United States 

Environmental Protection 

Agency

Benefit and Cost Analysis for the 

Proposed Effluent Limitations Guidelines 

and Standards for the Steam Electric 

Power Generating Point Source Category 

April 2013

EPA 821-R-13-004
Benefit and Cost Analysis for Proposed ELGs Table of Contents 

April 19, 2013 i 

Table of Contents 

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

1.1 STEAM ELECTRIC PLANTS................................................................................................................................ 1-1

1.2 REGULATORY OPTIONS CONSIDERED FOR THE PROPOSED ELGS..................................................................... 1-2

1.3 ANALYTIC FRAMEWORK.................................................................................................................................. 1-1

1.3.1 Constant Prices ........................................................................................................................................... 1-2

1.3.2 Discount Rate and Year .............................................................................................................................. 1-2

1.3.3 Period of Analysis ....................................................................................................................................... 1-3

1.3.4 Population and Income Growth .................................................................................................................. 1-3

1.4 ORGANIZATION OF THE BENEFIT AND COST ANALYSIS REPORT...................................................................... 1-3

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 Failures ................................................................................................................. 2-9

2.4.2 Water Supply and Use ................................................................................................................................ 2-9

2.4.3 Commercial Fisheries ............................................................................................................................... 2-10

2.4.4 Tourism ..................................................................................................................................................... 2-10

2.4.5 Property Values ........................................................................................................................................ 2-10

2.5 REDUCED AIR POLLUTION ............................................................................................................................. 2-11

2.6 REDUCED WATER WITHDRAWALS................................................................................................................. 2-11

2.7 SUMMARY OF BENEFITS CATEGORIES............................................................................................................ 2-11

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

3.1 REDUCED CANCER CASES FROM CONSUMPTION OF FISH CONTAMINATED WITH ARSENIC.............................. 3-2

3.1.1 Methodology and Data ................................................................................................................................ 3-2

3.1.2 Results ........................................................................................................................................................ 3-5

3.1.3 Revised Cancer Slope Factor ...................................................................................................................... 3-6

3.2 BENEFITS TO CHILDREN FROM REDUCED LEAD EXPOSURE VIA FISH CONSUMPTION ...................................... 3-6

3.2.1 Methodology and Data ................................................................................................................................ 3-6

3.2.2 Results ...................................................................................................................................................... 3-10

3.3 BENEFITS TO INFANTS FROM REDUCED EXPOSURE TO MERCURY.................................................................. 3-11

3.3.1 Methodology and Data .............................................................................................................................. 3-11

3.3.2 Results ...................................................................................................................................................... 3-13

3.4 BENEFITS TO SUBSISTENCE FISHERS.............................................................................................................. 3-13

3.5 POTENTIAL ADDITIONAL HEALTH BENEFITS ................................................................................................. 3-16

3.6 LIMITATIONS AND UNCERTAINTIES ............................................................................................................... 3-17

4 NON-MARKET 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
Benefit and Cost Analysis for Proposed ELGs Table of Contents 

April 19, 2013 ii

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

4.2 WILLINGNESS TO PAY FOR WATER QUALITY IMPROVEMENTS ...................................................................... 4-11

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

4.4 LIMITATIONS AND UNCERTAINTIES ............................................................................................................... 4-19

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 PROPOSED ELGS.......................................................................................... 5-2

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

5.3.2 Assessing Benefits of T&E Species Improvements from the Proposed 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 ........................................................................ 5-6

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

5.6 LIMITATIONS AND UNCERTAINTIES ............................................................................................................... 5-11

6 BENEFITS FROM GROUNDWATER QUALITY IMPROVEMENTS ......................................................... 6-1

6.1 METHODOLOGY AND DATA ............................................................................................................................. 6-1

6.1.1 Baseline Water Quality ............................................................................................................................... 6-1

6.1.2 Water Quality Improvements ...................................................................................................................... 6-1

6.1.3 Affected Households................................................................................................................................... 6-2

6.1.4 Monetary Values of Groundwater Quality Improvements .......................................................................... 6-3

6.2 RESULTS .......................................................................................................................................................... 6-5

6.3 LIMITATIONS AND UNCERTAINTIES ................................................................................................................. 6-5

7 BENEFITS FROM AVOIDED IMPOUNDMENT FAILURES ....................................................................... 7-1

7.1 METHODS AND DATA....................................................................................................................................... 7-1

7.1.1 Failure Probability ...................................................................................................................................... 7-1

7.1.2 Capacity Factor ........................................................................................................................................... 7-2

7.1.3 Failure Costs ............................................................................................................................................... 7-2

7.2 RESULTS .......................................................................................................................................................... 7-5

7.3 LIMITATIONS AND UNCERTAINTIES ................................................................................................................. 7-6

8 AIR-RELATED BENEFITS ................................................................................................................................. 8-1

8.1 DATA AND METHODOLOGY ............................................................................................................................. 8-1

8.1.1 Changes in Air Emissions ........................................................................................................................... 8-1

8.1.2 NOx and SO2 ............................................................................................................................................... 8-3

8.1.3 CO2 ............................................................................................................................................................. 8-6

8.1.4 Estimating Total Air-Related Benefits ........................................................................................................ 8-7

8.2 RESULTS .......................................................................................................................................................... 8-8

8.3 LIMITATIONS AND UNCERTAINTIES ................................................................................................................. 8-9

9 BENEFITS FROM REDUCED WATER WITHDRAWALS ........................................................................... 9-1

9.1 REDUCED GROUNDWATER WITHDRAWALS ..................................................................................................... 9-1

9.1.1 Methods ...................................................................................................................................................... 9-1

9.1.2 Results ........................................................................................................................................................ 9-2

9.1.3 Limitations and Uncertainties ..................................................................................................................... 9-2

10 SUMMARY OF TOTAL BENEFITS ................................................................................................................ 10-1

10.1 TOTAL ANNUALIZED BENEFITS ..................................................................................................................... 10-1

10.2 TIME PROFILE OF BENEFITS ........................................................................................................................... 10-0

10.3 INFERRED BENEFITS FOR REGULATORY OPTIONS NOT ANALYZED EXPLICITLY ............................................ 10-1
Benefit and Cost Analysis for Proposed ELGs Table of Contents 

April 19, 2013 iii

11 SUMMARY OF TOTAL SOCIAL COSTS ...................................................................................................... 11-4

11.1 OVERVIEW OF SOCIAL COSTS ANALYSIS FRAMEWORK ................................................................................. 11-4

11.2 KEY FINDINGS FOR REGULATORY OPTIONS................................................................................................... 11-5

12 BENEFITS AND SOCIAL COSTS .................................................................................................................... 12-2

12.1 COMPARISON OF BENEFITS AND SOCIAL COSTS BY OPTION .......................................................................... 12-2

12.2 ANALYSIS OF INCREMENTAL BENEFITS AND SOCIAL COSTS.......................................................................... 12-3

13 REFERENCES ..................................................................................................................................................... 13-1

APPENDIX A: DETAIL ON ESTIMATING AFFECTED POPULATION................................................................. 1

APPENDIX B: IEUBK MODEL DESCRIPTION AND APPLICATION .................................................................... 1

APPENDIX C: HUMAN HEALTH BENEFITS INCLUDING DOWNSTREAM REACHES .................................. 1

APPENDIX D: TSS, TN, AND TP ECOREGION-SPECIFIC SUBINDEX CURVES ................................................ 1

APPENDIX E: META-ANALYSIS RESULTS ............................................................................................................... 1

E.1 LITERATURE REVIEW OF WATER RESOURCE VALUATION STUDIES............................................................................ 1

E.2 TOTAL WTP META-ANALYSIS REGRESSION MODEL AND RESULTS........................................................................... 6

E.3 MODEL LIMITATIONS................................................................................................................................................18

APPENDIX F: IMPACTS OF STEAM ELECTRIC POLLUTANTS ON AQUATIC SPECIES .............................. 1

APPENDIX G: SENSITIVITY OF THE ESTIMATED BENEFITS FROM AVOIDED IMPOUNDMENT 

FAILURES TO FAILURE PROBABILITY.................................................................................................................... 1

APPENDIX H: CONCENTRATION RESPONSE FUNCTION USED IN THE ANALYSIS OF AIR-RELATED 

BENEFITS .......................................................................................................................................................................... 1

APPENDIX I: CO2-RELATED BENEFITS USING ALTERNATE SOCIAL COST OF CARBON VALUES ....... 1
Benefit and Cost Analysis for Proposed ELGs List of Abbreviations 

April 19, 2013 iv

List of Abbreviations 

BAT Best available technology economically achievable 

BMP Best management practice 

BOD Biochemical oxygen demand 

BPT Best practicable control technology currently available 

CAA Clean Air Act 

CCR Coal combustion residuals 

CSF Cancer slope factor 

CPI Consumer Price Index 

CWA Clean Water Act 

E&T Endangered and threatened 

EA Environmental Assessment 

EJ Environmental justice 

ELGs Effluent limitations guidelines and standards 

EO Executive Order 

EPA U.S. Environmental Protection Agency 

FC Fecal coliform 

FCA Fish consumption advisories 

FGD Flue gas desulfurization 

FGMC Flue gas mercury control 

GDP Gross domestic product 

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 

MATS Mercury and Air Toxics Standards 

MCL Maximum contaminant level 

NAICS North American Industry Classification System 

NERC North American Electric Reliability Corporation 

NHD National hydrography dataset 

NPDES National Pollutant Discharge Elimination System 

O&M Operation and maintenance 

OMB Office of Management and Budget 

PbB Blood lead concentration 

POTW Publicly owned treatment works 

PSES Pretreatment Standards for Existing Sources 

PSNS Pretreatment Standards for New Sources 

RSEI Risk-Screening Environmental Indicators 

SCC Social cost of carbon 

SPARROW SPAtially Referenced Regressions On Watershed attributes 

TDD Technical Development Document 

TN Total nitrogen 

TP Total phosphorus 

TRI Toxic Release Inventory 

TSS Total suspended solids 
Benefit and Cost Analysis for Proposed ELGs List of Abbreviations 

April 19, 2013 v 

USGS U.S. Geological Service 

WQI Water quality index 

WQL Water quality ladder 

WTP Willingness to pay 
Benefit and Cost Analysis for Proposed ELGs 1: Introduction 

April 19, 2013 1-1 

1 Introduction

EPA is proposing 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 proposed effluent limitation guidelines and standards 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 benefits and social costs of the proposed ELGs and complements 

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

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

Steam Electric Power Generating Point Source Category (EA) (U.S. EPA, 2013a; DCN SE01995). 

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

from implementation of the proposed ELGs. 

 Technical Support Document for the Proposed Effluent Limitations Guidelines and Standards for the 

Steam Electric Power Generating Point Source Category (TDD) (U.S. EPA, 2013b; DCN SE01964). 

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

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 proposed ELGs including facility specific compliance cost 

estimates, pollutant loadings, and non-water quality impact assessment. 

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

Steam Electric Power Generating Point Source Category (RIA) (U.S. EPA, 2013c; DCN SE03170). 

The RIA describes EPA‘s analysis of the costs and economic impacts of the proposed ELGs. In 

particular, it provides the basis for social cost estimates presented in this document. It also provides 

information pertinent to meeting several legislative and administrative requirements. 

The rest of this chapter discusses aspects of the proposed ELGs that are salient to EPA‘s analysis of the 

benefits and social costs of the regulation and summarizes key analytic assumptions used throughout this 

document. 

1.1 Steam Electric Plants

As defined in 40 CFR part 423, the steam electric industry covers establishments ―primarily engaged in the 

generation of electricity for distribution and/or sale, which results primarily from a process utilizing fossiltype fuels (coal, petroleum coke, oil, gas) or nuclear fuel in conjunction with a thermal cycle employing the 

steam water system as the thermodynamic medium.‖ (40 CFR part 423.10) EPA intends the industry 

definition to apply to operations where the generation of electricity is the predominant source of revenue 

and/or principal reason for operation. EPA considers steam electric processes to be those with at least one 
Benefits and Cost Analysis for Proposed ELGs 1: Introduction 

April 19, 2013 1-2 

prime mover that utilizes steam. For example, combined cycle systems are composed of one or more 

combustion turbines operating in conjunction with one or more steam turbines, and thus would be subject to 

the proposed ELGs. 

EPA estimated that 1,079 steam electric plants are subject to the proposed ELGs; a subset of these plants 

would need to modify their operations to meet new effluent limits and standards (refer to the TDD and RIA

for details). 

1.2 Regulatory Options Considered for the Proposed ELGs

EPA considered eight regulatory options for the proposed ELGs (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 technology bases for the options). Thus, EPA is proposing to revise or 

establish 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: flue gas desulfurization (FGD) wastewater, fly 

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

impoundments, wastewater from flue gas mercury control (FGMC) systems and gasification systems, and 

nonchemical metal cleaning wastes. 
Benefit and Cost Analysis for Proposed ELGs 1: Introduction 

April 19, 2013 1-0 

Table 1-1. Steam Electric Regulatory Options

Wastestreams

Technology Basis for BAT/NSPS/PSES/PSNS

Regulatory Options

1 3a 2 3b 3 4a 4 5

FGD

Wastewater

Chemical 

Precipitation

BPJ 

Determination

Chemical 

Precipitation + 

Biological 

Treatment

Chemical 

Precipitation + 

Biological 

Treatment

** 

Chemical 

Precipitation + 

Biological 

Treatment

Chemical 

Precipitation + 

Biological 

Treatment

Chemical 

Precipitation + 

Biological 

Treatment

Chemical 

Precipitation + 

Evaporation

Fly Ash 

Transport 

Water

Impoundment

(Equal to BPT)

Dry Handling Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Dry Handling Dry Handling Dry Handling Dry Handling

Bottom Ash 

Transport 

Water

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT) 

Dry Handling

/Closed Loop

**

Dry Handling 

/Closed Loop

Dry Handling 

/Closed Loop

Combustion 

Residual 

Leachate

Impoundment 

(Equal to BPT)

Impoundment 

(Equal to BPT)

Impoundment 

(Equal to BPT)

Impoundment 

(Equal to BPT)

Impoundment

(Equal to BPT)

Impoundment

(Equal to BPT)

Chemical 

Precipitation

Chemical 

Precipitation

FGMC

Wastewater

Impoundment

(Equal to BPT)

Dry Handling Impoundment 

(Equal to BPT)

Dry Handling Dry Handling Dry Handling Dry Handling Dry Handling

Gasification

Wastewater

Evaporation Evaporation Evaporation Evaporation Evaporation Evaporation Evaporation Evaporation

Nonchemical 

Metal Cleaning 

Wastes

Chemical 

Precipitation

Chemical 

Precipitation

Chemical 

Precipitation

Chemical 

Precipitation

Chemical 

Precipitation

Chemical 

Precipitation

Chemical 

Precipitation

Chemical 

Precipitation

** Requirement is subject to applicability threshold. For Option 3b FGD wastewater: Chemical Precipitation + Biological Treatment for units at a facility with a total 

wet-scrubbed capacity of 2,000 MW and more; BPJ determination for units at a facility with a total wet-scrubbed capacity <2,000 MW. For Option 4a bottom ash 

transport water: Dry handling/Closed loop for units >400 MW; Impoundment (Equal to BPT) for units ≤400 MW.

BPT = Best Practicable Control Technology Currently Available.

BPJ = Best Professional Judgment.

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs 1: Introduction 

April 19, 2013 1-1 

After considering these regulatory options, EPA identified Options 3a, 3b, 3 and 4a as the preferred options 

for regulation of pollutant discharges from existing sources (BAT and PSES). For new sources, EPA 

identified Option 4 as the preferred option for NSPS and PSNS. The preamble that accompanies the proposed 

regulation explains the rationale for EPA‘s determination.

The load reductions that would be achieved by the proposed regulatory options vary depending on the 

pollutant type, which are categorized as conventional (such as total suspended solids, biochemical oxygen 

demand, and oil and grease), priority (such as mercury, arsenic, and selenium), and non-conventional (such as 

phosphorus and total dissolved solids). Table 1-2 summarizes the estimated conventional, priority, nonconventional, and toxic-weighted pound equivalent1

pollutant reductions under each of the eight regulatory 

options. The table lists the options in increasing order of total toxic-weighted pollutant removals. 

Table 1-2. Pollutant Removal for Proposed ELGs Regulatory Options

Regulatory 

Option

Pollutant Load Reduction 

(million pounds per year)

Conventional

Pollutantsa Priority Pollutants

Nonconventional

Pollutantsb

Toxic-Weighted Pound 

Equivalent

Option 1c2.8 0.5 c-418 1.5

Option 3a 16.0 0.4 468 2.5

Option 2 2.8 0.7 1,155 2.6

Option 3b 17.1 0.6 914 3.4

Option 3 19.0 1.1 1,623 5.1

Option 4b

d

28.0 1.4 2,612 6.7

Option 4 35.0 1.7 3,328 7.8

Option 5 36.0 1.7 5,287 8.2

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).

c. Option 1 shows a negative removal for nonconventional pollutants because the mass of several pollutants (ammonia, chromium, 

TKN, and BOD) are not quantified at baseline, and because some pollutant discharge concentrations are higher under Option 1.

d. EPA estimated the pollutant removals for Option 4a based on approximated plant-level bottom ash loadings for those plants that have 

at least one generating unit with a nameplate capacity of 400 MW or less and at least one other generating unit with a nameplate 

capacity of greater than 400 MW. For more details on how EPA estimated these plant-level bottom ash loadings, see the memorandum 

entitled ―Steam Electric ELG Regulatory Option 4a Estimation Methodologies‖ (DCN SE03834).

Additionally, the regulatory options would eliminate or reduce water withdrawals associated with wet ash 

transport and wet FGD scrubbers. EPA estimates that power plants would reduce the use of water by 50 

billion gallons per year (136 million gallons per day) under Option 3a, by 52 billion gallons per year (143 

million gallons per day) under Option 3b, by 53 billion gallons per year (144 million gallons per day) under 

Option 3, and by 103 billion gallons per year (282 million gallons per day) under Option 4a. 

1.3 Analytic Framework 

The analytic framework of this BCA includes four basic components used consistently throughout the 

analysis of benefits and social costs of the proposed ELGs: 

1. All values are presented in 2010 dollars; 

2. Benefits and social costs are analyzed over a 24-year period (2017 to 2040); 

 

1

 For information on toxic weighted pound equivalents, see TDD (U.S. EPA, 2013b).
Benefits and Cost Analysis for Proposed ELGs 1: Introduction 

April 19, 2013 1-2 

3. Future benefits and social costs are discounted using rates of 3 percent and 7 percent back to the 

expected rule promulgation year of 2014; and 

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

These components are discussed in the sections below. 

Note that the benefits and social cost analyses presented in this document are generally performed at the level 

of individual steam electric plants (for all plants that were surveyed), with plant-level estimates multiplied by 

sample weights to represent all 1,079 steam electric plants and the affected resources.2,3

EPA estimated benefits for five of the eight regulatory options considered for the proposed ELGs (Options 1, 

2, 3, 4, and 5). EPA did not estimate the benefits of Options 3a, 3b and 4a. However, EPA used its 

understanding of the wastestreams and treatment technologies for these options, along with projections of 

pollutant reductions for all eight options, to infer total monetized benefits for Options 3a, 3b, and 4a. This is 

because the five options EPA analyzed can serve as upper and lower bounds for the benefits of Options 3a, 

3b, and 4a. Specifically, monetized benefits for Options 3a and 3b are likely to be between those for Options 

2 and 3. Similarly, monetized benefits for Option 4a are likely to be between those for Options 3 and 4. 

However, EPA is less confident that the approach used to infer benefits for Options 3a, 3b, and 4a based on 

the results of the other five options would yield reasonable estimates if applied to the individual categories of 

benefits (e.g., water quality, air emissions, avoided impoundment failure cleanup costs) and therefore the 

Agency did not infer values for individual benefit categories but presents category-specific estimates only for 

Options 1, 2, 3, 4, and 5. 

1.3.1 Constant Prices 

This BCA applies a year 2010 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 2010), 

and in those instances, EPA has updated the prices to 2010 by multiplying them by appropriate indexes, or 

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

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 consumer or household willingness-to-pay (WTP) 

surveys, such as WTP for surface water quality improvements applied in this BCA for monetizing ecological 

benefits of the proposed ELGs. This BCA updates these non-market prices as needed using appropriate 

indexes (e.g., Consumer Price Index (CPI)). 

1.3.2 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 (OMB, 2003; updated 2009). The same discount rates are used for both benefits and social 

costs. 

All future cost and benefit values are discounted back to the expected ELG promulgation year of 2014. 

 

2

 Refer to the TDD for a discussion of the 2010 Questionnaire for the Steam Electric Power Generating Effluent 

Guidelines (industry survey) and development and application of sample weights. 

3

 Benefits incur only to waterbodies affected by plants which have a sample weight of one. Benefits associated with 

waterbodies affected by other steam electric plants are equal to zero. Therefore, the use of sample weights does not 

introduce additional uncertainty on benefit estimates. 
Benefits and Cost Analysis for Proposed ELGs 1: Introduction 

April 19, 2013 1-3 

1.3.3 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 

would implement control technologies three years after their NPDES permit comes up for renewal after the 

rule promulgation (2014). Assuming that NPDES permits are renewed every five years, technology 

implementation across all steam electric plants would occur during the period of calendar years 2017 through 

2021.4 This schedule recognizes that control technology implementation is likely to be staggered over time 

across the universe of steam electric plants. 

The period of analysis extends to 2040 to capture the life of the longest-lived compliance technology at any 

steam electric plant (20 years), and the last year of technology implementation (2021). 

1.3.4 Population and Income Growth

To account for future population growth or decline, EPA adjusted affected population estimates based on 

growth projections contained in EPA‘s Environmental and Benefits Mapping and Analysis Program 

(BenMap) for the years 2014 through 2030. These projections are based on 2000 Census data as well as 

projections taken from the 2007 Woods and Poole projection estimates (Woods and Poole Economics, 2007). 

Because BenMap (at the time this analysis was conducted) does not include population estimates beyond 

2030, EPA used stepwise autoregressive forecasting to forecast population values at the state level between 

2030 and 2040. This method combines linear and autoregressive models.5

Also, since willingness-to-pay (WTP) is expected to increase as income increases, EPA took into account 

income growth for some analyses (e.g., WTP for water quality improvements and groundwater protection and 

value of a statistical life (VSL)). To estimate median state level household income between 2017 and 2040, 

EPA used historic state-specific median household income data from the U.S. Census Bureau‘s 2009 

Community Population Survey (U.S. Census Bureau, 2010b) for the years 1984 to 2009. The Consumer Price 

Index (CPI) was used to adjust all value to 2010 dollars (U.S. BLS, 2010) and a stepwise autoregressive 

forecasting method was used to estimate future annual state level median household income. For the health 

benefits analyses in Chapter 3, EPA applied the projected income data (described above) along with the 

income elasticity to adjust the VSL.6

 

1.4 Organization of the Benefit and Cost Analysis Report

This Benefits and Cost Analysis (BCA) report presents EPA‘s analysis of the benefits of the proposed ELGs, 

assessment of the total social costs, and comparison of the social costs and monetized benefits. 

 

4

 As specified in the preamble, certain limitations and standards based on any of the five main regulatory options being 

proposed for existing direct and indirect dischargers do not apply until July 1, 2017 (approximately three years from the 

effective date of the rule). The implementation period analyzed corresponds roughly to the timing of implementation for 

proposed BAT and PSES for existing direct and indirect dischargers. 

5

 We used PROC FORECAST in SAS, which provides a quick and automatic way to generate forecasts for many time 

series in one step. Details about PROC FORECAST can be found at 

http://www.dms.umontreal.ca/~duchesne/chap12.pdf 

6

 This method for adjusting VSL differs slightly from what BenMAP uses. EPA evaluated the effects of using the 

BenMAP income growth factors instead of the method used in this analysis and found negligible differences in the 

results. 
Benefits and Cost Analysis for Proposed ELGs 1: Introduction 

April 19, 2013 1-4 

The report is organized as follows: 

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

implementation of the proposed ELGs. 

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

health benefits. 

 Chapter 4: Non-Market Benefits from Water Quality Improvements discusses EPA‘s analysis of the 

water quality improvements resulting from the proposed 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 Groundwater Quality analyzes the benefits of reductions in the risk of 

groundwater contamination. 

 Chapter 7: Benefits from Avoided Impoundment Failures assesses the benefits of reducing the 

impacts of coal combustion residue (CCR) releases due to the failure of impoundments used by some 

steam electric plants to manage their CCR waste. 

 Chapter 8: 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 9: Benefits from Reduced Water Withdrawals discusses ancillary benefits arising from 

reduced surface water intake and groundwater use. 

 Chapter 10: Summary of Total Benefits summarizes results across benefit categories. 

 Chapter 11: Summary of Total Social Costs summarizes social costs of the proposed ELGs. 

 Chapter 12: Benefits and Social Costs 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 social costs of its actions. 

 Chapter 13: References provides references cited in the text. 

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

report.
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April 19, 2013 2-1 

2 Benefits Overview

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

proposed ELGs. EPA expects that benefits would accrue to society in several broad categories, including 

enhanced surface 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 would reduce pollutant loadings to receiving waters. Benefits of the proposed ELGs 

would also include secondary effects of the implementation of control technologies or other changes in plant 

operations, such as reduction in emissions of air pollutants (e.g., CO2, NOx, and 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 episodic impoundment failures. 

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 likely to be achieved by the proposed 

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, 2013a). 

Figure 2-1 summarizes the potential effects of the proposed 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 proposed 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.
Benefit and Cost Analysis for Proposed ELGs 2: Benefits Overview 

April 19, 2013 2-2 

Figure 2-1. Summary of Benefits Resulting from the Proposed ELGs.
Benefit and Cost Analysis for Proposed ELGs 2: Benefits Overview 

April 19, 2013 2-3 

2.1 Human Health Benefits Associated with Improvements in Surface Water 

Quality

Steam electric pollutants can cause a wide variety of adverse human health effects arising, for example, from 

drinking contaminated water (surface or groundwater) or consuming contaminated fish tissue. Metals 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, 2000a). More details on the fate, 

transport, and exposure risks of steam electric pollutants are provided in the EA (U.S. EPA, 2013a). 

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 such as cancer 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 proposed ELGs have the potential to provide human health benefits by reducing exposure to pollutants in 

water via two principal exposure pathways: (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 plant discharges. 

2.1.1 Fish Consumption

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

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

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

health from exposure to contaminated fish tissue: 

1. Incidence of cancer from fish consumption; 

2. Neurological effects to children ages 0 to 7 from exposure to lead; 

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

4. Reduced risk of non-cancer toxic effects from fish consumption. 

EPA was able to monetize only the first three of these four measures. Incidence of cancer was translated into 

an expected number of avoided mortality events and, on that basis, monetized. Lead and mercury impacts to 

children were evaluated in terms of potential intellectual impairment as measured by estimated changes in 

intelligence quotient (IQ). Details on these analyses are provided in the following sections of this report: 

 Reduced cancer cases from arsenic exposure (Section 3.1), 

 Reduced IQ losses in children ages 0 to 7 resulting from exposure to lead (Section 3.2), and 

 Reduced IQ losses among children exposed to mercury in-utero (Section 3.3).

The fourth effect (reduced risk of 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 1.1). 

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

effects. Willingness to pay (WTP) to avoid morbidity or mortality is generally considered to be a 
Benefit and Cost Analysis for Proposed ELGs 2: Benefits Overview 

April 19, 2013 2-4 

comprehensive measure of the costs of health care, losses in income, and pain and suffering of affected 

individuals and their caregivers. For example, the value of a statistical life (VSL) (see Section 3.1.1) is based 

on estimates of society‘s WTP to avoid the risk of premature mortality. Alternatively, the cost-of-illness 

approach, which is not used in this analysis, 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 3.2.1 and 3.3.1 for applications of 

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

EPA expects that there would 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: morbidity preceding cancer 

mortality from exposure to arsenic; neonatal mortality from exposure to lead (U.S. EPA, 2009a); effects to 

adults from exposure to lead (including increased incidence of hypertension, heart attack, strokes, and 

premature mortality, nervous system disorders, anemia and blood disorders, and other effects; U.S. EPA, 

2009a; 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 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 would result from the 

proposed 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. As such, EPA restricted the analysis of monetized health benefits from improved surface water quality 

to benefits arising from the consumption of contaminated fish tissue. Although treatment may not remove all 

contaminants from the drinking water supplies and there may be some incremental health-related benefits 
Benefit and Cost Analysis for Proposed ELGs 2: Benefits Overview 

April 19, 2013 2-5 

associated with reduced concentrations arising from the proposed ELGs, EPA expects that these benefits 

would not be substantial.7

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 aquatic water quality criteria (AWQC) limits. This benefit measure was obtained by comparing 

in-waterway pollutant concentrations to toxic effect levels. 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 proposed 

ELGs (Section 1.1). 

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

Accordingly, 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 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, 2013a). 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 plant discharges (U.S. EPA, 2013a). 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 that the ecological benefits from the proposed ELGs would include enhanced habitat for freshand 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 that the regulation would 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 would also favor recreational 

activities such as swimming, boating, fishing, and water skiing. Finally, the Agency expects that the 

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

resources. 

2.2.1 Improved Surface Water Quality

The proposed 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 plant discharges. Society 

values such ecological improvements by a number of mechanisms, including increased frequency and value 

of use of the improved habitat for recreational activities. In addition, individuals may also value the protection 

 

7

 There may also be market benefits associated with the decreased need for drinking water treatment, but EPA did not 

estimate these benefits as part of its analysis of the proposed ELG. 
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April 19, 2013 2-6 

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. 

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, 2009a; 

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

Reducing pollutant loads in steam electric 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 would also benefit from improved abundance and diversity of aquatic and terrestrial species. 

For example, wildlife viewers may 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, 2011). In addition, improved aesthetic quality of surface waters (e.g., clarity and 

odors) would enhance the recreational experience of wildlife viewers and other recreational users. 

(Schoch et al., 2011; U.S. EPA, 2011). 

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

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

 Swimming. Swimmers may 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 may 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 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 (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 DNR, 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 proposed ELGs by estimating in-waterway 

concentrations of nutrients and toxic pollutants discharged by steam electric plants and translating water 

quality measurements into a single numerical indicator (water quality index (WQI)). EPA used the expected 

change in WQI as a quantitative measure of ecological benefit for this regulatory analysis. Section 4.1 of this 

report provides detail on the parameters used in formulating the WQI and the WQI methodology and 

calculations. 
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April 19, 2013 2-7 

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, 2010b; 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., 

2003). In either case, values are estimated by statistical analysis of survey responses. 

Although the use of primary research to estimate values is generally preferred, 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 proposed ELGs. 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 115 estimates of total WTP (including both use 

and nonuse values) for water quality improvements, provided by the 45 original studies.8 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 and commercial fishing 

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

designated uses. 

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 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 

proposed ELGs would 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 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 derived only 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 proposed regulation on T&E species are 

beyond the schedule and resources available to EPA for this rulemaking. As an alternative, EPA used a 

 

8

 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.‖
Benefit and Cost Analysis for Proposed ELGs 2: Benefits Overview 

April 19, 2013 2-8 

benefit transfer approach that relies on information from existing studies (U.S. EPA, 2010b). 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). The estimated WTP equation 

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

populations resulting from the proposed 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 plants can also contaminate waterbody sediments. For example, 

adsorption of arsenic, selenium, and other pollutants found in steam electric 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 proposed ELGs would 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 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 proposed ELGs are expected 

to result in plants closing or significantly reducing their use of impoundments to manage coal combustion 

residuals (CCR). The associated reduction in the risk of groundwater contamination represents benefits 

attributable to the rule. 

To evaluate benefits of reduced contamination of groundwater aquifers in the vicinity of steam electric 

impoundments, EPA applied a benefit transfer approach based on the results of a meta-analysis of 

groundwater valuation studies conducted by Poe et al. (2001). Poe et al. (2001) used data from 13 studies that 

elicit household WTP values for reducing risk of groundwater contamination, to develop a meta-regression 

function based on economic variables (including changes in contamination probability and income, whether 

cancer was mentioned as a health concern, and the share of the population on public water supplies) and 

methodological variables (including elicitation methods). EPA used the estimated meta-regression function to 

estimate WTP for reducing risk of groundwater contamination for households that rely on private wells 

drawing water from aquifers surrounding steam electric plants. This analysis and results are presented in 

Chapter 6. 
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April 19, 2013 2-9 

2.4 Economic Productivity Benefits

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

impoundment failures and the reduction in the costs associated with the cleanup, environmental damages, and 

transaction costs from the resulting release of coal combustion residuals (CCR) into the environment. 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 Failures

Steam electric 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 proposed ELGs 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 failures. Benefits arising from the reduced 

risk of impoundment failures include avoided cleanup costs, environmental damage, and transaction costs. 

EPA quantified and monetized these benefits based on expected future impoundment structural failure rates, 

the volumes of CCR that would be released in the event of a failure, and the costs of spill cleanup, natural 

resource damages, and transaction costs. Chapter 7 describes this analysis. 

2.4.2 Water Supply and Use

The proposed 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 supply: The proposed ELGs are expected 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. 

Further, public drinking water sources do not always effectively remove bromides (a steam electric 

pollutant) from raw surface waters. Elevated bromide concentrations in source waters result in 

increased trihalomethanes (THMs) in drinking water (Pittsburgh Water and Sewer 

Authority/University of Pittsburgh School of Engineering, 2012). Drinking water utilities downstream 

of bromide sources are increasingly finding it difficult to meet drinking water standards for THMs. If 

water treatment is not sufficient, an alternate water source needs to be substituted (if available). Longterm 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. 

 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 
Benefit and Cost Analysis for Proposed ELGs 2: Benefits Overview 

April 19, 2013 2-10

entering the food chain. Further, eutrophication promotes cyanobacteria blooms that can kill livestock 

and wildlife that drink the contaminated surface water. 

EPA did not quantify or monetize benefits from enhanced quality of drinking and agricultural water sources 

arising from the proposed ELGs due to data limitations. 

2.4.3 Commercial Fisheries

Pollutants in steam electric 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 proposed ELGs. EPA‘s EA (see 

U.S. EPA, 2013a) shows that a small number of steam electric plants discharge to estuaries or marine waters; 

as a result, the benefits to commercial fisheries arising from the proposed ELGs are likely to be small. 

2.4.4 Tourism

The proposed 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 plants may lead to a reduction in 

tourism 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 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. 

2.4.5 Property Values

The proposed 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) 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 proposed 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. The expected changes in property values are likely to be 

small. In addition, there may be some overlap between changes in property values and the estimated total 

WTP for surface water quality improvements summarized in Section 2.2.1. 
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April 19, 2013 2-11

2.5 Reduced Air Pollution

The proposed ELGs are expected to affect air pollution through three main mechanisms: 1) additional 

auxiliary electricity use by steam electric 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 proposed 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 proposed 

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. 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 – most notably, premature mortality. 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 

used estimates of the social cost of carbon (SCC) obtained from the Interagency Working Group on Social 

Cost of Carbon (see IWGSCC 2010, p. 1) 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 8 details this 

analysis. 

2.6 Reduced Water Withdrawals

Steam electric plants use water 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 proposed ELGs are expected to reduce demand on aquifers by plants that rely on groundwater 

sources. 

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. 

Reduced water use from groundwater sources by steam electric plants would result in greater availability of 

groundwater supplies for alternative uses. EPA used an avoided cost method (based on the cost of 

desalination to replace groundwater supplies) to value the increased quantity of groundwater. This analysis is 

presented in Chapter 9. 

2.7 Summary of Benefits Categories

Table 2-1 summarizes the benefits of the proposed 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 threatened and endangered species, improved groundwater quality, reduced impoundment failures, reduced 
Benefit and Cost Analysis for Proposed ELGs 2: Benefits Overview 

April 19, 2013 2-12

air pollution, and reduced water withdrawals. Other benefit categories, including some human health risk 

reductions, 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. Benefits of Reduced Pollutant Discharges from Steam Electric Power Plants

Category Effect of Proposed ELGs

Benefits Analysis

Quantified Monetized

Methods (Report 

Chapter or Section

where Analysis is 

Detailed)

Human Health Benefits from Surface Water Quality Improvements

Reduced incidence of 

cancer 

Reduced exposure to arsenic from 

fish consumption

 

VSL (Section 3.1)

Reduced IQ losses to 

infants

Reduced in-utero mercury exposure 

from maternal fish consumption

 

IQ point valuation

(Section 3.3)

Reduced IQ losses to 

children ages 0 to 7

Reduced childhood exposure to lead 

from fish consumption

 

IQ point valuation

(Section 3.2)

Reduced need for 

specialized education

Reduced childhood exposure to lead 

from fish consumption

 

Avoided cost

(Section 3.2)

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 3.5)

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

 

Benefit transfer

(Chapter 4)

AestheticsaIncreased aesthetics from improved 

water clarity, color, odor, including 

nearby site amenities (residing, 

working, traveling)

 

Benefit transfer

(Chapter 4)

Non-use valuesa Enhanced existence, option, and 

bequest values from improved 

ecosystem health

 

Benefit transfer

(Chapter 4)

Aquatic and wildlife Reduced risks to aquatic life from 

exposure to steam electric pollutants

 

Benefit transfer

(Chapter 4)

Improved protection of 

T&E species

Improved T&E habitat and thus 

potential increase in T&E population

 

Benefit transfer

(Chapter 4)

Reduced sediment 

contamination 

Reduced deposition of toxic 

pollutants to sediment 

Qualitative 

discussion

Groundwater Quality Benefits

Groundwater quality Reduced groundwater contamination  Benefit transfer 

(Chapter 6)
Benefit and Cost Analysis for Proposed ELGs 2: Benefits Overview 

April 19, 2013 2-13

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

Category Effect of Proposed ELGs

Benefits Analysis

Quantified Monetized

Methods (Report 

Chapter or Section

where Analysis is 

Detailed)

Market and Productivity Benefits

Impoundment failures Reduced risk of impoundment 

failures due to changes in the use of 

impoundment  

Avoided cost of 

cleanup, natural 

resource damages, 

and transaction 

costs (Chapter 7)

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 from exposure to 

NOx, SO2 and particulate matter 

(PM2.5)

 

Benefit per ton of 

air pollutant 

removed (Chapter 

8)

Reduced air emissions of 

CO2

Avoided climate change /global 

warming impacts  

Social cost of 

carbon (SCC)

(Chapter 8)

Reduced Water Withdrawal Benefits

Reduced groundwater 

withdrawals

Increased availability of 

groundwater resources

 

Avoided cost

(Chapter 9)

Reduced surface water

withdrawals

Reduced impingement and 

entrainment mortality

Qualitative 

discussion

a. These values are implicit in the total WTP for water quality improvements.
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-1 

3 Human Health Benefits

EPA expects that the proposed ELGs would yield a range of 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, 2013a) provides details on the health effects caused by 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.9 This 

chapter presents EPA‘s analysis of human health benefits from reduced exposure to steam electric pollutants 

via the fish consumption pathway.10 The analyzed health benefit categories include: 1) reduced cancer cases 

from arsenic exposure, 2) reduced intelligence quotient (IQ) losses in children resulting from exposure to 

lead, and 3) reduced IQ losses among children exposed to mercury in-utero. 

The total quantified human health benefits included in this analysis represent only a subset of the potential 

health benefits expected to result from the proposed ELGs due to lack of data on a dose-response 

relationship11 between ingestion rates and potential other adverse health effects (such as kidney damage from 

cadmium or selenium exposure, cardiovascular impacts from lead exposure, gastrointestinal problems from 

zinc, thallium, or boron exposure, and others). Further, the analyses rely on several models that incorporate 

various assumptions that contribute to uncertainty in the estimated benefits and are subject to the limitations 

of the underlying water quality and fish tissue data developed in the EA (see U.S. EPA, 2013a). Beyond these 

limitations, the methodologies used to assess the human health benefits involve other limitations and 

uncertainties summarized in Section 3.6. 

EPA‘s analysis of the monetary value of human health benefits is based on data from the EA (see U.S. EPA, 

2013a for more details). The relevant data include COMIDs12 for receiving waters, ambient pollutant 

concentrations in receiving reaches under the baseline and post-compliance scenarios,13 pollutant 

concentrations in fish tissue, fish consumption rates among age cohorts for affected recreational anglers and 

subsistence fishers, and the daily average dose and the Lifetime Average Daily Dose (LADD, with units 

mg/kg BW/day) of pollutants for each age cohort for recreational anglers and subsistence fishers. Table 3-1

identifies the cohorts defined in the EA and used for this analysis. 

 

9

 Although reaches downstream of those receiving steam electric discharges may also be affected, this analysis focuses 

only on the benefits accrued from reaches receiving discharges directly from steam electric plants. 

10 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 Section 2.1.2: Drinking Water 

Consumption for details. 

11 A dose response relationship is an increase in incidences of an adverse health outcome per unit increase in exposure to 

a toxin. 

12 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. 

13 The baseline and post-compliance conditions account for discharges from steam electric plants only. It does not 

account for other pollutant sources to receiving waters (e.g., other industrial plant discharges, non-point sources, air 

deposition), which may contribute to elevated ambient concentrations but would not be affected by the proposed ELG. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-2 

Table 3-1. Cohorts Used for the Human Health Benefits Analysis

Cohort Recreational Anglers and 

their Households

Subsistence Fishers and their 

Households

Age (years)

0-1 0-1

2 2

3-5 3-5

6-10 6-10

11-15 11-15

16-20 16-20

Adult (21 or higher) Adult (21 or higher)

Source: U.S. EPA, 2013a.

