensatory education for each cohort and CBG. Summing across all cohorts 

and CBGs provides the total number of children who would require special education.

 

21 EPA updated lifetime earnings to 2013 dollars using the Consumer Price Index (2013=236.384; 2009 = 

214.537).

22 This assumption follows the methodology used by EPA in Economic and Environmental Benefits Analysis 

Document for the Final Effluent Limitations Guidelines and Standards for the Metal Products and Machinery 

Point Source Category (U.S. EPA, 2003).
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Equation 3-6. CompEd(i)(c) = ExCh(i) ∗ Pr20(i)(c) ∗ S

Where:

CompEd(i) = the number of children with PbB over 20 g/dL and IQ less than 70 (who would need 

compensatory education) for cohort c in CBG i

ExCh(i) = the number of affected children for cohort c in CBG i

Pr20(i) = the probability that a child’s PbB is above 20 g/dL for cohort c in CBG i

S = Share of children with PbB over 20 g/dL that would have IQ scores less than 70 (20%)

The U.S. Department of Education (Chambers, et al., 2003) estimated that average annual expenditures for a 

student with mental retardation are approximately $8,484 higher than for an average student. Updating to 

2013 dollars23 yields annual costs of $16,075 per child. EPA assumed that children with IQ less than 70 

would incur these additional costs each year for 12 years. Discounting future costs using a 3 percent discount 

rate yields a total compensatory education cost of approximately $164,806 per child with an IQ score less 

than 70 ($136,613 per child if using a 7 percent discount rate).

3.3.2 Results

Table 3-4 shows the benefits associated with avoided IQ losses from lead exposure via fish consumption, and 

Table 3-5 shows the benefits associated with a reduced need for specialized education. The final BAT/PSES 

option will generate annualized benefits of $0.8 million to $1.1 million using a 3 percent discount rate, and 

$0.1 million to $0.2 million using a 7 percent discount rate.

Table 3-4: Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead

Regulatory

Option

Average 

Annual 

Number of 

Affected

Children 0 to 7

Total 

Avoided 

IQ Losses, 

2021 to 

2042

Annualized Value of Avoided IQ Point Lossesa(Millions 2013$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Option A 3,326,127 853 $0.34 $0.48 $0.05 $0.08 

Option B 3,326,127 853 $0.34 $0.48 $0.05 $0.08 

Option C 3,326,127 1,285 $0.51 $0.72 $0.08 $0.12 

Option D 3,326,127 1,985 $0.79 $1.11 $0.13 $0.19 

Option E 3,326,127 1,985 $0.79 $1.11 $0.13 $0.19 

Source: U.S. EPA Analysis, 2015

a. Low bound assumes that the loss of one IQ point results in the loss of 1.76% of lifetime earnings (following Schwartz, 1994); 

high bound assumes that the loss of one IQ point results in the loss of 2.38% of lifetime earnings (following Salkever, 1995).

 

23 Updated to 2013 dollars using the Consumer Price Index for Education (2013 = 224.521; 1999 = 107.0).
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Table 3-5: Estimated Avoided Cost of Compensatory Education for Children with Blood Lead 

Concentrations above 20 g/dL and IQ Less than 70

Regulatory

Option

Number of 

Affected

Children 0 to 7

Decrease in Number 

of Cases of IQ < 70, 

in 2021 to 2042

Avoided Annual Cost (Millions; 2013$)a

3% Discount Rate 7% Discount Rate

Option A 3,326,127 1 $0.00 $0.00 

Option B 3,326,127 1 $0.00 $0.00 

Option C 3,326,127 1 $0.01 $0.00

Option D 3,326,127 2 $0.01 $0.01 

Option E 3,326,127 2 $0.01 $0.01 

Source: U.S. EPA Analysis, 2015

a. “-“ indicates that a value was not estimated and “$0.00” indicates that avoided annual cost is less than $0.01 million.

3.4 Benefits to Adults from Reduced Lead Exposure

The public health literature suggests that a wide spectrum of adverse health outcomes can occur from lead 

exposure in people of all ages. For example, recent evidence has suggested that exposure to lead in adults can 

result in CVD impacts; specifically, increases in hypertension, coronary heart disease, CVD, and CVD-related 

mortality (National Toxicology Program, 2012; U.S. EPA, 2013b). EPA has developed and externally peerreviewed concentration response functions for CVD-related mortality resulting from adult exposure to lead 

(Abt Associates, 2015 and U.S. EPA, 2015f). EPA is using these concentration response functions to estimate 

human health benefits from reduced adult exposure to lead following the implementation of the final ELGs.

3.4.1 Methods

This section summarizes EPA’s approach for estimating the benefits to human health due to reductions in 

adult exposure to lead following implementation of the final ELGs. EPA relied on two models to estimate 

baseline and post-compliance exposure for each population cohort:

 The first model estimates PbB in adults as a function of air-based background lead exposure and 

consumption of contaminated fish tissues. 

 The second model is a population life table model that estimates the gains in life years due to 

decreased risk of CVD mortality from the PbB reductions. 

EPA then aggregated the resulting cohort-specific gains in life expectancy to represent the total magnitude of 

the expected human health benefits for each ELG option. EPA estimated monetized benefits by applying a 

constant value per statistical life (VSL) to the estimated number of premature deaths avoided in each analysis 

year.

3.4.1.1 Modeling Blood Lead Concentrations in Adults

The Leggett model (Leggett, 1993), a physiologically-based pharmacokinetic model, has been used to 

estimate bone and blood lead levels in adults by U.S. EPA and others (California Office of Environmental 

Health Hazard Assessment (Cal OEHHA), 2013a, 2013b).

24 The model predicts PbB by explicitly modeling 

lead absorption, distribution, metabolism, and excretion for 21 body tissue compartments on a daily time step. 

Therefore, the model is able to address time-dependent conditions (e.g., changing exposures through time). 

 

24 EPA Office of Pollution Prevention and Toxics (OPPT) is using the Leggett+ model for the upcoming 

renovation and repair rule.
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Since its original publication, the model has been updated and improved to better fit observed data. Most 

recently, the Cal OEHHA has published an updated version of the Leggett model, Leggett+, which they have 

used for occupational health standards in California. EPA implemented the updated model in the R 

programming language using publicly available documentation and MATLAB code (Cal OEHHA, 2013a, 

2013b). The Agency modified the model inputs to more easily alter lead exposure due to contaminated fish 

consumption, and to reduce the complexity (and therefore computational time) of background exposure from 

air.25 Internal model parameters with respect to the transit of lead through the 21 body tissues are unchanged. 

Table 3-6 presents and describes parameters used to apply the Leggett+ model. 

Table 3-6: Parameters Used to Apply the Leggett+ Model

Parameter Units Value Notes

Age at Start of 

Exposure

Years 18 The Leggett+ model is parameterized to model 

PbB only for adults

Age in 2014 Years 25 – 65 in 10-year increments Data used for all individuals within decadal 

cohort

Duration of 

Exposure to 

Contaminated 

Fish

Years Within age cohort, number of 

years between 18 and year of 

ELG implementation.

Pre-2014 exposures limited to 27 years (based 

upon examination of model results). Pre-2014 

exposure calculated as:

Duration = Age in 2014 – 18.

Initial Blood 

Lead 

Concentration

μg/dL 1.5 CDC 2009; Schober 2006

Background Lead 

Intake

μg/day 1.8 Calculated to maintain PbB at 1.5 μg/dL with 

no fish exposure for a body mass of 74 kg (Cal 

OEHHA 2013a)

Body Mass kg Age Men Women Values from Table 8-24 of EPA (2011b). 

Values are held constant for all ages within 

each decade. Affects quantity of contaminated 

fish tissue consumed (see below). 

20s 82.5 71.4

30s 85.7 76.3

40s 88.0 76.3

50s 88.5 77.8

60s 88.2 75.6

Lead Intake from 

Contaminated 

Fish Tissue

mg/kg body 

mass / day

Average daily doses calculated 

as described in Section 3.2.2

Baseline (i.e., no ELG) values used until 2021; 

thereafter, option-specific values.

Initial Tissue 

Compartment 

Parameters

Various Various Values used were obtained from CAL OEHHA 

(2013a).

EPA ran the models for several population cohorts defined by sex, age, exposure (recreational and subsistence 

fishers), and geography (215,460 CBGs) to characterize variability in the relevant population characteristics. 

The model was run on a daily time-step for each of the combinations of input parameters (6 scenarios 

[baseline + 5 ELG options)] x 2 sexes [male/female] x 215,460 CBGs x 2 exposure cohorts [recreational vs. 

subsistence fishing] x 5 age cohorts [20s – 60s, by decade]). Simulation duration included two separate 

 

25 The Leggett+ model was designed to model airborne workplace exposure to air; in adapting the model to this 

analysis, EPA excised all parameters directly related to workplace exposure (including workplace airborne lead 

concentrations, breathing rate, transfer fraction of inhaled lead to blood, etc.) to improve model runtime. 

Background airborne ingestion remained consistent with the baseline Leggett+ model inputs.
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components: exposures pre- and post-2015. Exposure occurring before 2015 (beginning at the age of 18 for 

each decadal age group) was run once for each combination of input parameters. These parameter values were 

then used as initial parameters for all model runs (baseline and the five ELG options) simulating exposure 

after 2015. Exposure prior to 2015 was limited to 27 years in duration, because inspection of model results 

suggested that additional lead accumulation in body tissues was minimal beyond 25 years. Consequently, 

simulations under no-ELG conditions ranged in duration from 34 years (for the 20s age cohort: 7 years of pre2015 exposure plus 2015 – 2042, inclusive) to 54 years (for 50s and 60s age cohorts, 27 years of pre-2015

exposure plus 2015- 2042, inclusive), while the duration for all ELG-option simulations was 27 years (2015-

2042, inclusive).

For each model run, EPA averaged forecasted PbB outputs within forecast year. Figure 3-1 illustrates changes 

in PbB over time for two selected male subsistence fishing cohorts within a single CBG under the baseline 

and two of the regulatory options. 

Figure 3-1: Example changes in PbB through time under baseline, option B, and option D for cohorts 

in the 20s and 60s (age as of 2014). Differences between cohorts are driven by differences in body 

mass. Example data represent modeled PbBs of male subsistence fishers residing in CBG

560419753004. The majority of cohorts nationwide experience a smaller reduction in PbB due to the 

rule.

Relative to baseline, PbB was reduced for approximately 1.1 percent of the simulated population under 

options A and B, 2.0 percent of simulations under option C, and 2.3 percent of simulated individuals under 

options D and E. The extent of the reductions followed the spatial distribution and magnitude of loading 

reductions across the options. When reductions did occur, they were typically small (e.g., < 0.03 μg/dL in

2042 for options A – C, and < 0.08 μg/dL in 2042 for options D - E).

Relative to baseline, PbB was reduced by more than 1 percent of baseline – a decrease EPA considers to be a 

meaningful reduction level based upon CVD mortality rates – for between 0.03 percent to 1.18 percent of the 

simulated population under options A to E, respectively, with the final BAT/PSES (Option D) showing 1.18

percent of simulated individuals with a reduction of greater than 1 percent of baseline PbB. 
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3.4.1.2 Estimating Hazard Reduction under the Final ELGs

For each sex, single-year age, and exposure cohort (hereafter cohort), the analysis characterizes two basic 

survival analyses under the baseline and option scenarios: a death hazard function and a survival function

(details of the analysis are provided in Appendix E).

26 To estimate changes in mortality following ELG 

implementation, EPA characterized:

1. the baseline CVD death hazard function; 

2. the baseline non-CVD death hazard function; and 

3. an option-specific CVD death hazard function.

The main source of data for hazard estimation in key simulation elements (1) and (2) above is a life table, a 

collection of statistics that shows age-specific probabilities of survival and fecundity.27 EPA obtained life 

table data from two sources: the CDC’s National Center for Health Statistics (Arias, 2014) and the CDC’s 

Underlying Cause of Death Database (CDC 2012).28 After obtaining all necessary data, EPA calculated age 

and sex- specific baseline hazards for CVD mortality; a sample of life table data used in calculations is 

presented in Table 3-7.

Table 3-7: Sample of Inputs for Age- and Sex-Specific Hazard Functions

Age Sex

Proportion of 

Population

Mean Body Mass 

(kg)

CVD Rate 

(per 1,000)

20s M 0.0875 82.5 0.072149

30s M 0.0906 85.7 0.242882

40s M 0.0977 88.0 0.833447

50s M 0.0925 88.5 2.209863

60s M 0.0630 88.2 4.633724

20s F 0.0852 71.4 0.037076

30s F 0.0909 76.3 0.123911

40s F 0.0995 76.3 0.392972

50s F 0.0973 77.8 0.940202

60s F 0.0693 75.6 2.303575

Using CVD rates collected, EPA then calculated option-specific CVD death hazard functions based on the 

relationship between PbB and the CVD hazard ratio. To do this, EPA used a concentration-response function 

from a peer reviewed study which found an adjusted relative hazards of CVD mortality of 1.53 (1.21-1.94)

per 3.4-fold increase in PbB (Menke et al. 2006).

3.4.1.3 Estimating Premature Deaths Avoided Over Multiple Years

The VSL is the marginal rate of substitution between wealth and mortality risk in a defined time period, 

usually taken to be one year. Therefore, the product of VSL and the estimated aggregate reduction in risk of 

 

26 Collett (2003), pp. 10-12. 

27 An extensive discussion of life tables can be found in Shryock et al. (1980) Chapter 15.

28 Database query parameters included: Dataset: Underlying Cause of Death, 1999-2010; Autopsy: All; Gender: 

Female, Male; ICD-10 Codes: I00-I99 (Diseases of the Circulatory System); Place of Death: All; Race: All; 

Single-year ages 20 – 100+, inclusive; Years: 2006-2010; States: All; Urbanization: All; Calculate Rates Per: 

100,000; Rate Options: Default intercensal populations for years 2001-2009.
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premature death represents the affected population’s aggregate WTP to reduce its probability of death in one 

year. EPA estimated the benefits of multi-year mortality risk as the product of:

1. The reduction in initial age-specific mortality rate (i.e., the proportion of people alive at exact age x, 

who will die before attaining exact age x + 1 in year t); and

2. The number of individuals surviving to the beginning of year t. This value is calculated as the initial 

cohort population size in 2015 multiplied by the probability that these individuals survive to age x, 

and are alive at the beginning of year t to enjoy the benefits of the year’s mortality risk reduction. 

It is important to note that WTP for a specific reduction in risk may depend on (i) the time of payment, (ii) the 

conditions under which the individual can save and borrow against future income, and (iii) whether the 

individual knows about the change in survival probability ahead of time (Hammitt 2007). The earlier an 

individual learns of the altered probability of survival, the earlier she “can adjust by reallocating her planned 

future consumption and risk-reducing expenditures.” (Hammitt 2007).

3.4.2 Results

Table 3-8 summarizes the magnitude and economic value of human health benefits from reduced incidence of 

cardiovascular disease connected to adult lead exposure due to ELG implementation during the period 2019-

2042. At a 3 percent discount rate, the final BAT/PSES (Option D) has annualized benefits of $12.8 million; 

the annualized benefits are $10.7 million at a 7 percent discount rate.

Table 3-8: Summary of Estimated Health Benefits due to Decreased Risk of CVD Mortality 

during 2019-2042 based on the Economic Value of Avoided Premature Mortality (VSL)

Regulatory Option

Aggregate Reduction 

in Risk of Premature 

Death

Annualized Benefits (millions 2013$)

3 Percent 7 Percent

Option A 10.7 $3.77 $3.14

Option B 10.7 $3.77 $3.14

Option C 23.6 $8.35 $6.97

Option D 36.2 $12.80 $10.68

Option E 36.2 $12.80 $10.68

Source: EPA Analysis, 2015

3.5 Benefits to Children from Reduced Mercury Exposure

Mercury can have a variety of adverse health effects on adults and children (see U.S. EPA, 2015a). The final 

ELGs are expected to reduce the discharge of mercury to surface waters by steam electric power plants and 

therefore provide a range of human health benefits. Due to data limitations, however, EPA estimated only the 

benefits from reduced IQ losses among children exposed to mercury in-utero as a result of maternal 

consumption of contaminated fish. 

EPA identified the population of children exposed in-utero starting from the CBG-specific affected 

population described in Section 3.1. Because this analysis focuses only on infants born after implementation 

of the ELGs, EPA further limited the affected population by estimating the number of women between the 

ages of 15 and 42 potentially exposed to contaminated fish caught in the affected waterbodies, and 
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multiplying the result by ethnicity-specific average fertility rates.29 This yields the cohort-specific annual 

number of births for each CBG. 

The U.S. Department of Health and Human Services provides fertility rates by race for 2012 in the National 

Vital Statistics Report (Martin, et al., 2013). The fertility rate measures the number of births occurring per 

1,000 women between the ages of 15-44 in a particular year. Fertility rates were highest for Hispanic women 

at 74.4, followed by African Americans at 65.0, Asian or Pacific Islanders at 62.2, Caucasians at 58.6, and 

Tribal/Other at 47.0. 

3.5.1 Methods

EPA used the same ethnicity- and mode-specific consumption rates shown in Table 3-2 and calculated the 

CBG- and cohort-specific mercury ADD based on Equation 3-3. In this analysis, EPA used a linear doseresponse relationship between maternal mercury hair content and subsequent childhood IQ loss from Axelrad 

et al. (2007). Axelrad et al. (2007) developed a dose-response function based on data from three 

epidemiological studies in the Faroe Islands, New Zealand, and Seychelle Islands. According to their results, 

there is a 0.18 point IQ loss for each 1 part-per-million (ppm) increase in maternal hair mercury.

To estimate maternal hair mercury concentrations based on the daily intake (see Section 3.2.2), EPA used the 

median conversion factor derived by Swartout and Rice (2000), who estimated that a 0.08 μg/kg body weight 

increase in daily mercury dose is associated with a 1 ppm increase in hair concentration. Equation 3-7 shows 

EPA’s calculation of the total annual IQ decrement for a given receiving reach.

Equation 3-7. IQL(i)(c) = InExPop(i) ∗ MADD(i)(c) ∗ (

1

Conv

) ∗ DRF

Where:

IQL(i) = IQ losses associated with in-utero exposure to mercury from maternal consumption of fish 

contaminated with mercury for cohort c in CBG i

InExPop(i) = affected population of infants in CBG i (the number of births)

MADD(i)(c) = maternal ADD for cohort c in CBG i (μg/kg BW/day)

Conv = conversion factor for hair mercury concentration based on maternal mercury exposure (0.08 

ug/kg BW/day per 1 ppm increase in hair mercury)

DRF = dose response function for IQ decrement based on marginal increase in maternal hair mercury 

(0.18 point IQ decrement per 1 ppm increase in hair mercury)

Summing estimated IQ losses across all analyzed CBGs yields the total number of IQ points lost due to inutero mercury exposure from maternal fish consumption under each analyzed regulatory option. The benefits 

of the ELGs are calculated as the reduction in IQ points lost between the baseline and modeled postcompliance conditions.

The available economic literature provides little empirical data on society’s overall WTP to avoid a decrease 

in children’s IQ. To determine the value of avoided IQ losses, EPA used estimates of the changes in a child’s 

future expected lifetime earnings per one IQ point reduction and the cost of additional education. EPA used 

the same values of an IQ point presented in Section 3.3.1, which range from $2,107 to $14,883.

 

29 EPA acknowledges that fertility rates vary by age. However, the use of a single average fertility rate for all ages 

is not expected to bias results because the average fertility rate reflects the underlying distribution of fertility 

rates by age.
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3.5.2 Results

Table 3-9 shows the estimated benefits of avoided IQ point losses for infants exposed to mercury in-utero.

The final BAT/PSES option will generate annualized benefits of $2.9 million to $4.0 million at a 3 percent 

discount rate, and $0.5 million to $0.7 million at a 7 percent discount rate. 

Table 3-9: Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure

Regulatory

Option

Number of 

Affected 

Infants per 

Year

Total Avoided 

IQ Losses, 

2021 to 2042

Annualized Value of Avoided IQ Point Lossesa(Millions 2013$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Option A 418,953 3,239 $1.29 $1.81 $0.21 $0.31 

Option B 418,953 3,311 $1.32 $1.85 $0.21 $0.32 

Option C 418,953 6,001 $2.38 $3.35 $0.39 $0.58 

Option D 418,953 7,219 $2.87 $4.03 $0.47 $0.69 

Option E 418,953 7,898 $3.14 $4.41 $0.51 $0.76 

Source: U.S. EPA Analysis, 2015

a. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 

1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 

1995).

3.6 Reduced Cancer Cases from Arsenic Exposure

Among steam electric pollutants analyzed in the EA, arsenic is the only confirmed carcinogen with a 

published dose response function (see U.S. EPA, 2010b).30 EPA estimated the number of annual cancer cases 

associated with consumption of fish contaminated with arsenic from steam electric discharges under the 

baseline and each regulatory option. The reduction in the number of cancer cases from the baseline to postcompliance represents human health benefits attributable to the final ELGs. 

3.6.1 Methods

EPA used the cohort-specific arsenic LADD (see Section 3.2.2) to calculate the total number of cancer cases 

for each cohort for each CBG under the baseline and each of the regulatory options, based on Equation 3-8.

Equation 3-8. CC(i)(c) = ExPop(i)(c) ∗ CSF ∗ LADD(i)(c)

Where:

CC(i)(c) = the number of cancer cases for cohort c in CBG i

ExPop(i)(c) = the number of people affected for cohort c in CBG i

CSF = Cancer Slope Factor for skin cancer from arsenic [1.5 (mg/kg BW/day)-1].

LADD(i)(c) = Lifetime Average Daily Dose of arsenic for cohort c in CBG i (mg/kg BW/day).

For this analysis, EPA used the current Integrated Risk Information System (IRIS) CSF, which is based on 

incidences of skin cancer. EPA is currently revising its cancer assessment of arsenic to reflect new data on 

 

30 Although other pollutants, such as cadmium, are also likely to be carcinogenic (see U.S. Department of Health 

and Human Services (U.S. DHHS), 2008), EPA did not identify dose-response functions to quantify the effects 

of changes in these other pollutants.
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internal cancers. It is possible that the revised combined (lung and bladder cancer) CSF would be higher (e.g., 

the draft value is 25.7 per mg/kg BW/day), suggesting that the use of the current IRIS value may bias benefits 

downward.

Summing the number of cancer cases across all cohorts and all CBGs yields the total number of annual cancer 

cases under the baseline and each of the regulatory options. To estimate the number of avoided cancer cases, 

EPA subtracted the estimated number of cancer cases for each analyzed regulatory option from the estimated 

number of cancer cases under the baseline.

In the analysis of the proposed Steam Electric ELGs, EPA had used VSL to place a monetary value on 

avoiding a cancer case. This monetization approach inherently assumes that all cases would be fatal. Given 

that the majority of skin cancer cases (which are the basis for the health benefits arising from reduced arsenic 

exposure) are not fatal, this approach is likely to bias benefit estimates upward. For the analysis of the final 

rule, therefore, EPA revised the monetization approach to value skin cancer cases based on a COI approach.

Based on a literature review, EPA developed COI estimates for the skin cancer health endpoint associated 

with oral arsenic exposure (Abt Associates, 2014). The Agency found the following distribution of nonmelanoma skin cancers associated with arsenic:

 Basal cell carcinoma: 15%;

 Invasive squamous cell carcinoma: 19%;

 Non-invasive squamous cell carcinoma: 58%; and

 Combined: 8%.

These types of skin cancers have very low fatality rates. Diagnosis involves medical histories, physical 

exams, and skin biopsies, while treatments consist of minor surgeries and periodic follow-up visits. 

The COI estimates for skin cancers associated with arsenic include both direct medical expenditures and 

indirect opportunity costs. The direct medical costs were based on Medical Expenditure Panel Survey 

(MEPS) data on office-based provider visits and outpatient visits between 1996 and 2010, and represent the 

mean expenditures per patient.31 Diagnosis and surgery are one-time costs, while follow-up visits are periodic 

for all years after the surgery, with frequency depending on the type of cancer. The other component of the 

cost of an illness is the opportunity cost – i.e., the value of time lost during the illness. For non-melanoma 

skin cancer, EPA assumes that all patients in this analysis incur opportunity costs for diagnosis, surgery, and 

follow-up doctor visits. Table 3-10 shows the total costs for different types of skin cancer.

 

31 Expenditures are for entire procedures; for example, expenditures for a surgery include the surgery itself as well 

as the associated hospital stay.
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Table 3-10: Total Costs of Illness for Skin Cancera

Type of Cost Cost in First Year Cost in Subsequent Years

Basal Cell Skin Cancer

Medical care $1,112 $307 

Opportunity cost $282 $39 

Total cost for BCC $1,394 $347 

Non-invasive Squamous Cell Skin Cancer

Medical care $1,419 $266 

Opportunity cost $322 $34 

Total cost for non-invasive SCC $1,741 $300 

Invasive Squamous Cell Skin Cancer

Medical care $3,020 $388 

Opportunity cost $460 $50 

Total cost for invasive SCC $3,480 $439 

a. Updated to 2013$ using the consumer price index for medical care

Table 3-11 shows the weighted average skin cancer cost of illness estimate, based on the proportion of cases 

for each type of skin cancer. Overall, a skin cancer case from arsenic exposure results in costs of 

approximately $2,056 in the first year and $338 in the subsequent 14 years. Using a 3 percent discount rate, 

this equates to a $5,877 value of a skin cancer case, and using a 7 percent discount rate, the value is $5,015. 

Table 3-11: Weighted Average Skin Cancer Cost of Illnessa

Type Proportion of Cases COI (first year) COI (subsequent years)b

Non-invasive squamous cell 58% $1,741 $300

Invasive squamous cell 19% $3,480 $439

Basal cell 15% $1,394 $347

Combinationc8% $2,205 $362

Total/Weighted Average 100% $2,056 $338

a. updated to 2013$ using the consumer price index for medical care.

b. Assumes 14 subsequent years.

c. For “combination,” EPA calculated COI based on the average of basal cell, invasive squamous cell, and non-invasive squamous 

cell cases.

A reduction in pollutant loadings does not immediately result in cessation of adverse health effects. There is a 

lag between the time when exposures are reduced and the time when a reduction in risk occurs. Additionally, 

there may be a latency period between the initial exposure and the onset of the illness. The latency period 

between low-dose arsenic exposure and skin cancer is unknown (Karagas et al., 2001; Shannon and Strayer, 

1989), though some researchers postulate it could range from several years to decades (Karagas et al., 1998).

U.S. EPA (2010b) notes that the cessation lag for skin cancer from arsenic is unknown, but that the cessation 

lag for internal cancers from arsenic may be longer than for skin cancer, ranging from 15 to 50 years. For this 

analysis, EPA assumed that cancer cases resulting from arsenic would not occur for ten years after exposure 

and discounted the value of avoided cancer cases by an additional ten years.
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September 29, 2015 3-19

3.6.2 Results

Table 3-12 shows the estimated changes in incidence of cancer cases from exposure to arsenic in fish tissue 

under the ELGs and the annualized benefits calculated using 3 percent and 7 percent discount rates. Under 

both discount rates, annualized benefits are under $0.01 million.

Table 3-12: Annual Benefits from Reduced Cancer Cases due to Arsenic Exposure

Regulatory

Option

Annual 

Affected

Population

Reduced Cancer Cases, 

2019 to 2042 

Benefits (Millions 2013$)a

3% Discount 7% Discount

Option A 35,972,005 0.03 $0.00 $0.00 

Option B 35,972,005 0.03 $0.00 $0.00 

Option C 35,972,005 0.06 $0.00 $0.00 

Option D 35,972,005 0.09 $0.00 $0.00 

Option E 35,972,005 0.1 $0.00 $0.00 

Source: U.S. EPA Analysis, 2015

a. “-“ indicates that a value was not estimated and “$0.00” indicates that annual benefits are less than $0.01 million.

3.7 Total Monetized Human Health Benefits

Table 3-13 presents total monetized human health benefits for the final BAT/PSES (Option D) and alternate 

regulatory options. Using a 3 percent discount rate, benefits under Option D range from $16.5 million to 

$18.0 million ($11.3 million to $11.6 million using a 7 percent discount rate). Reduced lead exposure for 

adults and reduced mercury exposure for children represent the majority of total monetized human health 

benefits. 

Table 3-13: Total Monetized Human Health Benefits for ELG Options (millions of 2013$)

Discount 

Rate Option

Reduced Lead 

Exposure for 

Childrena

Reduced 

Lead 

Exposure 

for Adults

Reduced Mercury 

Exposure for 

Childrena

Reduced 

Cancer 

Cases from 

Arsenicb

Totala

Low High Low High Low High

3%

A $0.34 $0.48 $3.77 $1.29 $1.81 $0.00 $5.40 $6.06 

B $0.34 $0.48 $3.77 $1.32 $1.85 $0.00 $5.43 $6.10 

C $0.52 $0.73 $8.35 $2.38 $3.35 $0.00 $11.25 $12.43 

D $0.80 $1.12 $12.80 $2.87 $4.03 $0.00 $16.47 $17.95 

E $0.80 $1.12 $12.80 $3.14 $4.41 $0.00 $16.74 $18.33 

7%

A $0.05 $0.08 $3.14 $0.21 $0.31 $0.00 $3.40 $3.53 

B $0.05 $0.08 $3.14 $0.21 $0.32 $0.00 $3.40 $3.54 

C $0.08 $0.12 $6.97 $0.39 $0.58 $0.00 $7.44 $7.67 

D $0.14 $0.20 $10.68 $0.47 $0.69 $0.00 $11.29 $11.57 

E $0.14 $0.20 $10.68 $0.51 $0.76 $0.00 $11.33 $11.64 

Source: U.S. EPA Analysis, 2015

a. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 

1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 

1995).

b. “$0.00” indicates that annual benefits are less than $0.01 million.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3-20

3.8 Additional Measures of Human Health Benefits 

The benefits described above are only some of the human health effects expected to improve as a result of the 

final ELGs for which EPA was able to identify dose-response relationships. As noted in the introduction to 

this Chapter, pollutants in steam electric power plant discharges have been linked to additional adverse human 

health effects. To provide an additional measure of the potential health benefits of the final ELGs, EPA also 

estimated the expected reduction in the number of receiving reaches with pollutant concentrations in excess of 

human health-based AWQC. This analysis and its findings are not additive to the preceding analyses in this 

chapter, but represent another way of characterizing potential health benefits resulting from reduced exposure 

to steam electric pollutants. This analysis compares in-stream pollutant concentrations estimated for the 

baseline and each analyzed ELG option in receiving reaches and downstream reaches (see the EA; U.S. EPA,

2015a) to criteria established by EPA for protection of human health. EPA compared in-water concentrations 

of arsenic, copper, nickel, selenium, thallium, and zinc to EPA’s national recommended water quality criteria 

protective of human health used by states and tribes (U.S. EPA, 2012b). Pollutant concentrations in excess of 

these values indicate potential risks to human health. For another four steam electric pollutants (cadmium, 

chromium, lead, and mercury) for which there are no recommended criteria, EPA instead compared 

concentrations to MCLs (U.S. EPA, 2012a).

Table 3-14 shows the results of this analysis. EPA estimates that in-stream concentrations of steam electric 

pollutants exceed human health criteria or MCLs for at least one pollutant in 3,673 reaches nationwide as a

result of baseline steam electric pollutant discharges. EPA expects that the final BAT/PSES (Option D) will 

reduce the occurrence of concentrations in excess of human health-based criteria for 1,973 of the reaches, and 

eliminate all exceedances for 1,870 of those reaches. While Option D reduces concentrations in the remaining 

103 reaches relative to the baseline levels, and thereby improves water quality in these reaches, the reductions 

are not sufficient to bring concentrations below the human health criteria or MCLs. 

Table 3-14: Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric 

Pollutants

Regulatory

Option

Number of Reaches with 

Steam Electric Pollutanta

Concentrations Exceeding 

Human Health Criteria for at 

Least One Pollutant

Number of Reaches with Improved Water Quality, 

Relative to Baseline

Number of Reaches with 

Reduced Number of 

Exceedancesb

Number of Reaches with 

All Exceedances 

Eliminated 

Baseline 3,673 -- --

Option A 3,128 631 545

Option B 3,128 633 545

Option C 2,270 1,540 1,403

Option D 1,803 1,973 1,870

Option E 1,625 2,088 2,048

Source: U.S. EPA Analysis, 2015

a. Pollutants include arsenic, copper, nickel, selenium, thallium, zinc, cadmium, chromium, lead, and mercury.

b. The number of reaches with exceedances reduced includes those reaches where all exceedances are eliminated.

3.9 Implications of Revised Steam Electric Plant Loading Estimates

As described in Section 1.4.3, EPA revised its estimates of pollutant loadings in steam electric power plant 

discharges after completing the benefit analyses. The revisions affect baseline discharges of the three 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3-21

pollutants explicitly modeled to quantify and monetize human health benefits in this Chapter: lead, mercury, 

and arsenic. Table 3-15 summarizes changes in loading estimates between the original loads used in 

estimating benefits in this Chapter and the revised loads. The table shows total industry loads of lead, mercury 

and arsenic in the baseline and under the final ELGs (Option D), as well as pollutant removals achieved by 

the final ELGs. 

Table 3-15: Estimated Aggregate Changes in Pollutant Loadings for Lead, Mercury and Arsenic

(Pounds per Year).

Pollutant

Baseline Option D Removal under Option D

Original 

Values

Revised 

Values

% 

Change

Original 

Values

Revised 

Values

% 

Change

Original 

Values

Revised 

Values

% 

Change

Lead 14,588 7,674 -47% 350 331 -5% 14,238 7,343 -48%

Mercury 1,176 992 -16% 31 31 -1% 1,145 961 -16%

Arsenic 22,219 20,138 -9% 1,483 1,480 0% 20,737 18,658 -10%

Source: U.S. EPA Analysis, 2015

As shown in Table 3-15, the revisions mainly reduced the baseline loadings of lead, mercury and arsenic, and 

had a smaller relative impact on loadings post-compliance. Because baseline pollutant loads are lower than 

previously estimated, EPA expects the adverse health effects estimated in the baseline to also be lower than 

previously calculated, which correspondingly reduces the incidence of adverse health effects avoided by 

implementing the final ELGs and therefore expected improvements from the final ELGs.

For several reasons — notably the fact that revisions do not affect all plants equally, exposure also depends 

on other point sources, and many model functions are not linear —it would be inappropriate to simply scale

the monetized benefits based on the aggregate changes in loadings. While EPA cannot readily recalculate the 

benefits, the direction and magnitude of the change in pollutant removals in Table 3-15 indicate that it is 

likely that revisions to the loadings affect benefit estimates and that new benefit estimates would be lower 

than presented in this Chapter. The magnitude of this reduction is, however, uncertain.

3.10 Limitations and Uncertainties

The analysis presented in this chapter does not include all possible human health benefits associated with

post-compliance reductions in pollutant discharges due to lack of data on a dose-response relationship 

between ingestion rates and potential adverse health effects. Therefore, the total quantified human health 

benefits included in this analysis represent only a subset of the potential health benefits expected to result 

from the final rule. 

Additionally, the methodologies and data used in the analysis of health benefits associated with reduced 

incidences of adverse health outcomes due to consumption of fish contaminated with steam electric pollutants 

involve limitations and uncertainties, that add to the limitations and uncertainties inherited from the EA 

analysis and data (see U.S. EPA, 2015a). Table 3-16 summarizes the limitations and uncertainties and 

indicates the direction of the potential bias.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3-22

Table 3-16: Limitations and Uncertainties in the Analysis of Human Health Benefits

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

The analysis is based loadings that 

were subsequently revised by EPA 

Overestimate Revised loadings of lead, mercury, and arsenic are 

lower than the loadings used to estimate benefits. The 

changes indicate that the incidence of adverse health 

effects are likely lower in the baseline, and therefore 

that improvements due to the ELGs may be 

correspondingly lower.

The analysis does not consider the 

suitability of alternate fishing 

sites.

Uncertain Estimating the number of anglers fishing on receiving 

and downstream reaches based on the ratio of reach 

length to the total number of reach miles within the 

same 50-mile buffer area recognizes the effects of the 

quantity of competing fishing opportunities on the 

likelihood of fishing a given reach, but does not 

account for the differential quality of fishing sites. If 

the quality of substitute sites is distinctly worse or 

better (e.g., some sites have better access or designated 

fishing areas), the estimated benefits may be overstated 

or understated. 

Anglers are assumed to be 

distributed evenly (over the reach 

miles) over all available fishing 

sites within the 50-mile travel 

distance.

Uncertain EPA assumed that all anglers travel up to 50 miles and 

distribute their visits over all fishable sites within the 

area. In fact, recreational anglers may have preferred 

sites (e.g., a site located closer to their home) that they 

visit more frequently. The characteristics of these sites, 

notably ambient water concentrations and fishing 

advisories, affects exposure to pollutants, but EPA 

does not have data to support a more detailed analysis 

of fishing visits. The impact of the assumption on 

benefit estimates is uncertain since fewer/more anglers 

may be exposed to higher/lower fish tissue 

concentrations than assumed by EPA in the analysis.

The number of subsistence fishers 

was assumed to equal 5 percent of 

the total number of anglers fishing 

the affected reaches.

Uncertain The magnitude of subsistence fishing in the United 

States or individual states is not known. Assuming 

5 percent may understate or overstate the number of 

potentially affected subsistence fishers (and their 

households) overall, and ignores potential variability in 

subsistence rates across racial/ethnic groups.

EPA used a CSF for arsenic of 1.5 

cases per mg/kg BW/day based on 

skin cancer only.

Uncertain This is the current IRIS value and was based on 

incidences of skin cancer. EPA is currently revising its 

cancer assessment of arsenic to reflect new data on 

internal cancers. 

There is a linear 0.18 point IQ loss 

for each 1 ppm increase in 

maternal hair mercury.

Uncertain This dose-response function may over- or 

underestimate IQ impacts arising from mercury 

exposure if a linear function is not the best 

representation of the relationship between maternal 

body burden and IQ losses.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 3: Human Health Benefits

September 29, 2015 3-23

Table 3-16: Limitations and Uncertainties in the Analysis of Human Health Benefits

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

For the mercury- and lead-related 

benefits analyses, EPA assumed 

that IQ losses are an appropriate 

endpoint for quantifying adverse 

cognitive and neurological effects 

resulting from childhood or inutero exposures to lead and 

mercury (respectively).

Underestimate IQ may not be the most sensitive endpoint. 

Additionally, there are deficits in cognitive abilities 

that are not reflected in IQ scores, including acquisition 

and retention of information presented verbally and 

many motor skills (U.S. EPA, 2005). To the extent that 

these impacts create disadvantages for children 

exposed to mercury at current exposure levels or result 

in the absence of (or independent from) measurable IQ 

losses, this analysis may underestimate the benefits of 

the ELGs of reduced lead and mercury exposure.

The IEUBK model processes daily 

intake from “alternative sources” 

to 2 decimal places (ug/day). 

Underestimate Since the intakes are very small, some variation is 

missed by using the model (i.e., it does not capture 

very small changes).

EPA did not quantify the benefits 

associated with reduced adult 

exposure to mercury.

Underestimate The scientific literature suggests that exposure to 

mercury may have significant adverse health effects for

adults; if measurable effects are occurring at current 

exposure levels, excluding the benefits of reduced adult 

exposure results in an underestimate of benefits.

EPA assumed constant body mass 

for all males and females in the 

adult Pb and As analyses

Uncertain Male and female body mass estimates used in analyses 

are national estimates obtained from the CDC. The 

extent to which CBG-specific body masses varies will 

have an unknown effect on benefits

Uniform application of data from 

national life tables in adult Pb and 

As analyses

Uncertain By applying national averages, EPA assumes that 

mortality rates of all modeled cohorts (specific to 

location, fishing cohort, etc.) do not differ from the 

national mortality experience. EPA also assumes that 

age-specific mortality rates are constant through time.

CVD-related benefits of reduced 

Pb exposure are not tracked for 

individuals younger than age 20 in 

2014

Underestimate Benefits accruing to younger individuals are not 

tracked because the Leggett+ model is unable to model 

PbBs in youth and adolescents. 

EPA assumed no cessation lag for 

PbB effects 

Uncertain EPA assumed no time lag between changes in PbB and 

risk reductions. The accuracy of this assumption is 

unknown.

Concentration-response functions 

used in adult Pb and As analyses

Uncertain The overall applicability of the concentration-response 

functions are subject to uncertainty. Generalized 

concentration-response functions are used to model 

age-specific hazard ratios.
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4 Nonmarket Benefits from Water Quality Improvements

As discussed in the EA document (U.S. EPA, 2015a), heavy metals, nutrients, and other pollutants discharged 

by steam electric power plants have a wide range of effects on water resources located in the vicinity and 

downstream from the plants. These environmental changes affect environmental goods and services valued by 

humans, including recreation; commercial fishing; public and private property ownership; navigation; water 

supply and use; and existence services such as aquatic life, wildlife, and habitat designated uses. Some 

environmental goods and services (e.g., commercially caught fish) are traded in markets, and thus their value 

can be directly observed. Other environmental goods and services (e.g., recreation and support of aquatic life) 

cannot be bought or sold directly and thus do not have observable market values. These second types of 

environmental goods and services are classified as “nonmarket”. The expected changes in the nonmarket 

values of the water resources affected by the final ELGs (hereafter nonmarket benefits) are additive to the 

market benefits (e.g., avoided costs of producing various market goods and services) and benefits from 

improved groundwater quality estimated in other chapters (Freeman, 2003).

EPA’s approach to estimating the nonmarket benefits from water quality improvements resulting from the 

final ELGs involves 1) characterizing baseline and post-compliance water quality using a water quality index 

and 2) monetizing changes in the nonmarket value of affected water resources attributable to the final ELGs

using a meta-analysis of surface water valuation studies that provide data on the public’s WTP for water 

quality improvements. The analysis accounts for improvements in water quality resulting from changes in 

nutrient, sediment, and metals concentrations in reaches affected by discharges from steam electric power 

plants. 

4.1 Water Quality

To link water quality changes from reduced metal, nutrient and sediment discharges to effects on human uses 

and support for aquatic and terrestrial species habitat, EPA used a water quality index (WQI) which translates 

water quality measurements, gathered for multiple parameters that are indicative of various aspects of water 

quality, into a single numerical indicator. 

The WQI provides the link between specific pollutant levels, as reflected in individual index parameters (e.g., 

dissolved oxygen (DO) concentrations), and the presence of aquatic species and suitability for particular uses.

The WQI value, which is measured on a scale from 0 to 100, reflects varying water quality, with 0 for poor 

quality and 100 for excellent. 

The WQI used in this analysis is the same as was used for the proposed rule analysis.32 TheWQI includes the 

six parameters of the WQI previously used for the Final Construction and Development Rule – DO, 

biochemical oxygen demand (BOD), fecal coliform (FC), total nitrogen (TN), total phosphorus (TP), and total 

suspended solids (TSS) – and one additional subindex for metals, for a total of seven parameters.

33,34 As 

 

32 The WQI modifies the WQI used by EPA in the Environmental Impact and Benefits Assessment for Final 

Effluent Guidelines and Standards for the Construction and Development Category (also referred to as the C&D 

rule; U.S. EPA, 2009a), which builds on McClelland (1974) and on the methodology developed by Dunnette 

(1979) and subsequently updated by Cude (2001) to better account for spatial and morphologic variability in the 

natural characteristics of streams. A more detailed discussion of the history of the WQI framework is found in 

Chapter 10 of the C&D report (U.S. EPA, 2009b). 

33 EPA modified the WQI for freshwater waterbodies from the C&D analysis to include metals. This was done by 

incorporating elements of the WQI developed by the Canadian Council of Ministers of the Environment 
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September 29, 2015 4-2

discussed in Chapter 4 of the EA document (U.S. EPA, 2015a), lotic systems such as rivers and streams, 

account for the vast majority (82 percent) of water bodies receiving direct discharges from steam electric 

power plants, with 183 of the 222 immediately receiving waters. Lentic freshwater systems such as lakes

(with the exception of the Great Lakes), ponds, and reservoirs, represent 12 percent, the Great Lakes another 

5 percent, and estuaries the remaining 1 percent. EPA focused the national level model on rivers/streams and 

lakes/ponds/reservoirs as the most common affected waterbodies. Because of the specific hydrodynamics and 

scale of the analysis required to appropriately model and quantify receiving water concentrations in the Great 

Lakes and estuary systems, EPA looked at the changes in pollutant loadings and impacts to these systems in 

selected case studies (see EA document for details; U.S. EPA, 2015a). EPA did not quantify the benefits to 

these systems, leading to an underestimate of the benefits discussed in this chapter.

4.1.1 WQI Calculation

Implementing the WQI methodology involves three key steps: 1) obtaining water quality levels for each of 

seven parameters included in the WQI; 2) transforming parameter levels to subindex values expressed on a 

common scale; and 3) aggregating the individual parameter subindices to obtain an overall WQI value that 

reflects waterbody conditions across the seven parameters. These steps are repeated to calculate the WQI 

value for the baseline (i.e., in the absence of the final ELGs), and for each analyzed regulatory option.

The first step in the implementation of the WQI involves obtaining water quality levels for each parameter, 

and for each waterbody, under both baseline conditions and post-compliance conditions (see Section 0). Some 

parameter levels are field measurements while others are modeled values.

The second step involves transforming the parameter measurements into subindex values that express water 

quality conditions on a common scale of 0 to 100. EPA used the subindex transformation curves developed by 

Dunnette (1979) and Cude (2001) for the Oregon WQI for BOD, DO, and FC. For TSS, TN, and TP 

concentrations, EPA adapted the approach developed by Cude (2001) to account for the wide range of natural 

or background nutrient and sediment concentrations that result from the variability in geologic and other 

region-specific conditions, and to reflect the national context of the analysis. TSS, TN, and TP subindex 

curves were developed for each Level III ecoregion (U.S. EPA, 2009a) using baseline TSS, TN, and TP 

concentrations calculated in SPARROW at the E2RF1 reach level.35,36,37 For each of the 85 Level III 

ecoregions intersected by the E2RF1 reach network, EPA derived the transformation curves by assigning a 

 

(CCME) wherein the index values are calculated based on the scope, frequency, and amplitude of exceedances 

of specified numeric thresholds (CCME, 2001).

34 EPA analyzed changes to water quality resulting from the implementation of the final ELGs on receiving 

freshwater reaches. While steam electric plants also discharge to estuarine and coastal reaches, EPA did not 

estimate benefits from reducing pollutant loadings to these waterbodies due to the relatively small changes in 

concentrations expected.

35 The SPARROW (SPAtially Referenced Regressions On Watershed attributes) model was developed by the 

United States Geological Survey (USGS) for the regional interpretation of water-quality monitoring data. The 

model relates in-stream water-quality measurements to spatially referenced characteristics of watersheds, 

including contaminant sources and factors influencing terrestrial and aquatic transport. SPARROW empirically 

estimates the origin and fate of contaminants in river networks and quantifies uncertainties in model predictions. 

More information on SPARROW can be found at http://water.usgs.gov/nawqa/sparrow/FAQs/faq.html#1

36 EPA’s E2RF1 (Enhanced River File Version 2.0) is a digital stream networks used in SPARROW models. This 

dataset extends over the continental United States and includes approximately 62,000 stream reaches.

37 Following the approach EPA used for the C&D analysis, the selected data exclude outlier TSS concentrations, 

defined as values that exceed the 95th percentile based on the universe of all E2RF1 reaches modeled in 

SPARROW (U.S. EPA, 2009a). In the C&D analysis, the USGS and EPA had determined that these outlier 

values corresponded to headwater reaches and were an artifact of the model rather than expected concentrations. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-3

score of 100 to the 25th percentile of the reach-level TSS concentrations in the ecoregion (i.e., using the 25th 

percentile as a proxy for “reference” concentrations), and a score of 70 to the median concentration. An 

exponential equation was then fitted to the two concentration points following the approach used in Cude 

(2001). 

For this analysis, EPA also used a metals-specific subindex curve based on the number of Ambient Water 

Quality Criteria (AWQC) exceedances for metals in each waterbody. National freshwater chronic AWQC 

values are available for arsenic, cadmium, chromium, lead, mercury, nickel, selenium, and zinc. To develop 

this subindex curve, EPA used an approach developed by the Canadian Council of Ministers of the 

Environment (CCME 2001). The CCME water quality index is based on three attributes of water quality that 

relate to water quality objectives: scope (number of monitored parameters that exceed water quality standard 

or toxicological benchmark); frequency (number of individual measurements that do not meet objectives, 

relative to the total number of measurements for the time period of interest) and amplitude (i.e., amount by 

which measured values exceed the standards or benchmarks). Following the CCME approach, EPA’s metal 

subindex considers the number of parameters with exceedances of the relevant water quality criterion. With 

regards to frequency, EPA modeled long-term annual average concentrations in ambient water (see Section 0

and Appendix D for details), and therefore any exceedance of an AWQC may indicate that ambient 

concentrations exceed AWQCs most of the time (assumed to be 100 percent of the time). EPA did not 

consider amplitude, because if the annual average concentration exceeds the chronic AWQC then the water is 

impaired for that constituent and the level of exceedance is of secondary concern. Using this approach, the 

subindex curve for metals assigns the lowest subindex score of 0 to waters where exceedances are observed

for all eight metals analyzed, and a maximum score of 100 to waters where there are no exceedances. 

Intermediate values are distributed evenly between 0 and 100.

Table 4-1 presents parameter-specific functions used for transforming water quality data into water quality 

subindices for freshwater waterbodies for the six traditional pollutants. Table 4-2 presents the subindex values 

for metals. The equation parameters for each of the 85 ecoregion-specific TSS, TN, and TP subindex curves 

are provided in Appendix G.

Table 4-1: Freshwater Water Quality Subindices

Parameter Concentrations Concentration 

Unit

Subindex

Dissolved Oxygen (DO)

DO saturation ≤100%

DO DO ≤ 3.3 mg/L 10

DO 3.3 < DO < 10.5 mg/L -80.29+31.88×DO-1.401×DO2

DO DO ≥ 10.5 mg/L 100

100% < DO saturation ≤ 275%

DO N/A mg/L 100 × exp((DOsat - 100) × -1.197×10-2)

275% < DO saturation

DO N/A mg/L 10

Fecal Coliform (FC)

FC FC > 1,600 Lbs/100 mL 10

FC 50 < FC ≤ 1,600 Lbs/100 mL 98 × exp((FC - 50) × -9.9178×10-4)

FC FC ≤ 50 Lbs/100 mL 98
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Table 4-1: Freshwater Water Quality Subindices

Parameter Concentrations Concentration 

Unit

Subindex

Total Nitrogen (TN)a

TN TN > TN10 mg/L 10

TN TN100 < TN ≤ TN10 mg/L a × exp(TN×b); where a and b are ecoregionspecific values in Appendix G

TN TN ≤ TN100 mg/L 100

Total Phosphorus (TP)b

TP TP > TP10 mg/L 10

TP TP100 < TP ≤ TP10 mg/L a × exp(TP×b); where a and b are ecoregionspecific values in Appendix G

TP TP ≤ TP100 mg/L 100

Total Suspended Solids (TSS)c

TSS TSS > TSS10 mg/L 10

TSS TSS100 < TSS ≤ TSS10 mg/L a × exp(TSS×b); where a and b are ecoregionspecific values in Appendix G

TSS ≤ TSS100 mg/L 100

Biochemical Oxygen Demand, 5-day (BOD)

BOD BOD > 8 mg/L 10

BOD BOD ≤ 8 mg/L 100 × exp(BOD × -0.1993)

a. TN10 and TN100 are ecoregion-specific TN concentration values that correspond to subindex scores of 10 and 100, respectively.

Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001) 

b. TP10 and TP100 are ecoregion-specific TP concentration values that correspond to subindex scores of 10 and 100, respectively.

Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)

c. TSS10 and TSS100 are ecoregion-specific TSS concentration values that correspond to subindex scores of 10 and 100, 

respectively. Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)

Source: EPA analysis using methodology in Cude (2001).

Table 4-2: Freshwater Water Quality Subindex for Heavy Metals

Number of Metals with AWQC 

Exceedances

Subindex

0 100.0

1 87.5

2 75.0

3 62.5

4 50.0

5 37.5

6 25.0

7 12.5

8 0.0

The final step in implementing the WQI involves combining the individual parameter subindices into a single 

WQI value that reflects the overall water quality across the parameters. EPA calculated the overall WQI for a 

given reach using a geometric mean function and assigned all WQ parameters an equal weight of 0.143 (1/7th
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of the overall score). Unweighted scores for individual metrics of a WQI have previously been used in Cude 

(2001), CCME (2001), and Carruthers and Wazniak (2003). 

Equation 4-1 presents EPA’s calculation of the overall WQI score.

Equation 4-1.





n

i

Wi WQIr Qi

1

WQIr = the multiplicative water quality index (from 0 to 100) for reach r

Qi = the water quality subindex measure for parameter i

Wi = the weight of the i-th parameter (0.143)

n = the number of parameters (i.e., seven)

Once an overall WQI value is calculated, it can be related to suitability for potential uses. Vaughan (1986) 

developed a water quality ladder (WQL) that can be used to indicate whether water quality is suitable for 

various human uses (i.e., boating, rough fishing, game fishing, swimming, and drinking without treatment). 

Vaughan identified “minimally acceptable parameter concentration levels” for each of the five potential uses. 

Vaughan used a scale of zero to 10 instead of the WQI scale of zero to 100 to classify water quality based on 

its suitability for potential uses. Therefore, the WQI value corresponding to a given water quality use 

classification equals the WQL value multiplied by 10. Table 4-3 presents water use classifications and the 

corresponding WQL and WQI values.

Table 4-3: Water Quality Classifications

Water Quality Classification WQL Value WQI Value

... drinking without treatment 9.5 95

... swimming 7.0 70

... game fishing 5.0 50

... rough fishing 4.5 45

... boating 2.5 25

Source: Vaughan (1986)

4.1.2 Sources of Data on Ambient Water Quality

EPA used the following data sources to obtain ambient concentrations for the seven parameters included in 

the WQI:

 Outputs from USGS’s SPARROW models provided baseline and post-compliance concentrations of 

total nitrogen, total phosphorus, and total suspended solids. These calibrated national models are the 

same models previously used by EPA in the C&D rule analysis (U.S. EPA, 2009c). See Appendix D 

for further details.

 EPA estimated baseline and post-compliance metal concentrations using the water quality model 

component of EPA’s Risk-Screening Environmental Indicators (RSEI) model (U.S. EPA, 2012c).

EPA used estimates of metal loadings discharged from steam electric plants to directly receiving

reaches under the baseline and the five analyzed options (see EA for discussion of directly receiving 

reaches; U.S. EPA, 2015a). EPA input the loadings from steam electric plants in the RSEI model to 

estimate the long-term average concentrations in directly receiving and downstream reaches. These 
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loadings are used to complement discharges already included in RSEI for other facilities that report to 

TRI. See Appendix D for details. The number of exceedances per waterbody (each reach) was 

calculated by comparing baseline and post-compliance concentrations with EPA’s freshwater chronic 

aquatic life criteria values for each metal.

38 If the concentration was greater than the aquatic life 

criteria value for a given metal, EPA categorized the waterbody as having an AWQC exceedance for 

that metal. EPA then summed the total number of metals with AWQC exceedances (up to eight) for 

each waterbody under the baseline and under each analyzed regulatory option.

 The USGS National Water Information System (NWIS) provided concentration data for three 

parameters: 1) fecal coliform, 2) dissolved oxygen, and 3) biochemical oxygen demand. 39 EPA’s 

Storage and Retrieval (STORET) data warehouse provided additional data on fecal coliform counts 

and biochemical oxygen demand where NWIS data was unavailable (U.S. EPA, 2008a).40

Note that the concentration data input into the WQI typically represent long-term average concentrations. 

Table 4-4 summarizes the water quality modeling data used for estimating baseline and post-compliance 

metal, nutrient and sediment concentrations for reaches directly receiving steam electric plant discharge and 

for downstream reaches. 

Table 4-4: Water Quality Modeling Data used in Calculating the Baseline and Policy Metal, Nutrient 

and Sediment Concentrations

Reach Input Data Water Quality Model Model Output 

Reaches directly 

receiving steam 

electric plant 

discharge and 

downstream 

reaches (18,622 

NHD reaches total)

Baseline and policy metal loadings

to inland reaches directly receiving 

steam electric plant discharges

(U.S. EPA, 2015a). 

Metal loadings from other TRI 

dischargers in 2012.

Concentrations modeled in

RSEI

In-steam metal 

concentrations at the NHD 

level

Baseline and policy nutrient and 

sediment loadings to inland 

reaches directly receiving steam 

electric plant discharges (U.S. 

EPA, 2015a)

Baseline values for other nutrient 

and sediment sources in inland 

reaches (e.g., urban, agricultural 

and forested land, animal 

agriculture (nutrients), streambed 

(sediment), atmospheric deposition 

(nitrogen)).

Concentrations modeled in 

SPARROW

In-stream nutrient and 

sediment concentrations at 

the E2RF1 level

 

38 RSEI utilizes the USGS’s National Hydrology Dataset (NHD) which defines a reach as a continuous piece of 

surface water with similar hydrologic characteristics. In the NHD each reach is assigned a reach code; a reach 

may be composed of a single feature, like a lake or isolated stream, but reaches may also be composed of a 

number of contiguous features. Each reach code occurs only once throughout the nation and once assigned, a 

reach code is permanently associated with its reach. If the reach is deleted, its reach code is retired. The NHD 

reaches in this analysis range from 0.003 miles to 9.11 miles in length.

39 USGS’s NWIS dataset provides information on the occurrence, quantity, quality, distribution, and movement of 

surface and underground waters based on data collected at approximately 1.5 million sites in all 50 States, the 

District of Columbia, and U.S. territories. More information on NWIS can be found at 

http://waterdata.usgs.gov/nwis/

40 EPA’s STORET (STOrage and RETrieval) Data Warehouse is a repository for water quality, biological, and 

physical water monitoring data. More information can be found at http://www.epa.gov/storet/
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EPA used two different reach classification frameworks to assess in-stream water quality under the baseline 

and each of the regulatory options: the National Hydrography Dataset (NHD) network and the USGS’s 

Enhanced River File 1 (E2RF1). Metal concentrations were estimated for reaches indexed to the NHD 

network. In contrast, the SPARROW, NWIS, and STORET data are available for reaches indexed to the 

E2RF1 network and to USGS’s Hydrological Unit Code (HUC) watersheds. The WQI and benefits are 

ultimately calculated at the resolution of NHD reaches, but with adjustments made to data available only at 

the E2RF1 level to reflect differences in spatial scale. Thus, to reconcile the two levels of resolution, EPA 

mapped all modeled reaches from the E2RF1 to the NHD network using GIS and assigned the closest E2RF1 

ID to each NHD reach. Figure 4-1 illustrates the differences in scale between the E2RF1 network and the 

NHD network. 

Figure 4-1: Comparison between the NHD and E2RF1 Network in a Single Watershed.

The water quality analysis included a total of 18,622 NHD reaches, totaling 27,421 miles, that are potentially 

affected by steam electric plants under baseline conditions. Baseline concentrations for all WQI parameters 

were available for over 95 percent of the potentially affected NHD reaches. EPA used a successive average 

approach to address the data gaps in WQI parameters not described above (i.e., DO, BOD, fecal coliform) in 

the remaining inland reaches. The approach involves assigning the average of ambient concentrations for a 

WQI parameter within a hydrologic unit to reaches within the same hydrologic unit with missing data, and 

progressively expanding the geographical scope of the hydrologic unit (HUC8, HUC6, HUC4, and HUC2) to 
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fill in all missing data.41 This approach assumes that reaches located in the same watershed generally share 

similar characteristics. Using this estimation approach, EPA compiled baseline water quality data for all 

analyzed NHD reaches. Table 4-5 summarizes the data sources used to estimate baseline and postcompliance values by water quality parameters.

Table 4-5: Water Quality Data used in Calculating the Baseline and Policy WQI

Parameter Baseline value Post-compliance value

TN From SPARROW output (baseline run)

matched to NHD level

From SPARROW output (regulatory option run)

matched to NHD level

TP From SPARROW output (baseline run)

matched to NHD level

From SPARROW output (regulatory option run)

matched to NHD level

TSS From SPARROW output (baseline run)

matched to NHD level

From SPARROW output (regulatory option run)

matched to NHD level

DO Baseline value at the E2RF1 level matched to 

NHD levela

No change. Regulatory option value equal 

baseline value

BOD Baseline value at the E2RF1 level matched to 

NHD levela

No change. Regulatory option value equal 

baseline value

Fecal Coliform Baseline value at the E2RF1 level matched to 

NHD levela

No change. Regulatory option value equal 

baseline value

Metals Baseline exceedances at NHD level based on 

RSEI model outputs

Regulatory option exceedances at NHD level

based on RSEI model outputs.

a. Values based on STORET and NWIS data, complemented with data available for progressively larger geographical units 

(HUC8, HUC6, HUC4, and HUC2), as needed to fill in all missing data.

4.1.3 Baseline WQI

The water quality analysis included a total of 18,622 NHD reaches that are potentially affected by steam 

electric power generating plants under baseline conditions. Based on the estimated WQI value under the 

baseline scenario (WQI-BL), EPA categorized each of these 18,622 NHD reaches using five WQI ranges 

(WQI < 25, 25≤WQI<45, 45≤WQI<50, 50≤WQI<70, and 70≤WQI). WQI values of less than 25 indicate that 

water is not suitable for boating (the recreational use with the lowest required WQI), whereas WQI values 

greater than 70 indicate that waters are swimmable (the recreational use with the highest required WQI).42

 

41 Hydrologic Unit Codes (HUCs) are cataloguing numbers that uniquely identify hydrologic features such as 

surface drainage basins. The HUCs consist of 8 to 14 digits, with each set of 2 digits giving more specific 

information about the hydrologic feature. The first pair of values designate the region (of which there are 21), 

the next pair the subregion (total of 222), the third pair the basin or cataloguing unit (total of 352), and the 

fourth pair the subbasin, or accounting unit (total of 2,262) (USGS, 2007a). Digits after the first eight offer 

more detailed information, but are not always available for all waters. In this discussion, a HUC level refers to a 

set of waters that have that number of HUC digits in common. For example, the HUC6 level includes all 

reaches for which the first six digits of their HUC are the same.

42 EPA did not separately categorize waters where the WQI was greater than or equal to 90 (drinkable water), 

because the analysis did not examine other pollutants that may impact the drinkability of a water.
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Table 4-6: Percentage of Potentially Affected Inland Reach Miles by WQI Classification: Baseline 

Scenario 

Water Quality 

Classification Baseline WQ Number of Reaches

Percent of 

Potentially 

Affected 

Reaches

Number of 

Reach Miles

Percent of 

Potentially 

Affected Reach 

Miles

Unusable WQI<25 0 0.0% 0 0.0%

Suitable for 

Boating 25≤WQI<45 677 3.6% 781 2.8%

Suitable for Rough 

Fishing 45≤WQI<50 649 3.5% 722 2.6%

Suitable for Game 

Fishing 50≤WQI<70 6,771 36.4% 10,187 37.2%

Suitable for 

Swimming 70≤WQI 10,525 56.5% 15,731 57.4%

Total 18,622 100.0% 27,421 100.0%

Source: U.S. EPA Analysis, 2015

4.1.4 Estimated Changes in Water Quality (∆WQI) from the ELG Rule

To estimate benefits of water quality improvements expected to result from the final ELGs, EPA calculated 

the WQI for each analyzed regulatory option. As discussed in Section 0, EPA estimated changes in ambient 

concentrations of TN, TP and TSS using the USGS’s SPARROW model and metal concentrations using the 

water quality component of the RSEI model. In calculating the post-compliance WQI (WQI-PC), the Agency 

used option-specific metal, TN, TP, and TSS concentrations. The estimated post-compliance metal, nutrient,

and sediment concentrations reflect the expected reduction in pollutant discharges under each analyzed 

regulatory option. Although the final ELGs would also improve levels of other WQI parameters, such as BOD

and DO, these other parameters were held constant in this analysis for all regulatory options, due to data 

limitations. 

