United States
Environmental Protection
Agency
Office of Water EPA-821 -R-24-006
Washington, DC 20460 April 18, 2024
<&EPA Benefit and Cost Analysis for
Supplemental Effluent
Limitations Guidelines and
Standards for the Steam
Electric Power Generating
Point Source Category
-------
v»EPA
United States
Environmental Protection
Agency
Benefit and Cost Analysis for Supplemental
Effluent Limitations Guidelines and Standards
for the Steam Electric Power Generating Point
Source Category
EPA-821-R-24-006
April 18, 2024
U.S. Environmental Protection Agency
Office of Water (4303T)
Engineering and Analysis Division
1200 Pennsylvania Avenue, NW
Washington, DC 20460
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BCAfor Supplemental Steam Electric Power Generating ELGs
Acknowledgements and Disclaimer
This report was prepared by the U.S. Environmental Protection Agency. Neither the United States
Government nor any of its employees, contractors, subcontractors, or their employees make any warranty,
expressed or implied, or assume any legal liability or responsibility for any third party's use of or the results
of such use of any information, apparatus, product, or process discussed in this report, or represents that its
use by such party would not infringe on privately owned rights.
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BCA for Supplemental Steam Electric Power Generating ELGs Table of Contents
Table of Contents
Table of Contents i
List of Figures vi
List of Tables vii
Abbreviations xi
Executive Summary 1
1 Introduction 1-1
1.1 Steam Electric Power Plants 1-2
1.2 Baseline and Regulatory Options Analyzed 1-2
1.3 Analytic Framework 1-4
1.3.1 Constant Prices 1-5
1.3.2 Discount Rate and Year 1-5
1.3.3 Period of Analysis 1-5
1.3.4 Timing of Technology Installation and Loading Reductions 1-6
1.3.5 Annualization of future costs and benefits 1-6
1.3.6 Population and Income Growth 1-6
1.4 Organization of the Benefit and Cost Analysis Report 1-7
2 Benefits Overview 2-1
2.1 Human Health Impacts Associated with Changes in Surface Water Quality 2-4
2.1.1 Drinking Water 2-4
2.1.2 Fish Consumption 2-6
2.1.3 Complementary Measure of Human Health Impacts 2-8
2.2 Ecological and Recreational Impacts Associated with Changes in Surface Water Quality 2-9
2.2.1 Changes in Surface Water Quality 2-10
2.2.2 Impacts on Threatened and Endangered Species 2-11
2.2.3 Changes in Sediment Contamination 2-12
2.3 Water Supply and Use 2-12
2.3.1 Drinking Water Treatment Costs 2-12
2.3.2 Effects on Household Averting Expenditure 2-15
2.3.3 Irrigation and Other Agricultural Uses 2-15
2.4 Other Economic Effects 2-16
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BCA for Supplemental Steam Electric Power Generating ELGs Table of Contents
2.4.1 Reservoir Capacity 2-16
2.4.2 Sedimentation Changes in Navigational Waterways 2-16
2.4.3 Commercial Fisheries 2-17
2.4.4 Tourism 2-18
2.4.5 Property Values 2-18
2.5 Changes in Air Pollution 2-19
2.6 Summary of Benefits Categories 2-21
3 Water Quality Effects of Regulatory Options 3-1
3.1 Waters Affected by Steam Electric Power Plant Discharges 3-1
3.2 Changes in Pollutant Loadings 3-2
3.2.1 Implementation Timing 3-2
3.2.2 Results 3-3
3.3 Water Quality Downstream from Steam Electric Power Plants 3-7
3.4 Overall Water Quality Changes 3-8
3.4.1 WQI Data Sources 3-8
3.4.2 WQI Calculation 3-10
3.4.3 Baseline WQI 3-11
3.4.4 Estimated Changes in Water Quality (AWQI) from the Regulatory Options 3-11
3.5 Limitations and Uncertainty 3-12
4 Human Health Benefits from Changes in Pollutant Exposure via the Drinking Water Pathway 4-1
4.1 Background 4-1
4.2 Overview of the Analysis 4-2
4.3 Estimates of Changes in Halogen Concentrations in Source Water 4-4
4.3.1 Step 1: Modeling Bromide Concentrations in Surface Water 4-4
4.3.2 Step 2: Modeling Changes in Trihalomethanes in Treated Water Supplies 4-5
4.3.3 Step 3: Quantifying Population Exposure and Health Effects 4-11
4.3.4 Step 4: Quantifying the Monetary Value of Benefits 4-17
4.4 Results of Analysis of Human Health Benefits from Estimated Changes in Bromide Discharges
Analysis 4-18
4.5 Additional Measures of Human Health Effects from Exposure to Steam Electric Pollutants via
Drinking Water Pathway 4-20
4.6 Limitations and Uncertainties 4-22
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BCA for Supplemental Steam Electric Power Generating ELGs Table of Contents
5 Human Health Effects from Changes in Pollutant Exposure via the Fish Ingestion Pathway .... 5-1
5.1 Population in Scope of the Analysis 5-2
5.2 Pollutant Exposure from Fish Consumption 5-4
5.2.1 Fish Tissue Pollutant Concentrations 5-4
5.2.2 Average Daily Dose 5-5
5.3 Health Effects in Children from Changes in Lead Exposure 5-6
5.3.1 Data and Methodology 5-7
5.3.2 Results 5-9
5.4 Health Effects in Adults from Changes in Lead Exposure 5-9
5.4.1 Data and Methodology 5-10
5.4.2 Results 5-12
5.5 Heath Effects in Children from Changes in Mercury Exposure 5-13
5.5.1 Data and Methodology 5-13
5.5.2 Results 5-14
5.6 Estimated Changes in Cancer Cases from Arsenic Exposure 5-15
5.7 Monetary Values of Estimated Changes in Human Health Effects 5-15
5.8 Additional Measures of Potential Changes in Human Health Effects 5-16
5.9 Limitations and Uncertainties 5-17
6 Nonmarket Benefits from Water Quality Changes 6-1
6.1 Estimated Total WTP for Water Quality Changes 6-1
6.2 Sensitivity Analysis 6-3
6.3 Limitations and Uncertainties 6-4
7 Impacts and Benefits to Threatened and Endangered Species 7-1
7.1 Introduction 7-1
7.2 Baseline Status of Freshwater Fish Species 7-2
7.3 T&E Species Potentially Affected by the Regulatory Options 7-2
7.3.1 Identifying T&E Species Potentially Affected by the Regulatory Options 7-2
7.3.2 Estimating Effects of the Rule on T&E Species 7-3
7.4 Limitations and Uncertainties 7-6
8 Air Quality-Related Benefits 8-1
8.1 Changes in Air Emissions 8-1
8.2 Climate Change Benefits 8-5
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BCA for Supplemental Steam Electric Power Generating ELGs Table of Contents
8.2.1 Data and Methodology 8-5
8.2.2 Results 8-13
8.3 Human Health Benefits 8-19
8.3.1 Data and Methodology 8-19
8.3.2 Results 8-23
8.4 Annualized Air Quality-Related Benefits of Regulatory Options 8-2
8.5 Limitations and Uncertainties 8-4
9 Estimated Changes in Drinking Water Treatment and Dredging Costs 9-1
9.1 Changes in Drinking Water Treatment Costs 9-1
9.1.1 Data and Methodology 9-1
9.1.2 Results 9-4
9.2 Changes in Dredging Costs 9-6
9.2.1 Data and Methodology 9-7
9.2.2 Results 9-7
9.3 Limitation and Uncertainty 9-9
10 Summary of Estimated Total Monetized Benefits 10-1
11 Summary of Total Social Costs 11-1
11.1 Overview of Costs Analysis Framework 11-1
11.2 Key Findings for Regulatory Options 11-3
12 Benefits and Social Costs 12-1
12.1 Comparison of Benefits and Costs by Option 12-1
12.2 Analysis of Incremental Benefits and Costs 12-1
13 Cited References 13-1
A Changes to Benefits Methodology since 2020 Final Rule Analysis A-l
B Estimated Costs and Benefits Using Discount Rates from the Proposal B-l
C WQI Calculation and Regional Subindices C-l
D Additional Details on Modeling Change in Bladder Cancer Incidence from Change in TTHM
Exposure D-l
E Derivation of Ambient Water and Fish Tissue Concentrations in Downstream Reaches E-l
F Georeferencing Surface Water Intakes to the Medium-resolution Reach Network F-l
G Sensitivity Analysis for IQ Point-based Human Health Effects G-l
H Methodology for Estimating WTP for Water Quality Changes H-l
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BCA for Supplemental Steam Electric Power Generating ELGs Table of Contents
I Identification of Threatened and Endangered Species Potentially Affected by the Final Rule
Regulatory Options 1-1
J Methodology for Modeling Air Quality Changes for the Final Rule J-l
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BCAfor Supplemental Steam Electric Power Generating ELGs
List of Figures
List of Figures
Figure 2-1: Summary of Estimated Benefits Resulting from the Regulatory Options 2-3
Figure 4-1: Overview of Analysis of Estimated Human Health Benefits of Reducing Bromide Discharges. 4-3
Figure 4-2: Modeled Relationship between Changes in Bromide Concentration and Changes in TTHM
Concentrations based on Median Values in Regli et al. (2015) 4-10
Figure 4-3: Estimated Number of Bladder Cancer Cases Avoided under the Regulatory Options 4-19
Figure 4-4: Estimated Number of Cancer Deaths Avoided under the Regulatory Options 4-19
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BCAfor Supplemental Steam Electric Power Generating ELGs
List of Tables
List of Tables
Table 1-1: Regulatory Options Analyzed for the Final Rule 1-3
Table 2-1: Estimated Baseline Annual Pollutant Loadings and Changes in Loadings for Regulatory Options
Under Technology Implementation 2-1
Table 2-2: Drinking Water Maximum Contaminant Levels and Goals for Selected Pollutants in Steam Electric
FGD Wastewater, BA Transport Water, CRL, and Legacy Wastewater Discharges 2-4
Table 2-3: Estimated Welfare Effects of Changes in Pollutant Discharges from Steam Electric Power Plants 2-
21
Table 3-1: Annual Average Reductions in Total Pollutant Loading in Period 1 (2025-2029) and Period 2
(2030-2049) for Selected Pollutants in Steam Electric Power Plant Discharges, Compared to Baseline
(lb/year) 3-5
Table 3-2: Estimated Exceedances of National Recommended Water Quality Criteria under the Baseline and
Regulatory Options 3-9
Table 3-3: Water Quality Data used in Calculating WQI for the Baseline and Regulatory Options 3-10
Table 3-4: Estimated Percentage of Potentially Affected Reach Miles by WQI Classification: Baseline
Scenario 3-11
Table 3-5: Ranges of Estimated Water Quality Changes for Regulatory Options, Compared to Baseline ... 3-12
Table 3-6: Limitations and Uncertainties in Estimating Water Quality Effects of Regulatory Options 3-13
Table 4-1: Estimated Bromide Loading Reductions by Analysis Period and Regulatory Option 4-5
Table 4-2: Estimated Reaches, Surface Water Intakes, Public Water Systems, and Populations Potentially
Affected by Steam Electric Power Plant Discharges 4-6
Table 4-3: Estimated Distribution of Changes in Source Water and PWS-Level Bromide Concentrations by
Period and Regulatory Option, Compared to Baseline 4-8
Table 4-4: Estimated Increments of Change in TTHM Levels (j^ig/L) as a Function of Change in Bromide
Levels ((ig/L) 4-9
Table 4-5: Distribution of Estimated Changes in TTHM Concentration by the Number of PWS and
Population Served 4-10
Table 4-6: Summary of Data Sources Used in Lifetime Health Risk Model 4-14
Table 4-7: Summary of Sex- and Age-specific Mortality and Bladder Cancer Incidence Rates 4-16
Table 4-8: Estimated Bromide-related Bladder Cancer Mortality and Morbidity Monetized Benefits 4-20
Table 4-9: Estimated Distribution of Changes in Source Water and PWS-Level Arsenic, Lead, and Thallium
Concentrations by Period and Regulatory Option, Compared to Baseline 4-21
Table 4-10: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway 4-22
Table 5-1: Summary of Population Potentially Exposed to Contaminated Fish Living within 50 Miles of
Affected Reaches (as of 2021) 5-4
vii
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BCA for Supplemental Steam Electric Power Generating ELGs List of Tables
Table 5-2: Summary of Group-specific Consumption Rates for Fish Tissue Consumption Risk Analysis .... 5-5
Table 5-3: Value of an IQ Point (2023$) based on Expected Reductions in Lifetime Earnings, 2 Percent
Discount Rate 5-9
Table 5-4: Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead under the Regulatory
Options, Compared to Baseline 5-9
Table 5-5: Estimated Average Body Weights (kg) by Age and Gender 5-10
Table 5-6: Baseline Hazard Rates of CVD Mortality by Age and Gender 5-12
Table 5-7: Estimated Benefits from Avoided CVD Deaths for Adults Aged 40-80 For All Regulatory Options,
Compared to Baseline 5-13
Table 5-8: Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure under the
Regulatory Options, Compared to Baseline 5-15
Table 5-9: Estimated Benefits of Changes in Human Health Outcomes Associated with Fish Consumption
under the Regulatory Options, Compared to Baseline (Millions of2023$; 2% Discount Rate) 5-15
Table 5-10: Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric Pollutants . 5-
16
Table 5-11: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish Ingestion
Pathway 5-17
Table 6-1: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality Improvements
under the Regulatory Options, Compared to Baseline (Main Estimates) 6-3
Table 6-2: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality Changes under
the Regulatory Options, Compared to Baseline (Sensitivity Analysis) 6-4
Table 6-3: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits 6-4
Table 7-1: Number of T&E Species with Habitat Range Intersecting Reaches Immediately Receiving or
Downstream of Steam Electric Power Plant Discharges, by Group 7-3
Table 7-2: Higher Vulnerability T&E Species Whose Habitat May be Affected by the Regulatory Options
Compared to Baseline (Shading Highlights Change from Baseline) 7-5
Table 7-3: Limitations and Uncertainties in the Analysis of T&E Species Impacts and Benefits 7-7
Table 8-1: IPM Run Years 8-2
Table 8-2: Estimated Changes in Air Pollutant Emissions Due to Increase in Power Requirements at Steam
Electric Power Plants 2025-2049, Compared to Baseline 8-2
Table 8-3: Estimated Changes in Air Pollutant Emissions Due to Increase in Trucking at Steam Electric
Power Plants 2025-2049, Compared to Baseline 8-3
Table 8-4: Estimated Changes in Pollutant Emissions Due to Changes in Electricity Generation Profile,
Compared to Baseline 8-4
Table 8-5: Estimated Net Changes in Air Pollutant Emissions Due to Changes in Power Requirements,
Trucking, and Electricity Generation Profile, Compared to Baseline 8-4
viii
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BCAfor Supplemental Steam Electric Power Generating ELGs
List of Tables
Table 8-6: Estimates of the Social Cost of Greenhouse Gas by Year and Near-Term Ramsey Discount Rate,
2025-2049 8-12
Table 8-7: Estimated Undiscounted and Total Present Value of Climate Benefits from Changes in CO2 and
CH4 Emissions under the Final Rule, Compared to Baseline (Millions of 2023$) 8-14
Table 8-8: Estimated Annualized Climate Benefits from Changes in CO2 and CH4 Emissions under the Final
Rule during the Period of 2025-2049 by Categories of Air Emissions and SC-GHG Estimates, Compared
to Baseline (Millions of 2023$) 8-15
Table 8-9: Human Health Effects of Ambient Ozone and PM2 5 8-21
Table 8-10: Estimated Avoided PM2 5 and Ozone-Related Premature Deaths and Illnesses by Year for the
Final Rule (Option B), Compared to Baseline (95 Percent Confidence Interval) 8-0
Table 8-11: Estimated Discounted Economic Value of Avoided Ozone and PM2 5-Attributable Premature
Mortality and Illness for Option B (millions of 2023$) 8-2
Table 8-12: Total Annualized Air Quality-Related Benefits of Final Rule (Option B), Compared to the
Baseline, 2025-2049 (Millions of 2023$) 8-3
Table 8-13: Total Annualized Air Quality-Related Benefits of Regulatory Options Based on Extrapolation
from Option B, Compared to the Baseline, 2025-2049 (Millions of 2023$) 8-3
Table 8-14: Limitations and Uncertainties in Analysis of Air Quality-Related Benefits 8-4
Table 9-1: Average Percent Change in Source Water Concentrations of TN, TP, and TSS Compared to
Baseline 9-2
Table 9-2: Median Drinking Water Treatment Expenditures by System Size and Source Category 9-3
Table 9-3: Annualized Estimated Drinking Water Treatment Cost Savings under the Regulatory Options,
Compared to Baseline (Million 2023$, 2 Percent Discount Rate) 9-4
Table 9-4: Estimated Average System-Level Annual Changes in Drinking Water Treatment Costs for TN
under the Regulatory Options, Compared to Baseline (2023$) 9-4
Table 9-5: Estimated Average System-Level Annual Changes in Drinking Water Treatment Costs for TSS
under the Regulatory Options, Compared to Baseline (2023$) 9-6
Table 9-6: Estimated Annual Average Navigational Dredging Quantities and Costs at Affected Reaches Based
on Historical Averages 9-8
Table 9-7: Estimated Annualized Changes in Navigational Dredging Costs under the Regulatory Options,
Compared to Baseline 9-8
Table 9-8: Estimated Annualized Reservoir Dredging Volume and Costs based on Historical Averages 9-8
Table 9-9: Estimated Total Annualized Changes in Reservoir Dredging Volume and Costs under the
Regulatory Options, Compared to Baseline 9-9
Table 9-10: Limitations and Uncertainties in Analysis of Changes in Dredging Costs 9-9
Table 10-1: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared to
Baseline (Millions of 2023$; 2 Percent Discount) 10-1
Table 10-2: Time Profile of Monetized Benefits (Millions of 2023$) 10-2
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BCA for Supplemental Steam Electric Power Generating ELGs List of Tables
Table 11-1: Summary of Estimated Incremental Annualized Costs for Regulatory Options (Millions of 2023$,
2 Percent Discount Rate) 11-3
Table 11-2: Time Profile of Costs to Society (Millions of 2023$) - Lower Bound 11-3
Table 11-3: Time Profile of Costs to Society (Millions of 2023$) - Upper Bound 11-4
Table 12-1: Total Estimated Annualized Benefits and Costs by Regulatory Option and Discount Rate,
Compared to Baseline (Millions of 2023$, 2 Percent Discount Rate) 12-1
Table 12-2: Analysis of Estimated Incremental Net Benefit of the Regulatory Options, Compared to Baseline
and to Other Regulatory Options (Millions of 2023$, 2 Percent Discount Rate) 12-2
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BCAfor Supplemental Steam Electric Power Generating ELGs
Abbreviations
Abbreviations
ACS
American Community Survey
ADD
Average daily dose
ALE
Action level exceedance
As
Arsenic
ATSDR
Agency for Toxic Substances and Disease Registry
BA
Bottom ash
BAT
Best available technology economically achievable
BCA
Benefit-cost analysis
BEA
Bureau of Economic Analysis
BenMAP-CE Environmental Benefits Mapping and Analysis Program—Community Edition
BLL
Blood lead level
BLS
Bureau of Labor Statistics
BMP
Best management practices
BOD
Biochemical oxygen demand
BW
Body weight
CAMx
Comprehensive Air Quality Model with Extensions
CBG
Census Block Group
CCI
Construction Cost Index
CCME
Canadian Council of Ministers of the Environment
CCR
Coal combustion residuals
CDC
Center for Disease Control
CFR
Code of Federal Regulations
CIL
Climate Impact Lab
C02
Carbon dioxide
COD
Chemical oxygen demand
COI
Cost-of-illness
COPD
Chronic obstructive pulmonary disease
CPI
Consumer Price Index
CWA
Clean Water Act
CWS
Community Water System
CWWS
Community Water System Survey
D-FATE
Downstream Fate and Transport Equations
DBP
Disinfection byproduct
DBPR
Disinfectants and Disinfection Byproduct Rule
DCN
Document Control Number
DICE
Dynamic Integrated Climate and Economy
DO
Dissolved oxygen
DSCIM
Data-driven Spatial Climate Impact Model
E2RF1
Enhanced River File 1
EA
Environmental Assessment
EC
Elemental carbon
ECI
Employment Cost Index
xi
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BCAfor Supplemental Steam Electric Power Generating ELGs
Abbreviations
ECOS
Environmental Conservation Online System
EG
Emissions guidelines
EGU
Electricity generating unit
EJ
Environmental justice
ELGs
Effluent limitations guidelines and standards
EO
Executive Order
EPA
United States Environmental Protection Agency
EROM
Enhanced Runoff Method
ESA
Endangered Species Act
FaIR
Finite Amplitude Impulse Response
FC
Fecal coliform
FCA
Fish consumption advisories
FGD
Flue gas desulfurization
FUND
Climate Framework for Uncertainty, Negotiation, and Distribution
FR
Federal Register
FrEDI
Framework for Evaluating Damages and Impacts
GDP
Gross Domestic Product
GHG
Greenhouse gas
GIS
Geographic Information System
GIVE
Greenhouse Gas Impact Value Estimator
GMSL
Global mean sea level
GWP
Global warming potential
HAP
Hazardous air pollutant
HC1
Hydrogen chloride
Hg
Mercury
HRTR
High Residence Time Reduction
HUC
Hydrologic unit code
IAM
Integrated assessment model
IBI
Index of biotic integrity
IEUBK
Integrated Exposure, Uptake, and Biokinetics
IPCC
Intergovernmental Panel on Climate Change
IPM
Integrated Planning Model
ISA
Integrated science assessment
ISI
Influential Scientific Information
IRIS
Integrated Risk Information System
IQ
Intelligence quotient
LADD
Lifetime average daily dose
LML
Lowest measured level
LRTR
Low Residence Time Reduction
MATS
Mercury and Air Toxics Standards
MCL
Maximum contaminant level
MCLG
Maximum contaminant level goal
MDA1
Maximum daily 1-hour average
MDA8
Maximum daily 8-hour average
xii
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BCAfor Supplemental Steam Electric Power Generating ELGs
Abbreviations
MGD Million gallons per day
MRM Meta-regression model
NAAQS National Ambient Air Quality Standards
NARS National Aquatic Resources Survey
NEI National Emissions Inventory
NERC North American Electric Reliability Corporation
NHD National Hydrography Dataset
NLCD National Land Cover Dataset
NLFA National Listing Fish Advisory
NOAA National Oceanic and Atmospheric Administration
NOAEL No observed adverse effect level
NOx Nitrogen oxides
NPDES National Pollutant Discharge Elimination System
NRSA National Rivers and Streams Assessment
NRWQC National Recommended Water Quality Criteria
NSPS New source performance standard
NTU Nephelometric turbidity units
NWIS National Water Information System
03 Ozone
03V Ozone formed in VOC-limited chemical regimes
03N Ozone formed in NOx-limited chemical regimes
OA Organic aerosol
O&M Operation and maintenance
OMB Office of Management and Budget
OSAT/APCA Ozone Source Apportionment Technique/Anthropogenic Precursor Culpability Assessment
OWTP Willingness-to-pay for a one-point WQI improvement (one-point WTP)
PACE Policy Analysis of the Greenhouse Gas Effect
Pb Lead
PM2 5 Particulate matter (fine inhalable particles with diameters 2.5 |a,m and smaller)
PM10 Particulate matter (inhalable particles with diameters 10 |a,m and smaller)
ppm parts per million
PSAT Particulate Source Apportionment Technique
PSES Pretreatment Standards for Existing Sources
PV Present value
PWS Public water system
QA Quality assurance
QC Quality control
RIA Regulatory Impact Analysis
RFF Resources for the Future
SAB-HES Science Advisory Board Health Effect Subcommittee
SBREFA Small Business Regulatory Enforcement Fairness Act
SC-CO2 Social cost of carbon
SDWIS Safe Drinking Water Information System
Se Selenium
xiii
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BCAfor Supplemental Steam Electric Power Generating ELGs
Abbreviations
so2
Sulfur dioxide
SPARROW
SPAtially Referenced Regressions On Watershed attributes
ssc
Suspended solids concentration
SWFSC
Southwest Fisheries Science Center
T&E
Threatened and endangered
TDD
Technical Development Document
TDS
Total dissolved solids
TEC
Threshold effect concentration
TN
Total nitrogen
TP
Total phosphorus
TRI
Toxics Release Inventory
TSD
Technical support document
TSS
Total suspended solids
TTHM
Total trihalomethanes
TWTP
Total willingness-to-pay
U.S. FWS
United States Fish and Wildlife Service
USGS
United States Geological Survey
VIP
Voluntary Incentive Program
VOC
Volatile organic compounds
VSL
Value of a statistical life
WBD
Watershed Boundary Dataset
WQ
Water quality
WQI
Water quality index
WQI-BL
Baseline water quality index
WQI-PC
Post-technology implementation water quality index
WQL
Water quality ladder
WTP
Willingness-to-pay
xiv
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BCAfor Supplemental Steam Electric Power Generating ELGs
Executive Summary
Executive Summary
The U.S. Environmental Protection Agency (EPA) is finalizing revisions to the technology-based effluent
limitations guidelines and standards (ELGs) for the steam electric power generating point source category,
40 Code of Federal Regulations (CFR) part 423, which EPA promulgated in October 2020 (85 FR 64650).
The final rule revises certain best available technology economically achievable (BAT) effluent limitations
and pretreatment standards for existing sources (PSES) for three wastestreams: flue gas desulfurization (FGD)
wastewater, bottom ash (BA) transport water, and combustion residual leachate (CRL). EPA also sets new
source performance standards and pretreatment standards for new sources for CRL.1
Regulatory Options
EPA analyzed three regulatory options, summarized in Table ES-1. The options are labeled Option A through
Option C according to increasing stringency. All options include the same general technology basis for FGD
wastewater and BA transport water (zero discharge) and for CRL (chemical precipitation) but differ in terms
of the technology basis applicable to certain subcategories. For example, all three options use surface
impoundments as the basis for units retiring by 2028, and options A and B use chemical precipitation with
biological treatment for FGD wastewater or High Recycle Rate Systems (HRR) for BA transport water as the
bases for units retiring by 2034. Options B and C also use chemical precipitation as the basis for legacy
wastewater. EPA is finalizing ELGs based on Option B.