3.1 Reduced Cancer Cases from Consumption of Fish Contaminated with Arsenic

Among steam electric pollutants analyzed in the EA, arsenic is the only confirmed carcinogen with a 

published dose response function (see U.S. EPA, 2010c).14 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 analyzed regulatory option. The reduction in the number of cancer cases from the baseline 

to post-compliance represents human health benefits attributable to the proposed ELGs. 

3.1.1 Methodology and Data

This section describes the methodology and data used to 1) identify the population of recreational and 

subsistence fishers potentially exposed to steam electric pollutants (hereafter, affected population); 2) estimate 

the difference in the number of avoided cancer cases due to the proposed ELGs under each of the regulatory 

options; and 3) estimate the monetary value of avoiding cancer cases. 

3.1.1.1 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 receiving 

steam electric discharges, and their household members. EPA estimated the number of people who are likely 

to fish reaches receiving steam electric discharges 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 

receiving reaches, and information on anglers‘ awareness and adherence to FCAs. To account for the family 

members potentially exposed to steam electric pollutants, EPA multiplied the number of anglers estimated to 

fish receiving reaches by the average household size in a given state. Appendix A provides details on the 

assumptions and data used in estimating affected populations. Equation 3-1 shows the calculation of the 

affected population, ExPop(i)(s), for waterbody i in state s. 

Equation 3-1. ( )( ) ( ) ( ) ( )

 ( )

 ( ) ( )

The equation terms are defined as follows: 

 Total population living within 100-mile distance of receiving reach – Pop100(i): The number of 

anglers who fish receiving reaches depends on how far anglers typically travel to their fishing 

destinations. Viscusi et al. (2008) found that 78 percent of anglers live within 100 miles of their 

 

14 Although other pollutants, such as cadmium, are also likely to be carcinogenic (see U.S. DHHS, 2008), EPA did not 

identify dose-response functions to quantify the effects of changes in these other pollutants. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-3 

fishing destinations. Accordingly, EPA used a distance of 100 miles to estimate the number of 

individuals potentially exposed to steam electric pollutants via fish consumption. As a first step in 

estimating the affected population, EPA estimated the number of people residing within the 100-mile 

buffer of receiving reaches by overlaying the receiving reach COMIDs and population data from the 

2010 Census. 

 Fraction of people who fish15 in the population – %Fish(s): To determine what percentage of the total 

population participates in fishing, EPA used region-specific U.S. Fish and Wildlife Service (U.S. 

FWS, 2006) estimates of the population 16 and older who fish in conjunction with U.S. Census 

Bureau (2010b) data on the share of the total population that is over 16.16

 Adjustment for substitute sites – [M(i)/BufM(i)]: Anglers may live within 100 miles of more than one 

waterbody and may choose to fish in other nearby waterbodies rather than the receiving reach. To 

account for the substitution effect of other sites, EPA estimated the fraction of the total stream miles 

within a 100-mile buffer zone attributed to the receiving reach. EPA assumed that recreational fishing 

efforts are uniformly distributed across all reach miles in the 100-mile buffer zone. Therefore, the 

number of anglers fishing the receiving reach is estimated by multiplying the fraction of the total 

reach miles attributed to the receiving reach by the angler population residing within the 100-mile 

buffer zone. For example, if the buffer zone around a 0.5-mile receiving reach includes 500 total 

miles of streams, EPA limited the estimate of potentially affected anglers to 0.1 percent of anglers 

living within the 100-mile buffer zone. This analysis does not account for the quality of substitute 

sites and, as a result, under- or overstates the fraction of anglers fishing the receiving reach. 

 Adjustment for fish consumption advisories – A(i): EPA further adjusted the affected angler 

population to reflect the presence of FCAs. Based on EPA‘s review of studies documenting anglers‘

awareness of FCA and their behavioral responses to FCA, 17 approximately 57.0 percent to 

61.2 percent of anglers are aware of FCA, and 71.6 percent to 76.1 percent of those who are aware 

ignore FCA. Conservatively assuming that 61.2 percent of anglers are aware of applicable FCA 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 FCA.18 Therefore, for receiving reaches with 

FCA (as identified in the EA), EPA reduced the affected populations by 17.4 percent. According to 

U.S. FWS (2006) data, approximately 23.3 percent of anglers release all the fish they catch (―catchand-release‖ anglers). Anglers practicing ―catch-and-release‖ would not be exposed to steam electric 

pollutants via consumption of contaminated fish. EPA did not further adjust the affected population to 

account for this practice since the data used to calculate the lifetime average daily dose (LADD) due 

to fish consumption is based on actual consumption rates for all recreational anglers, including those 

that practice ―catch and release.‖19

 Number of exposed family members – PPH(s): To account for family members in anglers households 

who may be exposed to steam electric pollutants via consumption of recreationally caught fish, EPA 

used the U.S. Census Bureau (2010a) data on the average number of people per household by state. 

 

15 Consistent with the assumed 95 percentile fish consumption rate for subsistence fishers, EPA assumed that 5 percent 

of the fishing population consists of subsistence fishers, and that the remaining 95 percent of the fishing population 

consists of recreational anglers. 

16 See Exhibit A-1 in Appendix A for details. 

17 See Exhibit A-2 and Exhibit A-3 in Appendix A for details. 

18 This is calculated as 61.2% aware of advisories times 28.4% (100%-71.6%) who choose not to fish or otherwise don‘t 

eat fish caught in waterbodies affected by advisories. 

19 See the EA (U.S. EPA, 2012a) for a more detailed discussion of the development of lifetime average daily dose 

(LADD) estimates. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-4 

To account for future population growth or decline, EPA also adjusted the affected population estimates based 

on the projected population changes from EPA‘s Environmental and Benefits Mapping and Analysis Program 

(BenMap) for the years 2014 (the promulgation year) through 2030. These projections are based on 2000

Census data as well as projections taken from the 2007 Woods and Poole projection estimates (Woods and 

Poole Economics, 2007). Because BenMap (at the time this analysis was conducted) does not project 

population estimates past 2030, EPA used stepwise autoregressive forecasting to estimate population changes 

between 2030 and 2040.20

Many of the 100-mile buffers around receiving reaches span more than one state. To estimate affected 

populations within multi-state buffers, EPA used the share of the 100-mile buffer area in each state to 

estimated weighted averages for the following parameters: percent of the population participating in fishing, 

persons per household, and population growth. 

Finally, EPA disaggregated the total affected population into each cohort based on 1) U.S. Census data 

documenting the share of the total population in one-year age increments (U.S. Census Bureau, 2010b), and 

2) assumptions about the share of anglers who practice subsistence fishing (versus recreational). EPA 

assumed that 5 percent of the affected population would be considered subsistence fishers, consistent with the 

assumed 95th percentile fish consumption rate for high-risk subsistence fishers.21

3.1.1.2 Estimating Avoided Cancer Cases

In the EA (U.S. EPA, 2013a), EPA estimated the LADD for seven age cohorts for recreational and 

subsistence anglers (14 cohorts total, Table 3-1) for the baseline and each analyzed regulatory option. EPA 

used these data to calculate the total number of cancer cases for each cohort for each receiving reach under the 

baseline and each of the regulatory options, based on Equation 3-2. 

Equation 3-2. ( )( ) ( )( ) ( )( )

CC(i)(c) = the number of cancer cases for waterbody i in cohort c. 

ExPop(i)(c) = the number of people affected for waterbody i in cohort c. 

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 waterbody i in cohort c (mg/kg 

BW/day). 

Summing the number of cancer cases across all cohorts and all receiving reaches yields the total number of 

annual cancer cases under the baseline and each of the regulatory options.22 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. 

3.1.1.3 Monetary Value of Avoided Cancer Cases

EPA used the ―value of a statistical life‖ (VSL) approach to estimate the monetary value of benefits 

associated with avoided cancer cases. EPA‘s Guidelines for Preparing Economic Analyses (U.S. EPA, 2010a) 

 

20 See Exhibit A-4 in Appendix A for population growth projections over the analysis period. 

21 Recreational anglers consume less fish and would thus experience lower cancer risks than subsistence fishers. 

22 EPA multiplied results for receiving reaches associated with surveyed steam electric plants by the sample weight to 

estimate total cases for all 1,079 steam electric plants subject to the proposed ELG. Because benefits are incurred only 

for waterbodies affected by steam electric plants with a sample weight of one, the use of sample weights does not 

introduce additional uncertainty. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-5 

currently recommends a default VSL of $7.9 million (2008 dollars), based on a distribution fitted to 26 

published VSL estimates. Updated to 2010 dollars,23 the VSL is $8.0 million. 

The VSL is based on estimates of society‘s willingness-to-pay (WTP) to avoid the risk of premature mortality 

from unforeseen instant mortality with no significant period of morbidity. The VSL approach may overstate 

benefits of avoiding cancer cases because not all cancer cases are fatal. On the other hand, the use of VSL to 

value an avoided cancer case may underestimate the benefits of reduced cancer risk, since it does not consider 

the costs and other effects that usually precede premature mortality from cancer (e.g., pain, suffering, and 

medical costs). 

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 duration of the 

cessation lag is unknown but may be as long as decades.24 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. 

The value of cancer risk reduction is a ―normal good‖,25 and thus is expected to grow over time as real income 

grows. EPA used historic state-specific median household income data from the U.S. Census Bureau‘s 2009 

Community Population Survey (U.S. Census Bureau, 2010b) for the years 1984 to 2009, and applied a 

stepwise autoregressive forecasting method to estimate future annual state level median household income 

through 2040.26 For each year in the analysis, EPA adjusted the VSL to account for income growth 

projections and the mid-range income elasticity assumptions27 from U.S. EPA (2010a).

3.1.2 Results

Table 3-2 shows the estimated changes in incidence of cancer cases from exposure to arsenic in fish tissue 

under the proposed ELGs and the annualized benefits calculated using 3 percent and 7 percent discount rates. 

The table lists the regulatory options in order of their total toxic-weighted pollutant removals. 

EPA estimates that Option 3 would reduce the number of cancer cases between 2017 and 2040 from 0.9 under 

the baseline to 0.4, for annual benefits of $0.09 million at a 3 percent discount rate ($0.05 million at a 7 

percent discount rate). EPA expects Options 3a and 3b to have smaller human health effects and benefits than 

Option 3. Option 4 would provide further reductions in the number of cancer cases (down to 0.2 over the 

period of 2017 through 2040), for total annual benefits of $0.15 million at a 3 percent discount rate ($0.09 

million at a 7 percent discount rate). The reductions in cancer cases and benefits of Option 4a are expected to 

be between those of Options 3 and 4, i.e., 0.4 to 0.7 fewer cancer cases per year and benefits between $0.09 

million and $0.15 million, at a 3 percent discount rate. 

 

23 EPA adjusted the VSL using the Consumer Price Index (CPI); 2010 = 218.056; 2008 = 215.303. 

24 U.S. EPA (2010c) 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. 

25 A ―normal good‖ is one for which consumer demand increases as income increases.

26 EPA updated the median household income to 2010 dollars using the Consumer Price Index. 

27 ―Income elasticity‖ is the degree to which WTP increases as income increases. For this analysis, EPA assumed that for 

every 1 percent increase in income, there is a 0.4 percent increase in VSL. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-6 

Table 3-2. Benefits from Reduced Cancer Cases 

Scenario Affected

Population

Total Cancer Cases, 

2017 to 2040

Reduced Cancer 

Cases, 2017 to 2040

Annualized Benefits

(Millions; 2010$)

3% Discount 

Rate

7% Discount 

Rate

Baseline 137,476 0.9 NA NA NA

Option 1 137,476 0.9 0.0 $0.00 $0.00 

Option 3a 137,476 a a a a

Option 2 137,476 0.9 0.0 $0.00 $0.00 

Option 3b 137,476 a a a a

Option 3 137,476 0.5 0.4 $0.09 $0.05 

Option 4a 137,476 b b b b

Option 4 137,476 0.2 0.7 $0.15 $0.09 

Option 5 137,476 0.2 0.7 $0.16 $0.09 

a. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Options 3a and 3b to be less 

than those of Option 3, with values for Option 3a being lower than Option 3b. EPA does not know how the benefits of Options 3a

and 3b compare to those of Options 1 and 2.

b. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Option 4a to be between those 

of Options 3 and 4.

Source: U.S. EPA Analysis, 2013

EPA conducted the analysis of health related benefits only for the reaches that receive steam electric 

discharges directly. The proposed ELGs are expected to provide additional benefits by also reducing arsenic 

concentration in reaches downstream from steam electric plant discharges. To evaluate the potential 

significance of these benefits, EPA also conducted a sensitivity analysis using fish tissue concentrations 

modeled for downstream reaches based on a simple pollutant dilution model. Appendix C presents the results 

of this sensitivity analysis. 

3.1.3 Revised Cancer Slope Factor

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 Table 3-2. 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, 2010c). Higher CSF values would lead to higher estimated reduction in the 

number of cancer cases (lunch and bladder combined) and higher benefit estimates for the proposed ELG. 

3.2 Benefits to Children from Reduced Lead Exposure via Fish Consumption

Exposure to lead can cause a variety of adverse health effects in adults and children (see U.S. EPA, 2013a). 

Because of data limitations, EPA estimated only the benefits to pre-school (ages 0 to 7) children from reduced 

lead exposure via consumption of contaminated fish tissue. 

3.2.1 Methodology and Data

This section describes the methodology and data used to estimate benefits to pre-school children potentially 

exposed to lead from consumption of fish caught in the reaches receiving steam electric discharges and that 

suffer learning disabilities as a result of their exposure. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-7 

EPA estimated benefits from reduced exposure to lead to preschool children using blood lead concentration 

(PbB) as a biomarker of lead exposure. EPA first modeled PbB under the baseline and post-compliance 

scenarios, and then used a dose 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). 

3.2.1.1 Affected Populations

EPA used the methodology described in Section 3.1.1 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 recreational anglers and subsistence 

fishers household members (Table 3-1). EPA subdivided the age 6-10 cohort based on the age six percent of 

the population (U.S. Census Bureau, 2010b), thereby restricting the analysis to the portion of the population 

cohort under age 7.28

3.2.1.2 IQ Losses due to Lead Exposure

This analysis considers children who are born after implementation of the proposed 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 one-year 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. 

Appendix B provides for 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. 

For each receiving reach, EPA used the cohort-specific LADD provided in the EA (U.S. EPA, 2013a). 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 and each receiving reach under the baseline and post-compliance scenarios. 

A linear dose-response relationship between PbB and IQ losses in the study by Schwartz (1994) suggests that 

a decrease of 0.25 IQ points can be expected for every 1 g/dL increase in PbB. More recent studies have 

calculated a steeper slope factor for lead effects on cognitive abilities (i.e. the effect is greater) at lower PbB 

levels. Therefore, using a linear dose-response function may underestimate lead impacts at lower PbB levels. 

For example, in a pooled data analysis, Lanphear et al. (2005; as cited in 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 above7.5 g/dL. 

 

28 U.S. Census Bureau (2010b) indicates that 1.3 percent of the population is 6 years old. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-8 

The IEUBK model results show that the estimated mean PbB for children exposed to lead from fish 

consumption in the baseline is approximately 2.7 μg/dL.29 EPA therefore used the dose-response factor of 

2.94 points per g/dL from Lanphear et al. to estimate IQ losses. For each analyzed regulatory option, EPA 

multiplied the estimated average decrease in PbB under the post-compliance scenario by 2.94 IQ points lost 

(per g/dL increase in PbB). This calculation 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 a given 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 Census Bureau (2010b) 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 proposed ELGs, 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).30 Equation 3-3 shows this calculation for the annual increase in total IQ points. 

Equation 3-3. ( ( 

 

))

∆IQ = the difference in total IQ points between the baseline and regulatory option scenarios 

∆GM = the change in the average PbB in affected population of children (μg/dL) 

2.94 = slope of the dose response function (IQ points lost per ug/dL increase in PbB) 

ExCh = the number of affected children aged 0 to 7 

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. 

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 $138,030 when discounting future earnings at 7 percent, and 

$614,729 when discounting future earnings at 3 percent. 31 The resulting estimated values of an IQ point are 

summarized in Table 3-3. 

Table 3-3. Value of an IQ Pointa(2010$)

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 $10,819 $14,631

7 percent $2,429 $3,285

a. Values are not adjusted for the cost of education.

 

29 The value is based on IEUBK outputs. 

30 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. 

31 EPA updated lifetime earnings to 2010 dollars using the Consumer Price Index (2010 = 218.056; 2009 = 214.537). 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-9 

Decreased IQ also results in less education and, therefore, reduced education costs. Chambers (2004) 

estimates that the annual cost of education for an average student is $9,724.32 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 of $979 to $1,274. EPA subtracted these education costs from the value 

of lifetime earnings per IQ point in Table 3-3. 

The value of an IQ point reduction adjusted for the avoided cost of education ranges between $1,156 

(following Schwarz (1994) and discounting future earnings at 7 percent) to $13,651 (following Salkever 

(1995) and discounting future earnings 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. 

3.2.1.3 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.33

EPA estimated the number of children that would have PbB above 20 g/dL for each receiving reach 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.34 Equation 3-4 shows the 

calculation of the number of children requiring compensatory education for each receiving reach. Summing 

across all receiving reaches provides the total number of children who would require special education. 

Equation 3-4. ( ) ( ) ( ) 

CompEd(i) = the number of children with PbB over 20 g/dL and IQ less than 70 (who 

would need compensatory education) for reach i

ExCh(i) = the number of affected children for reach i

Pr20(i) = the probability that a child‘s PbB is above 20 g/dL for reach i

0.20 = 20% (share of children with PbB over 20 g/dL that would have IQ scores less than 

70) 

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 

2010 dollars35 yields annual costs of $15,805 per child. EPA assumed that children with IQ less than 70 

would incur these additional costs each year from age 7 to age 18. Discounting future costs using a 3 percent 

discount rate yields a total compensatory education cost of approximately $157,327 per child with an IQ score 

less than 70 ($125,537 per child if using a 7 percent discount rate). 

 

32 Estimates of the cost of education were updated to 2010 dollars using the Consumer Price Index for education (2010 = 

199.337; 2003 = 134.4) 

33 See Appendix B for a detailed description of the IEUBK model and its application in this analysis. 

34 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). 

35 All values were updated to 2010 dollars using the Consumer Price Index for Education (2010 = 199.337; 1999 = 

107.0). 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-10

3.2.2 Results

EPA estimates that the annual benefits of avoided IQ losses range between $2.2 and $3.2 million at a 

3 percent discount rate for Option 3, and $0.2 to $0.3 million at a 7 percent discount rate (Table 3-4). For 

Option 4, these benefits range between $5.6 and $7.9 million at a 3 percent discount rate ($0.4 to $0.8 million 

at a 7 percent discount rate). 

EPA estimates that the preferred options for existing sources would result in annual cost savings from reduced 

compensatory education requirements due to the decreased number of children with PbB above 20 ug/dL and 

IQs less than 70 valued at $0.02 million and $0.07 million annually, respectively for Options 3 and 4, at a 

3 percent discount rate ($0.01 million and $0.03 million annually at a 7 percent discount rate) (see Table 3-5).

As discussed in Section 3.1.2, EPA conducted the analysis of health related benefits only for the reaches that 

receive steam electric discharges directly. The proposed ELGs are expected to provide additional benefits by 

also reducing lead concentration in reaches downstream from steam electric plant discharges. To evaluate the 

potential significance of these benefits, EPA also conducted a sensitivity analysis using fish tissue 

concentrations modeled for downstream reaches based on a simple pollutant dilution model. Appendix C 

presents the results of this sensitivity analysis. 

Table 3-4. Estimated Benefits from Avoided IQ Losses for Children Ages 0 to 7

Scenario

Number of 

Affected

Children 0 to 7

Total Avoided 

IQ Losses, 2017 

to 2040

Annualized Value of Avoided IQ Point Losses a

(Millions; 2010$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Baseline 12,478 NA NA NA

Option 1 12,478 159.9 $0.06 $0.08 $0.00 $0.01

Option 3a 12,478 b b b b b

Option 2 12,478 159.9 $0.06 $0.08 $0.00 $0.01

Option 3b 12,478 b b b b b

Option 3 12,478 6,386.6 $2.21 $3.17 $0.16 $0.31

Option 4a 12,478 c c c c c

Option 4 12,478 15,837.6 $5.55 $7.94 $0.40 $0.80

Option 5 12,478 15,836.1 $5.55 $7.94 $0.40 $0.80

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).

b. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Options 3a and 3b to be less 

than those of Option 3, with values for Option 3a being lower than Option 3b. EPA does not know how the benefits of Options 3a

and 3b compare to those of Options 1 and 2.

c. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Option 4a to be between those 

of Options 3 and 4.

Source: U.S. EPA Analysis, 2013

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

Concentrations above 20 g/dL and IQ Less than 70

Scenario

Number of 

Affected

Children 

0 to 7

Number of Cases 

of PbB > 

20 ug/dL and IQ 

< 70, in 2017 to 

2040

Decrease in 

Number of Cases 

ofIQ < 70, in 

2017 to 2040

Avoided Annual Cost 

(Millions; 2010$)

3% Discount 

Rate

7% Discount 

Rate

Baseline 12,478 15.1 NA NA NA

Option 1 12,478 15.1 0.0 $0.00 $0.00 

Option 3a 12,478 a a a a
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-11

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

Concentrations above 20 g/dL and IQ Less than 70

Scenario

Number of 

Affected

Children 

0 to 7

Number of Cases 

of PbB > 

20 ug/dL and IQ 

< 70, in 2017 to 

2040

Decrease in 

Number of Cases 

ofIQ < 70, in 

2017 to 2040

Avoided Annual Cost 

(Millions; 2010$)

3% Discount 

Rate

7% Discount 

Rate

Option 2 12,478 15.1 0.0 $0.00 $0.00 

Option 3b 12,478 a a a a

Option 3 12,478 11.1 4.0 $0.02 $0.01 

Option 4a 12,478 b b b b

Option 4 12,478 2.6 12.5 $0.07 $0.03 

Option 5 12,478 2.6 12.5 $0.07 $0.03 

PbB = blood lead concentration.

a. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Options 3a and 3b to be less 

than those of Option 3, with values for Option 3a being lower than Option 3b. EPA does not know how the benefits of Options 3a

and 3b compare to those of Options 1 and 2.

b. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Option 4a to be between those 

of Options 3 and 4.

Source: U.S. EPA Analysis, 2013

3.3 Benefits to Infants from Reduced Exposure to Mercury

Mercury can have a variety of adverse health effects on adults and children (see U.S. EPA, 2013a). The 

proposed ELGs are expected to reduce the discharge of mercury to surface waters by steam electric 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. 

3.3.1 Methodology and Data

This section describes the methodology and data used to 1) identify the number of children in angler and 

substance fisher households who are likely to have in-utero exposure to mercury; 2) estimate IQ losses in the 

affected children under the baseline and each analyzed regulatory option; and 3) estimate the monetary value 

of benefits from reducing IQ losses among affected children. 

3.3.1.1 Affected Population

EPA identified the population of children exposed in-utero starting from the reach-specific affected 

population described in Section 3.1.1 (i.e., based on the number of recreational and subsistence fishers and 

adjusted to reflect fishing practices (advisories, catch and release), the availability of substitute locations, 

household size, and age cohorts). 

Because this analysis focuses only on infants born after implementation of the proposed ELGs, EPA further 

limited the affected population by estimating the number of women between the ages of 15 and 50 potentially 

exposed to contaminated fish caught in the waterbodies affected by mercury discharges from steam electric 

plants, and multiplying the result by the state-specific average fertility rate.36 This yields the annual number of 

births in the affected population. EPA acknowledges that fertility rates vary by age. However, the use of a 

 

36 The average state-specific fertility rates range from 10.7 to 20.6 babies per year per 1,000 people. See Exhibit A-5 in 

Appendix A for details. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-12

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. 

Two of the population cohorts utilized in the EA (U.S. EPA, 2013a) spanned ages 11 to 15 (one for 

recreational and one for subsistence, Table 3-1). EPA used the percentage of the population aged 11 to 15 that 

is aged 15 (U.S. Census Bureau, 2010b) to subdivide the two cohorts and estimate the number of 15-year-olds 

to be included as mothers to affected infants.37

3.3.1.2 IQ Losses due to in Utero Mercury Exposure

In this analysis, EPA used a linear dose-response 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 EA for details; U.S. EPA, 

2013a), EPA used the median conversion factor derived by Swartout and Rice (2000), who estimated that a a 

0.08 μg/kg body weight increase in daily mercury dose is associated with a 1 ppm increase in hair 

concentration. Equation 3-5 shows EPA‘s calculation of the total annual IQ decrement for a given receiving 

reach. 

Equation 3-5. ( ) ( ) ( ) ( 

 

) 

IQL(i) = IQ losses associated with in-utero exposure to mercury from maternal consumption 

of fish contaminated with mercury for reach i

InExPop(i) = affected population of infants for reach i (the number of births) 

MFish(i) = maternal mercury exposure from consumption of fish caught in the reaches 

receiving steam electric pollutant discharges (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 (IQL) across all receiving reaches yields the total number of IQ points lost due 

to in-utero mercury exposure from maternal fish consumption under each analyzed regulatory option. The 

benefits of the proposed ELGs are calculated as the reduction in IQ points lost between the baseline and 

modeled post-compliance conditions. 

3.3.1.3 Monetizing Benefits

Available economic literature provides little empirical data on society‘s overall WTP to avoid a reduction in 

an infant‘s IQ. To determine the value of avoided IQ losses, EPA used an estimate of the reduction in the 

present value of the infant‘s future expected lifetime earnings per one IQ point reduction minus the cost of 

avoided education. 

EPA used the same value of an IQ point presented in Table 3-3, and adjusted for avoided education cost (see 

Section 3.2.1), to estimate the value of avoided IQ losses from mercury exposure in utero. The estimated 

 

37 U.S. Census Bureau (2010b) indicates that 1.4 percent of the population is age 15. 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-13

values of an IQ point ranges from $1,156 (following Schwarz (1994) and discounting future earnings at 

7 percent) to $13,651 (following Salkever (1995) and discounting future earnings at 3 percent). This effect 

represents only one component of society‘s WTP to avoid IQ losses, and thus underestimates the total value 

of benefits to children from reduced exposure to mercury.

3.3.2 Results

Table 3-6 shows the estimated value of avoided IQ losses due to in-utero mercury exposure. The estimated 

annual benefits of avoided IQ losses from reduced maternal exposure to mercury under Option 3 range 

between $4.1 million and $5.8 million at a 3 percent discount rate (between $0.3 million and $0.6 million at a 

7 percent discount rate). These benefits accrue over the period of 2017 through 2040. Benefits for Option 4 

are about 1.8 times higher than for Option 3, with annualized value of avoided IQ losses ranging between 

$8.4 million and $12.1 million at a 3 percent discount rate ($0.6 million to $1.2 million at a 7 percent discount 

rate). 

As discussed above, EPA conducted the analysis of health related benefits only for the reaches that receive 

steam electric discharges directly. The proposed ELGs may reasonably be expected to provide additional 

benefits by also reducing mercury concentration in reaches downstream from steam electric plant discharges. 

To evaluate the potential significance of these benefits, EPA also conducted a sensitivity analysis using fish 

modeled tissue concentrations for downstream reaches based on a simple pollutant dilution model. Appendix 

C presents the results of this sensitivity analysis. 

Table 3-6. Estimated Benefits from Avoided IQ Losses Due to Reduced In-utero Mercury Exposure

Scenario

Number of 

Births in 

Affected

Population (per 

year)

Total Avoided 

IQ Losses, 2017 

to 2040

Annualized Value of Avoided IQ Lossesa

(Millions; 2010$)

3% Discount Rate 7% Discount Rate

Low Bound High 

Bound Low Bound High Bound

Baseline 1,932 NA NA NA

Option 1 1,932 8,895 $3.15 $4.51 $0.23 $0.46 

Option 3a 1,932 b b b b b

Option 2 1,932 9,096 $3.22 $4.61 $0.24 $0.47 

Option 3b 1,932 b b b b b

Option 3 1,932 11,547 $4.08 $5.83 $0.30 $0.59 

Option 4a 1,932 c c c c c

Option 4 1,932 23,970 $8.42 $12.05 $0.61 $1.21 

Option 5 1,932 24,004 $8.43 $12.06 $0.61 $1.22 

a. Low bound estimate assumes that the loss of one IQ point results in the loss of 1.76 percent of lifetime earnings (following 

Schwartz, 1994); high bound estimate assumes that the loss of one IQ point results in the loss of 2.38 percent of lifetime earnings 

(following Salkever, 1995).

b. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Options 3a and 3b to be less 

than those of Option 3, with values for Option 3a being lower than Option 3b. EPA does not know how the benefits of Options 3a

and 3b compare to those of Options 1 and 2.

c. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Option 4a to be between those 

of Options 3 and 4.

Source: U.S. EPA Analysis, 2013

3.4 Benefits to Subsistence Fishers

As noted in Section 3.1.1, EPA assumed for this analysis that 5 percent of the exposed population is 

subsistence fishers, and that the remaining 95 percent is recreational anglers. This is based on the assumed 

95th percentile fish consumption rate for subsistence fishers. These individuals consume more self-caught fish 
Benefit and Cost Analysis for Proposed ELGs 3: Human Health Benefits 

April 19, 2013 3-14

than recreational anglers and would therefore experience higher health risks associated with the consumption 

of contaminated fish tissue. 

Table 3-7 shows the annual human health benefit estimates for two analyzed options – Options 3 and 4 –

disaggregated into those accruing to recreational anglers and to subsistence fishers. Although subsistence 

fishers account for 5 percent of the total exposed population, they account for 18 percent to 50 percent of the 

total benefits, depending on the regulatory option and benefit category. 
Benefit and Cost Analysis for Proposed ELG 3: Human Health Benefits 

April 19, 2013 3-15

Table 3-7. Estimated Annualized Health Benefits to Recreational Anglers and Subsistence Fishers (Millions; 2010 $)

ELG 

Regulatory 

Option

Discount 

Rate Benefit Category

Recreational Anglers Subsistence Fishers Total Exposed 

Population

Annual Benefits % of Total Annual Benefits % of Total Annual Benefits

Low High Low High Low High

Option 3

3 percent

Avoided Cancer Cases from 

Exposure to Arsenic $0.08 81% $0.02 19% $0.09 

Avoided IQ Losses from 

Exposure to Lead $1.79 $2.56 81% $0.42 $0.60 19% $2.21 $3.17 

Avoided Compensatory 

Education from Exposure to Lead $0.01 50% $0.01 50% $0.02 

Avoided IQ Losses from in-Utero 

Exposure to Mercury $3.28 $4.70 81% $0.79 $1.14 19% $4.08 $5.83 

7 percent

Avoided Cancer Cases from 

Exposure to Arsenic $0.04 81% $0.01 19% $0.05 

Avoided IQ Losses from 

Exposure to Lead $0.13 $0.25 81% $0.03 $0.06 19% $0.16 $0.31 

Avoided Compensatory 

Education from Exposure to Lead $0.01 50% $0.01 50% $0.01 

Avoided IQ Losses from in-Utero 

Exposure to Mercury $0.24 $0.48 81% $0.06 $0.12 19% $0.30 $0.59 

Option 4

3 percent

Avoided Cancer Cases from 

Exposure to Arsenic $0.13 50% $0.13 50% $0.25 

Avoided IQ Losses from 

Exposure to Lead $4.53 $6.48 82% $1.02 $1.46 18% $5.55 $7.94 

Avoided Compensatory 

Education from Exposure to Lead $0.05 70% $0.02 30% $0.07 

Avoided IQ Losses from in-Utero 

Exposure to Mercury $6.78 $9.70 81% $1.64 $2.35 19% $8.42 $12.05 

7 percent

Avoided Cancer Cases from 

Exposure to Arsenic $0.07 50% $0.07 50% $0.14 

Avoided IQ Losses from 

Exposure to Lead $0.33 $0.65 82% $0.07 $0.15 18% $0.40 $0.80 

Avoided Compensatory 

Education from Exposure to Lead $0.02 71% $0.01 29% $0.03 

Avoided IQ Losses from in-Utero 

Exposure to Mercury $0.49 $0.98 81% $0.12 $0.24 19% $0.61 $1.21 
Benefit and Cost Analysis for Proposed ELG 3: Human Health Benefits 

April 19, 2013 3-16

3.5 Potential Additional Health Benefits

EPA expects that additional health benefits would arise from reduced exposure to pollutants in steam electric 

plant discharges; however, monetary valuation of these other health benefits is not possible due to lack of data 

on a dose-response relationship between pollutant ingestion rate and potential adverse health effects. To 

provide an additional measure of the potential health benefits of the proposed ELGs, EPA also estimated the 

expected reduction in the number of receiving reaches with pollutant concentrations in excess of human 

health-based aquatic water quality criteria (AWQC). This analysis and its findings are not additive to the 

preceding analyses of changes in cancer rates, lead-related effects, and mercury-related effects, 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 

regulatory option in the receiving reaches (see the EA; U.S. EPA, 2013a) 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). 

The analysis was performed on reaches that receive discharges from steam electric plants directly (i.e., 

excludes reaches located downstream of steam electric plants and that would also have changes in pollutant 

concentrations under the proposed ELGs).

Table 3-8 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 146 receiving reaches 

nationwide as the result of baseline steam electric pollutant discharges. EPA expects that Option 3 would 

eliminate the occurrence of concentrations in excess of human health-based criteria for 27 of the receiving 

reaches and reduce the number of exceedances in another 27 reaches. Options 3a and 3b would have effects 

that will be smaller or equal to those of Option 3. Option 4 would reduce the number of exceedances in 24 

reaches and eliminate them altogether in another 98 reaches. The Agency expects that Option 4a will reduce 

the number of exceedances between Options 3 and 4, i.e., eliminate all exceedances in 27 to 98 reaches, and 

reduce exceedances in 24 to 27 reaches. 

Table 3-8. Receiving Reaches Exceeding Human Health Criteria for Steam Electric Pollutants

Scenario

Number of Receiving Reaches 

with Steam Electric Pollutant 

Concentrations Exceeding 

Human Health Criteria for at 

Least One Pollutant

Number of Receiving Reaches with Improved Water 

Quality, Relative to Baseline

All Exceedances 

Eliminated

Number of Exceedances 

Reduced

Baseline 146 NA NA

Option 1 146 0 9

Option 3a a a a

Option 2 146 0 12

Option 3b a a a

Option 3 119 27 27

Option 4a b b b

Option 4 48 98 24

Option 5 50 96 21
Benefit and Cost Analysis for Proposed ELG 3: Human Health Benefits 

April 19, 2013 3-17

Table 3-8. Receiving Reaches Exceeding Human Health Criteria for Steam Electric Pollutants

Scenario

Number of Receiving Reaches 

with Steam Electric Pollutant 

Concentrations Exceeding 

Human Health Criteria for at 

Least One Pollutant

Number of Receiving Reaches with Improved Water 

Quality, Relative to Baseline

All Exceedances 

Eliminated

Number of Exceedances 

Reduced

a. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Options 3a and 3b to be less 

than those of Option 3, with values for Option 3a being lower than Option 3b. EPA does not know how the benefits of Options 3a

and 3b compare to those of Options 1 and 2.

b. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Option 4a to be between those 

of Options 3 and 4.

Source: U.S. EPA Analysis, 2013

3.6 Limitations and Uncertainties

This analysis 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 proposed ELGs. 

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. Table 3-9 summarizes the uncertainties and indicates the direction of the 

potential bias. 

Table 3-9. Uncertainties in the Analysis of Human Health Benefits

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

Background concentrations of

steam electric pollutants from 

upstream sources, contaminated 

sediments from previous 

discharges, or natural sources, are 

not considered in this analysis.

a

Uncertain Even if discharges of the pollutants are reduced or 

eliminated as a result of the proposed ELGs, background 

concentrations may persist for years. This may result in 

an over- or underestimate of the number of reaches for 

which the proposed ELGs would reduce the number of 

health-based water concentration criteria exceedances.

The analysis of health benefits 

does not include risks to 

recreational anglers and 

subsistence fishers downstream 

from receiving reaches.

a

Underestimate By omitting downstream effects, this analysis potentially 

understates baseline risks that could be reduced by the 

proposed ELGs, and therefore underestimates the 

benefits.

The analysis does not consider the 

suitability of alternate fishing sites.

Uncertain Estimating the number of anglers fishing receiving 

reaches based on the ratio of reach length to the total 

number of reach miles within the same 100-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 affected 

populations are likely to be overstated or understated.

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).
Benefit and Cost Analysis for Proposed ELG 3: Human Health Benefits 

April 19, 2013 3-18

Table 3-9. Uncertainties in the Analysis of Human Health Benefits

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

EPA used a CSF for arsenic of 1.5 

cases per mg/kg BW/day.

Uncertain This is the current IRIS value and was based on 

incidences of skin cancer. However, EPA is currently 

revising its cancer assessment of arsenic to reflect new 

data on internal cancers. It is possible that the revised 

combined (lung and bladder cancer) CSF would be higher 

(draft value is 25.7 per mg/kg BW/day), suggesting that 

the use of the current IRIS value may result in an 

underestimate of benefits.

There is a linear 0.18 point IQ loss

for each 1 ppm increase in 

maternal hair mercury.

Uncertain This dose-response function used in this analysis 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.

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 proposed ELG of

reduced lead and mercury exposure.

The IEUBK model processes daily 

intake from ―alternative sources‖ 

to 3 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 lead and mercury.

Underestimate The scientific literature suggests that exposure to lead and 

mercury may have significant adverse health effects for 

adults; if measurable effects at occurring at current 

exposure levels, excluding the benefits of reduced adult 

exposure to these pollutants results in an underestimate of 

benefits.

a. EPA is currently reviewing and revising its modeling methodology and plans to consider background concentrations and 

downstream reaches in a future revision to this analysis.
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-1 

4 Non-Market Benefits from Water Quality Improvements

As discussed in the environmental assessment (EA) document (U.S. EPA, 2013a), heavy metals, nutrients, 

and other pollutants discharged by steam electric 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, thus their value can be directly observed. Other environmental goods and services (e.g., recreation 

and support of aquatic life) that cannot be bought or sold directly and thus don‘t have observable market 

values. These second types of environmental goods and services are classified as ―nonmarket‖. The expected 

changes in nonmarket values of the water resources affected by the proposed ELG (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 

proposed 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 

proposed ELGs using a meta-analysis of surface water valuation studies that provide data on the public‘s 

willingness-to-pay (WTP) for water quality improvements.38 The analysis accounts for improvements in water 

quality resulting from changes in nutrient and metals concentrations in reaches affected by discharges from 

steam electric plants. 

4.1 Water Quality

To link water quality changes from reduced metal and nutrient 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 modifies the WQI used by EPA in the Environmental and Economic Benefits 

Assessment for the Final Construction and Development Rule (also referred to as the C&D rule; U.S. EPA, 

2009b), 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). To account for reductions in loadings of heavy metals 

 

38 The meta-analysis of surface water valuation studies for estimating benefits of water quality improvements resulting 

from the regulation follows EPA‘s approach in the analysis of Construction and Development (C&D) regulations (U.S. 

EPA, 2009b). The technical details involved in the estimation of meta-analyses are also presented in sources such as 

Bateman and Jones (2003), Johnston et al. (2005, 2006), Shrestha et al. (2007), and Rosenberger and Phipps (2007). EPA 

is presenting the meta-analysis approach in this analysis but there are other approaches that could be used to monetize 

the benefits. 
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-2 

resulting from the proposed ELGs, EPA modified the WQI for freshwater waterbodies39 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 (CCME) wherein the index values are calculated based on the scope, 

frequency, and amplitude of exceedances of specified numeric thresholds (CCME, 2001).TheWQI used in 

this analysis, therefore, 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. 

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 proposed 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 Sections 4.1.3

and 4.1.4). 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, 2009b) using baseline TSS, TN, and TP 

concentrations calculated in SPARROW at the E2RF1 reach level.40,41,42 For each of the 85 Level III 

ecoregions intersected by the E2RF1 reach network, EPA derived the transformation curves by assigning a 

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 

 

39 EPA analyzed changes to water quality resulting from the implementation of the proposed ELG 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 water bodies due to the relatively small changes in concentrations 

expected. 

40 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 instream 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

41 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. 

42 The selected data exclude outlier TSS concentration values, defined as values that exceed the 95th percentile based on 

the national population of all reaches. 
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-3 

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 CCME (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. Unlike the CCME index, however, the metal 

subindex does not differ depending on the frequency or amplitude. With regards to frequency, EPA modeled 

long-term annual average concentrations in ambient water (see Section 4.1.2 and EA 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. 

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 D.