The difference in the WQI between baseline conditions and a given regulatory option (hereafter denoted as 

∆WQI) is a measure of the change in water quality attributable to the final ELGs. Table 4-7 presents water 

quality changes for all reaches. The water quality analysis included a total of 18,622 NHD reaches that are 

potentially affected by steam electric power generating plants under baseline conditions. Of these potentially 

affected reaches, 104 have fewer AWQC exceedances for metals under Option D. The combination of these 

reaches and reaches with reductions in nutrient or sediment concentrations modeled in SPARROW amount to 

a total of 13,229 unique NHD reaches, or 71 percent of reaches with ∆WQI > 0 under Option D, comprising a 

total of 19,573 reach miles, or 71 percent of all reach miles. Among other options analyzed, the largest 

number of reaches affected by the ELGs occurs under Option E. Under Option E, there are 13,537 inland 

reaches with ∆WQI > 0, totaling 20,300 miles. Note that the changes are based on annual average 

concentrations and represent changes expected after compliance with the final ELGs. As discussed in Section 

1.5.3, the changes are assumed to start in 2021 and remain constant thereafter over the period of analysis 

through 2042. 
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Table 4-7: Water Quality Improvements from Final ELGs in All Benefiting Reaches

Change in WQI Number of Inland 

Reaches

Percentage of 

Potentially 

Affected Inland 

Reaches

(18,622 Reaches)

Reach Miles

Percentage of 

Potentially Affected 

Inland Reach Miles

(27,421 Miles)

Option A

ΔWQI = 0 13,843 74.34% 19,244 70.18%

0 < ΔWQI < 0.1 4,679 25.13% 7,998 29.17%

0.1 ≤ ΔWQI < 1 48 0.26% 99 0.36%

1 ≤ ΔWQI < 5 43 0.23% 72 0.26%

5 ≤ ΔWQI < 10 5 0.03% 3 0.01%

10 ≤ ΔWQI 4 0.02% 5 0.02%

Total 18,622 100.00% 27,421 100.00%

Option B

ΔWQI = 0 9,928 53.31% 13,991 51.02%

0 < ΔWQI < 0.1 7,148 38.38% 11,115 40.53%

0.1 ≤ ΔWQI < 1 1,428 7.67% 2,165 7.89%

1 ≤ ΔWQI < 5 100 0.54% 121 0.44%

5 ≤ ΔWQI < 10 11 0.06% 10 0.04%

10 ≤ ΔWQI 7 0.04% 20 0.07%

Total 18,622 100.00% 27,421 100.00%

Option C

ΔWQI = 0 7,922 42.54% 11,005 40.13%

0 < ΔWQI < 0.1 8,519 45.75% 13,122 47.85%

0.1 < ΔWQI < 1 2,011 10.80% 3,053 11.13%

1 ≤ ΔWQI <5 147 0.79% 202 0.74%

5 ≤ ΔWQI < 10 14 0.08% 19 0.07%

10 ≤ ΔWQI 9 0.05% 21 0.08%

Total 18,622 100.00% 27,421 100.00%

Option D

ΔWQI = 0 5,393 28.96% 7,848 28.62%

0 < ΔWQI < 0.1 10,915 58.61% 16,066 58.59%

0.1 < ΔWQI < 1 2,111 11.34% 3,223 11.76%

1 ≤ ΔWQI < 5 178 0.96% 242 0.88%

5 ≤ ΔWQI < 10 13 0.07% 18 0.07%

10 ≤ ΔWQI 12 0.06% 23 0.09%

Total 18,622 100.00% 27,421 100.00%
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Table 4-7: Water Quality Improvements from Final ELGs in All Benefiting Reaches

Change in WQI Number of Inland 

Reaches

Percentage of 

Potentially 

Affected Inland 

Reaches

(18,622 Reaches)

Reach Miles

Percentage of 

Potentially Affected 

Inland Reach Miles

(27,421 Miles)

Option E

ΔWQI = 0 5,085 27.31% 7,121 25.97%

0 < ΔWQI < 0.1 11,218 60.24% 16,777 61.18%

0.1 < ΔWQI < 1 2,106 11.31% 3,215 11.73%

1 ≤ ΔWQI < 5 187 1.00% 262 0.95%

5 ≤ ΔWQI < 10 13 0.07% 18 0.07%

10 ≤ ΔWQI 13 0.07% 28 0.10%

Total 18,622 100.00% 27,421 100.00%

Source: U.S. EPA Analysis, 2015

4.2 Methodology for Estimating WTP for Water Quality Improvements

To estimate the nonmarket benefits of the water quality improvements resulting from the final ELGs, EPA 

used results from a meta-analysis of stated preference studies summarized below and described in greater 

detail in Appendix H. This meta-analysis is a revised version of the 2009 meta-analysis used in the benefitcost analysis of the proposed ELGs (U.S. EPA, 2013a). As described in the EPA report (U.S. EPA 2015e), 

EPA made a number of improvements to the meta-regression model. In particular, the revised meta-model 

satisfies the adding-up condition, a theoretically desirable property. 43 This condition ensures that if the model 

were used to estimate WTP for the cumulative water quality change resulting from a number of CWA 

regulations, the benefits estimates would be equal to the sum of benefits from using the model to estimate 

WTP for water quality changes separately for each rule. EPA used the revised meta-analysis to estimate the 

sum of use and non-use values for water quality improvements resulting from the final ELGs. In addition, 

EPA views the meta-analysis here as a work in progress and the current model is not the final product. 

Nonetheless, EPA views the results reported here as a clear improvement over the analysis used at proposal.

The meta-analysis is based on a meta-dataset of 51 stated preference studies, published between 1985 and 

2011. Each of these studies used a stated preference approach to elicit survey respondents’ willingness to pay 

for water quality improvements. EPA entered values for all existing variables in the meta-data for each study 

included in the meta-dataset. In addition to the study variables included in the 2009 meta-data, EPA 

developed and coded new variables (for all studies) that address gaps in the 2009 meta-data. While the 2009 

meta-data largely relied on binary variables to distinguish broad categories of affected resources (e.g., single 

vs. multiple rivers, regional freshwater), EPA developed new variables to capture geospatial factors including 

extent of sampled market, surveyed populations and affected resources. EPA report “Meta-analysis of the 

Willingness-to-Pay for Water Quality Improvements” provides detail on variable development and coding 

(U.S. EPA 2015e). The variables in the revised MRM fall into four general categories:

 

43 For a WTP function WTP (WQI0, WQI2, Y0) to satisfy the adding-up property, it must meet the simple 

condition that WTP(WQI0, WQI1 , Y0) + WTP( WQI1, WQI2 , Y0 - WTP(WQI0, WQI1 , Y0) ) = WTP( WQI0, 

WQI2 , Y0) for all possible values of baseline water quality (WQI0), potential future water quality levels (WQI1

and WQI2), and baseline income (Y0).
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1. Study methodology and year variables characterize such features as the year in which a study was 

conducted, payment vehicle and elicitation formats, WTP estimation method, and publication type. 

These variables are included to explain differences in WTP across studies but are not expected to vary 

across benefit transfer for different policy applications. 

2. Region and surveyed populations variables characterize such features as the geographical region 

within the United States in which the study was conducted, the average income of respondent 

households and the representation of users and nonusers within the survey sample.

3. Sampled market and affected resource variables characterize features such as the geospatial scale (or 

size) of affected waterbodies, the size of the market area over which populations were sampled, as 

well as land cover and the quantity of substitute waterbodies. 

4. Water quality (baseline and change) variables characterize baseline conditions and the extent of the 

water quality change. To standardize the results across these studies, EPA expressed water quality 

(baseline and change) in each study using the 100-point WQI, if they did not already employ the WQI 

or WQL.

Using this meta-dataset, EPA then developed a meta-regression model that predicts how marginal WTP for 

water quality improvements depends on a variety of methodological, population, resource, and water quality 

change characteristics. In other words, the meta-regression model predicts the marginal WTP values that 

would be generated by a stated preference survey with a particular set of characteristics chosen to represent 

the water quality improvements and other specifics of the ELGs where possible, and best practices where not

possible. EPA developed two versions of the meta-regression model. Model 1 is used to provide EPA’s 

central estimate of non-market benefits and Model 2 is used to develop a range of estimates to account for 

uncertainty in the resulting WTP values. The two models differ only in how they account for the magnitude of 

the water quality improvements presented to respondents in the original stated preference studies:

 Model 1 assumes that individuals’ marginal WTP depends on the level of water quality, but not on 

the magnitude of the water quality change specified in the survey. This restriction means that, the 

meta-model satisfies the adding-up condition, a theoretically desirable property.

 Model 2 allows marginal WTP to depend not only on the level of water quality but also on the 

magnitude of the water quality change specified in the survey. The model allows for the possibility 

that marginal WTP for improving from 49 to 50 on the water quality index depends on whether 

respondents were asked to value a total water quality change of 10, 20, or 50 points on a WQI scale.

This model provides a better statistical fit to the meta-data, but it satisfies the adding-up conditions 

only if the same magnitude of the water quality change is considered (e.g., 10 points). To uniquely 

define the demand curve and satisfy the adding-up condition using this model, EPA treats the water 

quality change variable as a methodological variable and therefore must make an assumption about 

the size of the water quality change that would be appropriate to use in a stated preference survey

designed to value water quality changes resulting from the final ELGs. When the water quality 

change is fixed at the mean of the meta-data, the predicted WTP is very close to the central estimate 

from Model 1.

EPA used the two meta-regression models in a benefit transfer approach that follows standard methods 

described by Johnston et al. (2005), Shrestha et al. (2007), and Rosenberger and Phipps (2007). The benefit 
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transfer approach uses census block groups (CBGs) as the geographic unit of analysis.44 The transfer approach 

involved projecting benefits in each CBG and year, based on the following general benefit function: 

Equation 4-2. ln(MWTPY,B) = Intercept+ ∑(coefficienti

) × (independent variable valuei)

Where

ln(MWTPY,B) = The predicted natural log of marginal household WTP for a given year (Y) 

and CBG (B).

coefficient = A vector of variable coefficients from the meta-regression.

independent 

variable values

= A vector of independent variable values. Variables include baseline water 

quality level (WQI-BLY,B) and expected water quality under the regulatory 

option (WQI-PCY,B) for a given year and CBG.

Here, ln(MWTPY,B) is the dependent variable in the meta-analysis—the log of approximated marginal WTP

per household, in a given CBG B for water quality in a given year Y.

45 The baseline water quality level (WQIBLY,B) and expected water quality under the regulatory option (WQI-PCY,B) were based on water quality at 

waterbodies within a 100-mile buffer of the centroid of each CBG. A buffer of 100 miles is consistent with 

Viscusi et al. (2008) and with the assumption that the majority of recreational trips will occur within a 2-hour 

drive from home. Because marginal WTP is assumed to depend, according to Equation 4-2, on both baseline 

water quality level (WQI-BLY,B) and expected water quality under the regulatory option (WQI-PCY,B), EPA 

estimated the marginal WTP for water quality improvements resulting from the final ELGs at the mid-point of 

the range over which water quality was changed, WQIY,B = (1/2)( WQI-BLY,B + WQI-PCY,B)).

In this analysis, EPA estimated WTP for the households in each CBG for waters within a 100-mile radius of 

that CBG’s centroid. EPA chose the 100 mile-radius because households are likely to be most familiar with 

waterbodies and their qualities within the 100 mile distance. However, this assumption may be an 

underestimate of the distance beyond which households have familiarity with and WTP for waterbodies

affected by steam electric discharges and their quality. By focusing on a buffer around the CBG as a unit of 

analysis, rather than buffers around affected waterbodies, each household is included in the assessment 

exactly once, eliminating the potential for double-counting of households.46 Total national WTP is calculated 

as the sum of estimated CBG-level WTP across all block groups that have at least one affected waterbody 

within 100 miles. Using this approach, EPA is unable to analyze the WTP for CBGs with no affected waters 

within 100 miles. Appendix C describes the methodology used to identify the relevant populations. 

In each CBG and year, predicted WTP per household is tailored by choosing appropriate input values for the 

meta-analysis parameters describing the resource(s) valued, the extent of resource improvements (i.e., WQIPCY,B), the scale of resource improvements relative to the size of the buffer and relative to available 

substitutes, the characteristics of surveyed populations (e.g., users, nonusers), and other methodological 

 

44 A Census Block group is a group of Census Blocks (the smallest geographic unit for the Census) in a 

contiguous area that never crosses a State or county boundary. A block group typically contains a population 

between 600 and 3,000 individuals. There are 217,740 block groups in the 2010 Census. See 

http://www.census.gov/geo/maps-data/data/tallies/tractblock.html .

45 To satisfy the adding-up condition, as noted above, EPA normalized WTP values reported in the studies 

included in the meta-data so that the dependent variable is MWTP per WQI point. This ‘average’ marginal 

WTP value is an approximation of the MWTP value elicited in each survey scenario.

46 Population double-counting issues can arise when using “distance to waterbody” to assess simultaneous 

improvements to many waterbodies.
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variables. For example, EPA assumed that household income (an independent variable) changes over time, 

resulting in household WTP values that vary by year. 

Table 4-8 provides details on how EPA used the meta-analysis to predict household WTP for each CBG and 

year. The table presents the estimated regression equation intercept, variable coefficients (coefficienti) for the 

two models, and the corresponding independent variable names and assigned values. The meta-regression 

allows the Agency to forecast WTP based on assigned values for model variables that are chosen to represent 

a resource change in the final ELGs’ policy context. EPA assigned a value to each model variable 

corresponding with theory, characteristics of the water resources, and sites affected by the final ELGs and the 

policy context. This follows general guidance from Bergstrom and Taylor (2006) that meta-analysis benefit 

transfer should incorporate theoretical expectations and structures, at least in a weak form. 

In this instance, EPA assigned six study and methodology variables, (thesis, volunt, nonparam, non_reviewed, 

lump_sum, and WTP_median) a value of zero. One methodological variable, outliers_trim, was included with 

an assigned value of 1. Because the interpretation of the study year variable (Lnyear) is uncertain, EPA gave 

the variable a value of 3.0796, which is the 75th percentile of the year values in the meta-data. This value 

assignment reflects an equal probability that the variable represents a real time trend (in which case its value 

should be set to the most recent year of the analysis) and spurious effects (in which case its values should be 

set to the mean value from the meta-data). The choice experiment variable (ce) was set to 1 to reflect recent 

trends in the use of choice experiments within the environmental valuation literature. Model 2 includes an 

additional variable, water quality change (ln_quality_ch), which as discussed above allows the function to 

reflect differences in marginal WTP based on differences in the magnitude of changes presented to survey 

respondents when eliciting values. To ensure that the benefit transfer function satisfies the adding-up 

condition, this variable was treated as a demand curve shifter, similar to the methodological control variables, 

and held fixed for the benefit calculations. To estimate low and high values of WTP for water quality 

improvements resulting from the final ELGs, EPA estimated marginal WTP using two alternative settings of 

the ln_quality variable: WQI = 5 units and WQI = 50 units, which represent the low and high end of the 

range of values observed in the meta-data.

All but one of the region and surveyed population variables vary based on the characteristics of each CBG. 

For median household income, EPA used CBG-level median household income data from the U.S. Census

2010 (American Community Survey 5-year data) and used a stepwise autoregressive forecasting method to 

estimate future annual state level median household income. EPA set the variable nonusers_only to zero 

because water quality improvements are expected to enhance both use and non-use values of the affected 

resources and thus benefit both users and nonusers (a nonuser value of 1 implies WTP values that are 

representative of nonusers only, whereas the default value of 0 indicates that both users and nonusers are 

included in the surveyed population). EPA set the variable river to 1 and mult_type to 0 because the analysis 

focuses only on rivers and streams. Other waterbody types (e.g., lakes and estuaries) are excluded from the 

analysis. 

The geospatial variables corresponding to the sampled market and scale of the affected resources (ln_ar_agr, 

ln_ar_ratio , sub_proportion) vary based on attributes of the CBG and attributes of the nearby affected 

resources. For all options, the affected resource is based on the 18,622 NHD reaches potentially affected by 

steam electric power generating plant discharges under baseline conditions. The affected resource for each 

CBG is the portion of the 18,622 reaches that fall within the 100-mile buffer of the CBG. Spatial scale is held 

fixed across regulatory options. The variable corresponding to the sampled market (ln_ar_ratio) is set to the 

mean value across all CBGs included in the analysis of benefits from water quality improvements resulting 

from the final ELGs, and thus does not vary across affected CBGs. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-15

Because data on specific recreational uses of the water resources affected by the final ELGs are not available, 

the recreational use variables (swim_use, gamefish, boat_use) are set to zero, which corresponds to 

“unspecified” or “all” recreational uses in the meta-data.47 Water quality variables (Q and lnquality_ch) vary 

across CBGs and regulatory options based on the magnitude of the reach-length weighted average water 

quality improvement at affected resources within the 100-mile buffer of each CBG. 

Table 4-8: Independent Variable Assignments for Surface Water Quality Meta-Analysis

Variable 

Type Variable

Coefficient

Assigned 

Value Model Explanation

1

Model 

2

Study 

Methodology 

and Year

intercept -1.040 -6.14

Ce 0.377 0.423 1

Binary variable indicating that the study is a choice 

experiment. Set to one to reflect that choice 

experiments represent current state-of-art methods 

in stated preference literature.

thesis 0.866 0.774 0

Binary variable indicating that the study is a thesis 

or dissertation. Set to zero because studies 

published in peer-reviewed journals are preferred.

lnyear -0.412 -0.5 3.0796

Natural log of the year in which the study was 

conducted (i.e., data were collected), converted to 

an index by subtracting 1980. Set to the natural log 

of the 75th percentile of the year index value for 

studies in the metadata (21.7) to reflect uncertainty 

in the variable interpretation. If the variable 

represents a real time trend, the appropriate value 

should reflect the most recent year of the analysis. 

If it represents spurious effects, the values should 

reflect the mid-point from meta-data. Both 

interpretations are equally probable.

volunt -1.390 -1.184 0

Binary variable indicating that WTP was estimated 

using a payment vehicle described as voluntary as 

opposed to, for example, property taxes. Set to zero 

because hypothetical voluntary payment 

mechanisms are not incentive compatible (Mitchell 

and Carson 1989).

outliers_trim -0.367 -0.291 1

Binary variable indicating that outlier bids were 

excluded when estimating WTP. Set to one 

because WTP estimates that exclude outlier bids 

are preferable.

nonparam -0.408 -0.39 0

Binary variable indicating that regression analysis 

was not used to model WTP. Set to zero because 

use of the regression analysis to estimate WTP 

values is preferred.

non_reviewed -0.709 -0.871 0

Binary variable indicating that the study was not 

published in a peer-reviewed journal. Set to zero 

because studies published in peer-reviewed 

journals are preferred.

 

47 If a particular recreational use was not specified in the survey instrument, EPA assumed that survey respondents 

were thinking of all relevant uses. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-16

Table 4-8: Independent Variable Assignments for Surface Water Quality Meta-Analysis

Variable 

Type Variable

Coefficient

Assigned 

Value Model Explanation

1

Model 

2

lump_sum 0.843 0.773 0

Binary variable indicating that the study provided 

WTP as a one-time, lump sum or provided annual 

WTP values for a payment period of five years or 

less. Set to zero to reflect that the majority of 

studies from the meta-data estimated an annual 

WTP, and to produce an annual WTP prediction.

wtp_median -0.161 -0.151 0

Binary variable indicating that the WTP measure 

from the study is the median. Set to zero because 

only average or mean WTP values in combination 

with the number of affected households will 

mathematically yield total benefits if the 

distribution of WTP is not perfectly symmetrical.

Region and 

Surveyed 

Population northeast 1.180 0.593 Varies

Binary variable indicating that the affected 

population is located in a Northeast U.S. state, 

defined as ME, NH, VT, MA, RI, CT, and NY. Set 

based on the state in which the CBG is located. 

central 0.561 0.726 Varies

Binary variable indicating that the affected 

population is located in a Central U.S. state, 

defined as OH, MI, IN, IL, WI, MN, IA, MO, ND, 

SD, NE, KS, MT, WY, UT, and CO. Set based on 

the state in which the CBG is located.

south 1.400 1.563 Varies

Binary variable indicating that the affected 

population is located in a Southern U.S. state, 

defined as NC, SC, GA, FL, KY, TN, MS, AL, 

AR, LA, OK, TX, and NM. Set based on the state 

in which the CBG is located.

nonusers_only -0.586 -0.54 0

Dummy variable indicating that the sampled 

population included nonusers only; the alternative 

case includes all households. Set to zero to 

estimate the total value for aquatic habitat 

improvements for all households, including users 

and nonusers.

lnincome 0.333 0.96 Varies

Natural log of median household income values 

assigned separately for each CBG. Varies by year

based on the estimated income growth in future 

years.

Sampled 

Market and 

Affected 

Resource

mult_typea-0.827 -0.63 0

Binary variable indicating that multiple waterbody 

types are affected (e.g., river and lakes). Set to zero 

because calculations are based exclusively on 

rivers.

River -0.079 -0.174 1

Binary variable indicating that rivers are affected. 

Set to one because calculations are based 

exclusively on stream miles. EPA did not estimate 

water quality changes for other waterbody types 

(e.g., lakes and estuaries).

swim_use -0.234 -0.27 0

Binary variables that identify studies in which 

swimming, gamefish, and boating uses are 

specifically identified. Since data on specific 

recreational uses of the reaches affected by steam Gamefish 0.233 -0.01 0
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-17

Table 4-8: Independent Variable Assignments for Surface Water Quality Meta-Analysis

Variable 

Type Variable

Coefficient

Assigned 

Value Model Explanation

1

Model 

2

boat_use -0.725 -0.32 0

electric plant discharges are not available, set to 

zero, which corresponds to all recreational uses.

ln_ar_agr -0.271 -0.413 Varies

Natural log of the proportion of the affected 

resource area which is agricultural based on 

National Land Cover Database, reflecting the 

nature of development in the area surrounding the 

resource. Used Census county boundary layers to 

identify counties that intersect affected resources 

within the 100-mile buffer of each CBG. For 

intersecting counties, calculated the fraction of 

total land area that is agricultural using the

National Land Cover Dataset (NLCD). The

ln_ar_agr variable was coded in the metadata to 

reflect the area surrounding the affected resources.

ln_ar_ratio -0.034 -0.057 1.238

The natural log of the ratio of the sampled area 

(sa_area) relative to the affected resource area 

(defined as the total area of counties that intersect 

the affected resource(s)) (ar_total_area). Set to the 

mean value from the CBG’s with 100-mile buffers 

containing waters affected by the final ELGs.

sub_proportion 1.100 0.607 Varies

The size of the affected resources relative to 

available substitutes. Calculated as the ratio of 

affected reaches miles to the total number of reach 

miles within the buffer that are the same order(s) 

as the affected reaches within the buffer. Its value 

can range from 0 to 1.

Water 

Quality 

Q -0.015 -0.004 Varies

Because marginal WTP is assumed to depend on 

both baseline water quality and expected water 

quality under the regulatory option, this variable is 

set to the mid-point of the range of water quality 

changes due to the final ELGs, WQI Y,B = 

(1/2)(WQI-BLY,B + WQI-PC Y,B). Calculated as the 

length-weighted average WQI score for all 

potentially affected COMIDs within the 100-mile

buffer of each CBG.

lnquality_ch NA -0.746 ln(5) or 

ln(50)

Ln_quality_ch was set to the natural log of 

WQI=5 or WQI=50 for high and low estimates 

of the marginal WTP, respectively.

a. The meta-data includes six waterbody categories (1) river and stream, (2) lake, (3) all freshwater, (4) estuary, (5) river and lake, (6) 

salt pond/marshes, Variable multi-type takes on a value of 1 if the study focused on waterbody categories (3) and (6). EPA notes that 

the overall effect of this variable should be considered in conjunction with the regional dummies (e.g., a study of the Lake 

Okeechobee basin in Florida) and that only eight percent of all observations in the meta-data fall in the multiple waterbody categories.

4.3 Total WTP for Water Quality Improvements

EPA estimated economic values of water quality improvements at the CBG level. For each block group, EPA 

multiplied the coefficient estimates for each variable, taken from meta-analysis results (Table 4-8,

Coefficients: Model 1 and Model 2), by the variable levels calculated for each CBG or fixed at the levels 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-18

indicated above (Table 4-8, column 4). The sum of these products represents the predicted natural log of 

marginal household WTP (ln_MWTP) for a representative household in each CBG, as indicated by Table 4-8.

Equation 4-3 provides the discount formula used to calculate household benefits for each CBG.

Equation 4-3.

HWTPY,B = MWTPY,B × ∆WQIB

where:

HWTPY,B = Annual household WTP in 2013$ in year Y for households located in

the CBG (B),

MWTPY,B = Marginal WTP for water quality for a given year (Y) and the CBG

(B) estimated by the meta-analysis function and evaluated at the 

midpoint of the range over which water quality is changed,

∆WQIB = Estimated annual average water quality change for the CBG (B).

As summarized in Table 4-9, average annual household WTP estimates for the final ELGs (Option D) range 

from $0.32 on the low end (Model 2) to $1.77 on the high end (Model 2), with a central estimate of $0.35 

(Model 1). The average is calculated based on the 84.5 million households affected by Option D. We note that 

the central estimate does not fall at the midpoint of the range, but instead represents the value from Model 1 

which falls between the low and high bound estimates provided by Model 2.

Table 4-9: Household Willingness-to-Pay for Water Quality Improvements

Regulatory 

Option

Improving Reach 

Miles 

Number of Affected 

Households (Millions)

Average Annual WTP Per Household (2013$)

Low Central High

Option A 8,177 30.3 $0.17 $0.20 $0.92 

Option B 13,431 60.3 $0.28 $0.37 $1.58 

Option C 16,417 67.9 $0.33 $0.46 $1.85 

Option D 19,573 84.5 $0.32 $0.45 $1.77 

Option E 20,300 94.3 $0.31 $0.45 $1.75 

Source: U.S. EPA Analysis, 2015

To estimate total WTP (TWTP) for water quality improvements for each CBG, EPA multiplied the perhousehold WTP values for the estimated water quality improvement by the number of households within each

block group in a given year. EPA then calculated annualized total WTP values for each CBG with both a 

3 percent and 7 percent discount rate as shown below in Equation 4-4. As discussed in Chapter 1, benefits 

from water quality changes are estimated for all years between 2021 and 2042. For reaches directly receiving 

steam electric plant discharges, benefits are expected to begin accruing according to the technology 

implementation period of 2019 through 2023 described in Chapter 3 of the RIA (U.S. EPA, 2015b). 

Downstream reaches, however, can be affected not only by discharges from a given plant discharging directly 

to the reach, but also by any change in discharges from plants located upstream, which may have different 

compliance years. Therefore, for this analysis, EPA used a simplified assumption that all benefits begin 

accruing in 2021, which is the midpoint of the compliance period.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-19

Equation 4-4.

TWTPB = ( ∑

HWTPY,B × HHY,B

(1 + i)

Y−2015

2042

T=2021

) × (

i × (1 + i)

n

(1 + i)

n+1 − 1

)

where:

TWTPB = Total household WTP in 2013$ for households located in the CBG

(B),

HWTPY,B = Annual household WTP in 2013$ for households located in the CBG

(B) in year (Y),

HHY,B = the number of households residing in the CBG (B) in year (Y), 

T = Year when benefits are realized

i = Discount rate (3 or 7 percent) 

n = Duration of the analysis (22 years)48

EPA generated annual household counts for each CBG through the period of analysis based on projected 

population growth following the method described in Section 1.5.4. Table 4-10 presents the results for the 

3 percent and 7 percent discount rates. 

Table 4-10: Total Willingness-to-Pay for Water Quality Improvements

Regulatory 

Option

Number of Affected 

Households 

(Millions)

3% Discount Rate 7% Discount Rate

Low Central High Low Central High

Option A 30.3 $4.2 $4.9 $23.4 $3.3 $3.9 $18.6 

Option B 60.3 $15.0 $18.9 $83.7 $12.0 $15.2 $66.7 

Option C 67.9 $19.6 $26.0 $109.4 $15.7 $20.9 $87.3 

Option D 84.5 $23.2 $31.3 $129.5 $18.5 $25.1 $103.4 

Option E 94.3 $25.1 $34.0 $140.0 $20.0 $27.3 $111.7 

Source: U.S. EPA Analysis, 2015

EPA estimated that 19,573 reach miles would improve under the final ELGs. The total annualized benefits of 

water quality improvements resulting from reduced metal, nutrient and sediment discharges in these reaches 

range from $23.2 million to $129.5 million with a central estimate of $31.3 million using a 3 percent discount 

rate and $18.5 million to $103.4 million with a central estimate of $25.1 million using a 7 percent discount 

rate.

Readers may wonder why the central estimate is closer to the low end of the range than the high end. EPA 

tested several different functional forms for Model 2, and found that the model has the highest explanatory 

power (R-squared) when water quality change is included in logged form. This implies that water quality 

change has a nonlinear effect on MWTP. In particular, small initial increases in the scale of the water quality 

change scenario have a larger effect on MWTP than subsequent increases. This is the reason why the central 

estimate of MWTP (based on a water quality change scenario of approximately +20 units) is closer to the low 

MWTP estimate (based on a water quality change scenario of +50) than to the high MWTP estimate (based 

on a water quality change scenario of +5). In addition, when Model 2 is used in a benefits transfer application 

with a water quality change of +20, the mean of the meta-data, the results are very close to the results of 

 

48 See Section 1.5.3 for detail on the period of analysis. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-20

Model 1. This sensitivity analysis is included because a water quality change of +5 is closer to the size of 

water quality changes projected to result from the ELGs than the +20 analog to the central estimate, while the 

+50 represents the upper end of water quality changes in existing surveys (and the lower end of the sensitivity 

benefits range), for completeness.

In addition, EPA views its revisions to the meta-analysis employed here as a work in progress. As part of the 

revisions, EPA has identified some issues that require further analysis, and intends to continue this work after 

the Steam Electric ELGs are promulgated. EPA also intends to have the SAB review the results of that 

progress when it has reached a point of completion where SAB review is appropriate. In particular, EPA has 

identified the following issues to address: conducting additional robustness tests and cross validation, 

investigating model over-fitting, investigating whether a distance decay effect can be gleaned from the metadata (to substitute for the 100-mile radius assumption used in the benefits transfer application here), and to 

consider employing Bayesian estimation techniques. However, EPA views the results presented here as a 

sufficient improvement over the results presented at proposal to warrant their inclusion in this report.

4.4 Implications of Revised Steam Electric Plant Loading Estimates

As described in Section 1.4.3, EPA revised its estimates of pollutant loadings in steam electric power plant 

discharges after completing the benefit analyses. The revisions affect baseline discharges of the several 

pollutants explicitly modeled to quantify and monetize the water quality improvement benefits in this 

Chapter: the eight metals, TN, TP and TSS. Changes in baseline metal loadings range from a 47 percent 

reduction (for lead) to a 1 percent increase (for selenium) while changes in TN, TP, and TSS loadings are all 

less than 1 percent. The overall effect of changes in TN, TP, and TSS loadings is likely to be trivial given the 

magnitude of loading reductions (i.e., less than 1 percent). Impacts of the revised metal loadings on the WQI 

score is difficult to assess since the score is not based on a continuous scale, but depends instead on whether 

the modeled instream concentrations metals exceed the relevant AWQCs. To the extent that baseline 

concentrations calculated based on the original loads exceed the relevant AWQCs only slightly, small 

reductions in these concentrations due to the revised loads could increase the baseline WQI score, all else 

being equal, and therefore reduce the magnitude of improvements attributable to the ELGs.

For several reasons — notably the fact that revisions do not affect all plants equally, the metals subindex 

score is based on counts of exceedances, and the model functions are not linear — it would be inappropriate 

to simply scale the monetized benefits based on the aggregate changes in loadings. However, comparison of 

the initial and revised baseline loads, on the one hand, and the ratios of modeled concentrations based on the 

initial loads to AWQCs, on the other hand, provides insight on the potential significance of the revisions on 

benefit estimates. 

Table 4-11 summarizes changes in loading estimates between the original loads used in estimating benefits 

for Option D in this Chapter and the revised loads. The table shows total industry loads of the eight metals 

included in the WQI subindex, for the baseline and under the final ELGs (Option D).
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-21

Table 4-11: Estimated Aggregate Changes in Pollutant Loadings for Metals, Nutrients, and 

Suspended Solids.

Pollutant

Baseline Loadings Option D

Initial Revised % Change Initial Revised % Change

Arsenic 22,219 20,138 -9% 1,483 1,480 0%

Cadmium 10,925 8,289 -24% 636 630 -1%

Chromium (VI) 119 119 0% 0 0 0%

Lead 14,588 7,674 -47% 350 331 -5%

Mercury 1,176 992 -16% 31 31 -1%

Nickel 94,201 61,933 -34% 1,781 1,697 -5%

Selenium 112,999 114,533 1% 3,122 3,129 0%

Zinc 145,045 124,333 -14% 6,951 6,893 -1%

Total nitrogen 13,134,797 13,134,797 0% 69,969 70,285 0%

Total phosphorus 153,972 154,519 0% 31,630 31,667 0%

Total suspended 

solids

14,182,480 14,286,431 1% 1,661,665 1,665,345 0%

Source: U.S. EPA Analysis, 2015

While EPA cannot readily recalculate the benefits, the direction and relative magnitude of the change in 

pollutant removals in Table 4-11 suggest there could be reaches where modeled concentrations would no 

longer exceed the applicable AWQS after revising the loadings. The changes are most significant for the 

baseline and are most likely to affect benefit estimates in instances where the concentrations were only 

slightly above the AWQC. However, review of baseline exceedances shows that, of the 1,023 exceedances 

present in the baseline (in 567 reaches), 774 exceedances result from instream concentrations being more than 

1.5 times the relevant AWQC and 690 exceedances result from instream concentrations that are more than 

twice the AWQC. Instream concentrations would need to decline by at least 33 percent and 50 percent, 

respectively, to eliminate such baseline exceedances. Based on this data review, EPA concludes that the 

values presented in this Chapter based on the initial loadings provide reasonable insight into the water quality 

improvement benefits expected from the final ELGs.

Table 4-12: Number of Reaches with Baseline AWQC Exceedances Based on Initial Loadings.

Pollutant

Ratio of [baseline concentration]/[AWQC]

>1.0 >1.5 >2.0

Arsenic 167 128 124

Cadmium 151 100 75

Chromium (VI) 10 7 5

Lead 124 97 62

Mercury 17 11 7

Nickel 216 147 138

Selenium 91 68 47

Zinc 247 216 167

Source: U.S. EPA Analysis, 2015
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-22

4.5 Limitations and Uncertainties

Table 4-13 summarizes the limitations and uncertainties in the analysis of benefits associated with improved 

surface water quality and indicates the direction of any potential bias. 

Table 4-13: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits

Issue Effect on Benefits 

Estimate Notes

Revisions to pollutant loads used to estimate water quality improvements

The analysis is based 

loadings that were 

subsequently revised 

by EPA 

Overestimate Revised loadings of parameters included in the WQI (notably the 

eight metals), are lower than the loadings used to estimate 

benefits. The changes indicate that number of exceedances of 

AWQCs may be lower in the baseline, and therefore the resulting 

improvements due to the ELGs may be lower. As discussed in 

Section 4.4, however, the magnitude of the overstatement may be 

small.

Limitations inherent to the meta-analysis model and benefit transfer

Use of 100-mile buffer

for calculating water 

quality benefits for 

each CBG

Underestimate The distance between the surveyed households and the affected 

waterbodies is not well measured by any of the explanatory 

variables in the meta-regression model. EPA would expect 

values for water quality improvements to diminish with 

distance (all else equal) between the home and affected 

waterbody. The choice of 100 miles is based on typical driving 

distance to recreational sites (i.e., 2 hours or 100 miles). 

Therefore, EPA used 100 miles to approximate the distance

decay effect.

Selection of the WQI 

parameter value for 

estimating low and 

high WTP values 

Uncertain EPA set WQI to 5 and 50 units to estimate high and low benefit 

values based on Model 2. These values were based on the lowest 

and highest water quality changes included in the meta-data. To 

the extent that WQI = 50 is significantly larger than the water 

quality expected from the final ELGs it is likely to significantly 

understate the estimated WTP value. WQI = 5 is more 

consistent with the magnitude of water quality changes resulting 

from the final ELGs. 

Whether potential 

hypothetical bias is 

present in underlying

stated preference

results

Uncertain Following standard benefit transfer approaches, this analysis 

proceeds under the assumption that each source study provides a 

valid, unbiased estimate of the welfare measure under 

consideration (cf. Moeltner et al. 2007; Rosenberger and Phipps 

2007). To minimize potential hypothetical bias underlying stated 

preference studies included in meta-data, EPA set independent 

variable values to reflect best benefit transfer practices. 

Use of different water 

quality measures in 

the underlying metadata

Uncertain The estimation of WTP may be sensitive to differences in the 

environmental water quality measures across studies in the meta 

data. Studies that did not use the WQI were mapped to the WQI 

so a comparison could be made across studies. In preliminary 

model runs, EPA tested a dummy variable (WQI) that captures 

the effect of a study using (WQI=1) or not using (WQI=0) the 

WQI. The variable coefficient was not statistically different from 

zero, indicating no systematic bias in the mapping of studies that 

did not use the WQI. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 4: Non-Market Benefits

September 29, 2015 4-23

Table 4-13: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits

Issue Effect on Benefits 

Estimate Notes

Transfer error Uncertain Transfer error may occur when benefit estimates from a study site 

are adopted to forecast the benefits of a policy site. Rosenberger 

and Stanley (2006) define transfer error as the difference between 

the transferred and actual, generally unknown, value. While 

meta-analysis is fairly accurate when estimating benefit function, 

transfer error may be a problem in cases where the sample size is 

small. Meta-analyses have been shown to outperform other 

function-based transfer methods in many cases, but this result is 

not universal (Shrestha et al. 2007). This notwithstanding, results 

reviewed by Rosenberger and Phipps (2007) are “very 

promising” for the performance of meta-analytic benefit transfers 

relative to alternative transfer methods.

Use of the WQI to link water quality changes to effects on human uses and support for aquatic and terrestrial 

species

Omission of lakes and 

estuaries from analysis 

of benefits from water 

quality improvements 

Underestimate 12 percent of steam electric power generating plants discharge to 

the Great Lakes or estuaries. Due to limitations of the water 

quality models used in the analysis of the final ELGs, these 

waterbodies were excluded from the analysis. This omission is 

likely to underestimate benefits of water quality improvements 

from the final ELGs. 

Changes in WQI 

reflect only reductions 

in metal, nutrient, and 

total suspended 

sediment

concentrations

Uncertain The estimated changes in WQI reflect only water quality 

improvements resulting directly from reductions in metal,

nutrient and sediment concentrations. They do not include 

improvements in other water quality parameters (e.g., BOD, 

dissolved oxygen) that are part of the WQI. If the omitted water 

quality parameters also improve, then the analysis underestimates

the expected water quality changes.

In-stream metal 

concentrations are 

based only on loadings 

from steam electric 

power generating 

plants and other TRI 

dischargers 

Uncertain In-stream concentrations for heavy metals were estimated based 

on loadings from steam electric plant and other TRI dischargers 

only and, as a result, do not account for background 

concentrations of these pollutants from other sources, such as 

contaminated sediments, non-point sources, point sources that are 

not required to report to TRI, air deposition, etc. Not including 

other contributors to background metal concentrations in the 

analysis is likely to result in understatement of baseline 

concentrations of these pollutants and therefore of AWQC 

exceedances. The overall impact of this limitation on the 

estimated WTP for water quality improvement is uncertain but is 

expected to be small since the WTP function used in this analysis 

is most sensitive to the change in water quality.

Use of nonlinear 

subindex curves

Underestimate The methodology used to translate in-stream sediment and 

nutrient concentrations into subindex scores employs nonlinear 

transformation curves. Water quality changes that fall outside of 

the sensitive part of the transformation curve (i.e., above/below 

the upper/lower bounds, respectively) yield no benefit in the 

analysis.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

September 29, 2015 5-1

5 Impacts and Benefits to Threatened and Endangered Species

5.1 Introduction

Threatened and endangered (T&E) species are species vulnerable to future extinction or at risk of extinction 

in the near future, respectively. These designations reflect low or rapidly declining population levels, loss of 

essential habitat, or life history stages that are particularly vulnerable to environmental alteration. In many 

cases, T&E species are given special protection due to inherent vulnerabilities to habitat modification, 

disturbance, or other human impacts. This chapter examines the environmental impacts of steam electric 

power plant discharges on T&E species and the benefits associated with improvements resulting from the 

final ELGs and other regulatory options.

As described in the EA (U.S. EPA, 2015a), the chemical constituents of steam electric waste streams can pose 

serious threats to ecological health due to the bioaccumulative nature of many pollutants, high concentrations, 

and high loadings. Pollutants such as selenium, arsenic and mercury have been associated with fish kills, 

disruption of growth and reproductive cycles and behavioral and psychological alterations in aquatic 

organisms (U.S. EPA, 2009d; Appendix H). Additionally, high nutrient loads can lead to the eutrophication of 

waterbodies. Eutrophication can lead to increases in the occurrence and intensity of water column 

phytoplankton, including harmful algal blooms (e.g., nuisance and/or toxic species), which have been found 

to cause fatal poisoning in other animals, fish, and birds (Williams et al., 2001). Eutrophication may also 

result in the loss of critical submerged rooted aquatic plants (or macrophytes), and reduced DO, levels, 

leading to anoxic or hypoxic waters.

For species vulnerable to future extinction, even minor changes to growth and reproductive rates and small 

levels of mortality may represent a substantial portion of annual population growth. Consequently, steam 

electric power plant discharges may either lengthen recovery time, or hasten the demise of these species. For 

this reason, the final ELGs may have a significant impact on T&E species populations.

From an economic perspective, T&E species affected by steam electric power plant discharges may have both 

use and nonuse values. However, given the protected nature of T&E species and the fact that use activities 

generally constitute take, which is illegal unless permitted, the majority of T&E species do not have direct 

uses, the majority of the economic value for T&E species comes from nonuse values. Species-specific 

estimates of nonuse values held for the protection of T&E species can be most accurately derived by primary 

research using stated preference techniques. However, the cost, administrative burden, and time required to 

develop primary research estimates to value effects of the final rule on T&E species are beyond the schedule 

and resources available to EPA for this rulemaking. As an alternative, EPA used a benefit transfer approach 

that relies on information from existing studies (U.S. EPA, 2010c). 

In this chapter, EPA explores the current status of major freshwater taxa, identifies the extent to which the 

final ELGs can be expected to benefit species protected by the Endangered Species Act (ESA), and applies 

economic valuation studies to these T&E species to estimate WTP for these benefits.

5.2 Baseline Status of Freshwater Fish Species

Reviews of aquatic species’ conservation status over the past three decades have documented the effect of 

cumulative stressors on freshwater aquatic ecosystems, resulting in a significant decline in the biodiversity 

and condition of indigenous communities (Deacon et al., 1979; Williams et al., 1989; Williams et al., 1993; 

Taylor et al., 1996; Taylor et al., 2007; Jelks et al., 2008). Overall, aquatic species are disproportionately 
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imperiled relative to terrestrial species. For example, while 39 percent of freshwater and diadromous fish 

species (Jelks et al., 2008) are classified as T&E, a similar status review found that only 7 percent of North 

American bird and mammal species are currently imperiled (Wilcove and Master, 2005).

Approximately 39 percent of described fish species in North America are imperiled, with 700 fish taxa 

classified as vulnerable (230), threatened (190), or endangered (280) in addition to 61 taxa presumed extinct 

or functionally extirpated from nature (Jelks et al., 2008). These data show that the number of T&E species 

have increased by 98 percent and 179 percent when compared to similar reviews conducted by the American 

Fisheries Society in 1989 (Williams, Johnson et al. 1989) and 1979 (Deacon et al., 1979), respectively. 

Despite recent conservation efforts, including the listing of several species under the ESA, only 6 percent of 

the fish taxa assessed in 2008 had improved in status since the 1989 inventory (Jelks et al., 2008).

Several families of fish have strikingly high proportions of T&E species. Approximately 46 percent and 

44 percent of species within families Cyprinidae (carps and true minnows) and Percidae (darters and perches) 

are imperiled, respectively. Some families with few, wide-ranging species have even higher rates of 

imperilment, including the Acipenseridae (sturgeons; 88 percent) and Polyodontidae (paddlefish; 

100 percent). Families with species important to sport and commercial fisheries ranged from a low of 

22 percent for Centrarchidae (sunfishes) to a high of 61 percent for Salmonidae (salmon) (Jelks et al., 2008).

5.3 T&E Species Affected by the Final ELGs

To assess the potential effects of the final ELGs on T&E species, EPA constructed databases to determine 

which species are found in waters expected to improve due to a reduction in pollutant discharge from steam 

electric power plants. Notably, these databases exclude all species considered threatened or endangered by 

scientific organizations [e.g., the American Fisheries Society (Williams et al., 1993; Taylor et al., 2007; Jelks 

et al., 2008)] but not protected by the ESA. These databases allowed EPA to estimate the potential for adverse 

impacts of steam electric power plant discharges on T&E species, as well as benefits associated with the final

ELGs. 

5.3.1 Identifying T&E Species Potentially Affected by the Final ELGs

To estimate the effects of the final ELGs on T&E species, all affected species must first be identified. EPA 

identified all species currently listed or in consideration for listing under the ESA using the U.S. FWS

Environmental Conservation Online System (U.S. FWS, 2014a). Whenever possible, EPA obtained the 

geographical distribution of T&E species in geographic information system (GIS) format as polygon (shape) 

files, line files (for inhabitants of small creeks and rivers) and as a subset of geodatabase files. Data sources 

include U.S. FWS (2014b), the National Oceanic and Atmospheric Administration’s (NOAA’s) Office of 

Response and Restoration (NOAA, 2010), NatureServe (NatureServe, 2014), and NOAA National Marine 

Fisheries Service (NMFS, 2014a; NMFS, 2014b; NMFS, 2014c). For several freshwater species, geographic 

ranges were available only as 6-digit HUCs (NatureServe, 2014; U.S. FWS, 2014b). For these species, EPA 

created GIS data layers using a GIS HUC database obtained from the USGS (Steeves and Nebert, 1994). 

To determine the probability that individual T&E species could benefit from the final ELGs, EPA compiled 

data on locations of steam electric power plants and receiving waterbodies. The Agency used plant and outfall 

coordinates it had obtained through its 2010 Questionnaire for the Steam Electric Power Generating Effluent 

Guidelines (the industry survey) and georeferenced these coordinates to waterbodies (see EA for details; U.S. 

EPA, 2015a). The result of this analysis consists of the NHD Plus (COMIDs) identifiers of waterbodies that 

receive discharges from steam electric power plants and indicators of water quality under the baseline and 

each analyzed regulatory option. EPA queried these data to identify “affected areas” as those habitats where 

1) receiving waters do not meet water quality metrics recognized to cause harm in organisms under baseline 
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conditions; and 2) receiving waters exceed water quality metrics under the most stringent regulatory option

EPA analyzed (Option E). EPA used these data in ArcGIS to determine the T&E species with habitat extents 

overlapping the affected areas.

EPA constructed a screening database using the spatial data. This database included all T&E species whose 

habitat overlaps those waterbodies receiving effluent discharges from steam electric power plants. A buffer of 

500 m was chosen when constructing this database to account for any minor errors in outfall location and 

habitat maps.

After identifying T&E species potentially affected by the final ELGs, EPA classified the species on the basis 

of their vulnerability to changes in water quality. Species were classified as follows:

 High vulnerability – species living in aquatic habitats for several life history stages and/or species that 

obtain a majority of their food from aquatic sources.

 Moderate vulnerability – species living in aquatic habitats for one life history stage and/or species 

that obtain some of their food from aquatic sources.

 Low vulnerability – species whose habitats overlap bodies of water, but whose life history traits and 

food sources are terrestrial.

Life history data used to classify species were obtained from a wide variety of sources (Froese and Pauly,

2009; NatureServe, 2014; Alaska Fisheries Science Center (AFSC), 2010; Atlantic States Marine Fisheries 

Commission (ASMFC), 2010; Northeast Fisheries Science Center (NEFSC), 2010; Pacific Islands Fisheries 

Science Center (PIFSC), 2010a; PIFSC, 2010b; Southeast Fisheries Science Center (SEFSC), 2010; 

Southwest Fisheries Science Center (SWFSC), 2010; U.S. FWS, 2010).

The results of the spatial analysis and vulnerability classification process (as described above) are presented in 

Table 5-1. Appendix I lists all T&E species potentially affected by the final ELGs.

Table 5-1: T&E Species with Habitat Occurring within Waterbodies Affected by Steam Electric Power 

Plants

Species Group

Species Vulnerability

Species Count

Low Moderate High

Amphibians 0 2 2 4

Arachnids 4 0 0 4

Birds 9 2 1 12

Clams 0 0 33 33

Crustaceans 0 1 1 2

Fishes 0 0 17 17

Insects 10 2 1 13

Mammals 17 4 1 22

Reptiles 3 1 5 9

Snails 8 0 14 22

Total 51 12 75 138

Source: U.S. EPA Analysis, 2015

For the purposes of estimating benefits, EPA excluded all species with low and moderate vulnerability 

potentials based upon life history traits. For all species with high potential vulnerability, EPA conducted 

further analyses to identify those species likely to be affected by the final ELGs, rather than all species whose 
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life histories make them vulnerable. High vulnerability species meeting the following criteria were removed 

from further consideration:

 Species presumed to be extinct, including those not collected for a minimum of 30 years (e.g., 

Noturus trautmani).

 Endemic species living in waterbodies (e.g., isolated headwaters, natural springs) unlikely to be 

affected by steam electric power plant discharges (e.g., Gambusia georgei).

 Species protected by the ESA whose recovery plans i) do not include pollution or water quality issues 

as factors preventing recovery, and ii) identify habitat destruction (due to damming, stream 

channelization, water impoundments, wetland drainage, etc.) as a primary factor preventing recovery 

(e.g., Erimystax cahni).

 Listings due to non-native species introductions and/or hybridization with native or non-native 

congeners (e.g., Oncorhynchus clarki somias)

 Listings where water quality issues are identified as the primary issue preventing recovery, but where 

a specific industry or entity not within the scope of the final ELGs is identified as the culprit. (e.g., 

Erimystax cahni due to siltation from coal mining activity).

 Species about which very little is known, including geographic distribution. 

After eliminating the T&E species meeting these criteria, EPA identified a total of 15 species whose recovery 

may be enhanced by the final ELGs. 

5.3.2 Estimating Benefits of T&E Species Improvements from the Final ELGs

The final ELGs have the potential to positively affect the recovery trajectory for 15 T&E species. For each of 

these species, EPA estimated the magnitude of potential benefits by identifying inhabited waterbodies likely 

to meet AWQC for aquatic life as a consequence of the final ELGs and comparing these areas to the overall 

area of habitat occupied by T&E species.

First, for each T&E species affected by steam electric power plant discharges, EPA examined water quality in 

each of the waterbodies inhabited by each T&E species under baseline conditions, and under conditions 

projected to exist following implementation of the final ELGs. For each analyzed regulatory option, EPA 

identified waterbodies that 1) do not meet AWQC for wildlife under baseline conditions, but 2) have no 

wildlife AWQC exceedances following implementation of the final ELGs. For 11 species, there were no 

waterbodies that met these conditions, leaving three T&E fish species and one salamander species in nine 

states that may experience increases in population growth rates as a result of the final ELGs (Table 5-2). 

Table 5-2: T&E Species Whose Recovery May Benefit from the Final ELGs

Species Common Name State(s)

Acipenser brevirostrum Shortnose Sturgeon MD, SC

Cryptobranchus alleganiensis Hellbender salamander IL, KY, MO, OH, PA

Etheostoma chermocki Vermilion darter AL

Etheostoma etowahae Etowah darter GA

Source: U.S. EPA Analysis, 2015

EPA did not identify data sufficient to explicitly model population growth rates as a function of water quality 

for any of these species. Therefore, to estimate proportionate population increases as a result of the final 

ELGs, EPA identified the fraction of inhabited waterbodies that meet wildlife AWQC as a consequence of the 
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final ELGs. This fraction was used to estimate relative population changes in estimating the WTP for T&E 

species recovery. 

5.4 Estimating WTP for T&E Species Population Increases

5.4.1 Economic Valuation Methods

For several reasons, it is difficult to estimate the benefits of improving T&E species habitats resulting from 

the final ELGs. First, data required to estimate the response of T&E populations to improved habitats are 

rarely available. Second, the contribution of T&E species to ecosystem stability, ecosystem function, and life 

history remains relatively unknown. Third, much of the wildlife economic literature focuses on commercial 

and recreational benefits that are not relevant for many protected species (i.e., use values).There is a paucity 

of economic data focused on the benefits of preserving habitat for T&E species because nonuse values 

comprise the principal source of benefit estimates for most T&E species.

Analysis of nonuse benefits for T&E species affected by changes in pollutant discharges from steam electric 

power plants stemming from the final ELGs involves the following two steps: 1) quantifying the impacts of 

pollutant discharges from steam electric power plants on T&E species and estimating the change in these 

impacts as a consequence of reducing steam electric discharges; and 2) estimating an economic value of 

improving T&E habitats and populations as a consequence of the final ELGs. 

Benefit transfer involves extrapolating existing estimates of nonmarket values to the policy sites that

potentially differ from the original analytical situation in terms of geographic locations or affected species.

Ideally, the resource in question (i.e., T&E species), policy variables (change in species status, recovery 

interval, population size, etc.), and the geographic location and benefitting population (i.e., defined human 

population) are identical. Such a match rarely occurs. Despite differences in these variables, however, a 

benefit transfer approach can provide useful insights into the social benefits gained by reducing impacts on

T&E species.

5.4.2 Estimating WTP for Improved Protection of T&E Species

To estimate the potential economic values of increased T&E species populations affected by the final ELGs, 

EPA used a benefit transfer approach based on a meta-analysis of 31 stated preference studies eliciting WTP 

for changes in T&E populations (Richardson and Loomis, 2009). This meta-analysis is based on studies 

conducted in the United States that valued threatened, rare, or endangered fish, bird, reptile, or mammal 

species. Because the underlying meta-data does not contain amphibian valuation studies, EPA was unable to 

monetize any benefits for potential population increases of Hellbender salamander as a result of the final 

ELGs. Equation 5-1 contains the estimated WTP equation from the Richardson and Loomis (2009) paper that 

EPA used to monetize potential population increases resulting from the final ELGs. 

Equation 5-1. ln WTP (2006$) = −153.231 + 0.870 ln CHANGESIZE + 1.256 VISITOR + 1.020 FISH +

0.772MARINE + 0.826 BIRD – 0.603 ln RESPONSERATE + 2.767 CONJOINT +

1.024 CHARISMATIC – 0.903 MAIL + 0.078 STUDYYEAR.
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Table 5-3 lists the assigned variable values and definitions used in estimating per household WTP for 

improved protection of T&E species resulting from the final ELGs.

Table 5-3: Independent Variable Assignments for the T&E Meta-Regression

Variable Description Value Explanation

Intercept Intercept -153.231 -

ln ChangeSize Natural log of percentage change in the 

population of the species of interest Varies Log of percentage change in fishpopulation

Visitor

Dummy variable indicating if survey 

respondents are visitors rather than fulltime residents0

Primary beneficiaries are expected to be 

full-time state residents

Fish Dummy variable indicating population 

increases for fish species 1

Only freshwater T&E fish species are 

expected to be affected Marine Dummy variable indicating population increases for marine mammals 0

Bird Dummy variable indicating population 

increases for bird 0

Charismatic Dummy variable indicating a 

charismatic species Varies Sturgeon species are considered charismatic; minnow species are not

Conjoint Dummy variable indicating conjoint 

method surveys

0

Default value from Richardson and 

Loomis (2009) as only one underlying 

meta-study used conjoint analysis; the rest 

were CV studies

ln ResponseRate Natural log of the survey response rate 3.912 Mean value from Richardson and Loomis 

(2009) following the Johnston et al. (2006) 

approach where values for methodological 

attributes are set at mean values from the 

metadata 

Mail Indicates mail surveys 0.851

StudyYear Year of study 1992

Source: U.S. EPA Analysis, 2015

EPA does not currently have either species-specific estimates of the population effects of the final ELGs or 

population models to estimate future population changes for the affected T&E species due to improved 

aquatic habitat conditions. In the absence of such estimates, EPA used best professional judgment to assign a 

range of potential improvements in the T&E populations based on the expected reductions in AWQC 

exceedances under the post-compliance scenario. To estimate total population increases as a result of each 

analyzed regulatory option, EPA assumed minimal increases in population size of 0.5, 1, or 1.5 percent. EPA 

then weighted these population growth estimates within states by the proportion of reaches used by T&E 

species expected to meet wildlife-based AWQC under each option. The natural log of these weighted 

population growth estimates under each scenario was used to assign a value to the ChangeSize parameter 

estimate. EPA used the approach described in Johnston et al. (2006) and assigned mean study values from 

Richardson and Loomis (2009) for the methodological variables (ln ResponseRate, Mail, and StudyYear).

Although it could not find published literature to support dose-response relationships for any of the species 

assessed or any other numerical estimates of benefit that might occur to T&E species because of the rule, EPA 

believes that its low (0.5 percent), medium (1.0 percent) and high (1.5 percent) estimates of population 

growth for T&E species (occurring within affected reaches exceeding AWQC at baseline) because of the final 

ELG are reasonable. This is because of the high number of species filtered from further assessment (as 

described in Sections 5.3.1 and 5.3.2, only four species met all criteria for inclusion), because population 

increases were estimated to occur only in reaches meeting AWQC because of the rule (the majority of habitat 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 5: T&E Species Benefits

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used by T&E species assessed meets AWQC at baseline), and because few individuals must be saved to attain 

these growth estimates. For example, consider a T&E species with a state-level population of 10,000 

individuals (reasonable for threatened species, likely an over-estimate for endangered species and endemic 

species), residing in 10 reaches - only one of which does not meet AWQC criteria at baseline (but does under 

rule options), and a population growth rate of 0 in the baseline. This species would achieve EPA’s low, 

medium and high population increases if the ELG results in one fewer premature mortality every 5 years (low 

growth) and approximately one fewer premature mortality every 1.5 years (high growth) between 2019 and 

2042. This low level of effect needed to meet growth assumptions, when combined with known effects of 

wildlife living in areas with poor water quality, make this level of population increase reasonable.

Because population growth was assessed at the state level, EPA was unable to attribute benefits to a specific 

steam electric power plant and therefore to account for the timing of benefits based on the assumed control 

technology implementation year. EPA assumed that benefits begin accruing in 2021 for all states. This year is 

the midpoint of the period of 2019 through 2023 when plants are assumed to implement control technologies 

to comply with the revised effluent limits and standards. 

For each state, EPA estimated household WTP for improved protection of T&E species resulting from the 

final ELGs using Equation 5-1 and the independent variable assignments presented in Table 5-3. EPA 

estimated total annual benefits for the years between 2021 and 2042 by multiplying household WTP by the 

number of households in each state for a given year. EPA then calculated the value of benefits for each year 

and the annualized total WTP values for each state using 3 percent and 7 percent discount rates. 

5.5 Results

Table 5-4 presents the annualized total benefits calculated using discount rates of 3 percent and 7 percent. The 

monetized benefits to T&E species of Option D are concentrated in two states: Alabama (AL) and Georgia

(GA). The annualized benefits of Option D are $0.02 million for the medium population increase using a 

3 percent discount rate.
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Table 5-4: Estimated Annualized Benefits to T&E Species from WQ Improvements (Millions 2013$)a,b

Discount 

Rate State

Option A Option B Option C Option D Option E

Low Medium High Low Medium High Low Medium High Low Medium High Low Medium High

3%

AL <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

GA <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01

MD $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01

SC $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 <$0.01 $0.01 <$0.01 <$0.01 $0.01 <$0.01 <$0.01 $0.01

Total $0.01 $0.01 $0.02 $0.01 $0.01 $0.02 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03

7%

AL <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

GA <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01

MD $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01

SC $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

Total $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03

Source: U.S. EPA Analysis, 2015
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5.6 Implications of Revised Steam Electric Plant Loading Estimates

As described in Section 1.4.3, EPA revised its estimates of pollutant loadings in steam electric power plant 

discharges after completing the benefit analyses. The revisions affect baseline discharges of several pollutants 

with wildlife AWQC exceedances. For several reasons — notably the fact that revisions do not affect all 

plants equally, and that species vulnerability to steam electric pollutants is determined based on threshold 

effects — it would be inappropriate to simply scale the monetized benefits based on the aggregate changes in 

loadings. The impacts of the loading revisions on estimated benefits to E&T specifies may be similar to that 

discussed in Section 4.4 for water quality improvement benefits.

5.7 Limitations and Uncertainties

Table 5-5 summarizes the caveats, omissions, biases, and uncertainties known to affect EPA’s estimates of 

the benefits to T&E species and indicates the direction of the potential bias. 

Table 5-5: Limitations and Uncertainties in the Analysis of T&E Species Benefits 

Issue

Effect on 

Benefits 

Estimate

Notes

The analysis is based 

loadings that were 

subsequently revised by EPA 

Overestimate Revised loadings are lower than the loadings used to estimate 

benefits. The changes indicate that number of exceedances of 

AWQCs may be lower in the baseline, and therefore the

improvements resulting from the ELGs may be lower. As 

discussed in Section 4.4, however, the magnitude of the 

overstatement may be small.

Change in T&E populations 

due to the effect of steam 

electric ELGs is uncertain

Uncertain Data necessary to quantitatively estimate population changes are 

unavailable. Therefore, EPA used best professional judgment to 

assess reasonable changes in T&E populations. Actual effects of 

the final rule may be larger or smaller than projected changes in 

the population of T&E species assumed in this analysis.

Only those T&E species 

listed as threatened or 

endangered on the 

Endangered Species Act are 

included in the analysis

Underestimate The databases used to estimate benefits to T&E species exclude 

all species considered threatened or endangered by scientific 

organizations but not protected by the ESA. The magnitude of 

the underestimate is likely to be significant, since the proportion 

of imperiled fish and mussel species is high (e.g., Jelks et al 

2008, Taylor et al 2007).

Benefit estimates do not 

include monetized values for 

potential population increases 

in Hellbender salamander 

(Cryptobranchus 

alleganiensis a)

Underestimate It is likely that population increases in Cryptobranchus 

alleganiensis have value to the public. In addition to bequest, 

altruistic, and existence values, salamanders may have aesthetic

or cultural values. Salamanders also provide beneficial 

ecological services through indirect biotic control of species 

diversity and ecosystem processes, connection of energy and 

matter between aquatic and terrestrial landscapes, contributing 

to soil dynamics, and providing available stores of energy and 

nutrients for tertiary consumers (David and Welsh 2004).
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 5: Threatened and Endangered Species Benefits

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Table 5-5: Limitations and Uncertainties in the Analysis of T&E Species Benefits 

Issue

Effect on 

Benefits 

Estimate

Notes

Benefit estimates are likely to 

include only a subset of 

species that may be affected

Underestimate EPA did not consider species for which water quality was not 

listed as an important factor to species recovery. Because water 

quality issues may be important to species recovery even if not 

listed explicitly in species recovery plans this analysis may omit 

species that are likely to benefit from the final ELG.

Benefit transfer introduces 

uncertainties

Uncertain Value may over- or understate true WTP values (See Section 4

for more details).

Ecological roles filled by 

T&E species 

Underestimate WTP values are unlikely to include changes to food-webs and 

ecosystem stability as a consequence of the restoration (or loss) 

of T&E species.

Overlap between WTP 

estimates for T&E species 

and the WTP estimates for 

improvements in water 

quality

Overestimate There may be some overlap between WTP estimates for T&E 

species and the WTP estimates for improvements in water 

quality because WTP values for improvements in water quality 

may inherently include benefits to T&E species. However, none 

of the studies in EPA’s meta-analysis of WTP for water quality 

improvements specifically mentioned or otherwise prompted 

respondents to include benefits to T&E species populations (see 

Chapter 4); therefore, any overlap is likely to be minimal.

WTP estimates do not take 

into account possible 

substitution for effects for 

similar species. 

Overestimate WTP estimates may be affected by the availability of related 

species (Hoehn and Loomis, 1993), however Kahneman and 

Knetsch (1992) argue that substitution effects may not apply to 

the values associated with endangered species, because their 

uniqueness is the essence of their existence value.
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September 29, 2015 6-1

6 Benefits from Avoided Impoundment Failures

EPA has promulgated several rules affecting the steam electric industry recently: the Cooling Water Intake 

Structures (CWIS) rule for existing facilities (79 FR 48300), the CCR rule (80 FR 21302), and the CPP rule 

(FR publication forthcoming). EPA approached analyses associated with each rulemaking carefully. EPA also 

recognizes that the steam electric industry complying with three regulations cumulatively in a very short 

period of time may choose a different compliance path than assumed in the analyses. The cumulative effect 

introduces uncertainty on the compliance path, and thereby on the benefits and costs associated with these 

rules.

EPA expects that the operational changes prompted by the final ELGs will cause some plant owners to reduce 

their reliance on impoundments to manage coal combustion residuals. The CPP rule is likely to have similar 

effect on plant owners’ reliance on impoundments. These changes will affect the future probability and/or 

magnitude of impoundment failures and the resulting accidental, and sometimes catastrophic, releases of coal 

combustion residuals. This rule takes the CPP rule into account in the baseline whereas the CCR rule did not, 

though it included the proposed CPP rule as a sensitivity analysis. Because the timing was such that the CCR 

rule did not include compliance with CPP in its main analysis, the benefits and costs for the ELGs of 

complying with the full set of rules may be higher than reported here, although EPA has done its best to 

incorporate the effect of all three rules in this analysis.