The baseline for the benefit and social cost analyses reflects existing ELG requirements in absence of this
EPA action, i.e., the 2020 ELG. As detailed in this report, EPA calculated the difference between the baseline
and regulatory Options A through C to determine the net incremental effect of the regulatory options. In
general, the regulatory options are estimated to result in smaller pollutant loads, improved environmental
conditions, and net benefits.
Benefits of Regulatory Options
EPA estimated the potential social welfare effects of the regulatory options and, where possible, quantified
and monetized the benefits (see Chapters 3 through 9 for details of the methodology and results). Table ES-2
summarizes the benefits that EPA quantified and monetized.
EPA quantified but did not monetize other welfare effects of the regulatory options and discusses other effects
only qualitatively. Chapter 2 presents additional information on these welfare effects
EPA does not expect, and is not aware of, any planned new sources that would be subject to the requirements of this final rule.
ES-1
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BCAfor Supplemental Steam Electric Power Generating ELGs
Executive Summary
Table ES-1: Regulatory Options Analyzed for the Final Rule
Wastestream
Subcategory
Technology Basis for BAT/PSES Regulatory Options3
Baseline
(2020 Rule)
Option A
Option B
(Final Rule)
Option C
FGD
Wastewater
NA (default unless in subcategory)15
CP + Bio
ZLD
ZLD
ZLD
Boilers permanently ceasing the combustion
of coal by 2028
SI
SI
SI
SI
Boilers permanently ceasing the combustion
of coal by 2034
NS
CP + Bio
CP + Bio
NS
High FGD Flow Facilities or Low Utilization
Boilers
CP
NS
NS
NS
BA Transport
Water
NA (default unless in subcategory)15
HRR
ZLD
ZLD
ZLD
Boilers permanently ceasing the combustion
of coal by 2028
SI
SI
SI
SI
Boilers permanently ceasing the combustion
of coal by 2034
NS
HRR
HRR
NS
Low Utilization Boilers
BMP Plan
NS
NS
NS
CRL
NA (default)15
BPJ
CP
ZLD
ZLD
Discharges of unmanaged CRL
NA
NS
CP
CP
Boilers permanently ceasing the combustion
of coal by 2034
NA
CP
CP
NS
Legacy
Wastewater
Operate after 2024
NA
NS
CP
CP
Abbreviations: BMP = Best Management Practice; CP = Chemical Precipitation; HRR = High Recycle Rate Systems; SI = Surface Impoundment; ZLD = Zero Liquid Discharge; NS = Not
subcategorized (default technology basis applies); NA = Not applicable
a. See TDD for a description of these technologies (U.S. EPA, Agency for Toxic Substances and Disease Registry, 2009; Grandjean et al., 2014; Hollingsworth & Rudik, 2021; Mergler et
al., 2007; 2024f).
b. The table does not present existing subcategories included in the 2015 and 2020 rules as EPA did not reopen the existing subcategorization of oil-fired units or units with a
nameplate capacity of 50 MW or less.
Source: U.S. EPA Analysis, 2024
ES-2
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BCAfor Supplemental Steam Electric Power Generating ELGs
Executive Summary
Table ES-2: Summary of Total Annualized Benefits for Regulatory Options, Compared to Baseline
(Millions of 2023$; 2 Percent Discount)
Benefit Category
Option A
Option B
(Final Rule)
Option C
Human Health
Changes in IQ losses in children from exposure to lead via
fish ingestion3
<$0.01
<$0.01
<$0.01
Changes in cardiovascular disease premature mortality
from exposure to lead via fish ingestion
$0.16-$0.43
$0.16-$0.43
$0.16-$0.45
Changes in IQ losses in children from exposure to mercury
via fish ingestion
$1.71
$1.98
$2.00
Changes in cancer risk from disinfection by-products in
drinking water
$13.37
$13.37
$14.27
Ecological Conditions and Recreational Uses Changes
Use and nonuse values for water quality changes'5
$0.79
$1.24
$1.68
Market and Productivity Effects3
Changes in drinking water treatment costs
$0.45-$0.54
$0.46-$0.55
$0.59-$0.71
Changes in dredging costs3
<$0.01
<$0.01
<$0.01
Air Quality-Related Effects
Climate change effects from changes in greenhouse gas
emissions0
$1,200
$1,600
$1,900
Human health effects from changes in NOx, S02, and PM2.5
emissions'^
$1,200
$1,600
$2,000
Total®
$2,417
$3,217
$3,919
Additional non-monetized benefits
Other avoided adverse health effects (cancer and non-
cancer) from reduced exposure to pollutants discharged to
receiving waters; improvements in T&E species habitat and
potential effects on T&E species populations; changes in
property value from water quality improvements; changes
in ecosystem effects, visibility impairment, and human
health effects from direct exposure to N02, S02, and
hazardous air pollutants.
a. "<$0.01" indicates that monetary values are greater than $0 but less than $0.01 million.
b. Value reflects the main willingness-to-pay estimates. See Chapter 6 for details.
c. Values for air-quality related effects are rounded to two significant figures. EPA estimated the air quality-related benefits for
Option B. EPA extrapolated estimates of air quality-related benefits for Options A and C from the estimate for Option B that is based
on IPM outputs. See Chapter 8 for details.
d. The values reflect the LT estimates of human health effects from changes in PM2.5 and ozone levels. See Chapter 8 for details.
e. Values for individual benefit categories may not sum to the total due to independent rounding.
Source: U.S. EPA Analysis, 2024
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BCAfor Supplemental Steam Electric Power Generating ELGs
Executive Summary
Social Costs of Regulatory Options
Table ES-3 (below) presents the incremental social costs attributable to the regulatory options, calculated as
the difference between each option and the baseline. The regulatory options generally result in additional
costs across regulatory options and discount rates. Chapter 11 describes the social cost analysis. The
compliance costs of the regulatory options are detailed in the Regulatory Impact Analysis (RIA) (U.S. EPA,
2023k).
Comparison of Benefits and Social Costs of Regulatory Options
In accordance with the requirements of Executive Order (E.O.) 12866: Regulatory Planning and Review, as
amended by E.O. 13563: Improving Regulation and Regulatory Review and E.O. 14094: Modernizing
Regulatory Review. EPA compared the benefits and costs of each regulatory option. Table ES-4 presents the
monetized benefits and social costs attributable to the regulatory options, calculated as the difference between
each option and the baseline. The total social costs are presented as a range to reflect uncertainty regarding the
costs to meet limits for unmanaged CRL.
Table ES-3: Total Annualized Benefits and Social Costs by Regulatory Option and Discount Rate
(Millions of 2023$; 2 Percent Discount)
Regulatory Option
Total Monetized Benefits3'13
Total Social Costs3
Lower Bound
Upper Bound
Option A
$2,417
$433.2
$960.9
Option B (Final Rule)
$3,217
$536.2
$1,063.9
Option C
$3,919
$622.4
$1,150.1
a. EPA's benefits analysis did not account for the effects of loading reductions associated with limits for unmanaged CRL and legacy
wastewater, whereas the total costs account for outlays for meeting these limits. See Chapter 11 for details on the lower and upper
bound cost scenarios.
b. EPA estimated the air quality-related benefits for the final rule (Option B) only. EPA extrapolated estimates of air quality-related
benefits for Options A and C from the estimate for Option B that is based on IPM outputs. See Chapter 8 for details.
Source: U.S. EPA Analysis, 2024.
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BCAfor Supplemental Steam Electric Power Generating ELGs
1: Introduction
1 Introduction
EPA is finalizing revisions to the technology-based ELGs for the steam electric power generating point source
category, 40 CFRpart 423, which EPA previously proposed in March 29, 2023 (88 FR 18824). The final rule
revises certain effluent limitations promulgated in October 2020 (85 FR 64650) based on BAT and
pretreatment standards for existing sources for four wastestreams: flue gas desulphurization (FGD)
wastewater, bottom ash (BA) transport water, combustion residual leachate (CRL), and legacy wastewater.
EPA also sets new source performance standards and pretreatment standards for new sources for CRL.2
This document presents an analysis of the benefits and social costs of the regulatory options and complements
other analyses EPA conducted in support of this final rule, described in separate documents:
• Environmental Assessment for Supplemental Effluent Guidelines and Standards for the Steam
Electric Power Generating Point Source Category (EA; U.S. EPA, 2024b). The EA summarizes the
potential environmental and human health impacts that are estimated to result from the regulatory
options.
• Technical Development Document for Supplemental Effluent Guidelines and Standards for the Steam
Electric Power Generating Point Source Category (TDD; U.S. EPA, 2024f). The TDD summarizes
the technical and engineering analyses supporting the final rule. The TDD presents EPA's updated
analyses supporting the revisions to limitations and standards applicable to discharges of FGD
wastewater, BA transport water, leachate, and legacy wastewater. These updates include additional
data collection that has occurred since publication of the 2023 proposed rule, updates to the industry
(e.g., retirements, treatment updates), cost methodologies, pollutant removal estimates, and
explanations for the calculation of the effluent limitations and standards.
• Regulatory Impact Analysis for Supplemental Revisions to the Effluent Limitations Guidelines and
Standards for the Steam Electric Power Generating Point Source Category (RIA; U.S. EPA, 2024e).
The RIA describes EPA's analysis of the costs and economic impacts of the regulatory options. This
analysis provides the basis for social cost estimates presented in Chapter 11 of this document. The
RIA also provides information pertinent to meeting several legislative and administrative
requirements, including the Regulatory Flexibility Act of 1980 (as amended by the Small Business
Regulatory Enforcement Fairness Act [SBREFA] of 1996), the Unfunded Mandates Reform Act of
1995, Executive Order (E.O.) 13211 onActions Concerning Regulations That Significantly Affect
Energy Supply, Distribution, or Use, and others.
• Environmental Justice Analysis for Supplemental Revisions to the Effluent Limitations Guidelines and
Standards for the Steam Electric Power Generating Point Source Category (EJA; U.S. EPA, 2024c).
This report presents a profile of the communities and populations potentially impacted by this final
rule and an analysis of the distribution of impacts in the baseline and final rule changes.
The rest of this chapter discusses aspects of the regulatory options that are salient to EPA's analysis of the
benefits and social costs of the final rule and summarizes key analytic inputs used throughout this document.
EPA does not expect, and is not aware of, any planned new sources that would be subject to the requirements of this final rule.
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BCAfor Supplemental Steam Electric Power Generating ELGs
1: Introduction
The analyses of the regulatory options 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.
1.1 Steam Electric Power Plants
The ELGs for the Steam Electric Power Generating Point Source Category apply to a subset of the electric
power industry, namely those plants "with discharges resulting from the operation of a generating unit by an
establishment whose generation of electricity is the predominant source of revenue or principal reason for
operation, and whose generation of electricity results primarily from a process utilizing fossil-type fuel (coal,
oil, or gas), fuel derived from fossil fuel (e.g., petroleum coke, synthesis gas), or nuclear fuel in conjunction
with a thermal cycle employing the steam water system as the thermodynamic medium" (40 Code of Federal
Regulations [CFR] 423.10).
As described in the RIA, of the 858 steam electric power plants in the universe identified by EPA, only those
coal-fired power plants that discharge FGD wastewater, BA transport water, CRL or legacy wastewater may
incur compliance costs under the regulatory options analyzed for this final rule. After accounting for planned
retirements and fuel conversions, EPA estimated that 185 power plants will have coal-fired generating units
operating after December 31, 2028 and/or generate FGD wastewater, BA transport water, CRL or legacy
wastewater. Of those plants, an estimated 110 steam electric power plants generate the relevant wastestreams
and may incur costs to meet the effluent limits under one or more regulatory options. See TDD and RIA for
details (U.S. EPA, 2024e; 2024f).
1.2 Baseline and Regulatory Options Analyzed
EPA presents three regulatory options (see Table 1-1). These options differ in the stringency of controls and
applicability of these controls to generating units or plants based on generation capacity utilization, and
retirement or repowering status (see TDD for a detailed discussion of the options and the associated treatment
technology bases).
The baseline for this analysis reflects applicable requirements (in absence of the rule). The baseline includes
the 2020 rule (85 FR 64650). As discussed further in Section 2.2.2 of the RIA, the baseline for this analysis
also includes the effects of the 2020 CCR Part A rule.
The Agency estimated and presents in this report the water quality and other environmental effects of FGD
wastewater, BA transport water, leachate, and legacy wastewater discharges under both the 2020 rule baseline
and regulatory options A through C presented in Table 1-1. The Agency calculated the difference between the
baseline and the regulatory options to determine the net effect of each regulatory option. EPA is finalizing
Option B.
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BCAfor Supplemental Steam Electric Power Generating ELGs
1: Introduction
Table 1-1: Regulatory Options Analyzed for the Final Rule
Wastestream
Subcategory
Technology Basis for BAT/PSES Regulatory Options3
Baseline
(2020 Rule)
Option A
Option B
(Final Rule)
Option C
FGD
Wastewater
NA (default unless in subcategory)15
CP + Bio
ZLD
ZLD
ZLD
Boilers permanently ceasing the
combustion of coal by 2028
SI
SI
SI
SI
Boilers permanently ceasing the
combustion of coal by 2034
NS
CP + Bio
CP + Bio
NS
High FGD Flow Facilities or Low
Utilization Boilers
CP
NS
NS
NS
BA Transport
Water
NA (default unless in subcategory)15
HRR
ZLD
ZLD
ZLD
Boilers permanently ceasing the
combustion of coal by 2028
SI
SI
SI
SI
Boilers permanently ceasing the
combustion of coal by 2034
NS
HRR
HRR
NS
Low Utilization Boilers
BMP Plan
NS
NS
NS
CRL
NA (default)15
BPJ
CP
ZLD
ZLD
Discharges of unmanaged CRL
NA
NS
CP
CP
Boilers permanently ceasing the
combustion of coal by 2034
NA
CP
CP
NS
Legacy
wastewater
Operate after 2024
NA
NS
CP
CP
Abbreviations: BMP = Best Management Practice; CP = Chemical Precipitation; HRR = High Recycle Rate Systems; SI = Surface Impoundment; ZLD = Zero Liquid Discharge; NS = Not
subcategorized (default technology basis applies); NA = Not applicable
a. See TDD for a description of these technologies (U.S. EPA, 2024f).
b. The table does not present existing subcategories included in the 2015 and 2020 rules as EPA did not reopen the existing subcategorization of oil-fired units or units with a
nameplate capacity of 50 MW or less.
Source: U.S. EPA Analysis, 2024
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1: Introduction
1.3 Analytic Framework
The analytic framework of this benefit-cost analysis (BCA) includes basic components used consistently
throughout the analysis of benefits and social costs3 of the regulatory options:
1. All values are presented in 2023 dollars;
2. Future benefits and costs are discounted at 2 percent back to 2024;
3. Benefits and costs are analyzed over a 25-year period (2025 to 2049) which covers the years when
plants implement wastewater treatment technologies to meet the revised ELGs (2025-2029) and the
subsequent life of these technologies (20 years);
4. Technology installation and the resulting pollutant loading changes occur at the end of the estimated
wastewater treatment technology implementation year;
5. Benefits and costs are annualized over 25 years, based on the period of analysis described above;
6. Positive values represent net benefits (e.g., improvements in environmental conditions or social
welfare) compared to baseline; and
7. Future values account for annual U.S. population and income growth, unless noted otherwise.
These components are discussed in the sections below.
As was the case for the 2023 proposed rule, EPA's analysis of the regulatory options generally follows the
methodology the Agency used previously to analyze the 2015 and 2020 rules and the 2023 proposed rule
(U.S. EPA, 2015a, 2020b, 2024a). In analyzing the regulatory options, however, EPA made several changes
relative to the analysis of the 2020 rule and 2023 proposed rule:
• EPA used revised inputs that reflect the costs and loads estimated for each of the three regulatory
options (see TDD and RIA for details; U.S. EPA, 2024e; 2024f). Like the analysis of the 2020 final
rule and 2023 proposed rule, EPA estimated loading reductions for two periods (2025-2029 and 2030-
2049) during the overall period of analysis (2025-2049) to account for transitional conditions when
different plants are in the process of installing technologies to meet the ELGs.
• EPA updated the baseline industry information to incorporate changes in the universe and operational
characteristics of steam electric power plants such as electricity generating unit retirements and fuel
conversions since the analysis of the 2020 final rule and 2023 proposed rule. EPA also incorporated
updated information on the technologies and other controls that plants employ. See the TDD for
details on the changes (U.S. EPA, 2024f).
• Finally, EPA made certain changes to the methodologies to be consistent with approaches used by the
Agency for other rules and/or incorporate recent advances in environmental assessment, health risk,
and resource valuation research.
These changes are described in the relevant sections of this document, and summarized in Appendix A.
Unless otherwise noted, costs represented in this document are social costs.
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BCAfor Supplemental Steam Electric Power Generating ELGs
1: Introduction
1.3.1 Constant Prices
This BCA applies a year 2023 constant price level to all future monetary values of benefits and costs. Some
monetary values of benefits and costs are based on actual past market price data for goods or services, while
others are based on other measures of values, such as household willingness-to-pay (WTP) surveys used to
monetize ecological changes resulting from surface water quality changes. This BCA updates market and
non-market prices using the Consumer Price Index (CPI), Gross Domestic Product (GDP) implicit price
deflator, or Construction Cost Index (CCI). To update the value of a Statistical Life (VSL), EPA used the
GDP deflator and the elasticity of VSL with respect to income of 0.4, as recommended in EPA's Guidelines
for preparing Economic Analysis (U.S. EPA, 2010, updated 2014). EPA used the GDP deflator to update the
value of an IQ point, the CPI to update the WTP for surface water quality improvements and cost of illness
(COI) estimates, and the CCI to update the cost of dredging navigational waterways and reservoirs.
1.3.2 Discount Rate and Year
This BCA estimates the annualized value of future benefits and costs using a discount rate of 2 percent,
following current Office of Management and Budget (OMB) guidance in Circular A-4 (U.S. Office of
Management and Budget, 2023)4 Climate benefits are monetized using social cost of greenhouse gas (SC-
GHG) estimates calculated with near-term Ramsey discount rates of 1.5 percent, 2 percent, and 2.5 percent.
To calculate the annualized value of climate benefits, EPA uses the same discount rate as the near-term
Ramsey rate used to discount the climate benefits from future GHG changes. That is, future climate benefits
estimated with the SC-GHG at the near-term 2 percent Ramsey rate are discounted to the base year of the
analysis using a 2 percent rate. Section 8.2 provides additional details on the discounting of climate benefits.
All future cost and benefit values are discounted back to 2024, the rule promulgation year.5
In Appendix B, EPA presents the benefits and costs of the final rule using the discount rates used in the
proposal BCA, which followed the guidance applicable at the time the prior analysis was conducted (OMB,
2003).6
1.3.3 Period of Analysis
The rule benefits are projected to begin accruing when each plant implements the control technologies needed
to comply with any applicable BAT effluent limitations or pretreatment standards. As described in greater
4 The social costs presented in this BCA differ from the annualized pre-tax compliance costs described in Chapter 3 of the RIA or
the compliance costs modeled in IPM (Chapter 5 of the RIA) which use the estimated weighted average cost of capital for the
power sector of 3.76 percent to discount and annualize costs.
5 In its analysis of the 2015 rule, EPA presented benefits in 2013 dollars and discounted these benefits and costs to 2015 (see U.S.
Environmental Protection Agency. (2015a). Benefit and Cost Analysis for the Effluent Limitations Guidelines and Standards for
the Steam Electric Power Generating Point Source Category. (EPA-821-R-15-005). ), whereas the analysis of the 2020 rule used
2018 dollars and discounted benefits and costs to 2020 (see U.S. Environmental Protection Agency. (2020b). Benefit and Cost
Analysis for Revisions to the Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point
Source Category. (EPA-821-R-20-003). ).
6 In the prior version of Circular A-4, the OMB recommended that 3 percent be used when a regulation affects private
consumption, and 7 percent in evaluating a regulation that would mainly displace or alter the use of capital in the private sector
(U.S. Office of Management and Budget. (2003). Circular A-4: Regulatory Analysis. Retrieved from
https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/circulars/A4/a-4.pdf). OMB has long recognized that climate
effects should be discounted only at appropriate consumption-based discount rates. Because the SC-GHG estimates reflect net
climate change damages in terms of reduced consumption (or monetary consumption equivalents), the use of the social rate of
return on capital (7 percent under ibid.) to discount damages estimated in terms of reduced consumption would inappropriately
underestimate the impacts of climate change for the purposes of estimating the SC-GHG.