Table 4-1. Freshwater Water Quality Subindices

Parameter Concentrations Concentration Unit Subindex

Dissolved Oxygen (DO)

DO saturation ≤100%

DO ≤ 3.3 Mg/L 10

DO 3.3 < DO < 10.5 Mg/L -80.29+31.88*DO-1.401*DOb

DO 10.5 ≤DO Mg/L 100

100% < DO saturation ≤ 275%

DO N/A Mg/L 100 * exp((DOsat - 100) *-1.197 

E-2)

275% < DO saturation

DO N/A Mg/L 10

Fecal Coliform (FC)

FC ≤ 50 Lbs/100 mL 98

FC 50 < FC ≤ 1,600 Lbs/100 mL 98 * exp(FC - 50) * -9.9178 E-4

FC > 1,600 Lbs/100 mL 10

Total Nitrogen (TN)a

TN ≤ TN10 Mg/L 10

TN TN 10 < TN ≤ T TN 

100

Mg/L a * exp(TN*b); where a and b are 

ecoregion-specific values

TN > TN 100 Mg/L 100

Total Phosphorus (TP)

TP ≤ TP10 Mg/L 10

TP TP10 < TP ≤ TP 100 Mg/L a * exp(TP*b); where a and b are 

ecoregion-specific values

TP > TP S100 Mg/L 100
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

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Table 4-1. Freshwater Water Quality Subindices

Parameter Concentrations Concentration Unit Subindex

Total Suspended Solids (TSS)c

TSS ≤ TSS10 Mg/L 10

TSS TSS10 < TSS ≤ 

TSS100

Mg/L a * exp(TSS*b); where a and b are 

ecoregion-specific values

TSS > TSS100 Mg/L 100

Biochemical Oxygen Demand, 5-day (BOD)

BOD BOD≤ 8 Mg/L 100 * exp(BOD * -0.1993)

BOD BOD > 8 Mg/L 10

a. TN10 and TN100 are ecoregion-specific TN concentration values that correspond to subindex scores of 10 and 100, respectively.

b. TP10 and TP100 are ecoregion-specific TP concentration values that correspond to subindex scores of 10 and 100, respectively.

c. TSS10 and TSS100 are ecoregion-specific TSS concentration values that correspond to subindex scores of 10 and 100, respectively.

Source: EPA analysis using methodology in Cude (2001) .

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

Parameter Number of AWQC Exceedances Subindex

Metals 0 100

Metals 1 87.5

Metals 2 75

Metals 3 62.5

Metals 4 50

Metals 5 37.5

Metals 6 25

Metals 7 12.5

Metals 8 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

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. 
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-5 

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 and total phosphorus, and baseline concentrations of total suspended solids.43 See U.S. 

EPA (2009b) for a discussion of how EPA used the national SPARROW models to estimate ambient 

concentrations of TN, TP, and TSS. 

 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 each of 296 receiving 

reaches under the baseline and five of the eight regulatory options (see EA for discussion of receiving 

reaches; U.S. EPA, 2013a). In addition to these loadings, the RSEI model also incorporates 2010 

Toxic Release Inventory (TRI) data on annual average discharges from other industrial sources.44

EPA input the loadings from steam electric plant and TRI discharges in the RSEI model to estimate 

the long-term average concentrations in receiving and downstream reaches. The RSEI model uses a 

simple dilution and first-order decay equation (metals are treated as conservative substances). In 

the model, a plant is assumed to release its annual discharge at a constant rate throughout the year. 

Annual average concentrations are then estimated and tracked downstream throughout the 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 number of exceedances per waterbody (each receiving 

reach) was calculated by comparing baseline and post-compliance concentrations with EPA‘s 

freshwater chronic aquatic life criteria values for each metal45. If the concentration was greater than 

 

43 EPA did not model changes in TSS concentrations as a result of this proposed ELG. 

44 EPA removed from the 2010 TRI data the loadings associated with steam electric plants that report to TRI. For steam 

electric plants, EPA used the loadings estimated in the EA under the baseline and the regulatory options. Loadings for 

other TRI dischargers (non-steam electric plant and other industrial facilities) remained constant throughout all 

scenarios. 

45 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 
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-6 

the 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 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. 46 EPA‘s 

Storage and Retrieval (STORET) data warehouse provided additional data on fecal coliform counts 

and biochemical oxygen demand when NWIS data was unavailable (U.S. EPA, 2008a).47

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 and nutrient 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 and 

Nutrient Concentrations

Reach Input Data Water Quality Model Model Output 

Reach directly 

receiving steam 

electric plant 

discharge (296 

reaches) and 

downstream 

reaches

Baseline and policy metal loadings

to reaches directly receiving steam 

electric plant discharges (U.S. 

EPA, 2013a). 

Metal loadings from other TRI 

dischargers in 2010.

Concentrations modeled in

RSEI

In-steam metal 

concentrations at the NHD 

level

Baseline and policy nutrient

loadings to reaches directly 

receiving steam electric plant

discharges (U.S. EPA, 2013a)

Concentrations modeled in 

SPARROW

In-stream nutrient

concentrations at the 

E2RF1 level

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. To conduct the analysis, EPA 

analyzed the data at the level of spatial detail at which they exist, i.e., changes estimated at the level of E2RF1 

reaches are attributed to these reaches; changes estimated at the more detailed NHD level are attributed only 

to those (fewer) reach miles to which they apply. The WQI and benefits are ultimately calculated at the 

resolution of E2RF1 reaches, but with adjustments made to data available at the NHD level to reflect 

differences in spatial scale. Thus, to reconcile the two levels of resolution, EPA mapped all receiving reaches 

from the NHD to the E2RF1 network using GIS and assigned the closest E2RF1 ID to each NHD catchment. 

EPA then calculated the fraction of E2RF1 reach miles that correspond to the NHD reaches affected by steam 

electric plant discharges either directly (receiving reach) or from upstream sources (downstream reach). 

Figure 4-1 illustrates the differences in scale between the E2RF1 network and the NHD network. 

 

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. 

46 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/

47 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/
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

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

A total of 121 E2RF1 IDs had multiple NHD catchments with changes in metal concentrations. For these 

cases, EPA used a length weighted average to estimate E2RF1-level concentrations and the number of 

exceedances attributed to the E2RF1 reach. Table 4-5 contains a summary of the methods used by EPA to 

transfer metal concentrations from the NHD level to the E2RF1 level. 

Table 4-5. Methods Used to Transfer Metal Concentration Reductions from the NHD to the E2RF1 

Level 

Scenario Method Number of E2RF1 

Reaches

A single NHD reach is estimated to 

have metal concentration 

reductions along the E2RF1 reach

Water quality is calculated at E2RF1 level and the 

change is attributed to only the portion of the 

E2RF1 reach comprised of the NHD.

196

Multiple NHD reaches are 

estimated to have metal 

concentration reductions along the 

E2RF1 reach

The number of AWQC exceedances is calculated at 

the NHD level and then a reach length weighted 

average is used to calculate the number of AWQC 

exceedances at the E2RF1level using NHD lengths 

along the E2RF1 reach. 

121

Of the 296 NHD reaches directly receiving steam electric plant discharges, 61 are projected to experience 

changes in metal concentrations sufficient to eliminate one or more AWQC exceedances under the regulatory 

options EPA analyzed. Additionally, a total of 639 downstream NHD reaches are estimated to have fewer 

AWQC exceedances under the regulatory options. The combination of these NHD reaches and the E2RF1 

reaches with changes in nutrient concentrations modeled in SPARROW amount to a total of 3,030 unique 
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benefiting E2RF1 reaches comprising a total of 23,129 reach miles, the E2RF1 network in the contiguous 

United States is comprised of approximately 60,878 reaches (636,863 reach miles) . 

Baseline concentrations for all WQI parameters were available for a total of 2,965 of the 3,030 reaches. EPA 

used a successive average approach to address the data gaps in WQI parameters not modeled above (i.e., DO, 

BOD, fecal coliform) in the remaining freshwater 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 fill in all missing data.48 This approach assumes that reaches located in the 

same watershed generally share similar characteristics. Using this estimation approach, EPA was able to 

compile baseline water quality data for all freshwater reaches. Table 4-6 summarizes the data sources used to 

estimate baseline and post-compliance values by water quality parameters. 

Table 4-6. Water Quality Data used in Calculating the Baseline and Policy WQI

Parameter Baseline value Post-compliance value

TN From SPARROW output (baseline run) From SPARROW output (regulatory option run)

TP From SPARROW output (baseline run) From SPARROW output (regulatory option run)

TSS From SPARROW output (baseline run) No change. Regulatory option value equal 

baseline value

DO Baseline value at the E2RF1 levela No change. Regulatory option value equal 

baseline value

BOD Baseline value at the E2RF1 levela No change. Regulatory option value equal 

baseline value

Fecal Coliform Baseline value at the E2RF1 levela No change. Regulatory option value equal 

baseline value

Metals Baseline exceedances at NHD level Regulatory option exceedances at NHD level

when a single NHD reach is estimated to have 

reductions in AWQC exceedances along a given 

E2RF1. When multiple NHD reaches along the 

same E2RF1reach have reductions in AWQC 

exceedance, then regulatory option exceedances

are estimated at theE2RF1 level using a weighted 

average (see Table 4-5)

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

Based on the estimated WQI value under the baseline scenario, EPA categorized each E2RF1 reach 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 

 

48 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, 2007). 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. 
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

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WQI values greater than 70 indicate that waters are swimmable (the recreational use with the highest required 

WQI).49

Table 4-7. Percentage of Affected Reach Miles by WQI Classification: Baseline Scenario 

Water 

Quality 

Classification

Baseline 

WQ

Receiving 

Reach 

Miles

Percent of 

Receiving 

Reach 

Miles 

Analyzed

Downstream 

Reach Miles

Percent of 

Downstream 

Reach Miles 

Analyzed

All 

Reach 

Miles

Percent 

of Total 

Reach 

Miles 

Analyzed

Unusable WQI<25 0 0.0% 0 0.0% 0 0.0%

Suitable for 

Boating

25≤WQI<45 112 7.4% 851 3.9% 963 4.2%

Suitable for 

Rough Fishing

45≤WQI<50 100 6.6% 735 3.4% 835 3.6%

Suitable for 

Game Fishing

50≤WQI<70 649 42.8% 8,453 39.1% 9,102 39.4%

Suitable for 

Swimming

70≤WQI 656 43.3% 11,572 53.6% 12,229 52.9%

Total 1,517 100.0% 21,612 100.0% 23,129 100.0%

Note: Includes miles for the 58 E2RF1 reaches that contain both NHD catchments with direct loadings and NHD catchments with 

downstream metal reductions.

Source: U.S. EPA Analysis, 2013

4.1.4 Estimated Changes in Water Quality (∆WQI) from the Regulation

To estimate benefits of water quality improvements expected to result from the proposed ELGs, EPA 

calculated the WQI for each analyzed regulatory option. In calculating the post-compliance WQI, the Agency 

used option-specific metal, TN, and TP concentrations. The estimated post-compliance metal and nutrient 

concentrations reflect the expected reduction in pollutant discharges under each analyzed regulatory option. 

Although the proposed ELGs would also reduce loadings of other pollutants, such as BOD, these other 

parameters were held constant in this analysis for all regulatory options, due to data limitations. 

As discussed in Section 4.1.2, EPA estimated changes in ambient concentrations of TN and TP using the 

USGS‘s SPARROW model and metal concentrations using the water quality component of the RSEI model. 

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 proposed ELGs. Table 4-8 presents 

water quality changes for all reaches. EPA estimated that the total number of improved reach miles would 

range from 3,945 miles (Option 1) to 22,447 miles (Option 4). Option 3 is expected to improve water quality 

in 15,682 reach miles. These 15,682 reach miles comprise 67.8 percent of all analyzed reach miles, and 

2.9 percent of all E2RF1 miles located in the 42 states where benefits accrue. Option 4 improves the most 

number of reach miles, 22,447, or 97.1 percent of all analyzed reach miles and 4.1 percent of all E2RF1 

miles.50

 

49 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. 

50 Option 5 improves slightly fewer miles than Option 4 (22,441 vs. 22,447 miles) because, under Option 4, plants with 

both leachate and FGD waste streams are assumed to implement chemical precipitation and biological treatment for the 

combined streams. Under Option 5, plants are assumed to treat the two streams separately: FGD wastewater by 

evaporation and leachate using chemical precipitation (which removes less pollutant load than biological treatment). 
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-10

Table 4-8. Water Quality Improvements from Proposed ELGs in All Benefiting Reaches

Change In WQI Total Number of Improved 

Reaches

Total Improved Reach 

Miles

Percentage of Benefiting 

Reach Miles 

(23,129 Miles)

Percentage of E2RF1State 

River Miles (547,352 Miles) 

Benefiting 

Option 1

0 < ΔWQI < 1 438 3,474 15.0% 0.6%

1 ≤ ΔWQI < 2.5 27 387 1.7% 0.1%

2.5 ≤ ΔWQI < 10 6 36 0.7% <0.1%

10 ≤ ΔWQI 2 48 0.2% <0.1%

Total 473 3,945 17.1% 0.7%

Option 2

0 < ΔWQI < 1 1,681 12,126 52.4% 2.2%

1 ≤ ΔWQI < 2.5 17 225 1.0% <0.1%

2.5 ≤ ΔWQI < 10 28 284 1.2% 0.1%

10 ≤ ΔWQI 2 48 0.2% <0.1%

Total 1,728 12,683 54.8% 2.3%

Option 3

0 < ΔWQI < 1 2,065 14,928 64.5% 2.7%

1 ≤ ΔWQI < 2.5 26 372 1.6% 0.1%

2.5 ≤ ΔWQI < 10 31 309 1.3% 0.1%

10 ≤ ΔWQI 5 73 0.3% <0.1%

Total 2,127 15,682 67.8% 2.9%

Option 4

0 < ΔWQI < 1 2,747 20,708 89.5% 3.8%

1 ≤ ΔWQI < 2.5 100 932 4.0% 0.2%

2.5 ≤ ΔWQI < 10 62 699 3.0% 0.1%

10 ≤ ΔWQI 10 107 0.5% <0.1%

Total 2,919 22,447 97.1% 4.1%

Option 5

0 < ΔWQI < 1 2,748 20,726 89.6% 3.8%

1 ≤ ΔWQI < 2.5 98 909 3.9% 0.2%

2.5 ≤ ΔWQI < 10 62 699 3.0% 0.1%

10 ≤ ΔWQI 10 107 0.5% <0.1%

Total 2,918 22,441 97.0% 4.1%
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-11

4.2 Willingness to Pay for Water Quality Improvements

To estimate nonmarket benefits of water quality improvements resulting from the proposed ELG options, 

EPA used results of the meta-analysis of stated preference studies described in the Environmental and 

Economic Benefits Assessment for the Final Construction and Development Rule (U.S. EPA, 2009a). EPA‘s 

benefit transfer approach follows standard methods described by Johnston et al. (2005) , Shrestha et al. 

(2007), and Rosenberger and Phipps (2007). EPA‘s meta-analysis results imply a simple benefit function of 

the following general form: 

Equation 4-2. ( ) ∑( 

) ( )

ln(WTPY,A,S) = The predicted natural log of annual household WTP for a given year 

(Y), analytic category (A) and a given state (S)). Each analytic group 

corresponds to a combination of the baseline water quality category (WQI 

baseline) and the expected change in water quality under the regulatory 

option for a given year(∆WQI) 

coefficient i = A vector of variable coefficients from the meta-regression with i 

elements 

independent variable valuesi = A vector of independent variable values with i elements 

Here, ln(WTPY,A,S) is the dependent variable in the meta-analysis—the natural log of annual state-level 

household WTP for water quality improvements in a given year (Y). Water quality improvements are 

measured as a change in WQI from the baseline to post-compliance scenario (WQI). Household WTP vary 

by year, because household income (an independent variable) changes over time. The metadata include 

independent variables characterizing specific details of the resource(s) valued, such as the baseline resource 

conditions; the extent of resource improvements (i.e., WQI) and whether they occur in estuarine or 

freshwater; the geographic region and scale of resource improvements (e.g., regional improvements versus 

improvements in one or several waterbodies); characteristics of surveyed populations (e.g., users, nonusers); 

and other specific details of each study. Table 4-9 provides the estimated regression equation intercept (5.71), 

variable coefficients (coefficienti), and the corresponding independent variable names. This 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 proposed ELGs‘ policy context. 

Table 4-9. Independent Variable Assignments for Surface Water Quality Meta-Analysis

Variable 

Type Variable Coefficient Assigned Value Explanation

Study and 

Methodology 

Variables

intercept 5.7109 .

year_index -0.08043 9.68 Set to 9.68 to reflect the mean year that the studies 

in the dataset were conducted.

discrete -0.1248 1

Set to one to reflect survey efforts that employed 

discrete choice elicitation methods, which are 

preferred over other approaches, such as openended and payment card methods.

volunt -1.3233 0

Set to zero because hypothetical voluntary 

payment mechanisms are not incentive compatible 

(Mitchell and Carson 1989).

mail -0.2013 0

Set to zero because in-person interviews are 

preferred (if feasible).
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-12

Table 4-9. Independent Variable Assignments for Surface Water Quality Meta-Analysis

Variable 

Type Variable Coefficient Assigned Value Explanation

lump_sum 0.5569 0

Set to zero because the policy option would be 

paid for over a period of years.

WQI -0.3275 1

Set to one because of the methodological use of 

the WQI in the meta-analysis.

nonparam -0.6698 0

Set to zero because most studies used in the meta 

analysis used regression analysis to calculate 

willingness-to-pay values.

non_reviewed -0.2718 0

Set to zero because studies published in peerreviewed journals are preferred.

median_WTP -0.5358 0

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.

outlier_bids -0.8837 1

Set to one because survey data that exclude such 

responses are preferable; outlier bids are often 

excluded from the analysis of stated preference 

data because these bids (often identified as greater 

than a certain percentage of a respondent‘s

income) may indicate that a respondent did not 

consider his or her budget constraints and or 

supplementary goods.

Surveyed 

Population

income 0.0000027 Varies

Median household income values assigned 

separately for each state and varies by year based 

on the estimated income growth in future years.

nonusers -0.4036 0

Set to zero in order to estimate the total value for 

aquatic habitat improvements, including both use 

and nonuse values; for nonuser population, the 

total value of water resource improvements 

includes nonuse values only (Freeman 2003).

Waterbody 

Type Variables

single_river -0.4279 0

The expectation is that multiple freshwater bodies 

within a state would be affected by the proposed 

ELGs. Therefore regional_fresh is set to one.

single_lake -0.06316 0

multiple_river -1.4752 0

regional_fresh 0.1588 1

salt_pond 0.9849 0

Geographic 

Region and 

Scale 

Variables

num_riv_

pond 0.1173 0

Indicates the number of rivers or salt ponds

affected by the proposed ELGs, and is set to zero 

because this analysis assumes that the effluent 

guidelines would affect the entire 

watershed/region; this variable assignment is 

constant across study regions.

mr -0.8846 0 Regional variables are omitted from the predictive 

portion of the analysis (i.e., set to zero) because 

the regression results suggest that these variables 

may be picking spurious or other unexplained 

effects (e.g., author‘s effect).

mp 1.6337 0

Resource 

Improvement 

Variables

allmult -0.3728 1

Set to one because it is assumed that multiple 

species would benefit from water quality 

improvements
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-13

Table 4-9. Independent Variable Assignments for Surface Water Quality Meta-Analysis

Variable 

Type Variable Coefficient Assigned Value Explanation

nonspec -0.4042 0

Set to zero because it is assumed that multiple 

species would benefit from water quality 

improvements.

lnquality_ch 0.4065 Varies Set to the natural log of the average change in

water quality for each analytic scenario.

fish_use -0.3317 1

Set to one because the proposed ELGs are

expected to benefit a variety of aquatic species and 

therefore enhance recreational fishing 

opportunities.

fishplus 0.4432 0

Set to zero because the proposed ELGs are not 

expected to result in a fish population change of 50 

percent or greater.

lnbase 0.02610 Varies Set to the natural log of the average baseline water 

quality for each analytic scenario.

EPA assigned a value to each model variable corresponding with theory, characteristics of the water 

resources, and sites affected by the proposed 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, three of the methodology variables, 

discrete, WQI_study, and outlier_bids are all included with an assigned value of one. Year_index is given the 

value of 9.68, which corresponds to the mean year that the studies were conducted, 2002. Nonparam is set to 

zero because most studies included in metadata used parametric methods to estimate WTP values. Other study 

and methodology variables (volunt, mail, lump_sum, non_reviewed, median_WTP) are assigned a zero value. 

The median household income (income) varies by state and year. To estimate state level household income 

EPA used historic state-specific median household income data from the U.S. Census Bureau‘s 2009 

Community Population Survey (U.S. Census Bureau, 2010b). The data contained median household for the 

years 1984 to 2009. The Consumer Price Index (CPI) was used to inflate all values to 2010 dollars (U.S. BLS, 

2010) and a stepwise autoregressive forecasting method was used to estimate future annual state level median 

household income. The variable nonusers was set 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. For 

example, recreational users may benefit from enhanced recreational opportunities and non-users may benefit 

from the knowledge that water quality in their state has improved. 

The proposed ELGs are expected to affect water quality at a regional level because steam electric plants are 

located in 39 continental states and water quality improvements are expected in 42 continental states. The 

Agency set a dummy variable denoting multiple regions (mr) to zero because the expected magnitude of 

water quality improvement is relatively modest and, as a result, EPA‘s analysis focuses primarily on in-state 

or local resource improvements. The Mountain Plain regional dummy variable (mp) is set to zero because the 

magnitude of the regional effect suggests that spurious or otherwise unexplained effects (e.g., the effect of 

specific researchers who appear more than once in the data) may drive their overall magnitude. 

To account for the regional scale of the water quality effect resulting from the proposed ELGs, the variable 

regional_fresh is assigned a value of one. Other variables relating to waterbody type (i.e., single_lake, 

single_river, salt_pond, multiple_river, num_riv_pond) are set to zero. 

As discussed in Chapter 1, benefits from water quality changes are estimated for all years between 2017 and 

2040. For reaches directly receiving steam electric plant discharges, benefits are expected to begin accruing 
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-14

3 years after renewal of a plant‘s NPDES permit, after promulgation of the rule in 2014. This corresponds to 

the technology implementation window of 2017 through 2021. For example if a plant‘s implements control 

technologies to meet the revised effluent limits in 2020, the receiving reach would begin accumulating 

benefits in 2020 and have no benefits in 2017, 2018, and 2019. Downstream reaches, however, are affected 

not only by discharges from this plant, but also by any discharge from upstream. Consequently, for this 

analysis, EPA assumed that all benefits begin accruing in 2019, which is the midpoint of the compliance 

period.51

The majority of the studies in the meta-analysis valued improvements in multiple waterbodies or across a 

geographic region, and the studies that values a single resource generally valued such large waterbodies, for 

example the entirety the Minnesota River (355 miles) or the Chesapeake Bay (4,479 square miles), that if 

these waterbodies were translated to the E2RF1 or NHD network they would be comprised of multiple reach 

codes. Therefore, the valuation function from the meta-analysis cannot appropriately be applied to value 

changes in a single small waterbody. To estimate benefits, EPA grouped similar reaches located in the same 

state together for valuation purposes. These groupings correspond to 16 analytic categories reflecting 

combinations of four levels of baseline water quality conditions (25<WQI≤45, 45<WQI≤50, 50<WQI≤70, 

and 70<WQI) and four ranges of the projected water quality improvements (0<∆WQI<1.0, 1.0≤∆WQI<2.5, 

2.5≤∆WQI<10, and 10≤∆WQI).52. EPA then calculated the state-level weighted average of baseline and 

policy WQI values for each of the 16 groups. Reach length (in miles) was used as weight in this calculation. 

For example, if five reaches in Connecticut had a baseline water quality value between 50 and 70 and 

expected water quality improvements greater than 10 WQI, they would be grouped together and a reachlength weighted average baseline (WQI) and change in water quality (∆WQI) would be calculated for that 

group. The average baseline and change in water quality values are then used for estimating state-level WTP 

for a given analytic category. 

Water quality improvements resulting from the proposed ELGs are likely to enhance a variety of water 

resource uses, including fishing, swimming, and boating and benefit a variety of aquatic species. Therefore, 

EPA assigned variables denoting multiple species (allmult) and recreational fishing (fish_use) a value of one, 

and set variable denoting non-specified species (nonspec) to zero. EPA gave the variable fishplus is a value of 

zero because it is unlikely that the proposed ELGs would cause more than a 50 percent increase in the fish 

population. Finally, EPA assigned analytic group-specific values to the baseline water quality (baseline) and 

change in water quality (quality_ch). For a broader discussion of issues involved in the specification of 

variable levels for meta-analysis benefit transfer, see Johnston et al. (2005, 2006), among others. 

Economic values of water quality improvements are estimated at the state level and calculated by summing 

the WTP value for each analytic category within the state. For each analytic category, coefficient estimates 

for each variable, taken from meta-analysis results (Table 4-9, column 3), are multiplied by the variable levels 

chosen above (Table 4-9, column 4). The sum of these products represents the predicted natural log of WTP 

(ln_WTP), as indicated by Equation 4-2. The final step uses a standard formula to transform this predicted 

natural log into the desired household annual WTP estimate and multiply that value by the percentage of state 

E2RF1 miles benefiting in each analytical category. This formula is given by: 

 

51 The midpoint of the compliance period is used instead of a plant-specific year because downstream reaches may 

benefit from reduced loadings occurring in different years for different plants. 

52 None of the analyzed reaches are estimated to have baseline water quality below 25 WQI, therefore EPA did not 

estimate benefits to reaches with baseline WQI below 25. 
Benefit and Cost Analysis for Proposed ELGs 4: Benefits from Water Quality Improvements 

April 19, 2013 4-15

Equation 4-3. WTP WTP PercentRiv erMiles Y A S

 exp(ln_ Y A S  e/ 2)

2

, , , ,

 

exp(∙) = the exponential operator 

ln_WTPY,A,S = the predicted natural log of household WTP for a 

given year (Y), analytic category (A), and state (S) 

σe

2

 = the model residual variance (0.1876) 

PercentRiverMiles = the percent of state E2RF1 Miles within each analytic 

category 

EPA used a procedure described by Krinsky and Robb (1986) to estimate the 5th, average, and 95th percentiles 

of total WTP for each state, based on the results of the total WTP regression model. 53 These bounds 

characterize the uncertainty associated with the benefit results. This analysis provides confidence limits for 

WTP estimates related to the covariance of meta-analysis parameter estimates. It does not, however, assess 

the sensitivity of results to changes in meta-regression model assumptions or specifications (cf. Johnston et al. 

2005, 2006) or assumptions implied in benefit aggregation (cf. Loomis 1996; Loomis et al. 2000; Bateman et 

al. 2006). As noted above, EPA has made assumptions and specifications based on best benefit transfer 

practices (e.g., Johnston et al. (2005), Shrestha et al. (2007), and Rosenberger and Phipps (2007)). 

To calculate average household WTP for a given year, household WTP values were summed for all reaches 

for both users and nonusers. Equation 4-4 provides the discount formula used to calculate annualized benefits. 

Equation 4-4. 

















 

 

























 

(1 ) 1

(1 )

(1 )

1

2 4

2017

2014

, ,

n

n

Y

Y

Y A S

S

i

i i

i

WTP

AWTP

AWTPS = Annualized benefits in 2010$ for a given state (S) 

WTPY,A,S = WTP value for water quality improvements for a given year (Y), 

analytic category (A), and state (S) 

Y = Year when benefits are realized 

i = Discount rate (3 or 7 percent) 

n = Duration of the analysis (24 years) 

As shown in Table 4-10 and Table 4-11, average annual household WTP estimates for the proposed ELGs 

range from $0.00 for EPA Regions 1, 2, 9, and 10 under Option 1, to $2.43 in EPA Region 4 under Option 4 

(with a 3 percent discount rate), for the five regulatory options EPA analyzed. Under Option 3, the midpoint 

estimate of annualized household WTP values ranges from $0.01 per household in Region 10 to $2.19 per 

household in Region 4 with a 3 percent discount rate ($0.01 to $1.93 with a 7 percent discount rate). Option 4 

has the highest the midpoint estimates of $2.43 per household in Region 4, with a 3 percent discount rate. 

 

53 The procedure involves sampling the variance–covariance matrix of the estimated coefficients (matrix containing the 

variance of a random variable across the diagonal and the covariance, how random variables vary or change together, off 

the diagonal). WTP values are then calculated for each drawing from the variance–covariance matrix and an empirical 

distribution of WTP values is constructed. 
Benefit and Cost Analysis for Proposed ELG Regulation 4: Benefits from Water Quality Improvements 

April 19, 2013 4-16

Table 4-10. Average Household WTP for Water Quality Improvements (2010$; 3% Discount Rate)

EPA Region

Option 1 Option 2 Option 3 Option 4 Option 5

5% Mean 95% 5% Mean 95% 5% Mean 95% 5% Mean 95% 5% Mean 95%

1 $0.00 $0.00 $0.00 $0.01 $0.10 $0.36 $0.01 $0.10 $0.36 $0.04 $0.31 $0.96 $0.04 $0.31 $0.95

2 $0.00 $0.00 $0.00 $0.00 $0.02 $0.07 $0.00 $0.02 $0.07 $0.02 $0.19 $0.67 $0.02 $0.19 $0.66

3 $0.00 $0.03 $0.08 $0.09 $0.48 $1.34 $0.10 $0.54 $1.52 $0.55 $1.96 $4.76 $0.54 $1.94 $4.74

4 $0.09 $0.39 $1.03 $0.31 $1.61 $4.49 $0.44 $2.19 $6.00 $0.54 $2.43 $6.43 $0.52 $2.39 $6.35

5 $0.01 $0.09 $0.31 $0.15 $1.00 $3.03 $0.30 $1.46 $4.04 $0.58 $2.34 $5.99 $0.57 $2.32 $5.95

6 $0.07 $0.36 $1.05 $0.17 $0.79 $2.13 $0.18 $0.89 $2.43 $0.32 $1.47 $3.98 $0.31 $1.45 $3.94

7 $0.00 $0.08 $0.30 $0.03 $0.26 $0.84 $0.03 $0.29 $0.96 $0.13 $0.77 $2.31 $0.13 $0.76 $2.30

8 $0.06 $0.18 $0.48 $0.06 $0.25 $0.68 $0.07 $0.35 $1.04 $0.18 $0.79 $2.11 $0.18 $0.79 $2.11

9 $0.00 $0.00 $0.00 $0.00 $0.05 $0.19 $0.00 $0.07 $0.26 $0.03 $0.21 $0.60 $0.03 $0.21 $0.60

10 $0.00 $0.00 $0.00 $0.00 $0.01 $0.04 $0.00 $0.01 $0.04 $0.00 $0.01 $0.04 $0.00 $0.01 $0.04

Source: U.S. EPA Analysis, 2013

Table 4-11. Average Household WTP for Water Quality Improvements (2010$; 7% Discount Rate)

EPA Region

Option 1 Option 2 Option 3 Option 4 Option 5

5% Mean 95% 5% Mean 95% 5% Mean 95% 5% Mean 95% 5% Mean 95%

1 $0.00 $0.00 $0.00 $0.01 $0.09 $0.32 $0.01 $0.09 $0.32 $0.03 $0.27 $0.84 $0.03 $0.27 $0.84

2 $0.00 $0.00 $0.00 $0.00 $0.02 $0.06 $0.00 $0.02 $0.06 $0.01 $0.17 $0.58 $0.01 $0.17 $0.58

3 $0.00 $0.02 $0.07 $0.08 $0.42 $1.18 $0.09 $0.47 $1.33 $0.48 $1.72 $4.18 $0.48 $1.71 $4.16

4 $0.08 $0.34 $0.90 $0.27 $1.41 $3.95 $0.39 $1.93 $5.27 $0.47 $2.14 $5.65 $0.46 $2.10 $5.58

5 $0.01 $0.08 $0.27 $0.13 $0.88 $2.66 $0.26 $1.29 $3.55 $0.51 $2.05 $5.26 $0.50 $2.04 $5.23

6 $0.06 $0.32 $0.92 $0.15 $0.69 $1.87 $0.16 $0.78 $2.13 $0.28 $1.29 $3.49 $0.27 $1.27 $3.46

7 $0.00 $0.07 $0.27 $0.03 $0.23 $0.73 $0.03 $0.26 $0.84 $0.12 $0.68 $2.03 $0.12 $0.67 $2.02

8 $0.05 $0.16 $0.42 $0.06 $0.22 $0.59 $0.06 $0.31 $0.91 $0.16 $0.69 $1.85 $0.16 $0.69 $1.85

9 $0.00 $0.00 $0.00 $0.00 $0.05 $0.16 $0.00 $0.06 $0.23 $0.03 $0.18 $0.53 $0.03 $0.18 $0.53

10 $0.00 $0.00 $0.00 $0.00 $0.01 $0.03 $0.00 $0.01 $0.03 $0.00 $0.01 $0.04 $0.00 $0.01 $0.04

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELG Regulation 4: Benefits from Water Quality Improvements 

April 19, 2013 4-17

4.3 Total WTP for Water Quality Improvements

To estimate total WTP (TWTP) for water quality improvements in a given state, EPA multiplied the perhousehold WTP values for the estimated water quality improvement by the number of households within each 

state in a given year. The total WTP equation for each reach is provided below: 

Equation 4-5. TWTPY ,S WTPY ,A,S WQIbaseline WQI HHY ,S

 ( , ) 

TWTPY,S = the total state-level welfare change from improved water quality for all 16 

analytic categories (A) for a given year (Y) 

WTP Y,A,S = the estimated state-level per-household WTP for a given water quality 

improvement (i.e.,analytic category A) in year Y; analytic category A 

corresponds to a combination of the baseline water quality category(WQI 

baseline) and the expected change in water quality under the regulatory 

option for a given year(∆WQI) 

HH Y,S = the number of households residing in state (S) in a given year (Y) 

EPA generated annual state level population estimates through the period of analysis using the method 

described in Chapter 3. For the purposes of this analysis, EPA assumed that any changes in average 

household size over time would be negligible and average household size was kept constant for all years. 

EPA then calculated annualized total WTP values for each state with both a 3 and 7 percent discount rate 

using Equation 4-4. Annualized values were then summed across all states to calculate total annualized 

benefits of the proposed ELGs for each analyzed regulatory option, by EPA region and across all states. Table 

4-12 and Table 4-13 present the results for the 3 percent and 7 percent discount rates, respectively. 

As detailed by Loomis (1996), Loomis et al. (2000) and Bateman et al. (2006), among others, there are 

numerous uncertainties and assumptions in aggregating WTP across spatial jurisdictions. While these 

uncertainties are well known, the literature does not agree on appropriate, standardized guidance for benefit 

aggregations, and applied benefit-cost analysis almost universally requires simplifying assumptions in order 

to generate defensible welfare aggregations. In analyzing benefits of the proposed ELGs, EPA assumed that 

households would gain no benefits from water quality improvements in aquatic resources located outside of 

their state of residence. Consequently, the population considered in the benefits analysis of the proposed 

ELGs does not represent all the households that are likely to hold values for water resources in a given state. 

Residents of other states may hold values for water resources outside of their home state, in particular if such 

resources have personal, regional, or national significance. Even if per household WTP for out-of-state 

residents are small they can be very large in the aggregate if these values are held by a substantial fraction of 

the population.
Benefit and Cost Analysis for Proposed ELG Regulation 4: Benefits from Water Quality Improvements 

April 19, 2013 4-18

Table 4-12. Estimated Annualized Benefits for Water Quality Improvements (3% Discount Rate, Millions 2010$)a

EPA 

Region

Option 1 Option 2 Option 3 Option 4 Option 5

5% Mean 95% 5% Mean 95% 5% Mean 95% 5% Mean 95% 5% Mean 95%

1 $0.00 $0.00 $0.00 $0.03 $0.55 $1.97 $0.03 $0.55 $1.97 $0.00 $1.65 $5.05 $0.00 $1.64 $5.04

2 $0.00 $0.00 $0.00 $0.04 $0.41 $1.33 $0.04 $0.41 $1.33 $0.22 $2.51 $8.51 $0.22 $2.49 $8.48

3 $0.04 $0.25 $0.73 $0.86 $4.36 $11.89 $0.93 $4.91 $13.59 $4.54 $16.22 $39.46 $4.49 $16.09 $39.20

4 $0.49 $2.36 $6.43 $2.03 $11.21 $31.79 $3.25 $16.20 $44.21 $3.94 $17.80 $47.05 $3.82 $17.48 $46.51

5 $0.15 $1.20 $3.92 $1.52 $10.70 $32.60 $3.17 $16.00 $44.68 $6.07 $24.91 $64.28 $6.00 $24.72 $63.95

6 $1.23 $3.87 $9.21 $2.45 $8.56 $20.84 $2.52 $8.95 $21.98 $3.42 $12.10 $29.73 $3.31 $11.83 $29.29

7 $0.02 $0.45 $1.68 $0.13 $1.33 $4.45 $0.15 $1.48 $4.90 $0.67 $3.46 $10.00 $0.67 $3.44 $9.96

8 $0.04 $0.14 $0.38 $0.05 $0.27 $0.84 $0.09 $0.69 $2.29 $0.47 $2.26 $6.11 $0.47 $2.26 $6.11

9 $0.00 $0.00 $0.00 $0.00 $0.46 $1.61 $0.01 $0.63 $2.31 $0.29 $1.82 $5.23 $0.29 $1.82 $5.23

10 $0.00 $0.00 $0.00 $0.01 $0.09 $0.31 $0.01 $0.09 $0.31 $0.01 $0.11 $0.35 $0.01 $0.11 $0.35

Total $1.97 $8.28 $22.35 $7.13 $37.96 $107.65 $10.20 $49.90 $137.59 $19.63 $82.83 $215.79 $19.27 $81.89 $214.12

Source: U.S. EPA Analysis, 2013

Table 4-13. Estimated Annualized Benefits for Water Quality Improvements (7% Discount Rate, Millions 2010$)a

EPA 

Region

Option 1 Option 2 Option 3 Option 4 Option 5

5% Mean 95% 5% Mean 95% 5% Mean 95% 5% Mean 95% 5% Mean 95%

1 $0.00 $0.00 $0.00 $0.03 $0.46 $1.65 $0.03 $0.46 $1.65 $0.00 $1.38 $4.23 $0.00 $1.38 $4.23

2 $0.00 $0.00 $0.00 $0.03 $0.35 $1.12 $0.03 $0.35 $1.12 $0.18 $2.10 $7.13 $0.18 $2.09 $7.11

3 $0.03 $0.20 $0.61 $0.73 $3.66 $9.98 $0.78 $4.11 $11.39 $3.80 $13.57 $33.00 $3.76 $13.46 $32.78

4 $0.41 $1.97 $5.36 $1.69 $9.35 $26.50 $2.71 $13.50 $36.85 $3.29 $14.84 $39.22 $3.19 $14.58 $38.77

5 $0.12 $1.01 $3.29 $1.27 $8.98 $27.34 $2.66 $13.43 $37.47 $5.10 $20.89 $53.87 $5.04 $20.73 $53.59

6 $1.02 $3.23 $7.67 $2.05 $7.14 $17.37 $2.10 $7.46 $18.32 $2.85 $10.09 $24.78 $2.76 $9.87 $24.41

7 $0.01 $0.38 $1.41 $0.11 $1.11 $3.72 $0.12 $1.23 $4.09 $0.56 $2.89 $8.36 $0.56 $2.88 $8.32

8 $0.03 $0.12 $0.32 $0.04 $0.23 $0.70 $0.07 $0.57 $1.90 $0.39 $1.87 $5.06 $0.39 $1.87 $5.05

9 $0.00 $0.00 $0.00 $0.00 $0.38 $1.34 $0.01 $0.52 $1.91 $0.24 $1.50 $4.33 $0.24 $1.50 $4.33

10 $0.00 $0.00 $0.00 $0.01 $0.08 $0.26 $0.01 $0.08 $0.26 $0.01 $0.09 $0.29 $0.01 $0.09 $0.29

Total $1.64 $6.91 $18.65 $5.96 $31.73 $89.98 $8.53 $41.71 $114.97 $16.42 $69.23 $180.28 $16.12 $68.45 178.89

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELG Regulation 4: Benefits from Water Quality Improvements 

April 19, 2013 4-19

EPA estimated that between 3,945 and 22,447 reach miles would improve under the proposed ELGs, 

depending on the regulatory option. The total annualized benefits of water quality improvements resulting 

from reduced metal and nutrient pollution in these reaches range from $8.3 million to $82.8 million (with a 3 

percent discount rate) and from $6.9 million to $69.2 million with a 7 percent discount rate. 

Option 3 is expected to improve a total of 15,682 reach miles. The midpoint estimate of annualized benefits of 

improving these waterbodies is $49.9 million per year with a 3 percent discount rate ($41.7 million with a 

7 percent discount rate). EPA regions 4 and 5 are expected to accrue the most benefits with $16.2 and $16.0 

million annually (with a 3 percent discount rate). Options 3a and 3b are expected to result in the improvement 

of fewer miles, and provide lower estimated benefits that those of Option 3. Option 4a is expected to have 

benefits between those of Options 3 and 4, i.e., improve between 15,682 and 22,447 reach miles and provide 

annualized benefits estimated at between $49.9 and $82.8 million (midpoint estimate; 3 percent discount rate). 

4.4 Limitations and Uncertainties

Table 4-14 summarizes the uncertainties in the analysis of benefits associated with improved surface water 

quality and indicates the direction of the potential bias. 

Table 4-14. Uncertainties in the Analysis of Nonmarket Water Quality Benefits

Issue Effect on Benefits 

Estimate Notes

Limitations inherent to the meta-analysis model and benefit transfer

Assumption that

households will only

value improvements of 

waterbodies located in 

their state.