Benefits from the reduced risk of impoundment failures include avoided cleanup costs, environmental 

damage, and transaction costs. EPA’s analysis of the monetary value of avoided impoundment failures is 

based on the identification of impoundments that would be affected by each of the regulatory options. EPA 

estimated benefits from avoided impoundment failures based on the probability of a release for each 

impoundment in a given year, the capacity of the impoundment, and the cost (including cleanup costs, natural 

resource damages (NRD) and transaction costs) per gallon of coal combustion residuals slurry spilled. 

Benefits are calculated as the difference between expected failure costs for a regulatory option and expected 

failure costs under baseline conditions, over the period of 2019 through 2042.

6.1 Methods and Data

This section describes the methodology and data used to determine the baseline and post-compliance 

probability of impoundment failures, assign costs to the releases, and estimate the total present and annualized 

values of benefits resulting from the final ELGs.

As described below, the ELG analysis follows an approach similar to that used in the final CCR rule analysis 

(U.S. EPA, 2014), but uses ELG-specific data and assumptions that reflect differences in the impoundment 

universe and failure probability. For example, the final ELG analysis considers a universe of 1,070 

impoundments at 1,080 steam electric plants to which the ELGs apply (see Section 6.1.2), which is larger 

than the 735 impoundments analyzed for the CCR rule. Fifty-three of the impoundments in the Steam Electric 

universe are projected to close as a result of the CCR rule, leaving 1,017 impoundments in the ELG analysis 

baseline. The ELG analysis also uses assumptions that reflect implementation of the CCR rule in the ELG 

baseline. Thus, the risk of an impoundment failure applied to big impoundments subject to wall breaches 

(0.044 percent; see Section 6.1.1) reflects the residual risk remaining after implementation of the CCR rule, as 

compared to the higher historical rate of 0.09 percent used as baseline in the CCR rule analysis. 
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6.1.1 Baseline Failure Probability and Release Quantity

EPA estimated the future probability of coal combustion residuals releases from impoundments based on 

historical trends, accounting for the primary types of releases applicable to different types of impoundments. 

The approach builds on the methodology used by EPA Office of Resource Conservation and Recovery 

(ORCR) for the analysis of final regulations governing the disposal of CCR in impoundments (i.e., “CCR 

rule”; U.S. EPA, 2014). 

To determine the frequency of releases, EPA used data from a survey of impoundments conducted in support 

of the CCR rule (U.S. EPA, 2012d). The surveyed plants had a total of 656 CCR impoundments, and the 

survey obtained information from owners and operators about impoundment releases between 1999 and 2008. 

Two of the survey respondents also provided data for two additional releases that occurred in 1995 and 1998. 

In total, the survey provides data for 49 relevant historical releases49 over a total of 6,565 impoundment-year 

observations.50

In response to the benefit analysis for the proposed ELGs, EPA received several comments noting that 

seepage events, which account for 13 of the 49 release events, are not likely to result in significant cleanup or 

other types of costs. EPA excluded these 13 seepage events from the historical release data used to determine 

the frequency of relevant releases. The remaining 36 releases are summarized in Appendix J.

EPA sorted the 36 releases into two categories based on the circumstances and cause of the release: wall 

breaches (4 releases), which are structural failures of external perimeter embankments, and other releases (32 

releases). These other releases include overtopping (8 failures); miscellaneous causes (13 failures) such as 

sink hole, stack failure, pump failure, hydraulic dredging pipe failure, liner perforation, internal dike breach 

(not perimeter), seal failure, discharge structure disturbed during maintenance, and embankment slough; or 

unknown causes (11 releases). 

EPA assumes, and the historical release data shows, that the potential for wall breaches exists only for large 

impoundments that meet certain structural integrity “factor of safety” design criteria specified in the CCR 

rule. These impoundments (labeled big for the purpose of the analysis) are those with: 

 Height (impounding elevation) of five feet or more above the upstream toe, and storage volume of 20 

acre-feet or more, or

 Height (impounding elevation) of 20 feet or more above the upstream toe.

Impoundments that do not meet either criterion (labeled small) are assumed not susceptible to failure by wall 

breach, but susceptible only to other types of releases. Based on these criteria, the ORCR survey (U.S. EPA, 

2012d) includes 444 big impoundments and 212 small impoundments. EPA calculated the frequency of 

releases based on the 36 historic releases for each type of failure (wall breach vs. other) and type of 

impoundment (big vs. small). 

 

49 This differs from the risk of failure estimated for the proposed ELG. For the proposed rule analysis, EPA had 

counted 42 CCR pond damage cases rather than the current count of 49 cases. EPA corrected the survey 

database for nine cases that had been entered as single incidents but were in fact separate incidents, leading to 

an interim total of 52 cases. Two of these 52 cases did not involve coal combustion residuals ponds (they 

involved a metal cleaning waste basin and a coal pile sump) and one case did not involve the release of 

impounded coal combustion residuals material (it involved an oil and grease exceedance due to servicing of a 

pump located near a coal combustion residuals pond). 

50 The survey responses provided 10 years of data for 656 impoundments, an additional 4 years of data for one 

impoundment, and an additional year of data for another impoundment, for a total of 6,565 observations (656 × 

10 + 1×4 + 1×1).
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The probability of a release is assumed to be uniform over time and across all impoundments within a 

category (big or small), irrespective of other impoundment characteristics such as age, amount of coal 

combustion residuals managed, etc. In practice, the probability of a release may depend on impoundment 

characteristics and could therefore change as a result of the final rule. However, EPA did not have sufficient 

data to model the probability as a function of impoundment characteristics.

The impoundment survey conducted by ORCR (see U.S. EPA, 2012d) provides data on the release volume 

and impoundment capacity for 17 of the 36 documented releases. For each type of release and impoundment 

category, EPA calculated a “capacity factor” that represents the ratio of gallons of coal combustion residuals

released compared to the design capacity of the impoundment involved in the release.

Table 6-1 summarizes the historical probability of impoundment release and the capacity factor by release and 

impoundment type.

Table 6-1: Probabilities of CCR impoundment releases, based on analysis of 49 historical release 

events 1995–2008

Impoundment Type – Big 

(at least 5 feet high AND at least 20 acre-feet OR at least 20 feet high independent of volume)

Impoundments with observations for 10 years 444

Additional observations (impoundment-years) 4

Total number of observations 4,444

Release Type Wall Breach Other

Number of releases 4 26

Probability of a release – number of releases per impoundment per year 0.09% 0.59%

Capacity factor – volume released as percent of capacity 27.42% 2.91%

Impoundment Type – Small 

(Does not fit "big" criteria)

Impoundments with observations for 10 years 212

Additional observations (impoundment-years) 1

Total number of observations 2,121

Release Type Wall Breaches Other

Number of releases Not applicablea

6

Probability of a release – number of releases per impoundment per year Not applicablea

0.28%

Capacity factor – volume released as percent of capacity Not applicablea

0.41%

a. Small impoundments only incurred "other" releases (no wall breaches).

Source: U.S. EPA Analysis, 2015

EPA adjusted the release probabilities shown in Table 6-1 to account for implementation of the final CCR 

rule.51 The rule establishes minimum national criteria for the storage of CCR in surface impoundments at 

coal-fired electric utility plants, regulating CCR as a non-hazardous waste. It requires standardized pollution 

control measures as well as monitoring and corrective actions in the event of a leak. Additionally, it subjects 

all impoundments to location restrictions, design and operating conditions, groundwater monitoring, closure 

requirements, and post-closure care. For more details on the CCR rule, see U.S. EPA (2014). 

As detailed in U.S. EPA (2014), some plant owners may close their existing impoundments in response to the 

CCR rule, effectively eliminating the probability of a future release from these impoundments. For 

 

51 The CCR rule is not expected to affect the capacity factors in the event of a release.
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impoundments that remain operational, the CCR rule is expected to reduce the release rate through the 

implementation of structural integrity programs, including regular inspections and other safeguards. For these 

impoundments, EPA estimated that the CCR rule would reduce the probability of releases from big 

impoundments to 0.044 percent annually for wall breaches and to 0.16 percent for other releases (compared 

with 0.09 percent and 0.59 percent, respectively, in Table 6-1). For small impoundments, EPA expects that 

the CCR rule will reduce the probability of a release from 0.28 percent (Table 6-1) to 0.07 percent. See U.S. 

EPA (2014) for more detail on these release probability assumptions. 

Table 6-2 summarizes the release probability and capacity factor assumptions used for big and small 

impoundments in this analysis, taking the effects of the CCR rule into account.

Table 6-2: Release Probability and Capacity Factor Assumptions for the ELGs

Type

Annual Probability of Release Capacity Factor

Wall Breach Other Release Wall Breach Other Release

Big 0.044% 0.16% 27.42% 2.91%

Small NA 0.07% NA 0.41%

Source: U.S. EPA Analysis, 2015

6.1.2 Effects of the ELGs

The 1,080 steam electric power plants subject to the Steam Electric ELGs have a total of 1,070 

impoundments. Following implementation of the CCR rule, EPA expects 1,017 of these impoundments to 

continue operation as of 2023. As discussed in Section 1.3, EPA included the effects of the CPP rule in the 

baseline for the ELG analysis. EPA projects that the CPP rule will result in some generating unit ceasing 

operation (retiring) or converting, which will also affect impoundments that receive wastestream from the 

units. Due to the uncertainty in plant owners’ decision to continue to operate impoundments at plants where 

some but not all generating units are converted or retire, EPA estimated the impacts of the CPP in two ways:

 Low bound estimate: EPA adjusted impoundment capacity only for those plants where all units that 

send wastewater streams to an impoundment retire as a result of the CPP rule.

 High bound estimate: EPA adjusted impoundment capacity considering the share of the generating 

capacity of units that retire as a result of the CPP rule. 

This CPP-adjusted universe of impoundments (883 to 925 impoundments) represents the baseline for the ELG 

analysis, i.e., EPA evaluates the incremental impacts of the ELG on those impoundments EPA determined 

would handle coal combustion residuals wastestreams by the time the plant would comply with the ELGs

after accounting for both the CCR and CPP rules. 

EPA categorized these impoundments as big or small following the criteria outlined in Section 6.1.1. EPA 

used the Steam Electric Industry survey (U.S. EPA, 2010c) as the primary source of impoundment size data, 

supplemented by information collected in the CCR survey (see U.S. EPA 2012d) for impoundments that are 

common to both data sets.52 Table 6-3 summarizes the results.

 

52 To categorize each of the impoundments as big or small, EPA imputed impoundment height based on the 

volume, difference in elevation, and the reported maximum berm height from U.S. EPA (2010b). Together with 

this height data, EPA used the reported capacity to categorize impoundments that are clearly either big or small. 

EPA used ORCR impoundment data (from U.S. EPA 2010c) to categorize an additional 69 impoundments 

where there was clear overlap between the two impoundment data sets. EPA categorized an additional 187 

impoundments as big based on at least one height indicator being over 20 feet or at least one height indicator 
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Table 6-3: Steam Electric Impoundments by Size in ELG Baseline

Type

Number of Impoundments Impoundment Capacity

Count Percent of 

Total

Total (million gallons) Percent of 

Total Low Bound High Bound Low Bound High Bound

Big 632 668 72% 687,758 713,317 95%

Small 251 257 28% 34,336 34,348 5%

Total 883 925 100% 722,094 747,665 100%

Source: U.S. EPA Analysis, 2015

For each of these impoundments, EPA estimated the expected number of magnitude of releases for each year 

between 2019 and 2042, and estimated the expected costs associated with the releases. EPA did this 

calculation for the baseline and under each of the five regulatory options. 

Specifically, EPA used the probability of release established based on the historic data and revised to account 

for implementation of the CCR rule (Table 6-2) to estimate the expected number of releases from each of the 

steam electric power plant impoundments in the baseline. EPA used the applicable capacity factor to estimate 

the volume involved in a release from each impoundment. For example, EPA assumes that an “other” release 

from a big impoundment involves a volume of coal combustion residuals equal to 2.91 percent of the 

impoundment’s capacity. Figure 6-1 illustrates assumptions used for the baseline scenario. 

The Steam Electric ELG is anticipated to change how plant owners or operator handle their coal combustion 

residuals, potentially reducing the quantity of FGD solids or ash managed using impoundments. This lower 

coal combustion residuals volume is in turn expected to reduce the amount of coal combustion residuals

accumulating in the impoundments in any given year. For this analysis, EPA assumed that the amount of coal 

combustion residuals released in the event of a wall breach or other release is reduced in proportion to the 

reduction in the amount of coal combustion residuals handled by the impoundment. Thus, for each scenario, 

EPA assumed that an impoundment has the same expected number of releases post-compliance, but 

calculated the volume released by multiplying the capacity factor by an adjusted volume that reflects the 

reduction in the amount of coal combustion residuals handled wet (see U.S. EPA, 2015g; DCN SE05831(CBI 

version / DCN SE05832 (non-CBI version) for the calculations). EPA assumed that the reduction in volume 

occurs in the same year as the technology implementation year assumed in other parts of the analysis (see 

Section 1.5.3).

 

being over 5 feet together with capacity over 20 acre-feet. For impoundments for which no height data was 

provided, EPA categorized impoundments based on capacity only.
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Figure 6-1: Baseline Release Probability and Capacity Factor Assumptions 

1 Changes in the risk of impoundment failure in impoundments affected by the CPP rule are not attributable to the ELGs and do not 

generate benefits attributable to the ELGs in this analysis. 

6.1.3 Costs of a Release

The following sections discuss three categories of costs associated with impoundment releases: cleanup, 

NRD, and transaction costs. All dollar values are presented in year 2013 dollars.53

6.1.3.1 Cleanup Costs

EPA estimated per-gallon cleanup costs based on five historical impoundment failures. The average unit cost 

associated with these historical incidents is $1.35 per gallon released (see Table 6-4). Details on the five 

incidents are provided below and reflect additional research EPA conducted since the proposed ELG analysis. 

 

53 As needed, costs were updated to 2013 dollars using the Construction Cost Index from the Engineering News 

Report (unless otherwise indicated).
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Table 6-4: Documented Cleanup Costs from Impoundment Releases

Incident Volume Spilled 

(million gallons)

Cleanup Cost 

(millions; 2013$)

Unit Cleanup 

Cost (2013$)

Oak Creek 5 $13 $2.83

TVA Widows Creek 6 $10 $1.64

Martins Creek 100 $62 $0.62

Massey 230 $89 $0.39

TVA Kingston 1,100 $1,379 $1.25

Average 288 $311 $1.35

The Oak Creek release occurred in October 2011 when a bluff collapsed near an ongoing construction project 

at the Wisconsin Energy Oak Creek power plant, releasing 22,720 cubic yards (or 4.6 million gallons) of coal 

ash into Lake Michigan. The collapse also carried “debris from the construction worksite, including vehicles, 

heavy machinery, a filter press, a frac tank and four miscellaneous conex boxes filled with unknown amounts 

of miscellaneous equipment, down the bluff and into Lake Michigan.” (Wisconsin Department of Justice 

(DOJ), 2013). Wisconsin Energy’s immediate response to the spill included the placement of 1,500 feet of 

containment berms and booms and a geotechnical analysis of the stability of the bluff (Wisconsin DOJ, 2013; 

Jones and Behm, 2011). Additional response actions included excavation and removal of the material that had 

been spilled, with cleanup being complete by the end of November 2011 (Wisconsin DOJ, 2013). According 

to the U.S. EPA On-Scene Coordinator (U.S. EPA OSC, 2012), Wisconsin Energy reported spending 

$12.1 million on cleanup and restoration. After updating to 2013$ (for a total of $13 million), unit costs are 

$2.83 per gallon spilled.

The Tennessee Valley Authority (TVA) Widows Creek spill involved the release of 6.1 million gallons of 

gypsum, water, and fly ash. Updated to 2013$, cleanup costs for dredging of the creek and other activities 

were approximately $10 million (TVA, 2009), or $1.64 per gallon spilled.

The Martins Creek release involved the discharge of 100 million gallons of slurry over the course of 3 days, 

resulting from the failure of a wooden stop log. In its 2006 annual report (PPL Corporation, 2006), PPL 

Corporation estimated that the costs of the remediation effort were $48 million. After updating the revised 

costs to 2013$ (for a total cost of $62 million), the unit cost of this spill is $0.62 per gallon spilled.

The Massey Coal slurry spill involved the collapse of a 2,000 acre-foot (651.7 million gallon) surface 

impoundment on top of an idled underground mine. The Massey Energy 2002 annual financial report (10-K; 

Massey Energy, 2002) states that the release involved 230 million gallons and that Massey incurred a total of 

$58.3 million of cleanup costs in connection with the spill. Updated to 2013$ (for a total cost of $89 million), 

the unit cost for this spill is $0.39 per gallon spilled. 

The TVA Kingston release involved approximately 1.1 billion gallons of slurry being released from an ash 

pond onto 300 acres, primarily the Watts Bar Reservoir and shoreline property. The spill also damaged three 

homes, interrupted utility services, and blocked a local road (TVA, 2009). Cleanup activities included ash 

dredging and processing, ash disposition, infrastructure repair, dredge cell repair, dike reinforcement, 

construction of temporary ash storage basins, and others. Updated to 2013$, the cleanup costs were 

approximately $1.38 billion (TVA, 2010; 2011), or $1.25 per gallon spilled.

In early February 2014, a coal ash release occurred at the Dan River power plant operated by Duke Energy in 

North Carolina. The release of 24 to 27 million gallons of ash and ash pond water was caused by a break in a 

48-inch stormwater pipe beneath the ash basin (Duke Energy, 2014). Cleanup involved the removal of 2,500 
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tons of ash and contaminated sediment that settled against a dam, and an additional 500 tons that settled in 

other parts of the river and municipal water treatment settling tanks (Associated Press, 2014). 

Duke Energy has stated that company investors and insurers will pay for the cleanup rather than ratepayers 

(Duke Energy, 2014). Duke Energy’s second quarterly financial report in 2014 reports that the company spent 

approximately $20 million in repairs and remediation related to the spill through June 2014, and completed 

cleanup in July. Additionally, according to a plea agreement signed by Duke Energy subsidiaries on May 14, 

2015, Duke Energy agreed to spend $34 million for environmental damages, including a $24 million payment 

to the National Fish and Wildlife Foundation to benefit riparian areas of North Carolina and Virginia, plus 

$10 million in wetland mitigation bank credits. Total cleanup costs, transaction costs, and NRD were not 

available at the time that EPA conducted this analysis, however, and this incident was therefore not included 

in the cleanup costs shown in Table 6-4, nor used in this analysis.

6.1.3.2 Natural Resource Damages

Israel (2006) provides a detailed state-by-state summary of NRD programs, including some prominent cases

(arising from oil spills, chemical spills, and other incidents) in each state. Israel (2013) provides an updated 

version of the accounting with additional cases identified since the 2006 study. Appendix J lists the 137 NRD 

cases from Israel (2006; 2013) that provide quantitative estimates of NRD restoration and compensation 

costs.54

Releases resulting from impoundment failures may affect resources similar to those that were affected by 

some of the NRD cases identified in Israel (2006; 2013) and therefore would be expected to have similar 

NRD costs. Of the 137 NRD settlements identified in Israel (2006; 2013), EPA identified 65 cases that are 

relevant to this analysis based on the resources affected and the general circumstances of the releases. In 

particular, EPA excluded as potentially less relevant settlements for NRD, which, based on the description 

provided in Israel, involved damage to groundwater only or to ocean/coastal resources, or resulted from 

legacy pollution associated with Superfund sites. 

Table 6-5 shows summary statistics for the 65 NRD settlements EPA retained as relevant to this analysis,

compared to the summary statistics for the cleanup costs in Table 6-4. As shown in the table, the mean NRD 

settlement is approximately $13.7 million, or 4 percent of the mean total cleanup cost. 

 

54 NRD does not include cleanup costs (or legal and transaction costs, if reported) but includes only the resource 

restoration and compensation values. For example, in one case, Israel (2006) reported that "In total, the State's 

claim was $764 million, $342 million of which was restoration cost damages, $410 million of which was 

compensable value damages, and $12 million of which was assessment and legal costs." For this case, EPA 

used the sum of $342 million and $410 million as NRD (i.e., excluded assessment and legal costs). EPA used 

values for individual cases discussed in the study, focusing exclusively on NRD and excluding or subtracting 

assessment costs when those costs were reported separately. EPA also excluded NRD values that represent the 

aggregate of several cases when it was not possible to discern NRD for individual cases.
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Table 6-5: NRD Settlements Summary Statistics

Natural Resource Damages 

(2013$)a Cleanup Costs (2013$)b NRD as a Percent of Cleanup Costs

Minimum $44,000 $10,000,000 0%

Median $1,965,000 $62,000,000 3%

Mean $13,723,000 $311,000,000 4%

Maximum $439,545,000 $1,379,000,000 32%

a. Based on Israel (2006; 2013); updated using changes in the GDP. If a year is not provided for a case, EPA updated the value 

based on the year of the study that identified the case (i.e., 2006 or 2013).

b. Based on cleanup costs in Table 6-4.

To estimate expected NRD costs for future impoundment releases, EPA assumed that the NRD varies 

depending on the magnitude of the release in proportion to cleanup costs. To derive a per-gallon estimate of 

the expected NRD value for future releases, EPA divided the mean NRD settlement of $13.7 million by the 

mean cleanup cost ($311 million). This calculation yields an NRD estimate of 4 percent of cleanup costs, or 

$0.05 per gallon spilled (4 percent of $1.35 /gallon).

Note that this estimate provides an approximate value for NRD resulting from impoundment failures as a 

function of the volume of coal combustion residuals released, and is appropriate for analyses that look at 

NRD over a range of locations and circumstances. EPA expects that actual damages from any given release 

would be highly location- and release-specific.

6.1.3.3 Transaction Costs

For this analysis, transaction costs include the costs associated with negotiating NRD, determining 

responsibility among potentially responsible parties, and litigating details regarding settlements and 

remediation.55 EPA estimated transaction costs based on data showing transaction costs as a share of total 

cleanup costs at Superfund sites and the share of spending that represents total transaction costs. Table 6-6

shows the data sources. 

Table 6-6: Studies Summarizing Transaction Costs as a Share of Superfund 

Spending (for potentially responsible parties)

Acton (1995) 27%

Acton and Dixon (1992) 17%

Dixon, et al. (1993) 32%

Steinhardt et al. (1994) 33%

Average 27%

Multiplier (transaction costs as a share of cleanup cost)a 37%

a. Multiplier is calculated as Average/(1 - Average)

These data indicate that, on average, transaction costs represent 27 percent of total costs. As the purpose is to 

estimate unit costs per gallon spilled, transactions costs represent 37 percent of the subset of costs that are 

 

55 These activities involve services, whether performed by the complying entity or other parties, that EPA expects 

would be required in the absence of this final rule in the event of an impoundment failure. Accordingly, it is 

appropriate to account for the avoided resource cost of these services as social benefits in the benefit-cost 

analysis. Note that the transaction costs do not include fines, cleanup costs, damages, or other costs that 

constitute transfers or are already accounted for in the other categories analyzed separately.
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cleanup costs, calculated as 0.27/(1-0.27). Therefore, the estimated transaction costs per gallon of coal 

combustion residuals slurry spilled are $0.50 (37 percent of $1.35/gallon).

6.1.3.4 Total Release Costs

Table 6-7 summarizes unit costs for cleanup costs, NRD, and transaction costs. Total impoundment release 

costs are $1.90 per gallon spilled.56

Table 6-7: Unit Costs for Impoundment Releases (2013$)

Cost Component Unit Cost ($/gallon spilled)

Cleanup costs $1.35

NRD $0.05

Transaction costs $0.50

Total costs $1.90

6.2 Results

The final ELGs will provide benefits by reducing the impact of releases from impoundments that are expected 

to see reduced utilization as a result of the final rule, but would continue to operate in the absence of the final 

rule. For each of the impoundments included in the analysis, EPA calculated the difference between the 

annualized costs from future expected failures under the baseline and each analyzed regulatory option. The 

calculation involves the following steps:

 Multiplying the release rate for a given impoundment and year by the capacity factor, impoundment 

volume (adjusted if needed to reflect the effects of the ELG), and total unit costs for the release

(including cleanup, NRD, and transaction costs); 

 For each regulatory scenario, subtracting the costs of expected releases from the cost of expected 

releases under the baseline; and

 Discounting for future years, aggregating across the analysis time horizon (2019 to 2042), and 

annualizing over a 24-year period using rates of 3 percent and 7 percent.

Table 6-8 shows the total number of impoundment failures estimated over the period of 2019-2042 in the 

baseline and under each of the five regulatory options. Expected failures are reported as a range to reflect the 

range of the anticipated effects of the CPP rule discussed in Section 6.1.2. These values reflect the number 

and types of impoundments (big and small) and the associated failure probability for each type of failure (wall 

breach and other). Table 6-9 shows the estimated volume of coal combustion residuals released annually in 

these expected failures, after full compliance by all steam electric power generating plants. Estimates of the 

coal combustion residuals volume released reflect the size of the impoundments involved in the expected 

failures and the capacity factor for each type of failure.

The number of failures estimated in the ELG baseline (approximately 8 wall breaches and 33 other failures 

over 24 years) is consistent with the post-compliance failures estimated in the final CCR rule analysis (15 

wall breaches and 91 other releases over 100 years; see U.S. EPA 2014), given differences in the 

impoundment universe noted in the introduction to this Chapter. Similarly, the number of avoided failures 

estimated to result from implementation of the final ELGs (Option D), which is approximately 2 wall 

 

56 In the economic analysis of the proposed rule, EPA capped release costs for any single incident at $1.3 billion 

based on the total estimated cleanup costs of the TVA Kingston spill. However, EPA removed this cap for the 

final rule analysis, since future incidents could feasibly exceed the damages associated with Kingston.
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breaches and 7 other releases over 24 years, seems consistent with the number of avoided failures estimated 

for the final CCR rule (32 wall breaches and 429 other releases over 100 years; see U.S. EPA 2014), given the 

differences in the universe of impoundments and in assumptions regarding the failure rate for the ELG 

analysis.

Table 6-8: Total Expected Number of Releases in 2019 through 2042, by Failure Type

Regulatory 

Option

Expected Number of Failures Reduction in Expected Number of Failures

Wall Breaches Other Releases Wall Breaches Other Releases

Low Bound Estimate of CPP Effects

Baseline 7.7 32.6 0.0 0.0

Option A 7.3 30.8 0.4 1.7

Option B 7.3 30.8 0.4 1.7

Option C 6.7 28.1 1.0 4.5

Option D 6.1 25.7 1.6 6.9

Option E 6.1 25.7 1.6 6.9

High Bound Estimate of CPP Effects

Baseline 7.7 32.6 0.0 0.0

Option A 7.2 30.4 0.5 2.1

Option B 7.2 30.4 0.5 2.1

Option C 6.5 27.4 1.2 5.2

Option D 5.8 24.4 1.9 8.1

Option E 5.8 24.4 1.9 8.1

Source: U.S. EPA Analysis, 2015
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Table 6-9: Expected Total Coal Combustion Residuals Volume Released Annually, by Failure Type

(Million Gallons)

Regulatory 

Option

Estimated CCR Volume Released Estimated Reduction in CCR Volume 

Releaseda

Wall 

Breaches

Other 

Releases All Releases Wall Breaches

Other 

Releases All Releases

Low Bound Estimate of CPP Effects

Baseline 125 48 173 0 0 0

Option A 115 45 160 9 4 13

Option B 115 45 160 9 4 13

Option C 88 34 122 37 14 51

Option D 82 32 114 42 16 59

Option E 82 32 114 42 16 59

High Bound Estimate of CPP Effects

Baseline 125 48 173 0 0 0

Option A 114 44 158 10 4 - 4 14

Option B 114 44 158 10 4 - 4 14

Option C 86 33 119 39 14 - 15 54

Option D 79 31 110 45 16 - 18 63

Option E 79 31 110 45 16 - 18 63

Source: U.S. EPA Analysis, 2015

a. The values reflect reductions in the volume of coal combustion residuals released in expected failures for years 2023-2042, after 

compliance by all steam electric power generating plants. Reductions in years 2019-2022 are less than reported in this table due to

the assumed distribution of permit renewals and control technologies implementation over the period of 2019 through 2023.

Table 6-10 shows the total benefits of avoided impoundment failures, calculated as the sum of the avoided 

release costs across all impoundments expected to be affected by the final rule under each analyzed regulatory 

option. The range of benefits reflects the range of the anticipated effects of the CPP rule discussed in Section 

6.1.2. For each option, avoided wall breaches account for 72 percent of benefits, while other releases account 

for the remaining 28 percent.
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Table 6-10: Estimated Annualized Benefits of Avoided Impoundment Failures by Release Type 

(Millions; 2013$)a

Discount 

Rate

Regulatory 

Option

Wall Breaches Other Releases All Releases

Low Bound High Bound Low Bound High Bound Low Bound High Bound

3%

Option A $14.5 $16.5 $5.6 $6.4 $20.1 $22.9

Option B $14.5 $16.5 $5.6 $6.4 $20.1 $22.9

Option C $59.3 $62.7 $22.9 $24.2 $82.2 $86.9

Option D $68.8 $74.1 $26.7 $28.8 $95.6 $102.9

Option E $68.8 $74.1 $26.7 $28.8 $95.6 $102.9

7%

Option A $11.6 $13.3 $4.5 $5.1 $16.1 $18.4

Option B $11.6 $13.3 $4.5 $5.1 $16.1 $18.4

Option C $47.9 $50.7 $18.5 $19.6 $66.4 $70.3

Option D $55.9 $60.3 $21.7 $23.4 $77.7 $83.7

Option E $55.9 $60.3 $21.7 $23.4 $77.7 $83.7

Source: U.S. EPA Analysis, 2015

a. Baseline value of total failure costs minus option value of total failure costs.

6.3 Implications of Revised Steam Electric Plant Loading Estimates

As described in Section 1.4.3, EPA revised its estimates of pollutant loadings in steam electric power plant 

discharges after completing the benefit analyses. Estimated benefits from avoided impoundment failures do 

not depend on pollutant loadings and therefore the monetized benefit estimates provided in this Chapter are 

unaffected by revisions to steam electric plant loading estimates. 

6.4 Limitations and Uncertainties

Table 6-11 summarizes the limitations and uncertainties in the analysis of benefits associated with reduced 

impoundment failures arising from the final rule. The methodologies used in this analysis involve several 

simplifications and sources of limitations and uncertainties, as described below. These uncertainties add to the

limitations and uncertainties inherited from the EA analysis and data (see U.S. EPA, 2015a). Whether these 

limitations and uncertainties, taken together, are likely to result in an understatement or overstatement of the 

estimated benefits is not known.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 6: Benefits from Avoided Impoundment Failures

September 29, 2015 6-14

Table 6-11: Limitations and Uncertainties in Analysis of Avoided Risk of Impoundment Failure 

Benefits

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

The analysis assumes that, in the 

absence of the final rule, all 

impoundments would continue to 

operate in the baseline during the entire 

period of analysis.

Overestimate Plant owners may close existing impoundments or 

make other changes to their operations that would 

reduce the baseline probability of failure. Not 

accounting for these baseline conditions may 

overstate the benefits of the final rule.

The analysis accounts for projected 

closures of steam electric plant 

impoundments due to the CCR rule as of 

2023 (53 impoundments). The analysis 

does not account for additional projected 

closures in 2024-2042 due to the CCR 

rule.

Overestimate To the extent that additional impoundments are 

projected to close due to the CCR rule after 

implementation of the ELGs and EPA estimated 

benefits for these closures as part of the CCR rule 

analysis, EPA may be overstating the incremental

benefits of the ELGs. The magnitude of 

overstatement is unknown but is expected to be small 

given the relatively few closures projected overall in 

the CCR rule analysis (98 impoundments close by 

2114 out of the total of 735 impoundments analyzed; 

see U.S. EPA 2014), the timing of these projected 

future closures, and the effects of discounting on 

annualized benefit estimates. Furthermore, EPA’s 

adjustment to the ELG benefit analysis to reflect 

impoundment closures due to the CCR rule is 

consistent with the adjustment to ELG compliance 

costs. 

EPA estimated expected future 

impoundment releases from big and 

small impoundments based on uniform 

release rates for wall breaches and other 

releases. In practice, the probability of 

failure may depend on impoundment 

characteristics and management 

practices.

Uncertain Using a uniform failure rate may understate benefits 

of the final rule. Conversely, the historical failure 

rate may overstate projected failures under baseline 

conditions by not reflecting the effects of any recent 

changes in impoundment management practices 

(e.g., revised inspection and monitoring programs). 

The analysis uses a uniform cost of 

$1.90 per gallon spilled, including 

cleanup costs, natural resource damages, 

and transaction costs. 

Uncertain There is significant uncertainty involved in 

estimating the costs of unknown future release 

incidents, and these estimates are based on a small 

and highly variable sample of historic releases. The 

costs of future releases may be substantially higher 

or lower than the cost estimates applied in this 

analysis, depending on site-specific factors, 

including the ecosystems, infrastructure, and other 

resources damaged by the release.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air-Related Benefits

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7 Air-Related Benefits 

The final rule is expected to affect air pollution through three main mechanisms: 1) additional auxiliary 

electricity use by steam electric power plants to operate wastewater treatment, ash handling, and other 

systems needed to comply with the new effluent limits and standards; 2) additional transportation-related 

emissions due to the increased trucking of CCR waste to on-site or off-site landfills; and 3) the change in the 

profile of electricity generation due to relatively higher cost to generate electricity at plants incurring 

compliance costs for the ELGs. The different profile of generation can result in lower or higher air pollutant 

emissions due to differences in emission factors. Thus, small reductions in coal-based electricity generation as 

a result of the ELGs are compensated by increases in generation using other fuels or energy sources –

biomass, landfill gas, natural gas, nuclear power, oil, and wind power. For example, as detailed in Section 

10.6 of the RIA (U.S. EPA, 2015c), IPM projects a 0.3 percent decline in electricity generation from coal 

(3,276 GWh), as a result of the final ELGs (Option D); this decline is offset by a 0.1 percent increase in 

natural gas generation (1,964 GWh) and additional increases in electricity generation from waste coal, wind, 

and biomass. The changes in air emissions reflect the differences in emissions factors for these other fuels or 

sources of energy, as compared to coal.

In this analysis, EPA estimated the human health and other benefits resulting from net changes in emissions 

of three pollutants: NOX, SO2, and CO2. 

NOx and SOx (which include SO2 emissions quantified in this analysis) are known precursors to fine particles 

(PM2.5) air pollution, a criteria air pollutant that has been associated with a variety of adverse health effects –

most notably, premature mortality.57 In addition, in the presence of sunlight, NOX and VOCs can undergo a 

chemical reaction in the atmosphere to form ozone. Depending on localized concentrations of volatile organic 

compounds (VOCs), reducing NOX emissions would also reduce human exposure to ozone and the incidence 

of ozone-related health effects. Reducing emissions of SO2 and NOx would also reduce ambient exposure to 

SO2 and NO2, respectively. For the purpose of this analysis, EPA quantified only those benefits from 

associated reductions PM2.5.

58

CO2 is an important greenhouse gas that is linked to climate change effects, including: an increase in 

temperature; sea level rise; changes in weather patterns toward an intensified water cycle with stronger floods 

and droughts; and stress on ecosystems, especially in the Arctic, mountain and tropical areas, resulting in the 

shift of species habitat range. The expected economic losses from climate change include reduced agricultural 

yields, human health risks, property damages from increased flood frequencies, the loss of ecosystem 

services, etc. Increased CO2 levels also affect biological systems independent of climate change. For example, 

oceans become markedly more acidic, endangering coral reefs and potentially harming fisheries and other 

marine life (Intergovernmental Panel on Climate Change (IPCC), 2014). 

 

57 Sulfur oxides (SOx) include sulfur monoxide (SO), sulfur dioxide (SO2), sulfur trioxide (SO3) and other sulfur 

oxides. In this analysis, EPA analyzed changes in emissions of SO2 only. 

58 The Integrated Science Assessment for Particulate Matter (PM ISA) (U.S. EPA, 2009b) identified the human 

health effects associated with ambient PM2.5 exposure, which include premature morality and a variety of 

morbidity effects associated with acute and chronic exposures. Similarly, the Integrated Science Assessment for 

Ozone and Related Photochemical Oxidants (Ozone ISA) (U.S. EPA, 2013c) identified the human health effects 

associated with ambient ozone exposure, which include premature morality and a variety of morbidity effects 

associated with acute and chronic exposures.
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7.1 Data and Methodology

7.1.1 Changes in Air Emissions

As discussed in the RIA (Chapter 5: Electricity Market Analyses), EPA used the Integrated Planning Model 

(IPM) to estimate the electricity market-level effects of two of the five regulatory options (Options B and D; 

see Chapter 5 in RIA (U.S. EPA, 2015c)). IPM outputs include NOx, SO2, and CO2 emissions to air from 

electricity generating units (EGU). Comparing these emissions to those projected for the base case provides 

an assessment of the changes in air emissions resulting from changes in the profile of electricity generation 

under the final rule. EPA used four run years, 2020, 2025, 2030, and 2040, to represent the periods of 2019-

2022, 2023-2027, 2028-2033, and 2034-2042, respectively (for a more detailed discussion of the IPM analysis 

years, refer to Chapter 5 in RIA).

EPA developed separate estimates of air emissions associated with increases in electricity generation to power 

wastewater treatment systems by multiplying plant-specific additional electricity consumption estimated as 

part of the engineering analysis by plant- or North American Electric Reliability Corporation (NERC)-specific 

emission factors obtained from IPM for each analysis year. EPA estimated air emissions associated with 

increased trucking by multiplying the number of miles by average emission factors. Details of these two 

analyses are provided in the TDD. 

Table 7-1 through Table 7-3 summarize the estimated changes in emissions for the three mechanisms, the 

three pollutants, and the two regulatory options covered in this particular analysis. As shown in the tables, 

EPA estimates that changes in auxiliary service (Table 7-1) and transportation (Table 7-2) would result in an 

increase in emissions (positive values), while changes in the profile of electricity generation (Table 7-3)

would reduce CO2, SO2 and NOx emissions (negative values). Table 7-4 presents the net emissions changes 

across the three mechanisms.

The largest effect on projected air emissions is due to the change in the emissions profile of electricity 

generation at the market level. As presented in the RIA (Section 10.6: Executive Order 13211: Actions 

Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use), IPM projects small 

reductions in electricity generation coming from coal as a result of the ELGs (less than 0.1 percent for 

Option B; 0.3 percent for Option D), which is compensated with increases in generation using other fuels or 

energy sources – biomass, landfill gas, natural gas, nuclear power, and wind power. The changes in air 

emissions reflect the differences in emissions factors for these other fuels, as compared to coal.

Table 7-1: Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to 

Increase in Auxiliary Service at Steam Electric Power Plants, Relative to Baseline

Regulatory 

Option Year

Electricity 

Consumption 

(MWh)

CO2(Metric 

Tonnes/Year)

NOx (Tons/Year) SO2(Tons/Year)

Option B

2015-2018 0.0 0.0 0.0 0.0

2019 30,859.8 27,409.7 13.9 35.9

2020 41,529.5 36,972.7 20.5 45.4

2021 72,651.5 63,257.8 35.4 69.4

2022 86,488.4 75,759.4 45.2 75.6

2023-2042 102,168.7 89,632.5 59.6 86.7
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Table 7-1: Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to 

Increase in Auxiliary Service at Steam Electric Power Plants, Relative to Baseline

Regulatory 

Option Year

Electricity 

Consumption 

(MWh)

CO2(Metric 

Tonnes/Year)

NOx (Tons/Year) SO2(Tons/Year)

Option D

2015-2018 0.0 0.0 0.0 0.0

2019 60,680.1 55,222.0 35.3 59.2

2020 94,172.8 83,855.3 64.7 84.4

2021 152,706.0 134,385.6 95.7 132.0

2022 192,404.2 169,583.1 128.3 162.3

2023-2042 237,367.0 208,881.9 164.9 195.8

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table 7-2: Estimated Changes in Annual Air Pollutant Emissions due to Increased Trucking at 

Steam Electric Power Plants, Relative to Baseline

Option Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2

(Tons/Year)

Option B

2015-2018 0.0 0.0 0.0

2019 550.9 0.2 0.0

2020 691.5 0.3 0.0

2021 865.3 0.4 0.0

2022 925.2 0.4 0.0

2023-2042 982.5 0.4 0.0

Option D

2015-2018 0.0 0.0 0.0

2019 2,111.5 0.9 0.0

2020 2,480.3 1.1 0.0

2021 3,244.6 1.4 0.0

2022 3,641.2 1.6 0.0

2023-2042 3,767.8 1.6 0.0

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table 7-3: Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity 

Generation Profile, Relative to Baseline

Regulatory 

Option Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2(Tons/Year)

Option B

2015-2018 0.0 0.0 0.0

2019-2022 -2,057,293.5 -3,534.5 -4,620.6

2023-2027 -1,437,164.1 -3,323.9 -527.5

2028-2033 -246,295.3 -1,361.2 1,315.7

2034-2042 -1,186,462.9 -1,500.2 -1,608.4

Option D

2015-2018 0.0 0.0 0.0

2019-2022 -4,869,524.3 -14,614.0 -5,662.8

2023-2027 -2,555,361.0 -11,615.0 2,238.4

2028-2033 -2,089,591.4 -8,826.0 -984.2

2034-2042 -3,193,009.0 -10,638.8 -4,243.9

Source: U.S. EPA Analysis, 2015; see TDD for details.
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Table 7-4: Estimated Net Changes in Air Pollutant Emissions due to Increase in Auxiliary Service at 

Steam Electric Power Plants, Increased Trucking at Steam Electric Power Plants, and Changes in 

Electricity Generation Profile, Relative to Baseline

Regulatory 

Option Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2(Tons/Year)

Option B

2015-2018 0.0 0.0 0.0

2019 -2,029,332.9 -3,520.3 -4,584.6

2020 -2,019,629.3 -3,513.7 -4,575.1

2021 -1,993,170.4 -3,498.7 -4,551.2

2022 -1,980,609.0 -3,488.9 -4,545.0

2023-2027 -1,346,549.1 -3,263.9 -440.8

2028-2033 -155,680.3 -1,301.2 1,402.5

2034-2042 -1,095,847.9 -1,440.2 -1,521.7

Option D

2015-2018 0.0 0.0 0.0

2019 -4,812,190.8 -14,577.7 -5,603.6

2020 -4,783,188.7 -14,548.2 -5,578.4

2021 -4,731,894.1 -14,516.9 -5,530.8

2022 -4,696,300.0 -14,484.1 -5,500.5

2023-2027 -2,342,711.3 -11,448.4 2,434.3

2028-2033 -1,876,941.7 -8,659.4 -788.3

2034-2042 -2,980,359.3 -10,472.3 -4,048.0

Source: U.S. EPA Analysis, 2015

7.1.2 NOx and SO2

Detailed human health benefits analyses for air regulations typically involve the use of a sophisticated air 

quality model, such as the Community Multiscale Air Quality (CMAQ) Model, and BenMAP, EPA’s 

principal air pollution benefits analysis modeling tool. The air quality model estimates the changes in 

concentrations of criteria air pollutants in each cell of a grid resulting from changes in emissions to air (e.g., 

of NOx and SO2) under various policy scenarios. These criteria air pollutant changes are then input to 

BenMAP, which estimates the resulting changes in incidence in the exposed population of the adverse health 

effects associated with the pollutants and the corresonding monetized benefits (see (Abt Associates, 2012) for 

additional description of BenMAP). This detailed approach for human health benefits analysis of air 

regulations tends to be time- and resource-intensive. 

Recognizing that a less resource- and time-intensive approach is sometimes desirable, EPA relied on the best 

available methods of benefits transfer, which is the science and art of adapting primary research from similar 

contexts to estimate benefits for the environmental quality change under analysis. EPA’s Air Office 

developed estimates of national monetized benefits per ton of emissions avoided for use in estimating benefits 

without the need to conduct detailed air quality and human health benefits modeling. The benefit per ton 

values repsent the total monetized human health co-benefits, premature mortaility and premature morbidity, 

from the reduction in one ton of PM2.5 (or PM2.5 precursor such as NOX or SO2). Because the benefits per ton 

of emissions depend on both the type of emissions (e.g., NOx vs. SO2) and the geographic distribution 

(relative to population centers) of the emitting sources, EPA developed benefits per ton estimates for specific 

combinations of emission source categories and PM2.5 precursors. EPA used this approach, for example, in its 
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September 29, 2015 7-5

assessment of the benefits of PM and SO2 reductions for the Industrial Boiler and Process Heaters National 

Emissions Standards for Hazardous Air Pollutants (NESHAP) rule (U.S. EPA 2004a) and its analysis of the 

Mobile Source Area Toxics Rule (U.S. EPA 2004b) (See also Fann et al., 2012; Fann et al., 2009; Levy et al.,

2009; and Muller and Mendelsohn, 2009). 

EPA’s calculation of the benefits per ton values involved three principal steps, as described by Fann et al. 

(2009) and the Technical Support Document for the calculation of benefit per-ton estimates (U.S. EPA 

2008b):

1. Using an air quality model to estimate the changes in ambient PM2.5 concentrations resulting from 

specified precursor emissions reductions under various scenarios and then calculating the total tons 

(of the precursor emissions) reduced under each scenario;

2. Using BenMAP to estimate the changes in incidence of the associated health effects and the 

monetized benefits of those incidence reductions under each scenario; and

3. Estimating national benefits per ton by dividing the national monetized benefits by the total tons of 

emissions reduced under each scenario.

In the current analysis, benefits per ton estimates are needed for four combinations of emission type and 

source category involving NOx or SOx: 

 NOx from EGUs (to be applied to changes in market-level NOx emissions projected by IPM, and 

changes in emissions from auxiliary service);

 SOx from EGUs (to be applied to changes in market-level SOx emissions projected by IPM, and 

changes in emissions from auxiliary service);

 NOx from mobile sources (to be applied to changes in NOx emissions associated with transporting 

CCR waste to landfills); and

 SOx from mobile sources (to be applied to changes in SOx emissions associated with transporting 

CCR waste to landfills).

As described by Fann et al. (2009), “ambient PM2.5 is a complex mixture of primary and secondarily formed 

particles, resulting from interactions in the atmosphere and physical transport of emissions of particulate 

matter precursors, including available SO2, NOx, and NH3, meteorology (particularly temperature), and 

baseline levels and composition of PM2.5” (Fann et al. 2009, p.170). NOx and SOx differ in their propensity for 

becoming PM2.5. The benefits per ton estimates are based on the assumption that all fine particulates have the 

same potency for causing premature mortality (U.S. EPA 2011a).59

Fann et al. (2012) reported benefits per ton estimates for a variety of emission type/source category 

combinations, including all of those listed above, that are relevant to the current analysis, for the years 2005 

and 2016. Although they are not reported in Fann et al. (2012), EPA also obtained benefits per ton estimates 

for each of these categories for the years 2020, 2025, and 2030 directly from one of the study co-authors.60

For these additional years, the benefits per ton were calculated assuming the same change in ambient air 

quality as the author’s forecast for 2016, but accounting for the 2020, 2025 and 2030 projected baseline 

mortality and population to estimate the change in mortality risk

 

59 Benefits per ton estimates are available for other pollutants, such as direct PM2.5 emissions, but they were not 

included in this analysis because emissions factors were not available. The chemistry of PM formation is 

complex and nonlinear.

60 Provided in personal communication with Charles Fulcher, EPA Office of Air Quality Planning and Standards 

(OAQPS), on October 19, 2012.
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September 29, 2015 7-6

To be consistent with the rest of the analysis of the costs and benefits of the final ELGs, benefits per ton 

estimates are needed for each year from 2019 through 2042. Because the benefits per ton estimates for the 

years 2016, 2020, 2025, and 2030 are almost linear as a function of year, EPA interpolated benefits per ton 

values for the intermediate years (e.g., between 2020 and 2025) and projected values for the years from 2031 

through 2042 by linear regression, using (year, benefits per ton) data points for the years 2016, 2020, 2025, 

and 2030. Note, however, that the approximate linearity of the (year, benefits per ton) data points may be an 

artifact of the inability to project meteorological changes and thus changes in air quality for all years after 

2016, noted above. Thus, additional uncertainty was generated by using benefits per ton estimates for the 

future years that did not account for meteorological and air quality changes.

Assuming that the geographic distribution of controlled emitting sources in a source category (e.g., EGUs) 

and of emissions reductions in the current analysis are similar to the geographic distribution of emitting 

sources and emissions reduction in the analysis in Fann et al. (2012), EPA can derive a rough estimate of 

benefits from changes in air emissions by applying these benefits per ton estimates to the changes (in tons) of 

emissions resulting from compliance with the final rule. For example, the benefits from reduced emissions of 

NOx from EGUs under Option D can be estimated by multiplying emissions avoided under the regulatory 

option by the appropriate benefits per ton value.

As noted above, NOx and SOx are known precursors to PM2.5. Several adverse health effects have been 

associated with PM2.5, including premature mortality, non-fatal heart attacks, hospital admissions, emergency 

department visits, upper and lower respiratory symptoms, acute bronchitis, aggravated asthma, lost work days 

and acute respiratory symptoms. All of these health effects were included in the estimation of benefits that 

went into the calculation of benefits per ton in Fann et al. (2012).

A very large percentage, 98 percent, of the total monetized benefits of reducing PM2.5 concentrations are

attributable to avoided premature mortality. The appropriate method for valuing the reductions in premature 

mortality and development of an accepted value for projected reduction in the risk of premature mortality is 

still a discussion in the economics and public policy communities. Fann et al. (2012) used data from Krewski 

et al. (2009), a study of mortality and long-term exposure to PM2.5, to estimate the change in incidence of 

premature mortality associated with a given change in PM2.5 concentrations. This study is one of several 

credible peer-reviewed long-term exposure studies that EPA has used in benefits analyses of PM2.5.

When using long-term exposure studies, EPA has traditionally assumed that premature mortality avoided as a 

result of a reduction in PM2.5 concentrations in a given year do not all occur in that year. Instead, EPA 

assumes that the avoided PM2.5-related premature mortalities are distributed over a 20-year period, with most 

occurring in the earlier years. EPA values avoided premature mortality using VSL and then discounts that 

value back to the year of the analysis. Thus the numerator (the benefits) of the benefits per ton estimate for a 

given year is the value of morbidity avoided in that year plus the present discounted value of the stream of 

avoided premature mortalities over a twenty year period, discounted back to that year. For example, a benefits 

per ton estimate from Fann et al. (2012) for a ton removed in 2016 is the value of avoided morbidity in 2016 

plus the present discounted value of the stream of avoided premature mortalities associated with that ton from 

2016 to 2036, discounted back to 2016. 

EPA obtained two sets of benefits per ton estimates for this analysis for the years 2005, 2016, 2020, 2025, and 

2030: one set using a 3 percent discount rate and the other using a 7 percent discount rate.61 All benefits per 

ton estimates for years 2019 through 2042 were further discounted back to the year 2015 (using a 3 percent or 

7 percent discount rate, as appropriate). Because avoided premature mortalities are assumed to occur over a 

twenty-year period, and real income is likely to increase over time, these benefits per ton estimates reflect 

 

61 Provided in personal communication with Charles Fulcher, EPA/OAQPS, on October 19, 2012
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September 29, 2015 7-7

EPA’s estimated increases in the value for mortal risk reductions with respect to increases in real income. The 

income growth adjustment factors used are those in BenMAP (Abt Associates Inc., 2012). Table 7-5

summarizes the benefits per ton estimates EPA used for the different emission type and source category 

combinations involving NOx and SO2 in the analysis of the final rule. 

Table 7-5: National Benefits per Ton Estimates for NOx and SO2 Emissions (2013$/ton) from the 

Benefits per Ton Analysis Reported by Fann et al. (2012) a, b, c

Discount 

Rate Year

EGU Mobile Source (Onroad)

NOx SO2 NOx SO2

3%

2005 $3,791 $27,377 $4,633 $21,059 

2016 $5,475 $36,853 $7,687 $20,006 

2020 $5,686 $38,959 $8,108 $22,112 

2025 $6,107 $42,118 $8,845 $24,218 

2030 $6,528 $45,277 $9,582 $27,377 

7%

2005 $3,475 $25,271 $4,107 $18,953 

2016 $4,844 $32,641 $6,949 $17,900 

2020 $5,159 $34,747 $7,476 $20,006 

2025 $5,475 $37,906 $8,002 $22,112 

2030 $5,897 $41,065 $8,634 $24,218 

Source: U.S. EPA Analysis, 2015 based on Fann et al. (2012)

a. Provided for this analysis by Charles Fulcher, EPA/OAQPS on October 19, 2012. 

b. Mortality benefits based on Krewski et al. (2009).

c. Estimation of benefits per ton for 2016, 2020, 2025, and 2030 were based on year 2016 emissions modeling.

7.1.3 CO2

EPA estimated the global social benefits of CO2 emission reductions using the social cost of carbon (SCC) 

estimates developed by the Interagency Working Group on the Social Cost of Carbon (IWGSCC, 2010, 

2013a, 2013b, 2015a). This document refers to these estimates, which were developed by the U.S. 

government, as “SCC estimates.” The SCC is a metric that estimates the monetary value of impacts associated 

with marginal changes in CO2 emissions in a given year. It includes a wide range of anticipated climate 

impacts, such as net changes in agricultural productivity and human health, property damage from increased 

flood risk, and changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is used to quantify the benefits of reducing CO2 emissions, or the disbenefit from increasing 

emissions, in regulatory impact analyses.

The SCC estimates were developed over many years, using the best science available, and with input from the 

public. Specifically, an interagency working group (IWG) that included EPA and other executive branch 

agencies and offices used three integrated assessment models (IAMs) to develop the SCC estimates and 

recommended four global values for use in regulatory analyses. The SCC estimates were first released in 

February 2010 and updated in 2013 using new versions of each IAM. The 2013 update did not revisit the 

2010 modeling decisions with regards to the discount rate, reference case socioeconomic and emission 

scenarios, and equilibrium climate sensitivity distribution. Rather, improvements in the way damages are 

modeled are confined to those that have been incorporated into the latest versions of the models by the 

developers themselves and published in the peer-reviewed literature. The 2010 SCC Technical Support 

Document (TSD) (IWGSCC, 2010) provides a complete discussion of the methods used to develop these 

estimates and the current SCC TSD presents and discusses the 2013 update (including recent minor technical 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air-Related Benefits

September 29, 2015 7-8

corrections to the estimates) (IWGSCC, 2013b). In July 2015, IWGSCC published technical correction to the 

estimates (IWGSCC, 2015a); EPA uses these most current values for the benefit estimates presented in this 

chapter.

The 2010 SCC TSD noted a number of limitations to the SCC analysis, including the incomplete way in 

which the IAMs capture catastrophic and non-catastrophic impacts, their incomplete treatment of adaptation 

and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions 

regarding risk aversion. Currently IAMs do not assign value to all of the important physical, ecological, and 

economic impacts of climate change recognized in the climate change literature due to a lack of precise 

information on the nature of damages and because the science incorporated into these models understandably 

lags behind the most recent research. Nonetheless, these estimates and the discussion of their limitations 

represent the best available information about the social benefits of CO2 reductions to inform benefit-cost 

analysis. The new versions of the models offer some improvements in these areas, although further work is 

warranted.

Accordingly, EPA and other agencies continue to engage in research on modeling and valuation of climate 

impacts with the goal to improve these estimates. The EPA and other agencies also continue to consider 

feedback on the SCC estimates from stakeholders through a range of channels, including public comments on 

Agency rulemakings that use the SCC in supporting analyses and through regular interactions with 

stakeholders and research analysts implementing the SCC methodology used by the IWG. In addition, OMB’s 

Office of Information and Regulatory Affairs sought public comment on the approach used to develop the 

SCC estimates through a separate comment period that ended on February 26, 2014. See response to comment 

document (IWGCC, 2015b).

After careful evaluation of the full range of comments, the IWG continues to recommend the use of the SCC

estimates in regulatory impact analysis. With the release of the response to comments, the IWG announced 

plans to obtain expert independent advice from the National Academy of Sciences to ensure that the SCC

estimates continue to reflect the best available scientific and economic information on climate change. The 

NRC review will be informed by the public comments received and focus on the technical merits and 

challenges of potential approaches to improving the SCC estimates in future updates. 

Concurrent with OMB’s publication of the response to comments on SCC and announcement of the NRC 

process, OMB posted a revised TSD that includes two minor technical corrections to the current estimates. 

One technical correction addressed an inadvertent omission of climate change damages in the last year of 

analysis (2300) in one model and the second addressed a minor indexing error in another model. On average 

the revised SCC estimates are one dollar less than the mean SCC estimates reported in the November 2013 

TSD. The change in the estimates associated with the 95th percentile estimates when using a 3 percent 

discount rate is slightly larger, as those estimates are heavily influenced by the results from the model that 

was affected by the indexing error.

The four SCC estimates are: $13, $46, $68, and $130 per metric ton of CO2 emissions in the year 2020 (2013 

dollars).62 The first three values are based on the average SCC from the three IAMs, at discount rates of 5, 3, 

and 2.5 percent, respectively. Estimates of the SCC for several discount rates are included because the 

literature shows that the SCC is sensitive to assumptions about the discount rate, and because no consensus 

exists on the appropriate rate to use in an intergenerational context (where costs and benefits are incurred by 

different generations). The fourth value is the 95th percentile of the SCC across all three models at a 3 percent 

 

62 The SCC TSDs provide SCC in 2007 dollars, which are adjusted to 2013 dollars using the GDP Implicit Price 

Deflator. While the SCC values reported in Table 7-6 have been rounded to two significant digits, unrounded 

numbers were used to calculate the CO2 benefits.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air-Related Benefits

September 29, 2015 7-9

discount rate. It is included to represent higher-than-expected impacts from temperature change further out in 

the tails of the SCC distribution. The SCC increases over time because future emissions are expected to 

produce larger incremental damages as economies grow and physical and economic systems become more 

stressed in response to greater climate change.

These estimates are then discounted back to the year 2015 using the same discount rate used to estimate the 

SCC. For internal consistency, the annual benefits are discounted back to net present value terms using the 

same discount rate as each SCC estimate (i.e. 5 percent, 3 percent, and 2.5 percent) rather than the discount 

rates of 3 percent and 7 percent used to derive the net present value of other streams of costs and benefits of 

the final rule.63

EPA estimates the dollar value of the CO2-related benefits for each analysis year between 2019 and 2042 by 

applying the global SCC estimates, shown in Table 7-6, to the estimated reductions in CO2 emissions under 

the final rule. 

Table 7-6: Social Cost of Carbon Values (2013$/metric ton CO2)

Year 5% Discount Rate, 

Average

3% Discount Rate, 

Average

2.5% Discount 

Rate, Average

3% Discount Rate, 

95th Percentile

2019 $13 $45 $67 $130

2020 $13 $46 $68 $130

2021 $13 $46 $69 $140

2022 $14 $47 $70 $140

2025 $15 $50 $74 $150

2030 $18 $55 $80 $170

2035 $20 $60 $85 $180

2040 $23 $66 $92 $200

Source: IWGSCC, 2013b (values updated to 2013 dollars using GDP deflator (1.095)).

7.1.4 Estimating Total Air-Related Benefits 

EPA calculated the monetized air-related benefits of the final rule, under options B and D, in any given year 

(discounted back to the year 2015) by (1) multiplying the tons of emissions avoided for a given emissions 

type/source category combination in that year by the benefits per ton for that emissions type/source category 

combination for that year, and then (2) summing the benefits across all emissions type/source category 

combinations. The total benefit for year y, then, is calculated using Equation 7-1.

Equation 7-1.

2015

, ,

9

1

( )

y j y j

j

 Tons avoided  BPT



Where: 

j = 1, 2, and 3 denote NOx, SO2, and CO2, respectively, from market-level EGUs;

j = 4, 5, and 6 denote NOx, SO2, and CO2, respectively, associated with auxiliary service;

j = 7, 8, and 9 denote NOx, SO2, and CO2, respectively, associated with transportation; and 

BPTy,j

2015 is the present discounted value, discounted to the year 2015, of the benefits per ton for the jth 

emissions type/source category combination. 

 

63 See more discussion on the appropriate discounting of climate benefits using SCC in the 2010 SCC TSD

(IWGSCC, 2010). 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air-Related Benefits

September 29, 2015 7-10

The total present discounted value of benefits, discounted to the year 2015, PDV2015, is calculated using 

Equation 7-2.

Equation 7-2.    

 

2042

2015

2015

, ,

9

1

2015 ( )

y

y j y j

j

PDV Tons avoided BPT .

7.2 Results

Table 7-7 shows the estimated benefits from reductions in emissions of NOx, SO2, and CO2 in each of several 

selected years for the two regulatory options EPA analyzed. 

Table 7-7: Estimated Benefits from Reduced Air Emissions for Selected Years (millions; 2013$)a

Option Year 3% Discount Rate 7% Discount Rate

Option B

2019b

$287.7 $266.3

2020 $291.1 $270.0

2025 $106.3 $102.4

2030 -$46.5 -$41.4

Option D

2019b$514.5 $482.2

2020 $520.0 $488.8

2025 $85.4 $88.4

2030 $195.0 $186.2

Source: U.S. EPA Analysis, 2015

a. EPA used SCC values based on a 3 percent (average) discount rate to calculate total benefit values presented for both the 3 

percent and 7 percent discount rate.

b. The benefits per ton values used for year 2019 benefit calculation is assumed to be the same as the 2020 benefits per ton values.

Table 7-8 shows the annualized benefits from reductions in emissions of NOx, SO2, and CO2 for the two

regulatory options EPA analyzed. EPA annualized benefit estimates to enable consistent reporting across 

benefit categories (e.g., benefits from improvement in water quality). The total air-related benefits include 

benefits from CO2 emissions reductions calculated using average SCC values (average at 2.5 percent, average 

at 5 percent, and the 95th percentile at 3 percent). 

The annualized benefits of Options B and D are $110.2 million and $284.5 million, respectively, using a 

discount rate of 3 percent ($103.6 million and $248.6 million, respectively, using a discount rate of 7 

percent).64

 

64 The 3 percent average SCC estimate was used to value reductions in CO2 everywhere total benefits of the final 

rule are reported.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air-Related Benefits

September 29, 2015 7-11

Table 7-8: Estimated Annualized Benefits from Reduced Air Emissions (Millions; 2013$)

ELG Option Pollutant 3% Discount Rate 7% Discount Rate

Option B

NOx $12.7 $10.6 

SO2 $45.0 $40.5 

CO2

3% Avg $52.5 $52.5

5% Avg $15.3 $15.3

2.5% Avg $77.4 $77.4

3% 95th Percentile $157.7 $157.7

TOTAL

3% Avg $110.2 $103.6 

5% Avg $73.1 $66.5

2.5% Avg $135.2 $128.6

3% 95th Percentile $215.5 $208.9

Option D

NOx $63.1 $49.4 

SO2 $81.6 $59.4 

CO2

3% Avg $139.8 $139.8 

5% Avg $40.4 $40.4

2.5% Avg $206.8 $206.8

3% 95th Percentile $421.1 $421.1

TOTAL

3% Avg $284.5 $248.6

5% Avg $185.1 $149.2

2.5% Avg $351.4 $315.6

3% 95th Percentile $565.8 $529.9 

Source: U.S. EPA Analysis, 2015

7.3 Implications of Revised Steam Electric Plant Loading Estimates

As described in Section 1.4.3, EPA revised its estimates of pollutant loadings in steam electric power plant 

discharges after completing the benefit analyses. Estimated air-related benefits do not depend on pollutant 

loadings to receiving waters and therefore the monetized benefit estimates provided in this Chapter are 

unaffected by revisions to steam electric plant loading estimates. 

7.4 Limitations and Uncertainties

This analysis is subject to the standard sources of uncertainty found in any air pollution benefits analysis –

uncertainties surrounding the estimated emissions changes, the estimated changes in air pollutant 

concentrations resulting from changes in emissions, the estimated concentration-response relationships 

between the air pollutant and various health effects in the exposed population, and the estimated value of each 

health effect avoided. There is additional uncertainty in the SCC estimates, which reflect the projection of 

future harm from climate change, and the benefits per ton estimates. More details about the limitations and 

uncertainties associated with the air-benefit analysis are discussed in Table 7-9.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air-Related Benefits

September 29, 2015 7-12

Table 7-9: Limitations and Uncertainties in Analysis of Air-related Benefits

Issue Effect on Benefits 

Estimate Notes

Uncertainty in projecting the 

future harm from climate 

change.