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BCAfor Supplemental Steam Electric Power Generating ELGs
1: Introduction
detail in the NPRM, EPA is establishing availability timing for BAT limitations that is "as soon as possible"
after the effective date of any final rule but "no later than" five years from the effective date (i.e.. a 2029
deadline). As discussed in the RIA (in Chapter 3), for the purpose of the economic impact and benefit
analysis, EPA generally estimates that plants will implement control technologies to meet the applicable rule
limitations and standards as their permits are renewed, and no later than December 31, 2029. This schedule
recognizes that control technology implementation is likely to be staggered over time across the universe of
steam electric power plants.
The period of analysis extends to 2049 to capture the estimated life of the compliance technology at any
steam electric power plant (20 or more years), starting from the year of technology implementation, which
can be as late as 2029.
The different compliance years between options, wastestreams, and plants means that environmental changes
may occur in a staggered fashion over the analysis period as plants implement control technologies to meet
applicable limits under each option. To analyze environmental changes from the baseline and resulting
benefits, EPA used the annual average of loadings or other environmental changes (e.g., air emissions, water
withdrawals) projected during two distinct periods (2025-2029 and 2030-2049) within the overall analysis
period (2025-2049). Section 3.2 provides further details on the breakout of the analysis periods.
1.3.4 Timing of Technology Installation and Loading Reductions
For the purpose of the analysis of benefits and social costs, EPA estimates that plants meet revised applicable
limitations and standards by the end of their estimated technology implementation year and that any resulting
changes in loadings will be in effect at the start of the following year.
1.3.5 Annualization of future costs and benefits
Consistent with the timing of technology installation and loading reductions described above which is
modeled to occur at the end of the year, EPA uses the following equation to annualize the future stream of
costs and benefits:
Equation 1-1.
r(PV)
AV = —
(1 +r)[l - (1 + r)~n]
Where A V is the annualized value, PVis the present value, r is the discount rate (2 percent), and n is the
number of years (25 years) over which non-zero costs and benefits are modeled.
1.3.6 Population and Income Growth
To account for future population growth or decline, EPA used Woods & Poole population forecasts for the
United States (Woods & Poole Economics Inc., 2021). EPA used the growth projections for each year to
adjust affected population estimates for future years (i.e.. from 2025 to 2049).
Because WTP is expected to increase as income increases, EPA accounted for income growth for estimating
the value of avoided premature mortality based on the value of a statistical life (VSL) and WTP for water
quality improvements. To develop income adjustment factors, EPA calculated income growth factors using
historical and projected "real disposable personal income" estimates (U.S. Energy Information
Administration, 2021). For the VSL calculations, EPA used the VSL value in 1990 dollars ($4.8 million) and
adjusted for inflation using the U.S. Bureau of Labor Statistics' (2023) CPI and adjusted for income growth
using real GDP per capita and an income elasticity of 0.4 (U.S. EPA, 2010, updated 2014). Adjusted VSL
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BCAfor Supplemental Steam Electric Power Generating ELGs
1: Introduction
values ranged from $13.5 million in 2025 to $16.4 million in 2049. For the WTP for water quality
improvements, EPA multiplied income estimates by the income growth rate, relative to 2021, for the
applicable analysis period year (i.e., from 2025 to 2049).7
1.4 Organization of the Benefit and Cost Analysis Report
This BCA report presents EPA's analysis of the benefits of the regulatory options, assessment of the total
social costs, and comparison of the social costs and monetized benefits.
The remainder of this report is organized as follows:
• Chapter 2 provides an overview of the main benefits expected to result from the implementation of
the three regulatory options analyzed for this proposal.
• Chapter 3 describes EPA's estimates of the environmental changes resulting from the regulatory
options, including water quality modeling that underlays the Agency's estimates of several categories
of benefits.
• Chapters 4 and 5 details the methods and results of EPA's analysis of human health benefits from
changes in pollutant exposure via the drinking water and fish ingestion pathways, respectively.
• Chapter 6 discusses EPA's analysis of the nonmarket benefits of changes in surface water quality
resulting from the regulatory options.
• Chapter 7 discusses EPA's analysis of benefits to threatened and endangered (T&E) species.
• Chapter 8 describes EPA's analysis of benefits associated with changes in emissions of air pollutants
associated with energy use, transportation, and the profile of electricity generation for the regulatory
options.
• Chapter 9 describes benefits from changes in costs for drinking water treatment and dredging costs to
maintain navigational channels and reservoirs.
• Chapter 10 summarizes monetized benefits across benefit categories.
• Chapter 11 summarizes the social costs of the regulatory options.
• Chapter 12 compares the benefits and social costs of its actions in accordance with executive order
E.O. 12866: Regulatory Planning and Review (58 FR 51735, October 4, 1993), as amended by E.O.
13563: Improving Regulation and Regulatory Review (76 FR 3821, January 21, 2011) and E.O.
14094: Modernizing Regulatory Review (88 FR 21879, April 11, 2023).
• Chapter 13 provides references cited in the text.
There is a relatively strong consensus in economic literature that income elasticities of approximately "1" are appropriate for
adjusting WTP for water quality improvements in future years (Johnston, R. J., Besedin, E. Y., & Holland, B. M. (2019).
Modeling Distance Decay within Valuation Meta-Analysis. Environmental and resource economics, 72(3), 657-690.
https://doi.Org/littps://doi.org/10.1007/sl0640-018-0218-z ; Tyllianakis, E., & Skuras, D. (2016). The income elasticity of
Willingness-To-Pay (WTP) revisited: A meta-analysis of studies for restoring Good Ecological Status (GES) of water bodies
under the Water Framework Directive (WFD). Journal of environmental management, 182, 531-541.
https://doi.Org/10.1016/j.jenvman.2016.08.012 ). Therefore, EPA used an income elasticity of "1" in tliis analysis.
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BCAfor Supplemental Steam Electric Power Generating ELGs 1: Introduction
Several appendices provide additional details on selected aspects of analyses described in the main text of the
report.
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BCAfor Supplemental Steam Electric Power Generating ELGs
2: Benefits Overview
2 Benefits Overview
This chapter provides an overview of the estimated welfare effects to society resulting from changes in
pollutant loadings due to implementation of the main regulatory options analyzed for the final rule. EPA
expects the regulatory options to change discharge loads of various categories of pollutants when fully
implemented. The categories of pollutants include conventional pollutants (such as suspended solids,
biochemical oxygen demand (BOD), and oil and grease), priority pollutants (such as mercury [Hg], arsenic
[As], and selenium [Se]), and non-conventional pollutants (such as total nitrogen [TN], total phosphorus [TP],
chemical oxygen demand [COD] and total dissolved solids [TDS]).
Table 2-1 presents estimated annual pollutant loads in the baseline and changes in pollutant loads under full
implementation of the effluent limitations and standards for the regulatory options. The TDD provides further
detail on the loading changes (U.S. EPA, 2024f). As described in Section 3.2, EPA anticipates a transition
period and estimated loadings during interim years before all plants have implemented control technologies to
meet the applicable final ELGs under the regulatory options may differ from these values. EPA also
anticipates loading reductions for legacy wastewater to occur only when facilities dewater and close their
existing ponds, which may happen after the end of the period of analysis.
Table 2-1: Estimated Baseline Annual Pollutant Loadings and Changes in Loadings for Regulatory
Options Under Technology Implementation
Pollutant
Estimated Baseline
Total Pollutant
Loadings3
(pounds per year)
Estimated Changes in Pollutant Loadings3 from Baseline
(pounds per year)
Option A
Option B
(Final Rule)
Option C
Antimony
245
-179
-225
-245
Arsenic
742
-480
-667
-691
Barium
7,260
-4,500
-5,680
-6,180
Beryllium
31
-27
-27
-31
Boron
6,270,000
-4,590,000
-4,910,000
-5,620,000
Bromide
6,160,000
-5,730,000
-5,730,000
-6,160,000
Cadmium
534
-134
-494
-510
Chemical oxygen demand
117,000
-112,000
-112,000
-117,000
Chromium
20,500
-20,300
-20,400
-20,400
Copper
379
-164
-331
-346
Cyanide
21,900
-18,900
-18,900
-21,900
Lead
215
-124
-172
-185
Manganese
600,000
-253,000
-516,000
-557,000
Mercury
40
-11
-38
-38
Nickel
3,390
-654
-3,280
-3,310
Total nitrogen
492,000
-165,000
-165,000
-189,000
Total phosphorus
10,800
-7,670
-7,670
-8,710
Selenium
4,750
-181
-1,930
-2,020
Thallium
743
-207
-626
-657
Total dissolved solids
712,000,000
-496,000,000
-563,000,000
-640,000,000
Total suspended solids
878,000
-547,000
-767,000
-803,000
Zinc
6,440
-1,920
-6,180
-6,270
Note: Pollutant loadings and removals are rounded to three significant figures. See TDD for additional details on estimated loads
(U.S. EPA, 2024f).
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BCAfor Supplemental Steam Electric Power Generating ELGs 2: Benefits Overview
Table 2-1: Estimated Baseline Annual Pollutant Loadings and Changes in Loadings for Regulatory
Options Under Technology Implementation
Pollutant
Estimated Baseline
Total Pollutant
Loadings3
(pounds per year)
Estimated Changes in Pollutant Loadings3 from Baseline
(pounds per year)
Option A
Option B
(Final Rule)
Option C
a. Industry-wide pollutant loadings reflect full implementation of ELGs. Values shown in this table do not account for generating
unit retirements or conversions during the period of analysis which are estimated to reduce total industry loadings under the
baseline and regulatory options.
Source: U.S. EPA Analysis, 2024
In addition to water quality changes, effects of the regulatory options in comparison to the 2020 rule also
include other effects of the implementation of control technologies and changes in plant operations, such as
changes in emissions of air pollutants (e.g., carbon dioxide [CO2], fine particulate matter [PM2.5], nitrogen
oxides [NOx], and sulfur dioxide [SO2]) which result in benefits to society in the form of changes in
morbidity and mortality and CO2 impacts on environmental quality and economic activities.
This chapter also briefly discusses the effects of pollutants found in FGD wastewater, BA transport water,
CRL, and legacy wastewater and provides a framework for understanding the benefits expected to be
achieved under by the regulatory options. For a more detailed description of steam electric wastewater
pollutants, their fate, transport, and impacts on human health and environment, see the EA (U.S. EPA,
2024b).
Figure 2-1 summarizes the potential effects of the regulatory options, the expected environmental changes,
and categories of social welfare effects as well as EPA's approach to analyzing those welfare effects.
EPA was not able to bring the same depth of analysis to all categories of social welfare effects because of
imperfect understanding of the link between discharge changes or other environmental effects of the
regulatory options and welfare effect categories, and how society values some of these effects. EPA was able
to quantify and monetize some welfare effects, quantify but not monetize other welfare effects, and assess still
other welfare effects only qualitatively. The remainder of this chapter provides a qualitative discussion of the
social welfare effects applicable to the final rule, including human health effects, ecological effects, economic
productivity, and changes in air pollution. Some estimates of the monetary value of social welfare changes
presented in this document rely on models with a variety of limitations and uncertainties, as discussed in more
detail in Chapters 3 through 9 for the relevant benefit categories.
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Figure 2-1: Summary of Estimated Benefits Resulting from the Regulatory Options,
DBP = Disinfection byproducts; VSL = Value of Statistical Life; HH AWQC= human health ambient water quality criteria; COI = Cost of illness; WTP = Willingness to Pay; A'WQC= ambient water quality criteria
Source: U.S. EPA Analysis, 2024.
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2.1 Human Health Impacts Associated with Changes in Surface Water Quality
Pollutants present in steam electric power plant wastewater discharges can cause a variety of adverse human
health effects. Chapter 3 describes the approach EPA used to estimate changes in pollutant levels in waters.
More details on the fate, transport, and exposure risks of steam electric pollutants are provided in the EA
(U.S. EPA, 2024b).
Human health effects are typically analyzed by estimating the change in the expected number of adverse
human health events in the exposed population resulting from changes in effluent discharges. While some
health effects (e.g., 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 regulatory options affect human health risk by changing exposure to pollutants in water via two principal
exposure pathways discussed below: (1) treated water sourced from surface waters affected by steam electric
power plant discharges and (2) fish and shellfish taken from waterways affected by steam electric power plant
discharges. The regulatory options also affect human health risk by changing air emissions of pollutants via
shifts in the profile of electricity generation, changes in auxiliary electricity use, and transportation; these
effects are discussed separately in Section 2.5.
2.1.1 Drinking Water
Pollutants discharged by steam electric power plants to surface waters may affect the quality of water used for
public drinking supplies. People may then be exposed to harmful constituents in treated water through
ingestion, as well as inhalation and dermal absorption (e.g., showering, bathing). The pollutants may not be
removed adequately during treatment at a drinking water treatment plant, or constituents found in steam
electric power plant discharges may interact with drinking water treatment processes and contribute to the
formation of disinfection byproducts (DBPs).
Public drinking water supplies are subject to legally enforceable maximum contaminant levels (MCLs)
established by EPA (U.S. EPA, 2018b). 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. Table 2-2 shows the MCL and MCLG for selected constituents or
constituent derivatives of steam electric power plant effluent.
Table 2-2: Drinking Water Maximum Contaminant Levels and Goals for Selected Pollutants in Steam
Electric FGD Wastewater, BA Transport Water, CRL, and Legacy Wastewater Discharges
Pollutant
MCL
MCLG
(mg/L)
(mg/L)
Antimony
0.006
0.006
Arsenic
0.01
0
Barium
2.0
2.0
Beryllium
0.004
0.004
Bromate
0.010
0
Cadmium
0.005
0.005
Chromium (total)
0.1
0.1
Copper3
1.3
1.3
Cyanide (free cyanide)
0.2
0.2
Lead3
0.015
0
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Table 2-2: Drinking Water Maximum Contaminant Levels and Goals for Selected Pollutants in Steam
Electric FGD Wastewater, BA Transport Water, CRL, and Legacy Wastewater Discharges
Pollutant
MCL
MCLG
(mg/L)
(mg/L)
Mercury
0.002
0.002
Nitrate-Nitrite as N
10 (Nitrate); 1 (Nitrite)
10 (Nitrate); 1 (Nitrite)
Selenium
0.05
0.05
Thallium
0.002
0.0005
Total trihalomethanes15
0.080
Not applicable
bromodichloromethane
Not applicable
0
bromoform
Not applicable
0
dibromochloromethane
Not applicable
0.06
chloroform
Not applicable
0.07
a. MCL value is based on action level.
b. Bromide, a constituent found in steam electric power plant effluent, is a precursor for Total Trihalomethanes and three of its
subcomponents. Additional trihalomethanes may also be formed in the presence of iodine, a constituent also found in steam
electric power plant wastewater discharges.
Source: 40 CFR 141.53 as summarized in U.S. EPA (2018b): National Primary Drinking Water Regulation, EPA 816-F-09-004
Pursuant to MCLs, public drinking water supplies are tested and treated for pollutants that pose human health
risks. In analyzing the human health benefits of the regulatory options, EPA assumes that treated water meets
applicable MCLs in the baseline. Table 2-2 shows that for arsenic, bromate, lead, and certain trihalomethanes,
the MCLG is zero. For these pollutants and for those that have an MCL above the MCLG (thallium), there
may be incremental benefits from reducing concentrations even where they are below the MCL.
EPA used a mass balance approach to estimate the changes in halogen (bromide) levels in surface waters
downstream from steam electric power plant outfalls. Halogens can be precursors for halogenated disinfection
byproduct formation in treated drinking water, including trihalomethanes addressed by the total
trihalomethanes (TTHM) MCL. The occurrence of TTHM and other halogenated disinfection byproducts in
downstream drinking water depends on a number of environmental factors and site-specific processes at
drinking water treatment plants. There is some evidence of associations between adverse human health
effects, including bladder cancer, and exposure to sufficient levels of halogenated disinfection byproducts in
drinking water (Hrudey et al., 2015; Regli et al., 2015; U.S. EPA, 2005b; 2016c; Villanueva et al., 2004;
Villanueva et al., 2003). EPA quantitatively estimated the marginal effect of changes in surface water
bromide levels on drinking water TTHM levels and bladder cancer incidence in exposed populations. EPA
also monetized associated changes in human mortality and morbidity. EPA relied on the COI approach to
monetize the estimated reduction in non-fatal bladder cancer cases and the VSL to monetize benefits from
avoided fatal cancer cases (see Section 4.3.3). The COI approach allows valuation of a particular type of non-
fatal illness by placing monetary values on measures, such as lost productivity and the cost of health care and
medications, that can be monetized.
To assess potential for changes in health risk from exposure to arsenic, lead, and thallium in drinking water,
EPA estimated changes in pollutant levels in source waters downstream from steam electric power plants
under each regulatory option. This analysis is discussed in Section 4.3.2.3. EPA did not quantify or monetize
benefits from reduced exposure to arsenic, lead, and thallium via drinking water due to the relatively small
concentration changes in source waters downstream from steam electric plants. EPA however notes that coal
ash effluents can make water more corrosive by increasing the conductivity of source waters used by
downstream water systems and, as a result, increase lead leaching from water distribution infrastructure.
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2.1.2 Fish Consumption
Recreational and subsistence fishers (and their household members) who consume fish caught in the reaches
downstream of steam electric power plants may be affected by changes in pollutant concentrations in fish
tissue. EPA analyzed the following direct measures of change in risk to human health from exposure to
contaminated fish tissue:
• Neurological effects to children ages 0 to 7 from exposure to lead;
• Incidence of premature cardiovascular mortality in adults from exposure to lead;
• Neurological effects to infants from in-utero exposure to mercury;
• Incidence of skin cancer from exposure to arsenic8; and
• Reduced risk of other cancer and non-cancer toxic effects.
The Agency evaluated potential changes in intellectual impairment, or intelligence quotient (IQ), resulting
from changes in childhood and in-utero exposures to lead and mercury. EPA also estimated changes in the
incidence of cardiovascular premature mortality from exposure to lead and the number of avoided skin cancer
cases exposure to arsenic.
For constituents with human health ambient water quality criteria or oral reference dose (RfD),9 the change in
the risk of other cancer and non-cancer toxic effects from fish consumption is addressed indirectly in EPA's
assessment of changes in exceedances of these thresholds (see Section 5.8 and Section 4 and Appendix A of
the EA; U.S. EPA, 2024b).
EPA relied on VSL to estimate the value of avoided cardiovascular premature mortality and a COI approach
to estimate the value of changes in the incidence of skin cancer, which are generally non-fatal (see Section
5.6). Some health effects of changes in 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 because the available economic research provides little empirical
data on society's WTP to avoid IQ losses. Instead, EPA calculated monetary values for changes in
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 the monetary value from changes in exposure to lead and mercury. Employed alone, these
monetary values would underestimate society's WTP to avoid adverse neurological effects. See Sections 5.3
and 5.4 for applications of this method to valuing health effects in children and infants from changes in
8 In 2023, EPA released an update to the IRIS inorganic arsenic protocol. "U.S. EPA. IRIS Toxicological Review of Inorganic
Arsenic (Public Comment and External Review Draft)" to reflect new data on internal cancers including bladder, liver, kidney,
and lung cancers associated with arsenic exposure via ingestion (U.S. Environmental Protection Agency. (2023i). IRIS
Toxicological Review of Inorganic Arsenic (Public Comment and External Review Draft). (EPA/635/R-23/166). Retrieved from
https://iris.epa.gov/Document/&deid=253756). Because cancer slope factors for internal organs have not been finalized, the
Agency did not consider these effects in the analysis of the final rule.
9 An RfD is defined as an estimate of a daily oral exposure that likely would not result in the occurrence of adverse health effects
in humans, including sensitive individuals, during a lifetime. An RfD is typically established by applying uncertainty factors to
the lowest- or no observed adverse effect level (NOAEL) for the critical toxic effect of a pollutant.
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exposure to lead and mercury. This is the same approach EPA used in its analysis of the 2023 Proposed Lead
and Copper Rule Improvements (U.S. EPA, 2023f).
EPA received comments on the analysis of the 2023 proposed supplemental ELG that it did not evaluate
potential health impacts via the fish consumption pathway arising from changes in discharges of other steam
electric pollutants, such as aluminum, boron, cadmium, hexavalent chromium, manganese, selenium,
thallium, and zinc. Analyses of these health effects require data and information on the relationships between
ingestion rate and potential adverse health effects and on the economic value of potential adverse health
effects. Following a review of the available data, for the final rule EPA again did not quantify, nor was it able
to monetize, changes in health effects associated with exposure to these pollutants under the regulatory
options due to data limitations and uncertainty in the quantitative relationships. Despite numerous studies
conducted by EPA and other researchers, dose-response functions are available for only a subset of health
endpoints associated with steam electric wastewater pollutants. In addition, the available research does not
always allow complete economic evaluation, even for quantifiable health effects. For example, sufficient data
are not available to evaluate and monetize the following potential health effects from fish consumption:
neonatal mortality from in-utero exposure to lead and other impacts to children from exposure to lead, such as
decreased postnatal growth in children ages one to 16, delayed puberty, immunological effects, and decreased
hearing and motor function (Cleveland et al., 2008; NTP, 2012; U.S. EPA, 2024d; 2019e); effects to adults
from exposure to lead such as decreased kidney function, reproductive effects, immunological effects, cancer
and nervous system disorders (Aoki et al., 2016; Chowdhury et al., 2018; Clay, Portnykh & Severnini, 2021;
Grossman & Slusky, 2019; Lanphear et al., 2018; Navas-Acien, 2021; NTP, 2012; U.S. EPA, 2024d; 2019e;
2023f); neurological effects to children from exposure to mercury after birth (Grandjean et al., 2014); effects
to adults from exposure to mercury, including vision defects, hand-eye coordination, hearing loss, tremors,
cerebellar changes, premature mortality, and others (Hollingsworth & Rudik, 2021; Mergler et al., 2007;
Center for Disease Control and Prevention (CDC), 2009); and other cancer and non-cancer effects from
exposure to other steam electric pollutants (e.g., kidney, liver, and lung damage from exposure to cadmium,10
reproductive and developmental effects from exposure to arsenic, boron, and thallium, liver and blood effects
from exposure to hexavalent chromium, and neurological effects from exposure to manganese) (California
EPA, 2011; Oulhote et al., 2014; Roels et al., 2012; U.S. Department of Health and Human Services, 2012;
U.S. EPA, 2020g; Ginsberg, 2012).
In some cases, EPA did not quantify or monetize health effects because the estimated changes in pollutant
loadings and fish tissue concentrations are small and, combined with the available concentration-response or
valuation functions, unlikely to result in tangible benefits. For example, concentration-response functions are
available to characterize reductions in blood lead levels (caused by changes in lead exposure) and to translate
these reductions into changes in birth weight and avoided cases of attention deficit hyperactivity disorder
(ADHD). The corresponding COI estimates are also available. However, past analyses have shown that these
benefits account for a small portion of total benefits associated with reducing adult and children exposure to
10 Although dose response relationships between a dietary exposure to cadmium and adverse effects in kidney functions have been
developed for a cadmium exposure range of 0.003 to 0.014 mg/kg BW/d) (Ginsberg, G. L. (2012). Cadmium risk assessment in
relation to background risk of chronic kidney disease. Journal of Toxicology and Environmental Health, Part A, 75(7), 374-390.