Underestimate Residents of other states may hold values for water resources 

outside of their home state, in particular if such resources have 

personal, regional, or national significance. Even if per household 

WTP for out-of-state residents are small they can be very large in 

the aggregate if these values are held by a substantial fraction of 

the population

Potential hypothetical 

bias underlying

contingent valuation 

method (CVM) results

Uncertain Following standard benefit transfer approaches, including metaanalytic transfers, 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 

underlying meta-data

Underestimate 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. The dummy variable 

(WQI) captures the effect of a study using (WQI=1) or not using 

(WQI=0) the WQI. EPA observed that studies that did not use the 

WQI had lower WTP values. This indicates a potential systematic 

bias in the mapping of studies that did not use the WQI. In 

analyzing the benefits of the proposed ELGs, EPA set the WQI to 

one to reduce uncertainty in WTP estimates associated with 

studies that did not include WQI as a native survey instrument.
Benefit and Cost Analysis for Proposed ELG Regulation 4: Benefits from Water Quality Improvements 

April 19, 2013 4-20

Table 4-14. 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 metaanalysis is fairly accurate when estimating benefit function, 

transfer error may be a problem in cases where the sample size is 

small. While meta-analyses have been shown to outperform other 

function-based transfer methods in many cases, 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 discharges to effects on human uses and support for aquatic and 

terrestrial species

Changes in WQI 

reflect only reductions 

in metal and nutrient 

concentrations

Underestimate The estimated changes in WQI reflect only water quality 

improvements resulting directly from reductions in metal and 

nutrient concentrations. They do not include improvements in 

water quality indicators associated with other pollutant loadings 

(e.g., BOD, dissolved oxygen), nor do they consider 

improvements in other water quality variables such as TSS. 

Omitting some water quality parameters from the analysis is likely 

to result in underestimation of the expected water quality changes.

In-stream metal 

concentrations are 

based only on loadings 

from steam electric 

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. 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 Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 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 plant 

discharges on T&E species and the benefits associated with improvements resulting from the proposed ELGs. 

As described in the EA (U.S. EPA, 2013a), the chemical constituents of steam electric waste streams can pose 

serious threats to ecological health due to the bioaccumulative nature of many constituents, 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, 2009a; Appendix F). 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 dissolved 

oxygen (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 plant discharges may either lengthen recovery time, or hasten the demise of these species. For this 

reason, the proposed ELGs may have a significant impact on T&E species populations. 

From an economic perspective, T&E species affected by steam electric plant discharges may have both use 

and nonuse values. However, given the protected nature of T&E species and the fact that 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 proposed regulation on 

T&E species are beyond the schedule and resources available to EPA for this rulemaking. As an alternative, 

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

2010b). 

In this chapter, EPA explores the current status of major freshwater taxa, identifies the extent to which the 

proposed ELGs can be expected to benefit species protected by the Endangered Species Act, 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 

imperiled relative to terrestrial species. For example, while 39 percent of freshwater and diadromous fish 
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 5-2 

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 Endangered Species Act 

(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 Proposed ELGs

To assess the potential effects of the proposed 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 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 plant discharges on T&E species, as well as benefits associated with the 

proposed ELGs. 

5.3.1 Identifying T&E Species Potentially Affected by the Proposed ELGs

To estimate the effects of the proposed 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 (as of August 4, 2012) 

using the US Fish and Wildlife Service Environmental Conservation Online System (U.S. FWS, 2010a). 

Whenever possible, the geographical distribution of T&E species was obtained 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 the US Fish and Wildlife Service (U.S. FWS, 2010b), 

NOAA‘s Office of Response and Restoration (NOAA, 2010), NatureServe (NatureServe, 2009), and NOAA 

National Marine Fisheries Service (NMFS, 2010a; NMFS, 2010b; NMFS, 2010c). For several freshwater 

species, geographic ranges were available only as 6-digit hydrologic unit codes (HUC) (NatureServe, 2009; 

U.S. FWS, 2010b). 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 proposed ELGs, EPA 

compiled data on locations of steam electric 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, 2013a). The result of this analysis consists of the National Hydrography Dataset (NHD) 

Plus (COMIDs) identifiers of waterbodies that receive discharges from steam electric plants and indicators of 

water quality under the baseline and each analyzed regulatory option. EPA queried these data to identify 
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 5-3 

“affected areas” as those habitats where 1) receiving waters do not meet water quality metrics recognized to 

cause harm in organisms under baseline conditions; and 2) receiving waters exceed water quality metrics 

under the most stringent regulatory option EPA analyzed (Option 5). EPA used these data in ArcGIS to 

determine the T&E species with habitat extents overlapping the affected areas. 

EPA constructed two screening databases using the spatial data: 

 Database 1 – all T&E species whose habitat is within 5 miles of waterbodies affected by steam 

electric plant discharges. The 5-mile buffer was chosen to account for any minor errors in outfall 

location and habitat maps, and as a very rough approximation of a distance where benefits from the 

rule are most likely to affect species life history parameters (e.g., survival, reproduction). 

 Database 2 – all T&E species whose habitat overlaps those waterbodies affected by the effluent 

discharges from steam electric plants. 

After identifying T&E species potentially affected by the proposed 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, 2009; AFSC, 2010; ASMFC, 2010; NEFSC, 2010; PIFSC, 2010a; PIFSC, 2010b; SEFSC, 

2010; SWFSC, 2010; U.S. FWS, 2010c).

The results of the spatial analysis and vulnerability classification process for Database 1 and Database 2 (as 

described above) are presented in Table 5-1 and Table 5-2, respectively. More species were identified in 

Database 2 than in Database 1, because the impacted waterbodies are geographically larger than the area 

encompassed by the five-mile radius. Appendix F lists all T&E species potentially affected by the proposed 

ELGs. 

Table 5-1. T&E Species With Affected Habitat Within 5 Miles of Steam Electric Power Plants 

Species Group Species Vulnerability Species Count Low Moderate High

Amphibians 0 3 1 4

Arachnids 4 0 0 4

Birds 6 5 1 12

Clams 0 0 29 29

Crustaceans 0 1 1 2

Fishes 0 0 14 14

Insects 9 1 2 12

Mammals 15 2 0 17

Reptiles 2 0 4 6

Snails 9 0 16 25

Total 45 12 68 125

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 5-4 

Table 5-2. T&E Species with Habitat Occurring within Waterbodies Affected by Steam Electric Power 

Plants 

Species Group Species Vulnerability Species Count Low Moderate High

Amphibians 1 5 3 9

Arachnids 10 0 0 10

Birds 11 6 1 18

Clams 0 0 65 65

Crustaceans 0 3 3 6

Fishes 0 0 31 31

Insects 14 3 3 20

Mammals 26 6 1 33

Reptiles 2 0 4 6

Snails 10 0 16 26

Total 74 23 127 224

Source: U.S. EPA Analysis, 2013

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 proposed ELGs, rather than all species 

whose 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, etc.) unlikely to be 

affected by steam electric 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 proposed 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 proposed ELGs. 

5.3.2 Assessing Benefits of T&E Species Improvements from the Proposed ELGs

The proposed 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 ambient water quality criteria (AWQC) for aquatic life as a consequence of the proposed 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 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 proposed ELGs. For each analyzed regulatory option, EPA identified 
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 5-5 

waterbodies that 1) do not meet AWQC for wildlife under baseline conditions, but 2) have no wildlife AWQC 

exceedances following implementation of the proposed ELGs. For seven species, there were no waterbodies 

that met these conditions, leaving seven T&E fish species and one dragonfly species in 18 states and the 

District of Columbia that may experience increases in population growth rates as a result of the proposed 

ELGs (Table 5-3). 

Table 5-3. T&E Species Whose Recovery May Benefit from the Proposed ELGs

Species Common Name State(s)

Acipenser brevirostrum Shortnose sturgeon DE, DC, FL, MD, ME, NH, 

PA, SC, and VT

Acipenser oxyrinchus destoi Gulf sturgeon LA and MS

Etheostoma chermocki Vermilion darter GA

Notropis cahabae Cahaba shiner GA

Percina aurolineata Goldline darter GA

Percina rex Roanoke logperch NC and VA

Percina tanasi Snail darter AL, SC, and TN

Somatochlora hineana Hine's Emerald Dragonfly IL and IN

Source: U.S. EPA Analysis, 2013 

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 proposed 

ELGs, EPA identified the fraction of inhabited waterbodies that meet wildlife AWQC as a consequence of the 

proposed ELGs. This fraction was used to estimate relative population changes in estimating the willingnessto-pay (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 proposed 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 pollutant discharges from steam electric plants from 

the proposed ELGs involves the following steps: 1) quantifying the impacts of pollutant discharges from 

steam electric 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 proposed 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 (e.g., 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 discrepancies in these variables, however, a 

benefit transfer approach can provide useful insights into the social benefits gained by reducing impacts on 

T&E species. 
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 5-6 

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 proposed 

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 insect valuation studies, EPA was unable to 

monetize any benefits for potential population increases of Hine's Emerald Dragonfly as a result of the 

proposed 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 proposed ELGs. 

Equation 5-1. 

 ( ) 

 – – 

 

Table 5-4 lists the assigned variable values and definitions used in estimating per household WTP for 

improved protection of T&E species resulting from the proposed ELGs. 

Table 5-4. 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

EPA does not currently have either species-specific estimates of the population effects of the proposed 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 
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 5-7 

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). 

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 because of the proposed ELGs are reasonable and plausible. This is 

because few individuals must be saved to attain these growth estimates. For example, a T&E species with a 

state-level population of 10,000 individuals (which is reasonable for threatened species, and likely an overestimate for endangered species and endemic species) and a population growth rate of 0 in the baseline would 

achieve low, medium and high population increases if the proposed ELGs result in 2.1, 4.4 and 6.5 fewer 

premature mortalities per year between 2017 and 2040, respectively. Additionally, the number of avoided 

premature mortalities would scale with habitat utilization, which decreases the number of avoided premature 

mortalities necessary to achieve these results. 

Two states (South Carolina and Georgia) have multiple T&E species with potential population growth 

resulting from the proposed ELGs. For these two states, EPA estimated benefits only for the species expected 

to see the greatest proportional population increase. EPA did not sum WTP estimates for multiple species at 

the state level, because the underlying meta-data are based on single species valuations and these estimates do 

not account for species substitution or complementary effects. Therefore, these WTP estimates may be 

affected by the availability of related species (Hoehn and Loomis, 1993). Further, WTP varies by species 

characteristics such as size, charisma, and endemism (Loomis and Ekstrand, 1997; Morse-Jones et. al, 2012; 

Metrick and Weitzman, 1996). The approach used by EPA for this analysis may understate WTP for 

protection of multiple species as T&E valuation studies have shown that WTP is greater for the preservation 

of multiple species as opposed to a single species (Stanley, 2005; Loomis and Ekstrand, 1997; Nunes and van 

den Bergh, 2001; White et al. 1997; White et al. 2001). EPA was unable to account for benefits to multiple 

species as the value of multiple species is not equal to the aggregated value of a single species. For example, 

Stanley (2005) estimated an average annual household WTP value of $25 for the preservation of the riverside 

fairy shrimp in Orange County CA, and an average annual household WTP value of $52 to preserve all 32 

locally endangered species. Moreover, wildlife valuation studies have shown that there are diminishing 

marginal benefits for wildlife preservation (Morse-Jones et. al, 2012; Rollins and Lyke 1998). 

Because population growth was assessed at the state level, EPA was unable to attribute benefits to a specific 

steam electric 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 2019 for all states. This year is 

the midpoint of the period of 2017 through 2021 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 

proposed ELGs using Equation 5-1 and the independent variable assignments presented in Table 5-4. EPA 

estimated total annual benefits for the years between 2019 and 2040 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 5-5 and Table 5-6 present the annualized total benefits calculated using a 3 percent and 7 percent 

discount rate, respectively. Annualized benefits for Options 1 through 5 range from $3.9 million to $47.3 

million with a 3 percent discount rate ($3.2 million to $39.5 million with a 7 percent discount rate), depending 

on the regulatory option and estimated population increase. Under Option 3, eight states have potential 

increases in T&E species populations, with annualized benefits of $10.0 million with a 3 percent discount rate 
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 5-8 

($8.4 million with a 7 percent discount rate). Benefits of Option 4 are higher and more broadly distributed, 

with 17 states having potential increases in T&E species populations and annualized benefits at $33.3 million 

(medium population increase estimate) being more than three times larger than those of Option 3. 

As for the other benefit categories discussed in this report, EPA expects the benefits of Options 3a and 3b to 

be below those of Option 3, whereas Option 4a will have benefits between those of Options 3 and 4. 
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

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Table 5-5. Estimated Annualized Benefits to T&E Species from WQ Improvements (3% Discount Rate, Millions 2010$)a

State Option 1 Option 2 Option 3 Option 4 Option 5

Low Medium High Low Medium High Low Medium High Low Medium High Low Medium High

AL $0.47 $0.87 $1.23 $0.47 $0.87 $1.23 $0.47 $0.87 $1.23 $0.67 $1.23 $1.75 $0.67 $1.23 $1.75

DC - - - - - - - - - $0.07 $0.12 $0.17 $0.07 $0.12 $0.17

DE - - - - - - - - - $0.15 $0.28 $0.40 $0.15 $0.28 $0.40

GA $0.80 $1.46 $2.07 $0.80 $1.46 $2.07 $0.80 $1.46 $2.07 $0.80 $1.46 $2.07 $0.80 $1.46 $2.07

FL - - - - - - - - - $3.89 $7.11 $10.11 $3.89 $7.11 $10.11

LA $1.46 $2.66 $3.79 $1.46 $2.66 $3.79 $1.46 $2.66 $3.79 $2.66 $4.86 $6.92 $2.66 $4.86 $6.92

MA - - - - - - - - - $0.86 $1.57 $2.23 $0.86 $1.57 $2.23

MD - - - - - - - - - $0.60 $1.10 $1.56 $0.60 $1.10 $1.56

ME - - - - - - - - - $0.80 $1.46 $2.07 $0.80 $1.46 $2.07

MS $0.46 $0.85 $1.21 $0.46 $0.85 $1.21 $0.46 $0.85 $1.21 $1.21 $2.21 $3.14 $1.21 $2.21 $3.14

NC $0.36 $0.66 $0.93 $0.36 $0.66 $0.93 $0.36 $0.66 $0.93 $1.46 $2.66 $3.79 $1.46 $2.66 $3.79

NH - - - - - - - - - $0.47 $0.86 $1.22 $0.47 $0.86 $1.22

PA - - - - - - - - - $1.36 $2.49 $3.54 $1.36 $2.49 $3.54

SC - - - - - - $1.03 $1.88 $2.68 $1.03 $1.88 $2.68 $1.03 $1.88 $2.68

TN - - - - - - $0.35 $0.65 $0.92 $0.35 $0.65 $0.92 $0.35 $0.65 $0.92

VA $0.29 $0.54 $0.77 $0.29 $0.54 $0.77 $0.54 $0.99 $1.40 $1.60 $2.93 $4.17 $1.60 $2.93 $4.17

VT - - - - - - - - - $0.22 $0.40 $0.56 $0.22 $0.40 $0.56

Total $3.85 $7.03 $10.00 $3.85 $7.03 $10.00 $5.47 $10.00 $14.23 $18.20 $33.26 $47.32 $18.20 $33.26 $47.32

a Low, Medium, and High increases in population are based on a 0.5,1, and 1.5 percent increase in T&E species population growth resulting from WQ improvements in all reaches used by 

the T&E species in the state. EPA weighted these population growth estimates by the percent of all reaches used by T&E species that are expected to improve as a result of the proposed 

ELGs.

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

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Table 5-6. Estimated Annualized Benefits to T&E Species from WQ Improvements (7% Discount Rate, Millions 2010$)a

State Option 1 Option 2 Option 3 Option 4 Option 5

Low Medium High Low Medium High Low Medium High Low Medium High Low Medium High

AL $0.40 $0.73 $1.03 $0.40 $0.73 $1.03 $0.40 $0.73 $1.03 $0.57 $1.03 $1.47 $0.57 $1.03 $1.47

DC - - - - - - - - - $0.06 $0.10 $0.15 $0.06 $0.10 $0.15

DE - - - - - - - - - $0.13 $0.23 $0.33 $0.13 $0.23 $0.33

GA $0.66 $1.22 $1.73 $0.66 $1.22 $1.73 $0.66 $1.22 $1.73 $0.66 $1.22 $1.73 $0.66 $1.22 $1.73

FL - - - - - - - - - $3.23 $5.90 $8.40 $3.23 $5.90 $8.40

LA $1.22 $2.23 $3.17 $1.22 $2.23 $3.17 $1.22 $2.23 $3.17 $2.23 $4.07 $5.80 $2.23 $4.07 $5.80

MA - - - - - - - - - $0.72 $1.32 $1.88 $0.72 $1.32 $1.88

MD - - - - - - - - - $0.50 $0.92 $1.31 $0.50 $0.92 $1.31

ME - - - - - - - - - $0.67 $1.22 $1.74 $0.67 $1.22 $1.74

MS $0.39 $0.71 $1.01 $0.39 $0.71 $1.01 $0.39 $0.71 $1.01 $1.01 $1.85 $2.64 $1.01 $1.85 $2.64

NC $0.30 $0.55 $0.78 $0.30 $0.55 $0.78 $0.30 $0.55 $0.78 $1.22 $2.22 $3.16 $1.22 $2.22 $3.16

NH - - - - - - - - - $0.39 $0.71 $1.01 $0.39 $0.71 $1.01

PA - - - - - - - - - $1.15 $2.09 $2.98 $1.15 $2.09 $2.98

SC - - - - - - $0.86 $1.57 $2.23 $0.86 $1.57 $2.23 $0.86 $1.57 $2.23

TN - - - - - - $0.30 $0.54 $0.77 $0.30 $0.54 $0.77 $0.30 $0.54 $0.77

VA $0.25 $0.45 $0.64 $0.25 $0.45 $0.64 $0.45 $0.82 $1.17 $1.34 $2.44 $3.48 $1.34 $2.44 $3.48

VT - - - - - - - - - $0.18 $0.33 $0.47 $0.18 $0.33 $0.47

Total $3.22 $5.88 $8.37 $3.22 $5.88 $8.37 $4.57 $8.36 $11.90 $15.20 $27.79 $39.54 $15.20 $27.79 $39.54

a Low, Medium, and High increases in population are based on a 0.5,1, and 1.5 percent increase in T&E species population growth resulting from WQ improvements in all reaches used by 

the T&E species in the state. These population growth estimates were then weighted by the percent of all reaches used by T&E species that are expected to improve as a result of the 

proposed ELGs.

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

April 19, 2013 5-11

5.6 Limitations and Uncertainties

Table 5-7 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 5-7. Uncertainties in the Analysis of T&E Species Benefits 

Issue Effect on Benefits 

Estimate Notes

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 proposed ELGs 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)

A 5-mile buffer was used to 

screen T&E species. This is a 

very rough approximation of 

a distance where benefits 

may be most apparent

Uncertain Effects are likely to be species and waterbody specific. For 

some species, 5 miles may be an underestimate of the distance 

at which acute effects of pollution are felt. For other species, 5 

miles may be an overestimate. 

Benefit estimates do not 

include monetized values for 

potential population increases 

in Hine's Emerald Dragonfly

(Somatochlora hineana)

Underestimate It is likely that population increases in Somatochlora hineana 

have value to the public. In addition to bequest, altruistic, and 

existence values, dragonflies may have aesthetic or cultural 

values. Dragonflies also provide beneficial ecological services. 

They are voracious insectivores that prey on mosquitoes, flies, 

and other small insects. The estimated annual benefits of pest 

control attributable to insects are $4.5 billion in the United 

States (Losey and Vaughan, 2006).

Benefit estimates are likely to 

include only a subset of 

species that may be affected

Underestimate EPA was conservative when applying benefit transfer of values 

for species potentially affected. Water quality issues may be 

important to species recovery even if not listed explicitly in 

species recovery plans.

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.
Benefit and Cost Analysis for Proposed ELGs 5: Threatened and Endangered Species Benefits 

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Table 5-7. Uncertainties in the Analysis of T&E Species Benefits 

Issue Effect on Benefits 

Estimate Notes

WTP estimates represent 

population increases for only 

one species

Underestimate WTP for multiple species may be greater than WTP to preserve 

a single species (Stanley, 2005, Nunes and van den Bergh, 

2001, White et al. 1997, White et al. 2001; Loomis and 

Ekstrand, 1997). However, only a small number of benefiting 

states have potential population increases in multiple species.

Also, EPA‘s analysis values species that are likely to 

experience the largest population increase, Therefore, while 

estimating WTP for a single species may underestimate 

benefits, this underestimate is likely to be small.

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.
Benefit and Cost Analysis for Proposed ELGs 6: Benefits from Improved Groundwater Quality 

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6 Benefits from Groundwater Quality Improvements

Steam electric plants manage coal combustion residuals (CCR) such as flue gas desulfurization (FGD) solids, 

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 impoundment (e.g., settling pond), where the solids settle out of the 

water. In addition to solids, these impoundments typically contain water with high concentrations of steam 

electric pollutants, including dissolved metals. These pollutants may leach into groundwater aquifers, 

degrading water quality and potentially creating health hazards to households drawing drinking water from 

affected aquifers. The health hazards may arise from water consumption or dermal contact with pollutants in 

the water, and include a wide range of human health effects (e.g., cancer, kidney damage, nervous system 

damage and others). The EA (U.S. EPA, 2012a) describes the health effects caused by steam electric 

pollutants in more detail. 

EPA expects that some of the regulatory options would eliminate the future leaching of steam electric 

pollutants from surface impoundments to groundwater aquifers by prompting changes in the way steam 

electric plants handle CCR. The proposed ELGs are expected to reduce contaminations in both public and 

private drinking water wells. However, because public drinking water systems must treat water to reduce 

pollutant concentrations below maximum contaminant levels (MCLs) the proposed ELGs are not expected to 

generate significant benefits to the population served by public drinking water sources. Therefore, EPA‘s 

analysis of benefits from improved groundwater quality focused only on households relying on private wells 

that draw water from the aquifers located in the vicinity of steam electric plants. To estimate the monetary 

value of benefits from reduced groundwater contamination EPA used results of Poe et al.‘s (2001) metaanalysis.54

6.1 Methodology and Data

This section describes the methodology and data used to estimate 1) the baseline groundwater quality in the 

aquifers affected by leachate from steam electric impoundments; 2) changes in groundwater pollutant 

concentrations resulting from the proposed ELGs; 3) the number of households potentially exposed to 

impoundment leachate; and 4) the households‘ willingness-to-pay (WTP) for improving the quality of 

groundwater used for drinking water supply. 

6.1.1 Baseline Water Quality

EPA modeled baseline groundwater concentrations of steam electric pollutants that would be reduced by the 

proposed ELGs (including lead, mercury, thallium, cadmium, selenium, arsenic, and chromium) within a onemile radius from steam electric plant impoundments. Within the one-mile radius, EPA used 0.2 mile bands to 

account for differences in groundwater concentrations of pollutants for drinking water wells located in 

varying distances from an impoundment. 

6.1.2 Water Quality Improvements

EPA used drinking water MCLs55 as a benchmark to assess groundwater contamination in the aquifers 

affected by steam electric plant impoundment leachate. An MCL for drinking water is the highest level of a 

 

54 The meta-analysis is defined as a ―statistical analysis of a large collection of results for individual studies for the 

purpose of integrating findings‖ (Poe et al., 2001; p. 138).

55 See U.S. EPA (2012a) for details. 
Benefit and Cost Analysis for Proposed ELGs 6: Benefits from Improved Groundwater Quality 

April 19, 2013 6-2 

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 health risk. EPA sets the 

MCL as close to the MCLG as possible, considering the best available treatment technologies and costs. EPA 

compared the modeled baseline concentrations of steam electric pollutants in groundwater to MCL for 

drinking water to identify those aquifers exceeding MCLs in the baseline. 

Closing the impoundment (as may occur if the impoundment is no longer used to manage CCR waste under 

the proposed ELG regulatory options) would remove the source of future contamination for groundwater 

aquifers affected by leachate from steam electric impoundments and thus improve future groundwater quality. 

EPA was not able to model changes in groundwater concentrations that would result from implementation of 

the proposed ELGs due to data and practical limitations. To assess the potential magnitude of benefits from 

the proposed ELGs, EPA used a simplified approach in which EPA assumed that water quality in affected 

aquifers that exceed MCLs under the baseline would improve by 70 percent given that the source of 

contamination is removed.56 EPA assumed that groundwater quality in the affected aquifers that do not exceed 

MCLs under the baseline would improve by 30 percent, as a result of impoundment closures. 

Some impoundments are expected to see a reduction, but not the complete elimination, of the CCR waste they 

handle under the regulatory options (TDD; U.S. EPA, 2013b). For these impoundments, EPA assumed that 

the benefits would be proportional to the reduction in the amount of CCR waste managed by the 

impoundment (for example, benefits are proportional to the share of ash that is fly ash for regulatory options 

that address fly ash only). 

6.1.3 Affected Households

As noted above, EPA expects that, by reducing pollutant leaching from impoundments, the proposed ELGs 

would benefit households relying on private drinking water wells that draw water from groundwater aquifers 

in the vicinity of impoundments. 

EPA utilized a synthetic population database developed by RTI International to estimate the number of adults 

and children living in 0.2 mile radial distance bands surrounding impoundments, up to 1 mile away.57 The 

1990 Census provides the most recent data on groundwater users and indicates that, nationally, approximately 

15 percent of households utilize private groundwater wells. For this analysis, EPA applied the site-specific 

ratio of households using groundwater reflected in the 1990 Census data (the most recent Census dataset 

including private well data) at the block group level to the RTI synthetic population data (which are based on 

the 2000 Census) to determine the share of the impoundment-specific population using private groundwater 

wells. 

To estimate the number of households in each distance band, EPA divided the number of persons residing in 

each distance band by the state-specific average number of persons per household (based on U.S. Census 

Bureau, 2010a).58

 

56 Although the decommissioning and closure of the impoundment would remove the source of contamination for these 

aquifers, EPA did not assume that they would be improved by 100 percent (i.e., complete removal of contamination) 

because there may be some other nearby sources of contamination, and it may take a very long time for legacy 

contaminations from the impoundments to be completely eliminated. 

57 The synthetic population database provides higher resolution population estimates than typical census block data. For 

details on the database, see Wheaton et al. (2009). 

58 EPA does not have data to determine the number of households for 4 to 18 plants with impoundments affected by the 

proposed ELG, depending on the regulatory option. For these plants, EPA applied the average per-plant benefits of 

plants with sufficient data. 
Benefit and Cost Analysis for Proposed ELGs 6: Benefits from Improved Groundwater Quality 

April 19, 2013 6-3 

6.1.4 Monetary Values of Groundwater Quality Improvements

EPA used the results of Poe et al.‘s (2001) meta-analysis of groundwater valuation studies to estimate WTP 

for groundwater quality improvements resulting from the proposed ELGs. Poe et al. (2001) compiled metadata from 13 stated preference studies that elicit household WTP values for improvements in groundwater 

quality or quantity. In these studies, the estimated household‘s WTP range from $80 and $1,788 (updated to 

2010 dollars using the Consumer Price Index (CPI)). Poe et al. (2001) grouped the studies into three 

categories: 

 Studies that place values on specific changes in water quality, 

 Studies that evaluate water quality changes presented in more general terms, and 

 Studies that frame the valuation problem as affecting water quantity (as opposed to quality). 

Poe et al. (2001) used meta-data to model WTP for groundwater improvements as a function of ―core 

economic variables‖ (change in supply, changes in contamination probability, use versus nonuse values, 

income, and prices of substitutes), and ―methodological variables‖ (whether cancer is mentioned as a concern, 

whether locality is emphasized, percent of population on public water supply versus private wells, elicitation 

method, and analytical methods). 59 The authors report three separate regression functions: 1) core economic 

variables only, 2) ―full complete,‖ which includes all core economic and methodological variables, and 3) 

―short complete,‖ which retains all core economic variables, but removes the least significant methodological 

variables. To estimate per household WTP for groundwater quality improvements that would result from the 

proposed ELGs, EPA applied the ―full complete‖ regression function shown in Equation 6-1. 

Equation 6-1. 

 ( ) ( ( )) ( ( )) ( ) 

 ( ( )) ( ) ( ( )) ( ( )) ( ( )) 

 ( ( )) ( ) ( ( )) ( ( )) 

 ( ( )) ( ( )) ( ( ))

This function allows EPA to forecast WTP for improvements in groundwater quality based on assigned 

values to model variables that are chosen to represent a resource change (probability of contamination, ∆prob)

and other methodological variables. EPA assigned a value to each model variable following general guidance 

for meta-analysis benefit transfer from Bergstrom and Taylor (2006). Table 6-1 describes the explanatory 

variables, estimated coefficients, and assigned variable values used in this analysis. EPA assigned the value to 

the change in probability of groundwater contamination variable (∆prob) using the following assumptions: 

 EPA assumed a 70 percent change in the probability of contamination for aquifers that exceed MCLs 

in the baseline, and 

 EPA assumed a 30 percent change for aquifers that do not exceed MCLs in the baseline. 

EPA used state-specific median household income from the U.S. Census Bureau (2010a) to assign the income 

value (Income) for the households associated with each impoundment. 

EPA set the use variable [D(use)] to zero to capture both use and nonuse values, and set the change in supply 

[D(∆supply)] and quantity of change in supply (∆supply) variables to zero to isolate the effect of a change in 

groundwater quality (rather than quantity). EPA also set the price of substitutes [D($ sub)] value to zero since 

the substitute drinking water sources (such as bottled water) and their prices are not considered in this 

 

59 The authors used a weighted least square procedure with standard errors derived using the Huber-White consistent 

covariance estimator. This approach treats each study as the equivalent of a sample cluster with the potential for 

heteroskedasticity (i.e. differences in variance across clusters). 
Benefit and Cost Analysis for Proposed ELGs 6: Benefits from Improved Groundwater Quality 

April 19, 2013 6-4 

analysis. EPA set the percent on public water supply (Public%) variable to zero since the benefits are 

expected to accrue only to private well users. EPA assigned zero values to the methodology variables 

(D(OEaftDC), D(pcard), D(DC), D(DC-cam), and D(DC-util)). This assignment results in the application of 

the default methodology assumption of an open-ended contingent valuation question (with no dichotomous 

choice).The majority of studies in the meta-data used an open-ended survey format. 

Table 6-1. Estimated Meta-Regression for Household WTP for Groundwater Quality Improvements

Variable Type Variable Coefficient Assigned 

Value Description

Core Economic 

Variables

Constant -491.1078

D(use) -440.3059 0

Focus on filtration devices or containment strategies; 

excluding non-use values; set to zero to reflect 

residents‘ use and nonuse values of groundwater

D(∆prob) -174.1401 1

Whether a change in the probability of contamination 

was specified; set to 1 to reflect that site-specific 

changes were specified

∆prob 1085.1610 varies (0.3 

or 0.7)

Site specific change in probability of contamination (0.7 

if modeled concentration exceeds a human health 

criteria; 0.3 if not)

D(∆supply) 235.4335 0

Whether a change in supply was specified; set to zero 

since a change in supply is not expected

∆supply 289.1667 0

Change in groundwater supply; set to zero since a 

change in supply is not expected

D($ sub) -83.0452 0

Whether the price of substitutes was specified; set to 

zero since substitute prices are not considered in this 

analysis

I(thou) 8.4125 varies Site specific median household income (in thousands)

Methodological 

Variables D(canc) 186.8805 1

Whether cancer was mentioned as a concern; set to 1 

because steam electric discharges include arsenic which 

is a carcinogen

D(local) -121.1955 0

Whether a specific locality was emphasized; set to 0 

because benefits extend to other areas surrounding 

impoundments

Public% 210.4727 0

Percentage of respondents on public water supplies; set 

to zero since benefits analysis is restricted to private 

well users

D(OEaftDC) 73.8525 0

Open ended value elicitation following dichotomous 

choice; set to zero to reflect default survey design values

D(pcard) -93.5884 0

Payment card; set to zero to reflect default survey design 

values

D(DC) 185.2280 0

Dichotomous choice; set to zero to reflect default survey 

design values

D(DC-cam) -181.0027 0

Cameron approach; set to zero to reflect default survey 

design values

D(DC-util) 227.4003 0

Utility theoretical approach; set to zero to reflect default 

survey design values

Source: Poe et al. (2001); ―full complete‖ model

EPA used the meta-regression to estimate per household WTP for groundwater quality improvements for 

households residing in 0.2-mile radial bands within 1 mile of impoundments affected by the proposed ELGs. 

EPA then calculated the total benefits associated with reduced contamination of groundwater by multiplying 

the band-specific household WTP by the number of households residing in the 0.2 mile bands surrounding 
Benefit and Cost Analysis for Proposed ELGs 6: Benefits from Improved Groundwater Quality 

April 19, 2013 6-5 

each impoundment and summing across all bands of impoundments expected to close under a given 

regulatory option. 

6.2 Results

EPA estimated that, on average, 43 households use private wells within 1-mile of affected aquifers. As 

described above, EPA assumed that the proposed ELGs would reduce the probability of groundwater 

contamination in aquifers associated with 147 to 623 impoundments by 30 percent or 70 percent60

. The 

estimated household‘s WTP for groundwater improvements varied by aquifer depending on income and the 

expected change in probability of groundwater contamination (i.e., 70 percent or 30 percent). The average 

WTP for reducing the probability of groundwater contamination across all affected aquifers is approximately 

$442 per household, with a range of $111 (for aquifers with low income populations and smaller 

improvements in quality) to $976 (for aquifers with high income populations and larger improvements in 

quality). As shown in Table 6-2, the estimated annual benefits from reducing the probability of groundwater 

contamination are $1.6 million and $6.5 million, respectively for Options 3 and 4, using a 3 percent discount 

rate ($1.4 million and $5.5 million using a 7 percent discount rate). EPA expects the annualized benefits of 

Options 3a and 3b to be less than those of Option 3, (i.e., less than $0.7 million, at 3 percent discount rate) 

and the annualized benefits of Option 4a to be between those of Options 3 and 4 (i.e., between $1.6 and $6.5 

million, at 3 percent discount rate) . 

Table 6-2. Estimated Annualized Benefits of Reduced Probability of Groundwater Contamination from 

Impoundment Leaching (Millions; 2010$)

Regulatory Option Number of 

Impoundmentsa 3% Discount Rate 7% Discount Rate

Option 1 147 $0.65 $0.56

Option 3a b b b

Option 2 147 $0.65 $0.56

Option 3b b b b

Option 3 239 $1.64 $1.40

Option 4a c c c

Option 4 623 $6.49 $5.52

Option 5 623 $6.49 $5.52

a. Impoundments managing reduced CCR waste quantities as a result of the proposed ELG option (out of a total of 1,070 impoundments at steam 

electric plants).

b. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Options 3a and 3b to be less than 

those of Option 3, with values for Option 3a being lower than Option 3b. EPA does not know how the benefits of Options 3a and 3b

compare to those of Options 1 and 2.

c. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Option 4a to be between those of 

Options 3 and 4.

Source: U.S. EPA Analysis, 2013

6.3 Limitations and Uncertainties

Table 6-3 summarizes the limitations and uncertainties in the analysis of benefits associated with the reduced 

probability of groundwater contamination from impoundment leachate. While the degree of uncertainty in the 

estimated WTP for improved groundwater quality is greater than desirable, this approach allowed EPA to 

assess whether benefits are likely to occur and assess the potential magnitude of benefits to the affected 

groundwater users. EPA believes that benefits to groundwater are likely to occur and therefore this approach 

provides a useful insight into the benefits of the proposed ELGs. 

 

60 Approximately 40 percent of 0.2-mile radial bands around affected impoundments may be improved by 70 percent, 

while 60 percent may be improved by 30 percent. 
Benefit and Cost Analysis for Proposed ELGs 6: Benefits from Improved Groundwater Quality 

April 19, 2013 6-6 

Table 6-3. Uncertainties in the Analysis of Groundwater Quality Benefits

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

Background concentrations of steam 

electric pollutants are not 

considered in this analysis, nor are

the design of impoundments or the 

lag between reductions in leachate 

loads and water quality 

improvements.

Overestimate The analysis does not account for the presence of a liner 

or other design characteristics that reduce leaching. 

Even if leaching of steam electric pollutants from an 

unlined impoundment is reduced or eliminated as a 

result of the proposed ELGs, background concentrations 

may persist for years. The benefits of reduced 

groundwater leaching may be overestimated in the short 

term.

EPA used current MCL 

exceedances as a benchmark to 

determine whether the probability of 

contamination would be reduced by 

70 percent (for aquifers exceeding 

the health criteria in the baseline) or 

by 30 percent (for aquifers not 

exceeding the health criteria in the 

baseline) as a result of the proposed 

ELGs.

Uncertain Due to data limitations on the aquifers surrounding 

affected impoundments, it is not possible to model 

changes in pollutant concentrations resulting from 

implementation of the proposed ELGs. The overall 

effect of this assumption on benefit estimates is 

uncertain.

EPA assumed that the benefits 

accruing to aquifers surrounding 

impoundments affected by the 

proposed ELGs would be 

proportional to the reduction in the 

quantity of CCR waste managed by 

the impoundment.

Uncertain The relationship between the quantity of CCR waste 

managed by the impoundments, pollutant loads in the 

leachate, and groundwater quality in surrounding 

aquifers may not be linear.

EPA relied on 1990 Census data on 

the site-specific share of the 

population using private wells.

Overestimate The fraction of households using private wells has 

declined since 1990; as such this assumption may result 

in an overestimate of the number affected households.a

EPA used the best data available in the absence of more 

recent location-specific data.

To estimate benefits associated with 

decreased groundwater 

contamination, EPA relied on a 

meta-analysis of groundwater 

valuation studies (Poe et al., 2001).

Uncertain Poe et al., (2001) reported wide variations in WTP for 

groundwater quality improvements, as well as variations 

in the methods used to elicit and present those values.

Therefore, actual household WTP for groundwater 

quality improvements may be higher or lower.

a. For example, data from the 2009 American Housing Survey indicates that approximately one out of every 12 homes, or 8.3 percent of 

households, rely on private wells, while the 1990 Census data indicated that the share of households using private wells was closer to 15 percent. 

Data from the American Housing Survey is not detailed enough for use in this analysis.
Benefit and Cost Analysis for Proposed ELGs 7: Benefits from Avoided Impoundment Failures 

April 19, 2013 7-1 

7 Benefits from Avoided Impoundment Failures

The operational changes prompted by the proposed ELGs are expected to cause some plant owners to reduce 

their reliance on impoundments to handle coal combustion residuals. These changes would affect the future 

probability and/or magnitude of impoundment failures and the resulting accidental, and sometimes 

catastrophic releases, of coal combustion residuals (CCR). 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 CCR 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 

2014 through 2040. 

7.1 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 failure events, and estimate the total present and 

annualized values of benefits from avoided impoundment failures resulting from the proposed ELGs. 

7.1.1 Failure Probability

EPA determined future probability of impoundment failures based on historical trends. EPA used data from a 

survey of impoundments conducted in support of regulations being developed by EPA‘s Office of Resource 

Conservation and Recovery (ORCR; see U.S. EPA, 2010d) governing the disposal of CCR in impoundments. 

The survey sought information from plant owners and operators about impoundment releases that occurred 

during the period of 2000 through 2009 for a total of 726 impoundments. Responses to the survey provided 

historical data on 38 releases that occurred during the 10-year period; additionally, some plant owners 

included information about 4 release incidents that occurred during the period of 1995 through 1999 at 4 

impoundments.61 EPA used this data to estimate an overall average annual failure rate, as shown in Equation 

7-1. 

Equation 7-1. 

 

 

 

 

 

( ) ( )

 

EPA applied this baseline average failure rate to estimate an expected number of releases for the universe of 

1,070 impoundments at steam electric plants over the years 2019 through 2040. This expected number of 

releases is then used to calculate the expected failure costs under the baseline and each analyzed regulatory 

 

61 Thus, the survey responses provided 10 years of data for 722 impoundments and 15 years of data for 4 impoundments, 

for a total of 7,280 observations (4 impoundments times 15 years of data, plus 722 impoundments times 10 years of 

data). 
Benefit and Cost Analysis for Proposed ELGs 7: Benefits from Avoided Impoundment Failures 

April 19, 2013 7-2 

option. Note that this analysis does not account for the effect of best management practices (BMPs) –

including integrity inspections and preventive maintenance – that would help reduce the probability of 

impoundment failures.62

Under all scenarios (baseline and each analyzed regulatory option), the risk of failure was assumed to be zero 

for years 2014 through 2018 due to integrity site assessments conducted by EPA in 2009 through 2012, which 

are expected to prevent all failures for the first five years after the recommended ―action plan‖ to improve 

impoundment structures is completed (2014 through 2018). 

As described above, the baseline average failure rate is assumed to be uniform across all impoundments and 

years, irrespective of impoundment characteristics. In practice, the probability of a failure may depend on 

impoundment characteristics, including the amount of CCR managed, and may therefore change as a result of 

the proposed ELGs. To test the sensitivity of the benefit estimates to alternative assumptions about 

impoundment failure probability, EPA also used the survey data to develop a statistical model of the 

probability of impoundment failure as a function of the impoundment capacity and age. This approach 

resulted in impoundment-specific failure rates for each impoundment in each year of the analysis. Appendix G

presents the results of this sensitivity analysis. The sensitivity analysis highlights the potential magnitude of 

benefits from avoided impoundment failures (sensitivity benefits are 2.5 to 2.7 times higher than estimated 

below); however, because of the limited data available to develop and validate the statistical model, EPA did 

not use the model in the primary benefit estimates presented in this chapter but instead relied on historical 

failure rates. 