Uncertain When attempting to assess the incremental economic impacts 

of carbon dioxide emissions, the analyst faces a number of 

serious challenges. A report from the National Research 

Council (NRC, 2009) points out that any assessment will 

suffer from uncertainty, speculation, and lack of information 

about (1) future emissions of greenhouse gases, (2) the effects 

of past and future emissions on the climate system, (3) the 

impact of changes in climate on the physical and biological 

environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to 

quantify and monetize the harms associated with climate 

change will raise serious questions of science, economics, and 

ethics and should be viewed as provisional.

The Interagency Working Group on Social Cost of Carbon 

(IWGSCC, 2010, 2013a, 2013b) noted a number of 

limitations to the SCC analysis, including the incomplete way 

in which the integrated assessment models capture 

catastrophic and noncatastrophic impacts, the modeling of 

inter-regional and inter-sectoral linkages, their incomplete 

treatment of adaptation and technological change, uncertainty 

in the extrapolation of damages to high temperatures, and 

assumptions regarding risk aversion. The limited amount of 

research linking climate impacts to economic damages makes 

the interagency modeling exercise even more difficult. This 

said, the SCC estimates were developed using a defensible set 

of input assumptions that are grounded in the existing 

literature.

There is uncertainty 

associated with the effects of 

compliance costs on the 

forecast change in emissions 

from the electricity sector. 

Uncertain Compliance costs (capital, fixed or variable) will influence 

marginal generation decisions of plants affected by the final 

rule. In order to model the electricity market effects of the 

final rule, EPA made certain modeling assumptions that may 

influence the pattern of generation across the electricity 

sector, and therefore emissions. For example, EPA converted 

engineering capital costs to annual fixed operation and 

maintenance (O&M) costs in order to model the cost of 

complying with the final rule in IPM.

See RIA Chapter 5, and Section 10.6: Executive Order 13211: 

Actions Concerning Regulations That Significantly Affect 

Energy Supply, Distribution, or Use for additional discussion 

of how modeling assumptions may influence the forecast air 

pollution changes from the IPM modeling.

Differences between modeled and actual quantities of 

electricity generated and emission factors of dispatched 

generating units would affect the changes in air pollutants 

emissions and therefore the benefits resulting from these 

changes. EPA does not have information to quantify the 

magnitude of this uncertainty.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 7: Air-Related Benefits

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Table 7-9: Limitations and Uncertainties in Analysis of Air-related Benefits

Issue Effect on Benefits 

Estimate Notes

There is uncertainty 

associated with the effects of 

the Clean Power Plan rule

Uncertain The final Clean Power Plan provides states considerable 

flexibility in developing state implementation plans to meet 

the rate or mass targets. This flexibility provides states great 

leeway to meet key priorities. However, it induces a 

considerable degree of uncertainty in what the future electric 

power market will look like and the overall economic impacts 

of the final ELGs. For example, states may choose to comply 

with the Clean Power Plan in ways that will lead to fewer or 

more coal-fired steam ELG plant retirements than the IPM 

runs would indicate. Such differences may have an important 

impact on dispatch profiles and emission changes.

EPA used a reduced form 

approach (benefits per ton) to 

value air-related benefits of 

emissions changes. 

Uncertain As Fann et al. (2012) note, “... implicit in the benefit per ton 

assessment is that the key attributes of the modeling — e.g.

population distribution, source parameters, etc. — are not so 

different from the policy scenario as to affect the estimated 

benefits appreciably. Reduced form approaches assume a 

linear relationship between changes in emissions and benefits, 

an assumption that may not be valid for large changes in 

emissions” (Fann et al., 2012, p. 142). 

EPA used year-specific 

benefits per ton estimates to 

derive values for each year 

within the analysis period.

Uncertain Use of year-specific benefits per ton estimates from which to 

generate annual estimates introduces another layer of 

uncertainty into the analysis. In particular, because actual air 

quality modeling was carried out only for 2005 and 2016, the 

approximate linearity seen in the benefits per ton estimates for 

2016, 2020, 2025, and 2030 may be an artifact of assuming 

that air quality remains constant at 2016 levels. The benefits 

per ton estimates for intermediate years also do not take into 

account the likely non-linearity involved. If each year-specific 

benefits per ton is uncertain, then an annual estimate 

incorporating benefits per ton-based estimates may be more 

uncertain. As a result, the annual estimates can be considered 

only rough estimates.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 8: Benefits from Reduced Water Withdrawals

September 29, 2015 8-1

8 Benefits from Reduced Water Withdrawals

Steam electric power plants use vast quantities of water for ash transport and for operating wet FGD 

scrubbers. 

By eliminating or reducing water used in sluicing operations or prompting the recycling of water in FGD 

wastewater treatment systems, the ELGs are expected to reduce water withdrawal from both surface 

waterbodies and aquifers. The reduction in water use depends on the regulatory option.65 EPA estimates that 

power plants would reduce water withdrawals from between 45 billion gallons per year (0.12 million gallons 

per day) under Option and 209 to 222 billion gallons per year (0.57 to 0.61 million gallons per day) under 

Option E (see Chapter 11 of TDD for details). The final BAT/PSES option (Option D) will reduce water 

withdrawals at steam electric power plants by 143 to 155 billion gallons per year (0.39 to 0.42 million gallons 

per day).

The section below discusses the benefits resulting specifically from reductions in groundwater withdrawals 

(Section 8.1). Benefits associated with surface water withdrawals are discussed qualitatively in Chapter 2 and

in a separate report provided in the final rule record (see DCN SE05943).

8.1 Groundwater Withdrawals

Reduced water intake from groundwater sources by steam electric power plants are expected to result in 

increased availability of groundwater for local municipalities that rely on groundwater aquifers for drinking 

water supplies. These municipalities are expected to avoid the cost of supplementing drinking water supplies 

through alternative means, such as bulk drinking water purchases. The following sections describe EPA’s 

estimate of reduced groundwater withdrawal benefits.

EPA estimated the benefits of reduced groundwater withdrawals based on avoided costs of purchasing 

drinking water during periods of shortages in groundwater supply. 

8.1.1 Methods

EPA’s analysis of the final ELG options (U.S. EPA, 2015b) indicate that one plant located in Nebraska will 

reduce the volume of groundwater withdrawn as a result of the ELGs. EPA estimated that the plant will avoid 

withdrawing a total of 21,971 gallons per day (8 million gallons per year) by converting to dry handling for its 

bottom ash under Options D and E. Because the state is potentially or currently water-stressed (Tetra Tech, 

2011), the ELGs are likely to generate benefits from improved groundwater recharge. To estimate the value of 

improved groundwater supply, EPA relied on state-specific prices of bulk drinking water supplies, since 

municipalities may need to purchase supplementary supplies in response to groundwater shortages arising 

from excessive withdrawals. EPA recognizes that the assumption that a reduction in groundwater withdrawals 

in the water-stressed states may result in reduced groundwater shortages is somewhat speculative, but used 

this assumption to provide screening-level estimates of the potential benefits.

To estimate the monetary value of reduced groundwater withdrawal, EPA relied on current state-specific 

water prices ($1,192.06 per acre/foot for Nebraska). For each affected plant and regulatory option, EPA 

 

65 Depending on the BAT/PSES technology basis for fly and bottom ash wastewater, the regulatory option may 

eliminate or reduce water use associated with current wet sluicing operating systems at steam electric power 

plants. Specifically, reductions in intake flow are expected to occur at plants which convert to dry handling or 

recycle FGD wastewater .
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 8: Benefits from Reduced Water Withdrawals

September 29, 2015 8-2

multiplied the reduction in groundwater withdrawal (in gallons per year) by the estimated price of drinking 

water per gallon. EPA used a conversion factor of 325,851 to convert acre foot to gallons.

8.1.2 Results

Table 8-1 shows estimated annual benefits from reduced groundwater withdrawals. The annual benefits from 

the BAT/PSES option (Option D) for existing sources are $0.02 million using a 3 percent discount rate

($0.02 million using a 7 percent discount rate).

Table 8-1: Estimated Annualized Benefits from Reduced Groundwater Withdrawals (Millions; 2013$)

Regulatory Option

Reduction in

Groundwater Intakes 

(million gallons per year; 

full implementation)

3% Discount Rate 7% Discount Rate

Option A 0.0 $0.00 $0.00

Option B 0.0 $0.00 $0.00

Option C 0.0 $0.00 $0.00

Option D 8.0 $0.02 $0.02

Option E 8.0 $0.02 $0.02

Source: U.S. EPA Analysis, 2015

8.1.3 Implications of Revised Steam Electric Plant Loading Estimates

As described in Section 1.4.3, EPA revised its estimates of pollutant loadings in steam electric power plant 

discharges after completing the benefit analyses. Estimated water withdrawal benefits do not depend on 

pollutant loadings and therefore the monetized benefit estimates provided in this Chapter are unaffected by 

revisions to steam electric plant loading estimates. 

8.1.4 Limitations and Uncertainties

Table 8-2 summarizes the limitations and uncertainties in the analysis of benefits associated with reduced 

groundwater withdrawals. 

Table 8-2: Limitations and Uncertainties in Analysis of Reduced Groundwater Withdrawals

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

EPA assumed that municipalities 

would need to replace lost 

groundwater supplies with bulk 

drinking water purchases.

Uncertain

See below.

Municipalities may not need to replace groundwater 

withdrawn by steam electric power plants (in which case 

the benefits of the ELG may be overstated), or they may 

choose to replace the groundwater through other means, 

such as desalinization (in the case of Florida, in which 

case the benefits of the ELG may be understated).

EPA assumed a direct relationship 

between groundwater withdrawals 

in water-stressed states and 

groundwater shortages, i.e., that 

reducing demand for limited 

groundwater supplies would result 

in avoided costs for purchased 

water.

Overestimate EPA assumed that demand for additional water supply 

exists in the affected areas (Florida and Nebraska) due to 

potential draughts. However, the extent of this demand is 

uncertain. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 8: Benefits from Reduced Water Withdrawals

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Table 8-2: Limitations and Uncertainties in Analysis of Reduced Groundwater Withdrawals

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

Affected aquifer characteristics Uncertain If the affected aquifers are used for private wells only, the 

estimated benefits of improved groundwater recharge 

could be under- or overstated, depending on households 

WTP for protecting groundwater quantity.
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9 Benefits from Avoided Dredging Costs

EPA expects that the final rule will reduce discharge loads of various categories of pollutants including total 

suspended solids (TSS), thereby reducing the rate of sediment deposition to affected waterbodies, including 

navigable waterways and reservoirs that require dredging for maintenance.

Navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are an integral part of the 

United States’ transportation network. They are prone to reduced functionality due to sediment build-up, 

which can reduce the navigable depth and width of the waterway (Clark, Haverkamp, and Chapman, 1985). In 

many cases, costly periodic dredging is necessary to keep them passable. The final steam electric ELG could 

provide cost savings to government and private entities responsible for maintenance of navigable waterways 

by reducing the dredging volume. 

Reservoirs serve many functions, including storage of drinking and irrigation water supplies, flood control, 

hydropower supply, and recreation. Streams can carry sediment into reservoirs, where it can settle and cause 

buildup of silt layers over time, at a recorded average rate of 1.2 billion kilograms per reservoir every year 

(USGS, 2007b). Sedimentation reduces reservoir capacity (Graf, Wohl, Sinha, and Sabo, 2010) and the useful 

life of reservoirs unless measures such as dredging are taken to reclaim capacity (Clark, et al., 1985). EPA 

expects that the final ELG will provide cost savings by reducing dredging activity to reclaim capacity at 

existing reservoirs.

EPA estimates that the final ELG would result in modest cost savings from reducing the amount of sediment 

dredged from navigational waterways and reservoirs affected by pollutant discharges from steam electric 

power plants. Under Option D, benefits from reduced navigational waterway dredging are less than one 

thousand dollars, with both 3 and 7 percent discount rates; benefits from reduced reservoir dredging are less 

than two thousand dollars, with both 3 and 7 percent discount rates. EPA has revised sediment loadings 

following the completion of the benefit analysis However, because sediment loadings changed by 

approximately 1 percent the overall effect on the estimated benefits from reduced dredging of navigational 

waterways and reservoirs is likely to be trivial.

Appendix K provides a more detailed description of the methodology and results of this analysis. This 

appendix presents EPA’s analysis of the avoided dredging costs for navigable waterways and reservoirs under 

the five regulatory options. First, it describes EPA’s analysis of historic dredging locations and frequency. 

Next, it presents EPA’s approach for estimating sediment deposition and removal in dredged waterways and 

reservoirs under the ELG regulatory options and associated costs. It then presents estimated benefits.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

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10 Benefits from Enhanced Marketability of Coal Combustion Residuals

EPA expects that installation of waste stream treatment technologies to comply with final ELGs will affect 

the type of coal combustion residue (CCR) by prompting plants to convert from wet handling of fly ash, 

bottom ash, and/or flue gas desulfurization (FGD) waste to dry handling. Relative to wet ash, dry ash’s 

chemical and physical properties make it more suitable for re-use in a variety of applications like structural 

fill, concrete, and wallboard (U.S. EPA, 2015d; American Coal Ash Association, 2012). This change would in 

turn allow plants to more readily market CCR to beneficial uses. 

There are two main economic benefits to society of re-using CCR. First, plants that are able to beneficially reuse CCRs are able to offset the CCR disposal costs (e.g., trucking and landfills) that EPA counted in its cost 

and economic impact analyses of this final rule. These avoided costs are net of added costs that some plants 

may incur in preparing dry CCRs for reuse. Second, by replacing raw or virgin inputs with re-used CCR 

materials during the production of structural fill and concrete, society avoids the need for and cost of 

extracting and preparing the raw or virgin inputs. These benefits include both direct costs (e.g., operating 

machinery, costs of transport), and indirect costs (e.g., downstream environmental benefits).

This Chapter describes the methodology EPA used to estimate the avoided cost benefits resulting from the 

enhanced marketability of CCR. The approach builds on the methodology EPA used in analyzing changes in 

beneficial use of CCRs under the final RCRA Final Rule for Disposal of Coal Combustion Residuals 

Generated by the Electric Utility Industry (U.S. EPA, 2014).

10.1 Methods

10.1.1 Beneficial Use Applications

The methodology focuses on two CCR wastestreams, and two end uses that may be affected by the final 

ELGs:66 (1) fly ash as a substitute for Portland cement in concrete production and (2) fly- and bottom ashes as 

substitutes for sand and gravel in fill applications (Table 10-1).67

In this analysis, EPA assumes that the ELGs do not materially affect the total quantity of CCR generated by 

steam electric power plants but instead change only the relative shares of the CCR handled dry instead of wet.

Following the central assumptions of EPA’s cost and economic impact analyses for this rule (U.S. EPA, 

2015c), EPA assumes that plants generate a constant amount of CCR each year in the analytic time period.

Table 10-1: Applicable Beneficial Use Applications of CCRs, by CCR Category

CCR Category

Beneficial Use Application

Concrete Production Fill

Fly ash  

Bottom ash 

 

66 EPA does not expect the final ELGs to affect the quantity or handling of FGD gypsum, and this CCR use is 

therefore excluded from the benefit analysis.

67 Note that this assumed pattern simplifies actual uses. The American Coal Ash Association (ACAA) reports 

other important uses (i.e., each more than 1 million short tons in 2012) for fly ash, bottom ash, and FGD 

gypsum, including waste stabilization (fly ash), blended cement/feed for clinker (fly ash, bottom ash, and FGD 

gypsum), and mining applications (fly ash, FGD gypsum) (http://www.acaausa.org/Portals/9/Files/PDFs/revisedFINAL2012CCPSurveyReport.pdf). 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

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10.1.2 Marketable CCR by State

The demand for concrete and fill constrains the extent to which steam electric power plants will be able to 

shift CCRs from disposal to beneficial use. Transportation costs are another main constraint and make 

marketing of CCR across long distances less likely. USGS Minerals Commodity Summaries (e.g., Bolen, 

2014; van Oss, 2013) show that both sand and cement production vary across regions of the United States; 

thus, Steam Electric facilities in different parts of the country likely face different markets for CCR. 

CCR end use sites include a mix of industrial facilities (e.g., to produce cement) and dispersed sites (e.g., as 

structural fill). Without precise information about the location of these beneficial use sites within regions,

EPA used sand and cement production within the state surrounding a plant to approximate the regional market 

demand each plant may face in marketing dry CCR. This approach assumes that plants only market their CCR 

to sites within the plant’s state, and that state-level demand constrains the total amount of CCR that steam 

electric power plants in that state, as a group, will be able to market to various end uses. To determine the 

maximum quantity of CCRs that steam electric power plants located in a given state will be able to market 

(“marketable CCRs”), EPA compared state-level estimates of supply and demand for CCR.68 Procedures to 

estimate demand and supply are described below. Due to heterogeneity in state sizes, this assumption implies 

the geographic extent of, and volume of, CCR markets vary across states.

10.1.2.1 Demand for CCR

EPA assumed that the annual quantities of end products (e.g., cement, and construction sand and gravel)

produced and used in the United States represent the maximum annual demand for CCR in those applications, 

accounting for the share of the end products that may be replaced with CCR. 

 Total End Product Production. EPA estimated concrete and fill production based on U.S. Geological 

Survey “Minerals Commodity Summaries,” and developed 3-year average production statistics for 

cement and construction sand and gravel, respectively (Bolen, 2011, 2012, 2013, 2014; van Oss, 

2009, 2010, 2013). These are the same sources as used for the final CCR rule analysis, but with more 

current data. In estimating production, EPA used state-level sand and gravel production statistics 

directly (e.g., Bolen, 2010). Annual cement production statistics are presented by multi-state regions, 

and separately report production for Portland and masonry cements (e.g., Van Oss, 2010). Because 

Portland cement constitutes the majority of total cement production by weight (97% in 2013), EPA 

used total cement production as a proxy for Portland cement production. EPA downscaled regional 

cement production statistics to individual state(s) by state shares of regional population (US Census, 

2012). 

 End Product Input Replaceable with CCR. For each end product, EPA then estimated the portion of 

total production that could be replaced with CCR. For cement, EPA assumed that:

‒ Fly ash could replace 25% of cement used in concrete. This percentage represents the low

end of several replacement rates reported in industry literature while remaining consistent 

with the final CCR rule and other federal government documents. For example, ERMCO 

(2013) reports that fly ash additives constitute 21.7% of cement in the United States, and We 

Energies (2013, p. 65) concluded that fly ash could be substituted for up to 40% of Portland 

concrete by weight in making structural grade concrete. Government replacement rate 

 

68 The CCR Final Rule approach (Appendix S) used linear programming to estimate incremental changes in 

beneficial reuse due to the rule, accounting for county-level capacity constraints (maximum demand).Although 

our state-level market approximations more coarsely delineate markets, they also serve to constrain maximum 

reuse to estimated market capacity.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10-3

estimates tend to be somewhat lower than industry estimates; for example, the CCR rule 

assumed that “that fly ash makes up no more than 30% of ...cementitious material” 

(Appendix S in US EPA, 2014), based on resources from US EPA (2008)69, and the Federal 

Highway Administration Materials Group recommends a substitution rate of no more than 15 

to 25 percent in pavement applications (FHWA, 2015).

‒ 73 percent of cement is used in ready-mix concrete (ERMCO, 2013).Of the cement that fly 

ash can replace, only a portion is used in concrete; mortar and grout are other uses for 

cement. 

For fill, EPA assumed that:

‒ Fill is 36% of total sand and gravel production by state. In 2012, 36% of national 

construction sand and gravel production was used in fill and fill-like applications (Bolen, 

2013), which include concrete aggregates (including concrete sand); asphaltic concrete 

aggregates and other bituminous mixtures; road base and coverings; fill; and snow and ice 

control. 

‒ Beneficial reuse of CCRs in fill carries no stigma, and could potentially replace 100 percent

of sand/gravel use as fill. Consistent with the analysis of the CCR final rule, EPA assumed no 

stigma around the reuse of CCRs in fill applications. EPA notes, however, that there may

remain some potential for the public to negatively perceive CCR re-used in fill or concrete 

because they are unfamiliar with CCR and/or with re-use practices, but consume media 

information that incorrectly characterizes these products and practices as “toxic” (U.S. EPA, 

2010d) . Because EPA did not identify information estimating the national extent or impact of 

stigma around CCR reuse, EPA assumed no stigma in residential applications. This approach 

is consistent with the non-hazardous determination applied in the Final CCR Rule, which is 

designed to avoid stigma around uses of “hazardous” products. To the extent that stigma or 

perceptional effects does exist and leads to reduced marketability of CCRs, the analysis may 

over-state the re-use of CCRs in some applications (e.g., residential contexts).

10.1.2.2 CCR Supply

 Baseline Production. EPA estimated state-level baseline CCR supply by ash type, including (1) the

sum of plant-level dry fly and bottom ash production marketed, as reported by Steam Electric plants 

located within the state in response to the 2010 Questionnaire for the Steam Electric Power 

Generating Effluent Guidelines (U.S. EPA, 2010a); and (2) The state-level sum of additional CCRs 

available for beneficial reuse due to the final CCR rule. The CCR final rule analysis projected that in 

2025, the 3-year rolling average of the annual increase in national CCR beneficial uses will total 0.26 

million tons of fly ash in concrete, and 5.65 million tons of CCRs in structural fill.

70 EPA allocated

these national total supply of CCR to states with baseline cement and sand production. Using

population as a proxy for fill and concrete use (and thereby, a proxy for production), EPA allocated

the additional CCR to states based on each state’s population.

 Change in Dry CCR Supply due to Final Steam Electric ELGs. EPA used data on annual plant-level 

changes in the quantity of CCRs handled dry, by category – fly ash and bottom ash – and which could 

 

69 U.S. EPA (2008). Study on Increasing the Usage of Recovered Mineral Components in Federally Funded 

Projects Involving Procurement of Cement or Concrete to Address the Safe, Accountable, Flexible, Efficient 

Transportation Equity Act: A Legacy for Users, Report to Congress, June 3, 2008, EPA530-R-08-007.

70 CCR RIA, Exhibit 5-L.
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September 29, 2015 10-4

be more readily marketed for beneficial use after making changes to meet the final ELGs under each 

of the regulatory options (see TDD for details).71

Consistent with the screening-level cost and economic impact analyses for the final rule which assume that 

steam electric power plants generate a constant amount of electricity throughout the analysis period (see RIA; 

U.S. EPA 2015c), EPA assumed that steam electric power plants will produce the same quantity of dry CCRs 

every year in the analysis period, beginning in the technology implementation year. EPA determined the 

amount of plant-level changes in fly- and bottom ash CCRs marketable to the two beneficial use categories 

assuming that fly ash is first marketed to concrete, as it is the use with higher market value, and then the 

remaining fly and bottom ash CCRs marketed to fill, as possible given remaining demand.

10.1.2.3 Marketable CCR

Thirty six states produced cement in 2010, 2011, or 2012 (van Oss, 2009; 2010; 2013). Given baseline CCR 

supply, EPA estimated that additional CCR production due to conversion to dry ash handling systems to meet 

the final ELGs would be marketable in 14 (39 percent) of these states. Note that none of the steam electric 

power plants expected to increase dry CCR production due to the ELGs are located in states without cement 

production in the baseline.

All 50 states produce sand and gravel, and EPA estimated that the CCR supply (from existing marketing of 

CCR by steam electric power plants and projected CCR reuse) exceeds demand for fill in only one state (West 

Virginia). In all other states, demand data suggest that steam electric power plants can market all the 

additional CCR handled dry. 

Table 10-2 reports “marketability” estimates for all states combined. Marginal changes in dry CCR represent

approximately 5 percent of unmet demand for CCR as a substitute for Portland cement in concrete and 

2 percent of unmet demand for CCR as a substitute for sand and gravel in fill. EPA expects that some plants 

will market CCR to both cement and fill, and that a lack of demand for fly ash in cement will induce many to 

market fly ash to fill. Nearly all of the expected conversions to dry-handled fly and bottom ash could 

potentially be marketed for beneficial reuse within the plant’s state. 

Table 10-2: State-level Market Approximation (Short Tons).

Application

Baseline Production Marketable Changes due to ELGs

Production

Unmet Demand (% 

of production)

Fly Ash

(% Total ΔCCR)

Bottom Ash (% Total 

ΔCCR)

Concrete 13,400,062 3,277,184 (24%) 162,491 (12.2%) NA

Fill 320,701,683 312,705,505 (98%) 1,166,260 (87.8%) 4,314,946 (91.7%)

Source: U.S. EPA Analysis 2015

EPA excluded from subsequent valuation of tallied “incremental” marketable CCR any changes that occur at 

steam electric power plants which already market the wet ash in the baseline, and only calculated benefits 

associated with plants that do not currently market their CCR wet. While these plants may convert to dry 

handling and may therefore find it easier to market their CCR in the future, EPA assumed for simplicity that 

the amount of ash they could market for beneficial use will not change as a result of this conversion. As a 

 

71 Fly ash tonnages are a dry-basis (not moisture conditioned), and were only calculated for steam electric units 

flagged for transport and disposal costs, excluding those units flagged for costs associated with additional 

conveyance capacity. Bottom ash tonnages were estimated for all bottom ash handling conversions excluding 

units marked for bottom ash management costs. Tonnage estimates include the CCR population; conversions at 

plants flagged as a fly ash or bottom ash dry conversion under the CCR rule were zeroed. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10-5

result of this assumption, the analysis values 100 percent of fly ash conversions marketed to concrete, and 

93 percent of fly ash and bottom ash conversions marketed to fill.

10.1.3 Value to Society from Re-Using CCR

Replacing virgin materials like fill and cement with CCR wastes avoids some costs while introducing other 

costs. EPA used the social cost accounting framework summarized in Figure 10-1 to track benefits and costs 

of CCR re-use, notably changes in production and disposal pre- and post-ELGs. The remainder of this section 

provides more detail on cost and benefit categories. 

Figure 10-1: Cost Accounting Framework for the Beneficial Reuse Analysis.

Without Reuse With Reuse Net Change Valuation Framework

Raw Materials (sand, cement)

Extraction/ mining No extraction/ Mining  Raw material 

extraction

Life Cycle Analysisa

Processing No processing  Raw materials 

processing

Transport to market No transport to market  Raw materials 

transportation

CCR from Steam Electric Power Generation

Prepare CCR for 

disposal

Beneficiate

CCR for re-use

 Beneficiation Estimate beneficiation 

costs

Transport SE CCR to 

disposal site

Transport SE CCR to 

reuse site

No net change in SE

CCR transport costsb

Qualitative uncertainty 

analysis

Disposal No Disposal  Disposal costs to 

meet SE ELG

Estimate avoided 

disposal costs

a: As was done in the analysis of the final CCR rule, EPA selected the LCA approach to estimate costs that society avoids by 

replacing virgin materials with CCR, including environmental impacts. The market value of avoided raw materials is an alternative 

cost proxy which captures some, but not all of these production costs. LCA captures some, but not all costs from a market price 

framework, and thereby is also an approximate avoided social cost.

b: On net, EPA assumed no change in transportation cost between the base case and policy scenario. In the base case, some plants 

dispose of CCR on-site, while others dispose of it off-site. Re-use applications are assumed to occur off-site; however, EPA also 

assumed that, on net to society, total costs of transportation to disposal and re-use sites are approximately equal given our in-state 

market framework.

Avoided Costs. Re-using CCRs avoids several costs, including the environmental costs of extracting virgin 

materials and costs to steam electric power plants of disposing of their CCR.

 Compliance cost offset. By redirecting the CCR to beneficial re-use rather than disposing of it, steam 

electric power plants can avoid disposal O&M costs attributed to meeting the ELGs in the economic 

impact analysis (see RIA; U.S. EPA 2015c). EPA’s analysis of the Final CCR Rule assumed plants 

marketing CCR could avoid certain per-ton transportation and disposal costs based on the volume of 

CCR marketed, but that plants could not offset certain other costs of the rule (e.g., general liability 

insurance required under the CCR rule, but not under the Steam Electric ELGs). For this Final Rule, 

EPA also assumed plants could not offset capital costs associated with disposal systems (e.g., 

landfills), but could offset O&M costs include tipping fees at off-site landfills. In this analysis, EPA 

assumes Steam Electric power plants market 100% of dry CCR produced following the rule. Since 

EPA assumes that plants that market dry CCR are able to re-use 100% of ash by volume, EPA 

assumed plants could offset 100% of these O&M costs. To the extent that plants may not market 

100% of ash in all years, this assumption produces an upper bound estimate of potential cost offsets 

due to beneficial reuse.
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September 29, 2015 10-6

 Production cost offset. This offset is the avoided cost of virgin materials costs72 resulting from using 

CCR in place of the virgin materials (e.g., Portland cement, sand and gravel). Depending on who uses 

the CCR for beneficial purposes, these cost offsets (transfers) may accrue to the steam electric power 

plant, the secondary CCR user who either paid for or received the CCR for free, a third-party reseller, 

etc. 

 Environmental benefits. By avoiding the production of virgin materials needed for Portland cement 

and fill, society also gains a reduction in total environmental damages associated with extracting, 

transporting, and processing these raw materials (e.g., air emissions and resource consumption). Life 

Cycle Analysis (LCA) is a framework to assess the total environmental impact of a product based on 

an inventory of energy and material inputs and outputs associated with each step in producing the 

product. Typically, LCA addresses raw material acquisition, materials manufacture, production, 

use/reuse/maintenance, and waste management (SAIC, 2006), and estimates environmental impacts 

per unit of product produced (e.g., changes in energy consumption, water consumption, and air 

pollutant emissions). To estimate the total environmental benefits of avoided Portland cement and fill 

production in this analysis, EPA applied the existing LCA impacts for cement and sand and gravel 

used in EPA’s CCR Final Rule. 

Table 10-4 lists environmental impacts per ton of avoided virgin material, and Table 10-5 lists the 

estimated unit values of the avoided economic impacts of these environmental impacts. EPA applied 

benefit transfer of unit economic values based on the Final CCR Rule’s LCA approach, the Social 

Cost of Carbon (IWGSCC, 2013b), and human health benefits from reducing emissions of PM2.5

precursors (U.S. EPA based on Fann et al., 2012). 

As shown in Figure 10-1, production cost offsets overlap with benefits from avoided life-cycle impacts.

Specifically, the life-cycle impact framework captures many of the energy, water and other production costs 

that fill and concrete producers can avoid by re-using CCR products and avoiding the production of virgin 

materials. Since the life-cycle framework also captures avoided environmental impacts of production, the 

remainder of this analysis reports only the monetized benefits from the life-cycle framework to avoid 

potentially double-counting benefits. 

Additional Costs. Steam electric power plants that market CCRs for beneficial re-use do so when the re-use is 

economically preferable to disposal. Some plants may find it necessary to undertake additional preparation of 

dry CCRs before beneficial use (e.g., to bring CCR to ASTM standards), or may need to consider the cost of 

transporting CCR to a beneficial use destination. There is uncertainty in the degree to which steam electric 

power plants incur one or more of these additional costs. To provide a conservative estimate of avoided costs

(and benefits), EPA included additional costs for beneficiation.

 Beneficiation of fly ash prior to use in concrete: EPA assumed that 13 percent of fly ash requires 

beneficiation prior to use in concrete.

73 Electric Power Research Institute (2005) reports a range of 

beneficiation costs per ton of fly ash; the CCR Final Rule analysis estimated that a reasonable central 

tendency value for the cost of beneficiation is approximately $10 per ton (in year 2004$). EPA

updated this cost to 2013 dollars and applied this cost to 13 percent of marketable fly ash CCRs at 

each steam electric power plant.

 

72 For reference, the 3-year average (2008 to 2012) market price of sand and gravel used as fill is $4.36/ton (range: $2.38 -

$9.30 by state) (van Oss/USGS, 2010, 2011, 2013). The 3-year average (2008 to 2012) market price of Portland cement 

is $44.50/ton ($76.24 - $148.02 by state) (Bolen, 2011, 2012, 2013, 2014).

73 In the Final CCR rule analysis document, ORCR cites personal communication with David Goss, ACCA (June 

25, 2008) as the source of this assumption.
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September 29, 2015 10-7

EPA calculated monetary values of estimated changes in beneficial use of marketable CCRs based on the 

avoided costs by steam electric power plants, as well as the avoided resource impacts from displacing virgin 

materials. Table 10-3 lists rates and ratios for avoided costs and additionally-incurred costs associated with reusing CCR for beneficial uses. Table 10-4 and Table 10-5 list the quantity and value of avoided resource and 

environmental impacts.

Table 10-3: Economic Value of CCR Handling Costs per Unit (2013$).

Cost Type Beneficial Use Cost/Benefit

Avoided Costs of Disposal Sand and Gravel Used as Fill; 

Concrete

Varies by Steam Electric planta

Additionally Incurred Costs of Beneficiation Concrete $11.95 per tonb

a. EPA estimated the annual cost of CCR disposal by Steam Electric plant and year (U.S. EPA, 2015). 

b. Electric Power Research Institute (2005). 

Table 10-4: Avoided Resource and Environmental Impacts per Ton of Virgin Material Produced.

Impact Category Portland Cementa,c Fillb,c

Energy (MMBtu) 3.52 - 3.62* 0.04

Water (gal) 201 – 246,947* 1,208

Air Emissions Greenhouse gases (tons) 0.90 – 0.93* 0.0032

NOx (tons) 0.0019 2.68 x 10-5

SOx (tons) 2.43 x 10-48.50 x 10-6

a. Assumptions are based on Final CCR rule analysis. For energy use, water use, greenhouse gases, carbon monoxide, and PM10 

associated with portland cement, the range of values presented in this Table reflect data from Ecoinvent v2.2 as included in SimaPro

7.2 and the National Renewable Energy Laboratory’s U.S. Life Cycle Inventory Database, the latter of which is available at 

http://www.nrel.gov/lci/. The Portland cement values for NOx, PM2.5, SOx, and mercury were all derived from emissions data 

presented in U.S. EPA, Regulatory Impact Analysis: Amendments to the National Emission Standards for Hazardous Air Pollutants 

and New Source Performance Standards (NSPS) for the Portland Cement Manufacturing Industry Final Report, August 2010. The 

value for lead was derived from Ecoinvent v2.2 as included in SimaPro 7.2.

b. Assumptions are based on Final CCR rule analysis. Values for fill reflect the average of values for sand and clay from Ecoinvent 

v2.2 as included in SimaPro 7.2.

* Denotes cases in which EPA used the lower limit of the range for this scoping analysis.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10-8

Table 10-5: Economic Value of Avoided Resource and Environmental Impacts per Unit of Impact 

(2013$).

Impact Category Portland Cement Fill

Energy ($/MMBtu)a

$4.94 $19.15

Water ($/gal)b

$0.00003

Greenhouse gases ($/ton)c$36 to $62

NOx ($/ton)d

$5,694 to $8,836 $5,694 to $8,836

SOx ($/ton)d

$43,856 to $68,109 $43,856 to $68,109

a. Assumptions are based on Final CCR rule analysis. Derived from U.S. Department of Energy, Energy Consumption by 

Manufacturers, May 2013. Table 7.2. This is the same data source as that used for the final CCR rule, updated to 2013$ using GDP 

deflator.

b. Assumptions are based on Final CCR rule analysis, updated to 2013$ using GDP deflator. Value provided by H. Scott Matthews, 

Professor of Civil and Environmental Engineering and Engineering and Public Policy at Carnegie Mellon University and Research

Director of Carnegie Mellon University’s Green Design Institute, October 18, 2011.

c. Values shown are the range of yearly Social Cost of Carbon values from 2015 to 2042, using the Average Value at 3% discount rate 

(adjusted to 2013$ and converted from tonnes to tons). Values are derived from Interagency Working Group on Social Cost of 

Carbon, United States Government, Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory 

Impact Analysis Under Executive Order 12866, 2013 (revised June 2015). EPA also calculated benefits using the 5% average ($10 to 

$22), 2.5% average ($50 to $85), and 3% 95th percentile ($85 to $188) SCC estimates.

d. Values shown are the range of yearly benefit per ton estimates (2015 to 2043) linearly interpolated and extrapolated from BenMap 

benefit per ton estimates for 2016, 2020, 2025, and 2030. Benefit per ton unit impacts were provided in personal communication with 

Charles Fulcher, EPA/OAQPS, on October 19, 2012 and updated to 2013$ using GDP deflator. EPA applied values for the “Cement 

Kilns” sector to Portland cement, and values for the “area sources” aggregate sector to fill. Values shown are the central tendency by 

sector, at 3% discount rate.

10.2 Results

Table 10-6 summarizes the estimated change in dry and marketable CCRs beneficially used in concrete and 

fill applications due to final ELGs and other regulatory options, for steam electric power plants not marketing 

wet ash in the baseline. The analysis suggests that most marketable CCRs would be beneficially used in fill 

applications.

Table 10-6: Estimated Beneficial Use Applications of CCRs, by CCR Category

ELG Option and CCR Category

Beneficial Use Application (1,000 short tons)

Concrete Structural Fill

Option A or Option B

Fly ash 162 1,166

Bottom ash NA 0

Total 162 1,166

Option C

Fly ash 162 1,166

Bottom ash NA 2,813

Total 162 3,979

Option D or Option E

Fly ash 162 1,166

Bottom ash NA 3,763

Total 162 4,929
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September 29, 2015 10-9

Table 10-7 reports the estimated life cycle benefits of avoiding virgin materials in concrete and fill 

production. Table 10-8 reports the estimated annualized economic value of increased marketable CCRs by 

value category. Relative changes in total CCR beneficial use and total life cycle impacts under the final ELGs 

are consistent with the projected effects of the CCR rule on beneficial reuse (shown in last column of the 

table; U.S. EPA, 2014).

Table 10-7: Annual Avoided Resource and Environmental Impacts Given CCR Reuse in Concrete 

and Fill Applications.

Impact Category Fly Ash Bottom Ash Option Total

CCR RIA, 2030 3-

yr rolling avg

Option A or Option B

Energy (MMBtu) 618,618 0 618,618 910,000

Water (million gal) 1,442 0 1,442 21,000

Greenhouse gases (tons) 149,974 0 149,974 75,000

NOx (tons) 340 0 340 310

SOx (tons) 49 0 49 160

Option C

Energy (MMBtu) 618,618 112,536 731,154 910,000

Water (million gal) 1,442 3,399 4,840 21,000

Greenhouse gases (tons) 149,974 9,003 158,977 75,000

NOx (tons) 340 75 415 310

SOx (tons) 49 24 73 160

Option D or Option E

Energy (MMBtu) 618,618 150,515 769,133 910,000

Water (million gal) 1,442 4,546 5,987 21,000

Greenhouse gases (tons) 149,974 12,041 162,015 75,000

NOx (tons) 340 101 441 310

SOx (tons) 49 32 81 160

Note: Values in this table represent annual changes in a full-compliance year (e.g., starting in 2023). 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10-10

Table 10-8: Annualized Economic Value of Estimated Changes in Beneficial Use (Million 2013$) a,b.

Regulatory Option Impact Category 3% 7%

Option A or 

Option B

Avoided Disposal Costs to Steam Electric Plants $0.80 $0.68 

Beneficiation Costs -$0.21 -$0.16

Avoided Life Cycle Costs of Virgin Materials $10.74 $13.79 

Net Social Value $11.33 $14.31 

Option C

Avoided Disposal Costs to Steam Electric Plants $13.03 $10.87 

Beneficiation Costs -$0.21 -$0.16

Avoided Life Cycle Costs of Virgin Materials $12.18 $15.43 

Net Social Value $25.00 $26.14 

Option D or 

Option E

Avoided Disposal Costs to Steam Electric Plants $18.49 $15.46 

Beneficiation Costs -$0.21 -$0.16

Avoided Life Cycle Costs of Virgin Materialsc$12.52 $15.81 

Net Social Value $30.80 $31.11 

Notes: a. Annualized over 24 years (2015 - 2042). Values escalated using CCI and GDP through 2022; thereafter, assume no real 

change in prices above inflation. Avoided disposal costs to steam electric power plants include are annual O&M costs. B. 

b. EPA used SCC values based on a 3 percent (average) discount rate to calculate total benefit values presented for both the 3 

percent and 7 percent discount rate.

c. EPA also estimated life cycle benefits for Options D or E, at other SCC estimates (IWGSCC, 2013 (revised June 2015)). Results 

of LCA benefits at 5% average, 2.5% average, and 3% 9th percentile SCC estimates range from $5.12 million (5% average) to 

$19.90 million (3% 95th percentile) when discounted at 3%, and from $3.81 million (5% average) to $14.87 million (3% 95th

percentile).

As shown in Table 10-8, for Options requiring both fly and bottom ash technologies, much of the annualized 

economic value from changes in beneficial use comes from avoided disposal costs incurred by steam electric 

plants, even after accounting for any increased beneficiation costs. 

The estimates above translate into net annualized benefits of approximately $30.8 million (3 percent discount 

rate) for Options D or E. Estimated benefits for Option C are lower due to the reduction in the amount of 

marketable bottom ash relative to Options D and E (i.e., BAT/PSES standards for bottom ash apply only to 

units with greater than 400 MW capacity), and benefits for Options A and B are lower yet, as they derive 

from the incremental marketability of fly ash only. 

10.3 Implications of Revised Steam Electric Plant Loading Estimates

As described in Section 1.4.3, EPA revised its estimates of pollutant loadings in steam electric power plant 

discharges after completing the benefit analyses. Estimated benefits from the enhanced marketability of CCR 

do not depend on pollutant loadings to receiving waters and therefore the monetized benefit estimates 

provided in this Chapter are unaffected by revisions to steam electric plant loading estimates. 

10.4 Limitations and Uncertainties

Key uncertainties and limitations include:

 Benefits from marketed CCR are sensitive to assumptions about CCR generation. The analysis 

assumes that the amount of CCR generated by steam electric power plants is constant throughout the 

analysis, i.e., coal-fired plants generate a constant quantity of electricity. This is consistent with the 

screening-level cost and economic impact analysis framework (see RIA report). However, the 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 10: Ash Marketing

September 29, 2015 10-11

Department of Energy and EPA both project that generation from steam electric plants will decline 

over time, due to a variety of market factors, including environmental regulations. This decline would 

reduce the quantity of CCR generated, available to be marketed for beneficial use, and the associated 

benefits estimated in this memo. The Agency accounted for disposal costs in its economic impact 

analysis assuming that a constant quantity of CCR would need to be disposed of. Therefore, the 

assumption of constant CCR generation does not result in an overstatement of avoided disposal 

benefits since they offset costs already calculated elsewhere. In contrast, non-disposal related benefits 

could be lower when accounting for the reduction in CCR generation.

 As discussed above, EPA assumed that conversions to dry handling at plants already marketing wet 

ash in the baseline are not incremental economic benefits because there is no change in the total 

amount of CCR that is beneficially used. Relaxing this assumption could increase estimated cost 

offsets to the extent that these plants avoid certain compliance or operational costs that have been 

counted against the ELGs.

 Steam electric plants may not market all of their CCR within a given year; for example, they may 

instead temporarily store it. This affects both the time profile and value of beneficial reuse benefits. 

To the extent that steam electric plants may or may not be able to completely avoid the annual O&M 

costs of disposal, EPA’s analysis is an upper bound.

 Certain other dimensions of this analysis may lead to under-estimated benefits of CCR re-use. For 

example, EPA assumed that facilities only offset O&M costs of disposal. To the extent that some 

plants are actually able to avoid or offset capital costs of compliance or other longer-term costs given 

reduced need for landfilling (e.g., costs of securing and developing additional disposal sites), EPA’s 

analysis is a lower-bound on the benefits of dry handling CCR.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11-1

11 Summary of Total Monetized Benefits

11.1 Total Annualized Benefits

Table 11-1 and Table 11-2, on the next two pages, summarize the total annual monetized benefits using 3 

percent and 7 percent discount rates, respectively. Table 11-3 and Table 11-4 compile, for each of the five

analyzed regulatory options, the time profiles of total (non-discounted) monetized benefits. The tables also 

report the calculated present and annualized values of benefits at 3 percent and 7 percent discount rates, 

respectively. 

The estimated total monetized benefits of the five regulatory options EPA analyzed range from $41.1 million 

to $565.6 million per year using a 3 percent discount rate, depending on the option ($37.2 million to 

$478.4 million per year using a 7 percent discount rate), and whether the analysis includes air-related benefits. 

Option D has total benefits (including air-related benefits) of approximately $450.6 million to $565.6 million 

using a 3 percent discount rate and $387.3 million to $478.4 million using a 7 percent discount rate.

The monetized benefits of the final rule do not account for all benefits because they omit various sources of 

benefits to society from reduced steam electric pollutant discharges, such as reduction in certain non-cancer 

health risk (e.g., effects of cadmium on kidney functions and bone density) and reduced cost of drinking water 

treatment. See Chapter 2 for a discussion of categories of benefits EPA did not monetize.

Finally, EPA was able to estimate air-related benefits for Options B and D only (see Chapter 7). Benefits for 

options A, C and E are therefore understated to a greater degree than the other options; in particular, EPA 

expects that the benefits for Option E would be higher than those for Option D if air-related benefits were 

included. See Section 13.1 for an extrapolation of potential air-related benefits for these options.

As described in Section 1.4.3, EPA revised its estimates of pollutant loadings in steam electric power plant 

discharges after completing the benefit analyses. These revisions are likely to reduce benefit estimates in the 

following four categories: human health, nonmarket water quality benefits, benefits to threatened and 

endangered species and avoided dredging costs. For several reasons — notably the fact that revisions do not 

affect all plants equally and the model functions are not linear — it would be inappropriate to simply scale the 

monetized benefits based on the aggregate changes in loadings. Therefore, EPA qualitatively assessed effects 

of pollutant loading reductions on the relevant benefit categories (see Chapters 3, 4, 5, and 9 for detail). The 

Agency concluded that although the revised loadings are likely to reduce the estimated benefit in four benefit 

categories, the magnitude of these reductions is likely to be small compared to the total benefits of the rule.

Chapters 3 through 10 provide more detail on limitation and uncertainty inherent in the analysis of each 

benefit category.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11-2

Table 11-1: Summary of Total Annualized Benefits at 3 Percent (Millions; 2013$)

Benefit Category

Option A Option B Option C Option D Option E

Low Midf High Low Midf High Low Midf High Low Midf High Low Midf High

Human Health Benefitsg

$5.4 $5.7 $6.1 $5.4 $5.8 $6.1 $11.3 $11.8 $12.4 $16.5 $17.2 $17.9 $16.7 $17.5 $18.3

Reduced IQ losses in children 

from exposure to leada,g $0.3 $0.4 $0.5 $0.3 $0.4 $0.5 $0.5 $0.6 $0.7 $0.8 $1.0 $1.1 $0.8 $1.0 $1.1 

Reduced CVD in adults from 

exposure to leadg $3.8 $3.8 $8.4 $12.8 $12.8

Reduced IQ losses in children 

from exposure to mercurya,g $1.3 $1.6 $1.8 $1.3 $1.6 $1.9 $2.4 $2.9 $3.4 $2.9 $3.5 $4.0 $3.1 $3.8 $4.4

Avoided cancer cases from 

exposure to arsenicb,g <$0.1 <$0.1 <$0.1 <$0.1 <$0.1

Improved Ecological 

Conditions and Recreational 

Usesg

$4.2 $4.9 $23.4 $15.0 $18.9 $83.7 $19.6 $26.0 $109.4 $23.3 $31.3 $129.5 $25.1 $34.0 $140.0

Use and nonuse values for 

water quality improvementsg $4.2 $4.9 $23.4 $15.0 $18.9 $83.7 $19.6 $26.0 $109.4 $23.2 $31.3 $129.5 $25.1 $34.0 $140.0

Nonuse values of T&E 

speciesb,g <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1 <$0.1

Market and Productivity 

Benefits $31.5 $32.8 $34.2 $31.5 $32.8 $34.2 $107.2 $109.5 $111.9 $126.4 $130.0 $133.7 $126.4 $130.0 $133.7

Avoided impoundment failures $20.1 $21.5 $22.9 $20.1 $21.5 $22.9 $82.2 $84.5 $86.9 $95.6 $99.2 $102.9 $95.6 $99.2 $102.9

Reduced dredging costsb<$0.1 <$0.1 <$0.1 <$0.1 < $0.1

Ash marketing benefits $11.3 $11.3 $25.0 $30.8 $30.8

Air-related benefits NE $110.2 NE $284.5 NE

Reduced human health effects NE $57.8 NE $144.7 NE

Reduced CO2 emissionsc NE $52.5 NE $139.8 NE

Reduced water withdrawalsb<$0.1 <$0.1 <$0.1 <$0.1 <$0.1
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11-3

Table 11-1: Summary of Total Annualized Benefits at 3 Percent (Millions; 2013$)

Benefit Category

Option A Option B Option C Option D Option E

Low Midf High Low Midf High Low Midf High Low Midf High Low Midf High

Total (excluding air-related 

Benefits)d $41.1 $43.4 $63.7 $51.9 $57.5 $124.0 $138.1 $147.4 $233.7 $166.1 $178.5 $281.2 $168.3 $181.6 $292.0

Total (including air-related 

Benefits)d,e NE NE NE $162.2 $167.8 $234.2 NE NE NE $450.6 $463.0 $565.6 NE NE NE

Source: U.S. EPA Analysis, 2015

“NE” indicates that EPA did not estimate the benefits. Air-related benefits of Option A are expected to be less than those for Option B; air-related benefits for Option C are expected to be 

between those of Options B and D; and air-related benefits of Option E are expected to be greater than those for Option D.

a. Value includes reduced IQ losses and avoided cost of compensatory education in children from exposure to lead. For details see Chapter 3.

b. “< $0.1” indicates that the monetized annual benefits are positive but less than $0.1 million.

c. For the valuation of benefits from reductions in CO2 emissions EPA relied on the 3 percent average social cost of carbon estimate.

d. Values for individual benefit categories may not sum to the total due to independent rounding.

e. The total monetized benefits for options A, C, and E do not include air-related benefits. This category of benefits was analyzed for Options B and D only (see Chapter 7).

f. EPA estimated use and nonuse values for water quality improvements using two different meta-regression models of willingness-to-pay. One model provides the low and high bounds 

while a different model provides a central estimate (included in this table in the mid-range column). For this reason, the mid-range estimate differs from the midpoint of the range for this 

benefit category. For details, see Chapter 4.

g. Estimates for this benefit category do not reflect revised pollutant loadings, which could result in lower monetized benefits. See Section 1.4.3 for details.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11-4

Table 11-2: Summary of Total Annualized Benefits at 7 Percent (Millions; 2013$)

Benefit Category

Option A Option B Option C Option D Option E

Low Midf High Low Midf High Low Midf High Low Midf High Low Midf High

Human Health Benefitsg$3.4 $3.5 $6.7 $3.4 $3.5 $14.2 $7.4 $7.6 $7.7 $11.3 $11.4 $11.6 $11.3 $11.5 $11.6

Reduced IQ losses in children 

from exposure to lead

a,g $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.2 $0.2 $0.1 $0.2 $0.2

Reduced CVD in adults from 

exposure to leadg $3.1 $3.1 $7.0 $10.7 $10.7

Reduced IQ losses in children 

from exposure to mercurya,g $0.2 $0.3 $3.5 $0.2 $0.3 $11.0 $0.4 $0.5 $0.6 $0.5 $0.6 $0.7 $0.5 $0.6 $0.8

Avoided cancer cases from 

exposure to arsenicb,g <$0.1 <$0.1 <$0.1 <$0.1 <$0.1

Improved Ecological 

Conditions and Recreational 

Usesg

$3.3 $3.9 $18.6 $12.0 $15.2 $66.7 $15.7 $20.9 $87.4 $18.6 $25.1 $103.4 $20.1 $27.3 $111.7

Use and nonuse values for 

water quality improvementsg $3.3 $3.9 $18.6 $12.0 $15.2 $66.7 $15.7 $20.9 $87.3 $18.5 $25.1 $103.4 $20.0 $27.3 $111.7

Nonuse values of T&E 

speciesb,g <$0.1 <$0.1 <$0.1 <$0.1 < $0.1 <$0.1 <$0.1 < $0.1 <$0.1 <$0.1 < $0.1 <$0.1 <$0.1 < $0.1 <$0.1

Market and Productivity 

Benefits $30.4 $31.6 $32.7 $30.4 $31.6 $32.7 $92.5 $94.4 $96.4 $108.8 $111.8 $114.8 $108.8 $111.8 $83.7

Avoided impoundment failures $16.1 $17.3 $18.4 $16.1 $17.3 $18.4 $66.4 $68.3 $70.3 $77.7 $80.7 $83.7 $77.7 $80.7 $83.7

Reduced dredging costsb< $0.1 < $0.1 < $0.1 < $0.1 < $0.1

Ash marketing benefits $14.3 $14.3 $26.1 $31.1 $31.1

Air-Related Benefits NE $103.6 NE $248.6 NE

Reduced human health effects NE $51.1 NE $108.8 NE

Reduced CO2 emissionsc NE $52.5 NE $139.8 NE

Reduced water withdrawalsb<$0.1 <$0.1 <$0.1 <$0.1 <$0.1
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

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Table 11-2: Summary of Total Annualized Benefits at 7 Percent (Millions; 2013$)

Benefit Category

Option A Option B Option C Option D Option E

Low Midf High Low Midf High Low Midf High Low Midf High Low Midf High

Total (excluding air-related 

Benefits)d $37.2 $38.9 $58.0 $45.8 $50.2 $113.7 $115.6 $122.9 $191.4 $138.7 $148.4 $229.8 $140.2 $150.6 $207.1

Total (including air-related 

Benefits)d,e NE NE NE $149.4 $153.8 $217.3 NE NE NE $387.3 $397.0 $478.4 NE NE NE

Source: U.S. EPA Analysis, 2015

“NE” indicates that EPA did not estimate the benefits. Air-related benefits of Option A are expected to be less than those for Option B; air-related benefits for Option C are expected to be 

between those of Options B and D; and air-related benefits of Option E are expected to be greater than those for Option D.

a. Value includes reduced IQ losses and avoided cost of compensatory education in children from exposure to lead. For details see Chapter 3.

b. “< $0.1” indicates that the monetized annual benefits are positive but less than $0.1 million.

c. For the valuation of benefits from reductions in CO2 emissions EPA relied on the 3 percent average social cost of carbon estimate.

d. Values for individual benefit categories may not sum to the total due to independent rounding.

e. The total monetized benefits for options A, C, and E do not include air-related benefits. This category of benefits was analyzed for Options B and D only (see Chapter 7).

f. EPA estimated use and nonuse values for water quality improvements using two different meta-regression models of willingness-to-pay. One model provides the low and high bounds 

while a different model provides a central estimate (included in this table in the mid-range column). For this reason, the mid-range estimate differs from the midpoint of the range for this 

benefit category. For details, see Chapter 4.

g. Estimates for this benefit category do not reflect revised pollutant loadings, which could result in lower monetized benefits. See Section 1.4.3 for details.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11-6

11.2 Time Profile of Benefits

Table 11-3 and Table 11-4 compile the time profiles of total (non-discounted) monetized benefits including 

air-related benefits for Options B and D, and the calculated annualized values of benefits at 3 percent and 7 

percent discount rates, respectively. The time profile is based on mid-range (or central) estimates for benefits 

for which benefits are presented as a range in Table 11-1 and Table 11-2.

As shown in the tables, benefits under Option D increase from 2019 until 2022, then decline in 2023 before 

gradually increasing. This 2023 decline is due to an IPM-projected increase in SO2 relative to baseline 

(negative benefits). For Option B, there is a similar decline in 2023 as well as a larger decline in benefits in 

2028. As with Option D, these declines are attributable to IPM-projected increases in SO2 (negative benefits),

but also occur due to smaller estimated CO2 and NOx reductions when compared to earlier years. For both 

Options B and D (excluding the increases in SO2 in the given periods), IPM indicates a decline in net air 

emissions reductions between 2019 and 2033 followed by an increase in net reductions in 2034. For more 

details on the IPM projections, see Chapter 7.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

September 29, 2015 11-7

Table 11-3: Time Profile of Benefits at 3 Percent (Millions; 2013$) (Including Air-Related Benefits for 

Options B and D) 

Year Option A

a Option B Option Ca Option D Option Ea

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $3.8 $291.4 $16.4 $544.8 $30.3

2020 $5.5 $296.6 $39.9 $578.7 $58.7

2021 $18.7 $327.6 $83.3 $633.5 $115.1

2022 $31.0 $344.4 $155.8 $718.1 $193.2

2023 $57.2 $176.1 $189.9 $310.5 $232.6

2024 $58.0 $179.1 $191.6 $314.9 $235.1

2025 $58.4 $181.5 $192.3 $317.6 $235.9

2026 $58.6 $184.0 $192.6 $319.9 $236.2

2027 $58.7 $186.3 $192.7 $321.8 $236.2

2028 $58.7 $31.1 $192.7 $422.6 $236.0

2029 $58.6 $30.4 $192.5 $423.2 $235.5

2030 $58.5 $29.9 $192.3 $426.2 $235.1

2031 $58.6 $29.6 $192.3 $428.8 $234.8

2032 $58.6 $29.2 $192.2 $431.6 $234.4

2033 $58.6 $28.8 $192.1 $434.4 $234.1

2034 $58.6 $224.3 $192.1 $670.1 $233.8

2035 $58.7 $226.7 $192.1 $676.4 $233.6

2036 $58.7 $229.2 $192.1 $682.7 $233.5

2037 $58.8 $231.7 $192.2 $689.1 $233.4

2038 $58.9 $234.2 $192.4 $695.6 $233.4

2039 $59.0 $236.8 $192.5 $702.1 $233.4

2040 $59.2 $239.3 $192.7 $708.6 $233.5

2041 $59.3 $241.9 $192.9 $715.3 $233.7

2042 $59.3 $243.2 $193.1 $718.6 $233.8

Annualized Benefits, 

3%b $43.4 $167.8 $147.4 $463.0 $181.6

Source: U.S. EPA Analysis, 2015

a. Estimates for Options A, C and E do not include air-related benefits. This category of benefits was only estimated for Options B and 

D (see Chapter 7).

b. Total annualized year-specific benefits may not sum to the total annualized benefits due to rounding.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 11: Total Monetized Benefits

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Table 11-4: Time Profile of Benefits at 7 Percent (Millions; 2013$) (Including Air-Related Benefits for 

Options B and D) 

Year Option A

a Option B Option Ca Option D Option Ea

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $3.8 $270.0 $16.4 $512.5 $30.3

2020 $5.5 $275.5 $39.9 $547.5 $58.7

2021 $16.9 $304.5 $80.1 $597.4 $110.8

2022 $29.2 $321.3 $152.5 $682.1 $188.8

2023 $55.4 $170.3 $186.6 $309.7 $228.2

2024 $56.2 $173.3 $188.3 $314.0 $230.6

2025 $56.5 $175.7 $188.9 $316.4 $231.4

2026 $56.7 $178.2 $189.2 $318.8 $231.6

2027 $56.8 $180.4 $189.3 $320.7 $231.6

2028 $56.8 $34.2 $189.3 $409.6 $231.3

2029 $56.6 $33.5 $189.1 $410.2 $230.8

2030 $56.5 $33.0 $188.9 $413.0 $230.4

2031 $56.6 $32.7 $188.8 $415.6 $230.0

2032 $56.6 $32.3 $188.6 $418.3 $229.6

2033 $56.6 $31.9 $188.5 $421.1 $229.2

2034 $56.6 $214.9 $188.5 $641.9 $228.9

2035 $56.6 $217.3 $188.5 $648.1 $228.7

2036 $56.7 $219.8 $188.5 $654.4 $228.5

2037 $56.8 $222.3 $188.5 $660.7 $228.4

2038 $56.8 $224.8 $188.6 $667.1 $228.4

2039 $56.9 $227.3 $188.8 $673.5 $228.3

2040 $57.0 $229.8 $188.9 $680.0 $228.4

2041 $57.2 $232.4 $189.1 $686.6 $228.5

2042 $57.2 $233.7 $189.2 $689.8 $228.5

Annualized Benefits, 

7%b $38.9 $153.8 $122.9 $397.0 $150.6

Source: U.S. EPA Analysis, 2015

a. Estimates for Options A, C and E do not include air-related benefits. This category of benefits was only estimated for Options B and 

D (see Chapter 7).

b. Total annualized year-specific benefits may not sum to the total annualized benefits due to rounding. Additionally, note that SCC 

values are annualized at 3 percent, whereas other benefits are annualized at 7 percent.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 12: Total Social Costs

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12 Summary of Total Costs

This chapter develops EPA’s estimates of the costs to society resulting from the final ELGs. As analyzed in 

this chapter, the costs of regulatory actions are the opportunity costs to society of employing resources to 

prevent the environmental damage otherwise occurring from discharges of wastewater containing metals, 

nutrients, and other pollutants.

12.1 Overview of Costs Analysis Framework

RIA Chapter 3: Compliance Costs presents EPA’s development of costs to the 1,080 steam electric power 

plants subject to the final ELGs (U.S. EPA, 2015c). These costs are used as the basis of the social cost 

analysis. However, the compliance costs used to estimate total costs differ in their consideration of taxes from 

those reported in RIA Chapter 3, which were calculated for the purpose of estimating the private costs and the 

economic impacts of the ELGs. In the analysis of costs to society, 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 plants, by taxpayers in the form of lost tax revenues, or by some combination.

74

As described in Chapter 1, EPA assumed that steam electric power plants, in the aggregate, would implement 

control technologies during a 5-year period from 2019 to 2023. For this analysis, EPA developed a yearexplicit schedule of compliance outlays over the period of 2019 through 2042.

75 After creating a costincurrence schedule for each cost component, EPA summed the costs expected to be incurred in each year for 

each plant, then aggregated these costs to estimate the total costs for each year in the analysis period. 

After compliance costs were assigned to the year of occurrence, the Agency adjusted these costs for real 

change between their stated year and the year(s) of their incurrence as follows: 

 All technology costs, except planning, were adjusted to their incurrence year(s) using the 

Construction Cost Index (CCI) from McGraw Hill Construction and the Gross Domestic Product 

(GDP) deflator index published by the U.S. Bureau of Economic Analysis (BEA); 

 Planning costs were adjusted to their incurrence year(s) using the Employment Cost Index (ECI) 

Bureau of Labor Statistics (BLS) and GDP deflator. 

Note that the CCI and ECI adjustment factors were developed only through the year 2022; after these years, 

EPA assumed that the real change in prices is 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. 

After developing the year-explicit schedule of total 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 2015, using both 3 percent and 7 percent discount rates. These 

discount rate values reflect guidance from the OMB regulatory analysis guidance document, Circular A-4 

 

74 For the impact analyses, compliance costs are measured as they affect the financial performance of the 

regulated plants and firms. The economic impact analyses therefore consider the tax deductibility of compliance 

expenditures, as appropriate depending on the tax status of the complying entity.

75 The end of the analysis period, 2042, was determined based on the life of the longest-lived compliance 

technology implemented at any steam electric power plant (20 years), and the last year of technology 

implementation (2023). 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 12: Total Social Costs

September 29, 2015 12-2

(U.S. OMB, 2003). EPA calculated the constant annual equivalent value (annualized value), again using the 

two values of the discount rate, 3 percent and 7 percent, over a 24-year social cost analysis period. EPA 

assumed no re-installation of compliance technology during the period covered by the social cost analysis. 

To assess the economic costs of the ELGs to society, EPA relied first on the estimated costs to steam electric 

power plants for the labor, equipment, material, and other economic resources needed to comply with the 

ELGs. 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 final rule does not affect the aggregate 

quantity of electricity that would be sold to consumers and, thus, that the rule’s social cost would include no 

loss in consumer and producer surplus from lost electricity sales by the electricity industry in aggregate. 

Given the small impact of the final rule on electricity production cost for the total industry, EPA believes that 

this assumption is reasonable for the social cost analysis (for more details on the impacts of the final rule on 

electricity production cost, see RIA Chapter 5: Electricity Market Analyses).The social cost analysis considers 

costs on an as-incurred, year-by-year basis – that is, this analysis associates each cost component to the 

year(s) in which they are assumed to occur relative to the assumed promulgation and technology 

implementation years.76

Finally, as discussed in RIA Chapter 10 (Section 10.7: Paperwork Reduction Act of 1995), the final ELGs are

not expected to result in additional administrative costs for plants to implement, and state and federal NPDES 

permitting authorities to administer, the final ELGs. As a result, the social cost analysis focuses on the 

resource cost of compliance as the only direct cost incurred by society as a result of the ELGs. 

12.2 Key Findings for Regulatory Options

Table 12-1 presents annualized costs for each of the five regulatory options. At a 3 percent discount rate, 

estimated annualized costs range between $120.5 million under Option A and $536.0 million under Option E. 

The final BAT/PSES options for existing sources (Option D) have annualized costs of $479.5 million at a 

3 percent discount rate, and $471.2 million at a 7 percent discount rate.

77

Table 12-1: Summary of Annualized Costs (Millions; $2013)

Regulatory Option 3% Discount Rate 7% Discount Rate

Option A $120.5 $116.9

Option B $198.7 $194.7

Option C $383.5 $379.9

Option D $479.5 $471.2

Option E $536.0 $525.8

Source: U.S. EPA Analysis, 2015.