), dose response relationships are not available for lower exposure ranges. Since exposure to cadmium associated with fish
consumption caught in the reaches affected by steam electric discharges is below 0.001 mg/kg BW/d (RfD for cadmium) in 99.8
percent of the affected reaches (11,078 out of 11,080 reaches) in the baseline, EPA did not quantify changes in adverse health
effects associated with reduced exposure to cadmium via fish consumption.
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lead (e.g., see U.S. EPA, 2023f). EPA therefore focused its quantitative analysis on the health effects that
have been associated with the largest share of the benefits.
EPA recognizes that there may be cumulative or synergistic effects of pollutants that share the same toxicity
mechanism, affect the same body organ or system, or result in the same health endpoint. For example,
exposure to several pollutants discharged by steam electric plants (i.e., lead, mercury, manganese, and
aluminum) is associated with adverse neurological effects, in particular in fetuses and small children (Agency
for Toxic Substances and Disease Registry (ATSDR), 2009; Grandjean et al., 2014; NTP, 2012; Oulhote et
al., 2014; U.S. EPA, 2024d). However, data and resource limitations preclude a full analysis of such
cumulative or synergistic effects. A weight of evidence approach is typically used in qualitatively evaluating
the cumulative effect of a chemical mixture. Cumulative effects often depend on exposure doses as well as
potential threshold effects (ATSDR, 2004; 2009). While there are no existing methods to fully analyze and
monetize these effects, EPA quantified some of these effects in the EA (U.S. EPA, 2024b).
Due to these limitations, the total monetary value of changes in human health effects included in this analysis
represents only a subset of the potential health benefits that are expected to result from the regulatory options.
2.1.3 Complementary Measure of Human Health Impacts
EPA quantified, but did not monetize, changes in pollutant concentrations in excess of human health-based
national recommended water quality criteria (NRWQC). This analysis provides an approximate indication of
the change in cancer and non-cancer health risk by comparing the number of receiving reaches exceeding
health-based NRWQC for steam electric pollutants in the baseline to the number exceeding NRWQC under
the regulatory options (Section 5.8).
Because the NRWQC in this analysis are set at levels to protect human health through ingestion of water and
aquatic organisms, changes in the frequency at which human health-based NRWQC are exceeded could
translate into changes in risk to human health. This analysis should be viewed as an indirect indicator of
changes in risk to human health because it does not reflect the magnitude of human health risk changes or the
population over which those changes would occur.
In addition, EPA assessed the risk of non-cancer health effects from exposure to steam electric pollutants by
comparing the estimated exposure to the pollutant to the pollutant's RfD. To estimate a hazard quotient for a
given pollutant EPA divided an individual's oral exposure to the pollutant by the pollutant's oral RfD. A
hazard quotient less than one means that the pollutant dose to which an individual is exposed is less than the
RfD. For assessing exposures to mixtures of pollutants, EPA developed distributions of non-cancer health
hazard indices (HI) under the baseline and regulatory options by summing the individual hazard quotients for
those pollutants in the mixture that affect the same target organ or system (e.g., the kidneys, the respiratory
system).11 The shift in the affected stream miles from higher to lower hazard score values between the
baseline and regulatory options is the measure of benefit from reduced non-cancer health hazards (See Section
4 of the EA; U.S. EPA, 2024b).
11 HI values are interpreted similarly to hazard quotients. Values below one are generally considered to suggest that exposures are
not likely to result in appreciable risk of adverse health effects during a lifetime, and values above one are generally cause for
concern,
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2.2 Ecological and Recreational Impacts Associated with Changes in Surface Water Quality
The regulatory options may affect the value of ecosystem services provided by surface waters through
changes in the habitats or ecosystems (aquatic and terrestrial) that receive steam electric power plant
discharges.
The composition of steam electric power plant wastewater depends on a variety of factors, such as fuel
properties, air pollution control technologies, and wastewater management techniques. Wastewater often
contains toxic pollutants such as aluminum, arsenic, boron, cadmium, chromium, copper, iron, lead,
manganese, mercury, nickel, selenium, thallium, vanadium, molybdenum, and zinc (U.S. EPA, 2024b).
Discharges of these pollutants to surface water can have a wide variety of environmental effects, including
fish kills, reduction in the survival and growth of aquatic organisms, behavioral and physiological effects in
wildlife, and degradation of aquatic habitat in the vicinity of steam electric power plant discharges (U.S. EPA,
2024b). As presented in Table 2-1, steam electric plants discharge an estimated 492,000 pounds of nitrogen
and 10,800 pounds of phosphorus each year in the baseline. Excess nutrients in surface water contribute to
eutrophication which can also cause algal blooms and depress oxygen levels, further reducing the habitability
for game fish and other aquatic life (U.S. EPA, 2000; U.S. EPA, 2001; Li et al., 2013; Mallin & Cahoon,
2020). 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. The modeled changes in environmental impacts are small relative to the
changes estimated for the 2015 rule. Still, EPA expects the ecological impacts from the regulatory options
could include improved habitat conditions for fresh- and saltwater plants, invertebrates, fish, and amphibians,
as well as terrestrial wildlife and birds that prey on aquatic organisms exposed to steam electric pollutants.
The change in pollutant loadings has the potential to enhance ecosystem productivity in waterways and the
health of resident species, including T&E species. Loading reductions projected under the regulatory options
have the potential to impact the general health of fish and invertebrate populations, their propagation to
waters, and fisheries for both commercial and recreational purposes. Water quality improvements also have
the potential to enhance recreational activities such as swimming, boating, fishing, and water skiing. Finally,
the final rule has the potential to impact nonuse values (e.g., option, existence, and bequest values) of the
waters that receive steam electric power plant discharges.
Society values changes in ecosystem services by a number of mechanisms, including increased frequency of
use and improved quality of recreational activities (e.g., fishing, swimming, and boating). Individuals also
value the protection of habitats and species that may reside in waters that receive steam electric plant
discharges, even when those individuals do not use or anticipate future use of such waters for recreational or
other purposes, resulting in nonuse values. The sections below discuss selected categories of benefits
associated with changes in ecosystem services (additional economic productivity benefits associated with
changes in ecosystem services are discussed in Section 2.4).
EPA's analysis is intended to isolate possible effects of the regulatory options on aquatic ecosystems and
organisms, including T&E species; however, it does not account for the fact that the National Pollutant
Discharge Elimination System (NPDES) permit for each steam electric power plant, like all NPDES permits,
is required to have limits more stringent than the technology-based limits established by an ELG wherever
necessary to protect water quality standards. In cases where a NPDES permit would already provide for more
stringent limits in the baseline than those that would be required under the final ELG, the improvements
attributable to the rule will be less than estimated in this analysis.
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2.2.1 Changes in Surface Water Quality
EPA quantified potential environmental impacts from the regulatory options by estimating in-waterway
concentrations of FGD wastewater, BA transport water and CRL pollutants and translating water quality
estimates into a single numerical indicator, a water quality index (WQI). EPA used the estimated change in
WQI as a quantitative estimate of changes in aquatic ecosystem conditions for this regulatory analysis.
Section 3.4 of this report provides details on the parameters used in formulating the WQI and the WQI
methodology and calculations. In addition to estimating changes using the WQI, EPA compared estimated
pollutant concentrations to freshwater NRWQC for aquatic life (see Section 3.4.1.1). The EA details
comparisons of the estimated concentrations in immediate receiving and downstream reaches to the
freshwater acute and chronic NRWQC for aquatic life for individual pollutants (U.S. EPA, 2024b).
A variety of primary methods exist for estimating recreational use values, including both revealed and stated
preference methods (Freeman III, 2003). Where appropriate data are available or can be collected, revealed
preference methods can represent a preferred set of methods for estimating use values. Revealed preference
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, 2010, updated 2014; OMB, 2023; Johnston, Boyle, et al., 2017). Stated
preference methods rely on carefully designed surveys, which either (1) ask people about their WTP for
particular environmental improvements, such as increased protection of aquatic species or habitats with
particular attributes, or (2) ask people to choose between competing hypothetical "packages" of
environmental improvements and household cost (Bateman et al., 2006; Johnston, Boyle, et al., 2017). In
either case, values are estimated by statistical analysis of survey responses.
Although the use of primary research to estimate values is generally preferred because it affords the
opportunity for the valuation questions to closely match the policy scenario, the realities of the regulatory
process often dictate that benefit transfer is the only option for assessing certain types of non-market values
(Rosenberger & Johnston, 2008; Johnston et al., 2021). 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, Van Houtven & Pattanayak, 2002, p. 134). It involves
adapting research conducted for another purpose to estimate values within a particular policy context
(Bergstrom & De Civita, 1999; Johnston et al., 2021). Among benefit transfer methods, meta-analyses are
often more accurate compared to other types of transfer approaches due to the data synthesis from multiple
source studies (Rosenberger and Phipps, 2007; Johnston et al., 2021). However, EPA acknowledges that there
is still a potential for transfer errors (Shrestha, Rosenberger & Loomis, 2007) and no transfer method is
always superior (Johnston et al., 2021).
EPA followed the same methodology used in analyzing the 2015 and 2020 rules and the 2023 proposal (U.S.
EPA, 2015a, 2020b, 2023b) and relied on a benefit transfer approach based on an updated meta-analysis of
surface water valuation studies to estimate the use and non-use benefits of improved surface water quality
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under the regulatory options. The updates consisted of incorporating WTP estimates from more recent peer
reviewed studies into EPA's existing econometric model.12 This analysis is presented in Chapter 6.
2.2.2 Impacts on Threatened and Endangered Species
For T&E species, even minor changes to reproductive rates and small mortality levels may represent a
substantial portion of annual population growth. By reducing discharges of steam electric pollutants to aquatic
habitats, the regulatory options have the potential to impact 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 use activities, such as fishing or hunting, generally constitute "take" which is
illegal unless permitted, the majority of the economic value for T&E species comes from nonuse values.13
EPA quantified but did not monetize the potential benefits of the regulatory options on T&E species. EPA
constructed databases to determine which species have habitat ranges that intersect waters downstream from
steam electric power plants. EPA then queried these databases to identify "affected areas" of those habitats
where 1) receiving waters do not meet aquatic life-based NRWQC under the baseline conditions; and
2) receiving waters do meet aquatic life-based NRWQC under the regulatory options.14 Because NRWQC are
set at levels to protect aquatic organisms, reducing the frequency at which aquatic life-based NRWQC are
exceeded should translate into reduced effects to T&E species and potential improvement in species
populations. EPA's analysis does not account for the potential for the NPDES permit issuance process to
establish more stringent site-specific controls to meet applicable water quality standards (i.e.. water quality-
based effluent limits issued under Section 301(b)(1)(C)). The analysis may therefore overestimate any
potential impacts to T&E species and associated benefits.
EPA was unable to monetize the final rule's benefits on T&E species due to challenges in quantifying the
response of T&E populations to changes in water quality. Although numerous economic studies have
estimated WTP for T&E protection, these studies focused on estimating WTP to avoid species loss or
extinction, increase in the probability of survival, or an increase in species population levels (Subroy et al.,
2019; Richardson & Loomis, 2009). These studies, as summarized in Subroy et al. (2019), suggest that people
attach economic value to protection of T&E species ranging from $12.6 per household (in 2023$) for
Colorado pikeminnow to $208.5 (in 2023$) for lake sturgeon (both fish species).15 In addition, T&E species
may serve as a focus for eco-tourism and provide substantive economic benefit to local communities. For
example, Solomon, Corey-Luse and Halvorsen (2004) estimate that manatee viewing provides a net benefit
(tourism revenue minus the cost of manatee protection) of $14.1 million to $15.5 million (in 2023$) per year
for Citrus County, Florida.16
12 See ICF. (2022b). Revisions to the Water Quality Meta-Data andMeta-Regression Models after the 2020 Steam Electric Analysis
through December 2021 [Memorandum], for additional detail on updating the meta-analysis.
13 The U.S. Endangered Species Act (ESA) defines "take" to mean "to harass, harm, pursue, hunt, shoot, wound, kill, trap, capture,
or collect, or to attempt to engage in any such conduct." 16 U.S. Code § 1532
14 Because the regulatory options reduce pollutant loads, the opposite (receiving waters meet aquatic life-based NRWQC under the
baseline conditions but do not meet the NRWQC under the regulatory options) does not apply to this analysis.
15 Values adjusted from $8.32 and $138 per household per year (in 2006$), respectively, using the CPI.
16 Range adjusted from $8.2 million to $9 million (in 2001 $), using the CPI.
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2.2.3 Changes in Sediment Contamination
Effluent discharges from steam electric power plants can also contaminate waterbody sediments. For
example, sediment adsorption of arsenic, selenium, and other pollutants found in FGD wastewater, BA
transport water, CRL and legacy wastewater discharges can result in accumulation of contaminated sediment
on stream and lake beds (Ruhl et al., 2012), posing a particular threat to benthic (/'. 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 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).
In waters receiving direct discharges from steam electric power plants, EPA examined potential exposures of
ecological receptors (/'. e., sediment biota) to pollutants in contaminated sediment. Benthic organisms can be
affected by pollutant discharges such as mercury, nickel, selenium, and cadmium (U.S. EPA, 2024b). The
pollutants in steam electric power plant discharges may accumulate in living benthic organisms that obtain
their food from sediments and pose a threat to both the organism and humans consuming the organism. As
discussed in the EA, EPA modeled sediment pollutant concentrations in immediate receiving waters and
compared those concentrations to threshold effect concentrations (TECs) for sediment biota (U.S. EPA,
2024b). In 2015, EPA also evaluated potential risks to fish and waterfowl that feed on aquatic organisms with
elevated selenium levels and found that steam electric power plant selenium discharges elevated the risk of
adverse reproduction impacts among fish and mallards in immediate receiving waters (U.S. EPA, 2015b).
By reducing discharges of pollutants to receiving reaches, the final rule may reduce the contamination of
waterbody sediments, impacts to benthic organisms, and the probability that pollutants could 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 the associated benefits.
2.3 Water Supply and Use
The regulatory options are projected to reduce loadings of steam electric pollutants to surface waters relative
to the baseline, and thus they may affect the uses of these waters for drinking water supply and agriculture.
EPA implemented a treatment cost elasticity approach to quantify avoided drinking water treatment costs
from reductions in total nitrogen and total suspended solids. This analysis is summarized in this section and
described in more detail in Chapter 9 (see Section 9.1).
2.3.1 Drinking Water Treatment Costs
The regulatory options have the potential to affect drinking water treatment costs. Numerous studies have
shown an unequivocal link between higher treatment costs and lower source water quality (see Heberling et
al. (2022) for a non-exhaustive list of studies). Using data from 24 U.S. and non-U.S. studies, Price and
Heberling (2018) developed elasticities for various water quality parameters, including nitrogen
concentrations, phosphorus and sediment loadings, TOC, turbidity, and pH. EPA used these elasticities for
turbidity and nitrogen to estimate potential drinking water treatment cost savings. The effects of reductions in
other pollutants such as phosphorus, halogens, metals, and toxic chemicals are described qualitatively due to
uncertain elasticities between these parameters and drinking water treatment costs, the lack of information on
baseline concentrations of these pollutants at source water intakes, and to avoid the possibility of double-
counting treatment cost savings.
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2.3.1.1 Nutrients
Eutrophication, which is most commonly caused by an overabundance of nitrogen and phosphorus, is one of
the main causes of taste and odor impairment in drinking water and can have a major negative impact on
public perceptions of drinking water safety. The incremental cost of treating drinking water to address foul
tastes and odors due to excess nutrients and the presence of algal blooms can be substantial (Mosheim &
Ribaudo, 2017). Treatment may involve filtration, chemical treatment, or other processes (see Khera, Ransom
and Speth (2013) for more information on treatment practices that may be employed by small drinking water
systems). Recent work has estimated that drinking water systems nationwide incur nutrient pollution
treatment costs in excess of $225 million annually (Andarge, 2022). Price and Heberling (2018) combined
prior studies of the effect of nutrients on drinking water treatment costs, showing that a 1 percent change in
nitrogen (as nitrate) concentration in source water leads to lead to a 0.05 to 0.06 percent change in drinking
water treatment costs among all U.S. and non-U.S. studies. The one U.S. study with key controls for possible
confounders yielded an elasticity of 0.06, but EPA instead employed a range of elasticity values of 0.05 to
0.06 to incorporate uncertainty. EPA combines the range of elasticities with estimates of baseline drinking
water treatment costs to estimate the cost savings that are anticipated to accrue from this regulatory action.
Given the uncertainty in the treatment cost elasticity for phosphorus, EPA did not calculate cost changes with
respect to phosphorus. From nitrogen pollution reductions alone, EPA estimated annualized drinking water
treatment cost savings from $357,000 to $552,000 across all regulatory options assuming a 2 percent discount
rate. See details in Section 9.1.
2.3.1.2 Total Suspended Solids
Drinking water treatment costs associated with fluctuations in TSS have been quantified in prior EPA
regulatory analyses including the 2004 Meat and Poultry Products Effluent Limitation Guidelines and the
2009 Effluent Limitation Guidelines and Standards for the Construction and Development Industry (U.S.
EPA, 2004b, 2009b). Water systems address TSS using chemical treatment with coagulants such as alum or
ferrous sulfate. Coagulant application varies in dosage depending on the influent concentrations of TSS, and
thus water systems accrue variable costs in the form of coagulant purchases that vary with TSS in source
water. Treatment for TSS also produces coagulated sediment in proportion to the influent concentration of
TSS and the quantity of coagulant added, and disposal of this coagulated sediment results in additional
variable costs for drinking water systems. Elasticity estimates for TSS in Price and Heberling (2018) are
based on three studies, two of which date to 1987 and 1988. Only one of these studies included key controls,
suggesting that a 1 percent change in sediment loads results in drinking water treatment cost changes of
0.05 percent. The elasticity estimates for turbidity in Price and Heberling (2018) are more precisely estimated
across twelve studies, and the five studies controlling for key confounders suggest that a 1 percent increase in
turbidity increases drinking water treatment costs by 0.10 to 0.12 percent. EPA therefore converts TSS
measurements to turbidity levels and applies the turbidity elasticity from Price and Heberling (2018) to derive
treatment cost savings from TSS reductions. The approach of converting TSS to turbidity was also applied for
this benefit category in the 2009 Effluent Limitation Guidelines and Standards for the Construction and
Development Industry (U.S. EPA, 2009b). EPA estimates that annualized treatment cost savings from TSS
loading reductions are between $92,000 and $160,000 at a 2 percent discount rate. See details in Section 9.1.
2.3.1.3 Metals and Toxic Chemicals
EPA conducted a screening-level assessment to evaluate the potential for changes in costs incurred by public
drinking water systems from changes in metal and toxic concentrations in source waters and concluded that
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such changes, while they may exist, are likely to be negligible. The assessment involved identifying the
pollutants for which treatment costs may vary depending on source water quality, estimating changes in
downstream concentrations of these pollutants at the location of drinking water intakes, and determining
whether modeled water quality changes have the potential to affect drinking water treatment costs. Based on
this analysis, EPA determined that there are no drinking water systems drawing water at levels that exceed an
MCL for metals and other toxics17 listed in Table 2-2 such as selenium and cyanide under either the baseline
or the regulatory options (see Section 4.3.2.3 for details). EPA estimated no changes in MCL exceedances
under the regulatory options. Accordingly, EPA did not conduct an analysis of changes in treatment costs
incurred by public water systems (PWS) given the relatively small changes in source water quality expected
under the final rule and data gaps regarding effects on treatment system operations.
2.3.1.4 Halogens
Halogens found in source water can react during routine drinking water treatment to generate harmful DBPs
at levels that vary with site-specific conditions (Good & VanBriesen, 2017, 2019; Regli et al., 2015; U.S.
EPA, 2016c). EPA estimated the costs of controlling DBP levels to the MCL in treated water as part of the
Stage 2 Disinfectants and Disinfection Byproduct Rule (DBPR). These costs include treatment technology
changes as well as non-treatment costs such as routine monitoring and operational evaluations. PWS may
adjust their operations to control DBP levels, such as changing disinfectant dosage, moving the chlorination
point, or enhancing coagulation and softening. These changes carry "negligible costs" (U.S. EPA, 2005c,
pages 7-19). Where low-cost changes are insufficient to meet the MCL, PWS may need to incur irreversible
capital costs to upgrade their treatment process to use alternative disinfection technologies such as ozone,
ultraviolet light, or chloride dioxide; switch to chloramines for residual disinfection; or add a pre-treatment
stage to remove DBP precursors (e.g., microfiltration, ultrafiltration, aeration, or increased chlorine levels and
contact time). Some drinking water treatment facilities have already upgraded their treatment systems as a
direct result of halogen discharges from steam electric power plants (United States of America v. Duke
Energy, "United States of America v. Duke Energy," 2015; Rivin, 2015). However, not all treatment
technologies remove sufficient organic matter to control DBP formation to required levels (Watson, Farre &
Knight, 2012). Thus, increased halogens levels in raw source water could translate into permanently higher
drinking water treatment costs at some plants, in addition to posing increased human health risk. Conversely,
reducing halogen levels in source waters can reduce the health risk, even where treatment changes have
already occurred.18 In some cases, operation and maintenance (O&M) costs may also be reduced.
EPA quantified halogen treatment cost elasticities using estimated operation and maintenance cost changes
presented in Chen et al. (2010). According to the estimates in that study, a one percent change in bromide
concentration in source waters leads to 0.14 and 0.86 percent change in drinking water operation and
maintenance costs in small and large water systems, respectively, in California. However, EPA did not
estimate PWS-level avoided treatment costs from bromide reductions resulting from this regulatory action
due to significant uncertainty in these elasticities. To start, existing treatment technologies at the majority of
PWS are not designed to remove halogens from raw surface waters, and so the coastal drinking water systems
17 Modeled drinking water concentrations reflect discharged pollutant loads from steam electric plants and from other facilities
reporting to the Toxics Resources Inventory (TRI).
18 Regli, S., Chen, J., Messner, M., Elovitz, M. S., Letkiewicz, F. J., Pegram, R. A., Pepping, T. J.,. . . Wright, J. M. (2015).
Estimating potential increased bladder cancer risk due to increased bromide concentrations in sources of disinfected drinking
waters. Environmental Science & Technology, 49(22), 13094-13102. estimated benefits of reducing bromide across various
types of water treatment systems.
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studied in Chen et al. (2010), which already contend with issues of seawater intrusion, are likely not
representative of other drinking water systems. In addition, there are other environmental sources of halogens,
and EPA has insufficient data on baseline bromide concentrations at source waters affected by this regulatory
action. While significant uncertainty prevented an analysis of avoided treatment costs from bromide, the
Agency assessed the changes in levels of halogens downstream from steam electric power plant outfalls and
estimated health outcomes (avoided bladder cancer cases) associated with reduced DBP formation at
downstream PWS (see Section 2.1.1 for a discussion of this benefit category and Chapter 4 for details of the
analysis).19
2.3.1.5 Chloride and Dissolved solids
Finally, excess chloride and TDS can corrode distribution system pipes and lead to the buildup of scale (a
mineral deposit), reducing water flow (U.S EPA, 2023m). Increased corrosion in water distribution systems
can also increase the leaching of lead and copper. Stets et al. (2018) found a strong statistical connection
between source water chemistry (i.e.. the chloride-sulfate mass ratio) and the probability of lead action level
exceedances (ALEs) in drinking water facilities. Because corrosion in water distribution systems is a costly
problem, the regulatory options have the potential to reduce costs to drinking water systems by reducing
chloride and TDS loadings and, as a result, corrosivity of source water.