7.1.2 Capacity Factor

The impoundment survey conducted by ORCR (see U.S. EPA, 2010d) and described above provide data on 

the spill volume and impoundment capacity for 15 of the 42 documented releases. These data show spill 

volumes ranging from less than 0.01 percent to 37.9 percent of impoundment capacity, with an average of 

6.45 percent. EPA used this average ratio (spill volume is 6.45 percent of impoundment capacity) to estimate 

the average volume spilled in expected releases from each of the 1,070 Steam Electric plant impoundments, 

i.e., in the event of a failure, EPA assumed that a volume of CCR equal to 6.45 percent of the impoundment 

capacity would be released. 

7.1.3 Failure Costs

The following sections discuss three categories of costs associated with impoundment failures: cleanup, 

natural resources damages, and transaction costs. All dollar values are presented in year 2010 dollars.63

7.1.3.1 Cleanup Costs

EPA estimated per-gallon cleanup costs based on three historical impoundment failures. The average unit cost 

associated with these historical incidents 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, causing 250 million gallons of slurry to spill into 

two nearby streams. The cleanup costs of this spill were $65 million (Pincock, et al., 2001; Geotimes, 

2003), or approximately $0.26 per gallon spilled. 

 

62 The preamble accompanying this proposed rule describes the BMPs. 

63 As needed, costs were updated to 2010 dollars using the Construction Cost Index from the Engineering News Report. 
Benefit and Cost Analysis for Proposed ELGs 7: Benefits from Avoided Impoundment Failures 

April 19, 2013 7-3 

 The Martins Creek spill involved the discharge of 100 million gallons of slurry over the course of 3 

days, resulting from the failure of a wooden stop log. Cleanup costs were approximately $44 million 

(Nixon, 2006; Barker, 2009; Dewberry and Davis, 2009), or approximately $0.44 per gallon spilled. 

 The TVA Kingston spill involved the release of approximately 1.1 billion gallons of slurry. The 

cleanup costs were approximately $1.27 billion (TVA, 2010; 2011), or approximately $1.16 per 

gallon spilled. 

To estimate the expected cleanup costs of a potential future impoundment release as a function of each steam 

electric impoundment‘s capacity, EPA multiplied the impoundment capacity by 6.45 percent and applied the 

average per gallon cost ($0.62/gallon), as shown in Equation 7-2. 

Equation 7-2. CCLEANUPi = ICAPACITYi x 0.0645 x $0.62 

Where: 

CCLEANUPi = the cleanup cost associated with a release from impoundment i

ICAPACITYi = the capacity of impoundment i.

7.1.3.2 Natural Resource Damages

Israel (2006) provides a detailed state-by-state summary of NRD programs, including some prominent NRD 

cases (arising from oil spills, chemical spills, and other incidents) in each state.64 The median dollar value of 

NRD settlement from these cases was approximately $4 million in 2010 dollars.65 Releases resulting from 

impoundment failures may affect resources similar to those that were affected by the NRD cases identified in 

Israel (2006) and therefore have similar NRD costs. 

To calculate expected NRD costs for future impoundment releases, EPA assumed that the NRD costs vary 

depending on the magnitude of the release, as indicated by cleanup costs, and represent a certain percentage 

of cleanup costs. To derive a per-gallon estimate of the expected NRD value for future releases, EPA divided 

the median NRD settlement of $4 million by the median cleanup cost for cases discussed in the previous 

section ($65 million). This calculation yields an NRD estimate of 6 percent of cleanup costs, or $0.04 per 

gallon spilled (6 percent of $0.62/gallon).66 This estimate is meant to reflect an approximate value for NRD 

resulting from impoundment failures as a function of the volume of CCR slurry released; actual damages 

would be highly location- and failure-specific. 

EPA calculated the NRD costs associated with an expected impoundment failure as a function of 

impoundment capacity using Equation 7-3. 

 

64 In a separate study, Lemley and Skopura (2012) attempt to estimate the total damages to affected fish and wildlife 

from coal combustion waste at twenty-one surface impoundment sites. Estimation of the economic values is explained in 

the study‘s support document (http://pubs.acs.org/doi/suppl/10.1021/es301467q/suppl_file/es301467q_si_001.pdf). The 

results of the study, while perhaps useful for providing a general sense of the scale and types of potential damages, are 

not appropriate for use in this benefits analysis. The estimates are based on broad and uniform assumptions, and use 

approaches and values that do not meet EPA guidelines for economic analyses to support rulemaking. 

65 Natural resource damages do not include cleanup costs (or legal and transaction costs, if reported) but include 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 (excluded legal costs) as NRD. 

66 Note that this is a rough assumption that does not account for differences in the resources affected in each case. 
Benefit and Cost Analysis for Proposed ELGs 7: Benefits from Avoided Impoundment Failures 

April 19, 2013 7-4 

Equation 7-3. CNRDi = ICAPACITYi x 0.0645 x $0.04 

Where: 

CNRDi = the cost of natural resource damages associated with a release from impoundment i

ICAPACITYi = the capacity of impoundment i.

7.1.3.3 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. 67 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 7-1

shows the data sources. On average, transaction costs account for 27 percent of total spending. 

Table 7-1: 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)a37%

a. Multiplier is calculated as Average/(1 - Average)

These data indicate that transaction costs represent 27 percent of total costs, or an additional 37 percent of 

cleanup costs. Therefore, the estimated transaction costs per gallon of slurry spilled is $0.23 (37 percent of 

$0.62/gallon). Using these assumptions, EPA calculated the transaction costs associated with an expected 

impoundment failure as a function of impoundment capacity using Equation 7-4. 

Equation 7-4. CTRANSACTIONi = ICAPACITYi x 0.0645 x $0.23 

Where: 

CTRANSACTIONi = the transaction costs associated with a release from impoundment i

ICAPACITY = the capacity of impoundment i.

7.1.3.4 Total Costs

Table 7-2 summarizes unit costs for cleanup, NRD, and transaction. Total impoundment failure costs are 

$0.06 per gallon of impoundment capacity. In applying unit costs to any impoundment, EPA set a maximum 

cost of $1.3 billion for any single incident based on the total estimated cleanup cost of the TVA Kingston 

spill. 

 

67 These activities involve services, whether performed by the complying entity or other parties, that EPA expects would 

be required in the absence of this regulation 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 for the proposed 

rule. 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. 
Benefit and Cost Analysis for Proposed ELGs 7: Benefits from Avoided Impoundment Failures 

April 19, 2013 7-5 

Table 7-2: Unit Costs (2010$)

Cost Component Unit Cost

Cleanup ($/gallon spilled) $0.62

NRD ($/gallon spilled) $0.04

Transaction costs ($/gallon spilled) $0.23

Total costs ($/gallon spilled) $0.89

Total costs based on impoundment capacity ($/gallon capacity)a$0.06

a. Total release costs times the average volume spilled as a percentage of impoundment capacity (6.45 

percent)

Source: U.S. EPA Analysis, 2013

The proposed ELGs would provide benefits by reducing the impact of impoundment failures for 

impoundments that are expected to see reduced utilization as a result of the proposed ELGs, but would 

continue to operate in the absence of the regulation.68 For each of the 1,070 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 costs were estimated by multiplying the estimated cost per 

failure by the failure probability for a given impoundment and year (0.58 percent), discounting for future 

years, aggregating across the analysis time horizon (2017 to 2040), and annualizing over a 24-year period 

using rates of 3 percent and 7 percent.69 The benefits of each analyzed regulatory option therefore are the 

costs of expected releases under the baseline minus the costs of expected releases under the regulatory 

scenario. 

7.2 Results

Table 7-3 summarizes total benefits of avoided impoundment failures, calculated as the sum of the avoided 

failure costs across all impoundments expected to be affected by the proposed ELGs under each analyzed 

regulatory option. 

EPA estimates that Option 3 would generate annual benefits valued at $114.8 million, using a 3 percent 

discount rate. The results also provide relevant information for understanding the potential benefits of the 

other preferred options. Options 3a and 3b are expected to provide smaller benefits than Option 3. Option 4a 

is expected to provide benefits between those estimated for Options 3 and 4, i.e., between $114.8 million and 

$295.1 million, using a 3 percent discount rate. 

Table 7-3. Estimated Annualized Benefits of Avoided Surface Impoundment Failures (Millions; 

2010$)a

Regulatory Option 3% Discount Rate 7% Discount Rate

Option 1 $62.1 $52.2 

Option 3a b b

Option 2 $62.1 $52.2 

Option 3b b b

 

68 As described in Section 7.1.1, EPA assumed a uniform probability of failure of 0.58 percent per impoundment per 

year. In this analysis, the costs of impoundment failures vary in proportion to the expected volume of CCR waste that 

may be released, which is assumed to change as a result of the proposed ELG. The sensitivity analysis described in 

Appendix G instead assumes that the probability of failure depends on impoundment age and capacity; for this alternate 

analysis, EPA assumed that changes in the volume of CCR waste managed using impoundments due to the proposed 

ELG reduce impoundment capacity by an equivalent fraction and therefore reduce the probability of a failure, in addition 

to changing the volume of CCR waste that may be released in any given incident and the cost of that incident. 

69 Note that while the analysis period starts in 2014, plants are assumed to close or reduce use of impoundments 

according to the same schedule as control technology implementation (2017-2021). Additionally, as noted in Section 

7.1.1, EPA assumed zero probability of failure in years 2014 through 2019 as a result of integrity site assessments. 
Benefit and Cost Analysis for Proposed ELGs 7: Benefits from Avoided Impoundment Failures 

April 19, 2013 7-6 

Table 7-3. Estimated Annualized Benefits of Avoided Surface Impoundment Failures (Millions; 

2010$)a

Regulatory Option 3% Discount Rate 7% Discount Rate

Option 3 $114.8 $95.9 

Option 4a c c

Option 4 $295.1 $245.9 

Option 5 $295.1 $245.9 

a. Baseline value of total failure costs minus option value of total failure costs.

b. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Options 3a and 3b to be less 

than those of Option 3, with values for Option 3a being lower than Option 3b. EPA does not know how the benefits of Options 3a

and 3b compare to those of Options 1 and 2.

c. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Option 4a to be between those 

of Options 3 and 4.

Source: U.S. EPA Analysis, 2013

As described in Section 7.1.1, these estimates do not account for the effect of BMPs that would further reduce 

the probability of a failure and lead to higher benefits for the proposed ELGs. EPA does not have sufficient 

information to accurately quantify and monetize the benefits of implementing BMPs. Preventing all future 

impoundment failures would provide annual benefits estimated at up to $378 million (using a 3 percent 

discount rate), relative to the baseline probability of failure and costs. 

EPA does not anticipate BMPs to fully realize such benefits, however. Inspections and other BMPs aim to 

prevent future failures by identifying conditions that have contributed to past impoundment failures and 

releases (e.g., slope instability, structural defects, seepage, overtopping, and inadequate management 

practices; see NRC, 2002); they may not be as effective at preventing impoundment failures caused by 

unusual weather events or earthquakes. Further, data from the Steam Electric Industry Survey and 2010 CCR 

Impoundment Survey suggest that steam electric plants already implement inspections and monitoring 

programs of varying scope and frequency (U.S. EPA, 2010e; U.S. EPA, 2012d), but field assessments 

conducted subsequent to the 2010 CCR Impoundment Survey indicate that the existing inspection programs at 

some plants failed to identify embankment erosion, seepage and other conditions (U.S. EPA; 2012e). 

7.3 Limitations and Uncertainties

Table 7-4 summarizes the limitations and uncertainties in the analysis of benefits associated with reduced 

impoundment failures arising from the proposed ELGs. The methodologies used in this analysis involve 

several simplifications and sources of uncertainties, as described below. Whether these simplifications and 

uncertainties, taken together, are likely to result in an understatement or overstatement of the estimated 

benefits is not known. 

Table 7-4. 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 proposed ELGs, 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 proposed ELGs.
Benefit and Cost Analysis for Proposed ELGs 7: Benefits from Avoided Impoundment Failures 

April 19, 2013 7-7 

Table 7-4. Uncertainties in Analysis of Avoided Risk of Impoundment Failure Benefits

Uncertainty/Assumption Effect on Benefits 

Estimate Notes

EPA estimated expected future 

impoundment failures based on a 

uniform probability of failure 

(0.58 percent per impoundment per 

year). In practice, the probability of 

failure may depend on impoundment 

characteristics and management 

practices.

Uncertain EPA used historical data to evaluate benefits 

assuming the best-fit relationship between the 

probability of failure and impoundment age and 

capacity. This sensitivity analysis is described in 

Appendix G. The results suggest that using a uniform 

failure rate may understate benefits of the proposed 

ELGs. 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 

$0.89 per gallon spilled and assumes 

that, on average, each release involves a 

volume equal to 6.45 percent of the 

impoundment capacity. The analysis 

caps total costs at $1.3 billion per 

incident. In practice, failure costs depend 

on incident-specific characteristics. Cost 

estimates for future failures are therefore 

highly uncertain. 

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.

The analysis does not include benefits 

from inspection, monitoring and other 

BMPs

Underestimate BMPs are expected to help further reduce the 

probability of impoundment failures by identifying 

conditions associated with past releases. EPA does 

not have data to accurately estimate the additional 

benefits of implementing these BMPs.
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-1 

8 Air-Related Benefits 

The proposed ELGs are expected to affect air pollution through three main mechanisms: 1) additional 

auxiliary electricity use by steam electric 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 coal combustion residue (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 proposed 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 proposed 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 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: Nitrogen oxides (NOx), sulfur dioxide (SO2), and carbon dioxide (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.70 CO2 is an important greenhouse gas that is linked to climate changes 

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. 

8.1 Data and Methodology

8.1.1 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 eight regulatory options (Options 3 and 4; 

see Chapter 5 in RIA (U.S. EPA, 2013c)). 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 proposed ELGs. EPA used two run years, 2020 and 2030, to represent the periods of 2017-2024, 

and 2025-2035, respectively. For this analysis, EPA assumed that changes in emissions estimated for the 

period of 2025-2035 continue through 2040. 

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 NERC-specific emission factors obtained from IPM for each 

 

70 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. 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-2 

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 8-1 through Table 8-3 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 8-1) and transportation (Table 8-2) would result in an 

increase in emissions (positive values), while changes in the profile of electricity generation (Table 8-3) 

would reduce CO2 and SO2 emissions (negative values), but increase NOx emissions. Table 8-4 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 the use of coal for electricity generation as a result of the proposed ELGs (0.1 percent for 

Option 3; 0.3 percent for Option 4), which is 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. 

Table 8-1. Estimated Changes in Electricity Consumption and Air Pollutant Emissions due to Increase 

in Auxiliary Service at Steam Electric Plants, Relative to Baseline

Year Electricity 

Consumption (MWh)

CO2(Metric 

Tonnes/Year)

NOx (Tons/Year) SO2(Tons/Year)

Option 3

2014-2016 0 0.0 0.0 0.0

2017 77,536 71,038.4 89.9 56.4 

2018 161,756 149,500.9 133.5 146.2 

2019 206,958 191,097.8 162.9 185.7 

2020 276,720 254,786.9 215.4 237.9 

2021-2024 303,332 276,960.1 239.6 257.7 

2025-2040 303,332 276,680.4 197.1 274.0 

Option 4

2014-2016 0 0.0 0.0 0.0

2017 139,221 102,517.2 88.4 98.2 

2018 300,317 250,761.7 190.7 275.4 

2019 421,580 362,607.3 296.3 412.5 

2020 572,273 501,679.5 420.2 564.0 

2021-2024 673,780 592,847.9 512.8 675.9 

2025-2040 673,780 619,790.1 527.3 709.0 

Source: U.S. EPA Analysis, 2013; see TDD for details. 

Table 8-2. Estimated Changes in Annual Air Pollutant Emissions due to Increased Trucking at Steam 

Electric Plants, Relative to Baseline

Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2

(Tons/Year)

Option 3

2014-2016 0.0 0.0 0.0

2017 4,699.4 2.1 0.0 

2018 9,818.2 4.3 0.1 

2019 13,595.7 5.9 0.1 

2020 18,683.8 8.2 0.2 

2021-2040 22,548.7 9.9 0.2 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-3 

Table 8-2. Estimated Changes in Annual Air Pollutant Emissions due to Increased Trucking at Steam 

Electric Plants, Relative to Baseline

Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2

(Tons/Year)

Option 4

2014-2016 0.0 0.0 0.0

2017 8,484.0 3.7 0.1 

2018 20,826.9 9.1 0.2 

2019 28,277.0 12.4 0.3 

2020 37,520.9 16.4 0.4 

2017-2040 44,167.9 19.3 0.5 

Source: U.S. EPA Analysis, 2013l; see TDD for details 

Table 8-3. Estimated Changes in Annual Air Pollutant Emissions due to Changes in Electricity 

Generation Profile, Relative to Baseline

Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2

(Tons/Year)

Option 3

2014-2016 0.0 0.0 0.0

2017-2024 -1,354,732.8 1,714.6 -2,869.8

2025-2040 -1,461,681.0 1,119.1 -3,096.6

Option 4

2014-2016 0.0 0.0 0.0

2017-2024 -2,779,337.5 1,216.8 -706.8

2025-2040 -4,412,837.1 1,052.5 -4,247.1

Source: U.S. EPA Analysis, 2013; see Chapter 5 in RIA for details. 

Table 8-4. Estimated Net Changes in Air Pollutant Emissions due to Increase in Auxiliary Service at 

Steam Electric Plants, Increased Trucking at Steam Electric Plants, and Changes in Electricity 

Generation Profile, Relative to Baseline

Year CO2

(Metric Tonnes/Year) NOx (Tons/Year) SO2

(Tons/Year)

Option 3

2014-2016 0.0 0.0 0.0

2017 -1,278,995.0 1,806.5 -2,813.4 

2018 -1,195,413.8 1,852.4 -2,723.6 

2019 -1,150,039.3 1,883.4 -2,684.0 

2020 -1,081,262.1 1,938.2 -2,631.8 

2021-2024 -1,055,224.0 1,964.0 -2,611.9 

2025-2040 -1,162,451.8 1,326.1 -2,822.4 

Option 4

2014-2016 0.0 0.0 0.0

2017 -2,668,336.3 1,308.9 -608.4

2018 -2,507,748.9 1,416.6 -431.1

2019 -2,388,453.3 1,525.5 -294.0

2020 -2,240,137.1 1,653.4 -142.4

2021-2024 -2,142,321.8 1,748.9 -30.4

2025-2040 -3,748,879.1 1,599.0 -3,537.7

Source: U.S. EPA Analysis, 2013. 

8.1.2 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 the Environmental 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-4 

Benefits Mapping and Analysis Program (BenMAP), EPA‘s state-of-the-art 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 population of the adverse health effects associated with the pollutants and the corresonding 

monetized benefits (see Abt Associates (2010) 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 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. 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, benefits per ton estimates must be specific to a combination of 

emission type and source category. 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 assessment of 

the benefits of PM and SO2 reductions for the Industrial Boiler and Process Heaters 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 et al., 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. An air quality model was used to estimate the changes in ambient PM2.5 concentrations resulting from 

specified precursor emissions reductions under various scenarios; under each scenario the total tons 

(of the precursor emissions) reduced was calculated. 

2. BenMAP was used to estimate the changes in incidence of the associated health effects and the 

monetized benefits of those incidence reductions under each scenario; and 

3. National benefits per ton estimates were calculated 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 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-5 

becoming PM2.5. The benefits per ton estimates assume, however, that all fine particulates have the same 

potency for causing premature mortality (U.S. EPA 2011).71

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 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.72 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 

For this analysis, benefits per ton estimates are needed for each year from 2017 through 2040. 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 2040 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, although it was necessary to generate benefits per ton 

estimates for each year from 2017 through 2040 to be consistent with the rest of the analysis, 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 proposed ELGs. For example, the benefits from reduced 

emissions of NOx from EGUs under Option 3 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 of the total monetized benefits of reducing PM2.5 concentrations are attributable to 

avoided premature mortality. 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. Appendix H provides more details on the 

concentration response function used for this analysis. 

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 and then discounts that value back to 

 

71 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. 

72 Provided in personal communication with Charles Fulcher, EPA Office of Air Quality Planning and Standards 

(OAQPS), on October 19, 2012. 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-6 

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 2016 is the value of avoided morbidity in 2016 plus the present 

discounted value of the stream of avoided premature mortalities 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.73 All benefits per 

ton estimates for years 2016 through 2040 were further discounted back to the year 2014 (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 

EPA‘s estimated increases in WTP for mortal risk reductions with respect to increases in real income. The 

income growth adjustment factors used are those in BenMAP (Abt Associates Inc., 2010). Table 8-5

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 proposed ELGs. 

Table 8-5. National Benefits per Ton Estimates for NOx and SO2 Emissions (2010$/ton) from the 

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

Year EGU Mobile Source (Onroad)

NOx SO2 NOx SO2

3% Discount Rate

2005 $3,700 $27,000 $4,500 $20,000

2016 $5,200 $35,000 $7,400 $20,000

2020 $5,400 $36,000 $7,800 $21,000

2025 $5,900 $40,000 $8,500 $24,000

2030 $6,200 $42,000 $9,200 $26,000

7 % Discount Rate

2005 $3,300 $24,000 $4,000 $18,000

2016 $4,700 $31,000 $6,700 $18,000

2020 $4,900 $33,000 $7,100 $19,000

2025 $5,300 $36,000 $7,700 $21,000

2030 $5,600 $38,000 $8,300 $24,000

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.

Source: U.S. EPA Analysis, 2013 based on Fann et al. (2012)

8.1.3 CO2

Estimates of benefits per ton for CO2 were derived using estimates of the social cost of carbon (SCC) 

obtained from the Interagency Working Group on Social Cost of Carbon (IWGSCC, 2010).74

The SCC is an estimate of the monetized damages associated with an incremental increase in carbon 

emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural 

productivity, human health, property damages from increased flood risk, and the value of ecosystem services 

due to climate change. 

The interagency group selected four SCC values for use in regulatory analyses: $4.86, $22.12, $37.28, and 

$67.07 per metric ton of CO2 emissions in the year 2010, in 2010 dollars. The first three values are based on 

the average SCC from three integrated assessment models, at discount rates of 5, 3, and 2.5 percent, 

 

73 Provided in personal communication with Charles Fulcher, EPA/OAQPS, on October 19, 2012 

74 The interagency process included EPA, the Department of Transportation, and other executive branch entities. 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-7 

respectively. SCC values are provided for several discount rates because the literature shows that the SCC is 

quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate rate 

to use in an intergenerational context. The fourth value is the 95th percentile of the distribution of SCC from 

all three models using a 3 percent discount rate. It is included to represent higher-than-expected impacts from 

temperature change further out in the tails of the SCC distribution. Low probability, high impact events are 

incorporated into all of the SCC values through explicit consideration of their effects in two of the three 

integrated assessment models as well as the use of a probability density function for equilibrium climate 

sensitivity in all three models. Treating climate sensitivity probabilistically allows the estimation of SCC at 

higher temperature outcomes, which lead to higher projected damages. The SCC increases over time because 

future emissions are expected to produce larger incremental damages as physical and economic systems 

become more stressed in response to greater climatic change. The interagency group estimated the growth rate 

of the SCC directly using the three integrated assessment models rather than assuming a constant annual 

growth rate. This helps to ensure that the estimates are internally consistent with other modeling assumptions. 

The IWGSCC report provides SCC estimates at 5-year intervals, starting with the year 2010 and ending with 

the year 2050. EPA used linear interpolation to estimate SCC values for the intermediate years. Table 8-6

presents the SCC estimates used in this analysis. As with benefits per ton estimates for NOx and SO2, all 

estimates for years 2015 through 2040 were discounted back to the year 2014. 

Table 8-6. SCC Values (2010$/metric tonne CO2)

Year 5% Discount Rate, 

Average

3% Discount Rate, 

Average

2.5 Discount Rate, 

Average

3% Discount Rate, 

95th Percentile

2017 $6.30 $25.11 $41.03 $78.54

2018 $6.51 $25.63 $41.75 $80.09

2019 $6.72 $26.15 $42.37 $81.75

2020 $7.03 $26.66 $43.10 $83.40

2025 $8.47 $30.59 $47.44 $93.43

2030 $10.02 $33.90 $51.67 $103.35

2035 $11.57 $37.21 $56.01 $113.37

2040 $13.13 $40.51 $60.36 $123.29

Source: IWGSCC, 2010 (values updated to 2010 dollars).

EPA estimates the dollar value of the CO2-related benefits for each analysis year between 2017 and 2040 by 

applying the global SCC estimates, shown in Table 8-6, to the estimated reductions in CO2 emissions under 

the proposed ELGs. 

8.1.4 Estimating Total Air-Related Benefits 

EPA calculated the monetized air-related benefits of the proposed ELGs, under options 3 and 4, in any given 

year (discounted back to the year 2014) 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 8-1. 

Equation 8-1. 

2014

, ,

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; 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-8 

j = 7, 8, and 9 denote NOx, SO2, and CO2, respectively, associated with transportation; and 

 

 is the present discounted value, discounted to the year 2014, of the benefits per ton for the 

jth emissions type/source category combination. 

The total present discounted value of benefits, discounted to the year 2014, PDV2014, is calculated using 

Equation 8-2. 

Equation 8-2. 

   

 

2040

2014

2014

, ,

9

1

2014 ( )

y

y j y j

j

PDV Tons avoided BPT .

8.2 Results

Table 8-7 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 8-7. Estimated Benefits from Reduced Air Emissions for Selected Years (millions; 2010$)

Year

Option 3 Option 4

3% Discount Rate 7% Discount Rateb3% Discount Rate 7% Discount Rateb

2017a$121.9 $86.8 $82.9 $29.5

2020 $113.7 $84.9 $57.1 $12.3

2025 $140.6 $104.4 $246.7 $150.6

2030 $149.7 $111.5 $265.7 163.0

a The benefits per ton values used for year 2017 benefit calculation is assumed to be the same as the 2016 benefits per ton values.

b EPA used SCC values based on a 5 percent discount rate to calculate total benefit values presented for the 7 percent discount rate.

Table 8-8 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 discounted at 3 percent or 

5 percent, depending on the discount rate used for other categories of benefits (3 percent or 7 percent). 

Appendix I presents estimates of CO2-related benefits calculated using alternate SCC values (average at 

2.5 percent, average at 5 percent, and the 95th percentile at 3 percent). 

The annualized benefits of Options 3 and 4 are $127.6 million and $170.5 million, respectively, using a 

discount rate of 3 percent ($82.3 million and $74.6 million, respectively, using a discount rate of 7 percent). 

These estimates provide relevant information for understanding the potential benefits of the preferred options, 

including Options 3a, 3b, and 4a. As discussed in Chapter 5 of the RIA, EPA expects the impacts of Option 

4a on the profile of electricity generation – and therefore air emissions – to be between those of Options 3 and 

4. Accordingly, EPA expects the benefits of Option 4a to be between those of Options 3 and 4. Further, the 

Agency expects Options 3a and 3b to result in smaller changes than those projected under Option 3, resulting 

in smaller benefits than those estimated for Option 3. 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-9 

Table 8-8. Estimated Annualized Benefits from Reduced Air Emissions 

(Millions; 2010$)

Regulatory 

Option Pollutant 3% Discount Rate 7% Discount Ratea

Option 3

NOx -$8.5 -$7.0

SO2 $102.5 $80.5

CO2

a

$33.6 $8.8

TOTAL $127.6 $82.3

Option 4

NOx -$8.8 -$6.9

SO2 $84.7 $56.8

CO2

a

$94.6 $24.7

TOTAL $170.5 $74.6

a. EPA used SCC values based on a 5 percent discount rate, and discounted at 5 percent, for total 

benefit values presented for the 7 percent discount rate. 

Source: U.S. EPA Analysis, 2013

8.3 Limitations and Uncertainties

This analysis includes only those benefits associated with avoided mortality due to PM2.5 reduction. 

Therefore, the quantified human health benefits included in this analysis represent only a subset of the total 

potential air-related benefits expected to result from the proposed ELGs. There are also 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 8-9. 

Table 8-9. Uncertainties in Analysis of Air-related Benefits

Issue Effect on Benefits 

Estimate Notes

Analysis included only premature 

mortality.

Underestimate This analysis does not include all of the human health 

benefits associated with air pollution reductions because 

the benefits per ton estimates that were used are based 

only on mortality, and not morbidity endpoints. Thus the 

quantified human health benefits included in this 

analysis represent only a subset of the total potential 

health benefits expected to result from the proposed 

ELGs.

There is 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 Academies of Science (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.
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-10

Table 8-9. Uncertainties in Analysis of Air-related Benefits

Issue Effect on Benefits 

Estimate Notes

The Interagency Working Group on Social Cost of 

Carbon (IWGSCC, 2010) noted a number of limitations 

to the SCC analysis, including the incomplete way in 

which the integrated assessment models capture 

catastrophic and noncatastrophic impacts, 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. As noted in the SCC TSD, the U.S. 

government intends to revise these estimates over time, 

taking into account new research findings that were not 

available in 2010.

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 proposed ELGs. In order to model the 

electricity market effects of the proposed ELGs, 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 proposed ELGs 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.

EPA used a reduced form approach 

(benefits per ton) to value airrelated 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). 
Benefit and Cost Analysis for Proposed ELGs 8: Air-Related Benefits 

April 19, 2013 8-11

Table 8-9. Uncertainties in Analysis of Air-related Benefits

Issue Effect on Benefits 

Estimate Notes

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 yearspecific 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 Proposed ELGs 9: Benefits from Reduced Water Withdrawals 

April 19, 2013 9-1 

9 Benefits from Reduced Water Withdrawals

Steam electric plants use vast quantities of water for ash transport and for operating wet flue gas 

desulfurization (FGD) scrubbers. 

By eliminating or reducing water used in sluicing operations or prompting the recycling of water in FGD 

wastewater treatment systems, the proposed ELGs are expected to reduce water withdrawal from both surface 

waterbodies and aquifers. The reduction in water use depends on the regulatory option.75 EPA estimates that 

power plants would reduce the use of water by 54 billion gallons per year (147 million gallons per day) under 

Option 3 and by 158 billion gallons per year (432 million gallons per day) under Option 4 (see Chapter 11 of 

TDD for details). 

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. Accordingly, the rest of this chapter 

focuses solely on the benefits associated with reduced groundwater withdrawals. 

Reduced water intake from groundwater sources by steam electric plants would 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. 

9.1 Reduced Groundwater Withdrawals

EPA estimated the benefits of reduced groundwater withdrawals based on avoided costs of purchasing 

drinking water during periods of shortages in groundwater supply. 

9.1.1 Methods

EPA‘s analysis of the proposed ELG options (U.S. EPA, 2013b) indicate that up to two plants (one in Florida 

and one in Nebraska) would reduce the volume of groundwater withdrawn as a result of the proposed ELGs. 

Because the states are potentially or currently water-stressed (Tetra Tech, 2011), the proposed 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 ($730.94 per acre/foot for Florida and $1,169.53 per acre/foot for Nebraska). For each affected 

plant and regulatory option, EPA 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. 

 

75 The policy options for fly and bottom ash would eliminate or reduce water use associated with current wet sluicing 

operating systems at steam electric plants. Reductions in intake flow would occur at plants which convert to dry handling 

or recycle FGD wastewater under regulatory options as part of their treatment system. 
Benefit and Cost Analysis for Proposed ELGs 9: Benefits from Reduced Water Withdrawals 

April 19, 2013 9-2 

9.1.2 Results

Table 9-1 shows estimated annual benefits from reduced groundwater withdrawals. The annual benefits from 

the preferred options for existing sources are $0.03 million and $0.13 million, respectively for Options 3 and 

4, using a 3 percent discount rate ($0.01 million and $0.08 million using a 7 percent discount rate). As for 

other categories of benefits, EPA expects that Options 3a and 3b would have lower benefits than Option 3, 

and Option 4a would have benefits between those of Options 3 and 4. 

Table 9-1. Estimated Annualized Benefits from Reduced Groundwater Withdrawals (Millions; 2010$)

Regulatory Option

Reduction in 

Groundwater Intakes 

(million gallons per year; 

full implementation)

3% Discount Rate 7% Discount Rate

Option 1 0 $0.00 $0.00

Option 3a a a a

Option 2 0 $0.00 $0.00

Option 3b a a a

Option 3 16.0 $0.03 $0.01

Option 4a b b b

Option 4 47.5 $0.13 $0.08

Option 5 47.5 $0.13 $0.08

a. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Options 3a and 3b to be less 

than those of Option 3, with values for Option 3a being lower than Option 3b. EPA does not know how the benefits of Options 3a

and 3b compare to those of Options 1 and 2.

b. Value was not estimated for this regulatory option. EPA expects the effects – and the benefits – of Option 4a to be between those 

of Options 3 and 4

Source: U.S. EPA Analysis, 2013

9.1.3 Limitations and Uncertainties

Table 9-2 summarizes the limitations and uncertainties in the analysis of benefits associated with reduced 

groundwater withdrawals. 

Table 9-2. 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 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. 

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.
Benefit and Cost Analysis for Proposed ELGs 10: Total Benefits 

April 19, 2013 10-1 

10 Summary of Total Benefits

10.1 Total Annualized Benefits

Table 10-1 summarizes the total annual monetized benefits using 3 percent and 7 percent discount rates. 

Table 10-2 and Table 10-3 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 explicitly range from 

$82.0 million to $605.5 million per year using a 3 percent discount rate, depending on the regulatory option 

($65.9 million to $424.8 million per year using a 7 percent discount rate). Option 3 has total benefits of 

approximately $311.7 million, using a 3 percent discount rate ($230.4 million using a 7 percent discount rate), 

when including air-related benefits. 

EPA did not analyze the benefits of three preferred options for existing sources (Option 3a, Option 3b and 

Option 4a), but generally expects the annual total benefits of Options 3a and 3b to be less than those of 

Option 3 and the benefits of Option 4a to be between those of Option 3 and 4. Section 10.3 provides estimated 

total annualized benefits of Options 3a, 3b, and 4a as inferred from EPA‘s analyses of Options 1 through 5.

The monetized benefits of the proposed ELGs do not account for all benefits because they omit various 

sources of benefits to society from reduced steam electric pollutant discharges, such as non-cancer health 

benefits (other than IQ benefits from reduced childhood exposure to lead and in-utero exposure to mercury) 

and reduced cost of drinking water treatment. See Chapter 2 for a discussion of categories of benefits EPA 

did not monetize. In addition, EPA‘s analysis of human health benefits associated with water quality 

improvements includes only partial benefits for directly receiving reaches. Finally, EPA was able to estimate 

air-related benefits for Options 3 and 4 only (see Chapter 8). Benefits for options 1, 2 and 5 are therefore 

understated; in particular, EPA expects that the benefits for Option 5 would be higher than those for Option 4 

if air-related benefits were included. 

Chapters 3 through 9 provide more detail on limitation and uncertainty inherent in the analysis of each benefit 

category. 
Benefit and Cost Analysis for Proposed ELGs 10: Total Benefits 

April 19, 2013 10-2 

Table 10-1. Summary of Total Annualized Benefits (Millions; 2010$)

Benefit Category Option 1 Option 3a Option 2 Option 3b Option 3 Option 4a Option 4 Option 5

3% Discount Rate

Human Health Benefits

Avoided cancer cases from exposure to arsenic $0.0 NE $0.0 NE $0.1 NE $0.2 $0.2

Reduced IQ losses in children from exposure to leada

$0.1 NE $0.1 NE $2.7 NE $6.8 $6.8

Avoided Cost of Compensatory Education for 

Children with Blood Lead Concentrations above 

20 g/dL and IQ Less than 70a

$0.0 NE $0.0 NE $0.0 NE $0.1 $0.1

Reduced IQ losses associated with in-utero exposure 

to mercurya $3.8 NE $3.9 NE $5.0 NE $10.2 $10.2

Improved Ecological Conditions and Recreational Uses

Use and nonuse values for water quality

improvementsb $8.3 NE $38.0 NE $49.9 NE $82.8 $81.9

Nonuse values of T&E speciesc

$7.0 NE $7.0 NE $10.0 NE $33.3 $33.3

Groundwater Quality Benefits $0.7 NE $0.7 NE $1.6 NE $6.5 $6.5

Market and Productivity Benefits (Avoided 

Impoundment Failures) $62.1 NE $62.1 NE $114.8 NE $295.1 $295.1

Air-related Benefits NE NE NE NE $127.6 NE $170.5 NE

Reduced Water Withdrawals $0.0 NE $0.0 NE $0.0 NE $0.1 $0.1

Total (excluding air-related benefits)d

$82.0 f $111.7 f $184.1 f $435.0 $434.1

Range

($71.1 to 

$99.0)

($76.4 to 

$184.4)

($136.9 to 

$275.8)

($347.3 to 

$578.6)

($346.9 to 

$576.9)

Total (including air-related benefits)d

e f e f $311.7 f $605.5 e

Range

($264.5 to 

$403.3)

($517.8 to 

$749.1)
Benefit and Cost Analysis for Proposed ELGs 10: Total Benefits 

April 19, 2013 10-3 

Table 10-1. Summary of Total Annualized Benefits (Millions; 2010$)

Benefit Category Option 1 Option 3a Option 2 Option 3b Option 3 Option 4a Option 4 Option 5

7% Discount Rate

Human Health Benefits

Avoided cancer cases from exposure to arsenic $0.0 NE $0.0 NE $0.1 NE $0.1 $0.1

Reduced IQ losses in children from exposure to leada

$0.0 NE $0.0 NE $0.3 NE $0.6 $0.6

Avoided Cost of Compensatory Education for 

Children with Blood Lead Concentrations above 

20 g/dL and IQ Less than 70a

$0.0 NE $0.0 NE $0.0 NE $0.0 $0.0

Reduced IQ losses associated with in-utero exposure 

to mercurya $0.3 NE $0.4 NE $0.4 NE $0.9 $0.9

Improved Ecological Conditions and Recreational Uses

Use and nonuse values for water quality

improvementsb $6.9 NE $31.7 NE $41.7 NE $69.2 $68.5

Nonuse values of T&E speciesc

$5.9 NE $5.9 NE $8.4 NE $27.8 $27.8

Groundwater quality benefits $0.6 NE $0.6 NE $1.4 NE $5.5 $5.5

Market and Productivity Benefits (Avoided 

Impoundment Failures) $52.2 NE $52.2 NE $95.9 NE $245.9 $245.9

Air-related Benefits NE NE NE NE $82.3 NE $74.6 NE

Reduced Water Withdrawals $0.0 NE $0.0 NE $0.0 NE $0.1 $0.1

Total (excluding air-related benefits)d

$65.9 f $90.7 f $148.1 f $350.2 $349.4

Range

($57.3 to 

$79.7)

($61.6 to 

$151.0)

($109.5 to 

$223.7)

($278.7 to 

$467.9)

($284.0 to 

$472.1)

Total (including air-related benefits)d

e f e f $230.4 f $424.8 e

Range

($193.2 to 

$307.4)

($358.9 to 

$548.1)

NE = not estimated

a. Values provided are the mean for each option; for full range of benefits estimated, see Chapter 3.

b. Values provided are the mean for each option; for full range of benefits estimated, see Chapter 4. Benefits for Option 5 are slightly less than those for Option 4 because Option 5 

improves slightly fewer miles than Option 4 (22,441 vs. 22,447 miles). Under Option 4, plants with both leachate and FGD wastestreams are assumed to implement chemical 

precipitation and biological treatment for the combined streams whereas, under Option 5, these same plants are assumed to treat the two streams separately: FGD wastewater by 

evaporation and leachate using chemical precipitation (which removes less pollutant load than biological treatment).

c. Values provided are the mean for each option; for full range of benefits estimated, see Chapter 5. 

d. Values for individual benefit categories may not sum to the total due to independent rounding.

e. The total monetized benefits for options 1, 2, and 5 do not include air-related benefits. This category of benefits was analyzed for Options 3 and 4 only (see Chapter 8). Section 10.3

describes EPA‘s calculation of inferred air-related benefits for Options 1, 2, and 5 based on results for Options 3 and 4.

f. EPA did not estimate benefits for Options 3a, 3b and 4a, but expects the benefits of Options 3a and 3b to be less than those of Option 3 and the benefits of Option 4a to be between 

those of Options 3 and 4. Section 10.3 describes EPA‘s calculation of inferred total benefits for Options 3a, 3b, and 4a based on results for Options 1 through 5.

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs 10: Total Benefits 

April 19, 2013 10-0 

10.2 Time Profile of Benefits

Table 10-2 and Table 10-3 compile, for each of the regulatory options, the time profiles of total (nondiscounted) monetized benefits. The tables also report the calculated present and annualized values of benefits 

at 3 percent and 7 percent discount rates, respectively. 