Table 12-2 provides additional detail on the social cost calculations. The table compiles, for each of the five 

regulatory options EPA analyzed for the final ELG, the time profiles of compliance costs incurred. The table 

 

76 The specific assumptions of when each cost component is incurred can be found in Chapter 3: Compliance 

Costs of the RIA.

77 Similarities in the values obtained when discounting costs using 3 percent and 7 percent are due to the time 

profile of the costs, specifically the timing and relative magnitude of upfront capital costs versus ongoing O&M 

expenditures.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 12: Total Social Costs

September 29, 2015 12-3

also reports the calculated annualized values of costs at 3 percent and 7 percent discount rates.78 The 

maximum compliance outlays are incurred over the years 2019 through 2023, i.e., during the estimated 

window when steam electric power plants are expected to implement compliance technologies.

Table 12-2: Time Profile of Costs to Society (Millions; $2013)

Year Option A Option B Option C Option D Option E

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $388.8 $641.8 $1,143.4 $1,336.6 $1,427.1

2020 $132.3 $276.7 $631.1 $856.7 $950.3

2021 $390.2 $620.2 $1,243.4 $1,364.9 $1,498.0

2022 $201.7 $374.2 $882.5 $1,123.1 $1,331.8

2023 $286.6 $512.9 $1,058.8 $1,332.7 $1,497.4

2024 $75.7 $120.2 $215.3 $282.3 $317.9

2025 $83.0 $127.5 $222.9 $291.2 $327.5

2026 $78.3 $122.7 $219.0 $289.5 $326.0

2027 $80.9 $125.4 $219.9 $289.1 $325.9

2028 $79.0 $123.4 $218.6 $286.6 $324.1

2029 $84.9 $129.3 $230.9 $304.2 $341.2

2030 $83.2 $127.7 $227.1 $298.6 $334.2

2031 $85.4 $129.8 $232.1 $303.7 $340.0

2032 $84.6 $129.0 $229.5 $303.4 $340.0

2033 $85.1 $129.5 $230.7 $302.9 $339.7

2034 $77.8 $122.2 $218.0 $286.8 $324.2

2035 $83.1 $127.5 $223.8 $293.4 $330.5

2036 $79.0 $123.5 $221.5 $290.7 $326.3

2037 $82.6 $127.1 $224.0 $295.7 $332.0

2038 $81.4 $125.8 $224.0 $295.2 $331.5

2039 $84.4 $128.8 $229.4 $300.3 $336.7

2040 $83.9 $128.3 $226.5 $298.0 $334.5

2041 $83.8 $128.2 $226.9 $297.2 $333.2

2042 $83.3 $127.7 $224.9 $293.9 $329.5

Annualized Costs, 3% $120.5 $198.7 $383.5 $479.5 $536.0

Annualized Costs, 7% $116.9 $194.7 $379.9 $471.2 $525.8

Source: U.S. EPA Analysis, 2015.

 

78 Whereas EPA calculated the time profile of benefits using discount rate-specific social cost of carbon estimates 

and therefore obtained time profiles that are specific to the discount rates (see Table 11-3 and Table 11-4), the 

time profile of costs does not depend on the discount rate. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 13: Benefits and Social Costs

September 29, 2015 13-1

13 Benefits and Costs

This chapter compares total monetized benefits and costs for the five regulatory options analyzed for the final 

ELGs. Benefits and costs are compared on two bases: (1) for each of the options analyzed and (2) 

incrementally across options. The comparison of benefits and costs also satisfies the requirements of 

Executive Order 12866: Regulatory Planning and Review and Executive Order 13563: Improving Regulation 

and Regulatory Review (see Chapter 9: Other Administrative Requirements of the RIA; U.S. EPA, 2015c).

13.1 Comparison of Benefits and Costs by Option

Chapter 11 and Chapter 12 present estimates of the benefits and costs, respectively, for the regulatory options 

evaluated in developing the final ELGs.

Table 13-1 presents EPA’s estimates of benefits and costs of the regulatory options for existing steam electric 

power plants, at 3 percent and 7 percent discount rates, and annualized over 24 years. These values are all in 

2013 dollars and are based on the discounting of costs and benefits to 2015, the rule promulgation year.

As discussed in Chapter 11, EPA did not analyze air-related benefits for Options A, C, and E. The total 

monetized benefits for those options are therefore understated. To compare the costs and benefits of these 

options, EPA calculated the average ratio of total benefits with air-related benefits to total benefits without airrelated benefits for Options B and D,79 then applied the average ratio to Options A, C, and E to extrapolate 

total monetized benefits including air-related benefits for these options. These extrapolated, approximate

benefits are shown in Table 13-1.

Table 13-1: Total Annualized Benefits and Costs by Regulatory Option and Discount Rate (Millions; 

2013$)

Regulatory 

Option

Total Monetized Benefits Total Monetized Benefits, Including 

Extrapolated Valuesa Total 

Costs

Low Midb High Low Midb High

3% Discount Rate

Option A $41.1 $43.4 $63.7 $122.1 $122.2 $125.7 $120.5

Option B $162.2 $167.8 $234.2 $162.2 $167.8 $234.2 $198.7

Option C $138.1 $147.4 $233.7 $410.4 $414.5 $461.3 $383.5

Option D $450.6 $463.0 $565.6 $450.6 $463.0 $565.6 $479.5

Option E $168.3 $181.6 $292.0 $500.2 $510.8 $576.3 $536.0

 

79 This ratio averaged 2.97 for low benefits, 2.81 for mid-range, and 1.97 for high.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 13: Benefits and Social Costs

September 29, 2015 13-2

Table 13-1: Total Annualized Benefits and Costs by Regulatory Option and Discount Rate (Millions; 

2013$)

Regulatory 

Option

Total Monetized Benefits Total Monetized Benefits, Including 

Extrapolated Valuesa Total 

Costs

Low Midb High Low Midb High

7% Discount Rate

Option A $37.2 $38.9 $58.0 $110.5 $109.5 $114.4 $116.9

Option B $149.4 $153.8 $217.3 $149.4 $153.8 $217.3 $194.7

Option C $115.6 $122.9 $191.4 $343.7 $345.6 $377.8 $379.9

Option D $387.3 $397.0 $478.4 $387.3 $397.0 $478.4 $471.2

Option E $140.2 $150.6 $207.1 $416.8 $423.7 $408.7 $525.8

Source: U.S. EPA Analysis, 2015.

a. EPA did not analyze air-related benefits for Options A, C, and E. This category of benefits was only estimated for Options B and 

D (see Chapter 7). EPA adjusted the total benefits estimated for Options A, C and E by multiplying the totals without air-related 

benefits by the average ratio of [total with air-related benefits]/[total without air-related benefits] for Options B and D.

b. EPA estimated use and nonuse values for water quality improvements using two different meta-regression models of WTP. One 

model provides the low and high bounds while a different model provides a central estimate (included in this table under the midrange column). For this reason, the mid benefit estimate differs from the midpoint of the benefits range. For details, see Chapter 4.

13.2 Analysis of Incremental Benefits and Costs

In addition to comparing benefits and costs for each regulatory 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 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.

EPA conducted the incremental net benefit analysis by calculating, for the five regulatory options, the change 

in net benefits, from option to option, in moving from the least stringent option to successively more stringent 

options. As described in Chapter 1, the regulatory options differ in the technology basis used to determine 

effluent limits and standards for different wastestreams. Thus, the difference in benefits and costs across the 

options derives from the characteristics of the wastestreams controlled by an option, the relative effectiveness 

of the control technology in reducing pollutant loads, and the distribution and characteristics of steam electric 

power plants that would implement the technologies and of the receiving waterbodies.

As noted previously, however, the total monetized benefits for Options A, C, and E do not include air-related 

benefits; this benefit category is included in results for Options B and D only. Therefore, to allow for 

consistent calculation of incremental benefits as one moves from one option to the next, EPA used the 

extrapolated total benefits estimated for Options A, C, and D to include the air-related benefits for these 

options (see Table 13-1). 

As reported in Table 13-2, EPA estimates that the annual monetized costs exceed the mid-range annual 

monetized benefits for the final ELG by $16.5 million using a 3 percent discount rate and $74.2 million using 

a 7 percent discount rate.
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September 29, 2015 13-3

Using a 3 percent discount rate, the incremental net annual monetized benefits moving from Option A to 

Option B is -$33 million (the negative value indicates that the increase in costs is larger than the increase in 

benefits). Moving from Option B to Option C, the change is $62 million, with the positive value indicating 

that the increase in benefits is larger than the increase in costs. Moving from Option C to Option D, and from 

Option D to Option E, the change is negative, at -$48 million and -$9 million, respectively.

Table 13-2: Incremental Net Benefit Analysis (Millions; 2013$)

Regulatory 

Optiona

Total Annual Monetized 

Benefits, Including 

Adjusted or Inferred 

Values

Total 

Social 

Costs

Net Annual Monetized 

Benefitsa

Incremental Net Annual 

Monetized Benefitsb

Low Midd High Low Midd High Low Midd High

3% Discount Rate

Option A $122.1 $122.2 $125.7 $120.5 $1.6 $1.6 $5.2 - - -

Option B $162.2 $167.8 $234.2 $198.7 -$36.5 -$30.9 $35.6 -$38.1 -$32.6 $30.4

Option C $410.4 $414.5 $461.3 $383.5 $26.9 $31.0 $77.8 $63.4 $61.9 $42.2

Option D $450.6 $463.0 $565.6 $479.5 -$28.9 -$16.5 $86.1 -$55.8 -$47.5 $8.3

Option E $500.2 $510.8 $576.3 $536.0 -$35.8 -$25.2 $40.3 -$6.8 -$8.7 -$45.8

7% Discount Rate

Option A $110.5 $109.5 $114.4 $116.9 -$6.4 -$7.4 -$2.5 - - -

Option B $149.4 $153.8 $217.3 $194.7 -$45.3 -$40.9 $22.5 -$38.9 -$33.5 $25.0

Option C $343.7 $345.6 $377.8 $379.9 -$36.2 -$34.3 -$2.1 $9.1 $6.6 -$24.6

Option D $387.3 $397.0 $478.4 $471.2 -$83.9 -$74.2 $7.2 -$47.7 -$39.9 $9.2

Option E $416.8 $423.7 $408.7 $525.8 -$109.0 -$102.1 -$117.1 -$25.1 -$27.9 -$124.3

Source: U.S. EPA Analysis, 2015.

a. EPA did not analyze air-related benefits for Options A, C, and E. This category of benefits was only estimated for Options B and D

(see Chapter 7). EPA adjusted the total benefits estimated for Options A, C, and E by multiplying the totals without air-related 

benefits by the average ratio of [total with air-related benefits]/[total without air-related benefits] for Options B and D.

b. Net benefits are calculated by subtracting total annualized costs from total annual monetized 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.

d. EPA estimated use and nonuse values for water quality improvements using two different meta-regression models of WTP. One 

model provides the low and high bounds while a different model provides a central estimate (included in this table under the midrange column). For this reason, the mid benefit estimate differs from the midpoint of the benefits range. For details, see Chapter 4.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14-1

14 Environmental Justice

Executive Order (E.O.) 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 

(EJ) 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.

To meet the objectives of E.O. 12898, EPA examined whether the benefits from the final ELGs may be 

differentially distributed among population subgroups in the affected areas. EPA considered the following 

factors in this analysis: population characteristics, proximity to affected waters, exposure pathways, 

cumulative risk exposure, and susceptibility to environmental risk. For example, subsistence fishers rely on 

self-caught fish for a larger share of their food intake than do recreational fishermen, and as such may incur a 

larger share of benefits arising from the final ELGs.

As described in the following sections, EPA conducted two types of analyses to evaluate the EJ implications 

of the final ELGs: (1) summarizing the demographic characteristics of the households living in proximity to 

reaches that receive steam electric power plant discharges; and (2) analyzing the human health impacts from 

consuming self-caught fish on minority and/or low-income populations located within 50 miles of reaches 

affected by steam electric power plant discharges.80 This second analysis seeks to provide more specific 

insight on the distribution of adverse health effects and benefits and to assess whether minority and/or lowincome populations incur disproportionally high environmental impacts and/or are disproportionally excluded 

from realizing the benefits of this final rule. 

The following two sections describe (1) a comparison of the socio-economic characteristics of the populations 

that live in proximity to steam electric power plants to state and national averages, and (2) the evaluation of 

human health effects and benefits that accrue to populations in different socio-economic cohorts.

14.1 Socio-economic Characteristics of Populations Residing in Proximity to Steam 

Electric Power Plants

For the first analysis, EPA assessed the demographic characteristics of the populations within specified 

distances of reaches that receive steam electric power plant discharges. The receiving reaches are those to 

which plants discharge directly in the baseline; for this first analysis, EPA did not include additional reaches 

located downstream from the receiving reaches (see BCA for a discussion of receiving and downstream 

reaches, U.S. EPA 2015a). The analysis is similar to the profile EPA had developed to support the proposed 

ELG, but looks at populations living within different, closer distances of steam electric power plants. The 

change was made in part to addresses comments EPA received on the need to look at communities in closer 

proximity to the plants, instead of the 100-mile buffer the Agency had used at proposal (see comment 

response document; U.S. EPA 2015d).81

 

80 As detailed in the Chapter 3, EPA used a distance of 50 miles to determine the affected population.

81 Commenters recommended that EPA consider the characteristics of populations within 4 miles, 5 miles, 

15 miles and 30 miles of the plants (see U.S. EPA, 2015d).
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EPA collected population-specific Census data on: 

 the percent of the population below the poverty threshold,82 and 

 the population categorized in various racial/ethnic groups, from which EPA calculated the percent of 

the population that belongs to a minority racial/ethnic group.83

EPA compiled these data for CBGs located within specified distances (e.g., 1 mile, 3 miles, 15 miles, 30 

miles, and 50 miles) of the reaches receiving steam electric power plant discharges. EPA compared 

demographic metrics to state and national averages to identify communities where EJ concerns may exist. EJ 

concerns may exist in areas where the percent of the population below the poverty threshold is higher than the 

state or national average or the percent of the population that is minority is above the state or national 

average. 

This first analysis considers the spatial distribution of low-income and minority groups to determine whether 

these groups are more or less represented in the populations in proximity to reaches receiving steam electric 

power plant discharges. The specified distance buffers from the reaches are denoted below as the “benefit 

region.” Populations within the benefit regions may be affected by steam electric power plant discharges and 

other environmental impacts in the baseline, and would be expected to benefit from environmental 

improvements resulting from the final ELGs. If the population within a benefit region has a larger proportion 

of minority or low-income families than the state average, it may indicate that the final ELGs may benefit 

communities that have been historically exposed to a disproportionate share of environmental impacts and 

thus contribute to redressing existing EJ concerns. 

EPA used the U.S. Census Bureau’s American Community Survey (ACS) data for 2006 to 2010 to identify 

poverty status (Table C17002) at the state and CBG levels. EPA also used 2010 U.S. Census data (Summary 

File 1; Table 8 – P3) to identify the percent of the population that is minority at the CBG and state levels. 

EPA overlaid the data with GIS data of buffer zones of specified distances from receiving reaches to 

characterize the affected communities living in proximity to the reaches. Table 14-1 summarizes the socioeconomic characteristics of benefit regions defined using radial distances of 1, 3, 10, 15, 30 and 50 miles from 

the receiving reaches.

Table 14-1: Socio-economic Characteristics of Communities Living in Proximity to Receiving 

Reaches

Distance from receiving

reach

Total population 

(millions) Percent minority Percent below poverty level

1 mile 0.2 20.7% 16.4%

3 miles 1.1 23.4% 15.3%

15 miles 14.0 29.8% 13.6%

30 miles 37.4 31.5% 13.1%

50 miles 57.3 29.9% 13.1%

United States 306.3 36.0% 13.9%

Source: U.S. EPA analysis, 2015

 

82 Poverty status is based on data from the Census Bureau’s American Community Survey which determines 

poverty status by comparing annual income to a set of dollar values called poverty thresholds that vary by 

family size, number of children, and the age of the householder.

83 The racial/ethnic categories are based on available fish consumption data as well as the breakout of ethnic/racial 

populations in Census data, which distinguishes racial groups within Hispanic and non-Hispanic categories. 

Minority groups include: African American (non-Hispanic); Asian/Pacific Islander (non-Hispanic); 

Tribal/Native Alaskan (non-Hispanic); Other non-Hispanic; Mexican Hispanic, and Other Hispanic.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

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As shown in Table 14-1, approximately 200,000 people live within 1 mile of steam electric power plants 

currently discharging to surface waters, over 1.1 million live within 3 miles, and nearly 37.4 million people 

live within 30 miles. The statistics also show that a greater fraction of the communities living in close 

proximity to steam electric power plants is poor, when compared to the national average. Approximately 

16.4 percent of households in communities within 1 mile of steam electric power plants have income below 

the poverty level as compared to a national average of 13.9 percent. A smaller fraction of the population 

within 1 mile of the plants belongs to minority racial or ethnic groups (20.7 percent), than the national 

average (36.0 percent). As one moves further away from the steam electric power plants, the fraction of the 

community that is below the poverty threshold goes down while the percent minority increases, so that the 

overall composition of the communities approaches that of the U.S. population overall. Thus, looking at 

communities within 30 miles of steam electric power plants, 13.1 percent of the population is below the 

poverty level (vs. 13.9 percent nationally) while 31.5 percent belong to a minority group (vs. 36.0 percent 

nationally). 

The simple comparison to the national average masks important differences, however, between states, 

particularly given the non-uniform geographical distribution of plants across the country. EPA therefore also 

compared the demographic profile of affected communities to that of the state where they are located. Table 

14-2 summarizes the results of this comparison. Of the 37 states with communities within 1 mile of steam 

electric power plants, 11 states have communities with a higher percentage of households below the poverty 

threshold than the overall state, 17 have a higher percent of the population that is minority, and 10 have a 

higher proportion of poor and minority households. These results show the potential for localized differences 

indicative of potential EJ concern, but the overall comparison reveals no systematic difference in demographic 

characteristics of the populations living in proximity to the steam electric power plants that would indicate 

that any communities with EJ concern would be precluded from the benefits of the final ELGs.

Table 14-2: Socio-economic Characteristics of Affected Communities, Compared to State Average

Distance from 

receiving 

reach

Number of States 

with Affected 

Communitiesa

Number of States where Affected Communities...

are Poorer

have a Higher 

Proportion of 

Minority Population

are Poorer and have a 

Higher Proportion of 

Minority Population

... than the State Average

1 mile 37 11 17 10

3 miles 37 10 17 7

15 miles 38 21 16 19

30 miles 42 22 16 21

50 miles 45 19 9 12

Source: U.S. EPA analysis, 2015

a. “Affected communities” are Census Block Groups within the specified distance of one or more receiving reaches.

14.2 Distribution of Human Health Impacts and Benefits 

The second type of analysis looks at the distribution of environmental effects and benefits to further inform 

understanding of the potential EJ concerns and the extent to which the final rule may mitigate them.

A significant share of the benefits of the final ELGs comes from reducing discharges of harmful pollutants to 

surface waters and associated reductions in fish tissue contamination. EPA quantified the human health 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14-4

benefits resulting from reducing exposure to pollutants in fish tissue in individuals who consume fish caught 

in reaches immediately receiving or downstream from steam electric plant discharges. The analysis relied on 

CBG- level data to estimate the number and characteristics of individuals exposed to steam electric pollutants 

through the consumption of self-caught fish, and race and ethnicity-specific assumptions to estimate 

exposure. This analysis allows the agency to report the distribution of benefits across population subgroups, 

including subgroups who may have been historically exposed to a disproportionate share of environmental 

impacts.

This section presents results for the two types of anglers analyzed: recreational anglers and subsistence

fishers. Chapter 3 provides more details on the approach used to identify the affected population, derive 

exposure, quantify health effects and monetize benefits. 

EPA limited its analysis of the distribution of health effects and benefits to two pollutants (lead and mercury) 

due to the small benefits resulting from reducing arsenic discharges. Further, for recreational anglers, EPA 

focused on benefits accruing to infants and children due to the complexity of carrying the detailed socioeconomic data through the models used to quantify the changes in premature mortality from cardiovascular 

disease (CVD) in adults. The outputs from the CVD benefit analyses do not provide sufficient information to 

assess changes across socio-economic subgroups. However, EPA did account for changes in the incidence of 

CVD attributable to the final ELGs when comparing recreational and subsistence fishers (see Section 14.4).

14.3 Distribution of Benefits across Benefitting Populations

Table 14-3 summarizes the estimated number of individuals exposed to steam electric pollutants through 

consumption of self-caught fish in the general population and in population subgroups that may be indicative 

of EJ concerns. As shown in the table, of the approximately 36.0 million people exposed to steam electric 

pollutants, 13.9 percent are poor, 34.4 percent are minority, and 6.5 percent are both poor and minority. 

Overall, 41.9 percent of potentially exposed individuals are categorized in at least one or more EJ subgroup 

based on their poverty level or race/ethnicity, while 58.1 percent are neither minority nor poor.

Table 14-3: Characteristics of Population Potentially Exposed to Steam Electric Pollutants via 

Consumption of Self-caught Fish

Subgroup Minority Non-Minority Total

Poor 2,345,972 6.5% 2,668,083 7.4% 5,014,055 13.9%

Non-Poor 10,042,390 27.9% 20,915,560 58.1% 30,957,950 86.1%

Total 12,388,362 34.4% 23,583,643 65.6% 35,972,005 100%

Source: U.S. EPA Analysis, 2015

The distribution of adverse health effects is a function of the characteristics of the affected population (Table 

14-3), including age and sex,84 ethnicity-specific exposure factors,85 and reach water quality. Table 14-4

 

84 Some adverse health effects are analyzed only for individuals in certain age groups. For example, IQ point 

decrements from exposure to lead are calculated for children 0 to 7 years old and the baseline exposure 

therefore depends on the number of children within this age group in the affected population in each socioeconomic subgroup. IQ point decrements from exposure to mercury are calculated for infants born within the 

analysis period and baseline exposure depends on the number of women of childbearing age (and fertility rates) 

in the affected population. 

85 Ethnicity-specific factors that determine exposure to pollutants in fish tissue include the assumed fish

consumption rates and average fertility rate. For example, as described in Chapter 2 of the BCA, Asian/Pacific 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14-5

shows the distribution of selected adverse health effects in the baseline. Table 14-5 shows the distribution of 

adverse health effects avoided under each of the five options EPA analyzed in developing the final ELGs. 

Note that benefits follow the same distribution as avoided adverse health effects since each case is valued 

equally, irrespective of the socio-economic subgroup. 

The two tables show results for three selected subgroups: 

 Poor and minority (6.5 percent of the exposed population), 

 Poor or minority (i.e., but not both; 35.3 percent of the exposed population), and 

 All others (i.e., non-poor white; 58.1 percent of the exposed population). 

The first two subgroups are the primary interest of this analysis as potentially indicative of EJ concerns. 

Table 14-4: Distribution of Baseline IQ Point Decrements by Pollutant (2021 to 2042)

Pollutant 

Poor & Minority

(6.5% of 

Population)

Poor or Minority 

(35.3% of Population)

All Others

(58.1% of Population) 

Total

Lead 6,266,344 8.0% 29,663,242 38.0% 42,214,217 54.0% 78,143,802 100%

Mercury 91,838 9.3% 439,403 44.6% 454,081 46.1% 985,322 100%

Source: U.S. EPA Analysis, 2015

Table 14-5: Distribution of Avoided IQ Point Decrements Relative to the Baseline, by Pollutant 

(2021 to 2042)

Pollutant 

and 

Exposed 

Population

Regulatory 

Option

Poor & Minority

(6.5% of 

Population)

Poor or Minority 

(35.3% of 

Population)

All Others

(58.1% of 

Population) 

Total

Children 

Exposed to 

Lead

Option A 78 9.2% 313 36.6% 462 54.2% 853 100%

Option B 78 9.2% 313 36.6% 462 54.2% 853 100%

Option C 128 9.9% 518 40.3% 640 49.8% 1,285 100%

Option D 155 7.8% 693 34.9% 1,137 57.3% 1,985 100%

Option E 155 7.8% 693 34.9% 1,137 57.3% 1,985 100%

Infants 

Exposed to 

Mercury

Option A 287 8.9% 1,300 40.1% 1,652 51.0% 3,239 100%

Option B 293 8.9% 1,331 40.2% 1,687 50.9% 3,311 100%

Option C 471 7.9% 2,216 36.9% 3,314 55.2% 6,001 100%

Option D 540 7.5% 2,603 36.1% 4,076 56.5% 7,219 100%

Option E 591 7.5% 2,839 35.9% 4,469 56.6% 7,898 100%

Source: U.S. EPA Analysis, 2015

The distribution of baseline health effects and health improvements resulting from the ELGs can be compared 

to the relative share of the population exposed to steam electric pollutants (from Table 14-3) to assess the 

 

Islander anglers have daily consumption rates that are 1.4 times and 1.9 times those of While (non-Hispanic) 

anglers for recreational and subsistence fishing modes, respectively.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

September 29, 2015 14-6

degree to which the regulatory options contribute to mitigating any EJ concerns that may be present in the 

baseline. 

The poor and minority subgroup represents 6.5 percent of the potentially affected population, but accounts for 

8.0 percent and 9.3 percent of the estimated IQ point decrements in the exposed population in the baseline. 

Under Option D, the poor and minority subgroup also sees a disproportional share of the improvements, with 

7.8 percent and 7.5 percent of the avoided IQ point decrements from exposure to lead and mercury, 

respectively. Findings are similar for the poor or minority subgroup, i.e., this subgroup also incurs a 

disproportionate share of baseline adverse health effects, and of the improvements arising from the final 

ELGs. 

14.4 Subsistence Fishers

In the analysis of health benefits (see Chapter 3), EPA assumed that 5 percent of the exposed population are

subsistence fishers, and that the remaining 95 percent are recreational anglers. This is based on the assumed 

95th percentile fish consumption rate for subsistence fishers. Subsistence fishers consume more self-caught 

fish than recreational anglers and can therefore be expected to experience higher health risks associated with 

steam electric pollutants in fish tissue.

The results of the human health analysis suggest that subsistence fishers are disproportionally exposed to 

pollutants in steam electric power plant discharges via fish consumption and disproportionally incur adverse 

health effects from this exposure. As shows in Table 14-6 and Table 14-7, subsistence fishers incur 7 percent

to 17 percent of the baseline IQ decrements, even though they represent only 5 percent of the overall 

population, and account for 17 percent to 30 percent of the avoided health effects and benefits of the final 

rule.

Table 14-6: Distribution of Baseline IQ Point Decrements by Pollutant and Fishing Mode (2021 to 

2042)

Pollutant and Exposed 

Population

Subsistence Fishers 

(5 percent of population)

Recreational Fishers 

(95 percent of 

population) Total

Children Exposed to Lead 5,302,873 6.8% 72,840,929 93.2% 78,143,802 100%

Infants Exposed to Mercury 166,415 16.9% 818,907 83.1% 985,322 100%

Source: U.S. EPA Analysis, 2015

Table 14-7: Distribution of Avoided IQ Point Decrements Relative to the Baseline by Fishing Mode, 

and Pollutant (2021 to 2042)

Pollutant 

and 

Exposed 

Population

Regulatory 

Option

Subsistence Fishers 

(5 percent of population)

Recreational Fishers 

(95 percent of 

population) Total

Children 

Exposed to 

Lead

Option A 186 21.8% 667 78.2% 853 100%

Option B 186 21.8% 667 78.2% 853 100%

Option C 390 30.4% 895 69.6% 1,285 100%

Option D 605 30.5% 1,381 69.5% 1,985 100%

Option E 605 30.5% 1,381 69.5% 1,985 100%
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

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Table 14-7: Distribution of Avoided IQ Point Decrements Relative to the Baseline by Fishing Mode, 

and Pollutant (2021 to 2042)

Pollutant 

and 

Exposed 

Population

Regulatory 

Option

Subsistence Fishers 

(5 percent of population)

Recreational Fishers 

(95 percent of 

population) Total

Infants 

Exposed to 

Mercury

Option A 536 16.5% 2,703 83.5% 3,239 100%

Option B 548 16.5% 2,763 83.5% 3,311 100%

Option C 992 16.5% 5,009 83.5% 6,001 100%

Option D 1,194 16.5% 6,025 83.5% 7,219 100%

Option E 1,306 16.5% 6,592 83.5% 7,898 100%

Source: U.S. EPA Analysis, 2015

14.5 EJ Analysis Findings

Based on the EJ analyses discussed above, EPA determined that the final ELGs will not deny communities 

from the benefits of environmental improvements expected to result from compliance with the more stringent 

effluent limits. In fact, the distribution of avoided adverse health outcomes and benefits suggests that poor and 

minority communities may receive a greater share of the benefits from the final ELGs than their 

representation in the affected populations. The final ELGs may thus help redress environmental inequities that 

may be present in the baseline.

By reducing exposures to pollutants discharged by steam electric power plants, all of the evaluated options

would benefit communities with potential EJ concerns. Of the five options, Options C, D, and E are expected 

to provide significantly greater human health benefits than Options A and B, as indicated by higher reductions 

in adverse health effects from lead and mercury exposure. Improvements under Option C accrue to poor and 

minority population to a larger relative extent than do improvements under Options D and E, but Option D 

(on which the final limitations and standards are based) provides greater benefits overall than Option C, 

including to poor and minority populations exposed to steam electric pollutants through consumption of selfcaught fish. Thus, the final rule will further environmental justice objectives.

14.6 Limitations and Uncertainties

This EJ analysis inherits the limitations and uncertainties of the human health benefit analysis (see Chapter 3) 

regarding pollutant exposure, health effects, and valuation. In addition, however, the analysis also embeds 

uncertainty derived from the application of uniform assumptions across the population exposed to pollutant 

discharges when factors may instead vary across socioeconomic characteristics, including:

 EPA assumed that all fishers travel up to 50 miles; in fact, some anglers stay closer to home and 

certain EJ or sensitive subpopulations may tend to stay closer to home (e.g., poor people and 

subsistence fishers). These people may be exposed to relatively higher concentrations of pollutants.

 EPA assumed that subsistence fishers are 5 percent of all anglers, with this assumption applied 

uniformly across all socioeconomic groups. In fact, a relatively higher share of EJ groups may be 

subsistence fishers. This would tend to increase the inequities already in the baseline and further 

increase the mitigating effect of the ELGs in addressing these inequities.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 14: Environmental Justice

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 EPA applied uniform fishing participation rates, FCAs, and catch and release practices across the 

entire population. However, differences in behavior across socioeconomic groups may result in 

different distribution of baseline impacts and benefits.

In summary, use of average values across the entire US population (or within a state) instead of assumptions 

that reflect specific socioeconomic conditions may understate inequities present in the baseline and benefits to 

poor or minority populations, to the extent that different socioeconomic groups may be more likely to be 

exposed to pollutants from steam electric power plant discharges.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs 15: References

September 29, 2015 15-1

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*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix A: Changes to Benefits Analysis

September 29, 2015 A-1

Appendix A. Changes to Benefits Analysis since Proposal

The table below summarizes the principal changes EPA made to its benefits analysis for the final rule, as 

compared to the analysis of the proposed rule (in addition to changes to inputs such as costs and pollutant 

loads). EPA made changes to address comments it received on the proposed rule analysis and to incorporate 

updated, more recent data.

Table A-1: Changes to Benefits Analysis Since Proposal 

Report Section or 

Benefit Category

Analysis Component

[Proposed rule analysis value]

Changes to Analysis for Final Rule 

[Final rule analysis value ]

General assumptions Dollar year [all costs and benefits expressed 

in 2010 dollars]

Updated dollar year [2013]

Promulgation year [all costs and revenue 

streams discounted back to 2014]

Updated promulgation year [2015]

Period of social-costs and benefits analyses 

[2017-2040]

Updated period of social-costs and benefits 

analyses [2019-2042]

Technology implementation years [2017-

2021]

Updated technology implementation years 

[2019-2023]

General pollutant 

loadings and 

concentration 

assumptions

EA modeling of metal concentrations in 

immediately receiving reaches (national 

model) 

Updated immediately receiving reaches (see 

EA for details)

RSEI modeling of metal concentrations in 

immediate and downstream reaches model 

across nation

Adjusted loadings to reflect partitioning of 

pollutants in immediate reach, based on the 

EA national model.

Used updated baseline that reflects TRI 

releases for 2012.

SPARROW modeling of nutrient 

concentrations in receiving and downstream 

reaches [estimate changes in ambient 

pollutant (nutrient) concentrations in 

receiving and downstream reaches based on 

regression model]

No changes for nutrients. 

Estimated changes in suspended sediments 

and channel deposition using SPARROW

Human health benefits associated with reductions in fish tissue contamination

Assignment of 

populations to affected 

waterbodies to determine 

potential exposure

Potentially exposed population [people 

residing within 100 miles of an affected 

waterbody]

Revised the analysis to focus on Census 

Block Groups. Subdivided Census Block

Group population according to 

socioeconomic indicators. Used travel 

distance of 50 miles 

Fishing practices [addressed by consumption 

estimate]

For recreational anglers, adjusted exposed 

population to account for catch and release 

practices

Scope [immediately receiving reaches only] Included downstream reaches 

Pollutant exposure via 

consumption of selfcaught fish

Consumption rates [uniform consumption 

rates for recreational/subsistence fishers]

Consumption rates [ethnicity and fishing 

mode-specific consumption rates]

Avoided IQ losses in 

children from lead 

exposure

Exposure estimates and monetization Updated/verified the dose-response 

relationship between PbB and IQ based on 

most recent data for low level exposure (to 

address comment)
*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix A: Changes to Benefits Analysis

September 29, 2015 A-2

Table A-1: Changes to Benefits Analysis Since Proposal 

Report Section or 

Benefit Category

Analysis Component

[Proposed rule analysis value]

Changes to Analysis for Final Rule 

[Final rule analysis value ]

Monetization Used updated estimates of the cost of 

education

Avoided IQ losses in 

infants from mercury 

exposure

Exposure estimates [exposed population is 

infants born to women (15-50 years of age) 

in households that fish recreationally]

Modified definition of cohort of women of 

child-bearing age (15-42 years old)

Monetization Used updated estimates of the cost of 

education

Avoided cancer cases 

from arsenic exposure

Monetization [valuation based on value of 

statistical life (VSL)]

Changed valuation to use cost of illness 

(COI) as basis for monetizing avoided cancer 

cases since skin cancer is generally not fatal

Avoided cardiovascular 

disease in adults from 

lead exposure

N/A Estimated changes in number of CVD 

(Leggett model) based on ingestion of 

recreationally-caught fish. Monetization 

based on VSL 

Avoided exposure in 

drinking water

N/A Expanded discussion of the potential benefits 

of reducing pollutant concentrations below

MCLs

Other benefits

Non-market benefits from

surface water quality 

improvements

Data sources Added changes in suspended solid 

concentration (from SPARROW)

New data for monetization to reflect 

parameters in new meta-analysis function 

Water quality index No change

Willingness-to-pay function Revised meta-analysis to include spatial 

characteristics of the affected water 

resources: size of the market, waterbody 

characteristics (length and flow), availability 

of substitute sites, land use type in the 

abutting counties

Benefitting population Test alternative definition of the unit of the 

analysis as the Census Block Group; all 

households in a given Census Block Group

value all water quality changes in a 100 mile 

radius

Benefits from 

groundwater quality 

improvements

Willingness-to-pay for water quality Did not estimate benefits given promulgation 

Avoided human health hazards of the final CCR rule

Benefits to threatened and 

endangered species

Categorical analysis based on habitat 

overlap/proximity

Monetization [willingness to pay]

No change

Benefits from avoided 

impoundment failures

Projected impoundment use at steam electric 

power plants

Updated to account for projected effects of 

the CCR rule 

Average failure rate and expected impacts of 

failures

Expressed risk of failure and impacts for two 

impoundment categories (small, big) 

depending on size and failure type

Residual failure rate Revised to reflect effects of the CCR rule

Cleanup costs Updated to include more recent data and/or 

more detailed review
*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix A: Changes to Benefits Analysis

September 29, 2015 A-3

Table A-1: Changes to Benefits Analysis Since Proposal 

Report Section or 

Benefit Category

Analysis Component

[Proposed rule analysis value]

Changes to Analysis for Final Rule 

[Final rule analysis value ]

Transaction costs Updated to include more recent data and/or 

more detailed review

Natural resources damages Updated to include more recent data and/or 

more detailed review

Reduced emissions of 

NOX and SO2

Changes in air pollutant levels from IPM and 

engineering analysis

Benefit-per-ton estimates for avoided human 

health impacts

No change

Reduced emissions of 

CO2

Changes in air pollutant levels from IPM and 

engineering analysis

No change

Monetization [based on social cost of carbon 

(SCC) (Interagency Working Group on 

Social Cost of Carbon, 2010)]

Updated SCC to reflect most recent value in 

Interagency Working Group on Social Cost 

of Carbon (2013)

Benefits from reduced 

water withdrawals

Surface water withdrawals Expanded qualitative discussion 

Groundwater withdrawals No change

Enhanced CCR 

marketability for 

beneficial uses

N/A Estimated changes in beneficial uses of CCR 

due to conversions from wet to dry handling. 

Estimated benefits based on approach used 

for the final CCR rule analysis.

Avoided maintenance 

dredging costs

N/A Estimated reductions in volume of sediment 

dredged from waterways and reservoirs. 

Estimated benefits based on the avoided 

costs for maintenance dredging.

Reduced water treatment 

costs

Qualitative discussion Expanded qualitative discussion to provide 

more information on potential impacts of 

bromide discharges on disinfection 

byproducts (and benefits from reducing 

discharges)

Environmental Justice

Environmental justice Profile of affected populations [compare 

demographic characteristics of population 

affected by steam electric plant discharges to 

the state population, using 100-mile buffer 

distance]

Use Census Block Group as the unit of 

analysis; use various distance buffers that 

reflect distance travelled by different 

socioeconomic groups (1, 3, 15, 30, and 50 

miles), based on comments

N/A Evaluated EJ considerations across the 

regulatory options by explicitly analyzing 

distribution of human health benefits by 

socioeconomic subgroup
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-1

Appendix B. Analysis for Scenario without CPP Rule

EPA included the anticipated effects of the Clean Power Plan (CPP) rule in its baseline for the final ELG rule 

analysis, as described in Chapter 1. This baseline was developed based on information about conversions, 

retirements, and other changes EPA projected in response to the CPP rule, as proposed by EPA in June 2014. 

In particular, EPA updated its steam electric power plants profile to account for additional changes due to 

CPP implementation that affect plant costs for meeting the ELGs and pollutant loads (see TDD for details). 

The results presented in the main body of this document are based on this scenario with CPP.

This appendix presents the results of an alternative benefits analysis using a baseline that does not include the 

incremental conversions, retirements, and other changes projected to occur in response to the CPP rule. All 

other assumptions match those described in Chapters 3 through 10.

Table B-1. Pollutant Removal for Final ELGs Regulatory Options for Scenario without CPP

Regulatory 

Option

Pollutant Load Reduction (pounds per year)

Conventional

Pollutantsa Priority Pollutants

Nonconventional

Pollutantsb

Toxic-Weighted Pound 

Equivalent

Option A 5,810,000 285,000 151,000,000 1,110,000 

Option B 5,810,000 386,000 161,000,000 1,250,000

Option C 13,600,000 528,000 377,000,000 1,630,000 

Option D 18,500,000 618,000 514,000,000 1,870,000 

Option E 19,100,000 665,000 526,000,000 1,910,000 

a. The loadings reduction for conventional pollutants includes BOD and TSS. 

b. The loadings reduction for nonconventional pollutants excludes TDS and COD to avoid double-counting removals for certain 

pollutants that would also be measured by these bulk parameters (e.g., sodium, magnesium).

Additionally, under the scenario without CPP, EPA estimates that the final BAT/PSES option (Option D) will 

reduce surface water withdrawals at steam electric power plants by 209 to 222 billion gallons per year (0.57 to 

0.61 million gallons per day) and will avoid withdrawals of 8 million gallons of groundwater per year 

(21,971 gallons per day).

B.1 Human Health Benefits

B.1.1 Benefits to Children from Reduced Lead Exposure

Table B-2. Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead for Scenario 

without CPP

Regulatory 

Option

Average 

Annual 

Number of 

Affected

Children 0 to 7

Total 

Avoided IQ 

Losses, 2021 

to 2042

Annualized Value of Avoided IQ Point Lossesa(Millions

2013$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Option A 3,326,127 893 $0.35 $0.50 $0.06 $0.09 

Option B 3,326,127 893 $0.35 $0.50 $0.06 $0.09 

Option C 3,326,127 3,940 $1.57 $2.20 $0.25 $0.38 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-2

Table B-2. Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead for Scenario 

without CPP

Regulatory 

Option

Average 

Annual 

Number of 

Affected

Children 0 to 7

Total 

Avoided IQ 

Losses, 2021 

to 2042

Annualized Value of Avoided IQ Point Lossesa(Millions

2013$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Option D 3,326,127 4,693 $1.86 $2.62 $0.30 $0.45 

Option E 3,326,127 4,693 $1.86 $2.62 $0.30 $0.45 

Source: U.S. EPA Analysis, June 2015

a. Low bound assumes that the loss of one IQ point results in the loss of 1.76% of lifetime earnings (following Schwartz, 1994); 

high bound assumes that the loss of one IQ point results in the loss of 2.38% of lifetime earnings (following Salkever, 1995).

Table B-3. Estimated Avoided Cost of Compensatory Education for Children with Blood Lead 

Concentrations above 20 g/dL and IQ Less than 70afor Scenario without CPP

Regulatory 

Option

Number of 

Affected

Children 0 to 7

Decrease in Number 

of Cases of IQ < 70, 

in 2021 to 2042

Avoided Annual Cost (Millions; 2013$)

3% Discount Rate 7% Discount Rate

Option A 3,326,127 1 $0.00 $0.00 

Option B 3,326,127 1 $0.00 $0.00 

Option C 3,326,127 2 $0.01 $0.01 

Option D 3,326,127 3 $0.02 $0.01 

Option E 3,326,127 3 $0.02 $0.01 

Source: U.S. EPA Analysis, June 2015

a. “-“ indicates that a value was not estimated and “$0.00” indicates that avoided annual cost is less than $0.01.

B.1.2 Benefits to Adults from Reduced Lead Exposure

Table B-4. Summary of Health Benefits due to Decreased Risk of CVD Mortality during 2019-2042 

based on the Economic Value of Avoided Premature Mortality (VSL) for Scenario without CPP

Regulatory Option

Avoided premature 

deaths

Annualized Benefits (millions 2013$)

3 Percent 7 Percent

Option A 11.4 $4.03 $3.36

Option B 11.4 $4.03 $3.36

Option C 48.0 $16.98 $14.17

Option D 62.1 $21.93 $18.30

Option E 62.1 $21.93 $18.30

Source: EPA Analysis, June 2015
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-3

B.1.3 Benefits to Children from Reduced Mercury Exposure

Table B-5. Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure for 

Scenario without CPP

Regulatory 

Option

Number of 

Affected 

Infants per 

Year

Total Avoided 

IQ Losses, 

2021 to 2042

Annualized Value of Avoided IQ Point Lossesa(Millions 2013$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Option A 418,953 4,519 $1.80 $2.52 $0.29 $0.43 

Option B 418,953 4,610 $1.83 $2.57 $0.30 $0.44 

Option C 418,953 11,851 $4.71 $6.62 $0.76 $1.14 

Option D 418,953 13,351 $5.31 $7.46 $0.86 $1.28 

Option E 418,953 14,189 $5.64 $7.92 $0.92 $1.36 

Source: U.S. EPA Analysis, 2015

a. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 

1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 

1995).

B.1.4 Reduced Cancer Cases from Arsenic Exposure

Table B-6. Annual Benefits from Reduced Cancer Cases due to Arsenic Exposureafor Scenario 

without CPP

Regulatory 

Option

Annual 

Affected

Population

Reduced Cancer Cases, 

2019 to 2042 

Benefits (Millions 2013$)

3% Discount 7% Discount

Option A 35,972,005 0.04 $0.00 $0.00 

Option B 35,972,005 0.04 $0.00 $0.00 

Option C 35,972,005 0.13 $0.00 $0.00 

Option D 35,972,005 0.16 $0.00 $0.00 

Option E 35,972,005 0.17 $0.00 $0.00 

Source: U.S. EPA Analysis, 2015

a. “-“ indicates that a value was not estimated and “$0.00” indicates that annual benefits are less than $0.01 million.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-4

B.1.5 Total Monetized Human Health Benefits

Table B-7. Total Monetized Human Health Benefits for ELG Options (millions of 2013$)a,b for Scenario 

without CPP

Discount 

Rate Option

Reduced Lead 

Exposure for 

Children

Reduced 

Lead 

Exposure 

for Adults

Reduced Mercury 

Exposure for 

Children

Reduced 

Cancer 

Cases from 

Arsenic

Total

Low High Low High Low High

3%

A $0.35 $0.50 $4.03 $1.80 $2.52 $0.00 $6.18 $7.05 

B $0.35 $0.50 $4.03 $1.83 $2.57 $0.00 $6.21 $7.10 

C $1.58 $2.21 $16.98 $4.71 $6.62 $0.00 $23.27 $25.81 

D $1.88 $2.64 $21.93 $5.31 $7.46 $0.00 $29.12 $32.03 

E $1.88 $2.64 $21.93 $5.64 $7.92 $0.00 $29.45 $32.49 

7%

A $0.06 $0.09 $3.36 $0.29 $0.43 $0.00 $3.71 $3.88 

B $0.06 $0.09 $3.36 $0.30 $0.44 $0.00 $3.72 $3.89 

C $0.26 $0.39 $14.17 $0.76 $1.14 $0.00 $15.19 $15.70 

D $0.31 $0.46 $18.30 $0.86 $1.28 $0.00 $19.47 $20.04 

E $0.31 $0.46 $18.30 $0.92 $1.36 $0.00 $19.53 $20.12 

Source: U.S. EPA Analysis, June 2015

a. “$0.00” indicates that annual benefits are less than $0.01 million.

b. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 

1994); high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 

1995).

B.1.6 Additional Measures of Human Health Benefits

Table B-8. Reaches Exceeding Human Health Criteria for Steam Electric Pollutants for Scenario 

without CPP

Regulatory 

Option

Number of Reaches with 

Steam Electric Pollutanta

Concentrations Exceeding 

Human Health Criteria for at 

Least One Pollutant

Number of Reaches with Improved Water Quality, 

Relative to Baseline

Number of Reaches with 

Fewer Exceedancesb

Number of Reaches with All 

Exceedances Eliminated

Baseline 3,959 -- --

Option A ,3,334 712 625

Option B 3,334 714 625

Option C 2,400 1,716 1,559

Option D 1,904 2,215 2,055

Option E 1,753 2,303 2,206

Source: U.S. EPA Analysis, 2015

a. Pollutants include arsenic, copper, nickel, selenium, thallium, zinc, cadmium, chromium, lead, and mercury.

b. The number of reaches with exceedances reduced includes those reaches where all exceedances are eliminated.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-5

B.2 Non-Market Benefits for Water Quality Improvements

Table B-9: Total Willingness-to-Pay for Water Quality Improvements

Regulatory 

Option

Number of Affected 

Households 

(Millions)

3% Discount Rate 7% Discount Rate

Low Mean High Low Mean High

Option A 39.9 $5.1 $6.0 $28.4 $4.0 $4.8 $22.6 

Option B 70.4 $15.1 $19.2 $84.4 $12.1 $15.4 $67.3 

Option C 76.3 $20.7 $27.8 $115.5 $16.6 $22.3 $92.2 

Option D 94.8 $25.8 $35.3 $143.6 $20.6 $28.3 $114.6 

Option E 101.4 $27.6 $38.0 $154.0 $22.1 $30.5 $122.9 

Source: U.S. EPA Analysis, 2015

B.3 Impacts and Benefits to Threatened and Endangered Species

Table B-10. T&E Species with Habitat Occurring within Waterbodies Affected by Steam Electric Power 

Plants for Scenario without CPP

Species Group

Species Vulnerability

Species Count

Low Moderate High

Amphibians 0 4 2 6

Arachnids 4 0 0 4

Birds 10 2 1 13

Clams 0 0 37 37

Crustaceans 0 1 2 3

Fishes 0 0 21 21

Insects 10 2 1 13

Mammals 17 6 1 24

Reptiles 3 1 5 9

Snails 8 0 14 22

Total 52 16 84 152

Source: U.S. EPA Analysis, 2015
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-6

Table B-11. Estimated Annualized Benefits to T&E Species from WQ Improvements (Millions 2013$)a,b for Scenario without CPP

Discount 

Rate State

Option A Option B Option C Option D Option E

Low Medium High Low Medium High Low Medium High Low Medium High Low Medium High

3%

AL <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

GA <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01

MD $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01

SC $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 <$0.01 $0.01 <$0.01 <$0.01 $0.01 <$0.01 <$0.01 $0.01

Total $0.01 $0.01 $0.02 $0.01 $0.01 $0.02 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03

7%

AL <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

GA <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01

MD $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01 <$0.01 $0.01 $0.01

SC $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01 <$0.01

Total $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03 $0.01 $0.02 $0.03
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-7

B.4 Benefits from Avoided Impoundment Failures

Table B-12: Steam Electric Impoundments by Size in ELG Baseline for Scenario without CPP

Type

Number of Impoundments Impoundment Capacity

Count Percent of Total Total (million gallons) Percent of Total

Big 732 72% 1,031,833 96%

Small 285 28% 44,202 4%

Total 1,017 100% 1,076,036 100%

Table B-13. Estimated Annualized Benefits of Avoided Impoundment Failures by Release Type for 

Scenario without CPP (Millions; 2013$)a

Discount 

Rate Regulatory Option Wall Breaches Other Releases All Releases

3%

Option A $25.3 $9.8 $35.2 

Option B $25.3 $9.8 $35.2 

Option C $101.7 $39.4 $141.1 

Option D $136.3 $52.9 $189.1 

Option E $136.3 $52.9 $189.1 

7%

Option A $20.3 $7.9 $28.1 

Option B $20.3 $7.9 $28.1 

Option C $82.6 $31.9 $114.5 

Option D $110.6 $42.9 $153.5 

Option E $110.6 $42.9 $153.5 

Source: U.S. EPA Analysis, 2015

a. Baseline value of total failure costs minus option value of total failure costs.

B.5 Air-Related Benefits

Table B-14. Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to 

Increase in Auxiliary Service at Steam Electric Power Plants, Relative to Baseline for Scenario 

without CPP

Regulatory 

Option Year

Electricity 

Consumption 

(MWh)

CO2(Metric 

Tonnes/Year)

NOx (Tons/Year) SO2(Tons/Year)

Option B

2015-2018 0.0 0.0 0.0 0.0

2019 51,163.2 35,363.6 20.4 37.1

2020 69,022.4 47,657.8 28.2 51.5

2021 105,250.1 73,942.8 43.1 75.4

2022 119,561.0 86,906.2 54.3 81.7

2023-2042 139,648.1 102,655.1 70.9 93.0
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-8

Table B-14. Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to 

Increase in Auxiliary Service at Steam Electric Power Plants, Relative to Baseline for Scenario 

without CPP

Regulatory 

Option Year

Electricity 

Consumption 

(MWh)

CO2(Metric 

Tonnes/Year)

NOx (Tons/Year) SO2(Tons/Year)

Option D

2015-2018 0.0 0.0 0.0 0.0

2019 95,434.4 68,584.9 48.3 66.1

2020 146,933.1 101,292.8 80.3 93.3

2021 223,365.9 153,405.3 112.5 144.4

2022 270,636.7 191,064.6 146.4 176.0

2023-2042 339,202.2 238,871.5 193.5 212.4

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table B-15. Estimated Changes in Annual Air Pollutant Emissions due to Increased Trucking at 

Steam Electric Power Plants, Relative to Baseline for Scenario without CPP

Regulatory 

Option Year CO2

(Metric Tonnes/Year)

NOx (Tons/Year) SO2(Tons/Year)

Option B

2015-2018 0.0 0.0 0.0

2019 775.3 0.3 0.0

2020 918.4 0.4 0.0

2021 1,092.2 0.5 0.0

2022 1,143.4 0.5 0.0

2023-2042 1,218.8 0.5 0.0

Option D

2015-2018 0.0 0.0 0.0

2019 2,401.2 1.0 0.0

2020 2,772.4 1.2 0.0

2021 3,551.3 1.5 0.0

2022 3,972.7 1.7 0.0

2023-2042 4,277.3 1.9 0.0

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table B-16. Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity 

Generation Profile, Relative to Baseline for Scenario without CPP

Regulatory 

Option Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2(Tons/Year)

With CPP (Also Applied to Without CPP Scenario)

Option B

2015-2018 0.0 0.0 0.0

2019-2022 -2,057,293.5 -3,534.5 -4,620.6

2023-2027 -1,437,164.1 -3,323.9 -527.5

2028-2033 -246,295.3 -1,361.2 1,315.7

2034-2042 -1,186,462.9 -1,500.2 -1,608.4
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-9

Table B-16. Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity 

Generation Profile, Relative to Baseline for Scenario without CPP

Regulatory 

Option Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2(Tons/Year)

Option D

2015-2018 0.0 0.0 0.0

2019-2022 -4,869,524.3 -14,614.0 -5,662.8

2023-2027 -2,555,361.0 -11,615.0 2,238.4

2028-2033 -2,089,591.4 -8,826.0 -984.2

2034-2042 -3,193,009.0 -10,638.8 -4,243.9

Source: U.S. EPA Analysis, 2015; see TDD for details.

Table B-17. Estimated Net Changes in Air Pollutant Emissions due to Increase in Auxiliary Service 

at Steam Electric Power Plants, Increased Trucking at Steam Electric Power Plants, and Changes in 

Electricity Generation Profile, Relative to Baseline for Scenario without CPP

Regulatory 

Option Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2(Tons/Year)

Option B

2015-2018 0.0 0.0 0.0

2019 -2,021,154.6 -3,513.7 -4,583.5

2020 -2,008,717.3 -3,505.9 -4,569.1

2021 -1,982,258.5 -3,490.9 -4,545.2

2022 -1,969,243.9 -3,479.6 -4,538.8

2023-2027 -1,333,290.2 -3,252.5 -434.5

2028-2033 -142,421.36 -1,289.73 1,408.69

2034-2042 -1,082,588.96 -1,428.78 -1,515.47

Option D

2015-2018 0.0 0.0 0.0

2019 -4,798,538.2 -14,564.6 -5,596.7

2020 -4,765,459.0 -14,532.5 -5,569.4

2021 -4,712,567.7 -14,499.9 -5,518.4

2022 -4,674,486.9 -14,465.9 -5,486.8

2023-2027 -2,312,212.2 -11,419.6 2,450.9

2028-2033 -1,846,442.6 -8,630.6 -771.7

2034-2042 -2,949,860.2 -10,443.5 -4,031.4

Source: U.S. EPA Analysis, 2015
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-10

Table B-18. Estimated Benefits from Reduced Air Emissions for Selected Years for Scenario without 

CPP (millions; 2013$)a,b

Option Year 3% Discount Rate 7% Discount Rate

Option B

2019c

$287.2 $265.8

2020 $290.3 $269.2

2025 $105.3 $101.4

2030 -$47.5 -$42.4

Option D

2019c$513.5 $481.3

2020 $518.7 $487.6

2025 $83.0 $86.1

2030 $192.4 $183.7

Source: U.S. EPA Analysis, 2015

a. EPA used SCC values based on a 3 percent (average) discount rate to calculate total benefit values presented for both the 3 

percent and 7 percent discount rate.

b. EPA used the changes in annual air pollutant emissions due to changes in electricity generation profile for scenario with CPP 

(from IPM analysis) when calculating benefits for the scenario without CPP

c. The benefits per ton values used for year 2019 benefit calculation is assumed to be the same as the 2020 benefits per ton values.

Table B-19. Estimated Annualized Benefits from Reduced Air Emissions for Scenario without CPP 

(Millions; 2013$)a

ELG Option Pollutant 3% Discount Rate 7% Discount Rate

Option B

NOx $12.7 $10.6

SO2 $44.8 $40.4

CO2

3% Avg $51.9 $51.9 

5% Avg $15.1 $15.1

2.5% Avg $76.5 $76.5

3% 95th Percentile $155.9 $155.9

TOTAL

3% Avg $109.3 $102.8

5% Avg $72.6 $66.1

2.5% Avg $134.0 $127.4

3% 95th Percentile $213.4 $206.8 

Option D

NOx $62.9 $49.3 

SO2 $81.0 $58.9 

CO2

3% Avg $138.4 $138.4 

5% Avg $40.0 $40.0

2.5% Avg $204.7 $204.7

3% 95th Percentile $416.9 $416.9

TOTAL

3% Avg $282.4 $246.7

5% Avg $183.9 $148.3

2.5% Avg $348.6 $313.0

3% 95th Percentile $560.8 $525.2 

Source: U.S. EPA Analysis, 2015

a. EPA used the changes in annual air pollutant emissions due to changes in electricity generation profile for scenario with CPP 

(from IPM analysis) when calculating benefits for the scenario without CPP
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-11

B.6 Benefits from Reduced Water Withdrawals

Table B-20. Estimated Annualized Benefits from Reduced Groundwater Withdrawals (Millions; 

2013$)

Regulatory Option

Reduction in 

Groundwater Intakes 

(million gallons per year; 

full implementation)

3% Discount Rate 7% Discount Rate

Option A 0.0 $0.00 $0.00

Option B 0.0 $0.00 $0.00

Option C 0.0 $0.00 $0.00

Option D 8.0 $0.02 $0.02

Option E 8.0 $0.02 $0.02

Source: U.S. EPA Analysis, 2015

B.7 Benefits from Enhanced Marketability of Coal Combustion Residuals

Table B-212. State-level Market Approximation (Short Tons).

Application

Baseline Production Changes due to ELGs

Production

Unmet Demand (% 

of production)

Fly Ash

(% ΔCCR)

Bottom Ash 

(% ΔCCR)

Concrete 13,400,062 3,277,184 

(24%) 

166,535 

(9.6%)

-

Fill 320,701,683 312,705,505 (98%) 1,575,450 

(90.4%)

5,480,176 (92.7%)

Source: U.S. EPA Analysis 2015

Table B-23. Estimated Beneficial Use Applications of CCRs, by CCR Category

ELG Option and CCR Category

Beneficial Use Application (1,000 short tons)

Concrete Structural Fill

Option A or Option B

Fly ash 167 1,575

Bottom ash NA 0

Total 167 1,575

Option C

Fly ash 167 1,575

Bottom ash NA 3,431

Total 167 5,006

Option D or Option E

Fly ash 167 1,575

Bottom ash NA 4,821

Total 167 6,397
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-12

Table B-224. Annual Avoided Resource and Environmental Impacts Given CCR Reuse in Concrete 

and Fill Applications for Scenario Without CPP

Impact Category Fly Ash Bottom Ash Option Total

CCR RIA, 2030 

3-yr rolling avg

Option A or Option B

Concrete (mill. tons) 0.167 NA 0.167 0.21

Structural fill (mill. tons) 1.58 0 1.58 6.93

Energy (MMBtu) 657,548 0 657,548 910,000

Water (million gal) 1,937 0 1,937 21,000

Greenhouse gases (tons) 1,442,070 0 1,442,070 75,000

NOx (tons) 359 0 359 310

SOx (tons) 54 0 54 59

Option C

Concrete (mill. tons) 0.167 NA 0.167 0.21

Structural fill (mill. tons) 1.58 3.43 5.06 6.93

Energy (MMBtu) 657,548 137,232 794,780 910,000

Water (million gal) 1,937 4,144 6,081 21,000

Greenhouse gases (tons) 1,442,070 10,979 1,453,048 75,000

NOx (tons) 359 92 451 310

SOx (tons) 54 29 83 160

Option D or Option E

Concrete (mill. tons) 0.167 NA 0.167 0.21

Structural fill (mill. tons) 1.58 4.82 6.39 6.93

Energy (MMBtu) 657,548 192,848 850,396 910,000

Water (million gal) 1,937 5,824 7,761 21,000

Greenhouse gases (tons) 1,442,070 15,428 1,457,498 75,000

NOx (tons) 359 129 488 310

SOx (tons) 54 41 95 160

Note: Values in this table represent annual changes in a full-compliance year (e.g., starting in 2023). 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-13

Table B-25. Annualized Economic Value of Estimated Changes in Beneficial Use (Million 2013$).