2.3.2 Effects on Household Averting Expenditure
Households who perceive their tap water as unsafe frequently buy bottled water or engage in other averting
behaviors (e.g., use filtration systems) aimed at reducing potential exposure to harmful pollutants, and these
actions have associated costs. For example, Javidi and Pierce (2018) estimate the minimum expenditures on
bottled water by all U.S. households who perceive their tap water as unsafe at $7.0 billion (2023$) annually.20
In particular, frequent algal blooms are generating growing public concern due to their impact on drinking
water safety. A study by Liu and Klaiber (2023) found that averting behavior in response to a 3-day water
advisory due to a harmful algal bloom outbreak in 2014 in Toledo, Ohio persisted for up to a month with total
averting costs for each household averaging approximately $4.60.21 The regulatory options have the potential
to affect source water quality and, as a result, to affect households' perception of tap water safety and reliance
on bottled water to meet their consumption standards.
2.3.3 Irrigation and Other Agricultural Uses
Irrigation accounts for 42 percent of the total U.S. freshwater withdrawals and approximately 80 percent of
the Nation's consumptive water use. Irrigated agriculture provides important contributions to the U.S.
economy accounting for approximately 40 percent of the total farm sales (Hellerstein, Vilorio & Ribaudo,
2019). Pollutants in steam electric power plant discharges can affect the quality of water used for irrigation
and livestock watering. Although elevated nutrient concentrations in irrigation water would not adversely
19 EPA's separate proposed rulemaking to regulate discharges of per- and polyfluoroalkyl substances in drinking water could result
in implementation of drinking water treatment technologies that would reduce DBP levels during the analysis period.
20 Values adjusted from $5.65 billion per year (in 2017$), using the CPI.
21 The study relied on household level data for bottled water purchases to estimate household effect models of averting behavior.
The average increase in bottled water expenditures was calculated across all households in the affected areas, of which only some
households purchased bottled water after the 3-day advisory. Between 12 percent and 20 percent of households purchased bottled
water before the drinking water advisory. The share increased to 34 percent in the two weeks following the 3-day drinking water
advisory (66 percent did not purchase bottled water after the 3-day advisory). Values adjusted from $3.60 per household per year
(in 2014$), using the CPI.
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affect its usefulness for plants, other steam electric pollutants, such as arsenic, mercury, lead, cadmium, and
selenium have the potential to affect soil fertility and enter the food chain (National Research Council, 1993;
Zhang et al., 2018). For example, the same heavy metals found in oilfield produced waters (including barium,
lead, and chromium) have been shown to accumulate in soil, plants, and oranges (Zhang et al., 2018).
Additionally, nutrients can increase eutrophication, promoting cyanobacteria blooms that can kill livestock
and wildlife that drink the contaminated surface water. TDS can impair the utility of water for both irrigation
and livestock use. EPA did not quantify or monetize effects of quality changes in agricultural water sources
arising from the regulatory options due to data limitations on how costs vary with relatively small estimated
changes in water quality.
2.4 Other Economic Effects
The regulatory options may have other economic effects stemming from changes in sediment deposition in
reservoirs and navigational waterways; changes in tourism, commercial fish harvests, and property values.22
EPA estimated the changes in sediment deposition in reservoirs and navigational waterways. Chapter 9
discusses the associated benefits. Other benefit categories (e.g., effects on property values) are discussed
qualitatively in the following sections.
2.4.1 Reservoir Capacity
Reservoirs serve many functions, including storage of drinking and irrigation water supplies, flood control,
hydropower supply, and recreation. Streams can carry sediment into reservoirs, where it can settle and build
up overtime, reducing reservoir capacity and the useful life of reservoirs (Graf et al., 2010; Palinkas & Russ,
2019; Rahmani et al., 2018). Reservoir capacity has been diminishing over time. At a national scale, Randle et
al. (2021) found that total reservoir storage capacity has dropped from apeak of 850 Gm3 to 810 Gm3. At a
state scale, Rahmani et al. (2018) found that all 24 federally operated reservoirs in Kansas have collectively
lost 17 percent of their original capacity with the highest single-reservoir loss of 45 percent. Dredging and
other sediment management strategies can be used to reclaim capacity (Hargrove et al., 2010; Miranda, 2017;
Morris, 2020; Randle et al., 2021; Winkelman. M.O., Sens & Marcus, 2019).23 EPA expects that changes in
suspended solids discharges under the regulatory options could affect reservoir maintenance costs by
changing the frequency or volume of dredging activity. Changes in sediment loads could result in a modest
decrease in dredging costs in reservoirs under all regulatory options. See Section 9.2 for details.
2.4.2 Sedimentation Changes in Navigational Waterways
Navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are an integral part of the
United States' transportation network (Clark, Haverkamp & Chapman, 1985). Navigable channels are prone
to reduced functionality due to sediment build-up, which can reduce the navigable depth and width of the
waterway (Clark, Haverkamp & Chapman, 1985; Ribaudo & Johansson, 2006). For many navigable waters,
22 EPA estimated changes in the marketability of coal combustion ash as a benefit of the 2015 rule (U.S. Environmental Protection
Agency. (2015a). Benefit and Cost Analysis for the Effluent Limitations Guidelines and Standards for the Steam Electric Power
Generating Point Source Category. (EPA-821-R-15-005). ). However, based on the baseline for this rule which already requires
ash to be handled dry, EPA does not expect incremental changes in the amount of ash handled dry vs. wet and benefits from
increased marketing of coal combustion ash under any of the regulatory options.
23 Other sedimentation management strategies may be used instead of, or in combination with, dredging. This includes reducing
sediment yield through watershed management practices and routing sediments through or around reservoirs (Morris, G. L.
(2020). Classification of Management Alternatives to Combat Reservoir Sedimentation. Water, 12(3).
https://doi.org/10.3390/wl2030861 ; Randle, T. J., Morris, G. L., Tullos, D. D., Weirich, F. H., Kondolf, G. M., Moriasi, D. N.,
Annandale, G. W.,. . . Wegner, D. L. (2021). Sustaining United States reservoir storage capacity: Need for a new paradigm.
Journal of Hydrology, 602, 126686. https://doi.Org/https://doi.org/10.1016/j.jhydrol.2021.126686 ).
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periodic dredging is necessary to remove sediment and keep them passable. For example, the U.S. Army
Corps of Engineers (USACE) maintains the Southwest Pass24, the most highly utilized commercial deep-draft
waterway in the country, and its rapid-onset shoaling has led to prolonged periods of draft restrictions for
transiting vessels (e.g., reductions in the amount of cargo that can be transported per voyage). To counteract
channel shoaling, the USACE has dredged an annual average 25 million cubic yards of sediment since 2015
(Hartman et al., 2022). Dredging navigable waterways can be costly. Following the previous example, total
dredging expenditures in the Southwest Pass for the 2019 fiscal year amounted to $147.8 million (dredging
expenditures between the 2015 and 2018 fiscal years ranged from $66.0 million to $65.4 million) (Hartman et
al., 2022).
EPA estimated that all regulatory options would reduce sediment loadings to surface waters and reduce
dredging of navigational waterways. EPA quantified and monetized these benefits based on the avoided cost
for projected changes in future dredging volumes. Section 9.2 describes this analysis.
2.4.3 Commercial Fisheries
Pollutants in steam electric power plant discharges can reduce fish populations by inhibiting reproduction and
survival of aquatic species. These changes may negatively affect commercial fishing industries as well as
consumers of fish, shellfish, and fish and seafood products. Estuaries are particularly important breeding and
nursery areas for commercial fish and shellfish species (Alkire, Silldorff & Wang, 2020; Brame et al., 2019;
Beck et al., 2001). In some cases, excessive pollutant loadings can lead to the closure of shellfish beds,
thereby reducing shellfish harvests and causing economic losses from reduced harvests (Jin, Thunberg &
Hoagland, 2008; Trainer et al., 2007; Islam & Masaru, 2004). 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 harvests, which in
turn could lead to an increase in producer and consumer surplus. Conversely, an increase in pollutant loadings
could lead to negative impacts on fish and shellfish harvest.
EPA did not quantify or monetize impacts to commercial fisheries under the regulatory options. EPA
estimated that eight steam electric power plants discharge BA transport water, FGD wastewater, CRL or
legacy wastewater directly to the Great Lakes or to estuaries. Large distances and stream flows greatly reduce
the relative impact of steam electric power plants discharging upstream from these systems. Although
estimated decreases in annual average pollutant loads under the regulatory options may benefit local fish
populations and commercial harvest, the overall effects to commercial fisheries arising from the regulatory
options are difficult to quantify but are likely to be relatively small. Commercial species potentially affected
by steam electric discharges account for approximately 1 percent of total landings value in the United States.25
24 This is the entrance channel for a port system which encompasses waters ranging from the Mississippi River in Baton Rouge,
Louisiana to the Gulf of Mexico Project (Hartman, M. A., Mitchell, K. N., Dunkin, L. M., Lewis, J., Emery, B., Lenssen, N. F.,
& Copeland, R. (2022). Southwest Pass Sedimentation and Dredging Data Analysis. Journal of Waterway, Port, Coastal, and
Ocean Engineering, 148(2), 05021017. https://doi.org/doi:10.1061/(ASCE)WW.1943-5460.0000684 ).
25 Based on U.S. commercial fisheries landing values in 2019. EPA obtained commercial fisheries landing data for areas that may
be affected by steam electric discharges (Mississippi (Big Lake, connected to Biloxi Bay), Tampa, FL area (closest port to
Hillsborough Bay), Lake Eerie, and Lake Michigan) and compared the potentially affected commercial fisheries landing value to
total U.S. commercial fisheries landing value (marine and Great Lakes). EPA obtained commercial fishery landing value for
Mississippi and the U.S. from NOAA Fisheries (National Oceanic and Atmospheric Administration. (2022). NOAA Fisheries -
U.S. Commercial Fish Landings. https://www.fisheries.noaa.gov/foss/f?p=215:200:1735541630262:Mail:NO::: ), for the Tampa
area from the Florida Fish and Wildlife Conservation Commission (Florida Fish and Wildlife Conservation Commission. (2022).
Commercial Fisheries Landings Summaries. https://app.myfwc.com/FWRI/PFDM/ReportCreator.aspx ), and for the Great Lakes
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Moreover, most species of fish have numerous close substitutes. The economic literature suggests that when
there are plentiful substitute fish products (e.g., chicken is substitute for fish) the measure of consumer
welfare (consumer surplus) is unlikely to change as a result of small changes in fish landings, such as those
EPA expects under the regulatory options.
2.4.4 Tourism
Discharges of pollutants may also affect the tourism and recreation industries (e.g., boat rentals, sales at local
restaurants and hotels) and, as a result, local economies in the areas surrounding affected waters due to
changes in recreational opportunities (U.S. Bureau of Economic Analysis, 2021; Mojica & Fletcher, 2020;
Highfill & Franks, 2019). The effects of water quality on tourism are likely to be highly localized. Moreover,
since substitute tourism locations may be available, increased tourism in one location (e.g., the vicinity of
steam electric power plants) may lead to a reduction in tourism in other locations or vice versa. Due to the
relatively small water quality changes expected from the regulatory options (see Section 3.4 for details) and
availability of substitute sites, the overall effects on tourism and, as a result, social welfare is likely to be
negligible. Therefore, EPA did not quantify or monetize this benefit category.
2.4.5 Property Values
Discharges of pollutants may affect the aesthetic quality of water resources by altering water clarity, odor, and
color in the receiving and downstream reaches. Technologies implemented by steam electric power plants to
comply with the regulatory options remove nutrients and sediments to varying degrees and have varying
effects on water eutrophication, algae production, water turbidity, and other surface water characteristics.
Several studies (e.g., Austin, 2020; Bin & Czajkowski, 2013; Cassidy, Meeks & Moore, 2023; Gibbs et al.,
2002; Guignet et al., 2022; Irwin & Wolf, 2022; Kemp, Ng & Mohammad, 2017; Kuwayama, Olmstead &
Zheng, 2022; Leggett & Bockstael, 2000; Liu, Opaluch & Uchida, 2017; Mamun et al., 2023; Moore et al.,
2020; Netusil, Kincaid & Chang, 2014; Tang, Heintzelman & Holsen, 2018; Tuttle & Heintzelman, 2014;
Walsh, Milon & Scrogin, 2011; Walsh et al., 2017; Wolf, Klaiber & Gopalakrishnan, 2022) suggest that both
waterfront and non-waterfront properties are more desirable when located near unpolluted water. For
example, a meta-analysis of 18 hedonic studies (Guignet et al., 2022) suggests that, on average, a one-percent
increase in water clarity leads to a 0.19 percent increase in waterfront home prices and 0.04 percent increase
in non-waterfront homes prices within 500 meters of the waterbody.26 The authors also found that site specific
effects on home prices are likely to be influenced by the baseline water clarity and vary by region. A hedonic
analysis of property values across six Ohio counties (Wolf & Klaiber, 2017) found a decline in property
values from increased frequency of algal blooms in lakes between 11 percent and 17 percent for near lake
homes and 22 percent for lake adjacent homes. Public perception of potential health risks associated with
toxic pollutant discharges from steam electric plants may also have a negative impact on nearby property
values. For example, Austin (2020) finds that, in North Carolina, negative impacts of coal ash discharges on
drinking water led to a 12 to 14 percent decline in sale price for homes within one mile of a coal ash pond
after potential risks were made more salient by a state regulation. Therefore, the value of properties located in
from the Great Lakes Fishery Commission (Great Lakes Fishery Commission. (2022). Commercial fish production in the Great
Lakes 1867-2020. http://www.glfc.org/great-lakes-databases.php ). EPA assumed that all fish species in Lake Eerie and Lake
Michigan may be affected by steam electric discharges. For commercial fishery landings in Tampa and Mississippi, EPA
removed deep sea fish species (e.g., tuna, sharks, jacks, and octopus) from consideration of fish potentially affected by steam
electric power plant discharges since they are unlikely to use the estuarine areas where discharges occur.
26 These elasticities are based on the base meta-regression (see Model 1 in Table 3 on page 204, Guignet, D., Heberling, M. T.,
Papenfus, M., & Griot, O. (2022). Property values, water quality, and benefit transfer: A nationwide meta-analysis. Land
Economics, 050120-0062R1. ).
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proximity to waters affected by steam electric plant discharges may increase due to reductions in discharges
of FGD wastewater, BA transport water, CRL, and legacy wastewater.
EPA did not quantify or monetize the potential change in property values associated with the regulatory
options because the water quality metrics or pollutants addressed in existing studies do not provide a good
match to the list of pollutants covered by the steam electric ELG. As shown in Guignet et al. (2022), water
clarity is the most common water quality measure analyzed in the hedonic literature, followed by fecal
coliform and chlorophyll a 21 The magnitude of the potential effect on property values from reducing steam
electric discharges is uncertain. It depends on many factors, including the number of housing units located in
the vicinity of the affected waterbodies,28 community characteristics (e.g., residential density), housing stock
(e.g., single family or multiple family), and the effects of steam electric pollutants on the aesthetic quality of
surface water. Because changes in the aesthetic quality of surface waters (e.g., clarity) that may result from
the relatively small changes in pollutant concentrations under the regulatory options are difficult to quantify,
EPA did not estimate the impacts of the final rule on property values. In addition, there may be an overlap
between shifts in property values and the estimated total WTP for surface water quality changes discussed in
Section 2.2.1.
2.5 Changes in Air Pollution
The final rule is expected to affect air pollution through three main mechanisms: 1) changes in energy use by
steam electric power plants to operate wastewater treatment and other systems needed to comply with the
final rule; 2) changes in transportation-related emissions due to changes in trucking of CCR and other 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 ELG compliance costs. The three mechanisms can produce
changes in different directions. For example, increased energy use by power plant tend to increase air
emissions associated with power generation, but those changes are relatively small when compared to the
changes resulting from shifts in the electricity generation mix away from coal-fired generation and toward
sources with lower emission factors. These shifts in generation mix result tend to reduce overall emissions at
the national level, although the localized changes in air pollutant emissions may be positive or negative
depending on which electricity generating units produce more or less electricity as a result of these shifts.
As described in Chapter 5 of the RIA, EPA used the Integrated Planning Model (IPM®), a comprehensive
electricity market optimization model that can evaluate impacts within the context of regional and national
electricity markets, to analyze impacts of the final rule (i.e., Option B). Electricity market analyses using IPM
project that the final rule (Option B) will expand on the baseline trend by shifting away from coal fired
electric power generation toward generation from other energy sources, such as natural gas and renewables.
Relative to the baseline, IPM projects coal-fired generation to decline as a result of the final rule. These
changes are offset in part by an increase in natural gas generation, nuclear generation, and generation by
renewables. Differences in emissions factors across energy sources generally results in net reductions in air
27 The majority of recently published studies that were not included in ibid, also analyzed impacts on water clarity on home prices
(e.g., Irwin, N., & Wolf, D. (2022). Time is money: Water quality's impact on home liquidity and property values. Ecological
economics, 199,107482., Mamun, S., Castillo-Castillo, A., Swedberg, K., Zhang, J., Boyle, K. J., Cardoso, D., Kling, C. L.,. . .
Phaneuf, D. (2023). Valuing water quality in the United States using a national dataset on property values. Proceedings of the
National Academy of Sciences, 120(15), e2210417120. ).
28 In a review of 36 hedonic studies that focus on the impact of water quality on housing values, Guignet, D., Heberling, M. T.,
Papenfus, M., & Griot, O. (2022). Property values, water quality, and benefit transfer: A nationwide meta-analysis. Land
Economics, 050120-0062R1. note that some studies have detected property value impacts up to a mile away from impacted
waterways.
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emissions from electricity generating units across all modeled pollutants at the national level (CO2, SO2, NOx,
direct PM2.5,PMio, Hg, and hydrogen chloride (HC1)). Overall for the three mechanisms (auxiliary services,
transportation, and market-level generation), EPA estimates net reductions in CO2, SO2, and NOx emissions
as compared to the baseline at the national level. EPA also estimated small increases in methane (CH4)
emissions from transportation, but these increases are much smaller than the net reductions in CO2 emissions.
However, the distribution of the changes may result in localized increases even as the overall changes
nationwide are decreases, and air emissions of some pollutants may increase in some years and decrease in
others. See the RIA for details (U.S. EPA, 2024e).
CO2 is the most prevalent of the greenhouse gases, which are air pollutants that EPA has determined endanger
public health and welfare through their contribution to climate change. EPA used estimates of the social cost
of greenhouse gases (SC-GHG) - specifically, the social cost of carbon (SC-CO2) and of the social cost of
methane (SC-CH4) - to monetize the benefits of changes in CO2 and CH4 emissions as a result of the final
rule. The SC-GHG is the monetary value of the net harm to society associated with emitting a metric ton of
the GHG in question into the atmosphere in a given year, or the benefit of avoiding that increase. In principle,
the SC-GHG includes the value of all climate change impacts, including (but not limited to) changes in net
agricultural productivity, human health effects, property damage from increased flood risk and natural
disasters, disruption of energy systems, risk of conflict, environmental migration, and the value of ecosystem
services. Chapter 8 details this analysis.
NOx, and SO2 are known precursors to PM2.5, a criteria air pollutant that has been associated with a variety of
adverse health effects, including premature mortality and hospitalization for cardiovascular and respiratory
diseases (e.g., asthma, chronic obstructive pulmonary disease [COPD], and shortness of breath). EPA
quantified changes in direct PM2.5 emissions and in emissions of PM2.5 and ozone29 precursors NOx and SO2
and assessed impacts of those emission changes on air quality changes across the country using the
Comprehensive Air Quality Model with Extensions (CAMx) (Ramboll Environ International Corporation,
2016). EPA then used spatial fields of baseline and post-compliance air pollutant concentrations as input to
Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) to estimate incremental human
health effects (including the potential for premature mortality and morbidity) from changes in ambient air
pollutant concentrations (U.S. EPA, 2018a). Chapter 8 details this analysis.
The final rule may also affect air quality through changes in electricity generation units emissions of larger
particulate matter (PM10) and hazardous air pollutants (HAP) including mercury and hydrogen chloride. The
health effects of mercury are detailed in the EA (U.S. EPA, 2024b). Hydrogen chloride is a corrosive gas that
can cause irritation of the mucous membranes of the nose, throat, and respiratory tract. For more information
about the impacts of mercury and hydrogen chloride emissions, see the Mercury and Air Toxics Standards
(MATS) for Power Plants,M including the 2023 proposed National Emission Standards for Hazardous Air
Pollutants: Coal- and Oil-Fired Electric Utility Steam Generating Units Review of the Residual Risk and
Technology Review (88 FR 24854).
The final rule may also affect air quality if steam electric power plants alter their coal storing and handling
practices, since Jha and Muller (2018) found that a 10 percent increase in coal stockpiles held by U.S. power
29 Emissions of nitrogen oxides (NOx) lead to formation of both ozone and PM2.5 while SO2 emissions lead to formation of PM2.5
only.
30 See https://www.epa.gov/statioiiary-soiirces-air-pollutioii/mercnrv-and-air-toxics-standards.
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2: Benefits Overview
plants results in a 0.09 percent increase in average PM2 5 concentration levels within 25 miles of these plants.
In addition to health effects from air emissions, air pollution can create a haze that affects visibility. Reduced
visibility could impact views in national parks by softening the textures, fading colors, and obscuring distant
features and therefore reduce the value of recreational activities (e.g., Boyle et al., 2016; Pudoudyal, Paudel &
Green, 2013). A number of studies (e.g., Bayer, Keohane & Timmins, 2006; Beron, Murdoch & Thayer,
2001; Chay & Greenstone, 1998) also found that reduced air quality and visibility can negatively affect
residential property values.
2.6 Summary of Benefits Categories
Table 2-3 summarizes the potential social welfare effects of the regulatory options analyzed for the final rule
and the level of analysis applied to each category. As indicated in the table, only a subset of potential effects
can be quantified and monetized. The monetized welfare effects include reductions in some human health
risks, use and non-use values from surface water quality improvements, reduced costs for dredging reservoirs
and navigational waterways, and changes in air emissions. Other welfare effect categories, including changes
in waters exceeding NRWQC, were quantified but not monetized. Although EPA was not able to quantify or
monetize other welfare effects, including some other human health risks and impacts to commercial fisheries,
those unquantified benefits may be relatively small compared to other monetized benefits.31 EPA evaluated
these effects qualitatively as discussed above in Section 2.1 through Section 2.5.
Table 2-3: Estimated Welfare Effects of Changes in Pollutant Discharges from Steam Electric Power
Plants
Benefits Analysis
Category
Effect of Regulatory Options
Quantified
Monetized
Methods (Report
Chapter where
Analysis is Detailed)
Human Health Benefits from Surface Water Quality Improvements
Changes in human health
Changes in exposure to halogenated
VSL and COI (Chapter
effects (e.g., bladder
DBPs in drinking water
4)
cancer) associated with
halogenated DBP
exposure via drinking
water
IQ losses to children ages
Changes in childhood exposure to lead
IQ point valuation
0 to 7
from consumption of self-caught fisha
(Chapter 5)
Need for specialized
Changes in childhood exposure to lead
Qualitative discussion
education
from consumption of self-caught fisha
(Chapter 5)
Incidence of
Changes in exposure to lead from
VSL (Chapter 5)
cardiovascular disease in
consumption of self-caught fisha
adults
IQ losses in infants
Changes in in-utero mercury exposure
IQ point valuation
from maternal consumption of self-
(Chapter 5)
caught fisha
Incidence of skin cancer
Changes in exposure to arsenic from
COI (Chapter 5);
consumption of self-caught fisha
Qualitative discussion
(Chapter 2)
31 The 2015 and 2020 rules, which are included in the baseline for this analysis, significantly reduced toxic pollutant and nutrient
loadings, making additional reductions estimated for this final rule smaller, particularly when compared to the benefits that can
be quantified and monetized.