Table 10-2: Time Profile of Benefits at 3 Percent (Millions; 2010$) (Including Air-Related Benefits for 

Options 3 and 4)

Year Option 1a Option 3a Option 2a Option 3b Option 3 Option 4a Option 4 Option 5a

2014 $0.0 $0.0 $0.0 $0.0 $0.0 

2015 $0.0 $0.0 $0.0 $0.0 $0.0 

2016 $0.0 $0.0 $0.0 $0.0 $0.0 

2017 $1.1 $1.2 $123.1 $85.1 $3.2 

2018 $3.0 $3.1 $122.9 $84.2 $11.5 

2019 $82.9 $116.5 $285.3 $434.1 $367.4 

2020 $88.2 $122.2 $291.9 $467.4 $409.5 

2021 $97.9 $132.1 $337.3 $579.6 $527.4 

2022 $98.8 $133.4 $340.7 $584.1 $530.7 

2023 $100.2 $135.2 $345.4 $591.3 $536.4 

2024 $100.8 $136.0 $348.9 $596.1 $539.9 

2025 $101.1 $136.7 $369.2 $790.4 $542.6 

2026 $101.4 $137.3 $371.3 $795.9 $545.8 

2027 $101.6 $137.9 $374.4 $801.9 $547.4 

2028 $101.8 $138.4 $377.3 $807.6 $549.0 

2029 $102.1 $139.0 $380.3 $813.3 $550.6 

2030 $102.3 $139.6 $383.0 $819.0 $552.2 

2031 $102.5 $140.1 $386.1 $824.9 $553.5 

2032 $102.7 $140.6 $389.1 $830.8 $554.9 

2033 $102.9 $141.1 $391.9 $836.3 $556.3 

2034 $103.1 $141.6 $394.9 $842.2 $557.8 

2035 $103.3 $142.1 $397.8 $847.8 $559.2 

2036 $103.5 $142.6 $400.8 $853.7 $560.6 

2037 $103.8 $143.2 $403.6 $859.3 $562.1 

2038 $104.0 $143.7 $406.5 $864.9 $563.6 

2039 $104.2 $144.2 $409.5 $870.8 $565.0 

2040 $104.4 $144.8 $412.4 $876.4 $566.5 

Annualized Benefits, 3% $82.0 b $111.7 b $311.7 c $605.5 $434.1

a. Estimates for Options 1, 2 and 5 do not include air-related benefits. This category of benefits was only estimated for Options 3 and 

4 (see Chapter 8) 

b. EPA did not estimate the benefits of this regulatory option. EPA expects the benefits of Options 3a and 3b to be less than those of 

Option 3, with values for Option 3a being lower than those for Option 3b. See Section 10.3 for EPA‘s calculation of inferred total 

benefits for Options 3a and 3b. 

c. EPA did not estimate the benefits of this regulatory option. EPA expects the benefits of Option 4a to be between those of Options 3 

and 4. See Section 10.3 for EPA‘s calculation of inferred total benefits for Option 4a. 

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs 10: Total Benefits 

April 19, 2013 10-2 

Table 10-3: Time Profile of Benefits at 7 Percent (Millions; 2010$) (Including Air-Related Benefits for 

Options 3 and 4)

Year Option 1a Option 3a Option 2a Option 3b Option 3 Option 4a Option 4 Option 5c

2014 $0.0 $0.0 $0.0 $0.0 $0.0 

2015 $0.0 $0.0 $0.0 $0.0 $0.0 

2016 $0.0 $0.0 $0.0 $0.0 $0.0 

2017 $0.3 $0.3 $122.2 $82.9 $1.1 

2018 $0.8 $0.8 $119.7 $77.1 $4.4 

2019 $81.8 $115.5 $283.7 $430.5 $363.8 

2020 $87.1 $121.0 $290.0 $463.3 $405.4 

2021 $96.8 $131.0 $335.1 $574.8 $522.5 

2022 $97.0 $131.5 $337.7 $577.3 $523.9 

2023 $97.1 $132.0 $340.4 $580.1 $525.2 

2024 $97.3 $132.5 $343.0 $582.7 $526.5 

2025 $97.5 $133.0 $362.5 $775.7 $527.9 

2026 $97.7 $133.5 $363.9 $779.4 $529.3 

2027 $97.9 $134.1 $366.9 $785.3 $530.7 

2028 $98.1 $134.6 $369.7 $790.9 $532.2 

2029 $98.3 $135.2 $372.6 $796.4 $533.6 

2030 $98.5 $135.7 $375.3 $802.0 $535.1 

2031 $98.7 $136.2 $378.4 $807.7 $536.3 

2032 $98.8 $136.6 $381.2 $813.4 $537.5 

2033 $99.0 $137.1 $384.0 $818.8 $538.8 

2034 $99.2 $137.6 $386.9 $824.5 $540.0 

2035 $99.4 $138.1 $389.7 $829.9 $541.3 

2036 $99.5 $138.6 $392.6 $835.7 $542.6 

2037 $99.7 $139.1 $395.4 $841.2 $544.0 

2038 $99.9 $139.6 $398.3 $846.6 $545.3 

2039 $100.1 $140.1 $401.2 $852.4 $546.6 

2040 $100.3 $140.6 $404.0 $857.9 $547.9 

Annualized Benefits, 7% $65.9 b $90.7 b $230.4 c $424.8 $349.4 

a. Estimates for Options 1, 2 and 5 do not include air-related benefits. This category of benefits was only estimated for Options 3 and 

4 (see Chapter 8) 

b. EPA did not estimate the benefits of this regulatory option. EPA expects the benefits of Options 3a and 3b to be less than those of 

Option 3, with values for Option 3a being lower than those for Option 3b. See Section 10.3 for EPA‘s calculation of inferred total 

benefits for Options 3a and 3b. 

c. EPA did not estimate the benefits of this regulatory option. EPA expects the benefits of Option 4a to be between those of Options 3 

and 4. See Section 10.3 for EPA‘s calculation of inferred total benefits for Option 4a.

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs 11: Total Social Costs 

April 19, 2013 10-1 

10.3 Inferred Benefits for Regulatory Options not Analyzed Explicitly

As noted throughout this report, EPA calculated benefits for some of the options considered for this proposal. 

Benefits for these options, however, provide information relevant to understanding the potential magnitude of 

benefits under all proposed options, including Options 3a, 3b, and 4a. The facilities affected by Option 3a are 

a subset of Option 3 facilities; Option 3 benefit estimates therefore provide an upper bound estimate of 

benefits anticipated under Options 3a and 3b. In a similar way, EPA expects Option 4 to provide an upper 

bound estimate of benefits anticipated under Option 4a. 

As an illustrative analysis, EPA inferred the potential benefits associated with Options 3a and 3b by 

subtracting the benefits for Option 2 (scaled up to include a rough estimate of air emissions benefits) from the 

benefits for Option 3, because Option 3 includes a combination of the wastestreams and control technologies 

in Options 3a and 2. EPA inferred the potential benefits associated with Option 3b based on the pollutant 

loading reductions (pounds) projected for Option 3b relative to pollutant loading reductions projected for 

Option 2 (plus the fly ash dry handling benefits of Option 3a) because Option 3b includes both fly ash 

requirements and the Option 2 FGD wastewater treatment requirements for a subset of facilities. This is 

equivalent to interpolating benefits linearly between Options 3a and 3 based on relative pollutant removals. 

Similarly, EPA inferred the potential benefits associated with Option 4a based on the bottom ash pollutant 

loading reductions projected for this option, relative to bottom ash pollutant loading reductions projected for 

Option 4, plus the benefits of Option 3, because Option 4a includes all of the requirements of option 3 plus 

the bottom ash requirements of Option 4 for a subset of facilities. This is equivalent to interpolating benefits 

linearly between Options 3 and 4 based on relative pollutant removals. 

EPA used total benefits, including air-related benefits, to infer benefits of Options 3a, 3b, and 4a. The first 

step in the analysis involved adjusting the total benefits of Option 2 shown in Table 10-1 to reflect anticipated 

air-related benefits. EPA made this adjustment using the results for Options 3 and 4, which show the total 

benefits including air-related benefits being, on average, 1.54 times the total benefits excluding air-related 

benefits using a 3 percent discount rate. Figure 10-1 displays the annual benefits of the regulatory options in 

relation to their total pollutant loading reductions. 
Benefit and Cost Analysis for Proposed ELGs 11: Total Social Costs 

April 19, 2013 10-2 

Figure 10-1: Pollutant Loading Removal vs. Estimated (Solid Markers) or Inferred (Empty Markers) 

Annual Benefits of Regulatory Options at 3 Percent Discount Rate.76 

Source: U.S. EPA Analysis, 2013

 

76 Option 1 is not displayed on the figure. EPA estimated total pollutant removal for this option, relative to baseline, 

at -417 million pounds per year. As EPA explains in Section 10.4 of the TDD, this is because characterization data used 

to estimate pollutant concentrations for baseline FGD surface impoundments does not include concentration data for 

ammonia, hexavalent chromium, and TKN, whereas these pollutants are included in the post-compliance loadings. 
Benefit and Cost Analysis for Proposed ELGs 11: Total Social Costs 

April 19, 2013 10-3 

Table 10-4 summarizes total annualized benefits estimated, adjusted or inferred using the calculations 

described above for the eight options discussed in today‘s proposal. Note that there is significant uncertainty 

in values inferred because the methodology used does not account for differences in the pollutants, receiving 

waterbodies, and exposed populations between the options. 

Table 10-4. Total Monetized Benefits for the Proposed ELGs (Millions; 2010$)

Regulatory Option Method Total Monetized Benefits 

at 3% Discount Rate

Total Monetized Benefits 

at 7% Discount Rate

Option 1 Adjusted Estimatea$126.5 $91.2

Option 3a Inferenceb$139.4 $104.8

Option 2 Adjusted Estimatea$172.3 $125.6

Option 3b Inferenceb$205.5 $153.0

Option 3 Estimate $311.7 $230.4

Option 4a Inferenceb,c $482.5 $343.4

Option 4 Estimate $605.5 $424.8

Option 5 Adjusted Estimatea$669.6 $483.7

a. Estimated total benefits for Options 1, 2, and 5 do not include air-related benefits (see Section 10.1). To infer benefits for Options 

3a, 3b, and 4a, EPA calculated adjustments to the benefits of Options 1, 2, and 5 to include inferred air-related benefits. The 

calculation adjusts the total benefits presented in Table 10-1 based on the average ratio of [total benefits including air-related 

benefits] / [total benefits excluding air-related benefits] for Options 3 and 4 calculated using 3% and 7% discount rates.

b. EPA did not estimate benefits for Options 3a, 3b and 4a. EPA inferred benefits for Options 3a, 3b, and 4a for illustrative purposes 

using elements of the more rigorous analysis done to estimate benefits for Options 3 and 4.

c. At the time this analysis was conducted, EPA inferred benefits for Option 4a based on pollutant removals for this option of 

2,630 million pounds per year. As presented in the TDD (U.S. EPA, 2013b), however, the pollutant removals for this option are 

2,620 million pounds per year, which would result in total annual benefits of $480.8 million (at 3 percent discount and $342.3 

million (at 7 percent discount).

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs 11: Total Social Costs 

April 19, 2013 11-4 

11 Summary of Total Social Costs

This chapter develops EPA‘s estimates of the costs to society resulting from the proposed ELGs. As analyzed 

in this chapter, the social 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. 

11.1 Overview of Social Costs Analysis Framework

RIA Chapter 3: Compliance Costs presents EPA‘s development of costs to the 1,079 steam electric plants 

subject to the proposed ELGs (U.S. EPA, 2013c). These costs are used as the basis of the social cost analysis. 

However, the compliance costs used to estimate total social 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 proposed 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 or by all taxpayers in the form of lost tax revenues.77

As described in Chapter 1, EPA assumed that steam electric plants, in the aggregate, would implement 

control technologies during a 5-year period from 2017 to 2021. For this analysis, EPA developed a yearexplicit schedule of compliance outlays over the period of 2017 through 2040.78 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 2017; 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 social costs and adjusting them for predicted real change 

to the year of their incurrence, EPA calculated the present value of these cost outlays as of the promulgation 

year by discounting the cost in each year back to 2014, using both 3 percent and 7 percent discount rates. 

These discount rate values reflect guidance from the Office of Management and Budget (OMB) regulatory 

analysis guidance document, Circular A-4 (OMB, 2003; updated 2009). EPA calculated the constant annual 

equivalent value (annualized value), again using the two values of the discount rate, 3 percent and 7 percent, 

 

77 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. 

78 The end of the analysis period, 2040, was determined based on the life of the longest-lived compliance technology 

implemented at any steam electric plant (20 years), and the last year of technology implementation (2021). 
Benefit and Cost Analysis for Proposed ELGs 11: Total Social Costs 

April 19, 2013 11-5 

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 proposed ELGs to society, EPA relied first on the estimated costs to 

steam electric plants for the labor, equipment, material, and other economic resources needed to comply with 

the proposed 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 regulation does not affect the aggregate 

quantity of electricity that would be sold to consumers and, thus, that the regulation‘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 regulation 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 

proposed ELGs 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.79

Finally, as discussed in RIA Chapter 10 (Section 10.7: Paperwork Reduction Act of 1995), the proposed ELGs 

are not expected to result in additional administrative costs for plants to implement, and State and federal 

NPDES permitting authorities to administer, the proposed 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 proposed 

ELGs. 

11.2 Key Findings for Regulatory Options

Table 11-1 presents annualized social costs for each of the eight regulatory options, in order of increasing 

toxic-weighted pollutant removals. At a 3 percent discount rate, estimated annualized social costs range 

between $185 million under Option 3a and $2,329 million under Option 5. 

The preferred options for existing sources have annualized costs of $185 million, $281 million, $572 million 

and $954 million, respectively for Options 3a, 3b, 3 and 4a, at a 3 percent discount rate. At a 7 percent 

discount rate, annualized costs range between $165 million under Option 3a and $2,209 million under Option 

5, with the preferred options having costs of $165 million, $257 million, $545 million and $915 million, 

respectively for Options 3a, 3b, 3 and 4a. 

Table 11-1: Summary of Annualized Social Costs (Millions; $2010)

Regulatory Option 3% Discount Rate 7% Discount Rate

Option 1 $268.3 $259.2

Option 3a $185.2 $164.6

Option 2 $386.8 $380.8

Option 3b $281.4 $257.2

Option 3 $572.0 $545.3

Option 4a $954.1 $914.7

Option 4 $1,381.2 $1,323.2

Option 5 $2,328.7 $2,209.4

Source: U.S. EPA Analysis, 2013.

Table 11-2 provides additional detail on the social cost calculations. The table compiles, for each of the eight 

regulatory options, the time profiles of compliance costs incurred. The table also reports the calculated 

 

79 The specific assumptions of when each cost component is incurred can be found in Chapter 3: Compliance Costs of 

the RIA. 
Benefit and Cost Analysis for Proposed ELGs 11: Total Social Costs 

April 19, 2013 11-6 

annualized values of costs at 3 percent and 7 percent discount rates. The maximum compliance outlays are 

incurred over the years 2017 through 2021, i.e., during the estimated window when steam electric plants are 

expected to implement compliance technologies. 

Table 11-2: Time Profile of Costs to Society (Millions; $2010)

Year Option 1 Option 3a Option 2 Option 3b Option 3 Option 4a Option 4 Option 5

2014 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0

2015 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0

2016 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0

2017 $523.6 $105.9 $841.4 $168.1 $947.3 $1,402.0 $1,939.0 $3,179.5

2018 $615.2 $169.3 $1,003.5 $431.4 $1,172.8 $1,852.1 $2,628.7 $4,411.5

2019 $437.3 $203.8 $688.3 $339.5 $892.1 $1,613.0 $2,432.2 $3,769.9

2020 $533.5 $300.0 $799.3 $650.1 $1,099.3 $2,007.6 $2,798.9 $4,418.4

2021 $314.5 $274.0 $503.3 $370.1 $777.3 $1,757.5 $2,716.0 $4,012.1

2022 $214.0 $198.7 $286.0 $276.4 $484.7 $778.7 $1,123.7 $2,002.9

2023 $215.7 $195.9 $287.7 $271.8 $483.6 $777.0 $1,126.0 $2,005.9

2024 $219.7 $196.4 $291.7 $273.5 $488.1 $782.1 $1,132.2 $2,011.9

2025 $217.9 $197.8 $289.9 $272.5 $487.7 $783.9 $1,132.4 $2,012.4

2026 $220.8 $196.4 $292.8 $274.4 $489.2 $781.6 $1,133.0 $2,013.1

2027 $224.8 $203.7 $296.8 $281.5 $500.5 $800.6 $1,163.6 $2,043.4

2028 $223.3 $204.3 $295.2 $282.0 $499.5 $801.8 $1,160.0 $2,039.2

2029 $225.9 $203.9 $297.9 $281.7 $501.8 $803.0 $1,164.1 $2,044.0

2030 $226.2 $203.9 $298.2 $282.0 $502.1 $807.3 $1,169.6 $2,049.3

2031 $225.3 $203.8 $297.3 $281.8 $501.1 $804.1 $1,160.9 $2,040.8

2032 $217.1 $199.1 $289.1 $277.6 $488.2 $782.3 $1,133.5 $2,013.5

2033 $216.7 $197.3 $288.7 $273.6 $486.0 $782.0 $1,135.5 $2,015.2

2034 $218.7 $198.3 $290.7 $275.3 $489.0 $785.0 $1,132.1 $2,011.3

2035 $221.5 $199.8 $293.4 $275.6 $493.3 $792.4 $1,148.5 $2,028.4

2036 $223.4 $199.6 $295.4 $277.4 $495.0 $793.4 $1,150.9 $2,030.5

2037 $224.6 $203.2 $296.6 $281.1 $499.8 $798.2 $1,153.8 $2,033.5

2038 $224.3 $203.3 $296.3 $281.3 $499.6 $798.0 $1,157.5 $2,037.2

2039 $223.8 $203.1 $295.8 $280.8 $498.8 $795.8 $1,149.7 $2,029.1

2040 $223.3 $203.0 $295.2 $280.6 $498.2 $793.7 $1,140.1 $2,019.2

Annualized Costs, 3% $268.3 $185.2 $386.8 $281.4 $572.0 $954.1 $1,381.2 $2,328.7

Annualized Costs, 7% $259.2 $164.6 $380.8 $257.2 $545.3 $914.7 $1,323.2 $2,209.4

Source: U.S. EPA Analysis, 2013.
Benefit and Cost Analysis for Proposed ELGs 12: Benefits and Social Costs 

April 19, 2013 12-2 

12 Benefits and Social Costs

This chapter compares total monetized benefits and social costs for the eight regulatory options considered for 

the proposed 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, 2013c).

12.1 Comparison of Benefits and Social Costs by Option

Chapter 10 and Chapter 11 present estimates of the benefits and social costs, respectively, for the regulatory 

options evaluated in developing the proposed ELGs. 

Table 12-1 presents EPA‘s estimates of benefits and social costs of the regulatory options for existing steam 

electric plants, at 3 percent and 7 percent discount rates, and annualized over 24 years. These values are all in 

2010 dollars and are based on the discounting of costs and benefits to 2014, the rule promulgation year. As 

described in Section 10.3, EPA did not estimate benefits of Options 3a, 3b and 4a; the values included in 

Table 12-1 represent benefits inferred based on EPA‘s analyses of Options 1 through 5. 

Table 12-1. Total Annualized Benefits and Social Costs by Regulatory Option and Discount Rate

(Millions; 2010$)

Regulatory Option Total Monetized Benefitsa

Total Monetized Benefits,

Including Adjusted or Inferred 

Values

Total Social Costs

3% 7% 3% 7% 3% 7%

Option 1 $82.0 $65.9 $126.5 b

$91.2 b

$268.3 $259.2

Option 3a $139.4 c

$104.8 c

$185.2 $164.5

Option 2 $111.7 $90.7 $172.3 b

$125.6 b

$386.8 $380.8

Option 3b $205.5 c

$153.0 c

$281.4 $257.2

Option 3 $311.7 $230.4 $311.7 $230.4 $572.0 $545.3

Option 4a $482.5 c, d $343.4 c, d $954.1 $914.7

Option 4 $605.5 $424.8 $605.5 $424.8 $1,381.2 $1,323.2

Option 5 $434.1 $349.4 $669.6 b

$483.7 b

$2,328.8 $2,209.4

a. The benefit values are the estimated ―mean‖ values. Additional ―low‖ and ―high‖ value estimates are presented in Chapters 3 through 

9. 

b. EPA did not analyze air-related benefits for Options 1, 2, and 5. This category of benefits was only estimated for Options 3 and 4 

(see Chapter 8). To infer benefits of Options 3a, 3b, and 4a, EPA adjusted the total benefits estimated for Options 1, 2 and 5 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 3 and 4. See Section 10.3 for details.

c. EPA did not estimate the benefits of this regulatory option. EPA inferred benefits for Options 3a, 3b, and 4a for illustrative purposes 

using elements of the more rigorous analysis done to estimate benefits for Options 2, 3 and 4. See Section 10.3 for details.

d. At the time this analysis was conducted, EPA inferred benefits for Option 4a based on pollutant removals for this option of 2,630 

million pounds per year. As presented in the TDD (U.S. EPA, 2013b), however, the pollutant removals for this option are 2,620 million 

pounds per year, which would result in total annual benefits of $480.8 million (at 3 percent discount and $342.3 million (at 7 percent 

discount).

Source: U.S. EPA Analysis, 2013.
Benefit and Cost Analysis for Proposed ELGs 12: Benefits and Social Costs 

April 19, 2013 12-3 

12.2 Analysis of Incremental Benefits and Social 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 relative terms? 

In contrast, incremental analysis looks at the differential relationship of benefits and costs across options and 

poses a different question: as increasingly more costly options are considered, by what amount do benefits, 

costs, and net benefits (i.e., benefits minus costs) change from option to option? Incremental net benefit 

analysis provides insight into the net gain to society from imposing increasingly more costly requirements and 

can help regulatory decision-makers choose among a set of regulatory proposals that otherwise have a similar 

quantitative relationship between benefits and costs based on a one-option-at-a-time comparison. 

EPA conducted the incremental net benefit analysis by calculating, for the eight 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 plants that would implement the technologies and of the receiving waterbodies. 

As noted previously, however, the total monetized benefits for Options 1, 2, and 5 do not include air-related 

benefits; this benefit category is included in results for Options 3 and 4 only. Further, EPA did not estimate 

the benefits of Options 3a, 3b and 4a, but instead inferred these benefits based on the results for Options 1 

through 5. Therefore, to allow for consistent calculation of incremental benefits as one moves from one option 

to the next, EPA adjusted the total benefits estimated for Options 1, 2, and 5 to account for potential airrelated benefits of these options. EPA used the inferred benefits for Options 3a, 3b and 4a discussed in 

Section 10.3. 

As reported in Table 12-2, at a 3 percent discount rate, EPA estimates that annual social costs exceed mean 

annual monetized benefits by $142 million for Option 1 to $1.6 billion for Option 5. At a 7 percent discount 

rate, annual social costs exceed mean annual monetized benefits by $168 million to $1.7 billion for Options 1 

and 5. 

At a 3 percent discount rate, the incremental change in mean net benefits in moving from Option 1 to 

Option 3a is $96 million (the positive value indicates that the increase in benefits is larger than the increase in 

costs). Moving from Option 3a to Option 2, the incremental change is an additional -$169 million (the 

negative value indicates that the increase in costs is larger than the increase in benefits), while the increment 

is $139 million when moving from Option 2 to Option 3b, -$185 million when moving from Option 3b to 

Option 3, and -$211 million when moving from Option 3 to Option 4a. 
Benefit and Cost Analysis for Proposed ELGs 12: Benefits and Social Costs 

April 19, 2013 12-4 

Table 12-2. Incremental Net Benefit Analysis (Millions; $2010)

Regulatory 

Optiona

Total Annual Monetized 

Benefits, Including 

Adjusted or Inferred 

Values

Total Social Costs Net Annual Monetized 

Benefitsb

Incremental Net Annual 

Monetized Benefitsc

3% 7% 3% 7% 3% 7% 3% 7%

Option 1 $126.5 d$91.2 d$268.3 $259.2 -$141.9 -$167.9 -$141.9 -$167.9

Option 3a $139.4 e$104.8 e$185.2 $164.6 -$45.8 -$59.8 $96.1 $108.1

Option 2 $172.3 d$125.6 d$386.8 $380.8 -$214.5 -$255.2 -$168.7 -$195.4

Option 3b $205.5 e$153.0 e$281.4 $257.2 -$75.9 -$104.2 $138.6 $151.0

Option 3 $311.7 $230.4 $572.1 $545.3 -$260.4 -$314.9 -$184.5 -$210.7

Option 4a $482.5 e, f $343.1 e, f $954.1 $914.7 -$471.6 -$571.6 -$211.3 -$256.7

Option 4 $605.5 $424.8 $1,381.2 $1,323.2 -$775.7 -$898.5 -$304.1 -$326.9

Option 5 $669.6 d$483.7 d$2,328.8 $2,209.4 -$1,659.1 -$1,725.7 -$883.4 -$827.2

a. Options are presented in order of increasing benefits.

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 did not analyze air-related benefits for Options 1, 2, and 5. This category of benefits was only estimated for Options 3 and 4 

(see Chapter 8). To infer benefits of Options 3a, 3b, and 4a, EPA adjusted the total benefits estimated for Options 1, 2 and 5 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 3 and 4. See Section 10.3 for details.

e. EPA did not estimate the benefits of this regulatory option. EPA inferred benefits for Options 3a, 3b, and 4a for illustrative purposes 

using elements of the more rigorous analysis done to estimate benefits for Options 2, 3 and 4. See Section 10.3 for details.

f. At the time this analysis was conducted, EPA inferred benefits for Option 4a based on pollutant removals for this option of 2,630 

million pounds per year. As presented in the TDD (U.S. EPA, 2013b), however, the pollutant removals for this option are 2,620 million 

pounds per year, which would result in total annual benefits of $480.8 million (at 3 percent discount and $342.3 million (at 7 percent 

discount).

Source: U.S. EPA Analysis, 2013.
Benefit and Cost Analysis for Proposed ELGs 13: References 

April 19, 2013 13-1 

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Benefit and Cost Analysis for Proposed ELGs Appendix A: Affected Population 

April 19, 2013 A-1 

Appendix A: Detail on Estimating Affected Population

Exhibit A-1 shows the state- and region-specific population data and percent of anglers in the state or regional 

population. These data are used to identify the affected recreational angler population and their household 

members who are potentially exposed to steam electric pollutants via consumption of contaminated fish 

caught in receiving reaches (as described in Section 3.1.1). 

Exhibit A-1. Population Data and Recreational Fishing Rates by State 

% of Population Who 

Fisha Persons per Householdb Populationb

East North Central Average 11.51% 2.51

Illinois 11.51% 2.62 12,830,632

Indiana 11.51% 2.49 6,483,802

Michigan 11.51% 2.53 9,883,640

Ohio 11.51% 2.47 11,536,504

Wisconsin 11.51% 2.42 5,686,986

East South Central Average 14.02% 2.51

Alabama 14.02% 2.48 4,779,736

Kentucky 14.02% 2.47 4,339,367

Mississippi 14.02% 2.6 2,967,297

Tennessee 14.02% 2.49 6,346,105

Middle Atlantic Average 6.39% 2.59

New Jersey 6.39% 2.68 8,791,894

New York 6.39% 2.64 19,378,102

Pennsylvania 6.39% 2.46 12,702,379

Mountain Average 10.52% 2.66

Arizona 10.52% 2.76 6,329,017

Colorado 10.52% 2.53 5,029,196

Idaho 10.52% 2.64 1,567,582

Montana 10.52% 2.49 989,415

Nevada 10.52% 2.66 2,700,551

New Mexico 10.52% 2.61 2,059,179

Utah 10.52% 3.14 2,763,885

Wyoming 10.52% 2.45 563,626

New England Average 8.76% 2.48

Connecticut 8.76% 2.55 3,518,288

Maine 8.76% 2.36 1,328,361

Massachusetts 8.76% 2.54 6,547,629

New Hampshire 8.76% 2.54 1,316,470

Rhode Island 8.76% 2.52 1,052,567

Vermont 8.76% 2.39 625,741

Pacific Average 6.66% 2.72

Alaska 6.66% 2.82 710,231

California 6.66% 2.91 36,756,666

Hawaii 6.66% 2.84 1,360,301

Oregon 6.66% 2.49 3,831,074

Washington 6.66% 2.52 6,724,540

South Atlantic Average 10.99% 2.50

Delaware 10.99% 2.58 897,934
Benefit and Cost Analysis for Proposed ELGs Appendix A: Affected Population 

April 19, 2013 A-2 

Exhibit A-1. Population Data and Recreational Fishing Rates by State 

% of Population Who 

Fisha Persons per Householdb

Populationb

District of Columbia 10.99% 2.21 601,723

Florida 10.99% 2.52 18,801,310

Georgia 10.99% 2.7 9,687,653

Maryland 10.99% 2.63 5,773,552

North Carolina 10.99% 2.47 9,535,483

South Carolina 10.99% 2.52 4,625,364

Virginia 10.99% 2.54 8,001,024

West Virginia 10.99% 2.37 1,852,994

West North Central Average 16.78% 2.41

Iowa 16.78% 2.36 3,046,355

Kansas 16.78% 2.46 2,853,118

Minnesota 16.78% 2.45 5,303,925

Missouri 16.78% 2.47 5,988,927

Nebraska 16.78% 2.45 1,826,341

North Dakota 16.78% 2.24 672,591

South Dakota 16.78% 2.43 814,180

West South Central Average 12.29% 2.60

Arkansas 12.29% 2.48 2,855,390

Louisiana 12.29% 2.61 4,533,372

Oklahoma 12.29% 2.49 3,751,351

Texas 12.29% 2.81 25,145,561

Average 11.10% 2.55 6,101,167

a. Source: U.S. FWS (2006); based on data at Census Region.

b. Source: U.S. Census Bureau (2010a).

EPA conducted a literature review of anglers‘ awareness of and responses to fish consumption advisories 

(FCA), as summarized in Exhibit A-2 and Exhibit A-3. 

Exhibit A-2. Angler Awareness of Fish Consumption Advisories

Source Survey Area Percentage of Anglers Aware of Fish 

Consumption Advisories

U.S. DHHS, 1995 Florida Everglades 71.0%b

May and Burger, 1996 New York/New Jersey Harbor 60.0%b

Connelly et al., 1996 (as cited in Jakus, et 

al., 1997) Lake Ontario >95.0%a

Tilden et al., 1997 Great Lakes 49.9%b

Chiang, 1998 San Francisco Bay 48.5%b

Burger et al., 1999 (as cited in Burger, 

2004) New York/New Jersey Harbor 60.0%b

Williams et al., 2000 Indiana 73.7%b

Jakus et al., 2002 Maryland Chesapeake Bay 48.0%c

Burger, 2004 New York/New Jersey Harbor 45.0%b

Average - 57.0% to 61.2%d

a. Based on mail-in surveys of anglers with fishing licenses.

b. Based on with interviews with anglers on-site.

c. Based on a compilation of figures from the literature.

d. Lower estimate represents average without inclusion of estimate from Connelly et al. (1996).
Benefit and Cost Analysis for Proposed ELGs Appendix A: Affected Population 

April 19, 2013 A-3 

Exhibit A-3. Percentage of Anglers who “Ignore” Fish Consumption Advisories

Source Survey Area Percentage of Anglers who "Ignore" 

Fish Consumption Advisoriesa

Diana et al., 1993 (as cited in Jakus, et al., 

1997) Lake Ontario (New York) 70%

Velicer et al., 1994 (as cited in Jakus, et 

al., 1997) Lake Ontario (New York) Lowb

U.S. DHHS, 1995 Florida Everglades 74%

May and Burger, 1996 (as cited in Jakus, 

et al., 1997) New York/New Jersey Harbor 70%-88%

MacDonald et al., 1997 (as cited in Jakus, 

et al., 1997) Maine > 75%

Chiang, 1998 San Francisco Bay 40%

Jakus et al., 2002 Maryland Chesapeake Bay 74%

Average - 71.6% to 76.1%c

a. "Ignore", in this case, means consuming fish caught from contaminated waters with knowledge that a waterbody has FCA.

b. Sample was composed of angler group  ̳opinion leaders‘ whose actions may not be representative of the general angling population.

c. Higher estimate represents average without inclusion of the estimate from Chiang (1998), which is an outlier.

EPA calculated site-specific population growth projections (relative to 2010) based on data from the 2000

Census and projections from Woods and Poole Economics (2007). Exhibit A- 4 shows the average population 

growth for years 2010 through 2040. 

Exhibit A- 4. Average Population Growth, Relative to 2010a

Year Population Growth Year Population Growth

2010 1.00 2026 1.13

2011 1.01 2027 1.14

2012 1.02 2028 1.15

2013 1.02 2029 1.16

2014 1.03 2030 1.17

2015 1.04 2031 1.18

2016 1.05 2032 1.19

2017 1.06 2033 1.19

2018 1.06 2034 1.20

2019 1.07 2035 1.21

2020 1.08 2036 1.22

2021 1.09 2037 1.23

2022 1.10 2038 1.23

2023 1.11 2039 1.24

2024 1.11 2040 1.25

2025 1.12

a. Estimates are based on data from the 2000 Census and projections from the 2007 Woods & Poole projection estimates (Woods 

and Pool Economics, 2007).

Exhibit A-5 shows the state- and region-specific data on fertility and the number of births per year. These data 

were used to identify the number of affected infants susceptible to in-utero exposure to mercury due to 

maternal consumption of contaminated fish (as described in Section 3.3.1).
Benefit and Cost Analysis for Proposed ELGs Appendix A: Affected Population 

April 19, 2013 A-4 

Exhibit A-5. Fertility Data and the Number of Birth per Year

Populationa

Number of 

Women 15 

to 50b

Percent of 

Population 

who are 

Women 15 

to 50c

Number of 

Women (15 

to 50) who 

had a Birth 

in the Last 

12 Monthsb

Fertility 

Rated

Babies per 

year per 

1000 Peoplee

East North 

Central 2,309,405 25% 128,918 5.6% 13.9

Illinois 12,830,632 3,268,056 25% 185,373 5.7% 14.4

Indiana 6,483,802 1,573,390 24% 92,540 5.9% 14.3

Michigan 9,883,640 2,484,965 25% 131,068 5.3% 13.3

Ohio 11,536,504 2,835,019 25% 157,318 5.5% 13.6

Wisconsin 5,686,986 1,385,594 24% 78,292 5.7% 13.8

East South 

Central 1,122,715 24% 68,758 6.1% 14.9

Alabama 4,779,736 1,155,351 24% 67,289 5.8% 14.1

Kentucky 4,339,367 1,057,585 24% 61,009 5.8% 14.1

Mississippi 2,967,297 734,070 25% 46,721 6.4% 15.7

Tennessee 6,346,105 1,543,855 24% 100,013 6.5% 15.8

Middle Atlantic 3,397,994 25% 183,325 5.5% 13.6

New Jersey 8,791,894 2,168,090 25% 131,141 6.0% 14.9

New York 19,378,102 5,010,234 26% 256,497 5.1% 13.2

Pennsylvania 12,702,379 3,015,658 24% 162,336 5.4% 12.8

Mountain 667,432 24% 42,416 6.4% 15.3

Arizona 6,329,017 1,548,377 24% 92,263 6.0% 14.6

Colorado 5,029,196 1,253,964 25% 73,670 5.9% 14.6

Idaho 1,567,582 366,226 23% 25,718 7.0% 16.4

Montana 989,415 228,795 23% 11,761 5.1% 11.9

Nevada 2,700,551 632,088 23% 39,633 6.3% 14.7

New Mexico 2,059,179 484,932 24% 30,999 6.4% 15.1

Utah 2,763,885 696,422 25% 56,952 8.2% 20.6

Wyoming 563,626 128,655 23% 8,329 6.5% 14.8

New England 603,410 25% 29,668 4.9% 12.2

Connecticut 3,518,288 869,841 25% 42,687 4.9% 12.1

Maine 1,328,361 317,790 24% 14,234 4.5% 10.7

Massachusetts 6,547,629 1,676,571 26% 83,335 5.0% 12.7

New Hampshire 1,316,470 330,453 25% 15,368 4.7% 11.7

Rhode Island 1,052,567 271,493 26% 14,722 5.4% 14.0

Vermont 625,741 154,314 25% 7,660 5.0% 12.2

Pacific 2,485,786 24% 150,044 6.3% 15.3

Alaska 710,231 173,966 24% 12,233 7.0% 17.2

California 36,756,666 9,399,440 26% 562,297 6.0% 15.3

Hawaii 1,360,301 305,513 22% 19,404 6.4% 14.3

Oregon 3,831,074 917,183 24% 55,854 6.1% 14.6

Washington 6,724,540 1,632,826 24% 100,430 6.2% 14.9

South Atlantic 1,613,949 25% 89,988 5.4% 13.3

Delaware 897,934 218,161 24% 11,059 5.1% 12.3

District of 

Columbia 601,723 174,240 29% 6,915 4.0% 11.5

Florida 18,801,310 4,301,117 23% 236,097 5.5% 12.6
Benefit and Cost Analysis for Proposed ELGs Appendix A: Affected Population 

April 19, 2013 A-5 

Exhibit A-5. Fertility Data and the Number of Birth per Year

Populationa

Number of 

Women 15 

to 50b

Percent of 

Population 

who are 

Women 15 

to 50c

Number of 

Women (15 

to 50) who 

had a Birth 

in the Last 

12 Monthsb

Fertility 

Rated

Babies per 

year per 

1000 Peoplee

Georgia 9,687,653 2,518,541 26% 144,020 5.7% 14.9

Maryland 5,773,552 1,464,034 25% 80,639 5.5% 14.0

North Carolina 9,535,483 2,324,058 24% 131,969 5.7% 13.8

South Carolina 4,625,364 1,108,257 24% 68,314 6.2% 14.8

Virginia 8,001,024 1,987,524 25% 107,196 5.4% 13.4

West Virginia 1,852,994 429,609 23% 23,683 5.5% 12.8

West North 

Central 705,711 24% 43,565 6.3% 14.9

Iowa 3,046,355 715,128 23% 46,953 6.6% 15.4

Kansas 2,853,118 682,702 24% 43,792 6.4% 15.3

Minnesota 5,303,925 1,303,385 25% 84,639 6.5% 16.0

Missouri 5,988,927 1,465,150 24% 81,734 5.6% 13.6

Nebraska 1,826,341 429,867 24% 25,825 6.0% 14.1

North Dakota 672,591 154,315 23% 9,820 6.4% 14.6

South Dakota 814,180 189,429 23% 12,190 6.4% 15.0

West South 

Central 2,218,672 24% 146,147 6.3% 15.3

Arkansas 2,855,390 690,561 24% 39,910 5.8% 14.0

Louisiana 4,533,372 1,124,309 25% 71,036 6.3% 15.7

Oklahoma 3,751,351 885,806 24% 55,386 6.3% 14.8

Texas 25,145,561 6,174,011 25% 418,254 6.8% 16.6

Average 6,101,167 1,504,688 24% 87,974 5.9% 14.3

a. Source: U.S. Census Bureau (2010a).

b. Source: U.S. Census Bureau (2010c).

c. Number of Women of childbearing age (15 to 50) divided by Population.

d. Number of Women (15 to 50) who had a Birth in the Last 12 Months divided by the Number of Women 15 to 50.

e. Number of Women (15 to 50) who had a Birth in the Last 12 Months divided by Population times 1000.
Benefit and Cost Analysis for Proposed ELGs Appendix B: IEUBK Model 

April 19, 2013 B-1 

Appendix B: IEUBK Model Description and Application

The Integrated Exposure Uptake Biokinetic (IEUBK) model uses exposure, uptake, and biokinetic response 

information to estimate the blood lead concentration (PbB) level distribution for a population of children 

receiving similar exposures (U.S. EPA, 2009c). The estimated distribution may be used to predict the 

probability of elevated PbB levels in children exposed to environmental lead. The model has four 

components. Each component reflects a different aspect of the overall biological process: 

 The multimedia nature of exposure to lead via soil, house dust, drinking water, air, and food (i.e., 

the exposure component),

 The differential bioavailability of various sources of lead (i.e., the uptake component),

 The pharmacokinetics of internal distribution of lead to bone, blood, and other tissues (i.e., the 

biokinetic component), and 

 Inter-individual variability of PbB levels (i.e., the variability component).

The model uses estimated or measured lead concentration in fish tissues and other media to estimate a 

continuous exposure pattern for children from birth through the seventh birthday and predicts the geometric 

mean PbB for a population of children receiving similar exposures. The model also generates the percentage 

of children with PbB levels in excess of a user-specified level of concern (in the case of this analysis, 20 

ug/dL). 

The IEUBK model has default values for exposure and uptake parameters, which are shown in Exhibit B-1. 

However, users can also change these default values according to site- or population-specific factors. For this 

analysis, EPA entered the receiving reach-specific LADD for each cohort (a product of the EA; U.S. EPA, 

2012a) as an ―alternate source,‖ and assumed an uptake rate of 50 percent (consistent with the default dietary 

bioavailability rate). Additionally, EPA changed the default ―cutoff‖ PbB level from 10 ug/dL to 20 ug/dL to 

estimate the percent of children who would have PbB above that level. For all other variables, EPA used the 

default IEUBK values. 