Regulatory Option Impact Category 3% 7%

Option A or 

Option B

Avoided Disposal Costs to Steam Electric Plants $6.44 $5.46 

Beneficiation Costs -$0.21 -$0.16

Avoided Life Cycle Costs of Virgin Materials $11.34 $14.52 

Net Social Value $17.57 $19.82 

Option C

Avoided Disposal Costs to Steam Electric Plants $21.97 $18.39 

Beneficiation Costs -$0.21 -$0.16

Avoided Life Cycle Costs of Virgin Materials $13.25 $16.69 

Net Social Value $35.01 $34.92 

Option D or 

Option E

Avoided Disposal Costs to Steam Electric Plants $34.43 $28.59 

Beneficiation Costs -$0.21 -$0.16

Avoided Life Cycle Costs of Virgin Materials $13.94 $17.48 

Net Social Value $48.16 $45.90 

Notes: Annualized over 24 years (2015 - 2042). Values escalated using CCI and GDP through 2022; thereafter, assume no real 

change in prices above inflation. Avoided disposal costs to steam electric power plants include annual O&M costs.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-14

B.8 Total Monetized Benefits

Table B-26. Summary of Total Annualized Benefits at 3 Percent (Millions; 2013$)

Benefit Category

Option A Option B Option C Option D Option E

Low Midf High Low Midf High Low Midf High Low Midf High Low Midf High

Human Health Benefits $6.2 $6.6 $7.1 $6.2 $6.7 $7.1 $23.3 $24.5 $25.8 $29.1 $30.6 $32.0 $29.4 $31.0 $32.5

Reduced IQ losses in children 

from exposure to leada $0.4 $0.4 $0.5 $0.4 $0.4 $0.5 $1.6 $1.9 $2.2 $1.9 $2.3 $2.6 $1.9 $2.3 $2.6 

Reduced CVD in adults from 

exposure to lead $4.0 $4.0 $17.0 $21.9 $21.9

Reduced IQ losses in children 

from exposure to mercurya $1.8 $2.2 $2.5 $1.8 $2.2 $2.6 $4.7 $5.7 $6.6 $5.3 $6.4 $7.5 $5.6 $6.8 $7.9

Avoided cancer cases from 

exposure to arsenicb $0.0 $0.0 $0.0 $0.0 $0.0

Improved Ecological 

Conditions and Recreational 

Uses

$5.1 $6.1 $28.4 $15.2 $19.2 $84.4 $20.7 $27.8 $115.5 $25.8 $35.3 $143.7 $27.6 $38.0 $154.0

Use and nonuse values for 

water quality improvements $5.1 $6.0 $28.4 $15.1 $19.2 $84.4 $20.7 $27.8 $115.5 $25.8 $35.3 $143.6 $27.6 $38.0 $154.0

Nonuse values of T&E speciesb$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0

Market and Productivity 

Benefits $52.7 $52.7 $176.1 $237.3 $237.3

Avoided impoundment failures $35.2 $35.2 $141.1 $189.1 $189.1

Reduced dredging costsb$0.0 $0.0 $0.0 $0.0 $0.0

Ash marketing benefits $17.6 $17.6 $35.0 $48.2 $48.2

Air-related benefits NE $109.4 NE $282.4 NE

Reduced human health effects NE $57.5 NE $143.9 NE

Reduced CO2 emissionsc NE $51.9 NE $138.4 NE

Reduced water withdrawalsb$0.0 $0.0 $0.0 $0.0 $0.0

Total (excluding air-related 

Benefits)d $64.0 $65.4 $88.2 $74.1 $78.6 $144.3 $220.1 $228.4 $317.4 $292.2 $303.2 $413.0 $294.4 $306.3 $423.8

Total (including air-related 

Benefits)d,e NE NE NE $183.5 $188.0 $253.6 NE NE NE $574.6 $585.5 $695.4 NE NE NE

Source: U.S. EPA Analysis, 2015
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-15

Table B-26. Summary of Total Annualized Benefits at 3 Percent (Millions; 2013$)

Benefit Category

Option A Option B Option C Option D Option E

Low Midf High Low Midf High Low Midf High Low Midf High Low Midf High

“NE” indicates that EPA did not estimate the benefits. Air-related benefits of Option A are expected to be less than those for Option B; air-related benefits for Option C are expected to be 

between those of Options B and D; and air-related benefits of Option E are expected to be greater than those for Option D.

a. Value includes reduced IQ losses and avoided cost of compensatory education in children from exposure to lead. For details see Chapter 3.

b. “< $0.1” indicates that the monetized annual benefits are positive but less than $0.1 million.

c. For the valuation of benefits from reductions in CO2 emissions EPA relied on the 3 percent average social cost of carbon estimate.

d. Values for individual benefit categories may not sum to the total due to independent rounding.

e. The total monetized benefits for options A, C, and E do not include air-related benefits. This category of benefits was analyzed for Options B and D only (see Chapter 7).

f. EPA estimated use and nonuse values for water quality improvements using two different meta-regression models of willingness-to-pay. One model provides the low and high bounds 

while a different model provides a central estimate (included in this table in the mid-range column). For this reason, the mid-range estimate differs from the midpoint of the range for this 

benefit category. For details, see Chapter 4.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-16

Table B-27. Summary of Total Annualized Benefits at 7 Percent (Millions; 2013$)

Benefit Category

Option A Option B Option C Option D Option E

Low Midf High Low Midf High Low Midf High Low Midf High Low Midf High

Human Health Benefits $3.7 $3.8 $7.2 $3.7 $3.8 $22.2 $15.2 $15.4 $15.7 $19.5 $19.8 $20.0 $19.5 $19.8 $20.1

Reduced IQ losses in children 

from exposure to leada $0.1 $0.1 $0.1 $0.1 $0.1 $0.1 $0.3 $0.3 $0.4 $0.3 $0.4 $0.5 $0.3 $0.4 $0.5

Reduced CVD in adults from 

exposure to lead $3.4 $3.4 $14.2 $18.3 $18.3

Reduced IQ losses in children 

from exposure to mercurya $0.3 $0.4 $3.8 $0.3 $0.4 $18.7 $0.8 $1.0 $1.1 $0.9 $1.1 $1.3 $0.9 $1.1 $1.4

Avoided cancer cases from 

exposure to arsenicb $0.0 $0.0 $0.0 $0.0 $0.0

Improved Ecological 

Conditions and Recreational 

Uses

$4.1 $4.8 $22.6 $12.1 $15.4 $67.3 $16.6 $22.3 $92.2 $20.6 $28.3 $114.6 $22.1 $30.5 $122.9

Use and nonuse values for 

water quality improvements

$4.0 $4.8 $22.6 $12.1 $15.4 $67.3 $16.6 $22.3 $92.2 $20.6 $28.3 $114.6 $22.1 $30.5 $122.9

Nonuse values of T&E speciesb$0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0

Market and Productivity 

Benefits $47.9 $47.9 $149.4 $199.4 $199.4

Avoided impoundment failures $28.1 $28.1 $114.5 $153.5 $153.5

Reduced dredging costsb$0.0 $0.0 $0.0 $0.0 $0.0

Ash marketing benefits $19.8 $19.8 $34.9 $45.9 $45.9

Air-Related Benefits NE $102.8 NE $246.7 NE

Reduced human health effects NE $50.92 NE $108.28 NE

Reduced CO2 emissionsc NE $51.85 NE $138.42 NE

Reduced water withdrawalsb$0.0 $0.0 $0.0 $0.0 $0.0

Total (excluding air-related 

Benefits)d $55.7 $56.6 $77.7 $63.7 $67.1 $137.4 $181.2 $187.2 $257.4 $239.5 $247.5 $334.1 $241.0 $249.8 $296.5

Total (including air-related 

Benefits)d,e NE NE NE $166.5 $169.9 $240.2 NE NE NE $486.2 $494.2 $580.8 NE NE NE

Source: U.S. EPA Analysis, 2015

“NE” indicates that EPA did not estimate the benefits. Air-related benefits of Option A are expected to be less than those for Option B; air-related benefits for Option C are expected to be 

between those of Options B and D; and air-related benefits of Option E are expected to be greater than those for Option D.

a. Value includes reduced IQ losses and avoided cost of compensatory education in children from exposure to lead. For details see Chapter 3.

b. “< $0.1” indicates that the monetized annual benefits are positive but less than $0.1 million.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-17

Table B-27. Summary of Total Annualized Benefits at 7 Percent (Millions; 2013$)

Benefit Category

Option A Option B Option C Option D Option E

Low Midf High Low Midf High Low Midf High Low Midf High Low Midf High

c. For the valuation of benefits from reductions in CO2 emissions EPA relied on the 3 percent average social cost of carbon estimate.

d. Values for individual benefit categories may not sum to the total due to independent rounding.

e. The total monetized benefits for options A, C, and E do not include air-related benefits. This category of benefits was analyzed for Options B and D only (see Chapter 7).

f. EPA estimated use and nonuse values for water quality improvements using two different meta-regression models of willingness-to-pay. One model provides the low and high bounds 

while a different model provides a central estimate (included in this table in the mid-range column). For this reason, the mid-range estimate differs from the midpoint of the range for this 

benefit category. For details, see Chapter 4.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-18

Table B-28: Time Profile of Benefits at 3 Percent (Millions; 2013$) (Including Air-Related Benefits for 

Options B and D) for Scenario Without CPP

Year Option A

a Option B Option Ca Option D Option Ea

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $10.6 $297.9 $27.7 $555.8 $42.3

2020 $13.2 $303.5 $83.6 $630.0 $111.3

2021 $30.6 $337.8 $138.0 $714.3 $197.5

2022 $48.9 $360.5 $244.3 $833.9 $310.8

2023 $85.6 $202.5 $295.2 $471.8 $396.2

2024 $86.5 $205.5 $298.2 $477.4 $400.0

2025 $86.9 $208.0 $299.0 $480.2 $401.0

2026 $87.1 $210.4 $299.1 $482.3 $401.1

2027 $87.2 $212.7 $298.8 $483.7 $400.7

2028 $87.3 $57.5 $298.3 $583.9 $399.9

2029 $87.2 $56.9 $297.4 $583.9 $398.8

2030 $87.1 $56.3 $296.6 $586.2 $397.7

2031 $87.2 $56.0 $295.9 $588.0 $396.7

2032 $87.2 $55.6 $295.1 $590.1 $395.7

2033 $87.2 $55.2 $294.4 $592.3 $394.8

2034 $87.3 $250.6 $293.8 $827.4 $393.9

2035 $87.3 $253.1 $293.3 $833.1 $393.2

2036 $87.4 $255.6 $292.9 $838.9 $392.6

2037 $87.5 $258.1 $292.5 $844.8 $392.1

2038 $87.7 $260.6 $292.2 $850.8 $391.6

2039 $87.8 $263.1 $291.9 $856.8 $391.3

2040 $87.9 $265.7 $291.8 $863.0 $391.0

2041 $88.1 $268.3 $291.7 $869.2 $390.8

2042 $88.2 $269.6 $291.5 $872.2 $390.6

Annualized Benefits, 

3% $65.4 $188.0 $228.4 $585.5 $306.3

Source: U.S. EPA Analysis, 2015

a. Estimates for Options A, C and E do not include air-related benefits. This category of benefits was only estimated for Options B and 

D (see Chapter 7).

Table B-29: Time Profile of Benefits at 7 Percent (Millions; 2010$) (Including Air-Related Benefits for 

Options B and D) for Scenario Without CPP

Year Option A

a Option B Option Ca Option D Option Ea

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $10.6 $276.5 $27.7 $523.6 $42.3
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-19

Table B-29: Time Profile of Benefits at 7 Percent (Millions; 2010$) (Including Air-Related Benefits for 

Options B and D) for Scenario Without CPP

Year Option A

a Option B Option Ca Option D Option Ea

2020 $13.2 $282.4 $83.6 $598.9 $111.3

2021 $28.2 $314.1 $131.1 $674.4 $189.2

2022 $46.5 $336.8 $237.2 $794.0 $302.4

2023 $83.2 $196.2 $288.1 $467.1 $387.7

2024 $84.0 $199.2 $291.0 $472.6 $391.4

2025 $84.4 $201.6 $291.8 $475.1 $392.3

2026 $84.6 $204.1 $291.8 $477.2 $392.4

2027 $84.7 $206.3 $291.5 $478.6 $391.9

2028 $84.7 $60.1 $290.8 $566.8 $391.0

2029 $84.6 $59.4 $289.9 $566.7 $389.9

2030 $84.5 $58.8 $289.0 $568.8 $388.7

2031 $84.6 $58.5 $288.2 $570.6 $387.6

2032 $84.6 $58.1 $287.4 $572.6 $386.5

2033 $84.6 $57.7 $286.7 $574.7 $385.5

2034 $84.6 $240.6 $286.0 $794.8 $384.6

2035 $84.6 $243.1 $285.4 $800.4 $383.8

2036 $84.7 $245.5 $284.9 $806.1 $383.1

2037 $84.8 $248.0 $284.5 $811.9 $382.5

2038 $84.9 $250.5 $284.1 $817.8 $382.0

2039 $85.0 $253.0 $283.8 $823.8 $381.5

2040 $85.1 $255.5 $283.6 $829.8 $381.2

2041 $85.3 $258.1 $283.4 $836.0 $380.9

2042 $85.3 $259.4 $283.2 $838.8 $380.6

Annualized Benefits, 

7% $56.6 $169.9 $187.2 $494.2 $249.8

Source: U.S. EPA Analysis, 2015

a. Estimates for Options A, C and E do not include air-related benefits. This category of benefits was only estimated for Options B and 

D (see Chapter 7).

B.9 Total Costs

Table B-30: Summary of Annualized Costs for Scenario without CPP (Millions; $2013)

Regulatory Option 3% Discount Rate 7% Discount Rate

Option A $141.1 $137.1

Option B $238.9 $234.9

Option C $465.3 $461.4

Option D $640.5 $626.1

Option E $711.8 $695.8

Source: U.S. EPA Analysis, 2015.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-20

Table B-31: Time Profile of Costs to Society (Millions; $2013), Scenario Without CPP

Year Option A Option B Option C Option D Option E

2015 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $0.0 $0.0 $0.0 $0.0 $0.0

2018 $0.0 $0.0 $0.0 $0.0 $0.0

2019 $474.6 $820.7 $1,420.3 $1,758.7 $1,900.2

2020 $169.2 $377.9 $855.0 $1,218.1 $1,388.1

2021 $431.3 $696.7 $1,347.4 $1,657.5 $1,809.6

2022 $223.1 $415.8 $1,043.8 $1,367.5 $1,597.5

2023 $355.8 $618.1 $1,367.7 $1,843.9 $2,035.9

2024 $85.8 $141.8 $258.5 $391.0 $434.6

2025 $95.9 $151.9 $268.5 $401.8 $446.9

2026 $91.3 $147.3 $265.2 $401.5 $446.8

2027 $93.8 $149.8 $265.6 $400.0 $445.1

2028 $90.2 $146.2 $263.8 $398.0 $443.8

2029 $98.4 $154.4 $279.0 $419.2 $464.6

2030 $96.3 $152.3 $275.0 $414.1 $457.7

2031 $99.3 $155.2 $279.7 $419.0 $464.1

2032 $97.9 $153.9 $277.7 $416.8 $462.1

2033 $98.7 $154.7 $279.8 $420.4 $465.5

2034 $88.3 $144.2 $261.7 $395.6 $441.3

2035 $95.7 $151.7 $269.6 $404.1 $449.5

2036 $91.8 $147.8 $267.7 $404.2 $447.8

2037 $96.2 $152.2 $271.2 $407.8 $452.9

2038 $93.8 $149.8 $271.1 $408.9 $453.9

2039 $97.8 $153.8 $277.2 $415.4 $460.0

2040 $97.0 $153.0 $273.9 $411.3 $456.0

2041 $97.0 $153.0 $273.7 $409.1 $453.4

2042 $96.3 $152.3 $271.7 $405.2 $448.8

Annualized Costs, 3% $141.1 $238.9 $465.3 $640.5 $711.8

Annualized Costs, 7% $137.1 $234.9 $461.4 $626.1 $695.8

Source: U.S. EPA Analysis, 2015.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-21

B.10 Monetized Benefits and Costs Comparison

B.10.1 Comparison of Total Monetized Benefits and Costs

Table B-32. Total Annualized Benefits and Costs by Regulatory Option and Discount Rate (Millions; 

2013$)

Regulatory 

Option

Total Monetized Benefits Total Monetized Benefits, Including 

Extrapolated Valuesa

Total Costs

Low Mid High Low Mid High

3% Discount Rate

Option A $64.0 $65.4 $88.2 $145.4 $144.7 $152.8 $141.1

Option B $183.5 $188.0 $253.6 $183.5 $188.0 $253.6 $238.9

Option C $220.1 $228.4 $317.4 $499.9 $505.3 $549.8 $465.3

Option D $574.6 $585.5 $695.4 $574.6 $585.5 $695.4 $640.5

Option E $294.4 $306.3 $423.8 $668.6 $677.6 $734.0 $711.9

7% Discount Rate

Option A $55.7 $56.6 $77.7 $126.5 $125.1 $134.6 $137.1

Option B $166.5 $169.9 $240.2 $166.5 $169.9 $240.2 $234.9

Option C $181.2 $187.2 $257.4 $411.5 $414.1 $445.8 $461.4

Option D $486.2 $494.2 $580.8 $486.2 $494.2 $580.8 $626.1

Option E $241.0 $249.8 $296.5 $547.4 $552.6 $513.6 $695.8

Source: U.S. EPA Analysis, 2015.

a. EPA did not analyze air-related benefits for Options A, C, and E. This category of benefits was only estimated for Options B and D 

(see Chapter 7). EPA adjusted the total benefits estimated for Options A, C and E by multiplying the totals without air-related benefits 

by the average ratio of [total with air-related benefits]/[total without air-related benefits] for Options B and D.

B.10.2 Analysis of Incremental Monetized Benefits and Costs

Table B-33. Incremental Net Benefit Analysis for Scenario without CPP (Millions; 2013$)

Regulatory 

Optiona

Total Annual Monetized 

Benefits, Including 

Adjusted or Inferred 

Values

Total 

Social 

Costs

Net Annual Monetized

Benefitsa

Incremental Net Annual 

Monetized Benefitsb

Low Midd High Low Midd High Low Midd High

3% Discount Rate

Option A $145.4 $144.7 $152.8 $141.1 $4.3 $3.6 $11.7 - - -

Option B $183.5 $188.0 $253.6 $238.9 -$55.4 -$50.9 $14.7 -$59.7 -$54.5 $3.0

Option C $499.9 $505.3 $549.8 $465.3 $34.6 $40.0 $84.5 $90.0 $90.9 $69.8

Option D $574.6 $585.5 $695.4 $640.5 -$65.9 -$55.0 $54.9 -$100.5 -$95.0 -$29.7

Option E $668.6 $677.6 $734.0 $711.9 -$43.3 -$34.3 $22.1 $22.7 $20.7 -$32.7

7% Discount Rate

Option A $126.5 $125.1 $134.6 $137.1 -$10.6 -$12.0 -$2.5 - - -

Option B $166.5 $169.9 $240.2 $234.9 -$68.4 -$65.0 $5.3 -$57.8 -$53.0 $7.7

Option C $411.5 $414.1 $445.8 $461.4 -$49.9 -$47.3 -$15.6 $18.5 $17.6 -$20.9
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix B: Analysis without CPP Rule

September 29, 2015 B-22

Table B-33. Incremental Net Benefit Analysis for Scenario without CPP (Millions; 2013$)

Regulatory 

Optiona

Total Annual Monetized 

Benefits, Including 

Adjusted or Inferred 

Values

Total 

Social 

Costs

Net Annual Monetized

Benefitsa

Incremental Net Annual 

Monetized Benefitsb

Low Midd High Low Midd High Low Midd High

Option D $486.2 $494.2 $580.8 $626.1 -$139.9 -$131.9 -$45.3 -$90.0 -$84.6 -$29.7

Option E $547.4 $552.6 $513.6 $695.8 -$148.4 -$143.2 -$182.2 -$8.5 -$11.3 -$136.9

Source: U.S. EPA Analysis, 2015.

a. EPA did not analyze air-related benefits for Options A, C, and E. This category of benefits was only estimated for Options B and D

(see Chapter 7). EPA adjusted the total benefits estimated for Options A, C, and E by multiplying the totals without air-related 

benefits by the average ratio of [total with air-related benefits]/[total without air-related benefits] for Options B and D.

b. Net benefits are calculated by subtracting total annualized costs from total annual monetized 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.

d. EPA estimated use and nonuse values for water quality improvements using two different meta-regression models of WTP. One 

model provides the low and high bounds while a different model provides the mid estimate. For this reason, the mid benefit estimate 

differs from the midpoint of the benefits range. For details, see Chapter 4.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix C: Estimation of Exposed Population

September 29, 2015 C-1

Appendix C. Estimation of Exposed Population

The assessment uses the Census Block Group as the geographic unit of analysis, assigning a radial distance 

(e.g., 50 miles or 100 miles) from the Census Block Group centroid. EPA assumes that all modeled reaches 

within this range are viable fishing sites, with all unaffected reaches viable substitutes for affected reaches 

within the area around the Census Block Group.

By focusing on distance from the Census Block Group, rather than distances from affected reaches, each 

household is only included in the assessment once, eliminating the potential for double-counting of 

households that are near multiple affected waterbodies.

Figure C-1 presents a hypothetical example focusing on two Census Block Groups (square at the center of 

each circular area), each near five waterbodies with water quality changes under the ELGs (thick red lines).

The same approach is used to identify populations for the analysis of non-market benefits in Chapter 4. In 

that case, the circles represent the outer edge of the 100-mile buffer around each block group. Highlighted in 

red are the affected NHD reaches under regulatory options for which baseline WQI and ∆WQI would be 

estimated

Figure C-1. Illustration of Intersection of Census Block Groups and COMIDs.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix D: Water and Fish Tissue 

September 29, 2015 D-1

Appendix D. Derivation of Ambient Water and Fish Tissue 

Concentrations in Receiving and Downstream Reaches

This appendix describes the methodology EPA used to estimate in-stream and fish tissue concentrations under 

the baseline and each of the five ELG regulatory options. The concentrations are used as inputs to estimate the 

water quality improvements and human health benefits of the final rule. Specifically, EPA in-stream metal 

concentrations to analyze non-use benefits of water quality improvements (see Chapter 4), and to derive fish 

tissue concentrations used to analyze human health effects from consuming self-caught fish (see Chapter 3). 

Nutrient and suspended sediment concentrations are used to support analysis of non-use benefits from water 

quality improvements (see Chapter 4).

The overall modeling methodology is similar to that used at proposal (see Chapter 4 in U.S. EPA (2013a)) 

and builds on data and methods described in the Technical Development Document and Environmental 

Assessment documents for the final ELGs (U.S. EPA, 2015a; 2015b). The following sections discuss 

calculations of the metal concentrations in streams and fish tissue and nutrient and sediment concentrations in 

streams.

D.1 Metals

D.1.1 Estimating Water Concentrations in each Reach

EPA first estimated the baseline and post-compliance metal concentrations in reaches receiving steam electric 

power plant discharges and downstream reaches. 

The water quality model component of EPA’s Risk-Screening Environmental Indicators (RSEI) model (U.S. 

EPA, 2012c) was used to estimate water concentrations. The model tracks the fate and transport of discharged 

pollutants through a reach network defined based on the medium resolution National Hydrographic Dataset 

(NHD).86 The hydrography network represented in RSEI consists of approximately 2.5 million reaches

(unique COMIDs).87

 

86 RSEI utilizes the USGS’s National Hydrology Dataset (NHD) which defines a reach as a continuous piece of 

surface water with similar hydrologic characteristics. In the NHD each reach is assigned a reach code; a reach 

may be composed of a single feature, like a lake or isolated stream, but reaches may also be composed of a 

number of contiguous features. Each reach code occurs only once throughout the nation and once assigned, a 

reach code is permanently associated with its reach. If the reach is deleted, its reach code is retired. The NHD 

reaches in this analysis range from 0.003 miles to 9.11 miles in length.

87 Reaches represented in RSEI are those determined to be potentially fishable based on type and physical 

characteristics. As documented in U.S. EPA (2012c): “Certain criteria were applied to the NHDPlus dataset to 

select the reaches to be used in the model. Specifically, because RSEI calculates the movement of a chemical 

release downstream using flow and velocity data, qualifying reaches must have at least one downstream or 

upstream connecting reach and have a non-negative flow and velocity. RSEI will not calculate concentrations 

for certain types of reaches, such as coastlines, treatment reservoirs, and bays; the downstream path of any 

chemical is assumed to stop if one of these types of reach is encountered. Additionally, some types of reaches 

are excluded from the set of fishable reaches, such as pipelines, aqueducts, and certain types of reservoirs. 

NHDPlus does not separate canals (presumably fishable) and ditches (presumably not fishable), so RSEI 

excludes reaches in the canal/ditch category if the annual mean flow is less than 5 ft3/s. This is an arbitrary 

minimum, and is intended primarily to exclude ditches at the point of the facility discharge. For reaches 

designated as not fishable in NHDPlus, the chemical is still assumed to travel downstream to the next reach, 

which may or may not be fishable.”
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix D: Water and Fish Tissue 

September 29, 2015 D-2

The analysis involved the following key steps for the baseline and each of the five regulatory options:

 Summing plant-level loadings to the COMID. EPA summed the plant-level annual average loads 

(see TDD) for each unique COMID receiving plant discharges from steam electric power plants in the 

baseline. Chapter 4 in the EA report describes the approach EPA used to identify the receiving 

waterbodies (U.S. EPA 2015a). 

 Specifying loads in the water quality model. RSEI includes data on annual average pollutant 

loadings to surface waters from facilities that reported to the Toxic Release Inventory (TRI) in 2012.

EPA replaced the loadings provided for Steam Electric plant dischargers in the TRI data set with 

those obtained in Step 1. Loadings for other TRI reporters were left unchanged.

 Performing dilution and transport calculations. RSEI’s water quality model uses a simple dilution 

and first-order decay equation (where metals are treated as conservative substances) to estimate 

average annual water concentrations for each individual reach. In the model, a plant is assumed to 

release its annual load at a constant rate throughout the year. Each source-pollutant release is tracked 

downstream throughout the NHD reach network until one of three conditions occurs: 1) the release 

has traveled 300 km (186 miles) downstream; 2) the release has traveled a distance equivalent to one 

week of travel time; or 3) the concentration reaches 1 x 10-9 mg/L. The model calculates the 

concentration of the pollutant in a given reach based on the total mass transported to the reach from 

upstream sources and the reach mean flow (provided in NHDPlus as an attribute of each COMID).

EPA used the approach above to estimate annual average concentrations of ten metals: arsenic, cadmium, 

chromium VI, copper, lead, mercury, nickel, selenium, thallium, and zinc. The results show that, of the 

2.5 million reaches represented in RSEI:

 77,414 reaches (117,518 km) have non-zero concentrations, and 

 18,773 of these reaches (27,778 km) are affected by steam electric power plant discharges in the 

baseline. 

D.1.2 Estimating Fish Tissue Concentrations in each Reach

To support analysis of the human health benefits associated with water quality improvements (see Chapter 3), 

EPA estimated concentrations of arsenic, lead, and mercury in fish tissue based on the RSEI model outputs 

discussed above. 

The methodology follows the same general approach described in the EA document for estimating fish tissue 

concentrations for receiving reaches (U.S. EPA, 2015a), but applies the calculations to the larger set of 

reaches modeled using RSEI, which include not only the receiving reaches analyzed in the EA, but also 

downstream reaches. Further, the calculations use RSEI-estimated concentrations as inputs, which account not 

only for the steam electric discharges, but also other major dischargers that report to TRI.

The analysis involved the following key steps for the baseline and each of the five regulatory options:

 Obtaining the relationship between water concentrations and fish tissue concentrations. EPA 

used the results of the EA national model to parameterize the linear relationship between water 

concentrations in receiving reaches and composite fish tissue concentrations (representative of trophic 

levels 3 and 4 fish consumed) in these same reaches for each of the three metals. 

 Calculating fish tissue data for affected reaches. For reaches for which RSEI provides non-zero 

water concentrations (i.e., reaches affected by steam electric power plants or other TRI dischargers), 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix D: Water and Fish Tissue 

September 29, 2015 D-3

EPA used the relationship obtained in Step 1 to calculate a preliminary fish tissue concentration for 

each metal. 

 Imputing the fish tissue concentrations for all other modeled reaches. For reaches for which 

RSEI does not calculate water concentrations, EPA assigned background fish tissue concentrations 

based on the 10th percentile of the distribution of reported concentrations in fish tissue samples in the 

National Listing Fish Advisory (NLFA) data88 (see Table C-1). EPA found that the distribution of 

these samples was consistent with values reported in Wathen et al (2014) and used the 10th percentile 

as representative of background, “clean” reaches not affected by point source discharges. 

 Validating and adjusting the fish tissue concentrations based on empirical data, if needed. EPA 

then applied the same method used to validate and adjust estimated fish tissue data in the EA national 

model to ensure that the fish tissue concentrations calculated based on the RSEI outputs are 

reasonable when compared to measured data. The approach involves applying order-of-magnitude 

adjustments in cases where the preliminary concentrations are greater than empirical measurements 

for a given reach or geographic area by an order of magnitude or more. Section 5.1.2 of the EA 

describes the methodology in greater detail. 

The analysis provides background metal-specific composite fish fillet concentrations for each COMID 

modeled in RSEI.

Table C-1: Assumed Background Fish Tissue Concentrations, 

based on 10th percentile

Parameter Pollutant Concentration (mg/kg)

As 0.039

Hg 0.058

Pb 0.039

D.2 Nutrients and Suspended Sediment

EPA used the USGS’s SPARROW model to estimate nutrient and sediment concentrations in receiving and 

downstream reaches. The calibrated, national models used for this analysis are the same as those used to 

estimate in-stream concentrations of TN, TP and TSS in the Construction and Development Industry 

Category ELGs (see U.S. EPA, 2009c). The approach involved the following steps:

 Referencing the receiving reaches to E2RF1 reaches. EPA overlaid the medium resolution NHD 

and E2RF1 features in GIS to develop the crosswalk between the two hydrologic networks. 

 Summing the loads for each E2RF1. EPA summed the plant-level loadings over each E2RF1 in the 

baseline and under each of the five regulatory options. 

 Calculating the change in loading for each E2RF1. EPA calculated the difference between the 

baseline and post-compliance loadings under each of the five regulatory options.

 Specifying the change in loading in SPARROW. The national SPARROW models for nutrients do 

not have an explicit explanatory variable for point source loadings in mass units. In the TN and TP 

SPARROW models, point sources (e.g., wastewater treatment plants) are represented by a population 

variable. The national calibrated models show contributions of 2.2514 kg TN/capita and 

 

88 See http://map1.epa.gov/.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix D: Water and Fish Tissue 

September 29, 2015 D-4

0.2319 kg TP/capita for point sources. EPA used these calibrated loading factors to express the load 

reductions obtained under each of the regulatory options into population-equivalent in SPARROW. 

This population-equivalent loading was subtracted from the baseline population value for each reach 

when running the SPARROW model. For the suspended sediment model, EPA used the same 

approach as used for the C&D ELG analysis, which involved adjusting the mass flux attributed to the 

urban land explanatory variable in the model to subtract the change in loading achieved under each 

option, under the assumption that steam electric power plant loadings are implicitly accounted for in 

the urban land component of the model (see U.S. EPA, 2009c).

The model provides annual average post-compliance concentrations in each E2RF1, which EPA compared 

with baseline conditions obtained directly from the national, calibrated model.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix E: CVD Incidence and Mortality

September 29, 2015 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute E-1

Appendix E. Details on Modeling of Cardiovascular Disease Incidence 

and Mortality

E.1 Benefits to Adults from Reduced Lead Exposure

E.1.1 Hazard Reduction under Final ELGs

For each sex, single-year age, and exposure cohort (hereafter cohort), the analysis characterizes two basic 

survival analyses under the baseline and option scenarios: a death hazard function and a survival function.

89

A death hazard function (HF), h(x), for an individual surviving to age x years, is a function such that h(x)dx

gives the probability of death between ages x and x + dx (where dx → 0). Specifically, h(x)dx =

P(x < Xd ≤ x + dx| Xd > x), where Xd is person’s exact age at death. The death hazard function is also 

commonly known as the hazard rate, and less commonly as the force of mortality. At any point in time, an 

individual is at risk of death from competing causes, of which cardiovascular disease (CVD) is only one. As 

discussed in Beyersmann et al. (2009), competing risks can be modeled using cause-specific death hazard 

functions, which are additive (by the law of total probability):

Equation E-1. h(x) = h

CVD(x) + hOTH(x)

A survival function, S(x), is the probability that a person dies at some point after age x, specifically: S(x) =

P( Xd > x). The survival function and the hazard function are intrinsically linked, with the survival function 

determined completely by the hazard function: 

S(x) = exp{− ∫ h(v)dv x

0

}, where v represents a time period typically different than x.

Thus, to estimate changes in mortality such as those resulting from implementation of the final ELGs, it is 

necessary to characterize:

(1) baseline CVD death hazard function; 

(2) baseline other cause (i.e., non-CVD) death hazard function; and 

(3) option-specific CVD death hazard function. 

The main source of data for hazard estimation in key simulation elements (1) and (2) above is a life table, 

which is a collection of statistics that shows age-specific probabilities of survival and fecundity.90 Employed 

heavily in actuarial science, demography, population biology, ecology and epidemiology, data from life tables 

can be used to calculate probabilities of survival to a given age, age-specific life expectancy, population 

growth rates, and many other demographic characteristics.

The statistics reported in a life table can be used to approximate the (baseline) hazard function. Of a particular 

interest are the estimates of the initial age-specific mortality rate, qx: the proportion of people alive at exact 

age x, who will die before attaining exact age x + 1. The initial mortality rate is closely related to the hazard 

function:

Equation E-2. qx = 1 − S(x + 1)⁄S(x) = 1 − exp {− ∫ h(v)dv x+1

x

}.

 

89 Collett (2003), pp. 10-12. 

90 An extensive discussion of life tables can be found in Shryock et al. (1980) Chapter 15.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix E: CVD Incidence and Mortality

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Assuming a constant hazard function within each integer age, ∫ h(v)dv x+1

x

= ∫ hxdv x+1

x

= hx, estimates of 

the initial age-specific mortality rates qx reported in the life table can be used to obtain an estimate of the 

baseline hazard function:

Equation E-3. hx

b = ln{1 − q̂x

}

At each integer age x, this overall hazard function can be further decomposed into CVD death hazard function 

and other-cause death hazard function. To this end, annual age-specific CVD death rates (which are reported 

by the Center for Disease Control and Prevention (CDC) can be used to approximate baseline qx

CVD,b

and, 

consequently, obtain an estimate of the baseline CVD hazard function:

Equation E-4. hx

CVD,b = ln{1 − qxCVD,b

}

which is key simulation element (1) noted above. 

Additionally, the estimate of the baseline other hazards function can be derived as:

Equation E-5. hx

OTH,b

= hx

b − hx

CVD,b

providing key simulation element (2) from above.

EPA used a concentration-response function from a peer reviewed study, Menke et al. (2006). The study finds 

a multivariate adjusted relative hazards of CVD mortality of 1.53 (1.21-1.94) per 3.4-fold increase in PbB, 

based on a Cox proportional hazards model using the log of the blood lead level. The corresponding beta 

estimate (βBLL) for this Hazard Ratio (HR) is 0.35. Cox proportional hazards models, such as the one 

estimated by Menke et al (2006) are designed to estimate a multiplicative relationship between an outcome 

(i.e., the CVD death hazard function) and a set of predictors. Menke et al. (2006) assessed the HR in various 

subgroups, and noted that subgroup interaction terms were not statistically significant, supporting the use of 

the coefficient for the overall population in benefits estimation. A key assumption of this model, the 

proportional hazards assumption [met in Menke et al (2006)], is that the HR is constant through time. Based 

on this information, EPA derived an estimate for βBLL and used it to compute HRs for changes of different 

magnitudes. EPA calculated HR associated with a known PbB change (due to an ELG regulatory option) as:

Equation E-6. HRo = exp {βBLL ∙ ln [

BLLo

BLLb

]}

Hazard functions accounting for reduced CVD mortality following ELG implementation were then calculated 

as:

Equation E-7. hx

CVD,o = hxCVD,b

∙ HRo(eq. 7)

This equation provides key simulation element (3), above.

E.1.2 Estimating Premature Deaths Avoided Over Multiple Years

The value of a statistical life (VSL) is the marginal rate of substitution between wealth and mortality risk in a 

defined time period, usually taken to be one year. Therefore, the product of VSL and the estimated reduction 

in risk of premature death represents the affected population’s aggregate willingness to pay (WTP) to reduce 

its probability of death in one year. EPA estimated the benefits of multi-year mortality risk as the product of:

(1) The reduction in initial age-specific mortality rate (i.e., the proportion of people alive at exact age x, 

who will die before attaining exact age x + 1; commonly represented as qx) in year t; and
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix E: CVD Incidence and Mortality

September 29, 2015 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute E-3

(2) The number of individuals surviving to the beginning of year t. This value is calculated as the initial 

cohort population size in 2014 multiplied by the probability that these individuals survive to age x

[commonly represented as S(x)], and are alive at the beginning of year t to enjoy the benefits of the 

year’s mortality risk reduction. EPA used the survival probability under each ELG option (i.e., the 

survival probability reflecting cumulative reductions in mortality risk prior to year t) in each 

recursive step of this calculation.

Each pattern of annual mortality rates corresponds to a unique survival curve. Thus, each intervention-related 

change in mortality rates (over multiple years) will generate a unique change in the cohort’s survival curve. 

Figure E-1 illustrates such a change for an individual whose baseline initial mortality rate of 0.3 (i.e., a 30 

percent chance of death in one year) was reduced to 0.2 (i.e., a 20 percent chance of death in one year) by an 

intervention which begins in year 0 and continues indefinitely. The baseline survival curve is shown as a solid 

line, while the dashed line shows the effects of intervention.

Figure E-1: Illustration of a Hypothetical Policy Effect on a Survival Curve.

Each intervention-related change in survival curve can be summarized by either the gain in life expectancy 

(i.e., the area between the survival curves) or by the number of avoided premature deaths at each point in time 

(Hammitt 2007). The recursive calculation of the number of avoided premature deaths (described above) can 

be seen as a series of shifts of the survival curve. Each shift represents the incremental effect of a mortality 

risk reduction in year t (while keeping mortality rates in subsequent years at their baseline levels) on the 

survival curve. Therefore, these shifts are consistent with the cumulative impact of mortality risk reductions in 

years 0 to t − 1. Figure E-2 illustrates two such shifts: (1) the dashed survival curve reflects the impact of 

reduced mortality rate in year 0 on the baseline survival curve (i.e., the incremental impact of mortality risk 

reductions in year 0); (2) the dotted curve reflects the impact of reducing mortality rate in year 1 on the 

dashed survival curve (i.e., the incremental impact of mortality risk reductions in year 1). 

As noted earlier, the area between the survival curves represents the gain in life expectancy. Thus, each shift 

of the survival curve reflects an incremental gain in life expectancy (e.g., in Figure E-2, the area between 

solid and dashed lines represents the incremental gain in life expectancy because of reduced mortality in year 

0; the area between dashed and dotted lines represent incremental gain in life expectancy because of reduced 

mortality in year 1). By design, the sum of these incremental gains is equal to the original gain in life 

expectancy from a multi-year reduction. This confirms that EPA’s procedure generates an estimated reduction 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix E: CVD Incidence and Mortality

September 29, 2015 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute E-4

in the risk of premature death that is consistent with the estimated aggregate gain in life expectancy from the 

final ELGs. 

Figure E-2: A Recursive Illustration of a Policy Effect on Survival Rates.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix F: Human Health Sensitivity Analyses

September 29, 2015 F-1

Appendix F. Human Health Benefits Sensitivity Analysis

EPA conducted a series of sensitivity analyses to evaluate the impacts of varying assumptions related to a) the 

distance travelled by recreational and subsistence anglers, b) the form of the concentration-response function 

for lead, and c) the cancer slope function and cancer case valuation approach for the arsenic analysis. These 

analyses are summarized below.

F.1 Alternative Fishing Distance Assumption

The set of reaches that may represent a source of contaminated fish for recreational anglers and subsistence 

fishers in each CBG depends on the distance the typical angler travels to fish. In the human health benefits 

analysis (Chapter 3), EPA assumed that anglers typically travel up to 50 miles to fish, using this distance to 

estimate the relevant fishing sites for the population of anglers in each CBG. 

Viscusi et al. (2008) found that 78 percent of anglers live within 100 miles of their fishing destinations. EPA 

conducted a sensitivity analysis to assess the impact of the travel distance assumptions to the health analysis 

results. Using a different travel distance assumption tends to increase the number of alternate fishing sites 

visited by anglers within each CBG, but also increases the availability of substitute sites.

Table F-1 shows the affected population using this alternative fishing travel distance, and Table F-2 shows 

the results for the health benefit categories for which EPA conducted this sensitivity analysis.

Table F-1. Summary of Potentially Affected Population Living within 100 

Miles of Affected Reaches (baseline)

Total population 306,740,261

Total angler populationa42,714,703

Angler population potentially exposed to 

contaminated fishb 29,592,014

a. Total population living within 100 miles of an affected reach times the state-specific share of 

the population who fishes based on U.S. FWS (2011; between 9% and 23%).

b. Total angler population adjusted to reflect lower fishing/consumption rates for reaches with 

fish consumption advisories and catch-and-release practices. 

Table F-2. Total Monetized Human Health Benefits for ELG Options Using a 100-mile Buffer Zone

(millions of 2013$)a,b

Regulatory 

Option

Reduced lead 

exposure for 

children

Reduced 

lead 

exposure for 

adults

Reduced 

mercury 

exposure for 

children

Reduced 

cancer 

cases from 

arsenic

Total

Low High Low High Low High

3% Discount Rate

Option A $0.28 $0.40 $3.55 $1.37 $1.92 $0.00 $5.20 $5.87 

Option B $0.28 $0.40 $3.55 $1.39 $1.95 $0.00 $5.22 $5.90 

Option C $0.43 $0.60 $6.66 $2.36 $3.32 $0.00 $9.45 $10.58 

Option D $0.57 $0.80 $8.98 $2.93 $4.11 $0.00 $12.49 $13.90 

Option E $0.57 $0.80 $8.98 $3.30 $4.64 $0.00 $12.86 $14.42 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix F: Human Health Sensitivity Analyses

September 29, 2015 F-2

Table F-2. Total Monetized Human Health Benefits for ELG Options Using a 100-mile Buffer Zone

(millions of 2013$)a,b

Regulatory 

Option

Reduced lead 

exposure for 

children

Reduced 

lead 

exposure for 

adults

Reduced 

mercury 

exposure for 

children

Reduced 

cancer 

cases from 

arsenic

Total

Low High Low High Low High

7% Discount Rate

Option A $0.05 $0.07 $2.96 $0.22 $0.33 $0.00 $3.23 $3.36 

Option B $0.05 $0.07 $2.96 $0.23 $0.33 $0.00 $3.24 $3.37 

Option C $0.07 $0.11 $5.56 $0.38 $0.57 $0.00 $6.01 $6.24 

Option D $0.10 $0.14 $7.50 $0.48 $0.71 $0.00 $8.07 $8.35 

Option E $0.10 $0.14 $7.50 $0.54 $0.80 $0.00 $8.13 $8.44 

Source: U.S. EPA Analysis, 2015

a. “-“ indicates that a value was not estimated and “$0.00” indicates that annual benefits are less than $0.01 million.

b. Low bound assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following Schwartz, 1994); 

high bound assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings (following Salkever, 1995).

F.2 Log-Linear Concentration Response Function for IQ Impacts to Children from 

Lead Exposure

In the analysis of benefits to children from reduced lead intake via fish consumption, EPA used a linear 

concentration-response function to quantify the relationship between blood lead concentrations (PbB) and 

intelligence quotient (IQ). This linear function is based on a concentration-response function based on 

children with PbB below 7.5 g/dL since the average PbB among affected children is approximately 2.7 

g/dL (see Section 3.3). EPA received several comments stating that this approach was inappropriate and 

resulted in an overestimate of the benefits. 

Given the uncertainty surrounding the lead concentration response relationship in children, EPA conducted a 

sensitivity analysis applying a log-linear function used by EPA (2008b) in the RIA for the Lead National 

Ambient Air Quality Standards (NAAQS). This function uses a log-linear function for PbB above 1.47 μg/dL 

and a linear slope for PbB levels below that cut-point, as shown in Equation F-8 and Equation F-9, 

respectively.

Equation F-8. IQ Loss = β1 × ln (

PbB

cutpoint) + β2 × cutpoint

Equation F-9. IQ Loss = β2 × cutpoint

Where:

cutpoint = 1.47 g/dL

β1 = -3.04 (log-linear regression coefficient)

β1 = -2.1 (linear regression coefficient)

Table F-3 shows the results of the analysis of avoided IQ point losses among children from exposure to lead 

using this alternative concentration-response function (corresponding to Table 3-4 in Section 3.3).
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix F: Human Health Sensitivity Analyses

September 29, 2015 F-3

Table F-3. Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead using LogLinear Concentration Response Function (2013$)

Regulatory 

Option

Average Annual 

Number of 

Affected

Children 0 to 7

Total Avoided 

IQ Losses, 

2021 to 2042

Annualized Value of Avoided IQ Point Lossesa(Millions)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Option A 3,326,127 324 $0.13 $0.18 $0.02 $0.03 

Option B 3,326,127 324 $0.13 $0.18 $0.02 $0.03 

Option C 3,326,127 486 $0.19 $0.27 $0.03 $0.05 

Option D 3,326,127 816 $0.32 $0.46 $0.05 $0.08 

Option E 3,326,127 816 $0.32 $0.46 $0.05 $0.08 

Source: U.S. EPA Analysis, 2015

a. Low bound assumes that the loss of one IQ point results in the loss of 1.76% of lifetime earnings (following Schwartz, 1994); 

high bound assumes that the loss of one IQ point results in the loss of 2.38% of lifetime earnings (following Salkever, 1995).

F.3 Alternative Cancer Slope Factor and Case Valuation for Arsenic Analysis

The Integrated Risk Information System (IRIS) reports a cancer slope factor (CSF) of 1.5 cases per mg/kg 

BW/day, which is based on incidences of skin cancer. EPA applied the 1.5 cases per mg/kg BW/day CSF to 

estimate the benefits shown in Section 3.6. EPA is currently revising its cancer assessment of arsenic to reflect 

new data on internal cancers including bladder and lung cancers associated with arsenic exposure via oral 

ingestion. The draft CSF is substantially higher – at 25.7 per mg/kg BW/day for women and 16.9 per mg/kg

BW/day for men (U.S. EPA, 2010b). 

EPA conducted a sensitivity analysis using the more sensitive CSF, together with monetizing the avoided 

cases using the value of a statistical life (VSL; $8.548 million), reflective of the higher mortality rates 

associated with internal cancers. Table F-4 shows the results of this sensitivity analysis (corresponding to 

Table 3-12 in Section 3.6).

Table F-4. Annual Benefits from Reduced Cancer Cases due to Arsenic Exposure, using Alternative 

CSF and VSL

Regulatory 

Option

Annual Affected

Population

Reduced Cancer Cases, 

2019 to 2042 

Benefits (Millions; 2013$)

3% Discount 7% Discount

Option A 35,972,005 0.55 $0.13 $0.07 

Option B 35,972,005 0.55 $0.13 $0.07 

Option C 35,972,005 1.05 $0.25 $0.14 

Option D 35,972,005 1.59 $0.38 $0.21 

Option E 35,972,005 1.72 $0.41 $0.23 

Source: U.S. EPA Analysis, 2015
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G-1

Appendix G. WQI Regional Subindices

This appendix provides the ecoregion-specific parameters used in estimating the TSS, TN, or TP water 

quality subindex, as follows:

 If [WQ Parameter] ≤ WQ Parameter 100 Subindex = 100

 If WQ Parameter 100 < [WQ Parameter] ≤ WQ Parameter 10 Subindex = a exp(b [WQ Parameter])

 If [WQ Parameter] > WQ Parameter 10 Subindex = 10

 Where [WQ Parameter] is the measured concentration of either TSS, TN, or TP and WQ Parameter 10, 

WQ Parameter 100, a, and b are specified in Table G-1 for TSS, Table G-2 for TN, and Table G-3 for TP.

Table G-1: TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS100 TSS10

10.1.2 Columbia Plateau 126.56 -0.0038 63 668 

10.1.3 Northern Basin and Range 112.42 -0.0007 160 3,457 

10.1.4 Wyoming Basin 123.36 -0.001 220 2,513 

10.1.5 Central Basin and Range 121.22 -0.0018 109 1,386 

10.1.6 Colorado Plateaus 144.44 -0.001 363 2,670 

10.1.7 Arizona/New Mexico Plateau 126.76 -0.0004 668 6,349 

10.1.8 Snake River Plain 146.39 -0.0027 142 994 

10.2.1 Mojave Basin and Range 119.34 -0.0015 121 1,653 

10.2.2 Sonoran Desert 112.39 -0.0002 567 12,097 

10.2.4 Chihuahuan Desert 214.39 -0.0005 1,419 6,130 

11.1.1 California Coastal Sage, Chaparral, and Oak Woodlands 127.97 -0.0012 205 2,124 

11.1.2 Central California Valley 171.86 -0.0044 122 646 

11.1.3 Southern and Baja California Pine-Oak Mountains 115.12 -0.0007 197 3,491 

12.1.1 Madrean Archipelago 261.35 -0.0005 2,053 6,527 

13.1.1 Arizona/New Mexico Mountains 120.98 -0.0004 477 6,233 

15.4.1 Southern Florida Coastal Plain 116.95 -0.0405 4 61 

5.2.1 Northern Lakes and Forests 157.76 -0.0233 20 118 

5.2.2 Northern Minnesota Wetlands 154.99 -0.0186 24 147 

5.3.1 Northern Appalachian and Atlantic Maritime Highlands 174.99 -0.0261 21 110 

5.3.3 North Central Appalachians 245.15 -0.0176 51 182 

6.2.10 Middle Rockies 144.64 -0.0038 98 703 

6.2.11 Klamath Mountains 238.9 -0.0068 129 467 

6.2.12 Sierra Nevada 185.36 -0.0116 53 252 

6.2.13 Wasatch and Uinta Mountains 124.28 -0.0014 160 1,800 

6.2.14 Southern Rockies 153.42 -0.0031 140 881 

6.2.15 Idaho Batholith 184.23 -0.0142 43 205 

6.2.3 Columbia Mountains/Northern Rockies 180.7 -0.0168 35 172 

6.2.4 Canadian Rockies 396.62 -0.0308 45 119 

6.2.5 North Cascades 240.95 -0.0193 46 165 

6.2.7 Cascades 192.94 -0.0181 36 164 

6.2.8 Eastern Cascades Slopes and Foothills 178.82 -0.0145 40 199 

6.2.9 Blue Mountains 148.35 -0.0037 107 729 

7.1.7 Strait of Georgia/Puget Lowland 181.06 -0.0224 27 129 

7.1.8 Coast Range 174.78 -0.0114 49 251 

7.1.9 Willamette Valley 210.3 -0.0114 65 267 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G-2

Table G-1: TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS100 TSS10

8.1.1 Eastern Great Lakes and Hudson Lowlands 144.62 -0.0104 36 257 

8.1.2 Lake Erie Lowland 112.79 -0.0049 25 494 

8.1.3 Northern Appalachian Plateau and Uplands 322.68 -0.0113 103 307 

8.1.4 North Central Hardwood Forests 148.68 -0.0108 37 250 

8.1.5 Driftless Area 117.97 -0.0012 141 2,057 

8.1.6 S. Michigan/N. Indiana Drift Plains 191.44 -0.0143 46 206 

8.1.7 Northeastern Coastal Zone 158.48 -0.0164 28 168 

8.1.8 Maine/New Brunswick Plains and Hills 156.02 -0.025 18 110 

8.1.10 Erie Drift Plain 133.08 -0.0037 78 700 

8.2.1 Southeastern Wisconsin Till Plains 121.34 -0.0042 46 594 

8.2.2 Huron/Erie Lake Plains 145.17 -0.0058 65 461 

8.2.3 Central Corn Belt Plains 187.95 -0.0033 191 889 

8.2.4 Eastern Corn Belt Plains 235.18 -0.003 282 1,053 

8.3.1 Northern Piedmont 175.82 -0.0042 135 683 

8.3.2 Interior River Valleys and Hills 149.68 -0.0013 303 2,081 

8.3.3 Interior Plateau 220.47 -0.0037 217 836 

8.3.4 Piedmont 224.11 -0.0048 169 648 

8.3.5 Southeastern Plains 205.3 -0.0085 85 356 

8.3.6 Mississippi Valley Loess Plains 492.49 -0.0048 333 812 

8.3.7 South Central Plains 184.36 -0.0045 136 648 

8.3.8 East Central Texas Plains 162.32 -0.0013 362 2,144 

8.4.1 Ridge and Valley 186.83 -0.0063 99 465 

8.4.2 Central Appalachians 166.76 -0.0062 82 454 

8.4.3 Western Allegheny Plateau 183.67 -0.0032 190 910 

8.4.4 Blue Ridge 216.16 -0.0087 89 353 

8.4.5 Ozark Highlands 175.16 -0.0018 317 1,591 

8.4.6 Boston Mountains 329.77 -0.0062 193 564 

8.4.7 Arkansas Valley 283.25 -0.004 261 836 

8.4.8 Ouachita Mountains 212.77 -0.0048 157 637 

8.4.9 Southwestern Appalachians 207.09 -0.0071 103 427 

8.5.1 Middle Atlantic Coastal Plain 182.17 -0.0178 34 163 

8.5.2 Mississippi Alluvial Plain 131.35 -0.0029 93 888 

8.5.3 Southern Coastal Plain 138.62 -0.0144 23 183 

8.5.4 Atlantic Coastal Pine Barrens 283.76 -0.0463 23 72 

9.2.1 Aspen Parkland/Northern Glaciated Plains 136.43 -0.0005 640 5,226 

9.2.2 Lake Manitoba and Lake Agassiz Plain 174.13 -0.0042 131 680 

9.2.3 Western Corn Belt Plains 135.01 -0.0009 347 2,892 

9.2.4 Central Irregular Plains 201.19 -0.001 673 3,002 

9.3.1 Northwestern Glaciated Plains 133.98 -0.0006 483 4,325 

9.3.3 Northwestern Great Plains 130.6 -0.0004 636 6,424 

9.3.4 Nebraska Sand Hills 289.85 -0.0066 162 510 

9.4.1 High Plains 125.61 -0.0005 507 5,061 

9.4.2 Central Great Plains 156.84 -0.0005 925 5,505 

9.4.3 Southwestern Tablelands 137.77 -0.0003 1,280 8,743 

9.4.4 Flint Hills 270.93 -0.0009 1,084 3,666 

9.4.5 Cross Timbers 134.97 -0.0006 523 4,337 

9.4.6 Edwards Plateau 173.77 -0.001 544 2,855 

9.4.7 Texas Blackland Prairies 134.23 -0.0005 624 5,194 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G-3

Table G-1: TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS100 TSS10

9.5.1 Western Gulf Coastal Plain 124.47 -0.0025 88 1,009 

9.6.1 Southern Texas Plains/Interior Plains and Hills with 

Xerophytic Shrub and Oak Forest

166.67 -0.0003 1,602 9,378 

Table G-2: TN Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TN100 TN 10

10.1.2 Columbia Plateau 116.58 -0.663 0.23 3.70

10.1.3 Northern Basin and Range 126.97 -0.626 0.38 4.06

10.1.4 Wyoming Basin 124.89 -0.445 0.50 5.67

10.1.5 Central Basin and Range 116.66 -0.335 0.46 7.33

10.1.6 Colorado Plateaus 146.41 -0.588 0.65 4.56

10.1.7 Arizona/New Mexico Plateau 116.33 -0.286 0.53 8.58

10.1.8 Snake River Plain 129.93 -0.594 0.44 4.32

10.2.1 Mojave Basin and Range 136.69 -0.593 0.53 4.41

10.2.2 Sonoran Desert 117.99 -0.495 0.33 4.99

10.2.4 Chihuahuan Desert 104.2 -0.45 0.09 5.21

11.1.1 California Coastal Sage, Chaparral, and Oak Woodlands 123.22 -0.889 0.23 2.82

11.1.2 Central California Valley 126.07 -0.548 0.42 4.62

11.1.3 Southern and Baja California Pine-Oak Mountains 122.76 -0.564 0.36 4.45

12.1.1 Madrean Archipelago 130.61 -0.325 0.82 7.91

13.1.1 Arizona/New Mexico Mountains 141.64 -0.541 0.64 4.90

15.4.1 Southern Florida Coastal Plain 1000000 -29.36 0.33 0.39

5.2.1 Northern Lakes and Forests 141.98 -0.985 0.36 2.69

5.2.2 Northern Minnesota Wetlands 142.55 -0.781 0.45 3.40

5.3.1 Northern Appalachian and Atlantic Maritime Highlands 142.6 -0.854 0.42 3.11

5.3.3 North Central Appalachians 180.92 -0.897 0.66 3.23

6.2.10 Middle Rockies 136.51 -0.991 0.31 2.64

6.2.11 Klamath Mountains 140.34 -1.805 0.19 1.46

6.2.12 Sierra Nevada 143.02 -1.424 0.25 1.87

6.2.13 Wasatch and Uinta Mountains 129.75 -0.452 0.58 5.67

6.2.14 Southern Rockies 131.07 -0.66 0.41 3.90

6.2.15 Idaho Batholith 149.42 -1.775 0.23 1.52

6.2.3 Columbia Mountains/Northern Rockies 136.14 -1.599 0.19 1.63

6.2.4 Canadian Rockies 151.95 -2.098 0.20 1.30

6.2.5 North Cascades 155.86 -1.231 0.36 2.23

6.2.7 Cascades 143.07 -1.473 0.24 1.81

6.2.8 Eastern Cascades Slopes and Foothills 123.99 -1.07 0.20 2.35

6.2.9 Blue Mountains 125.19 -0.786 0.29 3.22

7.1.7 Strait of Georgia/Puget Lowland 121.09 -0.723 0.26 3.45

7.1.8 Coast Range 136.15 -1.021 0.30 2.56

7.1.9 Willamette Valley 135.01 -0.809 0.37 3.22

8.1.1 Eastern Great Lakes and Hudson Lowlands 158.18 -0.563 0.81 4.90

8.1.2 Lake Erie Lowland 156.27 -0.38 1.18 7.23

8.1.3 Northern Appalachian Plateau and Uplands 431.78 -0.435 3.36 8.66

8.1.4 North Central Hardwood Forests 163.4 -0.599 0.82 4.66

8.1.5 Driftless Area 126.18 -0.272 0.85 9.32

8.1.6 S. Michigan/N. Indiana Drift Plains 130.25 -0.149 1.78 17.23
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G-4

Table G-2: TN Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TN100 TN 10

8.1.7 Northeastern Coastal Zone 125.75 -0.159 1.44 15.92

8.1.8 Maine/New Brunswick Plains and Hills 139.55 -0.553 0.60 4.77

8.1.10 Erie Drift Plain 148.99 -1.256 0.32 2.15

8.2.1 Southeastern Wisconsin Till Plains 134.85 -0.16 1.87 16.26

8.2.2 Huron/Erie Lake Plains 119.06 -0.091 1.91 27.22

8.2.3 Central Corn Belt Plains 135.57 -0.087 3.50 29.96

8.2.4 Eastern Corn Belt Plains 149.12 -0.122 3.28 22.15

8.3.1 Northern Piedmont 146.34 -0.314 1.21 8.55

8.3.2 Interior River Valleys and Hills 120.48 -0.131 1.43 19.00

8.3.3 Interior Plateau 146.39 -0.446 0.85 6.02

8.3.4 Piedmont 148.67 -0.637 0.62 4.24

8.3.5 Southeastern Plains 138.73 -0.727 0.45 3.62

8.3.6 Mississippi Valley Loess Plains 123.15 -0.379 0.55 6.62

8.3.7 South Central Plains 149.84 -0.706 0.57 3.83

8.3.8 East Central Texas Plains 136 -0.344 0.89 7.59

8.4.1 Ridge and Valley 158.11 -0.659 0.70 4.19

8.4.2 Central Appalachians 161.22 -0.907 0.53 3.07

8.4.3 Western Allegheny Plateau 125.25 -0.44 0.51 5.74

8.4.4 Blue Ridge 158.16 -0.777 0.59 3.55

8.4.5 Ozark Highlands 145.69 -0.513 0.73 5.22

8.4.6 Boston Mountains 168.59 -1.108 0.47 2.55

8.4.7 Arkansas Valley 135.4 -0.47 0.64 5.54

8.4.8 Ouachita Mountains 162.34 -0.942 0.51 2.96

8.4.9 Southwestern Appalachians 143.42 -0.645 0.56 4.13

8.5.1 Middle Atlantic Coastal Plain 123.43 -0.444 0.47 5.66

8.5.2 Mississippi Alluvial Plain 119.57 -0.31 0.58 8.00

8.5.3 Southern Coastal Plain 118.73 -0.701 0.24 3.53

8.5.4 Atlantic Coastal Pine Barrens 110.04 -0.482 0.20 4.98

9.2.1 Aspen Parkland/Northern Glaciated Plains 141.62 -0.086 4.06 30.82

9.2.2 Lake Manitoba and Lake Agassiz Plain 119.49 -0.082 2.18 30.25

9.2.3 Western Corn Belt Plains 129.28 -0.074 3.48 34.59

9.2.4 Central Irregular Plains 142.81 -0.184 1.93 14.45

9.3.1 Northwestern Glaciated Plains 120.91 -0.386 0.49 6.46

9.3.3 Northwestern Great Plains 125.65 -0.404 0.56 6.26

9.3.4 Nebraska Sand Hills 113.81 -0.324 0.40 7.51

9.4.1 High Plains 121.41 -0.161 1.21 15.51

9.4.2 Central Great Plains 129.36 -0.178 1.44 14.38

9.4.3 Southwestern Tablelands 136.03 -0.413 0.74 6.32

9.4.4 Flint Hills 142.74 -0.343 1.04 7.75

9.4.5 Cross Timbers 130.87 -0.278 0.97 9.25

9.4.6 Edwards Plateau 141.98 -0.588 0.60 4.51

9.4.7 Texas Blackland Prairies 133.84 -0.243 1.20 10.68

9.5.1 Western Gulf Coastal Plain 106.22 -0.301 0.20 7.85

9.6.1 Southern Texas Plains/Interior Plains and Hills with 

Xerophytic Shrub and Oak Forest 102.35 -0.374 0.06 6.22
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G-5

Table G-3: TP Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TP100 TP 10

10.1.2 Columbia Plateau 147.39 -2.211 0.18 1.22

10.1.3 Northern Basin and Range 165.9 -2.78 0.18 1.01

10.1.4 Wyoming Basin 143.83 -1.57 0.23 1.70

10.1.5 Central Basin and Range 167.24 -2.541 0.20 1.11

10.1.6 Colorado Plateaus 123.74 -0.784 0.27 3.21

10.1.7 Arizona/New Mexico Plateau 168.68 -3.39 0.15 0.83

10.1.8 Snake River Plain 140.75 -1.106 0.31 2.39

10.2.1 Mojave Basin and Range 139.89 -0.978 0.34 2.70

10.2.2 Sonoran Desert 122.92 -1.578 0.13 1.59

10.2.4 Chihuahuan Desert 132.89 -3.737 0.08 0.69

11.1.1 California Coastal Sage, Chaparral, and Oak Woodlands 125.05 -1.918 0.12 1.32

11.1.2 Central California Valley 126.32 -2.138 0.11 1.19

11.1.3 Southern and Baja California Pine-Oak Mountains 212.01 -0.941 0.80 3.25

12.1.1 Madrean Archipelago 140.62 -1.331 0.26 1.99

13.1.1 Arizona/New Mexico Mountains 555.88 -306 0.01 0.01

15.4.1 Southern Florida Coastal Plain 157.9 -26.64 0.02 0.10

5.2.1 Northern Lakes and Forests 152.78 -16.37 0.03 0.17

5.2.2 Northern Minnesota Wetlands 171.4 -21.87 0.02 0.13

5.3.1 Northern Appalachian and Atlantic Maritime Highlands 260.92 -21.53 0.04 0.15

5.3.3 North Central Appalachians 157.84 -6.439 0.07 0.43

6.2.10 Middle Rockies 188.95 -15.04 0.04 0.20

6.2.11 Klamath Mountains 205.2 -19.13 0.04 0.16

6.2.12 Sierra Nevada 142.56 -2.752 0.13 0.97

6.2.13 Wasatch and Uinta Mountains 141.72 -5.463 0.06 0.49

6.2.14 Southern Rockies 185.94 -21.89 0.03 0.13

6.2.15 Idaho Batholith 168.85 -17.88 0.03 0.16

6.2.3 Columbia Mountains/Northern Rockies 197.1 -27.87 0.02 0.11

6.2.4 Canadian Rockies 289.57 -47.06 0.02 0.07

6.2.5 North Cascades 227.85 -26.77 0.03 0.12

6.2.7 Cascades 154.67 -10.55 0.04 0.26

6.2.8 Eastern Cascades Slopes and Foothills 141.59 -3.31 0.11 0.80

6.2.9 Blue Mountains 165.33 -13.83 0.04 0.20

7.1.7 Strait of Georgia/Puget Lowland 185.34 -14.77 0.04 0.20

7.1.8 Coast Range 159.54 -9.053 0.05 0.31

7.1.9 Willamette Valley 148.02 -7.95 0.05 0.34

8.1.1 Eastern Great Lakes and Hudson Lowlands 230.09 -9.614 0.09 0.33

8.1.2 Lake Erie Lowland 3440.2 -8.887 0.40 0.66

8.1.3 Northern Appalachian Plateau and Uplands 317.21 -13.87 0.08 0.25

8.1.4 North Central Hardwood Forests 132.65 -4.905 0.06 0.53

8.1.5 Driftless Area 141.49 -2.261 0.15 1.17

8.1.6 S. Michigan/N. Indiana Drift Plains 184.34 -5.59 0.11 0.52

8.1.7 Northeastern Coastal Zone 174 -9.944 0.06 0.29

8.1.8 Maine/New Brunswick Plains and Hills 174.73 -28.94 0.02 0.10

8.1.10 Erie Drift Plain 151.79 -3.59 0.12 0.76

8.2.1 Southeastern Wisconsin Till Plains 141.21 -1.577 0.22 1.68

8.2.2 Huron/Erie Lake Plains 247.17 -2.666 0.34 1.20

8.2.3 Central Corn Belt Plains 223.41 -3.555 0.23 0.87

8.2.4 Eastern Corn Belt Plains 196 -3.734 0.18 0.80
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix G: Subindex Curves

September 29, 2015 G-6

Table G-3: TP Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TP100 TP 10

8.3.1 Northern Piedmont 160.97 -2.567 0.19 1.08

8.3.2 Interior River Valleys and Hills 156.71 -3.616 0.12 0.76

8.3.3 Interior Plateau 197.72 -5.623 0.12 0.53

8.3.4 Piedmont 223.39 -9.266 0.09 0.34

8.3.5 Southeastern Plains 177.2 -5.69 0.10 0.51

8.3.6 Mississippi Valley Loess Plains 168 -4.659 0.11 0.61

8.3.7 South Central Plains 166.39 -1.677 0.30 1.68

8.3.8 East Central Texas Plains 178.13 -6.407 0.09 0.45

8.4.1 Ridge and Valley 225.67 -16.59 0.05 0.19

8.4.2 Central Appalachians 187.73 -8.367 0.08 0.35

8.4.3 Western Allegheny Plateau 174.12 -10.5 0.05 0.27

8.4.4 Blue Ridge 152.7 -2.889 0.15 0.94

8.4.5 Ozark Highlands 204.88 -7.364 0.10 0.41

8.4.6 Boston Mountains 287.21 -5.786 0.18 0.58

8.4.7 Arkansas Valley 158.46 -6.821 0.07 0.41

8.4.8 Ouachita Mountains 169.72 -7.296 0.07 0.39

8.4.9 Southwestern Appalachians 153.95 -6.816 0.06 0.40

8.5.1 Middle Atlantic Coastal Plain 141.25 -3.807 0.09 0.70

8.5.2 Mississippi Alluvial Plain 144.74 -7.676 0.05 0.35

8.5.3 Southern Coastal Plain 126.84 -8.388 0.03 0.30

8.5.4 Atlantic Coastal Pine Barrens 156.13 -0.69 0.65 3.98

9.2.1 Aspen Parkland/Northern Glaciated Plains 132.19 -1.087 0.26 2.38

9.2.2 Lake Manitoba and Lake Agassiz Plain 197.19 -1.683 0.40 1.77

9.2.3 Western Corn Belt Plains 200.96 -1.994 0.35 1.50

9.2.4 Central Irregular Plains 134.12 -1.646 0.18 1.58

9.3.1 Northwestern Glaciated Plains 143.32 -1.267 0.28 2.10

9.3.3 Northwestern Great Plains 185 -3.788 0.16 0.77

9.3.4 Nebraska Sand Hills 153.07 -0.946 0.45 2.88

9.4.1 High Plains 188.57 -1.178 0.54 2.49

9.4.2 Central Great Plains 139.57 -0.972 0.34 2.71

9.4.3 Southwestern Tablelands 218.92 -2.351 0.33 1.31

9.4.4 Flint Hills 131.7 -0.78 0.35 3.31

9.4.5 Cross Timbers 159.98 -1.384 0.34 2.00

9.4.6 Edwards Plateau 149.63 -1.064 0.38 2.54

9.4.7 Texas Blackland Prairies 127.17 -1.863 0.13 1.36

9.5.1 Western Gulf Coastal Plain 104.21 -0.513 0.08 4.57

9.6.1 Southern Texas Plains/Interior Plains and Hills with 

Xerophytic Shrub and Oak Forest 147.39 -2.211 0.18 1.22
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix H: Meta-Regression Models

September 29, 2015 H-1

Appendix H. Development of Meta-Regression Models of Willingness to 

Pay for Water Quality Improvements

This meta-regression is a revised version of the 2009 meta-regression used in the benefit analysis of the 

proposed ELGs to estimate the water quality improvement benefits of the proposed rule (U.S. EPA, 2013a). 