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Table 2-3: Estimated Welfare Effects of Changes in Pollutant Discharges from Steam Electric Power
Plants
Benefits Analysis
Category
Effect of Regulatory Options
Quantified
Monetized
Methods (Report
Chapter where
Analysis is Detailed)
Other adverse health
Changes in exposure to toxic pollutants
Human health criteria
effects (cancer and non-
(lead, cadmium, thallium, etc.) via fish
exceedances (Chapter
cancer)
consumption or drinking water
5); Exposure above
V
non-cancer health
thresholds (Chapter 4,
EA; U.S. EPA, 2024b);
Qualitative discussion
(Chapter 2)
Reduced adverse health
Changes in exposure to pollutants from
Qualitative discussion
effects (e.g., rash and
recreational water uses
(Chapter 2)
irritation from dermal
exposure to toxins in
HABs)
Ecological Condition and Recreational Use Effects from Surface Water Quality Changes
Aquatic and wildlife
Changes in ambient water quality in
habitatb
receiving reaches
Water-based recreation15
Changes in swimming, fishing, boating,
and near-water activities from water
quality changes
Benefit transfer
Aesthetics15
Changes in aesthetics from shifts in
V
V
(Chapter 6);
water clarity, color, odor, including
Qualitative discussion
nearby site amenities for residing,
(Chapter 2)
working, and traveling
Non-use values'5
Changes in existence, option, and
bequest values from improved
ecosystem health
Protection of T&E
Changes in T&E species habitat and
Habitat range
species
potential effects on T&E species
intersecting with
populations
V
reaches with NRWQC
exceedances (Chapter
7); Qualitative
discussion (Chapter 2)
Sediment contamination
Changes in deposition of toxic pollutants
to sediment
Qualitative discussion
(Chapter 2)
Water Supply and Use
Water treatment costs
Changes in quality of source water used
Avoided cost of
for drinking water
for drinking
V
V
drinking water
treatment (Chapter 9);
Qualitative discussion
(Chapter 2)
Water treatment costs
Changes in quality of source water used
Qualitative discussion
for irrigation and other
for irrigation and other agricultural uses
(Chapter 2)
agricultural uses
Other Economic Effects
Dredging costs
Changes in sedimentation and costs for
Avoided cost of
maintaining navigational waterways and
S
S
dredging (Chapter 9);
reservoir capacity
Qualitative discussion
(Chapter 2)
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Table 2-3: Estimated Welfare Effects of Changes in Pollutant Discharges from Steam Electric Power
Plants
Category
Effect of Regulatory Options
Benefits Analysis
Quantified
Monetized
Methods (Report
Chapter where
Analysis is Detailed)
Commercial fisheries
Changes in fisheries yield and harvest
quality due to aquatic habitat changes
Qualitative discussion
(Chapter 2)
Tourism industries
Changes in participation in water-based
recreation
Qualitative discussion
(Chapter 2)
Property values
Changes in property values from
changes in water quality
Qualitative discussion
(Chapter 2)
Air Quality-Related Effects
Air emissions of PM2.5,
NOx and S02
Changes in mortality and morbidity from
exposure to particulate matter (PM2.5)
emitted directly or linked to changes in
NOx and S02 emissions (precursors to
PM2.5 and ozone)
V
V
VSL and COI (Chapter
8); Qualitative
discussion (Chapter 2)
Air quality effects of coal
stockpiles
Air quality effects of storing and
handling coal at steam electric power
plants
Qualitative discussion
(Chapter 2)
Air emissions of NOx and
S02
Changes in ecosystem effects; visibility
impairment; and human health effects
from direct exposure to N02, S02, and
hazardous air pollutants.
Qualitative discussion
(Chapters 2 and 8)
Air emissions of C02 and
ch4
Changes in climate change effects
V
V
Social cost of
greenhouse gases (SC-
GHG) (Chapter 8)
a. Reductions in discharges of lead, mercury, and other toxic pollutants may reduce concentrations of these pollutants in open seas,
thus reducing levels of pollutants in high trophic level fish harvested commercially. There are unquantified benefits associated with
all of these end points for those who consume commercially harvested fish, but these benefits are very difficult to estimate.
b. These values are implicit in the total WTP for water quality improvements.
Source: U.S. EPA Analysis, 2024
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3 Water Quality Effects of Regulatory Options
Changes in the quality of surface waters, aquatic habitats and ecological functions under the regulatory
options depend on several factors, including the operational characteristics of steam electric power plants,
treatment technologies implemented to control pollutant levels, the timing of treatment technology
implementation, and the hydrography of reaches receiving steam electric pollutant discharges, among others.
This chapter describes the surface water quality changes projected under the regulatory options. EPA modeled
water quality based on loadings estimated for the baseline and for each of the three regulatory options (Option
A through Option C). The differences in concentrations between the baseline and option scenarios represent
the changes attributable to the regulatory options. These changes inform the analysis of several of the benefits
described in Chapter 2 and detailed in later chapters of this report.
The analyses use pollutant loading estimates detailed in the TDD (U.S. EPA, 2024f) and expand upon the
analysis of immediate receiving waters described in the EA (U.S. EPA, 2024b) by estimating changes in both
receiving and downstream reaches. The EA provides additional information on the effects of steam electric
power plant discharges on surface waters and how they may change under the regulatory options.
3.1 Waters Affected by Steam Electric Power Plant Discharges
EPA estimates the regulatory options potentially affect 232 steam electric power plants with coal-fired
generating units after December 31, 2028 and/or CRL or legacy wastewater discharges. EPA used the United
States Geological Survey (USGS) medium-resolution National Hydrography Dataset (NHD) (USGS, 2018) to
represent and identify waters affected by steam electric power plant discharges, and used additional attributes
provided in version 2 of the NHDPlus dataset (U.S. EPA, 2019g) to characterize these waters.
Of the plants represented in the analysis, EPA estimated that 110 plants have non-zero pollutant discharges
under the baseline or the regulatory options for the wastestreams modeled for the benefits analyses (FGD
wastewater, BA transport water, CRL, or legacy wastewater).32,33 In the aggregate, the 110 plants discharge to
126 waterbodies (as categorized in NHDPlus), including lakes, rivers, and estuaries.34 Receiving reaches that
lack NHD classification for both waterbody area type and stream order generally correspond to reaches that
do not have valid flow paths35 for analysis of the fate and transport of steam electric power plant discharges
(see Section 3.3). Eleven steam electric power plants discharge FGD wastewater, BA transport water, CRL or
legacy wastewater to tidal reaches or the Great Lakes directly or through immediate tributaries or to waters
not connected to the hydrographic network.36 EPA did not assess pollutant loadings and water quality changes
32 The benefits analyses do not include loadings from unmanaged CRL and therefore omit some plants that are estimated to have
only this wastestream. These plants may incur compliance costs to comply with limits for unmanaged CRL for any discharge that
a permitting authority deems is the functional equivalent of a direct discharge and require a permit, but changes in unmanaged
CRL loads were not modeled explicitly. Costs are included, however, in the social costs presented in Chapters 11 and 12.
33 Of these 110 plants, 12 plants discharge to more than one waterbody. Also, of the 110 plants, 104 plants have non-zero pollutant
discharges under the baseline or the regulatory options for FGD wastewater, BA transport water, or CRL (6 plants have estimated
loads for legacy wastewater only).
34 Some plants discharge waste streams to multiple (two or three) different receiving waters.
35 In NHDPlus, the flow path represents the distance traveled as one moves downstream from the reach to the terminus of the
stream network. An invalid flow path suggests that a reach is disconnected from the stream network.
30 Four plants (Edgewater, Elm Road, JH Campbell, and Oak Creek) discharge non-zero loads to Lake Michigan, one plant
(Monroe) discharges to Lake Erie, one plant (Bay Front) discharges to Lake Superior, and four plants (Big Bend, Jack Watson,
Crist, and Winyah) discharge to estuaries or other tidal waters either directly or through immediate tributaries. Because Great
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associated with these waterbodies because of the lack of a defined flow path in NHDPlus, and in the case of
Great Lakes and esturaries the complexity of flow patterns and the relatively small changes in concentrations
expected.37 Thus, EPA estimated changes in water quality downstream from 101 steam electric plants
associated with a total of 114 receiving reaches representing the waterbodies in NHDPlus.38
3.2 Changes in Pollutant Loadings
EPA estimated post-technology implementation pollutant loadings for each plant under the baseline and the
regulatory options. The TDD details the methodology (U.S. EPA, 2024f). The sections below discuss the
approach EPA used to develop a profile of loading changes over time under the baseline and each regulatory
option and summarize the results.
3.2.1 Implementation Timing
Benefits analyses account for the temporal profile of environmental changes as the public values changes
occurring in the future less than those that are more immediate (OMB, 2023). As discussed in Section 1.3.3,
for the purpose of the economic impact and benefit analysis, EPA generally estimates that plants will
implement control technologies to meet the applicable rule limitations and standards as their permits are
renewed, and no later than December 31, 2029. This schedule recognizes that control technology
implementation is likely to be staggered over time across the universe of steam electric power plants. This in
turn can translate into variations in pollutant loads to waters over time.
To estimate the benefits of the regulatory options, EPA first developed a time profile of loadings for each
scenario (i.e.. baseline and each regulatory option), electricity generating unit (EGU), wastestream, and
pollutant that reflects the baseline loadings, the estimated loadings under the applicable technology basis, the
estimated technology implementation year for the plant, and the timing of any retirements or repowerings.
Specifically, EPA used baseline loadings starting in 2025 through the applicable technology implementation
year, applicable technology-based loadings corresponding to the analyzed scenario (baseline or regulatory
option) for all years following a plant's modeled implementation year, and zero loadings following a unit's
retirement or repowering (where applicable).
EPA then used this year-explicit time profile to calculate the annual average loadings discharged by each
plant for two distinct periods within the overall period of analysis of 2025 through 2049:39
Lakes and estuaries are complex waterbodies accurately modeling water quality impacts to these waters would require the
application of more complex models that was not feasible within this rulemaking. Finally, one plant (Gerald Gentleman)
discharges to a reservoir not connected to the stream network.
37 EPA looked at the changes in pollutant loadings and impacts to these systems in selected case studies as part of the analysis of
the 2015 rule. See 2015 EA for details; U.S. Environmental Protection Agency. (2015b). Environmental Assessment for the
Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source Category. (EPA 821-R-15-
006).
38 EPA analyzed a total of 185 plants with plants with coal-fired generating units after December 31,2028 and/or that generate the
wastestreams within the scope of the final rule. Not all these plants have costs and/or loads under the baseline or regulatory
options, so while the modeling scope is all 185 plants, as discussed in this section, some plants have zero loads whereas others
discharge to waters that lack a valid flow path (e.g., Great Lakes and estuaries), leaving 104 plants for which EPA analyzed
changes in downstream water quality.
39 EPA had initially analyzed regulatory options for which the technology implementation deadline was set to of 2030 and the
average loads calculated for two periods that reflected that deadline (i.e., 2026-2030 and 2031-2050). While EPA later revised the
compliance deadline to 2029, the Agency did not recalculate the average loads but instead shifted the periods and the associated
loading reductions by one year (i.e., 2025-2029 and 2030-2049). Because of the timing of the retirement of some generating units
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• Period 1, which extends from 2025 through 2029, when the universe of plants would transition from
current (baseline) treatment practices to practices that achieve the revised limits, and
• Period 2, which extends from 2030 through 2049 and is the post-transition period during which the
full universe of plants is projected to employ treatment practices that achieve the revised limits.
The analysis accounts for each plant's technology implementation year(s) and for announced unit retirements
or repowerings. Using average annual values for two distinct periods instead of a single average over the
entire period of analysis enables EPA to better represent the rule implementation and capture the transitional
effects of the regulatory options. While using an annual average does not show the differences between the
baseline and regulatory options for individual years within Period 1, EPA considers that the average provides
a reasonable measure of the transitional effects of the regulatory options given the categories of benefits that
EPA is analyzing, which generally result from changes in multi-year processes.
As discussed in the RIA (U.S. EPA, 2024e), there is uncertainty in the exact timing of when individual steam
electric power plants would be implementing technologies to meet the final rule or the other regulatory
options. This benefits analysis uses the same plant- and wastestream-specific technology installation years
used in the cost and economic impact analyses. To the extent that technologies are implemented earlier or
later, the annualized loading values presented in this section may under- or overstate the annual loads during
the analysis period.
3.2.2 Results
Differences in the stringency of effluent limits and pretreatment standards and the timing of their applicability
to steam electric power plants (and the resulting treatment technology implementation) mean that changes in
pollutant loads between the regulatory options and the baseline vary over the period of analysis. Within the
period of analysis, the years 2025-2029 represent a period of transition as plants implement treatment
technologies to meet the revised limits under the regulatory options, whereas years 2030 through 2049 have
steady state loadings that reflect implementation of technologies across all plants.4"
Table 3-1 summarizes the average annual reductions during Period 1 and Period 2 in FGD wastewater, BA
transport water, CRL, legacy wastewater,41 and total loads for selected pollutants that inform EPA's analysis
of the benefits discussed in Chapters 4 through 7 and Chapters 9 and 10. The regulatory options are estimated
to result in either no change or in reductions in pollutant loadings under an option as compared to the
baseline, with the reductions generally increasing as one progresses from Option A to Option C. Further,
loading reductions are largest during Period 2 when all steam electric plants have implemented the treatment
technologies associated with the limits, as compared to the transition period represented by Period 1.
relative to technology installation, the loading reductions reflected in analysis for Period 2 are smaller than would have been
obtained had EPA recalculated the average loads to reflect the earlier compliance year. The difference ranges between 0 percent
and 7 percent, depending on the pollutant and regulatory option, with an average across pollutants of 2 percent for the final rule
(Option B).
40 This steady state reflects unit retirements and repowerings. EPA accounted for unit retirements and repowerings by zeroing out
the loadings starting in the year following the change in status.
41 Loading reductions associated with legacy wastewater limits will occur only as plants close and dewater their existing ponds.
There is uncertainty on when plants may do so. For the purpose of this benefits analysis, EPA conservatively assumed that pond
closures will occur after 2049 and therefore estimated no loading reductions during the period of analysis for Options B and C.
To the extent that facilities close their ponds earlier, then the analysis understates the benefits of these two options.
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Legacy wastewater discharges and loading reductions achieved by the legacy wastewater limits in the final
rule would occur only as plants close and dewater their existing ponds. Given the uncertainty on when plants
may do so, for the purpose of this analysis EPA estimated no loading reductions during the period of analysis.
Similarly, certain plants could be required to treat unmanaged CRL discharged from landfills, surface
impoundments, or other features to meet the limits in the final rule. These limits would apply only in cases
where a permitting authority deems, on a case-by-case basis, that the discharge is functionally equivalent to a
direct discharge and requires a permit. Because these discharges are uncertain, EPA did not include changes
in pollutant loads from unmanaged CRL in the main analysis. Because the cost analysis detailed in the RIA
(U.S. EPA, 2024e) and the social costs presented in Chapters 11 and 12 of this document includes these costs
(based on the assumption that plants treat legacy wastewater discharges in 2049 and comply with the
unmanaged CRL limits in the same year as limits for other wastestreams), the benefits of the final rule are
understated when compared to the social costs.
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Table 3-1: Annual Average Reductions in Total Pollutant Loading in Period 1 (2025-2029) and Period 2 (2030-2049) for Selected Pollutants in
Steam Electric Power Plant Discharges, Compared to Baseline (lb/year)
Pollutant
Option Aa
Option B (Final Rule)a
Option C
Si
<
CO
CRLC
FGD
Legacy
Total"
Si
<
CO
CRLC
FGD
Legacy®
Total"
Si
<
CO
CRLC
FGD
Legacy®
Total"
Period 1 (2025-2029)
Antimony
39
0
48
0
88
39
21
48
0
108
41
22
55
0
117
Arsenic
21
143
66
0
230
21
175
66
0
263
22
177
75
0
274
Barium
238
512
1,600
0
2,350
238
805
1,600
0
2,640
251
819
1,810
0
2,880
Beryllium
0
0
15
0
15
0
0
15
0
15
0
0
17
0
17
Boron
11,900
0
2,600,000
0
2,610,000
11,900
121,000
2,600,000
0
2,730,000
12,500
127,000
2,930,000
0
3,070,000
Bromide
2,430,000
11,400
0
0
2,440,000
2,430,000
11,400
0
0
2,440,000
2,670,000
12,000
0
0
2,690,000
Cadmium
2
22
48
0
71
2
45
48
0
94
2
46
54
0
101
Chromium
11
9,180
73
0
9,260
11
9,220
73
0
9,300
12
9,220
83
0
9,310
Copper
9
31
43
0
82
9
52
43
0
103
9
53
48
0
110
Cyanide
0
0
10,800
0
10,800
0
0
10,800
0
10,800
0
0
12,200
0
12,200
Lead
23
0
39
0
62
23
0
39
0
62
25
0
43
0
68
Manganese
342
672
143,000
0
144,000
342
15,600
143,000
0
159,000
361
16,400
161,000
0
178,000
Mercury
0
4
1
0
5
0
5
1
0
6
0
5
1
0
6
Nickel
39
198
72
0
309
39
247
72
0
358
41
250
81
0
372
TN
5,900
0
85,800
0
91,700
5,900
0
85,800
0
91,700
6,220
0
96,800
0
103,000
TP
496
0
3,690
0
4,190
496
0
3,690
0
4,190
523
0
4,160
0
4,680
Selenium
27
0
66
0
93
27
497
66
0
590
29
522
74
0
625
Thallium
3
2
112
0
117
3
9
112
0
123
3
9
126
0
138
TSS
29,900
137,000
99,300
0
267,000
29,900
185,000
99,300
0
314,000
31,500
187,000
112,000
0
330,000
Zinc
76
614
226
0
916
76
724
226
0
1,030
80
729
256
0
1,060
Period 2 (2030-2049)
Antimony
56
1
59
0
116
56
47
59
0
161
56
50
61
0
167
Arsenic
30
314
81
0
425
30
385
81
0
496
30
390
83
0
503
Barium
343
1,120
1,950
0
3,410
343
1,770
1,950
0
4,060
345
1,810
2,010
0
4,170
Beryllium
0
0
19
0
19
0
0
19
0
19
0
0
19
0
19
Boron
17,100
0
3,170,000
0
3,180,000
17,100
269,000
3,170,000
0
3,450,000
17,200
286,000
3,260,000
0
3,570,000
Bromide
4,600,000
16,400
0
0
4,620,000
4,600,000
16,400
0
0
4,620,000
4,630,000
16,600
0
0
4,650,000
Cadmium
2
47
58
0
107
2
99
58
0
159
2
102
60
0
164
Chromium
16
20,100
89
0
20,200
16
20,200
89
0
20,300
17
20,200
92
0
20,300
Copper
13
67
52
0
132
13
114
52
0
178
13
117
54
0
183
Cyanide
0
0
13,100
0
13,100
0
0
13,100
0
13,100
0
0
13,500
0
13,500
Lead
34
0
47
0
80
34
0
47
0
80
34
0
48
0
82
Manganese
493
1,470
174,000
0
176,000
493
34,700
174,000
0
209,000
496
36,900
180,000
0
217,000
Mercury
0
10
1
0
11
0
11
1
0
12
0
11
1
0
12
Nickel
56
433
88
0
577
56
542
88
0
686
57
549
90
0
696
TN
8,490
0
104,000
0
113,000
8,490
0
104,000
0
113,000
8,550
0
108,000
0
116,000
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Table 3-1: Annual Average Reductions in Total Pollutant Loading in Period 1 (2025-2029) and Period 2 (2030-2049) for Selected Pollutants in
Steam Electric Power Plant Discharges, Compared to Baseline (lb/year)
Pollutant
Option Aa
Option B (Final Rule)a
Option C
Si
<
CO
CRLC
FGD
Legacy
Total"
Si
<
CO
CRLC
FGD
Legacy®
Total"
Si
<
CO
CRLC
FGD
Legacy®
Total"
TP
714
0
4,500
0
5,210
714
0
4,500
0
5,210
719
0
4,630
0
5,350
Selenium
40
0
80
0
119
40
1,060
80
0
1,180
40
1,140
82
0
1,260
Thallium
4
5
136
0
144
4
19
136
0
159
4
20
140
0
164
TSS
43,000
301,000
121,000
0
465,000
43,000
406,000
121,000
0
570,000
43,300
413,000
125,000
0
581,000
Zinc
109
1,340
276
0
1,730
109
1,590
276
0
1,970
110
1,600
284
0
2,000
TN = Nitrogen, total (as N); TP = Phosphorus, total (as P); TSS = Total suspended solids
a. All numbers presented with three significant figures.
b. EPA did not estimate changes in ammonia, beryllium, and cyanide loadings associated with BA transport water.
c. EPA did not estimate changes in ammonia, beryllium, bromide, cyanide, lead, nitrogen, and phosphorus associated with CRL. Additionally, the unmanaged CRL loadings presented in
this table do not include unmanaged CRL discharged from landfills, surface impoundments, or other features which a permitting authority could deem, on a case-by-case basis, to be
functionally equivalent to a direct discharge. These loadings are not included in the benefits analyses, but costs for treating the unmanaged CRL discharges are included in the social
costs presented in Chapters 11 and 12.
d. FGD, BA, CRL and legacy wastewater loadings may not add up to the total due to independent rounding.
e. The loading reductions from legacy wastewater under Options B and C are estimated to occur only as plants close and dewater their ponds. For the purpose of this analysis, pond
closures are estimated to occur after 2049 (i.e., outside of the period of analysis) and therefore the loading reductions are zero across all pollutants for both options. Note that no legacy
wastewater loading reductions are anticipated under Option A irrespective of the assumed pond closure year.
Source: U.S. EPA Analysis, 2024.
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3.3 Water Quality Downstream from Steam Electric Power Plants
EPA used the estimated annual average changes in total pollutant loadings for Periods 1 and 2 to estimate
concentrations downstream from each plant. Using the same approach as for the analysis of the 2020 rule and
2023 proposal, EPA applied two models to estimate downstream concentrations from each plant for each
period:
• The D-FATE dilution model to estimate pollutant concentrations downstream from the plants. D-
FATE (Downstream Fate And Transport Equations) calculates concentrations in each downstream
medium-resolution NHD reach using annual average Enhanced Runoff Method (EROM) flows from
NHDPlus v2 and mass conservation principles.
• USGS's SPAtially Referenced Regressions On Watershed attributes (SPARROW) to estimate flow-
weighted nutrient (TN and TP) and suspended sediment concentrations. The SPARROW models
provide baseline and regulatory option concentrations of TN, TP, and suspended solids concentration
(SSC). EPA used the calibrated regional models published by the USGS (Ator, 2019; Hoos & Roland
Ii, 2019; Robertson & Saad, 2019; Wise, 2019; Wise, Anning & Miller, 2019). These models define
the stream network using the same medium-resolution NHD reaches used in D-FATE.