Exhibit B-1. Default Values for IEUBK Model Parameters

Parameter Default 

Value Units 

Indoor air lead concentration (% of outdoor) 30 %

Air (by year)

Air concentration 

Age = 0-1 year (0-11 mo) 

1-2 years (12-23 mo) 

2-3 years (24-35 mo) 

3-4 years (36-47 mo) 

4-5 years (48-59 mo) 

5-6 years (60-71 mo) 

6-7 years (72-84 mo) 

0.10

0.10

0.10

0.10

0.10

0.10

0.10

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

Time outdoors 

Age = 0-1 year (0-11 mo) 

1-2 years (12-23 mo) 

2-3 years (24-35 mo) 

3-7 years (36-84 mo) 

1

2

3

4

h/day

h/day

h/day

h/day
Benefit and Cost Analysis for Proposed ELGs Appendix B: IEUBK Model 

April 19, 2013 B-2 

Exhibit B-1. Default Values for IEUBK Model Parameters

Parameter Default 

Value Units 

Ventilation rate 

Age = 0-1 year (0-11 mo) 

1-2 years (12-23 mo) 

2-3 years (24-35 mo) 

3-4 years (36-47 mo) 

4-5 years (48-59 mo) 

5-6 years (60-71 mo) 

6-7 years (72-84 mo) 

2

3

5

5

5

7

7

m3/day

m3/day

m3/day

m3/day

m3/day

m3/day

m3/day

Lung absorption 32 %

Diet (by year)

Dietary lead intake 

Age = 0-1 year (0-11 mo) 

1-2 years (12-23 mo) 

2-3 years (24-35 mo) 

3-4 years (36-47 mo) 

4-5 years (48-59 mo) 

5-6 years (60-71 mo) 

6-7 years (72-84 mo) 

2.26

1.96

2.13

2.04

1.95

2.05

2.22

μg Pb /day

μg Pb /day

μg Pb /day

μg Pb /day

μg Pb /day

μg Pb /day

μg Pb /day

Alternate Diet Sources (by food class)

Concentration

Home-grown fruits 

Home-grown vegetables 

Fish from fishing 

Game animals from hunting 

0

0

0

0

μg Pb/g

μg Pb/g

μg Pb/g

μg Pb/g

Percent of food class

Home-grown fruits 

Home-grown vegetables 

Fish from fishing 

Game animals from hunting 

0

0

0

0

%

%

%

%

Drinking Water

Lead concentration in drinking water 4 μg/L

Ingestion rate 

Age = 0-1 year (0-11 mo) 

1-2 years (12-23 mo) 

2-3 years (24-35 mo) 

3-4 years (36-47 mo) 

4-5 years (48-59 mo) 

5-6 years (60-71 mo) 

6-7 years (72-84 mo) 

0.20

0.50

0.52

0.53

0.55

0.58

0.59

L/day

L/day

L/day

L/day

L/day

L/day

L/day

Alternate Drinking Water Sources

Concentration 

First-draw water 

Flushed water 

Fountain water 

4

1

10

μg/L

μg/L

μg/L

Percentage of total intake 

First-draw water 

Flushed water 

Fountain water 

50

35

15

%

%

%
Benefit and Cost Analysis for Proposed ELGs Appendix B: IEUBK Model 

April 19, 2013 B-3 

Exhibit B-1. Default Values for IEUBK Model Parameters

Parameter Default 

Value Units 

Soil/Dust (constant over time)

Concentration 

Soil 

Dust 

200

200

μg/g

μg/g

Soil/dust ingestion weighting factor (percent soil) 45 %

Soil/Dust Ingestion (by year)

Soil/Dust ingestion 

Age = 0-1 year (0-11 mo) 

1-2 years (12-23 mo) 

2-3 years (24-35 mo) 

3-4 years (36-47 mo) 

4-5 years (48-59 mo) 

5-6 years (60-71 mo) 

6-7 years (72-84 mo) 

0.085

0.135

0.135

0.135

0.100

0.090

0.085

g/day

g/day

g/day

g/day

g/day

g/day

g/day

Soil/Dust Multiple Source Analysis (constant)

Fraction of indoor dust lead attributable to soil (MSD) 

Ratio of dust lead concentration to outdoor air lead concentration 

0.70

100

unitless

μg Pb/g dust per μg 

Pb/m3 air

Soil/Dust Multiple Source Analysis with Alternative Household Dust Lead Sources (constant)

Concentration

Household dust

Secondary occupational dust

School dust

Daycare center dust

Second home

Interior lead-based paint

150

1,200

200

200

200

1,200

μg/L

μg/L

μg/L

μg/L

μg/L

μg/L

Percentage

Household dust

Secondary occupational dust

School dust

Daycare center dust

Second home

Interior lead-based paint

100

0

0

0

0

0

%

%

%

%

%

%

Bioavailability for All Gut Absorption Pathways

Total lead absorption (at low intake)

Diet

Drinking water

Soil

Dust

Alternate source

50

50

30

30

0

%

%

%

%

%

Fraction of total net absorption at low intake rate that is attributable to non 

saturable (passive) processes 0.2 unitless

Alternate Sources (by year)

Total lead intake

Age = 0-1 year (0-11 mo) 

1-2 years (12-23 mo) 

2-3 years (24-35 mo) 

3-4 years (36-47 mo) 

4-5 years (48-59 mo) 

0

0

0

0

0

μg/day

μg/day

μg/day

μg/day

μg/day
Benefit and Cost Analysis for Proposed ELGs Appendix B: IEUBK Model 

April 19, 2013 B-4 

Exhibit B-1. Default Values for IEUBK Model Parameters

Parameter Default 

Value Units 

5-6 years (60-71 mo) 

6-7 years (72-84 mo)

0

0

μg/day

μg/day

Maternal-to-Newborn Lead Exposure

Mothers blood lead concentration at childbirth 1.0 μg/L

Plotting and Risk Estimation

Geometric standard deviation (GSD) for blood lead 1.6 Unitless

Blood lead level of concern, or cutoff 10 μg/L

Computational Options

Iteration time step for numerical integration 4 h

Source: U.S. EPA (2007).
Benefit and Cost Analysis for Proposed ELGs Appendix C: Sensitivity Analysis – Human Health Benefits 

April 19, 2013 C-1 

Appendix C: Human Health Benefits Including Downstream Reaches

As discussed in Chapter 3, EPA analyzed the human health benefits of reducing steam electric pollutant 

discharges using only data for the reaches that receive steam electric discharges directly. However, the 

proposed ELGs are expected to provide additional human health benefits by also reducing pollutant 

concentrations in reaches downstream from steam electric plant discharges. To evaluate the potential 

significance of these benefits, EPA conducted a sensitivity analysis using fish tissue concentrations modeled 

for downstream reaches, based on ambient metal concentrations estimated using a simple dilution model. 

To expand the analysis to include downstream reaches, EPA first estimated baseline and post-compliance 

metals concentrations using the water quality model component of EPA‘s Risk-Screening Environmental 

Indicators (RSEI) model, which incorporates 2010 Toxic Release Inventory (TRI) data on annual average 

discharges.80 EPA input the loadings from steam electric plant and TRI discharges in the RSEI model to 

estimate the long-term average metals concentrations in receiving and downstream reaches. The RSEI model 

uses a simple dilution and first-order decay equation wherein metals are modeled as conservative substances. 

For each reach, EPA then used the modeled ambient water concentrations to derive fish tissue concentrations 

and average daily intake rates for each of the 14 age cohorts. 

Due to data and resources limitations, EPA restricted the downstream analysis to reaches that have baseline 

concentrations above 0.001 ug/L. EPA thus identified a set of 9,626 receiving and downstream reaches 

potentially affected by steam electric plant discharges, as compared with 296 reaches for the analysis 

discussed in Chapter 3. 

A series of tables provide the results of the sensitivity analysis for the five regulatory options EPA analyzed 

(Options 1, 2, 3, 4, and 5): Exhibit C-1 shows the benefits from reduced cancer cases, Exhibit C-2 shows the 

benefits from avoided IQ losses from lead exposure, Exhibit C-3 shows the benefits from a reduced need for 

compensatory education from lead exposure, and Exhibit C-4 shows the benefits from avoided IQ losses from 

in-utero mercury exposure. 

As shown by the results below, monetized human health benefits for the sensitivity analysis are significantly 

greater than those estimated for directly receiving reaches only, suggesting that changes in water quality 

downstream from steam electric plant discharges may provide non-trivial benefits to other affected 

populations. For example, total annualized human health benefits for Option 3 across the four benefit 

categories as presented in Exhibits C-1 through C-4 are $139 million (3 percent discount rate), as compared to 

$7.7 million when considering only directly receiving reaches in Chapter 3. EPA will continue to seek ways 

to refine its estimates of downstream effects to characterize associated human health benefits. 

Exhibit C-1. Benefits from Reduced Cancer Cases, Including Downstream Reaches

Scenario Affected 

Population

Total Cancer 

Cases, 2017 to 

2040

Reduced Cancer 

Cases, 2017 to 

2040

Annualized Benefits

(Millions; 2010$)

3% Discount 

Rate

7% Discount 

Rate

Baseline 1,738,165 24.6 NA NA NA

Option 1 1,738,165 24.5 0.2 $0.04 $0.02 

Option 2 1,738,165 24.5 0.1 $0.03 $0.02 

 

80 EPA removed from the 2010 TRI data the loadings associated with steam electric plants that report to TRI. For steam 

electric plants, EPA used the loadings estimated in the EA under the baseline and the regulatory options. Loadings for 

other TRI dischargers (non-steam electric plant and other industrial facilities) remained constant throughout all 

scenarios. 
Benefit and Cost Analysis for Proposed ELGs Appendix C: Sensitivity Analysis – Human Health Benefits 

April 19, 2013 C-2 

Option 3 1,738,165 10.6 14.1 $3.22 $1.83 

Option 4 1,738,165 2.2 22.4 $5.12 $2.92 

Option 5 1,738,165 2.1 22.5 $5.15 $2.93 

Exhibit C-2. Benefits from Avoided IQ Losses for Children Ages 0 to 7, Including Downstream 

Reaches

Scenario

Number of 

Affected

Children 0 to 7

Total Avoided IQ 

Losses, 2017 to 

2040

Annualized Value of Avoided IQ Point Losses a

(Millions; 2010$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Baseline 158,173 0 NA NA

Option 1 158,173 1,014 $0.35 $0.51 $0.03 $0.05

Option 2 158,173 1,014 $0.35 $0.51 $0.03 $0.05

Option 3 158,173 24,805 $8.65 $12.37 $0.62 $1.23

Option 4 158,173 85,462 $29.79 $42.61 $2.12 $4.24

Option 5 158,173 85,455 $29.79 $42.61 $2.12 $4.23

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).

Source: U.S. EPA Analysis, 2013

Exhibit C-3. Avoided Cost of Compensatory Education for Children with Blood Lead Concentrations 

above 20 g/dL and IQ Less than 70, Including Downstream Reaches

Scenario

Number of 

Affected Children 

0 to 7

Number of Cases 

of PbB > 20 

ug/dL and IQ < 

70, 2017 to 2040

Decrease in 

Number of Cases 

of 

IQ < 70, 2017 to 

2040

Avoided Annual Cost 

(Millions; 2010$)

3% Discount 

Rate

7% Discount 

Rate

Baseline 158,173 20.0 NA NA NA

Option 1 158,173 20.0 0.0 $0.00 $0.00 

Option 2 158,173 20.0 0.0 $0.00 $0.00 

Option 3 158,173 13.6 6.4 $0.04 $0.02 

Option 4 158,173 3.7 16.3 $0.09 $0.04 

Option 5 158,173 3.7 16.3 $0.09 $0.04 

PbB = blood lead concentration.

Source: U.S. EPA Analysis, 2013

Exhibit C-4. Benefits from Avoided IQ Losses Due to Reduced In-utero Mercury Exposure, Including 

Downstream Reaches 

Scenario

Number of Births 

in Affected

Population (per 

year)

Total Avoided IQ 

Losses, 2017 to 

2040

Annualized Value of Avoided IQ Lossesa

(Millions; 2010$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Baseline 25,087 NA NA NA

Option 1 25,087 151,415 $52.79 $75.49 $3.76 $7.50 

Option 2 25,087 154,843 $53.98 $77.20 $3.85 $7.67 

Option 3 25,087 302,903 $105.60 $151.02 $7.52 $15.01 

Option 4 25,087 565,449 $197.13 $281.92 $14.04 $28.02 

Option 5 25,087 566,493 $197.49 $282.44 $14.07 $28.07 

a. Low bound estimate assumes that the loss of one IQ point results in the loss of 1.76% of lifetime earnings (following Schwartz, 

1994); high bound estimate assumes that the loss of one IQ point results in the loss of 2.38% of lifetime earnings (following
Benefit and Cost Analysis for Proposed ELGs Appendix C: Sensitivity Analysis – Human Health Benefits 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute C-3 

Exhibit C-4. Benefits from Avoided IQ Losses Due to Reduced In-utero Mercury Exposure, Including 

Downstream Reaches 

Scenario

Number of Births 

in Affected

Population (per 

year)

Total Avoided IQ 

Losses, 2017 to 

2040

Annualized Value of Avoided IQ Lossesa

(Millions; 2010$)

3% Discount Rate 7% Discount Rate

Low Bound High Bound Low Bound High Bound

Salkever, 1995).

Source: U.S. EPA Analysis, 2013
Benefit and Cost Analysis for Proposed ELGs Appendix D: Subindex Curves 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute D-1 

Appendix D: TSS, TN, and TP Ecoregion-Specific Subindex Curves 

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 Exhibit D-1 for TSS, Exhibit D-2 for TN, and Exhibit D-3 for 

TP. 

Exhibit D-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 
Benefit and Cost Analysis for Proposed ELGs Appendix D: Subindex Curves 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute D-2 

Exhibit D-1: TSS Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TSS100 TSS10

7.1.9 Willamette Valley 210.3 -0.0114 65 267 

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 Proposed ELGs Appendix D: Subindex Curves 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute D-3 

Exhibit D-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 

Exhibit D-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

8.1.7 Northeastern Coastal Zone 125.75 -0.159 1.44 15.92
Benefit and Cost Analysis for Proposed ELGs Appendix D: Subindex Curves 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute D-4 

Exhibit D-2: TN Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TN100 TN 10

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

Exhibit D-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
Benefit and Cost Analysis for Proposed ELGs Appendix D: Subindex Curves 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute D-5 

Exhibit D-3: TP Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TP100 TP 10

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

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
Benefit and Cost Analysis for Proposed ELGs Appendix D: Subindex Curves 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute D-6 

Exhibit D-3: TP Subindex Curve Parameters, by Ecoregion

ID Ecoregion Name a b TP100 TP 10

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 Proposed ELGs Appendix E: Meta-Analysis 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute E-1 

Appendix E: Meta-Analysis Results

EPA used function-based benefit transfer to estimate benefits of surface water quality improvements due to 

reductions in in steam electric pollutant discharges due to the proposed ELGs. The benefit function was 

derived using meta-analysis, following the general approach of Johnston et al. (2005), Shrestha et al. (2007), 

and others, following conceptual methods outlined by Bergstrom and Taylor (2006). The recent literature has 

given increasing emphasis to the potential use of meta-analysis to conduct and inform function-based benefit 

transfer (Johnston et al. 2005; Bergstrom and Taylor 2006; Rosenberger and Stanley 2006; Shrestha et al. 

2007). For the present analysis, the meta-regression model was based on a model specification and data 

developed originally for the 316(b) Phase II Cooling Water Intake rule, and revised based on more recent 

studies to support benefits assessment of the Construction and Development ELGs (U.S. EPA, 2009b). 

Chapter A12, ―Methods for Estimating Non-use Benefits,‖ in the Regional Analysis document for the final 

Phase II rule provides details on the original meta-analysis (U.S. EPA, 2004c); revisions are detailed below. 

These revisions included adding new studies to the metadata, re-estimating the willingness-to-pay (WTP) 

function to better account for ecological services potentially affected by heavy metal contamination and 

nutrients, and testing additional functional forms and statistical approaches. 

As stated by Rosenberger and Johnston (2007, p. 1-2): 

One of the primary advantages of meta-analysis as a benefit transfer tool relates to its capacity to allow more appropriate 

adjustments of welfare measures based on patterns observed in the literature. Within a benefit transfer context, transfer error 

is often inversely related to the correspondence between a study site and a policy site among various dimensions 

(Rosenberger and Phipps 2007). The probability of finding a good fit between a single (or multiple) study site and a policy 

site, however, is usually low (Boyle and Bergstrom 1992; Spash and Vatn 2006). If, on the other hand, empirical studies 

contribute to a body of WTP estimates (i.e., metadata), and if empirical value estimates are systematically related to 

variations in resource, study, and site characteristics, then meta-regression analysis may provide a viable tool for estimating 

a more universal transfer function with distinct advantages over unit value or other function-based transfer methods 

(Johnston et al. 2003; Rosenberger and Loomis 2000a; Rosenberger and Stanley 2006). More specifically, Rosenberger and 

Phipps (2007) posit a meta-valuation function as the envelope of a set of empirically-defined valuation functions reported in 

the literature. 

In the present case, EPA identified 45 valuation studies that use stated preference techniques to elicit benefit 

values for water quality improvements. To examine the relative influence of study, economic, and resource 

characteristics on WTP for improving surface water quality, the Agency conducted a regression-based metaanalysis of 115 estimates of WTP for water quality improvements, provided by the 45 original studies. 

Analytic methods and model specifications follow established methods in the published literature (e.g., 

Johnston et al. 2005; Bergstrom and Taylor 2006). The estimated econometric model is used as the basis of a 

function-based benefit transfer, to calculate WTP for improving water quality in waterbodies affected by 

discharge from steam electric plants. 

The following discussion summarizes the results of EPA‘s meta-analysis of surface water valuation studies 

and the use of the resulting benefit function for transfer. 

E.1 Literature Review of Water Resource Valuation Studies

As outlined in the introduction, EPA conducted a meta-analysis of water resource valuation studies to 

examine the relative influence of study, economic, and resource characteristics on total WTP for water quality 

improvements. The Agency analyzed 45 studies, published between 1981 and 2008, that applied generally 

accepted, standard valuation methods to determine total (including use and nonuse) values associated with 

aquatic habitat improvements. These 45 studies all used stated preference techniques to assess WTP, but 

varied in other respects, including the survey administration methods used, the specific environmental change 
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valued, and the geographic region affected by the environmental changes. Studies using stated preference 

approaches are preferred to studies using revealed preference approaches, because they elicit total household 

WTP (including use and nonuse values). Revealed preference studies allow to estimate use values only. Data 

from the 45 studies result in a total of 115 observations for the meta-analysis because 30 studies provide more 

than one usable estimate of total WTP for aquatic habitat improvements. 

When constructing metadata for subsequent meta-analysis, analysts must determine the optimal scope of the 

metadata (Rosenberger and Johnston 2007), interpreted as the exact definition of the dependent variable in the 

meta-regression model, which, in turn, defines the set of source studies that can be considered for inclusion in 

the metadata. The primary tradeoff is often between maintaining close similarity among dependent variables 

versus including additional information (i.e., observations) in the metadata. Similarity in dependent variable 

definition and study attributes within the metadata can be important for two reasons. First, theory may dictate 

that certain types of estimated values are not strictly comparable (e.g., Hicksian compensating variation from 

a stated preference model versus Marshallian consumer surplus from a travel cost model). Second, model fit 

may be improved by narrowing the metadata, for example to include only valuation studies that use a 

particular valuation approach (e.g., stated preference methods, as in Johnston et al. (2005); or travel cost 

model estimates, as in Smith and Kaoru (1990)). Such study selection issues may be framed in terms of a 

requirement that at a minimum, studies included in metadata satisfy both commodity consistency and welfare 

consistency (Bergstrom and Taylor 2006). The former implies that ―the commodity (Q) being valued should 

be approximately the same within and across studies‖ (Bergstrom and Taylor 2006, p. 353). The latter implies 

that ―measures of WTP within and across studies ... should represent the same ... welfare change measure, or 

ex-post calibrations [are] made to account for theoretical differences between welfare change measures‖ 

(Bergstrom and Taylor 2006, p. 355). 

The requirement of welfare consistency implies that—outside of preference calibration approaches that 

explicitly account for theoretical differences between welfare constructs (e.g., Smith et al. 2002)—metaanalyses should not combine data representing theoretically distinct welfare measures. For example, the ad 

hoc combination of stated and revealed data for meta-analysis would generally violate the condition of 

welfare consistency. Although one may introduce independent variables in regression models (typically 

dummy variable intercept shifters) to account for such theoretical differences, Smith and Pattanayak (2002) 

argue that such methods are unlikely to represent appropriate adjustments for fundamental differences in 

theoretical welfare measures. 

E.1.1 Identifying Water Resource Valuation Studies

EPA identified surface water valuation studies used in the total WTP meta-analysis by conducting an in-depth 

search of the economic literature. EPA used a variety of sources and search methods to identify relevant 

studies: 

 Review of EPA‘s research and bibliographies dealing with non-market benefits associated with 

water quality changes 

 Systematic review of recent issues of key resource economics journals (e.g., Land Economics, 

Marine Resource Economics, Journal of Environmental Economics and Management) 

 Searches of online reference and abstract databases (e.g., Environmental Valuation Resource 

Inventory (EVRI), Benefits Use Valuation Database (BUVD), AgEcon Search) 

 Visits to home pages of authors known to have published stated preference studies and/or water 

quality valuation research 

 Searches of Web sites of agricultural and resource economics departments at several colleges 

and universities 
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 Searches of Web sites of organizations and agencies known to publish environmental and 

resource economics valuation research (e.g., Resources for the Future (RFF), National Center for 

Environmental Economics (NCEE)). 

From this review, EPA identified approximately 300 surface water valuation studies that were potentially 

relevant for this analysis and compiled a bibliographic database to organize the literature review process. 

Sixty-seven of these studies met the criteria identified for inclusion in the meta-analysis.81 These criteria were 

designed to ensure both commodity and welfare consistency as noted above, and include: 

 Specific amenity valued: Selected studies were limited to those in which the environmental 

quality change being valued affects ecological services provided by surface waterbodies, 

including aquatic life support, recreational activities (such as fishing, boating, and swimming), 

and nonuse value

 Values estimated: Selected studies were limited to those that used stated preference techniques 

to elicit household WTP. 

 Study location: Selected studies were limited to those that surveyed U.S. and Canadian 

populations to value resources 

 Research methods: Selected studies were limited to those that applied research methods 

supported by journal literature. 

The Agency compiled extensive information from the 67 selected studies. Of these studies, 45 were utilized in 

the model estimation. Reasons for the difference between the total number of studies in the final metadata 

(67) and studies represented in the final model (45) include unavailability of information for certain key 

regressors for all studies. 

The tradeoff between the number of regressors or independent variables that may be included in a metaregression analysis (K) and the number of studies that are included in the metadata (N) is a fundamental 

tradeoff in most meta-analyses in the valuation literature (Moeltner et al. 2007). That is, if a study considered 

for inclusion in the metadata does not provide information for a certain regressor that analysts might wish to 

include in the meta-regression, analysts must generally choose between omitting the regressor from the metaregression or omitting the study from the metadata. As a result, researchers wishing to increase the number of 

explanatory variables in meta-regression models (increasing K) often do so at the cost of reductions in the 

number of studies or observations in the metadata (reducing N). Conversely, increases in the number of 

observations in meta-regression models are sometimes only possible if one reduces the number of 

independent variables in the model. Hence, there is a tradeoff between the quantity of information in the 

metadata (i.e., the number of observations or studies) and the possible risk of omitted variables bias due to the 

omission of influential regressors. See Moeltner et al. (2007) for additional discussion of this ―N versus K‖ 

tradeoff in meta-analysis. 

The data set used in the meta-analysis includes the following information: 

 Full study citation 

 Study location 

 Sample data and description (e.g., size, response rate, income) 

 Resource characteristics (e.g., affected waterbody type, recreational uses, baseline quality) 

 

81 The remaining studies were either earlier unpublished versions or slightly modified versions of the included studies, 

focused on water resources outside of the United States and Canada, used secondary research methods, or valued 

environmental quality changes that were not directly linked to changes in water quality (e.g., change in recreational catch 

rates). 
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 Description of environmental quality change, including geographic scale, affected species, and 

affected recreational uses (e.g., water quality change from fishable to boatable) 

 Quantitative measure of environmental quality change measured in terms of improvements in 

Water Quality Index (WQI)82 and/or percentage reduction in pollutant concentration 

 Study WTP values updated to 2007 dollars 

 WTP estimation characteristics (i.e., parametric versus non-parametric, inclusion of protest bids 

and outlier bids, WTP description). 

E.1.2 Description of Studies Selected for Total WTP Meta-Analysis

The 45 studies that EPA used in the total WTP meta-analysis were conducted between 1981 and 2008, and 

applied standard, generally accepted stated preference valuation methods to assess WTP. Studies were 

excluded if they did not conform to general tenets of economic theory, or if they applied methods not 

generally accepted in the literature. 

All selected studies focus on environmental quality changes that affect surface water resources in the United 

States. Beyond this general similarity, the studies vary in several respects. Differences include the specific 

environmental change valued, scale of environmental improvement, geographic region affected by 

environmental changes, types of values estimated, survey administration methods, demographics of the 

survey sample, and statistical methods employed. The 45 studies include 25 journal articles, 6 reports, 5 Ph.D. 

dissertations, 7 academic or staff papers, 1 book, and 1 master‘s thesis.

The 45 studies selected for the meta-analysis provided 115 observations in the final data set because multiple 

estimates of WTP were available from 30 studies. The availability of multiple observations from single 

studies is common in meta-analyses of this type (e.g., Bateman and Jones 2003; Johnston et al. 2005). Some 

of the characteristics that allowed multiple observations to be derived from a single study include the extent of 

the amenity change, the respondent population type, elicitation method(s), waterbody type, number of 

waterbodies affected, recreational activities affected by the quality change, and species affected by the quality 

change. These variations are often due to experimental design driven by the key research questions or 

hypotheses. Exhibit E-1 lists key study and resource characteristics and indicates the number of observations 

derived from each study. 

Surveys in 26 studies were administered by mail; 9 studies collected information through personal interviews 

in the home, onsite, or in a centralized location, 9 surveys were conducted by telephone, and 1 conducted by 

computer administration. Study sample sizes range from 96 to 4,033 responses. 

 

82Additional details on the WQI and the use of the WQI in survey instruments are provided by McClelland (1974), 

Vaughan (1986), and Mitchell and Carson (1989, p. 342). This index is linked to specific pollutant levels, which in turn 

are linked to presence of aquatic species and suitability for particular recreational uses. The WQI allows the use of 

objective water quality parameters (e.g., dissolved oxygen concentrations) to characterize ecosystem services or uses 

provided by a given waterbody. 
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Exhibit E-1: Selected Summary Information for Studies

Author(s) and Year Observatio

ns

State Waterbody Type Type of Water Quality 

Improvement

Affected 

Recreational Uses

Aiken (1985) 1 CO all freshwater general water quality Fishing

Anderson and 

Edwards (1986)

1 RI salt 

impoundment/marshes

general water quality fishing and swimming

Azevedo et al. (2001) 5 IA Lake nutrients fishing and swimming

Bockstael et al. 

(1988)

1 DC, MD, 

VA

estuary general water quality; 

phosphorus and nitrogen

swimming, beach, 

boating, fishing, 

outings

Bockstael et al. 

(1989)

2 MD estuary general water quality Swimming

Breffle et al. (1999) 2 WI estuary general water quality Fishing

Cameron (1988) 1 TX Estuary general water quality Fishing

Cameron and 

Huppert (1989)

1 CA river/stream wildlife habitat game fishing

Carson and Mitchell 

(1993)

4 National multiple general water quality boating; fishing; 

swimming

Carson et al. (1994) 2 CA estuary DDT and PCBs Fishing

Clonts and Malone 

(1990)

3 AL river/stream general water quality multiple uses

Croke et al. (1986-

87)

9 IL river/stream general water quality multiple uses

De Zoysa (1995) 2 OH river and lake sediment and nutrients; 

wildlife habitat

multiple uses

Desvousges et al. 

(1987)

12 PA river/stream general water quality multiple uses

Hayes et al. (1992) 2 RI estuary general water quality swimming; fishing

Herriges and Shogren 

(1996)

2 IA Lake general water quality boating and fishing

Hite (2002) 2 MS river/stream general water quality multiple uses

Huang et al. (1997) 2 NC estuary general water quality Fishing

Hushak and Bielen 

(1999)

2 OH, MI river/stream general water quality multiple uses

Kaoru (1993) 1 MA salt 

impoundment/marshes

fecal coliform Fishing

Lant and Roberts 

(1990)

3 IA, IL river/stream sediment boating, fishing, and 

swimming

Lant and Tobin 

(1989)

9 IA, IL river/stream general water quality boating, fishing

Lichtkoppler and 

Blaine (1999)

1 OH multiple PCBs and general water 

quality

all recreational uses

Lindsey (1994) 8 MD estuary nutrients multiple uses

Lipton (2004) 1 MD estuary general water quality Boating

Loomis (1996) 1 WA river/stream general water quality Fishing

Loomis et al. (2000) 2 CO river/stream general water quality multiple uses

Lyke (1993) 2 WI lake general water quality Fishing

Matthews et al. 

(1999)

2 MN river/stream phosphorus boating and fishing

Olsen et al. (1991) 3 ID, MT, 

OR, WA

river/stream wildlife habitat Fishing

Opaluch et al. (1998) 1 NY estuary general water quality Shellfishing

Roberts and Leitch 

(1997)

1 MN, SD lake general water quality multiple uses

Rowe et al. (1985) 1 CO river/stream general water quality boating, fishing, and 

swimming

Sanders et al. (1990) 4 CO river/stream general water quality Swimming

Schulze et al. (1995) 2 MT river and lake hazardous pollutants boating, fishing, and 

swimming

Shrestha and 

Alavalapati (2004)

2 FL multiple phosphorus and wildlife 

habitat

multiple uses
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Exhibit E-1: Selected Summary Information for Studies

Author(s) and Year Observatio

ns

State Waterbody Type Type of Water Quality 

Improvement

Affected 

Recreational Uses

Stumborg et al. 

(2001)

2 WI lake phosphorus multiple uses

Sutherland and 

Walsh (1985)

1 MT river and lake general water quality Swimming

Viscusi et al. (2008) 2 National river and lake general water quality multiple uses

Welle (1986) 6 MN all freshwater acid rain game fishing and 

wildlife viewing

Wey (1990) 2 RI salt impoundment/ 

marshes

general water quality Other

Whitehead et al. 

(2002)

1 NC river/stream general water quality fishing, boating, 

swimming

Whitehead and 

Groothuis (1992)

3 NC river/stream sediment and nutrients multiple uses

Whitehead et al. 

(1995)

2 NC estuary general water quality boating, fishing, and 

swimming

Whittington et al. 

(1994)

1 TX estuary heavy metals and 

pesticides

multiple uses

The Agency‘s review of the relevant economic literature showed that available surface water valuation studies 

focus primarily on general water quality rather than specific pollutants or changes. Even in cases in which 

specific pollutants are the primary policy issue, the stated preference surveys from which welfare estimates 

are derived often characterize water quality changes only in general (i.e., non-pollutant specific) terms. 

Hence, the associated welfare measures are conditioned on this general description. Of the 45 studies, 26 

presented only WTP values for changes in general water quality (approximately 60 percent). Of the studies 

that did address specific changes, eight specified nutrients and/or sediment, four addressed hazardous 

pollutants including heavy metals and pesticides, three addressed wildlife habitat, one addressed acid 

deposition, one addressed fecal coliform bacteria, one presented values for both changes in general water 

quality and nutrient reductions, and one presented values for both changes in general water quality and 

wildlife habitat. Preliminary model estimates showed no evidence that the type of pollutant considered had a 

statistically significant influence on WTP across and within studies. For this reason, EPA used a standardized 

scale to define both the baseline water quality and the water quality change valued in the original study. 

Additional details are provided below. 

From these 45 studies, the Agency compiled a data set for the meta-analysis of total WTP values. EPA 

specified a regression model based on these data to estimate a range of total household benefits for surface 

water and aquatic habitat improvements. General empirical methods follow those outlined by Johnston et al. 

(2005), following standard approaches in the meta-analysis literature. The model and results are described in 

the next section. 

E.2 Total WTP Meta-Analysis Regression Model and Results

EPA estimated both trans-log and semi-log meta-regression models based on 115 WTP estimates for 

improvements in water resources, derived from 45 original studies.83 These metadata, the model specification, 

model results, and interpretation of the results are described in the following sections. EPA, however, notes 

that only the trans-log model is used in the analysis of benefits of the proposed ELGs. The alternative 

specification (semi-log) is presented for comparative purposes only. 

 

83In its analysis of nonuse benefits for the final 316(b) Phase II rule, EPA also specified trans- and semi-log regression 

models similar to the models estimated for Phase II discussed in this section. See Chapter A12, ―Methods for Estimating 

Non-use Benefits,‖ in the Regional Analysis document for further details regarding both the log-log and semi-log 

regression models estimated in EPA‘s analysis of nonuse benefits for the final Phase II rule (USEPA 2004c); 

http://www.epa.gov/ost/316b/casestudy/final.htm). 
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In a frequently cited work, Glass (1976) characterizes meta-analysis as ―the statistical analysis of a large 

collection of results for individual studies for the purposes of integrating the findings. It provides a rigorous 

alternative to the casual, narrative discussion of research studies which is commonly used to make some sense 

of the rapidly expanding research literature‖ (p. 3; cited in Poe et al. 2001, p. 138). Meta-analysis is being 

increasingly explored as a potential means to estimate resource values in cases where original targeted 

research is impractical, or as a means to reveal systematic components of WTP (e.g., Smith and Osborne 

1996; Santos 1998; Rosenberger and Loomis 2000a; Poe et al. 2001; Woodward and Wui 2001; Bateman and 

Jones 2003). While the literature often urges caution in the use and interpretation of benefit transfers for direct 

policy application (e.g., Desvousges et al. 1998; Poe et al. 2001; Navrud and Ready 2007), such methods are 

―widely used in the United States by government agencies to facilitate benefit-cost analysis of public policies 

and projects affecting natural resources‖ (Bergstrom and De Civita 1999). Transfers based on meta-analysis 

are likewise common in both the United States and Canada (Bergstrom and De Civita 1999; Bergstrom and 

Taylor 2006). 

Depending on the suitability of available data, a meta-analysis can provide a superior alternative to the 

calculation and use of a simple arithmetic mean WTP over the available observations, as it allows estimation 

of the systematic influence of study, economic, and natural resource attributes on WTP (U.S. EPA, 2010b; 

Rosenberger and Phipps 2007; Shrestha et al. 2007). The primary advantage of a regression-based (statistical) 

approach is that it accounts for differences among study characteristics that may contribute to changes in 

WTP, to the extent permitted by available data (Johnston et al. 2005; Rosenberger and Phipps 2007). An 

additional advantage is that meta-analysis can reveal systematic factors influencing WTP, allowing analysts to 

assess whether, for example, WTP estimates are (on average) sensitive to scope (Smith and Osborne 1996). 

There is, however, some controversy regarding whether regression-based meta-analyses should be used for 

direct benefit transfer. Many contemporary sources in the literature note the potential ability of regressionbased meta-analyses to generate benefit functions better able to adjust and forecast benefits at policy sites in 

question, and either explicitly or implicitly favor the use of meta-analysis over alternative benefit transfer 

approaches (e.g., Johnston et al. 2005; Bergstrom and Taylor 2006; Moeltner et al. 2007; Rosenberger and 

Johnston 2007; Rosenberger and Phipps 2007; Shrestha et al. 2007). EPA (USEPA 2000b) characterizes 

meta-analysis as ―the most rigorous‖ benefit transfer method. In contrast, the EPA Science Advisory (2007) 

Board‘s ―Advisory on EPA‘s Issues in Valuing Mortality Risk Reduction‖ recommends against the use of 

regression-based meta-analysis for VSL (value of statistical life) transfers, and other authors advise caution in 

such uses (Navrud and Ready 2007). The primary disagreement is whether it is appropriate to use metaanalysis results as a reduced form model to estimate benefits, and whether the empirical ability of metaanalysis in many cases to generate benefit transfers with reduced transfer errors offsets the lack of an 

underlying theoretical model to ―calibrate‖ benefit estimates across studies (cf. Smith and Pattanayak 2002; 

Smith et al. 2002). While the Agency recognizes this ongoing controversy, it notes that there are a large 

number of practitioners and publications supporting the use of regression-based meta-analysis for benefit 

transfer. It also removes the element of subjective judgment associated with selecting a single study or value 

for benefit transfer. Hence, the following model is presented as a means to provide a benefit function that 

capitalizes on the substantial information available for existing water quality valuation studies, 

notwithstanding potential concerns voiced by some regarding the use of meta-analyses for such purposes. 

E.2.1 Metadata Total WTP Regression Model

Meta-analysis is largely an empirical, data-driven process, but one in which variable and model selection is 

guided by theory (Bergstrom and Taylor 2006). Given a reliance on information available from the underlying 

studies that comprise the metadata, meta-analysis models most often represent a middle ground between 

model specifications that would be most theoretically appropriate and those specifications that are possible 

given available data. Smith and Osborne (1996), Rosenberger and Loomis (2000a), Poe et al. (2001), 
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Bateman and Jones (2003), Dalhuisen et al. (2003), Johnston et al. (2005, 2006), Bergstrom and Taylor 

(2006), Moeltner et al. (2007), and others provide insight into the mechanics of specifying and estimating 

meta-equations in resource economics applications. 

Past meta-analyses have incorporated a range of different statistical methods, with none universally accepted 

as superior (Johnston et al. 2005). EPA followed recent work of Bateman and Jones (2003) and Johnston et al. 

(2005) in applying a multilevel model specification to the metadata, to address potential correlation among 

observations gathered from single studies. Also following prior work (e.g., Smith and Osborne 1996; Poe et 

al. 2001), EPA applied the Huber-White robust variance estimation. As described by Smith and Osborne 

(1996, p. 293), ―this approach treats each study as the equivalent of a sample cluster with the potential for 

heteroskedasticity...across clusters.‖ Weighted models are avoided following the arguments of Bateman and 

Jones (2003).84

To guide development of the model and variable specifications, EPA relied upon a set of general principles. 

These principles are designed to help prevent excessive data manipulations and other factors that may lead to 

misleading model results. The general principles include, all else being equal: 

 Fewer and simpler data transformations are preferred to more extensive ones. 

 In the absence of overriding theoretical considerations, continuous variables are generally 

preferred to discrete variables derived from underlying continuous distributions. 

 Models should attempt to capture elements of the scope and scale of resource changes. 

 Models should distinguish WTP associated with different types of resources and resource uses, 

particularly where relevant to the policy question at hand. 

 Following the ―weak structural utility theoretic‖ (WSUT) approach of Bergstrom and Taylor 

(2006, p. 352), exogenous structural constraints are avoided to afford the flexibility necessary to 

appropriately model empirical patterns that may not necessarily flow from an underlying 

theoretical modeling structure. The dependent variable in the meta-analysis is the natural 

logarithm of estimated household WTP for water quality improvements in aquatic habitat, as 

reported in each original study. For this analysis, original study values were adjusted to 2007$ 

based on the relative change in Consumer Price Index (CPI) from the study year to 2007. Total 

WTP over the sample ranged from $5.33 to $502.70, with a mean value of $83.09. 

As noted above, two model specifications are estimated (cf. Johnston et al. 2005). For the first specification, 

all right-hand-side variables are linear, resulting in a standard semi-log functional form. The second 

specification is identical to the semi-log model, except for the specification of the explanatory variable 

measuring water quality change and baseline as natural logs. This results in a trans-log functional form, also 

common in empirical applications (Johnston et al. 2001, 2005). Both the semi-log and trans-log models have 

advantages related to (1) their fit to the data, (2) the intuitive results that are provided, and (3) their common 

use in the empirical valuation and meta-analysis literature (e.g., Smith and Osborne 1996; Santos 1998; 

Johnston et al. 2001, 2005, 2006). The trans-log model, however, has the additional structural advantage that 

estimated WTP is necessarily zero when water quality change is also zero (cf. Johnston et al. 2001)—a 

property suggested by theory that analysts may wish to weight against model fit considerations when 

choosing a model for benefit estimation. While linear forms are also common in this literature (Rosenberger 

 

84For comparison, models were also estimated using ordinary least squares (OLS) with robust variance estimation, 

weighted least squares with robust variance estimation, and multilevel models with standard (non-robust) variance 

estimation. None of these models outperformed the illustrated model in terms of overall model significance and fit, and 

statistical significance of individual coefficients. 
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and Loomis 2000a, 2000b; Poe et al. 2001; Bateman and Jones 2003), specifications requiring more intensive 

data transformations (e.g., Box-Cox, log-log) are less common. 

As noted in the preceding section, the metadata include independent variables characterizing specific details 

of the resource(s) valued such as the baseline resource conditions; the extent of resource improvements and 

whether they occur in estuarine or freshwater; the geographic region and scale of resource improvements 

(e.g., the number of waterbodies); resource characteristics (e.g., baseline conditions, the extent of water 

quality change, and ecological services affected by resource improvements); characteristics of surveyed 

populations (e.g., users, nonusers); and other specific details of each study. For ease of exposition, these 

variables are categorized into those characterizing (1) study and methodology, (2) surveyed populations, (3) 

geographic region and scale, and (4) resource improvements. Attributes included within each category are 

summarized below. 

Study and methodology variables characterize such features as: 

 The year in which a study was conducted 

 The payment vehicle and elicitation format (e.g., discrete choice versus open-ended, voluntary 

versus non-voluntary, interview versus mail versus phone) 

 WTP estimation methods and conventions (e.g., approaches to protest and outlier bids, use of 

parametric versus non-parametric statistical methods, estimation of mean or median WTP, the 

use of annual or lump-sum payments) 

 Whether the original survey represented water quality changes using the WQI. 