EPA made a number of improvements to the meta-regression model, including updating the set of studies and 

introducing GIS explanatory variables into the analysis. In particular, the revised meta-model satisfies the 

adding-up condition, a theoretically desirable property.91 This condition ensures that if the model were used to 

estimate willingness to pay (WTP) for the cumulative water quality change resulting from a number of CWA 

regulations, the benefits estimates would be equal to the sum of benefits from using the model to estimate 

WTP for water quality changes separately for each rule. EPA used the revised meta-analysis to estimate the 

sum of use and non-use values for water quality improvements resulting from the final ELGs.

The following sections describe EPA’s literature review to identify additional studies, meta-data development 

and coding, model specification, regression results, and limitations and uncertainties associated with the 

model. 

H.1 Literature review to identify additional studies

EPA used the 2009 meta-data (U.S. EPA, 2013a) as the starting point for the current revisions and conducted 

a literature review for additional studies to include in the metadata. The Agency followed Stanley et al. 

(2013)’s guidelines for meta-regression analyses in economics in documenting the literature search including 

(a) the exact databases and other sources searched, (b) the precise combination of keywords, and (c) date 

completed. To identify new studies, EPA relied on the following:

 Searches of general literature databases and search engines (EBSCO, Google Scholar, Google). 

Search keywords were selected to be sufficiently broad so not to miss relevant studies:

‒ First terms: (1) water quality, (2) water clarity, (3) nutrient removal/improvement, (4) water 

quality index/ladder, (5) clean water, (6) water pollution reduction, (7) water habitat 

improvement, and (8) stream flow.

‒ Second terms: (1) willingness to pay, (2) stated preference, (3) contingent valuation, (4) 

choice experiments, (5) contingent activity, and (6) conjoint analysis

 Searches of online reference and abstract databases (Environmental Valuation Resource Inventory 

(EVRI), Benefits Use Valuation Database (BUVD), AgEcon Search, RePEc/IDEAs, and the Oregon 

State University College of Forestry Recreation Use Values Database);

 Visits to webpages of authors and university programs known to publish stated preference studies 

and/or water quality valuation research;92

 

91 If WQI0 < WQI1 < WQI2, then for a WTP function WTP (WQI0, WQI2, Y0) to satisfy the adding-up property, 

it must meet the condition that WTP(WQI0, WQI1 , Y0) + WTP(WQI1, WQI2 , Y0 - WTP(WQI0, WQI1, Y0)) 

= WTP( WQI0, WQI2 , Y0) for all possible values of baseline water quality (WQI0), potential future water 

quality levels (WQI1 and WQI2), and baseline income (Y0).

92 This included G. Poe (Cornell), J. Bergstrom (University of Georgia), T. Haab (Ohio State), K. Viscusi 

(Vanderbilt), R. Carson (U.C. San Diego), W. H. Desvousges, J. Whitehead (Appalachian State), K. Boyle 

(Virginia Tech), R. Rosenberger (Oregon State), J. Loomis (Colorado State), J. Corrigan (Kenyon College), and 

R.H. von Haefen (North Carolina State). 
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September 29, 2015 H-2

 Searches of web sites for Resources for the Future and EPA’s National Center for Environmental 

Economics, both known to conduct environmental and resource economics valuation research;

 Searches of key resource economics journals for 2005 to 2013 (Land Economics, Environmental and 

Resource Economics, Marine Resource Economics, Journal of Environmental Economics and 

Management, Water Resources Research, and Ecological Economics). EPA focused these journalspecific searches on the recent period of 2005 to 2013 given the likelihood that older studies were 

captured under the steps above or captured during literature reviews conducted for prior versions of 

the MRM; and

 Review of bibliographies of other valuation meta-analyses (Van Houtven et al. 2007; Ge et al. 2013; 

Brander and Brouwer 2011).

Studies identified during the search were screened according to the following criteria prior to inclusion in the 

metadata to ensure validity, consistency, and applicability. The identification and review of studies was 

completed and verified by multiple individuals following recommendations of Stanley et al. (2013):

 Commodity consistency – Study must value water quality changes affecting ecosystem services 

provided by waterbodies, including recreational activities (such as fishing, boating, and swimming), 

aquatic life support and other nonuse values.93 EPA did not include studies that estimate WTP for 

improvements in surface waters used primarily for drinking water.

 Welfare consistency – The study must use general accepted stated preference approaches and report 

theoretically comparable Hicksian welfare measures (Boyle et al. 2013). 

 Amenity detail – As described by Johnston et al. (2005), “the study must provide sufficient 

information regarding resource, context, and study attributes to warrant inclusion in the meta-data” 

(p.223). 

 Study location – The study must be conducted in the U.S.

 Research methods – The study must apply research methods that are supported by the literature and 

that provide WTP estimates consistent with neoclassical welfare theory.

 Duplicative studies – Some studies may be released in multiple forms, such as working papers, 

conference papers, journal articles, and book chapters. We included only the latest version of the 

study (e.g., journal article) when there are multiple versions. Studies are screened to remove 

duplicative analysis and analysis that were subsequently revised and published. Multiple values can 

be included for a study if they address a different component of the study data or estimate values for 

alternative resource changes.

Table H-1 summarizes studies in the revised metadata after identifying and screening additional studies. In 

total, the revised metadata includes 51 stated preference studies that estimated total WTP (use plus nonuse) 

per household for water quality changes in U.S. waterbodies. The studies address various waterbody types 

including, rivers, lakes, salt ponds/marshes, and estuaries. The fifteen studies added to the metadata during the 

current revisions are shaded in Table H-1. Two of the new papers (Corrigan et al. 2009; Whitehead 2006) 

replaced unpublished versions of the same studies (Azevedo et al. 2001; Whitehead et al. 2002). The revised 

metadata excludes two studies by Viscusi et al. (2008) and Carson and Mitchell (1993). Carson and Mitchell 

(1993) estimate WTP for water quality improvements nation-wide. It is excluded because its national scale 

 

93 For example, a study that estimates WTP for recreational fishing improvements due to fish stocking would not 

be included in the meta-data. However, a study that estimates WTP for recreational fishing improvements due 

to nutrient reductions would be included in the meta-data, presuming it satisfies all other criteria.
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September 29, 2015 H-3

means that the size of the affected resource area is substantially different from other studies in the meta-data, 

which at most, assess improvements within a region. On the other hand, Viscusi et al. (2008) asked 

respondents in a national survey to value water quality improvements within 100 miles of their home. To be 

included in the metadata, a WTP observation must correspond to a specific affected resource or set of 

resources that can be delineated in GIS. Viscusi et al. (2008) does not fit into EPA’s data coding framework 

because the affected resource varies across the sample, with each respondent asked to think about their own 

area. The WTP estimates reported by Viscusi et al. (2008) reflect an average across many affected areas, 

rather than WTP for a specific affected resource. Many studies contribute multiple observations due to instudy variations in factors such as number or type affected waterbodies, magnitude of water quality 

improvement, sampled market area, and elicitation methods. The inclusion of multiple observations per study 

is standard in resource valuation metadata (Nelson and Kennedy 2009). In total, the 51 studies provide 140 

WTP observations. 

Table H-1: Primary Studies in the MetadataA,B,C,D

Author(s) and 

Publication Year

Obs. in 

Metadata State(s) Water Body Type(s)

Willingness to Pay per Household

(2007$)

Mean Min. Max.

Aiken (1985) 1 CO river and lake 193.18 193.18 193.18

Anderson and 

Edwards (1986)

1 RI salt pond/marshes 180.71 180.71 180.71

Banzhaf et al. (2006) 2 NY Lake 57.47 54.09 60.85

Banzhaf et al. (2011) 1 VA, WV, 

TN, NC, GA

river/stream 31.30 31.30 31.30

Bockstael et al (1988) 1 DC, MD, VA Estuary 149.03 149.03 149.03

Bockstael et al. (1989) 2 MD Estuary 158.30 75.67 240.93

Borisova et al. (2008) 3 WV, VA river/stream 44.94 18.05 65.82

Cameron and Huppert 

(1989)

1 CA Estuary 49.53 49.53 49.53

Carson et al. (1994) 2 CA estuary 59.40 41.21 77.59

Clonts and Malone 

(1990)

3 AL river/stream 103.20 78.31 127.48

Collins and 

Rosenberger (2007)

1 WV river/stream 18.19 18.19 18.19

Collins et al. (2009) 7 WV river/stream 120.52 2.84 217.57

Corrigan et al. (2009) 1 IA Lake 123.30 123.30 123.30

Croke et al. (1987) 9 IL river/stream 77.47 61.31 93.68

De Zoysa (1995) 1 OH river/stream 70.18 70.18 70.18

Desvousges et al. 

(1987)

12 PA river/stream 59.19 19.84 137.26

Downstream 

Strategies (2008)

2 PA river/stream 12.74 10.70 14.77

Farber and Griner 

(2000)

6 PA river/stream 76.16 16.58 148.59

Hayes et al. (1992) 2 RI estuary 397.44 390.68 404.19

Herriges and Shogren 

(1996)

2 IA Lake 134.55 61.71 207.40

Hite (2002) 2 MS river/stream 60.08 58.24 61.93

Huang et al. (1997) 2 NC estuary 258.65 255.01 262.29
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Table H-1: Primary Studies in the MetadataA,B,C,D

Author(s) and 

Publication Year

Obs. in 

Metadata State(s) Water Body Type(s)

Willingness to Pay per Household

(2007$)

Mean Min. Max.

Irvin et al. (2007) 4 OH all freshwater 21.67 19.65 23.23

Johnston et al. (1999) 1 RI river/stream 180.95 180.95 180.95

Kaoru (1993) 1 MA salt pond/marshes 218.61 218.61 218.61

Lant and Roberts 

(1990)

3 IA, IL river/stream 143.93 124.04 154.31

Lant and Tobin (1989) 9 IA, IL river/stream 55.63 40.58 67.64

Lichtkoppler and 

Blaine (1999)

1 OH river and lake 41.93 41.93 41.93

Lindsey (1994) 8 MD estuary 66.80 33.40 102.20

Lipton (2004) 1 MD estuary 63.98 63.98 63.98

Londoño Cadavid and 

Ando (2013)

2 IL river/stream 38.68 35.93 41.44

Loomis (1996) 1 WA river/stream 93.07 93.07 93.07

Lyke (1993) 2 WI river and lake 78.75 59.75 97.74

Matthews et al. (1999) 2 MN river/stream 21.73 18.14 25.32

Opaluch et al. (1998) 1 NY estuary 138.47 138.47 138.47

Roberts and Leitch 

(1997)

1 MN, SD Lake 8.35 8.35 8.35

Rowe et al. (1985) 1 CO river/stream 134.59 134.59 134.59

Sanders et al. (1990) 4 CO river/stream 160.69 81.01 210.04

Schulze et al. (1995) 2 MT river/stream 20.84 17.34 24.33

Shrestha and 

Alavalapati (2004)

2 FL river and lake 156.46 137.97 174.95

Stumborg et al. (2001) 2 WI Lake 84.29 66.73 101.86

Sutherland and Walsh 

(1985)

1 MT river and lake 146.03 146.03 146.03

Takatsuka (2004) 4 TN river/stream 286.88 181.90 391.85

Wattage (1993) 3 IA river/stream 53.89 40.24 74.59

Welle (1986) 6 MN Lake 167.28 109.60 238.42

Welle and Hodgson 

(2011)

3 MN Lake 145.10 10.59 285.06

Wey (1990) 2 RI salt pond/marshes 147.26 63.95 230.58

Whitehead and 

Groothuis (1992)

3 NC river/stream 41.01 31.90 53.16

Whitehead (2006) 3 NC river/stream 187.18 27.52 365.54
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Table H-1: Primary Studies in the MetadataA,B,C,D

Author(s) and 

Publication Year

Obs. in 

Metadata State(s) Water Body Type(s)

Willingness to Pay per Household

(2007$)

Mean Min. Max.

Whitehead et al. 

(1995)

2 NC estuary 95.44 78.29 112.59

Whittington et al. 

(1994)

1 TX estuary 194.72 194.72 194.72

Notes:

(A): Shading indicates studies added since the 2009 meta-analysis. 

(B): Journal publications by Corrigan et al. (2009) and Whitehead (2006) replaced staff papers authored by Azevedo et al. (2001) 

and Whitehead (2002), respectively.

(C): A study by Olsen et al. (1991) from the 2009 meta-analysis was excluded from the current metadata following robustness 

testing and additional review of study details.

(D): The revised metadata excludes studies by Viscusi et al. (2008) and Carson and Mitchell (1993) that surveyed households 

across the U.S.

H.2 Variable Development and Coding 

EPA entered values for all existing variables in the metadata for each new study identified during the 

literature review. Next, EPA developed and coded new variables (for all studies) that address gaps in the 2009 

metadata. The variables in the revised MRM fall into four general categories:

Study methodology and year variables characterize such features as the year in which a study was conducted,

payment vehicle and elicitation formats, WTP estimation method, and publication type. These variables are 

included to explain differences in WTP across studies but are not expected to vary across benefit transfer for 

different policy applications. 

Region and surveyed populations variables characterize such features as the US region in which the study was 

conducted, the average income of respondent households and the representation of users and nonusers within 

the survey sample.

Sampled market and affected resource variables characterize features such as the geospatial scale (or size) of 

affected water bodies, the size of the market area over which populations were sampled, as well as land cover 

and the quantity of substitute water bodies. 

Water quality (baseline and change) variables characterize baseline conditions and the extent of the water 

quality change.

Table H-2 presents and defines key variables from the metadata by category. Shading indicates that the 

variable was developed during these revisions. The development of new variables focused primarily on 

developing variables that capture geospatial factors including extent of sampled market, surveyed populations 

and affected resources.94 The 2009 MRM largely relied on binary variables to distinguish broad categories of 

affected resources (e.g., single vs multiple rivers, regional freshwater, etc.). Extensive GIS mapping is 

required in order to quantitatively incorporate new spatial factors using continuous variables because many 

primary studies give broad outlines but omit the detailed geospatial information regarding affected resources 

and sampled populations. This type of supplementation has been used by others to include various types of 

 

94 EPA also coded one new methodological variable, ce, to identify studies that used a choice experiment 

approach.
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information into valuation MRMs other than that necessary for the current metadata (e.g., Ghermandi et al. 

2010; Ghermandi and Nunes 2013). By linking to publicly available GIS data layers, EPA provides a

consistent basis for the construction and coding of the new, spatial variables. The following subsections 

describe both development and coding of water quality and spatial variables for surveyed populations, extent 

of sampled market, study site, and affected resources. In accordance with Stanley et al. (2013) guidelines, all 

variable coding was reviewed by multiple individuals and documented clearly in the metadata and data 

dictionary.

Annual WTP values are typically desired for policy analysis to support a comparison of annualized costs and 

benefits. The metadata includes total WTP as reported by the original study, adjusted to 2007$.95 For over 80 

percent of observations (114 studies), total WTP reflects annual payments in perpetuity (113 studies) and one 

observation reflects annual WTP for a 10-year payment period. For the remaining 26 observations, WTP 

reflects a short-term payment period (11 observations use a one-time lump sum payment, 1 observation uses a 

3-year payment period, and 14 observations use a 5-year payment period). EPA used the lump_sum binary 

variable to account for short-term payment periods (lump_sum=1). EPA also tested models with WTP values 

for short-term payment periods converted to perpetual streams but found that results were sensitive to 

discount rate assumptions and that these models did not provide any practical advantages for benefit transfer 

over models using the lump_sum variable.

Table H-2: Definition and Summary Statistics for Model VariablesA

Variable Definition Units Mean St. Dev.

Study Methodology and Year

Ce Binary variable indicating that the study is a choice 

experiment

Binary 

(Range: 0 or 1)

0.1071 0.3104

Thesis Binary variable indicating that the study is a thesis. Binary 

(Range: 0 or 1)

0.1143 0.3193

Lnyear Natural log of the year in which the study was 

conducted (i.e., data was collected), converted to an 

index by subtracting 1980.

Natural log of 

years (year 

ranges from 

1981 to 2011).

2.2127 0.9282

Volunt Binary variable indicating that WTP was estimated 

using a payment vehicle described as voluntary as 

opposed to, for example, property taxes. 

Binary 

(Range: 0 or 1)

0.0857 0.2809

outliers_trim Binary variable indicating that outlier bids were 

excluded when estimating WTP. 

Binary 

(Range: 0 or 1)

0.1929 0.3960

nonparam Binary variable indicating that WTP was estimated 

using non-parametric methods.

Binary 

(Range: 0 or 1)

0.4286 0.4966

non_reviewed Binary variable indicating that the study was not 

published in a peer-reviewed journal. 

Binary 

(Range: 0 or 1)

0.2357 0.4260

lump_sum Binary variable indicating that the study provided 

WTP as a one-time, lump sum or provided annual 

WTP values for a payment period of five years or 

less.. This variable enables the benefit transfer 

analyst to estimate annual WTP values by setting 

lump_sum=0. 

Binary 

(Range: 0 or 1)

0.1857 0.3903

 

95 EPA used $2007 because the a majority of observations were already converted to $2007 as part of the 2009 

meta-analysis used ELGs (U.S. EPA, 2013a). 
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Table H-2: Definition and Summary Statistics for Model VariablesA

Variable Definition Units Mean St. Dev.

wtp_median Binary variable indicating that the WTP measure 

from the study is the median.

Binary 

(Range: 0 or 1)

0.0714 0.2585

Region and Surveyed Populations

northeast Binary variable indicating that the survey included 

respondents from states within the Northeast U.S., 

defined as ME, NH, VT, MA, RI, CT, and NY. This 

is equivalent to EPA Region 1 plus NY or the 

Northeast USDA region. 

Binary 

(Range: 0 or 1)

0.0714 0.2585

Central Binary variable indicating that the survey included 

respondents from states within the Central U.S., 

defined as OH, MI, IN, IL, WI, MN, IA, MO, ND, 

SD, NE, KS, MT, WY, UT, and CO. This is 

equivalent to EPA regions 5, 7, and 8 or the 

Midwest and Mountain Plains USDA regions. 

Binary 

(Range: 0 or 1)

0.3643 0.4830

southB,C Binary variable indicating that the survey included 

respondents from states within the Southern U.S, 

defined as NC, SC, GA, FL, KY, TN, MS, AL, AR, 

LA, OK, TX, and NM. This is equivalent EPA 

regions 4 and 6, or the Southeast and Southwest 

USDA regions. 

Binary 

(Range: 0 or 1)

0.1571 0.3652

nonusers Binary variable indicating that the survey was 

implemented over a population of recreational 

nonusers (default category for this variable is a 

survey of any population that includes both users 

and nonusers). 

Binary 

(Range: 0 or 1)

0.0857 0.2809

lnincome Natural log of the median income (in 2007$) for the 

sample area of each study based on historical U.S. 

Census data. It was designed to provide a consistent 

income variable given differences in reporting of 

respondent income across studies in the metadata 

(i.e., mean vs. median). Also, some studies do not 

report respondent income. This variable was 

estimated for all studies in the metadata regardless 

of whether the study reported summary statistics for 

respondent income. 

Natural log of 

income 

(2007$) 

(sample area 

income ranges 

from $34,332 

to $78,444)

10.7453 0.1731

Sampled Market and Affected Resource

mult_bod Binary variable indicating that the survey addressed 

multiple waterbody types. The eight waterbody type 

categories are (1) river/stream, (2) lake, (3) all 

freshwater, (4) estuary, (5) wetlands, (6) river and 

lake, (7) salt pond/marshes, and (8) multiple 

(estuary and fresh water). Takes on a value of 1 if 

the study is coded as category (3), (6), or (8). 

Binary 

(Range: 0 or 1)

0.0786 0.2700

River Binary variable that takes on a value of 1 if the 

study affects a river, such that river length>0, and 

zero otherwise.

Binary 

(Range: 0 or 1)

0.6857 0.4659

swim_use Binary variable indicating that the affected use(s) 

stated in the study include swimming.

Binary 

(Range: 0 or 1)

0.2643 0.4425
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Table H-2: Definition and Summary Statistics for Model VariablesA

Variable Definition Units Mean St. Dev.

gamefish Binary variable indicating that the affected use 

stated in the study is gamefishing. 

Binary 

(Range: 0 or 1)

0.0571 0.2329

boat_use Binary variable indicating that the affected use(s) 

stated in the study includes boating.

Binary 

(Range: 0 or 1)

0.1143 0.3193

ln_ar_agr Natural log of the proportion of the affected 

resource area which is agricultural based on NLCD, 

reflecting the nature of development in the area 

surrounding the resource. The affected resource 

area is defined as all counties that intersect the 

affected resource(s). EPA also tested a variable for 

the fraction of lands that is developed land classes, 

but it did not improve model fit. 

Natural log of 

proportion 

(Range: 0 to 1)

-1.4329 0.9031

ln_ar_ratio A ratio of the sampled area, in km2, relative to the 

affected resource area. When not explicitly reported 

in the study, the affected resource area is measured 

as the total area of counties that intersect the 

affected resource(s), to create the variable 

ar_total_area. From here, ln_ar_ratio = 

log(sa_area / ar_total_area), where sa_area is the 

size of the sampled area in km2.

Natural log of 

ratio 

(km2/km2)

-1.1278 2.6067

sub_proportion The proportion of water bodies of the same 

hydrological type affected by the water quality 

change, within affected state(s). For rivers, this is 

measured as the length of the affected river reaches 

as a proportion of all reaches of the same order. For 

lakes and ponds, this is defined as the area of the 

affected water body as a proportion of all water 

bodies of the same National Hydrography Dataset 

classification. For bays and estuaries, this is defined 

as the shoreline length of the water body as a 

proportion of all analogous (e.g., coastal) shoreline 

lengths. To account for observations where multiple 

waterbody types are affected, the variable 

sub_proportion is defined as maximum of separate 

substitute proportions for rivers, lakes, and 

estuaries/bays. The affected resource appears in 

both the numerator and denominator when 

calculating sub_proportion.

Proportion 

(Range: 0 to 1) 

(km/km)

0.1880 0.2911

Water Quality Baseline and Change

lnquality_ch Natural log of the change in mean water quality 

(quality_ch), specified on the WQI (McClelland 

1974; Mitchell and Carson 1989). 

WQI units 2.9070 0.6039

Lnbase Natural log of baseline water quality, specified on 

the WQI (McClelland 1974; Mitchell and Carson 

1989).

WQI units 3.5889 0.6697
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Table H-2: Definition and Summary Statistics for Model VariablesA

Variable Definition Units Mean St. Dev.

Notes:

(A): Shading indicates that the variable was developed and coded during the current set of revisions.

(B): EPA merged studies from the Southeast and Southwest region into a single group because the metadata includes only one 

observation from the Southwest region, Whittington et al. (1994) which studied estuaries in Texas

(C): The USDA regions omitted from the regional binary variables are the Mid-Atlantic region (NJ, DE, MD, DC, PA, WV, and 

VA) and the Western region (WA, OR, ID, CA, NV, AZ, and AK).

H.2.1 Study Methodology and Year

As often found in meta-analyses within the valuation and benefit transfer literature (Navrud and Ready 2007), 

a variety of study and methodology effects can be shown to influence WTP for water quality improvements. 

The 2009 metadata included numerous variables characterizing factors including, but not limited to 

publication type, elicitation method, response rate, treatment of outliers, payment period, and study year. EPA 

coded values for these variables for the new primary studies following the variable definitions from the 2009 

MRM. The majority of the methodological variables in the metadata are binary, with 1 indicating that a given 

methodology was used in the primary study. Some others, such as study year, are continuous. Most of the 

methodological variables available in the metadata did not improve model fit and were not included in the 

revised MRM. Given that choice experiments are being used with increased frequency in the recent literature, 

EPA coded a new binary variable, ce, to flag observations that are based on a choice experiment framework. 

H.2.2 Region and Surveyed Populations

The 2009 metadata included variables describing the location of the study by state and region of the country 

although these were used in a fairly simplistic way in the meta-analysis. EPA expanded the metadata by 

delineating sampled area for each of the 140 WTP observations as GIS polygons. The sampled area is defined 

as the geographic area over which the primary survey was fielded (i.e., the population it is meant to be 

representative of). The Agency based its GIS delineation on the description of the study area from the study 

documentation and matched to polygon boundaries from publicly available GIS datasets. The sampled area 

can vary across observations from the same primary study if the WTP values are based on separate survey 

samples (this is true for 5 of 51).

The sampled areas are typically defined by either jurisdictional boundaries or watershed boundaries:

For sampled areas defined by jurisdictional boundaries (e.g., states, counties, or cities), EPA defined 

boundaries using the Census Topologically Integrated Geographic Encoding and Referencing (TIGER) state 

and county shapefiles for 1990, 2000, and 2010. By using the shapefile vintage that most closely corresponds 

to the year in which the study was fielded, EPA is able to accurately identify boundaries at the time of the 

study.

For sampled areas defined by watersheds, EPA approximated sampled area boundaries by matching to the 

U.S. Geologic Survey Hydrologic Unit Code (HUC) Watershed Boundary Dataset (WBD) to approximate the 

boundaries of the study area.96 The Agency first matched the watershed polygon in the HUC dataset based on 

 

96 The HUC WBD is a package of watershed shapefiles for the contiguous U.S. subdivided to seven levels of 

resolution. Each resolution level is assigned a specific number of HUC digits, with the number of digits 

increasing with higher resolutions. The lowest resolution is the HUC 2-digit, or HUC-2, watershed which divide 

the country into 21 distinct drainage regions. The highest classification is the HUC12 dataset, consisting of 
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September 29, 2015 H-10

the watershed name presented in the study. If the name could not be matched, EPA consulted the survey 

materials or maps included in the study to either locate the corresponding HUC watershed or use the materials 

to trace a watershed polygon manually. If maps are not provided, then EPA used any additional locational 

information presented in the study to identify a HUC watershed to serve as a proxy for the study watershed. 

This approach generally involved more interpretation than if the study defined the sampled area based on 

jurisdictional boundaries (as in previous bullet).97

Income is inconsistently reported across primary studies, with some reporting median income, others 

reporting mean income, and others not reporting any income statistics for the sample. After delineating the 

sample area for all studies, EPA calculated income statistics for the population within the sampled area using 

georeferenced historic U.S. Census income data to overcome reporting deficiencies and improve consistency 

across observations. Historical median income data is available for 1980, 1990, 2000, and 2010. Each 

sampled area was matched to the data vintage that is nearest to the year in which the study was fielded. EPA 

derived sampled area income as a population-weighted average of median income across counties that 

intersect the sampled are polygons. The population weights were adjusted based on the fraction of county area 

that is within the sample area. Resulting income estimates were adjusted to 2007$ using the Consumer Price 

Index (CPI).

The 2009 metadata included regional dummies for the study location based on the boundaries of U.S. 

Department of Agriculture (USDA) regions.98 The USDA regions were used as the starting point for regional 

assignments. The boundaries of the seven USDA regions are closely aligned with the ten EPA regions, EPA 

generated two new binary variables that combine some regions for which effects were found to be similar. 

Northeast identifies studies that sampled respondents in the Northeast U.S. (as far south as New York, central 

identifies studies that sampled in the Midwest or Mountain Plains regions of the U.S, and south identifies

those that sampled in the Southeast and Southwest U.S. (as far west as New Mexico).99 The state boundaries 

for each variable are listed in Table H-2. 

EPA also calculated the continuous variables sa_area, defined as the size of the area sample in square 

kilometers. Sa_area feeds into the index variable, ln_ar_ratio, described in detail in the following subsection. 

H.2.3 Reconciliation of Water Quality Baseline and Change

An important component metadata development is the reconciliation of variables across observations 

(Johnston et al. 2005; Smith and Pattanayak 2002; Smith et al. 2002; Van Houtven et al. 2007). Although the 

calculation and reconciliation of most independent variables requires little explanation, there are some 

variables for which additional detail is warranted. These include variables characterizing surface water quality 

and its measurement. To reconcile measures of water quality across studies, EPA adapted the prior approach 

 

approximately 98,000 watersheds. Additional information about the HUC watershed and delineation process 

can be found at http://water.usgs.gov/GIS/huc.html.

97 EPA encourages authors of future studies to describe the sampled area clearly, to avoid the need for judgment 

or interpretation when including studies in future meta-analyses.

98 Using ecoregion boundaries to define regional dummies is not feasible because it would result in a very small 

number of observations per region. Each USDA region includes several ecoregions and thus is better suited for 

grouping studies.

99 The default region corresponds to the Mid-Atlantic states (DE, MD, NJ, PA, VA, WV). It also includes the state 

of California because only two studies (3 observations) correspond to California and because similarly to the 

Mid-Atlantic states it is a coastal state. No meta-data observations correspond to other states in the Western 

U.S. (i..e, OR, WA, NV, ID, HI, AZ, AK). 
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of Johnston et al. (2005) and van Houtven et al. (2007), mapping water quality changes to the 100-point 

Water Quality Index (WQI).

A large number of the original studies in the metadata (10 studies and 31 percent of observations) include the 

10-point water quality ladder (WQL) measures as a native component of the original primary studies either 

using its numerical values (e.g., 2.5, 5.0, etc.) or descriptive levels (e.g., boatable, gamefishing).100 In other 

cases, EPA used descriptive information from studies to map baseline and post-improvement to points on the 

WQL. To calculate 100-point WQI values EPA multiplied the reported WQL values by ten. In most cases, 

descriptions correspond closely to levels on WQL, rendering mapping straightforward. EPA used the 

following guidelines for mapping:

WQL assignments are made based on consideration of information provided in the primary study 

documentation and, if available, the survey materials. The assignments are not supplemented by external 

water quality databases or reports. They should reflect the descriptions and metrics presented to respondents 

within the valuation scenarios.

Identify recreational uses (e.g., boating, fishing, and swimming) provided in the description of baseline and 

improved (or declined) conditions. These uses may be stated directly or embedded in the definition of water 

quality metrics (e.g., low, moderate, high) and can include additional descriptive information such as effects 

on sensitive aquatic species or indication of presence of or amount of specific pollutants.

Only consider those metrics that can be reasonably mapped to the WQL. Ancillary improvements from the 

improvement plans, such as shoreline trash pickup or terrestrial bird species, are not considered.

Exclude observations that describe an improvement in recreational use that is not tied to a water quality 

improvement (e.g., an increase in fish abundance not based on water quality improvement).

Start by assigning values corresponding to use thresholds defined on the WQL (e.g., 2.5, 4.5). Assign 

intermediate values when the changes occur within a specific use category or changes extend beyond the 

minimum for provision of use. EPA notes that the majority of assignments directly match values 

corresponding to WQL use thresholds. 

To the extent possible, assigned WQL values should reflect the “affected” portion of resource(s) described in 

the survey, that is, the portion of the resource that is subject to water quality changes under the valuation 

scenario.

In some cases, uses may be supported intermittently due to algae blooms, for example. If provided, use 

information regarding the frequency of service provision to calculate intermediate WQL values by weighting 

use threshold values.

For some types of environmental contamination, such as high acidity, the survey may state that the affected 

resource supports swimming but has degraded fisheries. It supports a higher use on the WQL (swimming), but 

not a lower use (gamefishing). . In these cases, we specify baseline WQL and WQL changes based on fishing 

conditions, the use that is actually improved under the valuation scenario (e.g., a baseline of 4.5 for rough 

fishing and a post-improvement value of 5.0 for game fishing).

For more detail on water quality description from the original studies and the assigned baseline and improved 

water quality conditions for each study see Appendix B in Peer Review Package for Meta-analysis of the 

Willingness-to-Pay for Water Quality Improvements (U.S. EPA, 2015e).

 

100 The WQL (Vaughan 1986) is expressed on a scale of 0 to 10 and can be mapped to the WQI by multiplying by 

10. Refer to Chapter 10 and Appendix G of the benefits analysis for the C&D Effluent Guidelines (U.S. EPA 

2009) for additional detail on the WQI and WQL.
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EPA did not identify any systematic variation in results associated with studies for which the WQI was a 

native component, versus those for which quality changes were mapped to the WQI. A binary variable 

indicating that the observation used the WQL (CHNG_WQL) was not individually significant and did not 

improve model fit.

H.2.4 Delineation of the Affected Resource and Substitutes

WTP for WQI changes is likely better explained in concert with geospatial factors, given the geographic 

heterogeneity in the metadata. In the context of water quality valuation, the geospatial scale of a water quality 

change reflects the area over which the environmental change occurs. EPA expects, all else being equal, that 

WTP for water quality improvements would be directly related to the size and number of affected 

waterbodies. EPA defines an affected resource as a waterbody that experience a water quality change under 

the valuation scenarios presented in the primary study. The number of affected waterbodies assessed in the 

primary studies varies widely across the metadata from single waterbodies to all waterbodies throughout a 

state or small region. The existing metadata identifies the geographic scale of the affected resources using 

four categories: (1) single waterbody, (2) multiple waterbodies, (3) a small region, or (4) a large region. Table 

H-3 summarizes the number and percent of observations in each of the four geographic scale categories. As 

part of the revisions, EPA delineated the affected waterbodies by matching waterbody descriptions and maps 

from the primary studies to publicly available georeferenced datasets. By delineating resources using GIS, 

EPA is able to incorporate detailed spatial and hydrological features of the waterbodies. As with sampled 

area, some primary studies provide separate WTP values for multiple affected resources or sets of affected 

resources. These are always treated as separate observations within the metadata. 

Table H-3: Summary of Scale of Resource Changes in the Meta-data.

Category Variable Description Observation 

Count

% of 

Observations

Single 

Waterbody Single Single, discrete waterbody such as a river or lake. 49 35.0%

Multiple 

Waterbodies Multi Multiple discrete waterbodies (e.g., 2 rivers) 13 9.3%

Small Region Sm_reg Small region, includes watersheds and 

state-wide analyses 77 55.0%

Large Region Lg_reg Large region, includes one estuary 

analysis that includes multiple states. 1 0.7%

Total - - 140 100.0%

The datasets used to map affected resources vary by affected waterbody type. For rivers and lakes, the 

Agency opted to match to the NHDPlus dataset over other publicly available georeferenced hydrologic 

datasets (e.g., E2RF1) because it is a comprehensive, national dataset of hydrologic features such as rivers, 

lakes, ponds and catchments, and contains tabular data that can be linked to these hydrologic features. For 

estuaries and coastlines, EPA used the National Oceanic and Atmospheric Administration (NOAA) Global 

Self-Consistent Hierarchical, High-Resolution Geography Database (GSHHD). EPA attempted to match the 

name of the affected resource to the datasets, and if a match could not be found, used narrative descriptions 

and maps from the primary study to manually select the affected waterbodies in ArcGIS. Once delineated, 

EPA calculated affected shoreline length for all waterbody types as proxy for the human/water interface, 

although some uses actually occur in open water. For rivers, shoreline length is double the reach length to 

reflect both shorelines. EPA also calculated the area of affected lakes.
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Observed mean WTP in a primary study is also related to the extent of market analyzed (Loomis 2000; 

Loomis and Rosenberger 2006). It is established in economic literature that mean WTP often declines as 

distance increases to an affected resource (i.e., distance decay) (Bateman et al. 2006; Jørgensen et al. 2013; 

Schaafsma et al. 2012). Because the scale of the sampled market area varies greatly across primary studies 

and past work has shown that such differences can have important implications for welfare estimates, the 

accuracy of benefit transfers depends on the ability to adjust for the distance between affected populations and 

the resource (Bateman et al. 2006; Johnston and Duke 2009; Johnston and Ramachandran 2013; Loomis and 

Rosenberger 2006). The size of the sampled area (sa_area) in the metadata varies greatly across the metadata 

mean of 49,065 mi2 with 5th and 95th percentiles of 13.3 and 104,094 mi2, respectively. Sa_area is not 

correlated with the size of the affected resource because various factors affect researcher’s choice of survey 

region including budget and resource constraints and study objectives. As a result, the metadata does not 

provide clear information regarding the extent of the market for the affected resources or distance effects. 

Modeling results indicated that model performance was enhanced, in terms of model fit, variable significance, 

and consistency with theoretical expectations, when resource size was specified as a function of sampled 

market area (sa_area). The ln_ar_ratio variable reflects the size of the sampled market relative to the size of 

the affected resource, defined as the (natural log of the) size of the sampled market area (sa_area) divided by 

the geographic area encompassing the affected waters in square kilometers (ar_total_area). Ar_total_area is 

calculated based on the area of counties that insect the affected resource. For example, if a study valued all 

freshwater in Iowa, then ar_total_area would equal the area of Iowa. On other hand, ar_total_area would 

equal the area of a single Iowa county if the affected resource falls entirely within the county boundary. The 

area ratio, ln_ar_ratio, is expected to have negative sign reflecting two effects. First, holding all else constant, 

stated preference surveys over larger market areas imply greater distances between individual households and 

affected waterbodies, and thus lower WTP per household. Secondly, improvements to more waters within the 

sampled area should be associated with greater WTP, ceteris paribus. 

About 55 percent of observations in the metadata estimated WTP for water quality changes in a single 

watershed, group of watersheds, or a state. The other 45 percent focused on a single waterbody or small set of 

waterbodies (e.g., one lake or 3 lakes). Specifying the affected resource area requires some assumptions if the 

affected resource is defined as a river or lake (e.g., EPA assumed that counties intersecting the affected water 

bodies represent the affected resource area). As noted above, EPA used intersecting counties to define all 

affected resources, including discrete rivers, as geographic areas while avoiding the arbitrary selection of a 

boundary distance.

Relative to the alternatives, ln_ar_ratio provides a more intuitive means to capture the scale of the affected 

resource occurring throughout an area, such as watershed or region which is likely to be the case in the 

context of national rulemakings. Model fit decreases when the affected area ratio is included in non-logged 

form and the variable (ar_ratio) is no longer individually significant. Logging the ratio is also intuitive 

because WTP is zero when the resource size is zero. EPA considered, but did not select, a variety of 

alternative specifications of the geographic scale and extent of market variables (e.g., shoreline length).

The availability and quality of substitutes in the surrounding geographic area are also expected to influence 

welfare estimates (Loomis and Rosenberger 2006; Schaafsma et al. 2012). All else equal, WTP should be 

negatively related to substitute availability. EPA developed a continuous variable, sub_proportion, defined as 

the ratio of affected waterbodies to available use and nonuse substitutes within the state. EPA expects that the 

relationship between distance and substitutes will differ for resource users and nonusers. For users, there is “a 

positive cost of access which depends on distance” (Hanley et al. 2003, p.300), therefore, sites that are farther 

away will generally be less attractive substitutes all other things being equal. On the other hand, “there seems 

no theoretical reason to expect such a [distance decay] trend to be seen within the responses of those who are 
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September 29, 2015 H-14

present non-users” (Bateman et al. 2006, p. 453). There is some evidence that a sense of spatial and cultural 

identity or “ownership” may be important for nonuse values for some environmental resources (Bateman et 

al. 2006; Hanley et al. 2003). Hanley et al. (2003) provides the example that “I may have stronger non-use 

values for Scottish wildlife sites if I am Scottish than for English wildlife sites” (p.300). In the U.S., this may 

be manifested as affiliation with one’s state of residence. Given these factors, EPA considers the state to be a 

reasonable basis for the calculating the substitute proportion. 

The substitute proportion variable (sub_proportion) represents a potential advance over less sophisticated 

binary variables traditionally used to represent the size or scope of affected resources. For studies restricted to 

rivers, the calculation of substitutes is restricted to rivers of the same stream order(s) as the affected resource 

to ensure comparability. For non-river inland waterbodies (e.g., lakes and ponds), substitute proportion is 

calculated based on area relative to area of lakes within the state(s).101 For estuaries and coasts, EPA 

calculated the denominator as all coastline miles in the state(s). EPA also tested models with separate 

substitute variables for each waterbody type, but model performance was not improved. 

By delineating an affected area, in addition to the waterbodies themselves, EPA is also able to analyze the 

development characteristics of areas bordering the waterbodies. In particular, EPA estimated the fraction of 

the land in the study area used for agriculture (ln_ar_agr). EPA used the National Land Cover Dataset 

(NLCD) to develop a land cover profile of the affected resource area. The NLCD is a high-resolution (30 

meter) spatial dataset of land cover across the contiguous U.S. and available for the years 1992, 2001 and 

2006. The NLCD has 16 or 21 categories of land cover depending on the year. For the purpose of consistency 

and simplicity, EPA defined the agricultural land as the sum of “cultivated cropland” and “pasture/hay”. The 

Agency used the change in agricultural land between NLCD vintages to interpolate values for the actual year 

of the study. The agricultural fraction is calculated as the area-weighted average of counties within the 

affected resource area. Areas dominated by agricultural land uses have particular characteristics which 

suggest that WTP for water quality improvements could be lower than other types of areas, ceteris paribus:

 First, unlike non-agricultural rural areas, heavily agricultural areas have generally been altered from 

their natural ecosystem conditions, and do not tend to be highly prized for water-based recreation (or 

characterized as pristine natural areas) – this would be expected to decrease WTP for improvements 

in agricultural areas compared to many other rural areas.

 Second, unlike more heavily populated suburban or urban areas, agricultural lands do not have the 

population base to support well-developed and used recreational areas. Improvements to water bodies 

in suburban areas, for example, are often highly valued because these areas support extensive 

recreational and other uses.

 Third, a greater proportion of the population in agricultural areas has employment linked to farm 

activities that may be associated directly or indirectly with water pollution. Those whose employment 

depends on farming activities may be hesitant to support programs to improve water quality (in stated 

preference surveys or otherwise), for fear that these policies may lead to greater restrictions on farms 

and farming activities.

For all of these reasons, one would expect areas dominated by agriculture to have lower WTP to improve 

water quality, again holding all else constant. This intuition is strongly supported by model results, as 

discussed below. EPA also developed and tested an analogous variable based on the fraction of land that is in 

developed land use categories but it not improve model fit. 

 

101 NHDPlus does not provide widths of river and streams. 
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H.3 Model Specification 

EPA tested a number of meta-regression models based on WTP estimates for improvements in water 

resources, derived from 51 original studies. However, only the marginal WTP model (“Model 1”) and its 

variant (“Model 2”) are used in the analysis of benefits of the final ELG because these models satisfy addingup conditions (Diamond 1996). Model specification for the marginal WTP model, results, and interpretation 

of the results are described in the following sections. EPA’s Peer Review Package for Meta-analysis of the 

Willingness-to-Pay for Water Quality Improvements (U.S. EPA, 2015e) and memorandum entitled 

Accounting for Scope in the MRM2 Meta-Regression Model (Abt Associates 2015) provide additional detail 

on model selection, testing and alternative specifications.102

H.3.1 Marginal Willingness to Pay (Model 1)

In the meta-regression model, marginal willingness-to-pay from observation i, MWTPi ≡

∂WTPi

∂Q

, is modeled 

as follows:

ln(MWTPi

) = α0 + α1Xi + f(Qi

; β) + εi(1)

In this equation, α0 is a constant, Xiis a vector of study and resource characteristics that act as demand 

shifters, Qiis the absolute water quality index level at which marginal willingness-to-pay is being evaluated, 

and εiis a zero-mean normally-distributed error term. The function f(⋅) expresses the core relationship 

between absolute water quality Qi and the log of marginal WTP, and β is a vector of parameters that define 

the functional form of f(⋅). If f(⋅) is assumed to be linear in Qi:

f(Qi; β) ≡ β ⋅ Qi(2)

then marginal WTP can be expressed as follows:

ln(MWTPi

) = α0 + α1Xi + β ⋅ Qi + εi

(3)

However, MWTPiis not observed directly in the meta-data, and instead must be calculated from total 

willingness-to-pay as part of the estimation routine. This would require a nonlinear least squares or maximum 

likelihood estimation approach. As a simpler alternative, EPA assumed that marginal WTP can be 

approximated by average WTP per unit of water quality:

MWTPi ≈

WTPi

ΔQi

(4)

with this approximation assumed to be valid at some point between Qi0 and Qi1, here approximated by the 

midpoint of the water quality change valued for that meta-data observation:

 

102 Note that Model 1 here is the model referred to as MRM2 in the Peer Review Package, while Model 2 here is 

the model referred to as MRM2-S in the Accounting for Scope memorandum.
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September 29, 2015 H-16

Qi =

Qi0+Qi1

2

(5)

Substituting Equations (4) and (5) into Equation (3) provides the version of the marginal WTP model that is 

ultimately used as a regression model (Model 1): 

ln (

WTPi

ΔQi

) = α0 + α1Xi + β ⋅ (

Qi0+Qi1

2

) + εi(6)

The simplest version of this regression uses an unweighted OLS approach to generate a vector of estimated 

parameter values (α̂0,α̂1, β̂, σ̂

2

), where σ̂

2

is the sum of squared residuals divided by N-K, where N is the 

number of observations and K is the number of parameters. Model 1 then substitutes these estimated values 

into Equation (3) above, to get an expression that can be used to predict the log of marginal WTP:

ln(MWTPi

) = α̂0 + α̂1Xi + β̂ ⋅ Qi + εi

(7)

Taking the exponent of both sides produces the following expression for marginal WTP evaluated for a 

particular set of study and resource characteristics X and a particular water quality index level Q:

MWTP = exp(α̂0 + α̂1X + σ̂

2

/2) ⋅ exp(β̂ ⋅ Q) (8)

Small changes in water quality (e.g., less than one unit) could be valued using an approximation that involves 

multiplying predicted MWTP from equation (8) by the amount of the water quality change. Larger water 

quality changes must account for the curvature of the marginal WTP function, and so must be calculated 

using the integral:

WTP = ∫ exp(α̂0 + α̂1X + σ̂

2

/2) ⋅ exp(β̂ ⋅ Q)

Q1

Q=Q0

dQ (9)

which is equal to:

WTP = exp(α̂0 + α̂1X + σ̂

2

/2) ⋅

exp(β̂⋅Q1)−exp(β̂⋅Q0)

β̂

(10)

Because 98 percent of reach miles affected by the final ELG would experience WQI changes that range 

between 0 and 1 EPA estimated WTP for water quality improvements by multiplying MWTP by the amount 

of water quality changes expected from the final ELG, as described in Chapter 4.

H.3.2 Marginal Willingness to Pay (Model 2)

A key feature of Model 1, as described in Equation (1) above, is that marginal WTP does not depend on the 

magnitude of the water quality change being valued. In other words, the model assumes that marginal WTP 

for the one-unit change from 49 to 50 on the water quality index is the same, regardless of whether survey 

respondents live in an area where baseline water quality is 35 or 49, and whether the expected total water 

quality change is 30 points or 2 points, respectively.
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The meta-data, however, do show a relationship between marginal WTP and the water quality change. This 

relationship could occur for any number of reasons, including the transformations associated with studies that 

did not use the WQI or WQL directly, or an omitted variable that is correlated with the water quality 

improvement. More specifically, the meta-data show that survey respondents express high marginal WTP for 

modest improvements in water quality, but then express much lower marginal willingness to pay for 

additional improvements. For more detail see a memorandum entitled Accounting for Scope in the MRM2 

Meta-Regression Model (Abt Associates 2015).

To address this relationship in the meta-data while satisfying the adding up condition, EPA developed a 

variant of the marginal WTP model that includes water quality change as a study methodological 

characteristic. Similar to other methodological parameters (e.g., the use of a choice experiment format, or a 

lump sum payment), the water quality change parameter could be set to any appropriate value when the metamodel is used for benefit transfer. Importantly, because water quality change would be treated as a 

methodological characteristic instead of as a scenario characteristic, the value assigned to the water quality 

change parameter need not correspond to the specific water quality change being valued in the benefit transfer 

application. For the same reason, the resulting model will still satisfy the adding-up property. 

The following equation presents an expanded version of marginal WTP that allows the water quality change 

ΔQito enter as a methodological characteristic (hereafter Model 2):

ln(MWTPi

) = α0 + α1Xi + g(ΔQi

; φ) + f(Qi; β) + εi(11)

In this equation, ΔQiis the magnitude of the water quality change for meta-data observation i, and φ is a 

vector of parameters that specify the functional form for the function g(⋅). For simplicity, EPA modeled the 

function g(⋅) as a linear function of the water quality change:

g(ΔQi; φ) ≡ φ ⋅ ΔQi(12)

Substituting Equations (4), (5), and (12) into Equation (11) gives the following regression model:

ln (

WTPi

ΔQi

) = α0 + α1Xi + φ ⋅ ΔQi + β ⋅ (

Qi0+Qi1

2

) + εi(13)

The estimated parameters from this equation can be used to generate an expression for both marginal WTP 

and total WTP:

MWTP = exp(α̂0 + α̂1X + φ ⋅ ΔQ + σ̂

2

/2) ⋅ exp(β̂ ⋅ Q) (14)

WTP = exp(α̂0 + α̂1X + φ ⋅ ΔQ + σ̂

2

/2) ⋅

exp(β̂⋅Q1)−exp(β̂⋅Q0)

β̂

(15)

Because ΔQ is treated as a study methodological characteristic, not as a characteristic of the benefit transfer 

water quality improvement scenario, the parameter ΔQ need not be set equal to Q1 − Q0 in equations (14) and 

(15).

H.4 Regression Results

Table H-4 presents regression results for Model 1 and Model 2. The models presented in Table H-4 were 

selected after the estimation of numerous preliminary models with different specifications and groups of 
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September 29, 2015 H-18

independent variables. The selection was based on both statistical fit and correspondence with theoretical 

expectations. EPA estimated the models using robust standard errors to account for study-level dependencies. 

Measures of fit for the illustrated models are good (R2= 0.641 and 0.773). These compare favorably to prior 

meta-analyses in the published literature.

The model performs well, with intuitive results for virtually all statistically significant variables. The model 

identifies numerous statistically significant coefficients for variables characterizing (1) study methodology, 

(2) region and surveyed populations, (3) extent of the market, study site, and affected resources, and 4) water 

quality. In total in Model 1, 15 out of 23 non-intercept coefficient estimates are statistically significant at 

p<0.05, and 12 of 23 are significant at p<0.01. The sub_proportion geospatial variable is significant at p<0.01 

with signs on coefficients that are consistent with expectations.

Given the trans-log functional form, these results for ln_ar_ratio imply diminishing marginal returns to scale 

(e.g., WTP per unit of water quality improvement declines as the scope of water quality change increases). 

Marginal WTP is lower among studies surveying only nonusers (nonusers), another expected result, because 

users may hold both use and nonuse values, while nonusers hold only nonuse values, by definition. 

Methodological variables also show expected patterns. For example, households are willing to pay a greater 

nominal amount in a one-shot payment (lump_sum) than they would be willing to pay in repeated annual 

payments.103 Marginal WTP also varies according to the type of uses potentially affected by the proposed 

changes (particularly boat_use). In some cases, these variables may be correlated with water quality 

condition. However, counterexamples exist: for example, waters may be swimmable but the study focuses on 

improvements in game fishing (e.g., Banzhaf et al., 2006). EPA found that model fit was better with 

swim_use, boat_use, and gamefish included.104 Thus, dropping these variables could result in omitted variable 

bias. 

As expected, sub_proportion has a positive coefficient, indicating that WTP increases as the affected resource 

constitutes a larger fraction of available substitutes. The coefficient for ln_ar_ratio is negative because the 

sampled area (sa_area) is in the numerator indicating that marginal WTP decreases as the sampled market 

area increases relative to the affected resource. This is consistent with the expectation that a larger sample 

area will include a greater proportion of respondents who are resource nonusers, who are less familiar with 

the resource, and/or who live at a greater distance from the affected waters.

A negative coefficient on the agricultural area variable (ln_ar_agr ) suggests that areas dominated by 

agriculture have lower WTP to improve water quality, holding all else constant. Because areas dominated by 

agriculture may be significantly different in terms of both resource and population characteristics, as 

discussed in the preceding section, this result is not surprising. EPA also notes that removing this variable 

causes substantial changes elsewhere in the model, a sign that removing this variable could cause non-trivial 

omitted variables bias.

105

The absolute water quality variable (Qavg2) shows that water quality has a larger effect on marginal WTP 

under Model 1, compared to Model 2. The coefficient is -0.017 under Model 1 and -0.004 under Model 2.

 

103 As discussed in Section 3, EPA also tested models with WTP values for short-term payment periods converted 

to perpetual streams but found that results were sensitive to discount rate assumptions and that these models did 

not provide any practical advantages for benefit transfer over models including the lump_sum variable.

104 Peer Review Package for Meta-analysis of the Willingness-to-Pay for Water Quality Improvements (U.S. EPA,

2015e)

105 EPA notes that some peer reviewers questioned inclusion of this variable in response to the peer review (U.S. 

EPA, 2015e). In this analysis, the Agency followed the econometric guidance suggesting that if the inclusion of 

a variable within a model is questionable, the default solution should be to include the variable (including an 

irrelevant variable only reduces efficiency; omitting a relevant variable creates bias).
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September 29, 2015 H-19

This pattern suggests that after accounting for the change in water quality, absolute water quality may have 

less of an effect on marginal WTP. However, EPA notes that a t-test would fail to reject the hypothesis that 

these coefficients are equal.

Model 2 also includes a water quality change variable: lnquality_ch. This variable is negative and highly 

significant. This pattern indicates that marginal WTP is decreasing in the size of the water quality change 

being valued. This is consistent with the patterns from the raw meta-data, as discussed in detail in a 

memorandum entitled Accounting for Scope in the MRM2 Meta-Regression Model (Abt Associates 2015).

Overall, a comparison of the R2values from the two models indicates Model 2 has greater explanatory power 

than Model 1. Model 1has a R2value of 0.641. In contrast, the R2value for Model 2 is 0.773. Given that 

Model 1 already has 23 variables and a constant, this substantial increase in R2from one additional variable 

suggests that log water quality change is an important variable to include in the model.

Table H-4: Regression Results 

Parameter

Model 1 Model 2

Parameter Estimate Robust Standard 

Error Parameter Estimate Robust Standard Error

ce 0.3772 0.3241 0.4233* 0.1991

thesis 0.8664** 0.2721 0.7735** 0.1828

lnyear -0.4115** 0.1308 -0.5000** 0.0911

volunt -1.3915** 0.2405 -1.1837** 0.2040

OUTLIER_BIDS -0.3673 0.2025 -0.2912* 0.1238

nonparam -0.4076** 0.1215 -0.3902** 0.1322

non_reviewed -0.7094** 0.2008 -0.8708** 0.1536

lump_sum 0.8427** 0.1895 0.7732** 0.1338

WTP_median -0.1612 0.3333 -0.1507 0.1800

northeast 1.1785** 0.3389 0.5932* 0.2366

central 0.5607* 0.2154 0.7262** 0.1616

south 1.4028** 0.2361 1.5625** 0.1698

nonusers -0.5858** 0.1466 -0.5403** 0.1133

lnincome1 0.3327 0.4756 0.9595** 0.3570

mult_bod -0.8273** 0.2124 -0.6300** 0.1797

river1 -0.0789 0.1748 -0.1738 0.1194

swim_use -0.2342 0.1781 -0.2697* 0.1223

gamefish 0.2331 0.3894 -0.0103 0.2454

boat_use -0.7251** 0.2389 -0.3204 0.1715

ln_ar_agr -0.2713* 0.1106 -0.4134** 0.0875

ln_ar_ratio1 -0.0340 0.0350 -0.0573 0.0292

sub_proportion 1.0983** 0.2738 0.6066** 0.2003

Qavg2 -0.0147* 0.0063 -0.0041 0.0050

lnquality_ch -0.7456** 0.0947

_cons -1.0388 5.3335 -6.1401 3.9505

Observations: 140 140

R

2

0.641 0.773
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September 29, 2015 H-20

Table H-4: Regression Results 

Parameter

Model 1 Model 2

Parameter Estimate Robust Standard 

Error Parameter Estimate Robust Standard Error

Weights Yes Yes

Random Effects: No No

Notes: * denotes p<.05; ** denotes p<.01. All standard errors are clustered by study.

Application of Model 2 to estimating benefits of improved water quality resulting from the final ELG requires 

selection of an appropriate value for the water quality change parameter. There are several potential 

hypotheses that could explain why survey respondents’ implied marginal WTP appears to depend on the 

overall magnitude of the change in water quality conditions.106 Overall, the meta-data do not provide any 

simple way of testing or ruling out these different possibilities. This makes choosing a single value for the 

water quality change parameter in Model 2 difficult. Therefore, EPA conducted a sensitivity analysis, using 

the range of water quality change scenarios found in the meta-dataset (e.g., water quality change equal to +5 

units and +50 units). This analysis characterizes the range of values that would be generated if EPA were to 

conduct a well-designed stated preference survey to elicit marginal WTP for the types of water quality 

improvements expected under the final ELG and future CWA regulations. Note that a water quality 

improvement of +5 is closer to the levels of water quality improvement in the benefits transfer application 

here.

H.5 Limitations and Uncertainty 

The validity and reliability of benefit transfer—including that based on meta-analysis—depends on a variety 

of factors. While benefit transfer can provide valid measures of use and nonuse benefits, tests of its 

performance have provided mixed results (e.g., Desvousges et al. 1998; Vandenberg et al. 2001; Smith et al. 

2002; Shrestha et al. 2007). Nonetheless, benefit transfers are increasingly applied as a core component of 

benefit cost analyses conducted by EPA and other government agencies (Bergstrom and De Civita 1999; 

Rosenberger and Phipps 2007). Moreover, Smith et al. (2002, p. 134) argue that “nearly all benefit cost 

analyses rely on benefit transfers, whether they acknowledge it or not.” Given the increasing [or as Smith et 

al. (2002) might argue, universal] use of benefit transfers, an increasing focus is on the empirical properties of 

applied transfer methods and models.

Although the statistical performance of the models is good, EPA notes several limitations applicable to both 

Model 1 and Model 2. These limitations stem largely from information available from the original studies, as 

well as degrees of freedom and statistical significance. An important factor in any benefit transfer is the 

ability of the study site or estimated valuation equation to approximate the resource and context under which 

benefit estimates are desired. As is common, the meta-analysis model presented here provides a close but not 

perfect match to the context in which values are desired. Although all of the studies used in the meta-analysis 

valued changes in water quality improvements, many studies did not specify the cause of water quality 

impairment in the baseline or focused on causes that are different from the pollutant of concern in the 

regulation (i.e., heavy metals and nutrients). Preliminary models, however, suggest no systematic patterns in 

WTP associated with such factors, at least in the present meta-data. 

 

106 Accounting for Scope in the MRM2 Meta-Regression Model (Abt Associates 2015).
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix H: Meta-Regression Models

September 29, 2015 H-21

Additional limitations relate to the paucity of demographic variables available for inclusion in the model. The 

only demographic variable incorporated in the analysis (lnincome1) was not statistically significant. Other 

demographic variables are unavailable. 

The estimated model produces statistically significant coefficients and allows estimation of WTP based on 

study and site characteristics. However, strictly speaking, model findings are relative to the specific case 

studies considered, and must be viewed within the context of the 140-observation data set, with all the 

appropriate caveats. Although this represents a fairly standard-to-large sample size for a meta-analysis in this 

context (the 51 studies in the analysis gather data from tens of thousands of respondents), it is relatively small 

relative to other statistical applications in resource and environmental economics. Model results are also 

subject to choices regarding functional form and statistical approach, although many of the primary model 

effects are robust to reasonable changes in functional form and/or statistical methods. The rationale for the 

specific functional form chosen here is detailed above.

As in all cases, results of the meta-analysis are dependent on the sample of studies available for the given 

resource change (Navrud and Ready 2007), and may be subject to various selection biases if the available 

literature does not provide a representative, unbiased perspective on welfare estimates associated with 

resource changes (Rosenberger and Johnston 2007). In this case, however, the Agency took various steps to 

ameliorate such potential biases, including the incorporation of both peer-reviewed and gray literature to 

avoid possible publication biases (Rosenberger and Johnston 2007), and the use of a comprehensive literature 

review in the attempt to avoid—as much as possible—other types of selection biases. 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I-1

Appendix I. Impacts of Steam Electric Pollutants on Aquatic Species

Table I-1: Common Coal-combustion Wastewater Pollutants (adapted from EPA, 2009d)

Compound Potential Environmental Concern

Arsenic Frequently observed in high concentrations in coal combustion wastewater; causes poisoning 

of the liver in fish and developmental abnormalities; is associated with an increased risk of 

cancer in humans in the liver and bladder.

BOD Can cause fish kills because of a lack of available oxygen; increases the toxicity of other 

pollutants, such as mercury. Has been associated with FGD wastewaters that use organic 

acids for enhanced SO2 removal in the scrubber.

Boron Frequently observed in high concentrations in coal combustion wastewater; leachate into 

groundwater has exceeded state drinking water standards; human exposure to high 

concentrations can cause nausea, vomiting, and diarrhea. Can be toxic to vegetation.

Cadmium Elevated levels are characteristic of coal combustion wastewater-impacted systems; 

organisms with elevated levels have exhibited tissue damage and organ abnormalities.

Chlorides Sometimes observed at high concentrations in coal combustion wastewater (dependent on 

FGD system practices); elevated levels observed in fish with liver and blood abnormalities.

Chromium Elevated levels have been observed in groundwater receiving coal combustion wastewater 

leachate; invertebrates with elevated levels require more energy to support their metabolism 

and therefore exhibit diminished growth.

Copper Coal combustion wastewater can contain high levels; invertebrates with elevated levels 

require more energy to support their metabolism and therefore exhibit diminished growth.

Iron Leachate from impoundments has caused elevated concentrations in nearby surface water; 

biota with elevated levels have exhibited sublethal effects including metabolic changes and 

abnormalities of the liver and kidneys.

Lead Concentrations in coal combustion wastewater are elevated initially, but lead settles out 

quickly; leachate has caused groundwater to exceed state drinking water standards. Human 

exposure to high concentrations of lead in drinking water can cause serious damage to the 

brain, kidneys, nervous system, and red blood cells.

Manganese Coal combustion wastewater leachate has caused elevated concentrations in nearby 

groundwater and surface water; biota with elevated levels have exhibited sublethal effects 

including metabolic changes and abnormalities of the liver and kidneys.

Mercury Biota with elevated levels have exhibited sublethal effects including metabolic changes and 

abnormalities of the liver and kidneys; can convert into methylmercury, increasing the 

potential for bioaccumulation; human exposure at levels above the MCL for relatively short 

periods of time can result in kidney damage.

Nitrogen Frequently observed at elevated levels in coal combustion wastewater; may cause 

eutrophication of aquatic environments.

pH Acidic conditions are often observed in coal combustion wastewater; acidic conditions may 

cause other coal combustion wastewater constituents to dissolve, increasing the fate and 

transport potential of pollutants and increasing the potential for bioaccumulation in aquatic 

organisms.

Phosphorus Frequently observed at elevated levels in coal combustion wastewater; may cause 

eutrophication of aquatic environments.

Selenium Frequently observed at high concentrations in coal combustion wastewater; readily 

bioaccumulates; elevated concentrations have caused fish kills and numerous sublethal 

effects (e.g., increased metabolic rates, decreased growth rates, reproductive failure) to 

aquatic and terrestrial organisms. Short term exposure at levels above the MCL can cause 

hair and fingernail changes; damage to the peripheral nervous system; fatigue and irritability 

in humans. Long term exposure can result in damage to the kidney, liver, and nervous and

circulatory systems.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I-2

Table I-1: Common Coal-combustion Wastewater Pollutants (adapted from EPA, 2009d)

Compound Potential Environmental Concern

Total Dissolved Solids High levels are frequently observed in coal combustion wastewater; elevated levels can be a 

stress on aquatic organisms with potential toxic effects; elevated levels can have impacts on 

agriculture & wetlands.

Zinc Frequently observed at elevated concentrations in coal combustion wastewater; biota with 

elevated levels have exhibited sublethal effects such as requiring more energy to support 

their metabolism and therefore exhibiting diminished growth, and abnormalities of the liver 

and kidneys.