The models represent discharges to reaches represented in the NHD. As discussed in Section 3.1, EPA
omitted wastestreams discharged by 11 steam electric power plants to the Great Lakes, estuaries or other
waters that lack a valid flowpath.
In the D-FATE model, EPA used stream routing and flow attribute information from the medium-resolution
NHDPlus v2 to track masses of pollutants from steam electric power plant discharges and other pollutant
sources as they travel through the hydrographic network. For each point source discharger, the D-FATE
model estimates pollutant concentrations for the receiving reach and all downstream reaches based on NHD
mean annual flows. In-stream flows are kept constant (i.e.. discharges have no effect on flows). EPA notes
that steam electric power plant discharges frequently constitute a return of flow withdrawn for plant use from
the same surface water. In addition, FGD and BA wastewater discharges generally comprise a very small
fraction of annual mean flows in the NHDPlus v2 dataset42
Following the approach used in the analysis of the 2015 and 2020 rules and the 2023 proposal (U.S. EPA,
2015a, 2020b, 2023c) to estimate pollutant concentrations, EPA also included loadings from major
dischargers (in addition to the steam electric power plants) that reported to the Toxics Release Inventory
(TRI). EPA used loadings reported to the TRI in 2021 43 TRI data were available for a subset of toxics:
arsenic, barium, chromium, copper, lead, manganese, mercury, nickel, selenium, thallium, and zinc. EPA
summed reach-specific concentrations from TRI dischargers and concentration estimates resulting from steam
electric power plant loadings to represent water quality impacts from multiple sources. The pollutant
concentrations calculated in the D-FATE model are used to derive fish tissue concentrations used to analyze
human health effects from consuming self-caught fish (see Chapter 5), analyze nonmarket benefits of water
42 Steam electric power plant FGD discharge rates are typically approximately 1 million gallons per day (MGD), whereas the
annual mean stream flows in receiving waters average approximately 15,000 MGD.
43 EPA had used 2019 TRI loadings for the analysis of the 2023 proposed rule. According to EPA TRI National Analysis, TRI
releases to water reported in 2021 were approximately 2 percent lower, in the aggregate, than releases reported in 2019
(196.4 million pounds versus 200.9 million pounds) (U.S. Environmental Protection Agency. (2023r, March 15, 2023). TRI
National Analysis: Water Releases. Retrieved November 28,2023 from https://www.epa.gov/trinationalanalysis/water-releases).
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quality improvements (see Chapter 6), and assess potential impacts to T&E species whose habitat ranges
intersect with waters affected by steam electric plant discharges (see Chapter 7).
3.4 Overall Water Quality Changes
Following the approach used in the analysis of the 2015 and 2020 rules and 2023 proposal (U.S. EPA, 2015a;
2020b, 2023c), EPA used a WQI to link water quality changes from reduced toxics, nutrient and sediment
discharges to effects on human uses and support for aquatic and terrestrial species habitat. The WQI translates
water quality measurements, gathered for multiple parameters (e.g., dissolved oxygen [DO], nutrients) that
are indicative of various aspects of water quality, into a single numerical indicator. The WQI ranges from 10
to 100 with low values indicating poor quality and high values indicating good water quality.
As detailed in U.S. EPA (2015a), the WQI includes seven parameters: DO, BOD, fecal coliform (FC), TN,
TP, suspended solids, and one aggregate subindex for toxics. The pollutants considered in the aggregate
subindex for toxics are those that are discharged by modeled steam electric power plants or 2021 TRI
dischargers and that have chronic aquatic life-based NRWQC. Pollutants that meet these qualifications
include arsenic, cadmium, hexavalent chromium, copper, lead, mercury, nickel, selenium, and zinc. See the
EA for details on NRWQC (U.S. EPA, 2024b). The subindex curve for toxics assigns the lowest WQI value
of 0 to waters where exceedances are observed for the nine toxics analyzed, and a maximum WQI value of
100 to waters where there are no exceedances. Intermediate values are distributed between 100 and 0 in
proportion to the number of exceedances.
3.4.1 WQI Data Sources
To calculate the WQI, EPA used modeled NRWQC exceedances for toxics (using concentrations from D-
FATE) and modeled concentrations for TN, TP, and total suspended solids (TSS) from the respective
SPARROW regional models. Following the approach used for the 2020 rule and 2023 proposal analyses, the
USGS National Water Information System (NWIS) provided concentration data for three parameters that are
held constant between the baseline and regulatory options: 1) fecal coliform, 2) dissolved oxygen, and 3)
biochemical oxygen demand (see Section 3.4.1.2).44,45
3.4.1.1 Exceedances of Water Quality Standards and Criteria
For each regulatory option, EPA identified reaches that do not meet NRWQC for aquatic life in Periods 1 and
2.46 Table 3-2 summarizes the number of reaches with estimated exceedances of NRWQC in the baseline and
44 USGS's NWIS 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/.
45 The 2020 rule and 2023 proposal analysis used data ranging from 2007-2017. This dataset was updated for this analysis to
include data ranging from 2007-2022.
40 Aquatic life criteria are the highest concentration of pollutants in water that are not expected to pose a significant risk to the
majority of species in a given environment. For most pollutants, aquatic NRWQC are more stringent than human health NRWQC
and thus provide a more conservative estimate of potential water quality impairment. Chronic criteria are derived using longer
term (7-day to greater than 28-day) toxicity tests if available, or an acute-to-chronic ratio procedure where the acute criteria is
derived using short term (48-hour to 96-hour) toxicity tests (U.S. Environmental Protection Agency. (2017a). Chapter 3: Water
Quality Criteria. Water Quality Standards Handbook. (EPA 823-B-17-001). Retrieved from
https://www.epa.gov/sites/production/files/2014-10/documents/liandbook-chapter3.pdf). More information on aquatic NRWQC
can be found at https://www.epa.gov/wac/national-recommended-water-qualitv-criteria-aauatic-life-criteria-table and in the EA
(U.S. Environmental Protection Agency. (2023g). Environmental Assessment for Proposed Supplemental Revisions to the
Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source Category. ).
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under the regulatory options. In Period 2, the final rule (Option B) is estimated to eliminate all exceedances of
chronic criteria for 5 reaches (of 40 reaches with at least one exceedance), and eliminate all exceedances of
acute criteria for all four reaches with baseline exceedances.
Table 3-2: Estimated Exceedances of National Recommended
Water Quality Criteria under the Baseline and Regulatory Options
Regulatory Option
Number of Reaches with at Least One
NRWQC Exceedance
Chronic
Acute
Period 1 (2025-2029)
Baseline
42
4
Option A
42
2
Option B (Final Rule)
40
2
Option C
40
2
Period 2 (2030-2049)
Baseline
40
4
Option A
40
2
Option B (Final Rule)
35
0
Option C
35
0
Source: U.S. EPA Analysis, 2024
Refer to the EA for additional discussion of comparisons of receiving and downstream water pollutant
concentrations to acute and chronic aquatic NRWQC (U.S. EPA, 2024b).
3.4.1.2 Sources for Ambient Water Quality Data
Following the approach used for the analysis of the 2020 rule and 2023 proposal, EPA used average
monitoring values for fecal coliform, dissolved oxygen, and biochemical oxygen demand for 2007-2022
where available. EPA used a successive average approach to assign average values for the three WQI
parameters not explicitly modeled (i.e.. DO, BOD, fecal coliform). The approach, which adapts a common
sequential averaging imputation technique, involves assigning the average of ambient concentrations for a
given 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 (Hydrologic unit code (HUC8, HUC6,
HUC4, and HUC2) to fill in all missing data.47 This approach is based on the assumption that reaches located
in the same watershed generally share similar characteristics. Using this estimation approach, EPA compiled
ambient water quality data and/or estimates for all analyzed NHD reaches. As discussed below, the values of
the three WQI parameters not explicitly modeled are kept constant for the baseline and regulatory policy
scenarios. This approach has not been peer reviewed, but it has been used by EPA for several prior rules and
reviewed by the public during the associated comment periods.
47 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 22), the next pair the subregion (approximately 245), the
third pair the basin or accounting unit (approximately 405), and the fourth pair the subbasin, or cataloguing unit (approximately
2,400) (U.S. Geological Survey. (2007). National Hydrography Dataset (NHD). Retrieved from http://nlid.usgs.gov/data.html,
U.S. Geological Survey. (2022). Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD).
Retrieved from https://pubs.usgs.gOv/tm/l l/a3/pdf/tml l-a3_5ed.pdf). Digits after the first eight offer more detailed information
at the watershed and subwatershed levels. 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.
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The water quality analysis included a total of 11,607 medium-resolution NHD reaches that are potentially
affected by steam electric power plants under the baseline. Of these 11,607 NHD reaches, EPA estimated
concentrations for 11,080 reaches from steam electric power plants. Table 3-3 summarizes the data sources
used to estimate baseline and regulatory option values by water quality parameter.
Table 3-3: Water Quality Data used in Calculating WQI for the Baseline and Regulatory Options
Parameter
Baseline
Regulatory Option
TN
Concentrations calculated using SPARROW
(baseline run)
Concentrations calculated using SPARROW
(regulatory option run)
TP
Concentrations calculated using SPARROW
(baseline run)
Concentrations calculated using SPARROW
(regulatory option run)
TSS
Concentrations calculated using SPARROW
(baseline run)
Concentrations calculated using SPARROW
(regulatory option run)
DO
Observed values averaged at the WBD
watershed level
No change. Regulatory option value set equal
to baseline value
BOD
Observed values averaged at the WBD
watershed level
No change. Regulatory option value set equal
to baseline value
Fecal Coliform
Observed values averaged at the WBD
watershed level
No change. Regulatory option value set equal
to baseline value
Toxics
Baseline exceedances calculated using D-FATE
model
Regulatory option exceedances calculated
using D-FATE model
WBD = Watershed Boundary Dataset. The WBD is a companion dataset to the NHD
Source: U.S. EPA Analysis, 2022.
3.4.2 WQI Calculation
EPA used the approach described in the BCA for the 2015 and 2020 rules and 2023 proposal (U.S. EPA,
2015a, 2020b, 2023c) to estimate WQI values for each reach under the baseline and each option. EPA used
updated subindex curves for TN, TP, and TSS previously used for the 2023 proposed revisions to the ELGs
for the Meat and Poultry Products Point Source Category (U.S. EPA, 2023d) and reflect data from the 2013-
2014 and 2018-2019 National Rivers and Streams Assessments (NRSA) (U.S. EPA, 2020e, 2023j) 48
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 for each reach to
calculate the WQI value for the baseline, and for each analyzed regulatory option. See details of the
calculations in Appendix C, including the subindex curves used to transform levels of individual parameters.
The scope of this analysis is the same as that for the analysis of nonmarket benefits of water quality
48 The NRSA is a component of EPA's National Aquatic Resources Survey (NARS). The NRSA provides information on the
conditions of the nation's rivers and streams and is conducted at regular intervals (2008-2009, 2013-2014, and 2018-2019) using
a consistent approach. This enables comparison of stream conditions over time. Hie NRSA has several interesting features to
support the development of a water quality index: it is based on a statistical representation of rivers and streams, it provides data
for key indicators of biological, chemical and physical conditions, and includes both measured data and a categorical assessment
of the conditions (poor, fair, good) for selected indicators. In particular, the 2013-2014 and 2018-2019 surveys provide
categorical assessments of chemical conditions related to TN and TP.
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improvements discussed in Chapter 6, which focuses on reaches within 300 km of a steam electric plant
outfall.49
3.4.3 Baseline WQI
The WQI value can be related to suitability for potential uses. Vaughan (1986) developed a water quality
ladder (WQL) that can be used to indicate whether water quality is suitable for various human uses (i.e.,
boating, rough fishing, game fishing, swimming, and drinking without treatment). Vaughan identified
"minimally acceptable parameter concentration levels" for each of the five potential uses. Vaughan used a
scale with a top value of 10 instead of the WQI scale with a top value of 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.
Based on the estimated WQI value under the baseline scenario (WQI-BL), EPA categorized each of the
11,080 NHD reaches using five WQI ranges (WQI < 25, 25
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for each reach and for each of the two periods. As discussed in Section 1.1, EPA estimated changes in
ambient concentrations of TN, TP and TSS using the USGS's SPARROW models and toxics concentrations
using the D-FATE model. Although the regulatory options would also indirectly affect levels of other WQI
parameters, such as BOD and DO, these other parameters were held constant in this analysis for all regulatory
options, due to methodological and data limitations.
The difference in the WQI between baseline conditions and a given regulatory option (hereafter denoted as
AWQI) is a measure of the change in water quality attributable to the regulatory option. Table 3-5 presents
water quality change ranges for the analyzed regulatory options under each analysis period.
Table 3-5: Ranges of Estimated Water Quality Changes for Regulatory Options, Compared to
Baseline
Regulatory
Option
Minimum
AWQI
Maximum
AWQI
25th Percentile
AWQI
Median AWQI
75th Percentile
AWQI
AWQI
Interquartile
Range
Period 1 (2025-2029)
Option A
0
1.70
0
7.90xl0"6
3.39xl0"4
3.39xl0"4
Option B (Final Rule)
0
1.70
0
7.91xl0"6
3.39xl0"4
3.39xl0"4
Option C
0
1.70
0
7.91xl0"6
4.69xl0"4
4.69xl0"4
Period 2 (2030-2049)
Option A
0
10.17
0
1.83xl0"5
4.02xl0"4
4.02xl0"4
Option B (Final Rule)
0
10.17
0
1.89xl0"5
4.54xl0"4
4.54xl0"4
Option C
0
10.17
0
2.67xl0"5
4.97xl0"4
4.97xl0"4
Source: U.S. EPA Analysis, 2024
3.5 Limitations and Uncertainty
The methodologies and data used in the estimation of the environmental effects of the regulatory options
involve limitations and uncertainties. Table 3-6 summarizes the limitations and uncertainties and indicates the
direction of the potential bias. Uncertainties associated with some of the input data are covered in greater
detail in other documents. Regarding the uncertainties associated with use of the NHDPlus attribute data, see
the NHDPlus v2 documentation (U.S. EPA, 2019g). Regarding the uncertainties associated with estimated
loads, see the TDD (U.S. EPA, 2024f).
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Table 3-6: Limitations and Uncertainties in Estimating Water Quality Effects of Regulatory Options
Uncertainty/Limitation
Effect on Water
Quality Effects
Estimation
Notes
Limited data are available to validate
water quality concentrations
estimated in D-FATE
Uncertain
The modeled concentrations reflect only a subset of
pollutant sources (e.g., steam electric power plant
discharges and TRI releases) whereas monitoring data
also reflect other sources such as bottom sediments,
air deposition, and other point and non-point sources
of pollution. TRI releases are also reported by the
facilities and could potentially suffer from misreporting
or faulty estimation techniques. EPA comparisons of D-
FATE estimates to monitoring data available for
selected locations and parameters (e.g., bromide
concentrations downstream of steam electric power
plant discharges) confirmed that D-FATE provides
reasonable values. Also refer to the 2015 EA for
discussion of model validation for selected case studies
(U.S. EPA, 2015b)
Steam electric power plant
discharges have no effects on reach
annual average or seasonal flows
Overestimate
The degree of overestimation in the estimation of
pollutant concentrations, if any, would be small given
that steam electric power plant discharge flows tend to
be very small as compared to flows in modeled
receiving and downstream reaches. Further, EPA
acknowledges that the effect of steam electric power
plant discharges on reach flows may vary seasonally
due to low- and high-flow periods.
Ambient water toxics concentrations
are based only on loadings from
steam electric power plants and
other TRI discharges.
Uncertain
Concentration estimates do not account for
background concentrations of these pollutants from
other sources, such as legacy pollution in sediments,
non-point sources, point sources that are not required
to report to TRI, air deposition, etc. Not including other
contributors to background toxics concentrations in
the analysis is likely to result in understatement of
baseline concentrations of these pollutants and
therefore of NRWQC exceedances. The effect on WQI
calculations is uncertain.
Annual loadings are estimated based
on EPA's estimated plant-specific
technology implementation years
Uncertain
To the extent that technologies are implemented
earlier or later, the Period 1 annualized loading values
presented in this section may under- or overstate the
annual loads during the analysis period. The effect of
this uncertainty is limited to Period 1 since loads reach
a steady-state level by the technology implementation
deadlines applicable to the regulatory options (e.g., by
the end of 2029)
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Table 3-6: Limitations and Uncertainties in Estimating Water Quality Effects of Regulatory Options
Uncertainty/Limitation
Effect on Water
Quality Effects
Estimation
Notes
Changes in WQI reflect only
reductions in toxics, nutrient, and
sediment concentrations.
Underestimate
The estimated changes in WQI reflect only water
quality changes resulting directly from changes in
toxics, nutrient and sediment concentrations. They do
not include changes in other water quality parameters
(e.g., BOD, dissolved oxygen) that are part of the WQI
and for which EPA used constant values. Because the
omitted water quality parameters are also likely to
respond to changes in pollutant loads (e.g., dissolved
oxygen levels respond to changes in nutrient levels),
the analysis underestimates the water quality changes.
EPA used regional averages of
monitoring data from 2007-2022 for
fecal coliform, dissolved oxygen, and
biochemical oxygen demand, when
location-specific data were not
available.
Uncertain
The monitoring values were averaged over
progressively larger hydrologic units to fill in any
missing data. As a result, WQI values may not reflect
certain constituent fluctuations resulting from the
various regulatory options and/or may be limited in
their temporal and spatial relevance. Note that the
analysis keeps these parameters constant under both
the baseline and regulatory options. Modeled changes
due to the regulatory options are not affected by this
uncertainty.
Use of nonlinear subindex curves
Uncertain
The methodology used to translate sediment and
nutrient concentrations into subindex scores (see
Section 3.4.2 and Appendix C) 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 change in the analysis and no
benefits in the analysis described in Chapter 6.
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4 Human Health Benefits from Changes in Pollutant Exposure via the
Drinking Water Pathway
EPA expects that the changes in pollutant loadings from the regulatory options relative to the 2020 rule could
affect several aspects of human health by changing bromide and other pollutant discharges to surface waters
and, as a result, pollutant concentrations in the reaches that serve as sources of drinking water. The EA
provides details on the health effects of steam electric pollutants (U.S. EPA, 2024b).
As described in Section 2.1, human health benefits deriving from changes in pollutant loadings to receiving
waters include those associated with changes in exposure to pollutants via treated drinking water use and fish
consumption. This chapter addresses the first exposure pathway: drinking water. Chapter 5 addresses the fish
consumption pathway.
The changes in pollutant loadings from the regulatory options relative to the 2020 rule could affect human
health by changing halogen and other pollutant discharges to surface waters and, as a result, pollutant
concentrations in the reaches that serve as sources of drinking water. The EA presents background
information regarding the potential impacts of halogen discharges on drinking water quality and human health
(U.S. EPA, 2024b). Section 4.1 provides background information on trihalomethane precursor development.
Sections 4.2 through 4.4 present EPA's analysis of human health effects from changes in bromide discharges.
Section 4.5 summarizes potential impacts on source waters from changes in other pollutant discharges.
Section 4.6 discusses uncertainty and limitations associated with the analysis presented in this chapter.
4.1 Background
FGD wastewater and BA transport water discharges contain variable quantities of bromide due to the natural
presence of bromide in coal feedstock and from additions of halogens, including bromide-containing salts,
and use of brominated activated carbon products to enhance air emissions control (Kolker et al., 2012).
Wastewater treatment technologies employed at steam electric power plants vary widely in their ability to
remove bromide. A number of studies have documented elevated bromide levels in surface water due to steam
electric power plant discharges (e.g., Cornwell et al., 2018; Good & VanBriesen, 2016, 2017; McTigue et al.,
2014; Ruhl et al., 2012; States et al., 2013; U.S. EPA, 2017c; 2019c) and have attributed measured increases
in bromide levels to the increasing number of installed wet FGD devices at steam electric power plants. FGD
wastewaters have been shown to contain relatively high levels of bromide relative to other industrial
wastewaters. Modeling studies have sought to quantify the potential for drinking water sources to be affected
by FGD wastewater discharges (Good & VanBriesen, 2019).
Bromide does not undergo significant physical (e.g., sorption, volatilization), chemical or biological
transformation in freshwater environments and is commonly used as a tracer in solute transport and mixing
field studies. Surface waters transport bromide discharges to downstream drinking water treatment facility
intakes where they are drawn into the treatment systems.
Although the bromide ion has a low degree of toxicity (World Health Organization, 2009), it can contribute to
the formation of brominated DBPs during drinking water disinfection processes, including chlorination,
chloramination, and ozonation. Bromate, a regulated DBP under the Safe Drinking Water Act (SDWA),
forms when bromine reacts directly with ozone. Chlorine reacts with bromide to produce hypobromite (BrO),
which reacts with organic matter to form brominated and mixed chloro-bromo DBPs, including three of the
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four regulated trihalomethanes51 (THM4, also referred to as total trihalomethanes (TTHM) in this discussion)
and two of the five regulated haloacetic acids52 (HAA5). Additional unregulated brominated DBPs have been
cited as an emerging class of water supply contaminants that can potentially pose health risks to humans
(Richardson et al., 2007; NTP, 2018; U.S. EPA, 2016c).
There is a substantial body of literature on trihalomethane precursor occurrence, trihalomethane formation
mechanisms in drinking water treatment plants, and relationships between source water bromide levels and
TTHM levels in treated drinking water. The formation of TTHM in a particular drinking water treatment plant
is a function of several factors including chlorine, bromide, organic material, temperature, and pH levels as
well as system residence times. There is also substantial evidence linking TTHM exposure to bladder cancer
incidence (U.S. EPA, 2016c). Bromodichloromethane and bromoform are likely to be carcinogenic to humans
by all exposure routes and there is evidence suggestive of dibromochloromethane's carcinogenicity (NTP,
2018; U.S. EPA, 2016c). The relationships between exposure to DBPs, specifically TTHMs and other
halogenated compounds resulting from water chlorination, and bladder cancer are further discussed in Section
4.3.3.2 and U.S. EPA (2019b).
4.2 Overview of the Analysis
Figure 4-1 illustrates EPA's approach for quantifying and valuing the human health effects of altering
bromide discharges from steam electric power plants. The analysis entails estimating in-stream changes in
bromide levels between conditions under the baseline and each of the three regulatory options (Step 1);
estimating the change in source water bromide levels and corresponding changes in TTHM concentrations in
treated water supplies (Step 2); relating these estimated changes to changes in exposure and the subsequent
changes in the incidence of bladder cancers53 in the exposed population (Step 3); and estimating the
associated monetary value of benefits (Step 4). This approach was implemented in EPA's 2019 proposed rule
and the 2023 proposal (U.S. EPA, 2019b, 2023c) and relies on findings from a peer-reviewed paper by Regli
et al. (2015) that built on the approach taken in the Stage 2 Disinfectants and Disinfection Byproduct Rule
(DBPR) (U.S. EPA, 2005c) to derive a slope factor to relate changes in lifetime bladder cancer risk to changes
in TTHM exposure. This analysis also incorporates National Cancer Institute's Surveillance, Epidemiology,
and End Results (SEER) program data to model incidence of bladder cancers by age and sex, cancer stage,
changes in lifetime cancer risk attributable to the regulatory options, and survival outcomes. The life-table
modeling approach used by EPA to estimate changes in health outcomes is a widely used method in public
health, insurance, medical research, and other studies and was used for analysis of lead-associated health
effects in the 2015 rule. The main advantage of this approach is that it allows for explicitly accounting for age
and cancer stage-specific patterns in cancer outcomes, as well as for other causes of mortality in the affected
population.