Surveyed populations variables characterize such features as: 

 The average income of respondents

 Whether the survey specifically targeted nonusers. 

Geographic region and scale variables characterize such features as: 

 The number of waterbodies affected by the policy 

 Whether the study considered water quality improvements in all waterbodies in a region 

 The geographic area of the country in which the study was conducted. 

Resource improvement variables characterize such features as: 

 The extent of water quality change estimated as a difference between the baseline and postchange water quality index 

 Baseline water quality index 

 Those studies for which recreational uses such as fishing are specifically noted in the survey 

 Aquatic species affected by resource improvements (e.g., game fish and shellfish) 

 Those studies identifying large increases in fish populations (i.e., greater than 50 percent). 

Although the interpretation and calculation of most independent variables requires little explanation, a few 

variables require additional detail. These include the variables characterizing surface water quality and its 

measurement. Many (23) observations in the metadata characterize quality changes using variants of the WQI 

(e.g., Mitchell and Carson 1989). This scale is linked to specific pollutant levels, which, in turn, are linked to 

the presence of aquatic species and recreational uses. However, some observations provide water quality 

measures using other, primarily descriptive, means that differ from the WQI. 

To allow consistent comparisons of water quality change using a single scale, EPA mapped all water quality 

measures to the original WQI developed by McClelland (1974). WQI values were therefore developed for 

those studies that did not originally use this index. This scale was chosen for two reasons: 
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 WQI values are linked to specific pollutant levels, which, in turn, are linked to the presence of 

aquatic species and suitability for particular recreational uses. Therefore, the WQI can be used to 

link water quality changes from reduced nutrient and heavy metal connections to effects on 

human uses and support for aquatic species habitat. 

 A large number of the original studies in the metadata included WQI measures as ―native‖ 

components of the original surveys. Hence, for these studies, no additional transformations were 

required. 

While not all studies in the metadata included the WQI as a native survey component, in most cases the 

descriptions of water quality (present in the studies that did not apply the WQI) rendered mapping of water 

quality measures to the WQI straightforward. In cases where baseline and improved (or declined) water 

quality was not defined by suitability for recreational activities (e.g., boating, fishing, and swimming) or 

corresponding qualitative measures (e.g., poor, fair, good), EPA used descriptive information available from 

studies (e.g., amount/indication of the presence of specific pollutants, historical decline of the quality of the 

resource) to approximate the baseline level of water quality and the magnitude of the change.85 For studies 

that valued discrete changes in the size of species populations, EPA characterized the baseline quality based 

on the current presence and prevalence of the species at hand, and assumed population increases to 

correspond to modest increases in water quality in order to be conservative.86 To account for the uncertainty 

involved in mapping those studies that are not based on the WQI, EPA introduced the binary variable WQI, 

which indicates those studies in which WQI measurements were an original component of the survey 

instrument. This approach is based largely on the published methods of Johnston et al. (2005), drawn from 

prior Agency work for the 316(b) Phase II Cooling Water Intake rule. 

Variables incorporated in the final model are listed and described in Exhibit E-2.

 

85For example, a study by Huang et al. (1997) described current water quality as degraded from 1981 levels in terms of 

reduced fish catches (60 percent) and reduced number of open shellfish beds (25 percent). However, because the water 

resource was still supporting recreational fishery, the baseline water quality was set to ―fishable‖ on the WQI.

86 For example, a study by Lyke (1993) describes the baseline conditions as follows: (1) ―there are no naturally 

reproducing lake trout in Lake Michigan; all lake trout found there are from hatcheries.‖ (2) ―Lake Superior stocks of 

self-reproducing lake trout were much reduced, but not wiped out, and both natural and hatchery-raised lake trout are 

found there.‖ These baseline conditions correspond to the ―game-fishable‖ level on the WQI. The study estimates WTP 

for restoring natural populations of lake trout to the Wisconsin Great Lakes. Therefore, the expected change that would 

occur within the ―game-fishable‖ category is likely to be small. 
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Exhibit E-2: Variables and Descriptive Statistics for the Total WTP Regression Model

Variable Description Units and 

Measurement

Mean

(Std. Dev.)

ln_WTP Natural log of WTP for specified resource improvements. Natural log of 

dollars (Range: 

2.12 to 6.22)

4.42

(0.75)

year_indx Year in which the study was conducted, converted to an index by 

subtracting 1980.

Year index 

(Range: 1 to 28)

9.68

(6.42)

discrete Binary (dummy) variable indicating that WTP was estimated 

using a discrete choice survey instrument.

Binary 

(Range: 0 or 1)

0.28

(0.45)

volunt Binary (dummy) 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) 0.05

(0.22)

mail Binary (dummy) variable indicating that the survey was 

conducted through mail (default value for this dummy is a phone 

survey).

Binary 

(Range: 0 or 1) 0.48

(0.52)

lump_sum Binary (dummy) variable indicating that payments were to occur 

on something other than an annual basis over a long period of 

time, such as property taxes. For example, some studies 

specified that payments would occur over a five-year period.

Binary 

(Range: 0 or 1) 0.13

(0.34)

nonparam Binary (dummy) variable indicating that WTP was estimated 

using non-parametric methods.

Binary 

(Range: 0 or 1)

0.50

(0.50)

quality_ch The change in mean water quality, specified on the WQI 

(McClelland 1974; Mitchell and Carson 1989). Defined as the 

difference between baseline and post-compliance quality. Where 

the original study (survey) did not use the WQI, EPA mapped 

water quality descriptions to analogous levels on the WQI to 

derive water quality change (see text).

WQI units 

(Range: 2.5 to 65) 21.3

(10.4)

lnquality_cha Natural log of the change in mean water quality (quality_ch), 

specified on the WQI (McClelland 1974; Mitchell and Carson 

1989).

WQI units 

(Range: 0.92 to 

4.17)

2.91

(0.59)

WQI Binary (dummy) variable indicating that the original survey 

reported resource changes using a standard WQI.

Binary 

(Range: 0 or 1)

0.34

(0.48)

non-reviewed Binary (dummy) variable indicating that the study was not 

published in a peer-reviewed journal. 

Binary

(Range: 0 or 1)

0.32

(0.47)

outlier_bids Binary (dummy) variable indicating that outlier bids were 

excluded when estimating WTP.

Binary 

(Range: 0 or 1)

0.94

(0.24)

median_WTP Binary (dummy) variable indicating that the study reported 

median, not mean, WTP.

Binary 

(Range: 0 or 1)

0.04

(0.20)

income Mean income of survey respondent, either as reported by the 

original survey or calculated by EPA based on U.S. Census 

Bureau averages for the original surveyed region.

Dollars 

(Range: 34,955 to 

158,347)

5,7049.59

(13,946.64)

nonusers Binary (dummy) variable indicating that the survey was 

implemented over a population of nonusers (default category for 

this dummy is a survey of any population that includes users).

Binary 

(Range: 0 or 1) 0.09

(0.29)

single_riverb Binary (dummy) variable indicating that resource change 

explicitly took place over a single river (default is a change in an 

estuary or that takes place on a national scale).

Binary 

(Range: 0 or 1) 0.23

(0.42)

single_lakec Binary (dummy) variable indicating that resource change 

explicitly took place over a single lake (default is a change in an 

estuary or that takes place on a national scale).

Binary 

(Range: 0 or 1)

0.08

(0.28)
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Exhibit E-2: Variables and Descriptive Statistics for the Total WTP Regression Model

Variable Description Units and 

Measurement

Mean

(Std. Dev.)

regional_fresh Binary (dummy) variable indicating that resource change 

explicitly took place over an entire region such as a state (default 

is a change in an estuary or a change that takes place on a 

national scale).

Binary 

(Range: 0 or 1) 0.36

(0.48)

multiple_river Binary (dummy) variable indicating that resource change 

explicitly took place over multiple rivers (default is a change in 

an estuary or that takes place on a national scale).

Binary 

(Range: 0 or 1) 0.06

(0.24)

salt_pond Binary (dummy) variable indicating that resource change 

explicitly took place over multiple salt impoundments (default is 

a change in an estuary or that takes place on a national scale).

Binary 

(Range: 0 or 1) 0.03

(0.18)

num_riv_pond Number of rivers or salt impoundments affected by policy; if 

unspecified num_riv_pond = 0. (In the present data, only studies 

addressing rivers and lakes specified >1 number of waterbodies. 

All others specified either 1 waterbody, or the number was 

unspecified.)

Number of 

specified rivers or 

impoundments 

(Range: 0 to 15)

1.01

(2.98)

mr Binary (dummy) variable indicating that the survey included 

respondents from more than one of the EPA regions.

Binary 

(Range: 0 or 1)

0.08

(0.26)

mp Binary (dummy) variable indicating that the survey included 

respondents from the Mountain Plain region.d

Binary 

(Range: 0 or 1)

0.02

(0.13)

allmult Binary (dummy) variable indicating that either all or multiple 

aquatic species are affected by the resource change.

Binary

(Range: 0 or 1)

0.18

(0.39)

nonspec Binary (dummy) variable indicating that the study did not 

specify what species would be affected by water quality 

improvements. 

Binary

(Range: 0 or 1) 0.42

(0.50)

fish_use Binary (dummy) variable identifying studies in which changes in 

fishing use are specifically noted in the survey.

Binary 

(Range: 0 or 1)

0.56

(0.50)

fishplus Binary (dummy) variable identifying studies in which a fish 

population or harvest change of 50 percent or greater is reported 

in the survey.

Binary 

(Range: 0 or 1) 0.08

(0.28)

baseline Baseline water quality, specified on the WQI. WQI units (Range: 

1.61 to 5.2)

39.79

(20.37)

lnbase Natural log of baseline water quality, specified on the WQI. WQI units (Range: 

5 to 70)

3.46

(0.80)

a. The variable quality_ch is defined earlier in this table as the difference between baseline and post-compliance quality, specified on the WQI 

(Mitchell and Carson 1989).

b. Examples of rivers and streams considered in the studies include the Columbia, Potomac, Elwha, Eagle, and Tar-Pamlico rivers.

c. Includes one study that focused on a segment of the Lake Erie shoreline.

d. The Mountain Plain region includes the following states: Colorado, Iowa, Kansas, Missouri, Montana, Nebraska, North Dakota, South Dakota, 

Utah, Wyoming.

E.2.2 Total WTP Regression Model and Results

As noted above, EPA estimated the meta-analysis regression using a multilevel, random-effects specification. 

This model follows the general approaches of Bateman and Jones (2003) and Johnston et al. (2005), among 

others. Multilevel (or hierarchical) models may be estimated as either random-effects or random-coefficients 

models, and are described in detail elsewhere (Singer 1998). The fundamental distinction between these and 

classical linear models is the two-part modeling of the equation error to account for hierarchical data. Here, 

the metadata are comprised of multiple observations per study, and there is a corresponding possibility of 

correlated errors among observations that share a common study or author. 

The common approach to modeling such potential correlation is to divide the residual variance of estimates 

into two parts, a random error that is independently and identically distributed across all studies and for each 

observation, and a random effect that represents systematic variation related to each study. The model is 
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estimated as a two-level hierarchy, with level one corresponding to WTP estimates (individual observations), 

and level two corresponding to individual studies. The random effect may be interpreted as a deviation from 

the mean equation intercept associated with individual studies (Bateman and Jones 2003). The model is 

estimated using a maximum likelihood estimator, assuming that random effects show a multivariate normal 

distribution. Following Bateman and Jones (2003), observations are unweighted. Covariances are obtained 

using the Huber-White covariance estimator (Smith and Osborne 1996). Random-effects models such as the 

multilevel model applied here are becoming increasingly standard in resource economics applications, and are 

estimable using a variety of readily available software packages. 

A Note on Model Specification 

As noted above, EPA considered two functional forms in this analysis: semi-log and trans-log. In both cases, 

the dependent variable is the natural log of estimated household WTP for surface water quality improvement. 

For Model One, all right-hand-side variables are linear, resulting in a semi-log functional form common in 

meta-analysis (e.g., Smith and Osborne 1996; Johnston et al. 2003). While linear forms are also common 

(Rosenberger and Loomis 2000a, 2000b; Poe et al. 2001; Bateman and Jones 2003), the semi-log and translog forms were chosen based on statistical performance and ability to capture curvature in the valuation 

function, and because they allow independent variables to influence WTP in a multiplicative rather than 

additive manner. 

Model Two is a trans-log model, identical to the semi-log specification save for the inclusion of water quality 

measures (baseline and quality_ch) as natural logarithms. This form—common in the hedonic modeling 

literature (Johnston et al. 2001) and illustrated by Johnston et al. (2005) within the meta-analysis literature—

shares many advantages of the semi-log functional form, but also incorporates the desirable quality that WTP 

is constrained to zero when quality change is also equal to zero. 

Following standard econometric practice and the ―weak structural utility theory‖ approach to meta-analysis 

summarized above (Bergstrom and Taylor 2006), the final models are specified based on guidance from 

theory and prior literature. For example, Arrow et al. (1993) made a fundamental distinction between discrete 

choice and open-ended payment mechanisms (such as iterative bidding and payment cards). Hence, this 

distinction is made in the final model (i.e., including the variable discrete_ch). Similarly, other ―survey 

methodology‖ variables in the model were chosen based on theoretical considerations and prior findings in

the literature (e.g., voluntary versus mandatory payment vehicles, parametric versus non-parametric, 

treatment of protest and outlier bids, use of mean versus median WTP). Also included are variables 

characterizing the scope and scale of the resource change, based on theoretical expectations that such factors 

should be relevant to welfare estimates. 

Few variables were excluded solely because of lack of statistical significance. Individual variables were only 

excluded if they could not be shown to be statistically significant in any version of the model (restricted or 

unrestricted), and there was no overriding rationale for retaining the variable in the model. For example, 

variables distinguishing different types of discrete choice instruments (e.g., conjoint versus dichotomous 

choice) added no significant explanatory power to the model (p = 0.58). 

It is important to note that although empirical considerations play a role in model development, certain 

variables were retained in the model for theoretical reasons, even if significance levels were low. Such 

specification of meta-analysis models using a combination of theoretical guidance and empirical 

considerations is standard in modeling efforts (Bergstrom and Taylor 2006). 

Exhibit E-3 presents results of the total WTP regression model. 
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Exhibit E-3: Estimated Multilevel Model Results for the Trans-log and Semi-log Total WTP 

Regression Models: WTP for Aquatic Habitat Improvements

Variable

Trans-log Model Semi-log Model

Parameter Estimate Standard Error Parameter Estimate Standard Error

intercept 5.7109 c0.9352 6.0946 c0.5127

year_indx -0.08043 c0.01482 -0.06707c0.01430

discrete -0.1248 0.2230 -0.1696 0.1735

volunt -1.3233 c0.1653 -1.3049c0.1661

nonparam -0.6698c0.1434 -0.6892c0.1164

income 2.698 x 10-63.9 x 10-6-1.16 x 10-73.238 x 10-6

WQI -0.3275 0.2692 -0.3631a0.2096

outlier_bids -0.8837c0.2855 -0.7829c0.2164

single_river -0.4279 c0.1412 -0.2724b0.1222

single_lake -0.06316 0.2386 -0.1268 0.2223

multiple_river -1.4752c0.3540 -1.5054c0.3184

regional_fresh 0.1588 0.1505 0.2219a0.1168

salt_pond 0.9849c0.3580 1.1357c0.3142

num_riv_pond 0.1173c0. 02806 0.1145c0.02510

mr -0.8846c0.1832 -0.7932c0.1343

mp 1.6337c0.2980 1.5168c0.2574

nonusers -0.4036c0.1314 -0.4451c0.1301

allmult -0.3728b0.1644 -0.4044c0.1266

nonspec -0.4042b0.1731 -0.2988 0.1351

quality ch (lnquality_ch in 

trans-log model) 0.4065a

0.1488 0.03208c0.006337

fish_use -0.3317b0.1291 -0.4480c0.1034

fishplus 0.4432b0.1820 0.4017c0.1217

baseline (lnbase in trans-log 

model) 0.02610 0.1183 0.005205 0.003739

mail -0.2013 0.1466 -0.3073c0.1155

lump_sum 0.5569b0.2387 0.7188c0.1873

non_reviewed -0.2718a0.1625 -0.3744c0.1234

median_wtp -0.5358c0.1875 -0.4675b0.1898

-2 Log Likelihood 133.9c115.5b

Covariance Factors:

Study Level (σu) 0 0

Residual (σ e) 0.1876c0.1599c

a. Significant at the 0.10 level.

b. Significant at the 0.05 level.

c. Significant at the 0.01 level.

E.2.3 Interpretation of Total WTP Regression Analysis Results

Regression results reveal strong systematic elements influencing WTP. The analysis finds both statistically 

significant and intuitive patterns that influence WTP for surface water quality improvements. In general, the 

statistical fit of the three estimated equations is good; model results suggest a considerable systematic 

component of WTP variation that allows forecasting of WTP based on site and study characteristics. 

Likelihood ratio tests show that model variables are jointly significant at the p<0.01 level for the trans-log 

model and the p < 0.05 level for the semi-log model. In both models, the majority of independent variables 

are statistically significant at p<0.05, with most statistically significant at p<0.01. Signs of significant 

parameter estimates generally correspond with intuition, where prior expectations exist. As shown in Exhibit 

E-3, the random effect is statistically insignificant (i.e., study level covariance factors are zero). Considering 

these factors, the statistical performance of both models compare favorably to prior meta-analyses in the 

valuation literature. 
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Despite differences in the functional form of the two models, statistical results are robust across models. In 

most cases, coefficient magnitudes and standard errors vary to only a small degree. Measures of equation fit 

are similar, and both models are significant at the p<0.05 level or better. Such results mirror those of Johnston 

et al. (2005), whose earlier meta-analysis of WTP for water quality improvements finds a high degree of 

robustness to changes in model specification. 

The initial discussion emphasizes results of the trans-log model. Although policy implications of both model 

specifications are nearly identical for moderate to relatively large water quality improvements (e.g., more than 

5 percent increase in WQI), the trans-log model provides more accurate WTP estimates when the expected 

water quality change is very small (e.g., less than 1 percent increase in WQI). 

One of the primary means to assess the validity of benefit transfers—and the only one that may be applied in 

cases wherein the true value for the study site is unknown—is value surface tests (Bergstrom and De Civita 

1999). These tests involve assessments of ways in which ―different factors may cause values to vary across 

sites, providing guidance for adjustments needed to make a valid transfer of value estimates from the study 

site(s) to the policy site‖ (Bergstrom and De Civita 1999). Following general approaches for such value 

surface assessments—which generally involve comparisons of empirical patterns found via meta-analysis to 

theoretical expectations or norms—the Agency concluded that most results of the estimated value surface 

suggest an appropriate benefit function. Results of these value surface assessments are detailed in the 

following sections. 

Resource Improvement Effects 

Seven variables characterize resource improvements and uses; most are of the expected sign. The coefficient 

on the quality_ch variable is positive and statistically significant (p<0.01), indicating that larger water quality 

improvements generate larger WTP. This is an important result, and indicates that WTP is sensitive to the 

scope of water quality improvements. The estimated model showed that WTP values are not sensitive to the 

baseline water quality from which water quality change would occur. The estimated parameter on the variable 

baseline representing the baseline water quality from which water quality change would occur is not 

statistically significant (p>0.1). This finding differs from that of Johnston et al. (2005), which shows that 

WTP for marginal water quality improvements declines as baseline water quality improves. Here, the value is 

positive but is only significant at p<0.17 in the semi-log model. In the trans-log model, this parameter is not 

statistically significant. The reason for this result is unknown, but may be related to highly valued uses that 

are associated with larger values on the WQI. For example, increases beginning at higher levels on the WQI 

may cross thresholds allowing such highly valued uses as swimming and drinking, such that increases at these 

high-quality levels may be valued more highly than otherwise similar changes at lower baselines, which may 

not allow such uses. Such thresholds, or other non-convex preference patterns, may lead to unexpected results 

for the baseline water quality variable (baseline).

Both models reveal that water quality changes associated with recreational fishing uses lead to a significant 

decrease in total WTP values, compared to improvements that do not affect fishing. The variable fish_use

identifies those studies that specified effects of water quality improvements on recreational fishing (e.g., 

increase in catch rates). The associated parameter estimate is significant (p<0.05) and has the negative sign. In 

contrast, the variable fishplus identifies those studies for which the associated survey identified particularly 

large gains in fish populations or harvest rates (>50 percent). The positive and statistically significant result 

(p<0.05) indicates that large gains in fish populations or harvests are associated with statistically significant 

increases in total WTP. Results suggest that while water quality improvements targeting fishing uses may not 

be valued particularly highly on average, very large resultant improvements in fish populations or harvests are 

associated with increases in WTP, ceteris paribus. 
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The variables all_mult and nonspec indicate that water quality improvements affect multiple species 

(all_mult) or unspecified species (nonspec), respectively. The default category from which these variables 

allow systematic variations in WTP is a focus on particular aquatic species affected by water quality (e.g., 

shellfish or game fish). The associated coefficients are negative, indicating that WTP is lower when a survey 

instrument does not specify what aquatic species would be affected (nonspec) or when all or multiple species 

are affected (all_mult). The latter finding seems to be counterintuitive at first. However, when a survey 

instrument focuses on the effect of water quality on particular species, it is a likely indication that these 

effects are of significant concern to the affected communities, which typically leads to a higher WTP. That is, 

this result suggests that WTP is higher when water improvements can be shown to offer targeted benefits to 

specific, and often high-profile, species groups—as opposed to cases in which improvements benefit an often 

poorly characterized group of species. 

Geographic Region and Scale Effects 

Ten binary variables characterize geographic region and scale; seven are statistically significant at p<0.10. 

The default category from which these variables allow systematic variations in WTP is an estuarine 

waterbody. Also included in this default are a small number of observations addressing national level 

improvements. Compared to this baseline, WTP associated with rivers is lower (single_river and

multiple_river both have negative and significant values). Single_lake has a negative value, but it is not 

significant. WTP for water quality gains in salt impoundments (salt_pond) is higher than for estuaries 

(p<0.05). This is not surprising since water quality gains in salt impoundments correspond to an increase in 

the number of acres of shellfish beds. 

Of particular importance for the general validity of empirical findings, the model results further suggest that 

WTP is sensitive to the number of waterbodies under consideration and geographic scale of improvement 

(regional_fresh). Of the waterbody categories distinguished above, both rivers and salt impoundments 

allowed variation in numbers of affected waterbodies explicitly described by the survey. This variation is 

captured by the variable num_riv_pond.87 The associated parameter estimate is statistically significant 

(p<0.01) and indicates that WTP increases with the number of waterbodies considered. The parameter 

estimate on the regional_fresh variable is positive and significant (p<0.10) in the semi-log model, indicating 

that large-scale regional water quality improvements lead to an increase in WTP. These results, combined 

with the statistical significance of the water quality change variables noted above, suggest that WTP values 

(in this case for water quality improvements) are strongly sensitive to scope, both in terms of the number of 

waterbodies considered, geographic scale of improvement, and the magnitude of water quality change. In the 

trans-log model, the regional_fresh variable is positive but not statistically significant (p>0.30). 

Finally, the regional indicator variables mp and mult_reg are statistically significant at p<0.01, suggesting that 

there are significant differences among WTP estimates from surveys in different geographical regions of the 

United States. The parameter estimate on the mult_reg variable is negative, indicating that WTP for non-local 

water quality improvements (e.g., out of state) are lower compared to in-state or local resource improvements. 

This is consistent with prior findings that WTP for water quality improvements declines with the distance 

from the resource (Bateman et al. 2006). The magnitude of the Pacific Mountain (mp) regional effect suggests 

that spurious or otherwise unexplained effects (e.g., the effect of specific researchers who appear more than 

once in the data) may drive their overall magnitude. For example, the size of the positive parameter estimate 

associated with WTP in the Pacific Mountain dummy (mp) leads in many cases to relatively large increases in 

WTP for Pacific Northwest policies. Hence, EPA believes that particular, spurious, or unexplained aspects of 

studies from this region may have caused the associated parameter estimate to have a larger-than-expected 

 

87 Technically, this variable is the sum of two interaction variables: (1) an interaction between multiple_river and the number of 

waterbodies noted in the survey (0 if unspecified) and (2) an interaction between salt_pond and the number of waterbodies noted in 

the survey (0 if unspecified).
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influence on WTP. Although effects of regional dummy variables often escape simple, intuitive 

characterization, EPA notes that they are often statistically significant in meta-analysis found in the valuation 

literature. Similar issues are found by Johnston et al. (2005), for example. 

Surveyed Populations Effects 

Only two variables, nonusers and income, are used to characterize surveyed populations. In particular, the 

nonusers variable is of substantial policy relevance. The negative and strongly significant (p<.01) parameter 

estimate indicates that surveys of nonusers only, who by definition have only nonuse values for the resource 

improvements in question (cf. Freeman 2003, p. 142), generate lower WTP values than surveys that include 

users, who may have both use and nonuse values. Based on this statistically significant result, it is possible to 

use this model to estimate nonuse values, interpreted as the mean WTP values estimated by surveys of 

nonusers only. Such methods, however, may underestimate nonuse values of the general population, if the 

nonuse values of users exceed those of nonusers (Whitehead and Blomquist 1991a,b). 

The income parameter estimate is positive in the trans-log model, as expected, but is not statistically 

significant. Such lack of statistical significance for income parameters is not uncommon in meta-analyses 

found in the literature (e.g., Johnston et al. 2005). 

Study and Methodology Effects 

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. 

While expected, this does indicate that the methodological approach influences WTP, as argued by Arrow et 

al. (1993). Of nine variables characterizing study and methodological effects, eight are statistically significant 

at p<0.10. Among these is the year in which a study was conducted (year_indx, a continuous variable), with 

later studies associated with lower WTP. This is the expected result, as the focus of survey design over time 

has often been on the reduction of survey biases that would otherwise result in an overstatement of WTP 

(Arrow et al. 1993). 

Model results reveal that voluntary (voluntary=1) payment vehicles (i.e., surveys that describe hypothetical 

payments as voluntary) are associated with reduced WTP estimates. This result counters common intuition 

and empirical findings that voluntary payment vehicles are associated with overstatements of true WTP 

(Carson et al. 2000). The reason for this counter-intuitive finding is unknown, but may reflect an 

unwillingness among respondents to offer large voluntary payments, given the fear that others will free-ride 

(Johnston et al. 2005). Reduced WTP estimates are also associated with studies applying non-parametric 

methods to WTP estimation (nonparam). Survey elicitation method does not have a strong effect in this 

model; studies using discrete choice formats have lower WTP values, but this difference is not statistically 

significant. 

Smaller WTP estimates are associated with studies that eliminate or trim outlier bids when estimating WTP 

(outlier_bids=1; p<0.01). Studies that report median WTP (median_WTP; p<0.01) have lower WTP values. 

Lower WTP is associated with the use of the WQI in the original survey (WQI=1). This parameter is, 

however, significant in the semi-log model only (p<0.1). As is the case with a variety of study design 

variables, there is no necessary expectation with respect to the direction of this effect. Nonetheless, this 

finding might suggest the capacity of such scales to clarify the specific magnitude and implications of water 

quality change, and hence (perhaps) reduce methodological misspecification or symbolic biases that might act 

to systematically inflate estimated WTP. 

Survey format variables also have an effect on WTP, as might be expected. Mail has a negative and 

statistically significant coefficient (p<0.01) in the semi-log model, compared to the default of telephone 

surveys or interviews. This parameter is negative, but not statistically significant (p>0.17) in the trans-log 
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model. It may be possible that the interview and telephone survey format results in larger WTP values either 

because the respondents are better able to understand the valuation scenario, or because respondents may feel 

pressure from interviewers to bias their WTP estimates upward. Finally, studies that ask respondents to report 

an annual payment (as opposed to a lump_sum payment) have higher WTP estimates (p<0.05). This likely to 

reflect the fact that annual payments are regarded as an infinite contribution and may reflect the respondent‘s 

uncertainly regarding his future income and budget constraints. 

E.2.4 Model Selection

To select the model for estimating benefits of water quality improvements from the regulation, EPA 

calculated WTP values for a range of WQI changes using both semi-log and trans-log models. In all cases the 

baseline WQI is set to 50, which approximates the average WQI value across all RF1 reaches in the United 

States. EPA assigned values to other independent variables corresponding with theory, characteristics of the 

water resource, and the policy context. Section 4 provides a complete list of values assigned to the remaining 

independent regressors. 

As shown in Exhibit E-4 both the semi-log and trans-log models yield similar WTP for water quality changes 

greater than five points as measured by WQI. However, the semi-log model is not sensitive to very small 

water quality changes (i.e., changes less than one point on the WQI index). Because the expected water 

quality changes from the regulation are relatively small, the Agency selected the trans-log specification for 

estimating benefits from the proposed ELGs, as a more conservative option. 

Exhibit E-4: Comparison of WTP for Different Changes in WQI Based on Semi-log and 

Trans-log Models

Model

20 Point 

Change in 

WQI

10 Point 

Change in 

WQI

5 Point 

Change in 

WQI

1 Point 

Change in 

WQI

0.50 Point 

Change 

in WQI

0.10 Point 

Change 

in WQI

0.01 Point 

Change 

in WQI

Trans-log 105.0186 79.2313 59.7760 31.0738 23.4436 12.1869 4.7796

Semi-log 89.0567 64.6167 55.0407 48.4122 47.6419 47.0345 46.8989

Semi-log Error Term = 0.1599.

Trans-log Error Term =0.1876.

E.3 Model Limitations

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 model is good, EPA notes several limitations of the model. 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 
Benefit and Cost Analysis for Proposed ELGs Appendix E: Meta-Analysis 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute E-19

metals and nutrients). Preliminary models, however, suggest no systematic patterns in WTP associated with 

such factors, at least in the present metadata. 

Additional limitations relate to the paucity of demographic variables available for inclusion in the model. The 

only demographic variable incorporated in the analysis (income) was not statistically significant. Moreover, 

other demographic variables are unavailable. 

The estimated model is statistically significant 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 115-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 45 

studies in the analysis gather data from a total of 23,589 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 (the semi-log form) 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. 

The relatively large (positive) magnitude of the parameter estimate for the Pacific Mountain U.S. regional 

dummy variable (mp) leads EPA to question the appropriate interpretation of this effect. While it is 

theoretically possible that WTP for water quality changes is substantially higher in the Pacific Northwest 

(e.g., people who live in this region are outdoor enthusiasts), the magnitude of the effect suggested by the 

model seems unlikely from an intuitive perspective. As suggested above, it is possible that spurious, 

unexplained factors influence the magnitude of this parameter in the present model. However, assessments of 

preliminary model runs suggest that this effect is relatively robust given the present data and selection of 

variables available. Nonetheless, EPA recommends that the magnitude of the predicted shift in WTP 

associated with the Pacific Mountain region should be viewed with caution. 
Benefit and Cost Analysis for Proposed ELGs Appendix F: Impacts of Pollutants on Aquatic Species 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute F-1 

Appendix F: Impacts of Steam Electric Pollutants on Aquatic Species

Exhibit F-1: Common Coal-combustion Wastewater Pollutants (adapted from EPA, 2009a)

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 Proposed ELGs Appendix F: Impacts of Pollutants on Aquatic Species 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute F-2 

Exhibit F-1: Common Coal-combustion Wastewater Pollutants (adapted from EPA, 2009a)

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.

Exhibit F-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric 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

Cicurina baronia Arachnids Low

Cicurina madla Arachnids Low

Cicurina venii Arachnids Low

Cicurina vespera Arachnids Low
Benefit and Cost Analysis for Proposed ELGs Appendix F: Impacts of Pollutants on Aquatic Species 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute F-3 

Exhibit F-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Plants

Species Species Group Vulnerability

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

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
Benefit and Cost Analysis for Proposed ELGs Appendix F: Impacts of Pollutants on Aquatic Species 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute F-4 

Exhibit F-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Plants

Species Species Group Vulnerability

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

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
Benefit and Cost Analysis for Proposed ELGs Appendix F: Impacts of Pollutants on Aquatic Species 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute F-5 

Exhibit F-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Plants

Species Species Group Vulnerability

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

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
Benefit and Cost Analysis for Proposed ELGs Appendix F: Impacts of Pollutants on Aquatic Species 

April 19, 2013 Internal Draft – Deliberative, Predecisional – Do not Quote, Cite, or Distribute F-6 

Exhibit F-2: T&E Species with Habitat Overlapping Waterbodies Affected by Steam Electric Plants

Species Species Group Vulnerability

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

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 Proposed ELGs Appendix G: Sensitivity Analysis – Impoundment Failure Probability 

April 19, 2013 G-1 

Appendix G: Sensitivity of the Estimated Benefits from Avoided 

Impoundment Failures to Failure Probability

To estimate the value of avoided impoundment failures, EPA estimated an overall average failure rate based 

on data from a survey of impoundments conducted in support of regulations being developed by EPA‘s Office 

of Resource Conservation and Recovery (ORCR; see U.S. EPA, 2010d). This analysis, presented in Chapter 

7, applies a uniform probability of failure of 0.58 percent to all impoundments over the entire analysis period. 

In practice, the probability of impoundment failure may depend on impoundment characteristics. To evaluate 

the sensitivity of the impoundment failure benefits to differing assumptions, EPA used ORCR‘s survey data 

to develop a statistical model of the probability of impoundment failure as a function of the impoundment 

capacity.88 Equation G-1 shows the estimated logistic regression model. 

Equation G-1. ( ) 

 

 

( )

Where: 

 ( 

) 

Where: 

Probability of Failure(i)= the probability of impoundment i failing 

capacityi = the capacity of impoundment i

ui = unobserved effect component specific to the universe of impoundments analyzed (varies with i) 

Table G-1 presents the specifications of the logistic regression model that best fit the survey data. 

Table G-1. Logistic Regression Model

Parameter Coefficient Standard Error P>|z| 95% Confidence Interval

0

-7.0244 0.7776 0.017 0.0529 0.5279

1 0.2904 0.1212 0.000 -8.5485 -5.5002

Prob > Chi2 = 0.0166

Applying this model to the universe of 1,070 impoundments at steam electric plants subject to the ELGs and 

data on impoundment capacities under the baseline and each regulatory scenario,89 EPA conducted this 

analysis for five regulatory options: Options 1, 2, 3, 4, and 5. EPA estimated impoundment-specific failure 

rates for the years 2019 through 2040. Under all scenarios (baseline and each analyzed regulatory option), the 

probability of failure was assumed to be zero for years 2014 and 2018 due to integrity site assessments 

conducted by EPA in 2009 through 2012, which are expected to prevent all failures for the first five years 

after the recommended ―action plan‖ to improve impoundment structures is completed (2014 through 2018). 

 

88 While EPA tested other model specifications that included impoundment age as an explanatory variable, this variable 

was not statistically significant in the best-fit model. Additionally, although other factors – such as the involvement of a 

professional engineer, site-specific structural integrity inspections, statewide integrity inspection requirements, 

impoundment height, and other factors – affect the probability of failure, EPA was unable to include these variables in 

the model due to data limitations. 

89 EPA used data on impoundment capacities from its 2010 Questionnaire for the Steam Electric Power Generating 

Effluent Guidelines (the industry survey) to determine the baseline impoundment capacity and estimated changes in 

capacity based on estimated reductions in flows of wastewater managed by an impoundment under each of the regulatory 

options. 
Benefit and Cost Analysis for Proposed ELGs Appendix G: Sensitivity Analysis – Impoundment Failure Probability 

April 19, 2013 G-2 

The average annual probability of failure is 0.53 percent in the baseline, across all 1,070 impoundments at 

steam electric plants (the probability of failure ranges between 0.04 percent and 2.75 percent, depending on 

impoundment capacity). Changes due to the proposed ELGs reduce the average annual probability of failure 

to 0.46 percent under Options 1 and 2, 0.42 percent under Option 3, and 0.19 percent under Options 4 and 5. 

Table G-2 shows the results of this sensitivity analysis (in lower part of the table) as compared to results using 

the uniform average probability of failure of 0.58 percent (in upper part of the table). As shown in the table, 

the alternative approach to calculating the probability of failure for each impoundment results in higher 

benefits than using the uniform average failure rate. For example, EPA estimates that Option 3 generates 

annual avoided impoundment failure benefits valued at $367.9million, using a 3 percent discount rate ($306.6 

million using a 7 percent discount rate), which is about 3.2 times the benefits estimated using a uniform 

average failure rate. 

Table G-2. Annual Benefits of Avoided Surface Impoundment Failures (millions; 2010 $)

Regulatory Option 3% Discount Rate 7% Discount Rate

Uniform Average Failure Rate (0.58 percent for all impoundments)

Option 1 $62.1 $52.2 

Option 2 $62.1 $52.2 

Option 3 $114.8 $95.9 

Option 4 $295.1 $245.9 

Option 5 $295.1 $245.9 

Failure Probability based on Regression Results (Impoundment- and option-specific rate)

Option 1 $168.7 $142.2

Option 2 $168.7 $142.2

Option 3 $367.9 $306.6

Option 4 $918.0 $764.4

Option 5 $918.0 $764.4

a. Baseline value of total failure costs minus option value of total failure costs

Note that this analysis does not account for the effect of best management practices (BMPs) – including 

integrity inspections and preventive maintenance – that are expected to further reduce the probability of 

impoundment failures under the proposed ELGs. As discussed in Chapter 7, EPA does not have sufficient 

information to accurately quantify and monetize the benefits of implementing BMPs. Preventing all future 

impoundment failures would provide annual benefits estimated at up to $1,108.2 million (using 3 percent 

discount and applying the regression equation described in this appendix). 

EPA does not anticipate BMPs to fully realize such benefits, however. Inspections and other BMPs aim to 

prevent future failures by identifying conditions that have contributed to past impoundment failures and 

releases (e.g., slope instability, structural defects, seepage, overtopping, and inadequate management 

practices; see NRC, 2002); they are not expected to be as effective at preventing impoundment failures caused 

by unusual weather events or earthquakes. Further, data from the Steam Electric Industry Survey and 2010 

CCR Impoundment Survey suggest that steam electric plants already implement inspections and monitoring 

programs of varying scope and frequency (U.S. EPA, 2010e; U.S. EPA, 2012d), but field assessments 

conducted subsequent to the 2010 CCR Impoundment Survey indicate that the existing inspection programs at 

some plants failed to identify embankment erosion, seepage, and other conditions (U.S. EPA; 2012e).
Benefit and Cost Analysis for Proposed ELGs Appendix H: C-R Function for Analysis of Air-related Benefits 

April 19, 2013 H-1 

Appendix H: Concentration Response Function Used in the Analysis of 

Air-Related Benefits

Most of the concentration response (C-R) functions relating criteria air pollutants to population incidence of 

an adverse health effect are log-linear (or exponential) in form: 

Equation H-1. 

x

y e



 

where x is the ambient air pollutant concentration (PM2.5 or O3 in this analysis), y is the incidence of the 

adverse health effect corresponding to x, β is the coefficient of ambient concentration of the air pollutant 

(describing the extent of change in y with a unit change in x), and α is the incidence of the adverse health 

effect when there is no ambient air pollutant. Each epidemiological study provides b (an estimate of β). 

Let x0 denote the baseline (upper) level of the ambient air pollutant and x1 denote the ―control scenario‖ 

(lower) level. In addition, let y0 denote the baseline incidence of the health effect (corresponding to the 

baseline ambient pollutant level, x0) and y1 denote the incidence after the rule is implemented, corresponding 

to ambient pollutant level, x1. Equation H-1 and the estimate, b, can be used to derive the following estimated 

relationship between the absolute reduction in ambient air pollutant level, Δx = (x0 – x1), and the 

corresponding reduction in health effect incidence, Δy: 

Equation H-2.    

b x

y y y y e

    0  1  0

 1

For this analysis, EPA used the values shown in Table H-1. 

Table H-1: Summary of Studies and Concentration-Response Functions Used to Estimate

PM2.5-Related Benefits 

Health Endpoint Study Location Age Range

PM2.5

Coefficient

(Beta)

Standard Error

Mortality, All 

Causes

Krewski et al. 

(2009)

116 U.S. cities 30+ 0.00583 0.00096
Benefit and Cost Analysis for Proposed ELGs Appendix I: CO2-related Benefits using Alternate SCC Values 

April 19, 2013 I-1 

Appendix I: CO2-Related Benefits using Alternate Social Cost of Carbon 

Values

As discussed in Chapter 8, the Interagency Working Group on Social Cost of Carbon (IWGSCC, 2010) 

developed social cost of carbon (SCC) values for three discount rates: 2.5 percent (average), 3 percent 

(average and 95th percentile), and 5 percent (average). 

In this appendix, we present the annualized benefits from reduced CO2 emissions using alternate SCC values. 

Note that EPA used the SCC for the 3 percent discount rate (average) and 5 percent discount rate (average) to 

estimate benefits presented in Chapter 8. 

Table I-1. Annualized Benefits from Reduced CO2 Emissions (Millions; 2010$)

Regulatory Option 5% Discount Rate, 

Average

3% Discount Rate, 

Average

2.5% Discount Rate, 

Average

3% Discount Rate, 

95th percentile

Option 3 $8.8 $33.6 $52.7 $102.5

Option 4 $24.7 $94.6 $148.7 $288.8