Table I-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plants

Species Species Group Vulnerability

Acipenser brevirostrum Fishes High

Acipenser medirostris Fishes High

Acipenser oxyrinchus desotoi Fishes High

Acipenser oxyrinchus oxyrinchus Fishes High

Alasmidonta heterodon Clams High

Alasmidonta raveneliana Clams High

Amblema neislerii Clams High

Amblyopsis rosae Fishes High

Ambystoma bishopi Amphibians Moderate

Ambystoma cingulatum Amphibians Moderate

Ambystoma macrodactylum Amphibians Moderate

Ambystoma tigrinum Amphibians Moderate

Ammodramus savannarum floridanus Birds Low

Anguispira picta Snails Low

Antrobia culveri Snails High

Antrolana lira Crustaceans High

Aphelocoma coerulescens Birds Low

Athearnia anthonyi Snails High

Batrisodes texanus Insects Low

Batrisodes venyivi Insects Low

Boloria acrocnema Insects Low

Brachylagus idahoensis Mammals Low

Brachyramphus marmoratus Birds Moderate

Brychius hungerfordi Insects High

Bufo houstonensis Amphibians Moderate

Cambarus aculabrum Crustaceans High

Campeloma decampi Snails High

Campephilus principalis Birds Low

Canis lupus Mammals Low

Canis rufus Mammals Moderate

Charadrius melodus Birds Moderate

Cicindela dorsalis dorsalis Insects Moderate

Cicindela nevadica lincolniana Insects Moderate

Cicindela puritana Insects Moderate
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I-3

Table I-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plants

Species Species Group Vulnerability

Cicurina baronia Arachnids Low

Cicurina madla Arachnids Low

Cicurina venii Arachnids Low

Cicurina vespera Arachnids Low

Corynorhinus (=Plecotus) townsendii ingens Mammals Low

Corynorhinus (=Plecotus) townsendii virginianus Mammals Low

Cryptobranchus alleganiensis Amphibians High

Cyprinella caerulea Fishes High

Cyprogenia stegaria Clams High

Dendroica chrysoparia Birds Low

Dendroica kirtlandii Birds Low

Discus macclintocki Snails Low

Dromus dromas Clams High

Drymarchon corais couperi Reptiles Low

Elimia crenatella Snails High

Elliptio chipolaensis Clams High

Elliptio spinosa Clams High

Elliptio steinstansana Clams High

Elliptoideus sloatianus Clams High

Enhydra lutris nereis Mammals Moderate

Epioblasma brevidens Clams High

Epioblasma capsaeformis Clams High

Epioblasma florentina curtisii Clams High

Epioblasma florentina florentina Clams High

Epioblasma florentina walkeri Clams High

Epioblasma florentina walkeri (=E. walkeri) Clams High

Epioblasma metastriata Clams High

Epioblasma obliquata obliquata Clams High

Epioblasma obliquata perobliqua Clams High

Epioblasma othcaloogensis Clams High

Epioblasma torulosa rangiana Clams High

Epioblasma torulosa torulosa Clams High

Epioblasma turgidula Clams High

Etheostoma chermocki Fishes High

Etheostoma chienense Fishes High

Etheostoma etowahae Fishes High

Etheostoma fonticola Fishes High

Etheostoma nianguae Fishes High

Etheostoma nuchale Fishes High

Etheostoma phytophilum Fishes High

Etheostoma scotti Fishes High

Etheostoma sellare Fishes High

Eubalaena glacialis Mammals Low

Eurycea nana Amphibians High
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I-4

Table I-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plants

Species Species Group Vulnerability

Fusconaia cor Clams High

Fusconaia cuneolus Clams High

Gambusia georgei Fishes High

Gammarus acherondytes Crustaceans Moderate

Glaucomys sabrinus coloratus Mammals Low

Glaucomys sabrinus fuscus Mammals Low

Gopherus polyphemus Reptiles Low

Graptemys flavimaculata Reptiles High

Graptemys oculifera Reptiles High

Grus americana Birds Moderate

Grus canadensis pulla Birds Moderate

Hemistena lata Clams High

Heraclides aristodemus ponceanus Insects Low

Herpailurus (=Felis) yagouaroundi cacomitli Mammals Low

Hesperia leonardus montana Insects Low

Heterelmis comalensis Insects High

Juturnia kosteri Snails Low

Lampsilis abrupta Clams High

Lampsilis altilis Clams High

Lampsilis higginsii Clams High

Lampsilis perovalis Clams High

Lampsilis subangulata Clams High

Lampsilis virescens Clams High

Lasmigona decorata Clams High

Leopardus (=Felis) pardalis Mammals Low

Leopardus (=Felis) wiedii Mammals Low

Leptodea leptodon Clams High

Leptonycteris nivalis Mammals Low

Leptoxis ampla Snails High

Leptoxis foremani Snails High

Leptoxis plicata Snails High

Leptoxis taeniata Snails High

Lepyrium showalteri Snails High

Lioplax cyclostomaformis Snails High

Lycaeides melissa samuelis Insects Low

Lynx canadensis Mammals Low

Margaritifera hembeli Clams High

Medionidus acutissimus Clams High

Medionidus parvulus Clams High

Medionidus penicillatus Clams High

Medionidus simpsonianus Clams High

Mesodon clarki nantahala Snails Low

Mesodon magazinensis Snails Low

Microhexura montivaga Arachnids Low
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I-5

Table I-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plants

Species Species Group Vulnerability

Microtus pennsylvanicus dukecampbelli Mammals Moderate

Mustela nigripes Mammals Low

Mycteria americana Birds Moderate

Myotis grisescens Mammals Moderate

Neoleptoneta microps Arachnids Low

Neonympha mitchellii francisci Insects Low

Neonympha mitchellii mitchellii Insects Low

Neotoma floridana smalli Mammals Low

Nicrophorus americanus Insects Low

Notropis cahabae Fishes High

Noturus crypticus Fishes High

Noturus placidus Fishes High

Obovaria retusa Clams High

Odocoileus virginianus clavium Mammals Moderate

Odocoileus virginianus leucurus Mammals Moderate

Oncorhynchus clarkii stomias Clams High

Oncorhynchus clarkii stomias Fishes High

Oncorhynchus kisutch Fishes High

Orcinus orca Mammals Low

Orthalicus reses (not incl. nesodryas) Snails Low

Palaemonias alabamae Crustaceans Moderate

Palaemonias ganteri Crustaceans Moderate

Panthera onca Mammals Low

Pegias fabula Clams High

Percina antesella Fishes High

Percina aurolineata Fishes High

Percina rex Fishes High

Percina tanasi Fishes High

Peromyscus gossypinus allapaticola Mammals Low

Peromyscus polionotus ammobates Mammals Low

Peromyscus polionotus niveiventris Mammals Low

Peromyscus polionotus phasma Mammals Low

Phoebastria (=Diomedea) albatrus Birds Low

Phoxinus cumberlandensis Fishes High

Picoides borealis Birds Low

Plethobasus cicatricosus Clams High

Plethobasus cooperianus Clams High

Plethodon nettingi Amphibians Low

Pleurobema clava Clams High

Pleurobema collina Clams High

Pleurobema curtum Clams High

Pleurobema decisum Clams High

Pleurobema furvum Clams High

Pleurobema georgianum Clams High
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I-6

Table I-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plants

Species Species Group Vulnerability

Pleurobema hanleyianum Clams High

Pleurobema marshalli Clams High

Pleurobema perovatum Clams High

Pleurobema plenum Clams High

Pleurobema pyriforme Clams High

Pleurobema taitianum Clams High

Pleurocera foremani Snails High

Polyborus plancus audubonii Birds Low

Polygyriscus virginianus Snails Low

Potamilus capax Clams High

Potamilus inflatus Clams High

Pseudemys alabamensis Reptiles High

Ptychobranchus greenii Clams High

Ptychocheilus lucius Fishes High

Puma (=Felis) concolor coryi Mammals Low

Pyrgulopsis (=Marstonia) pachyta Snails High

Pyrgulopsis neomexicana Snails High

Pyrgulopsis ogmorhaphe Snails High

Pyrgulopsis roswellensis Snails Low

Quadrula fragosa Clams High

Quadrula intermedia Clams High

Quadrula sparsa Clams High

Quadrula stapes Clams High

Rangifer tarandus caribou Mammals Low

Rhadine exilis Insects Low

Rhadine infernalis Insects Low

Rhadine persephone Insects Low

Rhinichthys osculus thermalis Clams High

Salvelinus confluentus Fishes High

Scaphirhynchus albus Fishes High

Scaphirhynchus suttkusi Fishes High

Sciurus niger cinereus Mammals Low

Somatochlora hineana Insects High

Sterna antillarum Birds High

Sternotherus depressus Reptiles High

Strix occidentalis caurina Birds Low

Strix occidentalis lucida Birds Low

Stygobromus (=Stygonectes) pecki Crustaceans High

Stygoparnus comalensis Insects Low

Succinea chittenangoensis Snails Low

Sylvilagus palustris hefneri Mammals High

Tartarocreagris texana Arachnids Low

Texamaurops reddelli Insects Low

Texella cokendolpheri Arachnids Low
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix I: Impacts on Aquatic Species

September 29, 2015 I-7

Table I-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Power Plants

Species Species Group Vulnerability

Texella reddelli Arachnids Low

Texella reyesi Arachnids Low

Toxolasma cylindrellus Clams High

Trichechus manatus Mammals Low

Triodopsis platysayoides Snails Low

Tryonia alamosae Snails High

Tulotoma magnifica Snails High

Tympanuchus cupido attwateri Birds Low

Typhlomolge rathbuni Amphibians High

Ursus americanus luteolus Mammals Low

Vermivora bachmanii Birds Moderate

Villosa trabalis Clams High

Xyrauchen texanus Fishes High

Zapus hudsonius preblei Mammals Low
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-1

Appendix J. Supporting Data for Impoundment Release Analysis

Table J-1: Historical Releases from EPA Survey (U.S. EPA, 2012d)

Facility State Release 

Year

Unit 

Height 

(feet)

Storage 

Capacity 

(ac-ft)

Category Gallons 

Released

Capacity 

Factor Release Description

Meredosia Power 

Station IL 2006 24 700 Big 500 0% other 2006 - less than 500 gallons spilled from the Fly Ash Pond

Chesterfield Power 

Station VA 2005 19 740 Big U other 2005 unusual discharge

Cliffside Power 

Station NC 2005 38 Big U other

10/7/2005 the Cliffside Steam Station 

experienced a significant localized flood 

event. The floodwaters from the Suck 

Creek entered into the retired Units 1-4 

ash basin, topped the top of the dam and 

washed away part of the basin's dike. 

Notifications were

Bowen Power Station GA 2002 45 3676 Big U other 7/28/02 sink hole; 11 cubic yards of ash 

sediment reached creek.

Bowen Power Station GA 2008 45 3676 Big U other

9/9/08 Stack Failure. Approximately 40 

tons of ash left plant property and flowed 

to near by residential properties and 

approximately 2 tons of ash reached 

Euharlee Creek

Harllee Branch 

Power Station GA 2000 83 1050 Big 35000 0% other discharge of slurry 12/1/00

R. M. Schahfer 

Power Station IN 1998 4 48 Small U other no details March 1998

Sherburne County 

Power Station MN 2007 57 6198 Big 600 0% other 600 Gal spill May 2007

Sherburne County 

Power Station MN 2008 41 620 Big 8000 0% other

Spring 2008, the piping used to transmit 

the fine fraction of the bottom ash from 

hydraulic dredging of the bottom ash pond 

broke and approximately 8000 gallons of 

water and as was discharged over the

Bottom Ash Pond embankment to the gro
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-2

Table J-1: Historical Releases from EPA Survey (U.S. EPA, 2012d)

Facility State Release 

Year

Unit 

Height 

(feet)

Storage 

Capacity 

(ac-ft)

Category Gallons 

Released

Capacity 

Factor Release Description

Dave Johnston Power 

Station WY 2009 0 3 Small 14400 1% other Jan 2009 - 14,400 gallons of process water overflowed the canal

Naughton Power 

Station WY 56 3370 Big 9249802 1% other 55,000 cu. Yds of fly ash was spilled outside the pond boundries

PPL Martins Creek 

Power Station PA 2005 43 1085 Big 1E+08 28% other 2005 - Fly Ash spill (100 Million Gal) estimated 10 acres

Colstrip Steam 

Electric Station MT 2003 25 245 Big 2700 0% other 3/18/03 spill +/- 2700

Colstrip Steam 

Electric Station MT 1995 88 4370 Big 100 0% other 10/11/95 small spill +/- 100 gallons C Cell

Colstrip Steam 

Electric Station MT 2000 88 4370 Big 50 0% other 8/29/00 small spill +/- 50 gallons C Cell

Colstrip Steam 

Electric Station MT 2006 88 4370 Big 2000 0% other 2/1/06 hole in HDPE liner C Cell +/- 2000 gallons

Roxboro Power 

Station NC 2008 37.5 53 Big U wall breach 2008 Pond Breach

W. H. Weatherspoon 

Power Station NC 2001 28 1375 Big U other Breach of an internal dike 2001

Winyah Power 

Station SC 2008 30 1700 Big U other 2/14/08 Release of wastewater - seal failure

Johnsonville Power 

Station TN 2004 30 2702 Big U other

Reported release of small quantity of 

ceneospheres on 03/27/2004 when 

discharge structure was disturbed during 

maintenance.

Kingston Power 

Station TN 2003 50 8907 Big U other

11/07/2003 an ash release occurred to land 

from slough in the Dredge Cell 

embankment.

Kingston Power 

Station TN 2006 50 8907 Big U other

11/01/2006 an ash release occurred to land 

from slough in the Dredge Cell 

embankment.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-3

Table J-1: Historical Releases from EPA Survey (U.S. EPA, 2012d)

Facility State Release 

Year

Unit 

Height 

(feet)

Storage 

Capacity 

(ac-ft)

Category Gallons 

Released

Capacity 

Factor Release Description

Kingston Power 

Station TN 2008 50 8907 Big 1.1E+09 38% wall breach

A release into the Emory River occurred 

on 12/22/2008 from the Dredge Cell 

embankment failure. No reports found of 

releases from the Main Ash Pond or S

Widows Creek Power 

Station AL 2008 115 11709 Big U other Reported release of small quantity of ceneospheres 01/30/2008.

Widows Creek Power 

Station AL 2004 115 11709 Big U other

Reported release of small quantity of 

ceneosperes 12/10/2004 due to intense 

precipitation.

Widows Creek Power 

Station AL 2009 150 10961 Big U other An abandoned decant weir in Pond 2B failed on 01/09/2009

Eagle Valley 

Generating Station IN 2008 38 415 Big 30000000 22% wall breach 1 levee breache (1/30/2008)- State notified. 30 million gallons spill.

Eagle Valley 

Generating Station IN 2007 28 415 Big 30000000 22% wall breach 1 levee breache (2/14/2007)- State notified. 30 million gallons spill.

Limestone Electric 

Generating Station TX 2000 0 50 Small 500 0% other

May 19, 2000: Approximately 500 gallons 

of water was discharged resulting from a 

severe rainfall event. The pH of the 

discharge was 8.5 su, TSS was 88 mg/1 

and Selenium was <0.010 mg/1. The 

discharge ultimately made its way to Lynn 

Creek. Event was repor

LaCygne Generating 

Station KS 2007 45 9298 Big U other July 2007 Due to unusual rainfall events

LaCygne Generating 

Station KS 2007 45 9298 Big U other Sept 2007 Due to unusual rainfall events

LaCygne Generating 

Station KS 2009 45 9298 Big U other May 2009- Due to unusual rainfall events

Riverside Generating 

Station IA 2002 15 140 Big U other April 14, 2002- Caused by Mississippi River flooding, fixed using drilled grout
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-4

Table J-1: Historical Releases from EPA Survey (U.S. EPA, 2012d)

Facility State Release 

Year

Unit 

Height 

(feet)

Storage 

Capacity 

(ac-ft)

Category Gallons 

Released

Capacity 

Factor Release Description

Interstate Power & 

Light Co - M.L. 

Kapp Generating 

Station

IA 2009 10 2 Small U other

3/13/2009, IPL reported to the Iowa DNR 

an unpermitted release of water from this 

pond at a location which sealed the pond 

from a previous discharge channel. This 

water leakage was immediately repaired 

on 3/13/2009.

PSCo Comanche 

Station CO 2007 0 12 Small 2500 0% other April 9, 2007- between 2,000-3,000 gallons spilled from a broken pipe

PSCo Valmont 

Station CO 2008 0 16 Small 4204.455 0% other Feb 14, 2008 - 25 cu yd spill of bottom ash slurry

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

1 AL $491,976 2006 $596,000 Shelby County Train 

Derailment

soybean spill leading to damage of 

aquatic life (fish, mussels, and 

snails)

Included

2 AL

$1,000,000,000 2011 $1,081,352,000

Deepwater Horizon oil spill in Gulf of Mexico

Excluded Ocean

$600,000,000 2013 $600,000,000 Excluded Ocean

3 AK $644,017 1997 $1,257,000 M/V Kuroshima oil spill along coastline Excluded Ocean

4 AK $1,000,000,000 1989 $2,968,963,000 Exxon Valdez Exxon Valdez Excluded Ocean

5 AZ $7,000,000 2013* $7,000,000 ASARCO LLC three historic mining sites Excluded CERCLA

6 AZ $6,800,000 2012 $7,031,000 Freeport-McMoRan Corp 

Morenci Mine hazardous substance release Included

7 AR $2,000,000 1987 $6,898,000

Vertac Chemical 

Corporation Superfund 

Site

dioxin release from 

herbicide/pesticide plant Excluded CERCLA

8 CA $16,300,000 1990 $45,789,000 American Trader Oil 

Spill

biological injury (effects on fish 

and birds) from a 416,598 gallon 

crude oil spill

Excluded Ocean
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-5

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

9 CA $14,000,000 2001 $22,133,000 Cantara Loop/ Dunsmuir 

Chemical Spill

spill of 19,000 gallons of an 

herbicide which damaged habitat 

and fish

Included

10 CA $30,000,000 2005 $38,481,000 Montrose Chemical long-time accidental and 

purposeful discharges of DDT Excluded CERCLA

11 CA $32,300,000 2007 $37,469,000 Cosco Busan Oil Spill spill of 53,000 gallons of bunker 

fuel Excluded Ocean

12 CA $5,400,000 1986 $19,761,000 Apex Houston Oil Spill spill of 25,800 gallons of crude oil, 

killing 10,577 birds Excluded Ocean

13 CA $10,800,000 1988 $34,538,000 Shell/Martinez Oil Spill release of 400,000 gallons of crude 

oil into creek Included

14 CA $2,700,000 1991 $7,346,000 Exxon Mobil/Santa Clara 

River Oil Spill spill of 74,000 gallons of crude oil Included

15 CA $1,400,000 1992 $3,596,000 Avila I Oil Spill spill of 24,200 gallons of crude oil 

into Pacific Ocean Excluded Ocean

16 CA $1,400,000 1993 $3,419,000 McGrath Oil Spill

release of 87,150 gallons of crude 

oil into McGrath Lake and the 

Pacific Ocean

Excluded Ocean

17 CA $7,100,000 1994 $16,318,000 ARCO/Santa Clara River 

Oil Spill

release of 190,000 gallons of crude 

oil into 16 miles of river Included

18 CA $3,625,000 1996 $7,517,000 SS Cape Mohican Oil 

Spill

96,000 gallons of intermediate fuel 

oil released, with 40,000 gallons 

spilling into San Francisco Bay

Excluded Ocean bay

19 CA $4,820,000 1997 $9,405,000 M/V Kure/Humboldt Bay 

Oil Spill

bunker fuel oil spill killing birds 

and affecting saltmarsh, mudflats, 

kayaking, camping, surfing

Excluded Ocean bay

20 CA $1,900,000 1997 $3,707,000 Torch/Platform Irene Oil 

Spill

oil release into ocean, killing 700 

birds, affecting sandy and rocky 

shoreline habitat

Excluded Ocean

21 CA $3,900,000 1998 $7,208,000 T/V Command Oil Spill

3,000 gallons of intermediate 

bunker oil discharged, damaging 

birds, shoreline habitat, 

recreational beach use

Excluded Ocean
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-6

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

22 CA $6,710,000 1999 $11,661,000

M/V 

Stuvyesant/Humboldt Oil 

Spill

2,000 gallons of intermediate fuel 

oil spilled into Pacific Ocean, 

killing 2,405 birds, damaging 

coastal beaches, shrimp, fish, and 

beach use

Excluded Ocean

23 CA $358,000 2000 $584,000 East Walker River Oil 

Spill

3,600 gallons of fuel oil spilled, 

impacting 15 miles of river Included

24 CA $6,000,000 2000 $9,795,000 Avila II Oil 

Contamination

numerous pipeline leaks caused 

underground plume of oil products Excluded Groundwater only

25 CA $1,150,000 2004 $1,573,000 Kinder-Morgan/Suisun 

Marsh Oil Spill

spill of 70,000 gallons of diesel 

into marsh Excluded Ocean

26 CA $950,000 2013* $950,000

Searles Valley 

Minerals/Searles Lake 

(Trona)

Hypersaline industrial wastewater 

discharged into large ponds Included

27 CA $9,000,000 2013* $9,000,000 Guadalupe Oil Field 

Contamination

pipeline leaks causing 80 plumes 

of diluent Excluded Ocean

28 CA $9,000,000 2013* $9,000,000 Iron Mountain Mine 

CERCLA Site

acid mine drainage over many 

decades Excluded CERCLA

29 CA $6,750,000 2013* $6,750,000 New Almaden Mine 

CERCLA Site

release of mercury in Guadalupe 

River watershed and south San 

Francisco Bay

Excluded CERCLA

30 CA $2,850,000 2013* $2,850,000 Chevron/Castro

oil and mercury dischargers 

contaminating intertidal and subtidal mudflats

Included

31 CA $850,000 2009 $990,000 Dubai Star/San Francisco 

Bay Spill

400 gallons of intermediate fuel oil 

spilled, affecting shoreline, birds, 

human uses

Excluded Ocean

32 CA $22,700,000 2007 $26,333,000 S.S. Jacob Luckenbach 

Oil Spill

457,000 gallons of fuel released 

from a sunken ship, killing 51,569 

birds between 1990 to 2003 and 

sea otters

Excluded Ocean

33 CO $194,000,000 2009 $226,019,000 California Gulch Site superfund site with surface water 

and habitat remediation needs Excluded CERCLA
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-7

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

34 CO $27,500,000 1992 $70,639,000 Rocky Mountain Arsenal former weapons and chemicals 

manufacturing site Excluded CERCLA

35 CO $10,000,000 1994 $22,983,000 Rocky Flats

former nuclear weapons 

manufacturer with plutonium, 

uranium, volatile organic 

compounds, metals, radionuclide

materials, nitrates, and asbestos 

contamination

Excluded CERCLA

36 CT $15,000,000 1977 $120,787,000 Housatonic and 

Connecticut Rivers

PCB contamination from longtime 

discharges from a power plant Excluded

37 CT $2,700,000 1967 $52,632,000 Quinnipiac River Basin

settlement funds to replace lost 

drinking water source from a 

landfill

Excluded CERCLA

38 DE $27,500,000 2010 $30,881,000 Athos I discharge of 265,000 gallons into 

Delaware River and tributaries Included

39 FL $3,100,000 1999 $5,387,000 Tampa Bay Oil Spill

362,000 gallons of fuel oil and 

other petroleum products, 

damaging beaches of shellfish beds

Excluded Ocean

40 FL $1,200,000 2006* $1,455,000 Sapp Battery

metal contaminations in soil, 

surface water, groundwater, and 

wetlands from battery salvage

Excluded

41 FL $77,000 2013* $77,000 Whitehouse Oil Pits 

Superfund Site

assessment costs and restoration of 

a wetland Excluded CERCLA

42 FL $3,600,000 1997 $7,025,000 Alafia River

discharge of 50 million gallons of 

process water from a 

phosphogypsum stack breach

Included

43 GA $11,800,000 2006 $14,303,000 R.J. Schlumberger PCB contamination Excluded CERCLA

44 HI $5,800,000 2013 $5,800,000 M/V Cape Flattery

oil spill damaging 20 acres of coral 

reef, fish, algae, sea urchins, and 

other reef animals

Excluded Ocean

45 ID $498,500,000 2009 $580,775,000 Bunker Hill Mining 

Superfund Site

100 million tons of mining wastes 

in a river system; wildlife habitat 

damage, poisoning of birds and 

other wildlife

Excluded CERCLA
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-8

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

46 ID $60,000,000 1995 $131,504,000 Blackbird Mine 

Superfund Site

mining damage to surface water 

and wildlife (Chinook salmon) Excluded CERCLA

47 IL $1,843,000 2011 $1,993,000 Former Indian Refinery

damages from various 

contaminants to groundwater, 

surface water, soils, and adjacent 

properties

Included

48 IL $450,000 2007 $522,000 Saline Branch and Salt 

Fork River

2 fish kills from sudden ammonia 

releases Included

49 IL $263,000 2001 $416,000 Marathon Oil Company

numerous spills of crude oil and 

refined petroleum products 

impacting 29 counties

Included

50 IL $154,648 2001 $244,000 Vesuvius USA 

Corporation

spill of an industrial chemical into 

3 tributaries of the Embarras River Included

51 IL $105,000 2013* $105,000 Williams Pipeline 

Company

pipeline leak of 10,000 gallons of 

gasoline and diesel oil into 

tributary of Salt Creek

Included

52 IN $86,000,000 1998 $158,936,000 Grand Calumet

dredging after PCB, oil, benzene, 

cyanide, and heavy metal 

contamination from Stell 

manufacturer, plus restoration of 

habitat

Excluded CERCLA

53 IN $6,250,000 2000 $10,203,000 White River

excessive chemical discharges 

killing 4.6 million fish; note that 

the settlement was for $14 million, 

$6.25 million of which was for 

NRD

Included

54 IN $600,000 2000 $979,000 American Chemical 

Services

discharge of chemicals from 

storage drums Included

55 IN $31,309 1985 $121,000 I. Jones Recycling, Inc. hazardous waste release Included

56 IN $200,000 2009 $233,000 Lakeland Disposal 

Landfill

waste leakage into groundwater, 

surface water, and sediments Included

57 IN $597,000 1999 $1,037,000 Waste Inc. Landfill liquid waste drainage into aquifer, 

creek, and surrounding wetlands Included
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-9

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

58 KS $1,200,000 2008 $1,369,000 Cherokee County 

Superfund Site

runoff from zinc and lead mine 

tailings that entered local streams 

and contaminated groundwater

Excluded CERCLA

59 KY $2,500,000 2007 $2,900,000 Russellville Plant PCB pollution in groundwater, 

streams, and rivers Included

60 LA $750,000 2011 $811,000 Calcasieu Estuary and 

Bayou Verdine

release of hazardous substances to 

soil and water, impacting assorted 

benthos and other marine resources

Excluded CERCLA

61 ME $1,000,000 1996 $2,074,000 Julie N Oil Spill Oil spill; 130 acres of habitat 

enhancement/acquisition Included

62 ME $125,000 1994 $287,000 F. O'Connor Superfund 

Site

groundwater damage from PCBs 

and solvents Excluded CERCLA

63 ME $930,000 1997 $1,815,000 Maine Yankee

petroleum, solvents, and 

radiological materials from nuclear 

power plant

Included

64 ME $160,440 2013* $160,000 S.D. Warren Facility

waste from manufacture of coated 

paper which contaminated 

groundwater

Excluded Groundwater 

only

65 MD $2,700,000 2000 $4,408,000 PEPCO Spill

oil spill; restoration of wetlands, 

oyster beds, waterfowl nesting 

areas, and terrapin habitat

Included

66 MD $507,300 2007 $588,000 Spectron, Inc. Superfund 

Site

contamination and oil from site 

which migrated to Little Elk Creek Excluded CERCLA

67 MA $20,200,000 1992 $51,888,000 AVX/New Bedford 

Harbor

water column, sediments, shellfish, 

birds, anadromous fish, 

recreational fishing, beach usage

Excluded CERCLA

68 MA $1,353,000 1993 $3,304,000 Charles George Landfill

landfill pollution, gases, leachate, 

contamination, migratory birds, 

fish

Included

69 MA $157,000 1995 $344,000 PSC Resources groundwater and wetlands Included

70 MA $3,100,000 1998 $5,729,000 Nyanza/ Sudbury River

surface water (riverine habitat), 

wetlands, fisheries, other wildlife, 

recreational use

Included
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-10

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

71 MA $30,000 1999 $52,000 Hallmark/ Mystic River surface water (riverine habitat), 

recreational use Included

72 MA $19,700,000 2000 $32,159,000 General Electric/ 

Housatonic River

ground and surface water, nesting 

habitats, recreational fishing and 

boating, various aquatic organisms 

and birds

Included

73 MA $30,000 2003 $44,000 Sulfuric Acid Spill/ 

North River

Various aquatic resources, aquatic 

fish, amphibians, invertebrates, and 

plant species

Included

74 MA $500,000 2004 $684,000 Coal Tar Deposits/ CT 

River

various aquatic resources, 

endangered species Included

75 MA $142,000 2004 $194,000 Posavina Oil Spill/ 

Chelsea Creek

coastal land and habitat, salt water 

vegetation, migratory birds, fish Excluded Ocean

76 MA $1,300,000 2007 $1,508,000

Textron Systems 

Corporation/Mass 

Military Reservation 

Superfund Site

groundwater Excluded CERCLA

77 MA $312,500 2008 $357,000 Global/Irving Chelsea 

Creek Oil Spill

surface water, shoreline, wetlands, 

salt marsh Included

78 MA $747,000 2010 $839,000 Rubchinuk Landfill Site community use Included

79 MA $1,650,000 2010 $1,853,000 Sutton Brook Disposal 

Area Superfund Site

groundwater, biological resources 

and their habitats Excluded CERCLA

80 MA $1,094,000 2011 $1,183,000 Blackburn and Union

Privileges Superfund Site

groundwater, biological resources 

and their habitats Excluded CERCLA

81 MA $6,076,000 2011 $6,570,000 Bouchard B-120 

Buzzards Bay Oil Spill

aquatic and shoreline, ram island 

shoreline, recreation and shellfish, 

and piping plovers

Excluded Ocean bay

82 MA $875,000 2012 $905,000 GM Assembly Plant in 

Framingham

streambed, banks, and surrounding 

wetlands; birds, wildlife, and 

benthic macroinvertebrates

Included

83 MA $4,250,000 2012 $4,395,000

Pharmacia Corp./Bayer 

CropScience Superfund 

Site

wetland, river, and lake habitat; 

fish, turtles, amphibians, and 

migratory birds

Excluded CERCLA
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-11

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

84 MI $807,490 1989 $2,397,000 Verona Well Field groundwater contamination from 

leaking solvents Excluded Groundwater only

85 MI $26,200,000 1998 $48,420,000 Saginaw River and Bay PCB release into Saginaw River Included

86 MS $3,000,000 1999 $5,214,000 Genesis Pipeline Spill

crude oil spill; surface water, 

sediments, shoreline habitat, 

wildlife

Included

87 MO $49,000 2013* $49,000 Cominco/Halliburton seven lead and copper metal 

concentrate spill sites Included

88 MO $20,100,000 2009 $23,417,000 Newton County Mine 

Tailings Superfund Site

releases of cadmium, lead, and 

zinc to groundwater, surface water, 

sediments, aquatic and terrestrial 

plants and organisms, aquatic 

mammals, fish, aquatic and 

terrestrial invertebrates, and 

migratory birds

Excluded CERCLA

89 MO $41,200,000 2009 $48,000,000 Southeast Missouri Lead 

Mining District

four mine sites impacting surface 

water, geological resources, 

groundwater, and aquatic and 

terrestrial biota

Excluded CERCLA

90 MT

$138,000,000 1999 $239,823,000

Atlantic Richfield 

Company

decades of mining and mineral 

processing releasing hazardous 

substances

Included

$169,000,000 2008 $192,848,000 Included

$5,900,000 2009 $6,874,000 Included

91 MT $37,000,000 2008 $42,221,000 Mike Horse Dam

Dam failure in 1975 due to heavy 

rains. Contaminated tailings were 

washed into the Beartrap Creek 

and the Upper Blackfoot River

Included

92 MT $1,600,000 2012 $1,654,000 Silvertip Pipeline Oil 

Spill

63,000 gallons of oil spilled into 

Yellowstone River Included

93 NV $859,528 2013 $860,000 Rio Tinto Mine abandoned copper mine waste 

disposal in Mill Creek Included

94 NH $1,500,000 2005 $1,924,000 Coakley Landfille 

Superfund Site

contamination of wetlands with 

various pollutants; restoration of 

338 acres of degraded saltmarsh

Excluded CERCLA
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-12

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

95 NJ $20,000 2013* $20,000 In re Former OwensIllinois Closure Site contamination of groundwater Excluded Groundwater 

only

96 NJ $40,462,000 2013* $40,462,000 In re Phelps Dodge Site hazardous waste discharge into 

groundwater Excluded Groundwater only

97 NJ $150,000 2009 $175,000 In re Jimmie's Raceway 

Service Station

contamination of soil and 

groundwater from gas station Excluded Groundwater only

98 NJ $3,218,700 2009 $3,750,000 Combe Fill South 

Landfill Superfund Site

contamination of groundwater and 

nearby brook Excluded CERCLA

99 NM $1,000,000 2000 $1,632,000 Sparton Technology Site

damage to groundwater from 

discarded solvents and plating 

wastes

Excluded Groundwater 

only

100 NM $1,100,000 2006* $1,333,000 Albuquerque ATSF Site railroad tie treating plant; damages 

to groundwater and wildlife habitat Included

101 NM

$13,000,000 2010 $14,598,000

Freeport McMoRan damages to groundwater, wildlife, 

and wildlife habitat

Excluded Groundwater 

only

$5,500,000 2012 $5,687,000 Included

102 NM $30,000 2004 $41,000 SOHIO L-Bar Facility uranium tailings which 

contaminated groundwater Excluded Groundwater only

103 NM $7,500,000 2006 $9,091,000

State of New Mexico v 

General Electric 

Company et al.

contaminated groundwater from a 

Superfund site Excluded CERCLA

104 NY $20,300,000 2013 $20,300,000 St Lawrence River in 

Massena PCB contamination in a river Excluded

105 NY $12,000,000 2006 $14,545,000 Lake Ontario System release of dangerous chemicals 

into Lake Ontario System Included

106 OH $13,750,000 2008 $15,690,000 Fernald Uranium 

Products

uranium products (over 1,000 

acres) damaging groundwater Excluded CERCLA

107 OH $5,500,000 2012 $5,687,000 Ashtabula River remediation of contaminated 

sediment Excluded CERCLA

108 OH $2,040,000 2006 $2,473,000 Ohio River

restoration of damage to mussels, 

fish, and snails from contamination 

from a metals company

Included
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-13

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

109 OK $300,000 2007 $348,000 Double Eagle Superfund 

Site

contamination of groundwater by 

hazardous waste Excluded CERCLA

110 OR $50,000 2013* $50,000 Whitaker Slough Cleanup contamination of Whitaker Slough

by electroplating wastewater Included

111 OR $100,000 2013* $100,000 Johnson Lake overflow from settling ponds and 

stormwater discharges Included

112 OR $300,000 2009 $350,000 Union Carbide Site

Claims related to waste from 

carbide and ferroallow processing 

contaminating the Columbia 

Slough.

Included

113 PA $21,000,000 2009 $24,466,000 Palmerton Zinc 

Superfund Site

injuries to aquatic and terrestrial 

resources from zinc and other 

metals

Excluded CERCLA

114 PA $7,350,000 2006 $8,909,000 Sinnemahoning Creek 

Watershed

spill of sodium hydroxide from 

train derailment, damaging several 

waterbodies

Included

115 RI $8,000,000 2013* $8,000,000 North Cape Oil Spill

828,000 gallons of home heating 

oil spilled, killing at least 9 million 

lobsters, thousands of birds, and 

millions of clams, crabs, and fish 

Excluded Ocean

116 RI $1,415,000 2005 $1,815,000 Calf Pasture Point and 

Allen Harbor Landfill

discharge of chemical wastes 

(chlorinated hydrocarbons and 

VOCs) into groundwater

Excluded Groundwater 

only

117 RI $6,000,000 2011 $6,488,000 Buzzard's Bay

oil spill injuring shoreline and 

aquatic resources, piping plovers, 

and recreational uses

Excluded Ocean bay

118 RI $750,000 2013 $750,000 Davis Liquid Waste 

Superfund Site contamination of groundwater Excluded CERCLA

119 SC $121,000 2012 $125,000 Cooper River/Charleston 

Harbo

release of 12,500 gallons of fuel oil 

affecting shoreline habitats, 

sediments, migratory birds, 

shellfish beds, and recreational 

shrimp baiting

Included
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-14

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

120 SD $4,000,000 2006* $4,848,000

South Dakota v 

Homestake Mining 

Company

damage to surface and 

groundwater from metals (from 

mining)

Included

121 TN $543,203 2002 $831,000 Obed Wild and Scenic 

River Site

oil spill and fire in tributaries of 

the Obed Wild and Scenic River Included

122 TN $50,000 2010 $56,000

U.S. Department of 

Energy's Oak Ridge 

Reservation

damages to fishing from release of 

hazardous substances and 

radioactive compounds

Included

123 TX $3,120,000 2012 $3,226,000 Malone Service Co. 

Disposal Facility

hazardous waste material that 

contaminated groundwater and 

migrated to Galveston Bay

Excluded CERC LA

124 UT $37,000,000 1995 $81,094,000 Southwest Jordan Valley

$28 million in restoration of 

groundwater, damaged by historic 

mining, $9 in compensation

Included

125 UT $2,580,000 2007 $2,993,000 Ensign-Bickford Trojan 

Facility

discharges from explosives 

manufacturing facility into 

groundwater (3 mile plume)

Excluded Groundwater 

only

126 UT $3,500,000 2011 $3,785,000 Red Butte Creek Oil Spill pipeline rupture releasing oil into 

creek and other waters Included

127 VA $3,700,000 2003 $5,399,000 Tazewell County Spill

1,300 gallons of a rubber 

accelerant, which damaged 

endangered species of freshwater 

mussels

Included

128 VA $2,500,000 2001 $3,952,000 Powell River

six million gallons of coal slurry 

release to river watershed (20 

miles downstream), impacting fish, 

endangered mussels, aquatic 

habitat, bats, and migratory birds

Included

129 WA $5,000,000 2006* $6,061,000 Elliot Bay/ Duwamish 

River

habitat development and 

restoration after damage from 

sewer discharges

Included

130 WA $5,500,000 2005 $7,055,000 Skykomish Facility discharge of diesel fueil which 

leaked to water table Included
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix J: Impoundment Release Analysis Supporting Data 

September 29, 2015 J-15

Table J-2: 137 NRD Settlements Documented by Israel for 1967 to 2013 (Israel 2006; 2013)

Row State Original NRD 

Amount Year Updated NRD Amount Case Notes

Included 

or 

Excluded

Reason for 

Exclusion

131 WA $9,000,000 1994 $20,685,000 Tenyo Maru

release of 354,800 gallones of fuel 

oil and 97,800 gallons of diesel 

fuel, affecting coastal waters and 

shorelines and killing 4,300 

seabirds

Excluded Ocean

132 WA $512,857 2008 $585,000

Crystal Mountan 

Emergency Generation 

Facility

release of 18,200 gallons of diesel 

fuel into creek Included

133 WV $2,040,000 2006 $2,473,000 Ohio River

restoration of native freshwater 

mussels, snails, and fish in Ohio 

River

Included

134 WV $500,000 2009 $583,000 Consol Energy/Dunkard 

Creek

discharge of mining wastewater 

containing chloride, resulting in an 

algae bloom that killed thousands 

of fish, mussels, and amphibians

Included

135 WI $35,000,000 2013* $35,000,000 Fox River/Green Bay contamination from PCB and other 

discharges from paper mills Excluded CERCLA

136 WI $1,900,000 2012 $1,965,000 Ashland Lakefront contaminated sediments Included

137 WY $50,000 2013* $50,000 Gasoline Spill restocking a surface water after a 

fish kills from a gasoline spill Included
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-1

Appendix K. Methodology for Benefits from Avoided Dredging Costs

The following subsections describe EPA’s methodology for estimating sediment dredging benefits by 

applying the unit dredging costs to changes in the volume of sediment deposited to navigable waterways and 

reservoirs under the five regulatory options. The section is organized as follows:

 Review and analysis of historical dredging data including dredging locations, intervals, and costs;

 Estimation of sediment deposition to dredged waterways and reservoirs using SPARROW; and

 Estimation of dredging costs savings under regulatory options based on changes in sediment 

deposition and unit cost of dredging.

EPA notes that the methodology presented here focuses solely on avoided dredging costs based on the volume 

of sediment. Due to data limitations, the analysis does not quantify environmental benefits of reducing the 

frequency of maintenance dredging, nor does it account for potentially lower costs associated with disposing 

of dredged material spoils due to reduced discharge of toxic metals.

K.1 Review and Analysis of Historical Dredging Data

EPA used data from the United States Army Corps of Engineers (USACE) Dredging Information System

(USACE, 2013) to analyze baseline navigational dredging activity and to estimate the cost per cubic yard of 

sediment dredged. The system catalogs all USACE dredging contracts and USACE-conducted dredging jobs 

from 1998 to 2012, including the location of dredging activities, start and stop dates, the volume of sediment 

dredged, and the cost of the dredging job. The system does not report separate cost components, but costs 

typically include (Sohngen & Rausch, 1998):

 Cost of dredging sediment from the waterway’s channel bed and loading onto a boat,

 Cost of transporting dredged material to a disposal facility, and

 Cost of confining or disposing of the dredged material. 

EPA reviewed available information about sediment removal methods at existing U.S. reservoirs. The review 

indicated that dredging is a practical and common approach to sediment removal in existing reservoirs. With 

the exception of draining a reservoir and excavating settled sediment (which is typically more expensive and 

less common than dredging), dredging is the only feasible option for sediment removal in many reservoirs

(Morris and Fan, 1997). Other methods to counteract sedimentation or reclaim capacity at existing reservoirs 

include:

 Sediment routing – a group of techniques that allow sediment-laden water to pass around or through a 

reservoir without allowing the sediment to settle, optimized to address sediment-laden flows from 

events such as storms and floods (Morris and Fan, 1997). EPA was not able to find any information 

on the prevalence or effectiveness of these techniques.107

 Flushing – the scouring of accumulated sediment from a reservoir by partially or fully draining it and 

allowing the erosive force of the draining water to carry sediment through and downstream of the 

reservoir. The practice does not appear to be very common in the United States as it creates 

 

107 Including sediment pools during initial construction and maintaining these pools can also reduce sediment 

deposition (Crowder 1987).
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-2

extraordinarily high sediment concentrations downstream of the reservoir,

108 and the reservoir may 

still require dredging (Morris and Fan, 1997).

Theoretically, reductions in sediment loadings could delay the construction of new reservoir capacity and 

associated construction costs, including land for the reservoir itself, embankments, mitigation lands, 

appurtenances to the dam (if applicable), such as pump stations, and pipelines to connect the reservoir to the 

treatment plant or raw water users. In practice, it may not be feasible to construct new capacity in many cases 

due to space constraints or adverse ecological effects associated with reservoir construction. Furthermore, the 

ELG regulatory options result in only marginal changes in sediment deposition to existing reservoirs that are 

unlikely to be of sufficient magnitude to influence decision-making regarding capacity expansion.

Based on this review, EPA focused its benefits analysis for reservoirs on dredging over other capacity 

reclamation approaches due to the frequent use and broad feasibility of dredging. Given likely similarities in 

cost components for navigational dredging and reservoir dredging, EPA used regional estimates of cost per 

cubic yard of sediment dredged based on the USACE Dredging Information System as inputs for its analysis 

of avoided reservoir dredging costs. The Agency recognizes that dredging costs are highly variable and driven 

my factors including costs for dewatering sites, weather, topography, and characteristics of bottom sediments, 

and that at some sites dredging costs could exceed the costs of other alternatives, potentially including the unit 

cost of constructing new capacity.109 EPA notes that the volume of sediment that can be dredged from 

reservoirs may also affected by the availability of funds or nearby disposal sites (Morris & Fan, 1997).

K.1.1 Dredging Location, Recurrence Interval, and Dredging Volumes

Each observation in the Dredging Information System corresponds to a given date range and location at which 

dredging occurred, referred to here as “dredging occurrence.” Many locations have multiple dredging 

occurrences because recurrent dredging is may be necessary to maintain navigability. EPA uses the term 

“dredging job” to refer to multiple dredging occurrences at the same location. For each dredging job, EPA 

identified:

 The number of occurrences from 1998 to 2012. EPA merged dredging occurrences at the same 

location that were less than 30 days apart because these may be continuations of the same dredging 

occurrence, rather than a new dredging occurrence. If determined to be a continuation of prior 

occurrence, EPA summed the volume of sediment dredged and costs of the records to generate a 

single observation in the database.

 Average dredging volume from 1998 to 2012. EPA divided the total quantity of sediment dredged for 

each job (single location) over the past 15 years by the number of occurrences of that job to calculate 

an average quantity of sediment dredged for an occurrence of that job. EPA assumed that this quantity 

of sediment would be dredged each time the job occurs in the future under the baseline scenario, and 

that it would be reduced due to the final ELGs

 

108 Concentration can sometimes exceed 1,000,000 mg/L and thus may require special permissions (Morris and

Fan, 1997)

109 Alan Plummer Associates (2005) compared the cost of dredging to the costs of constructing additional 

reservoir capacity for reservoirs in Texas and found that dredging unit costs are at least twice that of securing 

storage in new reservoirs. New reservoir costs are approximately $1 for each cubic yard of water stored in the 

conservation pool (2011$, updated based on the construction cost index), whereas dredging costs for large-sized 

dredging projects are approximately $2 per cubic yard (2011$, updated based on the construction cost index) 

and, depending on variability, could cost more than two times that amount.
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September 29, 2015 K-3

 The time elapsed between dredging occurrences. EPA calculated an average frequency of recurrence 

for each dredging job in years by dividing 15 (the number of data years, 1998-2012) by the number of 

occurrences of that job.

The number of dredging jobs in the system varies by region, and is likely to be influenced by the size of the 

region, the number of navigable waterways, and their economic importance. 

Where the USACE Dredging Information System did not contain cost or quantity of sediment dredged for

a listed dredging occurrence, EPA estimated the missing information from other jobs. The Agency

calculated 10th, 50th, and 90th percentile costs and quantity boundaries and used these to fill in all

incomplete records in each EPA region. EPA used the 10th and 90th percentile cost and amount dredged 

estimates in the low and high total dredging cost estimates, respectively. The cost data were adjusted to 2013$ 

using the construction cost index.

K.1.2 Determining Affected Navigational Dredging Jobs and Unit Costs 

For this analysis, EPA mapped each navigational dredging job with latitude-longitude coordinates to the 

nearest reach modeled in SPARROW (E2RF1 reaches), within a one-mile radius. For jobs for which latitudelongitude coordinates were not provided, EPA used alternate job location information such as the name of the 

job (usually the waterway dredged) and the USACE district that performed the job.110 EPA excluded dredging 

jobs that are greater than 1 mile from a SPARROW reach from the analysis.111

This approach matched 29 unique dredging jobs and 98 dredging occurrences to analyzed E2RF1 reaches, 

equivalent to 0.8 percent of the dredging occurrences with coordinates reported in the Dredging Information 

System. The remaining 99 percent of occurrences were either greater than 1 mile from a modeled reach. Table 

K-1 summarizes dredging jobs and recurrence intervals in the affected reaches. The recurrence interval ranges 

from 1 to 15 years across all affected reaches, with an average of 9.6 years.

Table K-2 summarizes the average cost of dredging. Costs vary considerably across affected reaches, from 

approximately $$1.59 per cubic yard at Establishment Bar in North Carolina to $28.08 per cubic yard at 

Bonum Creek in Virginia. The average unit cost of dredging for the entire coterminous United States is 

$$5.56 per cubic yard.

 

110 EPA reviewed to the data to identify cases where latitude-longitude coordinates appear to have been 

misreported based on district and reach information. Where possible, EPA populated missing coordinates by 

interpolating from other occurrences of the same dredging job.

111 To identify the nearest reach segment, EPA used an unprojected version of the ERF 1.2 (Enhanced Reach File) 

from USGS. For this analysis, EPA researched latitude-longitude coordinates for jobs where they were not 

provided and linked them to Reach File Version 1.0 (RF1) reaches. Each latitude/longitude of interest was 

matched to the nearest point in the ERF 1.2 universe of points using a spherical model of the earth and a 

standard haversine distance formula. No reach types were excluded from consideration in the nearest reach 

calculation
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-4

Table K-1: Navigational Dredging Jobs and Recurrence Intervals from 1998 to 2012 within Affected 

Reaches

Number of Affected 

Dredging Jobs

Number of 

Recurring 

Jobs

Number of Single 

Occurrence Jobs

Average Interval for 

Single Occurrence and 

Recurring Jobs (years)

Average Interval 

for Recurring 

Jobs (years)

29 15 15 9.6 3.7

Note: Includes dredging jobs with latitude-longitude coordinates that could be mapped to affected E2RF1 reaches.

Source: Dredging Information System (USACE, 2013).

Table K-2: Historic Dredging Costs from 1998 to 2012 within Affected Reaches

Number of 

Affected Reaches

Total Sediment 

Removed (millions of 

cubic yards)

Total Cost (millions of 

2013$)

Average Cost per cubic 

yard (2013$)

29 72.0 $754.0 $10.5

Note: Only includes jobs EPA was able to map to affected reaches.

Source: Dredging Information System (USACE, 2013). 

K.1.3 Determining Reservoir Dredging Locations

EPA relied upon the “reservoir” flags within the E2RF1 dataset to identify reservoir locations. SPARROW 

models reservoirs as impoundments located on the main reach network. For the purposes of this analysis, 

EPA assumed, consistent with Crowder (1987), that all sediment entering reservoirs must be removed in order 

to maintain current water storage capacity.

K.2 Sediment Deposition in Navigable Waterways and Reservoirs

EPA estimated annual sediment deposition to waterways using sedimentation outputs from SPARROW. EPA 

assumed that all sediment deposited within historically dredged waterways will be dredged at some future 

point. Likewise, EPA assumes that all sediment deposited in the affected reaches with reservoirs will also be 

dredged. 

As described in Chapter 4, the SPARROW outputs reflect changes in annual sediment deposition based on 

the changes in sediment loadings under the final ELGs. EPA assumed that reduced deposition starts in 2021, 

which is the midpoint of the period during which steam electric plants are expected to implement changes to 

meet the final rule limitations and standards. 

K.3 Estimating Dredging Costs under Baseline and Regulatory Options

Benefits under the final ELGs are calculated for each year from 2021 to 2042 as the difference between 

baseline dredging costs and post-compliance dredging costs. Each waterway or reservoir will have unique 

benefits based on sediment deposition, dredging frequency, and the unit cost of dredging. Dredging costs only 

occur in years when the waterbody or reservoir is projected to be dredged.

K.3.1 Estimating Annualized Dredging Costs for Navigational Waterbodies

For each dredging job location, EPA assumes that future dredging occurrences will happen at the same 

frequency as in the past. The volume of sediment removed in each dredging occurrence is equal to the amount 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-5

of sediment deposited in that reach since the prior occurrence at that job location. For example, if dredging 

occurs every two years at given job location, then the first analyzed occurrence project for the second year of 

the analysis period (2022). The volume of sediment dredged in that occurrence is the sum of sediment 

deposited in the current year and prior year (2021 and 2022). The dredging cost incurred in 2022 is the 

product of the cubic yards dredged and unit cost per cubic yard. By the end of the 2042, the reach will have 

been dredged ten times. 

Equation J-1 presents the calculation of annual dredging costs for navigational waterways for the baseline and 

regulatory options. EPA discounted the costs following the analytic framework described in Chapter 1.

Equation J-1 AV = ∑ (

Qb,t ×(1−R)×C

(1+d)

t−2015 )

I × (

d×(1+d)

n

(1+d)n+1−1

)

Where:

AV = Annualized value 

I = The index for the dredging occurrence. The number of occurrences is 

based on the total number of periods (22) and the recurrence interval for 

the dredging job. The recurrence interval is calculated by dividing 15 

years by the number of occurrences of the job in the USACE dredging 

data.

Qb = Cubic yards of dredged materials under the baseline scenario in a given 

year t

T = Year of dredging occurrence

R = Percentage of cubic yards of dredge material remaining under the 

regulatory scenario relative to baseline

C = Cost per cubic yard of sediment dredged for the dredging job location 

(2013$)

D = Annual discount rate. EPA used both 3 percent and 7 percent, in 

accordance with OMB guidance (Office of Management and Budget, 

2003)

N = Number of periods for annualization (24 years for this benefits analysis)

EPA conducted a sensitivity analysis for navigational dredging by varying assumptions for projected future 

dredging occurrence, generating low, medium, and high estimates for navigational dredging:

 For medium and high estimates, EPA assumed that single occurrence dredging jobs occur once every 

15 years (i.e., the number of data years). Single occurrence jobs are dropped from future projections 

for the low estimate.

 For low and medium estimates, dredging is interpreted as occurring at the end of each interval (i.e., 

2022 in the example described above). For the high estimate, EPA assumes that dredging occurs at 

the beginning of the interval, rather than the end (e.g., 2021). This second approach tends to generate 

greater discounted benefits because jobs occur sooner and are discounted less.

 EPA varied costs for jobs lacking actual cost values and dredging volumes in the Dredging 

Information System. EPA assigned 10th, 50th, and 90th percentile costs for low, mean, and high 

estimates, respectively.

 For the mean estimate, EPA assumed a minimum recurrence interval of 90 days. Occurrences that 

began less than 90 days from the end of the prior occurrence at the same job location were considered 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-6

to be continuation, and combined in the data. EPA used intervals of 30 and 180 days for the low and 

high estimates, respectively.

These assumptions are summarized in Table K-3.

Table K-3: Parameters Used in Sensitivity Analysis of Avoided Dredging Costs 

Parameter 

Value

Treatment of Single 

Occurrences Treatment of Interval

Percentile of Cost 

Estimates Used for 

Jobs Lacking Cost 

Data

Minimum Recurrence 

Interval

Low Excluded Start 10th Percentile 30 days

Mean Included End 50th Percentile 90 days

High Included End 90th Percentile 180 days

K.3.2 Estimating Annualized Dredging Costs for Reservoirs

The frequency of reservoir dredging is highly site-specific, depending on many factors including the average 

sediment concentration of the influent river or stream, the flow regime, the size of the reservoir and excess 

storage capacity, and any sediment routing practices. For this analysis, EPA chose a general frequency of 

reservoir dredging based on information presented by the USACE in a Final Dredged Material Management 

Plan and Environmental Impact Statement for reservoirs in Washington (USACE, 2002). The report states 

that “dredging cycles may vary from 2 to 10 years” (USACE, 2002, p. 66). EPA used these frequencies as 

high and low estimates and 6 years as a mean estimate. This approach provides a range of benefits estimates 

to account for uncertainty in the frequency of reservoir dredging. 

EPA was unable to identify a comprehensive source of cost data for reservoir dredging.112 The Agency used 

the average unit cost of dredging from the analysis of USACE Dredging Information System Data grouped by 

EPA region. Because this cost is given per cubic yard, the sediment attenuation in reservoirs given by 

SPARROW will be converted from kilograms to cubic yards using a sediment density of 1.5 g/cm3 (Hargrove 

2007). This translates to a conversion of 1,147 kilograms per cubic yard. Equation K-2 summarizes the 

calculation of annualized avoided costs for an affected reservoir. The total annualized avoided cost is the sum 

of annualized cost savings at all affected reservoirs. 

Equation K-2: ACr = ∑ [

(Qr,b,t−Qr,s,t)

1147

×C

(1+d)

t−2014 ]

I × [

d×(1+d)

n

(1+d)

d+1−1

]

Where:

AC = Annualized avoided cost of dredging all sediment settling in 

reservoir r

r = Reservoir reach ID number

I = The assumed interval in years of reservoir dredging; varied 

between 2, 6, and 10

Qb = Quantity of sediment present at baseline (kg)

Qs = Quantity of sediment present under the regulatory option (kg)

1,147 = Number of kilograms per cubic yard of sediment

 

112 Some limited information on the costs of reservoir dredging is available in the literature. For example, Crowder 

(1987) provides a unit cost but provides not empirical basis for the estimate.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-7

C = Regional average of historic dredging job cost per cubic yard, 

1998-2012.

r = ELG regulatory option (Option A – Option E)

t = Year of the dredging occurrence

d = Annual discount rate. EPA used both 3 percent and 7 percent.

n = Number of periods for annualization (24 years for the benefits 

analysis)

K.4 Results

Benefits under each regulatory option are equal to avoided costs, calculated as the difference in total 

annualized dredging costs at baseline and under regulatory options. The subsections below summarize 

navigational dredging and reservoir dredging benefits. 

K.4.1 Navigational Dredging Benefits

Table K-4 presents estimates of baseline sediment dredging from 2021 to 2042 and low, mean, and high cost 

estimates. Total baseline navigational dredging costs range from $ 36 thousands to $48 thousands per year, 

using a 3 percent discount rate, and between $29 to $42 thousands using a 7 percent discount rate. Table K-5 

presents estimated benefits for navigational dredging for the five regulatory options. Annualized benefits for 

Option D are less than one thousand, with both 3 and 7 percent discount rates.

Table K-4: Annualized Dredging Costs at Affected Reaches under the Baseline (Thousands of 2013$)

Total Sediment Dredged

(millions cubic yards)

Costs at 3% discount rate

(thousands of 2013$ per year)

Costs at 7% discount rate

(thousands of 2013$ per year)

Low Mean High Low Mean High Low Mean High

96.4 97.3 125.5 36.2 36.5 47.9 29.2 29.4 42.1

Source: EPA analysis, 2015.

Table K-5: Annualized Benefits from Reduced Dredging Costs (Thousands of 2013$)

Regulatory 

Option

Total Reduction in Sediment 

Dredged (Baseline – Option; 

cubic yards)

3% discount rate

(thousands of 2013$ per year)

7% discount rate

(thousands of 2013$ per year)

Low Mean High Low Mean High Low Mean High

Option A 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Option B 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Option C 763.8 819.6 2,606.4 0.4 0.4 0.9 0.3 0.3 0.9

Option D 1,496.7 1,735.0 6,648.3 0.8 0.8 2.2 0.6 0.6 2.0

Option E 3,122.7 3,361.5 8,530.1 1.5 1.5 3.0 1.1 1.2 2.7

Source: EPA analysis, 2015.

K.4.2 Reservoir Dredging Benefits

Table K-6 presents the total amount of sediment that is estimated to be dredged in 2021 from reservoirs, and 

the estimated annualized cost of dredging under the baseline scenario, including low, mean, and high 

estimates. Estimated dredging costs for the reservoirs range between $477.6 million and $577.8 million with a 
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-8

three percent discount rate and $344.4 million and $480.0 million with a seven percent discount rate under the 

baseline scenario.

Table K-6: Annualized Reservoir Dredging Costs under the Baseline (Millions 2013$)

Total Sediment Dredged 

(millions cubic yards) (2021-

2042)

3% Discount Rate ($/year) 7% Discount Rate ($/year)

Low Mean High Low Mean High Low Mean High

2,934.6 2,641.2 3,228.1 477.6 469.5 577.8 344.4 378.9 480.0

Source: EPA analysis, 2015.

The difference between the anticipated dredging costs under the baseline and a particular regulatory option

represents the avoided costs of that regulatory option. Table K-7 presents reductions in sedimentation and 

subsequent avoided costs from reduced reservoir dredging for each regulatory option, including low, mean, 

and high estimates under these regulatory options. Because the range of estimates is relatively small between 

the low and high estimates, the values presented below in the discussion below are mean estimates unless 

otherwise stated. 

Avoided costs from a reduction in reservoir sedimentation vary depending on the regulatory option, the 

assumed frequency of reservoir dredging, and the discount rate. Annualized benefits for Option D are less 

than two thousand dollars with both 3 and 7 percent discount rates.

Table K-7: Total Annualized Benefits of Reduced Reservoir Dredging (2013$)

Regulatory 

Option

Total Reduction in Sediment 

Dredged (cubic yards) (2021-

2040)a

3% Discount Rate (thousands 

of 2013$ per year)

7% Discount Rate thousands of 

2013$ per year)

Low Mean High Low Mean High Low Mean High

Option A 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Option B 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Option C 8,106.0 7,295.4 8,916.6 1.6 1.6 2.0 1.2 1.3 1.6

Option D 9,624.1 8,661.7 10,586.5 1.9 1.9 2.3 1.4 1.5 1.9

Option E 11,114.0 10,002.6 12,225.4 2.2 2.2 2.7 1.6 1.8 2.2

Source: EPA analysis, 2015.

K.5 Limitations and Uncertainties

Table K-8 summarizes key uncertainties and limitations for the analysis of sediment dredging benefits. Note 

that the SPARROW model used to estimate sediment deposition also has a number of limitations, described 

in Environmental Impact and Benefits Assessment for Final Effluent Guidelines and Standards for the 

Construction and Development Category (U.S. EPA 2009c).
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-9

Table K-8. Limitations and Uncertainties in the Analysis of Avoided Costs of Navigational Waterway 

Dredging 

Issue Effect on Benefits 

Estimate Notes

Lack of standardized job 

names in USACE Database 

Underestimate The USACE dredging database identifies dredging jobs by 

name, usually the name of the dredged waterbody, but lacks 

standardized naming conventions. It is possible that the same 

waterbody is dredged under different job names. For the low 

cost estimates, this may result in the exclusion of dredging job 

names that only appear once in the database, but in fact were 

carried out in the same waterbodies as a differently named job. 

This effect would tend to underestimate benefits in EPA’s low 

estimates.

Analysis restricted to jobs 

reported in USACE 

Database for 1998 to 2012

Underestimate The USACE database is limited to USACE dredging contracts 

from 1998 to 2012. It does not capture dredging jobs contracted 

by other organizations or jobs occurring before 1998 or after 

2012.

Lack of latitude and 

longitude data in USACE 

Database 

Underestimate Many dredging occurrences lack or have incomplete latitude 

coordinates. As a result, EPA was only able to map about 71 

percent of all dredging occurrences with records in the data. 

EPA did not attempt to use other methods, such as Google 

Earth, to map dredging locations to the E2RF1 reaches due to 

resource constraints. The result is a downward bias in benefits 

estimates because the analysis excludes some dredging jobs that 

may benefit under the final rule. 

Do not estimate benefits to 

waterways that are not 

dredged

Underestimate EPA’s dredging analysis is limited to navigable waterways that 

have been dredged in the past and reservoirs flagged within the 

E2RF1 dataset. Other waterbodies not identified by these data 

could require dredging in the future and benefit from sediment 

reductions under the proposed rule. Thus, EPA’s estimates may 

be underestimated. 

Omission of reservoirs 

located off E2RF1 channels

Underestimate The benefits analysis for modeled watersheds explicitly omits 

any reservoirs that are not located on the E2RF1 network. The 

omission of other reservoirs is likely to bias estimated reservoir 

dredging benefits downward. 

Lack of site specific data on 

sediment density 

Uncertain EPA used a single sediment density estimate to convert between 

sediment mass and volume. This may reduce the accuracy of 

resulting benefits estimates because sediment density is related 

soil type in the area and is not uniform. The direction of this 

potential bias is uncertain.

Lack of data on the 

frequency of reservoir 

dredging and the amount of 

sediment dredged

Uncertain There is significant uncertainty as to the types of reservoirs that 

are dredged and the unit cost of dredging. The appropriateness 

of benefits estimates for reservoirs is conditional on assumption 

that water storage volume will be maintained. Actual benefits 

may diverge from estimates presented here if reservoirs take 

response actions other than dredging to address sediment loads. 

Assumption that excess 

sediment is removed from 

reservoirs by dredging 

rather than building new 

reservoir capacity

Uncertain EPA’s analysis of reservoir benefits assumes that all excess 

sediment is removed by dredging. Reservoir capacity could be 

replaced with new reservoirs in some cases. The unit cost of 

constructing new reservoirs may be higher or lower than 

dredging depending on site-specific characteristics.
Benefit and Cost Analysis for Steam Electric Power Generating ELGs Appendix K: Avoided Dredging Methodology

September 29, 2015 K-10

Table K-8. Limitations and Uncertainties in the Analysis of Avoided Costs of Navigational Waterway 

Dredging 

Issue Effect on Benefits 

Estimate Notes

Omission of natural water 

storage facilities from the 

analysis 

Underestimate The SPARROW model does not take into account sediment 

build-up in natural water storage facilities such as glacial lakes 

and ponds. Any activity to mitigate sedimentation in these 

waterbodies is not included in this benefits analysis. This may 

bias benefits estimates downward.