51 The four regulated trihalomethanes are bromodichloromethane, bromoform, chloroform, and dibromochloromethane.
52 The five regulated haloacetic acids are dibromoacetic acid, dichloroacetic acid, monobromoacetic acid, monochloroacetic acid,
and trichloroacetic acid.
53 Regli, S., Chen, J., Messner, M., Elovitz, M. S., Letkiewicz, F. J., Pegram, R. A., Pepping, T. J.,. . . Wright, J. M. (2015).
Estimating potential increased bladder cancer risk due to increased bromide concentrations in sources of disinfected drinking
waters. Environmental Science & Technology, 49(22), 13094-13102. estimated the additional lifetime risk from a 1 (ig/L
increase in TTHM. This relationship holds over the TTHM range expected for systems in compliance with the Stage 2
Disinfectants and Disinfection Byproduct Rule.
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Figure 4-1: Overview of Analysis of Estimated Human Health Benefits of Reducing Bromide
Discharges.
Br loadings from
steam electric
plants
Legend:
Analysis
compone nt
Data/Inputs
Analysis step
Valuation
endpoint
Source: U.S. EPA Analysis, 2024.
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4.3 Estimates of Changes in Halogen Concentrations in Source Water
EPA estimated the change in halogen levels in the source water for PWS that have intakes downstream from
steam electric power plants. Halogens such as bromide are precursors for halogenated disinfection byproduct
formation in treated drinking water, including certain trihalomethanes addressed by the TTHM MCL. Higher
halogen levels in PWS source waters have been associated with higher levels of halogenated DBPs in treated
drinking water. The formation of DBPs varies with site-specific factors. In vitro toxicology studies with
bacteria and mammalian cells have documented evidence of genotoxic (including mutagenic), cytotoxic,
tumorigenic, and developmental toxicity properties of iodinated DBPs, but the available data are insufficient
at this time to determine the extent of iodinated DBP's contribution to adverse human health effects from
exposure to treated drinking water (Richardson et al., 2007; U.S. EPA, 2016c; National Toxicology Program,
2018). Populations exposed to changes in halogenated disinfection byproduct levels in their drinking water
under the regulatory options could experience changes in the incidence of adverse health effects, and in turn
the total counts of these health effects.
In this section, the Agency presents the number of PWS with modeled changes in bromide concentration in
their source water, the magnitude and direction of these changes, and the PWS service population estimated to
experience a change in DBP exposure levels due to changes in source water bromide levels.
4.3.1 Step 1: Modeling Bromide Concentrations in Surface Water
EPA estimated steam electric power plant-level bromide loadings associated with FGD wastewater and BA
transport water for the baseline and the regulatory options.54 This chapter presents EPA's best estimate of
changes in bromide loadings under each of the regulatory options.
EPA used the D-FATE model described in Section 3.3 to estimate in-stream bromide concentrations
downstream from 38 steam electric power plants that EPA estimated have non-zero bromide loads (i.e..
discharge FGD wastewater and/or BA transport water) under the baseline or regulatory options. EPA first
estimated the annual average bromide loads in Period 1 and Period 2 (see Section 3.2.1). EPA then estimated
concentrations in the receiving reach and each downstream reach in Period 1 and Period 2, using conservation
of mass principles, until the load reaches the hydrographic network terminus (e.g., Great Lake, estuary).55
EPA summed individual contributions from all plants to estimate total in-stream concentrations under the
baseline and the regulatory options in Period 1 and Period 2. Finally, EPA estimated the change in bromide
concentrations in each reach as the difference between each regulatory option and the baseline. The modeled
change is not dependent on bromide contributions from other sources (e.g., waterbody background levels).
As summarized in Table 4-1, regulatory options A and B are estimated to result in the same bromide loading
reductions, whereas bromide loading reductions are slightly higher under Option C. The reductions are higher
in Period 2 than in Period 1 under all regulatory options.
54 EPA did not estimate bromide loadings associated with CRL discharges.
55 As discussed in Section 3.1, EPA did not estimate concentration changes in the Great Lakes or estuaries.
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Table 4-1: Estimated Bromide Loading Reductions by Analysis Period and
Regulatory Option
Number of Steam Electric
Total Bromide Load
Regulatory Option
Plants with non-Zero
Changes
Reduction (lbs/year)
Period 1 (2025-2029)
Option A
32
2,444,904
Option B (Final Rule)
32
2,444,904
Option C
32
2,686,485
Period 2 (2030-2049)
Option A
37
4,615,175
Option B (Final Rule)
37
4,615,175
Option C
38
4,647,249
Source: U.S. EPA Analysis, 2024
4.3.2 Step 2: Modeling Changes in Trihalomethanes in Treated Water Supplies
4.3.2.1 Affected Public Water Systems
For the final rule, EPA updated the universe of PWS potentially affected by steam electric plant discharges to
reflect adjustments to the universe of plants projected to be subject to the rule and their associated receiving
and downstream reaches. EPA also collected more recent information about the operating characteristics of
the water systems (e.g., population served, facility status, wholesale water purchases). EPA used Safe
Drinking Water Information System (SDWIS) fourth quarter data for 2022.
EPA's SDWIS database56 provides the latitude and longitude of surface water facilities57, including source
water intakes for public drinking water treatment systems. To identify potentially affected PWS, the Agency
georeferenced each permanent surface water facility associated with non-transient community water systems
to the NHD medium-resolution stream network used in D-FATE.58 Appendix F describes the methodology
EPA used to identify the NHD water feature for each facility. The SDWIS database also includes information
on PWS primary sources (e.g., whether a PWS relies primarily on groundwater or surface water for their
source water), operational status, and population served, among other attributes. For this analysis, EPA used
the subset of facilities that identify surface water as their primary water source (specifically surface water
intakes and reservoirs) and are categorized as "active" and "permanent" in SDWIS. This subset of facilities
corresponds to PWS that are more likely to be affected by upstream bromide releases on an ongoing basis, as
compared to other systems that may use surface water sources only sporadically. This approach identifies
populations most likely to experience changes in long-term halogenated DBP exposures and associated health
effects due to the regulatory options.
50 EPA used intake locations and PWS data from the fourth quarter report for 2022. Intake location data are protected from
disclosure due to security concerns. SDWIS public data records are available from the Federal Reporting Services system at
https://ofmpub.epa.gov/apex/sfdw/.
57 Surface water facilities include any part of a PWS that aids in obtaining, treating, and distributing drinking water. Facilities in the
SDWIS database may include groundwater wells, consecutive connections between buyer and seller PWS, pump stations,
reservoirs, and intakes, among others.
58 This analysis does not include intakes that draw from the Great Lakes or other water bodies not analyzed in the D-FATE model.
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PWS can be either directly or indirectly affected by steam electric power plant discharges. Directly affected
PWS are systems with surface water intakes drawing directly from reaches downstream from steam electric
power plants discharging bromide.59 Other PWS are indirectly affected because they purchase their source
water from another PWS via a "consecutive connection" instead of withdrawing directly from a surface water
or groundwater source. For these systems, SDWIS provides information on the PWS that supplies the
purchased water. EPA used SDWIS data to identify PWS that may be indirectly affected by steam electric
power plant discharges because they purchase water from a directly affected PWS. The total potentially
exposed population consists of the people served by either directly or indirectly affected systems.
Table 4-2 summarizes the number of intakes, PWS, and total populations potentially affected by steam
electric power plant discharges via the drinking water pathway, and the subset of those intakes and PWS
affected by bromide discharges. In this analysis, the average distance from the steam electric power plant
discharge point to the drinking water treatment plant intake is 71 miles and approximately 19 percent of the
intakes are located within 30 miles of a steam electric power plant outfall. A subset of these PWS is
downstream of FGD wastewater and BA transport water discharges containing bromide,6" specifically
118 affected reaches have intakes used by 151 PWS serving a total of 15.7 million people, directly or
indirectly.
Table 4-2: Estimated Reaches, Surface Water Intakes, Public Water Systems, and Populations
Potentially Affected by Steam Electric Power Plant Discharges
PWS Impact Category
Number of Reaches
with Drinking Water
Intakes
Number of Intakes
Downstream of
Steam Electric Power
Plants
Number of PWS
Total Population
Served (Million
People)
Reaches downstream from steam electric plant discharges
Direct3
223
283
234
18.4
Indirect
Not applicable
Not applicable
682
10.8
Total
223
283
916
29.2
Reaches downstream from steam electric plant with non-zero bromide loads
Direct"
118
151
131
11.5
Indirect
Not applicable
Not applicable
366
4.1
Total
118
151
497
15.7
a. Includes 16 systems with both intakes downstream of steam electric power plant discharges and that purchase water from other
systems with intakes downstream of steam electric power plant discharges.
b. Includes 7 systems with both intakes downstream of steam electric power plant discharges and that purchase water from other
systems with intakes downstream of steam electric power plant discharges.
Source: U.S. EPA Analysis, 2024
4.3.2.2 System-Level Changes in Bromide Concentrations in Source Water
EPA estimated the change in bromide concentrations in the source water for each PWS that could result from
the regulatory options. In this discussion, the term "system" refers to PWS and their associated drinking water
59 To identify potentially affected PWS, EPA looked at all downstream reaches starting from the immediate reach receiving the
steam electric power plant discharge to the reach identified as the terminus of the stream network.
o0 Note that when plants retire, bromide may still be present in CRL. The present analysis considers bromide discharges from FGD
wastewater and BA transport water only.
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treatment operations, whereas the term "facility" refers to the intake that is drawing untreated water from a
source reach for treatment at the PWS level.
To estimate changes in bromide concentrations at the PWS level, EPA obtained the number of active
permanent surface water sources used by each PWS based on SDWIS data. SDWIS does not provide
information on respective source flow contributions from surface water and groundwater facilities for a given
PWS. For drinking water treatment systems that have both surface water and groundwater facilities, EPA
assessed changes from surface water sources only. This approach is reasonable given that the analysis is
limited to the PWS for which SDWIS identifies surface water as primary source.
For intakes located on reaches modeled in D-FATE, EPA calculated the reach-level change in bromide
concentration as the difference between the regulatory option and the baseline conditions. Some PWS rely on
a single intake facility for their source water supply. If the source water reach associated with this single
intake is affected by steam electric power plant bromide discharges, the system-level changes in bromide
concentration at the PWS would equal the estimated change in bromide concentration of the source water
reach. Other PWS rely on multiple intake facilities that may be located along different source water reaches.
System-level changes in bromide concentrations at these PWS are an average of the estimated changes in
bromide concentrations associated with each source water reach. For any additional intakes not located on the
modeled reaches and for intakes relying on groundwater sources, EPA estimated zero change in bromide
concentration. Because SDWIS does not provide information on source flows contributed by intake facilities
used by a given PWS, EPA calculated the system-level change in bromide concentration assuming each active
permanent source facility contributes equally to the total volume of water treated by the PWS. For example,
the PWS-level change in bromide concentration for a PWS with three intakes, of which one intake is directly
affected by steam electric power plant discharges, is estimated as one third of the modeled reach
concentration change ([ABr + 0 + 0]/3).
EPA addressed water purchases similarly, but with the change in bromide concentration associated with the
consecutive connection set equal to the PWS-level change estimated for the seller PWS instead of a reach-
level change. For facilities affected only indirectly by steam electric power plant discharges, EPA assumed
zero change in bromide concentrations for any other unaffected source facility associated with the buyer. EPA
also assumed that each permanent source facility contributes an equal share of the total volume of water
distributed by the buyer. For the seven PWS classified as both directly and indirectly affected by steam
electric power plant bromide discharges, EPA assessed the total change in bromide concentration as the
average of the change in concentration from both directly-drawn and purchased water.
Table 4-3 summarizes the distribution of changes in bromide concentrations under the regulatory options for
the two analysis periods. The changes depends on the Period, option, source water reach, and PWS but are
generally consistent with the changes in bromide loadings associated with FGD and bottom ash transport
wastewaters under each regulatory option (see Table 3-1). During Periods 1 and 2, all options show either
reductions or no changes in bromide concentrations for all source waters and PWS. For all options, the
magnitude and scope (the number of reaches, PWS, and population served) of the bromide reductions are
larger during Period 2 than during Period 1.
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Table 4-3: Estimated Distribution of Changes in Source Water and PWS-Level Bromide Concentrations by Period and Regulatory Option,
Compared to Baseline
ABr Range (|ig/L)
Number of Source Water Reaches
Number of PWSa
Population Served by PWS
Reduction ABr
No ABr (ABr = 0)
Reduction ABr
No ABr (ABr = 0)
Reduction ABr
No ABr (ABr = 0)
Period 1 (2025-2029)
Option A
Oto 10
109
13
451
65
13,539,103
3,380,007
10 to 30
1
0
2
0
2,521
0
50 to 75
1
0
3
0
123,386
0
Option B (Final Rule)
Oto 10
109
13
451
65
13,539,103
3,380,007
10 to 30
1
0
2
0
2,521
0
50 to 75
1
0
3
0
123,386
0
Option C
Oto 10
109
13
451
65
13,539,103
3,380,007
10 to 30
1
0
2
0
2,521
0
50 to 75
1
0
3
0
123,386
0
Period 2 (2030-2049)
Option A
Oto 10
117
1
473
36
15,095,692
1,669,547
10 to 30
0
9
0
156,392
0
>75
1
0
3
0
123,386
0
Option B (Final Rule)
Oto 10
117
1
473
36
15,095,692
1,669,547
10 to 30
0
9
0
156,392
0
>75
1
0
3
0
123,386
0
Option C
Oto 10
118
0
485
24
15,598,789
1,166,450
10 to 30
5
0
9
0
156,392
0
>75
1
0
3
0
123,386
0
a. Includes systems potentially directly and/or indirectly affected by steam electric power plant discharges.
Source: U.S. EPA Analysis, 2024.
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4.3.2.3 Changes in TTHM Concentration in Treated Water Supplies
The prior step provides the estimated PWS-level change in bromide concentration in the blend of source
waters used by a given system. The step described in this section provides the estimated PWS-level change in
TTHM concentration associated with this change in bromide concentration.
Regli et al. (2015) applied the Surface Water Analytical Tool (SWAT) version 1.1, which models TTHM
concentrations in drinking water treatment plants as a function of precursor levels, source water quality (e.g.,
bromide and organic material levels), water temperature, treatment processes (e.g., pH, residence time), and
disinfectant dose (e.g., chlorine levels) to predict the distribution of changes in TTHM concentrations in
finished water associated with defined increments of changes in bromide concentration in source waters. That
study estimated the distribution of increments of change in TTHM concentration for a subset of the
population of PWS characterized in the 1997-1998 Information Collection Rule (ICR) dataset. Table 4-4
summarizes the results from the Regli et al. (2015) analysis.
Table 4-4: Estimated Increments of Change in TTHM Levels (ng/L) as a Function of Change in
Bromide Levels (ng/L)
Change in bromide
Change in TTHM concentration (|ig/L)
concentration
Minimum
5th
Median
Mean
LO
(Ti
Maximum
(Hg/L)
Percentile
Percentile
10
0.0
0.1
1.1
1.3
3.4
10.1
30
0.0
0.3
2.6
3.2
8.3
23.7
50
0.0
0.5
3.7
4.6
11.6
33.2
75
0.0
0.6
4.9
6.0
14.8
42.1
100
0.0
0.8
5.8
7.1
17.5
49.3
Source: Regli et al. (2015), Table 2.
For this analysis, EPA used the results from Regli et al. (2015) to predict TTHM concentration changes for
each water treatment plant with changes in bromide concentrations in their source water due to the regulatory
options. Figure 4-2 shows the relationship (dashed line) between the change in bromide concentration and the
change in TTHM concentration based on fitting a polynomial curve through the median estimates from Table
4-4 (circular markers). EPA used the equation of the best-fit curve61 to estimate changes in TTHM
concentration as a function of changes in bromide concentration within the bromide concentration range
presented in Regli et al. (2015) (0 to 100 (ig/L). Estimates of TTHM concentration changes presented in the
remainder of this section reflect median changes from Regli et al. (2015).62 EPA evaluated the sensitivity of
benefits estimates to the relationship between changes in bromide and changes in TTHM using the 5th and 95th
percentile estimates in Table 4-4 in the 2019 and 2023 proposed rules (U.S. EPA, 2019b, 2023b).
01 The polynomial curve fits observations in Table 4-4 with residuals of zero over the range of observations.
62 While Regli, S., Chen, J., Messner, M., Elovitz, M. S., Letkiewicz, F. J., Pegram, R. A., Pepping, T. J.,. . . Wright, J. M. (2015).
Estimating potential increased bladder cancer risk due to increased bromide concentrations in sources of disinfected drinking
waters. Environmental Science & Technology', 49(22), 13094-13102. show similar mean and median changes in TTHM
concentrations across the range of changes in bromide concentrations, EPA used the median to minimize potential influence of
outlier values or skew in the distribution. Mean changes in TTHM for changes in bromide levels of 10, 30, 50, 75, and 100 jug/L
were 1.3, 3.2,4.6, 6.0 and 7.1 jug/L, respectively. Median changes in TTHM for changes in bromide levels of 10, 30, 50, 75, and
100 jug/L were 1.1, 2.6, 3.7, 4.9, and 5.8 |ig/L, respectively.
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Figure 4-2: Modeled Relationship between Changes in Bromide Concentration and Changes in TTHM
Concentrations based on Median Values in Regli et al. (2015).
If A Br >100 ug/L:
ATTHMsoto = 5.80 + 0.022 (ABr -100)
if A Br<100ng/L:
ATTHMm = -8.30x10s ABr4 + 1.96xl05 ABr3 -1.81xl03 ABR2 + 1.26x1a1 ABr
/
0 20 40 60 80 100 120 140 160
ABr (ug/L)
Source: U.S. EPA Analysis, 2024, based on Regli et al. (2015).
Table 4-5 shows the distribution of modeled absolute changes in TTHM concentrations and the potentially
exposed populations under each of the regulatory options. As shown in the table, the magnitude of estimated
bromide concentration changes is generally less than 10 |a,g/L, corresponding to estimated changes in TTHM
concentrations of less than 1.1 |ag/L. Compared to the baseline, all options are estimated to reduce TTHM
concentrations in treated water.
Table 4-5: Distribution of Estimated Changes in TTHM Concentration by the Number of PWS and
Population Served
Absolute ABr range3
(Hg/L)
Absolute ATTHM range3
(Hg/L)
Number of PWSb
Total population served
(million people)c
Perioc
1 (2025-2029)
Option A
>0 to 10
0.00 to 1.09
451
13.54
10 to 30
1.81 to 1.81
2
0.00
30 to 50
3.82 to 3.82
3
0.12
Option B (Final Rule)
>0 to 10
0.00 to 1.09
451
13.54
10 to 30
1.81 to 1.81
2
0.00
30 to 50
3.82 to 3.82
3
0.12
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Table 4-5: Distribution of Estimated Changes in TTHM Concentration by the Number of PWS and
Population Served
Absolute ABr range3
(Hg/L)
Absolute ATTHM range3
(Hg/L)
Number of PWSb
Total population served
(million people)c
Option C
>0 to 10
0.00 to 1.09
451
13.54
10 to 30
1.81 to 1.81
2
0.00
30 to 50
3.82 to 3.82
3
0.12
Period 2 (2030-2049)
Option A
>0 to 10
0.00 to 0.95
473
15.10
10 to 30
1.23 to 1.82
9
0.16
30 to 50
N/A
0
0.00
50 to 75
N/A
0
0.00
>75
6.48 to 6.48
3
0.12
Option B (Final Rule)
>0 to 10
0.00 to 0.95
473
15.10
10 to 30
1.23 to 1.82
9
0.16
30 to 50
N/A
0
0.00
50 to 75
N/A
0
0.00
>75
6.48 to 6.48
3
0.12
Option C
>0 to 10
0.00 to 0.95
485
15.60
10 to 30
1.23 to 1.82
9
0.16
30 to 50
N/A
0
0.00
50 to 75
N/A
0
0.00
>75
6.48 to 6.48
3
0.12
N/A: Not applicable (i.e., there are no observations within the specified ABr range)
Source: U.S. EPA Analysis, 2024.
4.3.3 Step 3: Quantifying Population Exposure and Health Effects
EPA used the following steps to quantify changes in human health resulting from changes in TTHM levels in
drinking water supplies:
• Characterize the exposed populations;
• Estimate changes in individual health risk; and
• Quantify the changes in adverse health outcomes.
4.3.3.1 Exposed Populations
The exposed populations consist of people served by each affected PWS. SDWIS provides the total
population served by each PWS but does not provide detailed information about the geographic extent of the
service area. For the final rule, EPA determined the service area of each PWS using a multi-tiered approach
based on data availability. EPA first used service areas (SA) identified in the Hydroshare Community Water
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Systems Service Boundaries (CWSSB) dataset (SimpleLab EPIC, 2022),63 then 2022 TIGER ZIP code
tabulated areas (ZCTAs), and finally county boundaries when no other data were available.64 Over 95 percent
of PWS with facilities downstream from steam electric plants had boundaries defined in the CWSBB dataset.
Three percent of the PWS service areas were matched based on the ZIP code, and approximately one percent
were matched based on the county.
EPA overlaid the service area boundaries to the Census block group (CBG) data in the 2021 American
Community Survey (U.S. Census Bureau, 2021) to distribute the total population served by each PWS by age
group to model health effects as described in Section 4.3.3.3.
EPA assumed that all individuals served by a given PWS are exposed to the same modeled changes in TTHM
levels for the PWS, i.e., there are no differences in TTHM concentrations in different parts of the water
distribution system.
4.3.3.2 Health Impact Function
The relationship between exposure to DBPs, specifically trihalomethanes and other halogenated compounds
resulting from water chlorination, and bladder cancer has been the subject of multiple epidemiological studies
(Cantor et al., 2010; U.S. EPA, 2005c; NTP, 2018), a meta-analysis (Villanueva et al., 2003; Costet et al.,
2011), and pooled analysis (Villanueva et al., 2004). The relationship between trihalomethane levels and
bladder cancer in the Villanueva et al. (2004) study was used to support the benefits analysis for EPA's Stage
2 DBP Rule65 which specifically aimed to reduce the potential health risks from DBPs (U.S. EPA, 2005c).
Regli et al. (2015) conducted an analysis of potential bladder cancer risks associated with increased bromide
levels in surface source water. To estimate risks associated with modeled TTHM levels, they built on the
approach taken in EPA's Stage 2 DBP Rule, i.e., deriving a slope factor from the pooled analysis of
Villanueva et al. (2004). They showed that the overall pooled exposure-response relationship for TTHM is
linear over a range of relevant doses. The linear relationship predicted an incremental lifetime cancer risk of 1
in ten thousand exposed individuals (10 4) per 1 (ig/L increase in TTHM. The linear model proposed by Regli
et al. (2015) provides a basis for estimating the dose-response relationship associated with changes in TTHM
levels estimated for the regulatory options. The linear slope factor enables estimates of the total number of
cancer cases associated with lifetime exposures to different TTHM levels.
EPA used the relationship estimated by Regli et al. (2015) to model the impact of changes in TTHM
concentration in treated water on the lifetime bladder cancer risk:
Equation 4-1. 0(x) = 0(0) ¦ exp (0.00427 ¦ x),
where 0 (x) are the odds of lifetime bladder cancer incidence for an individual exposed to a lifetime average
TTHM concentration in residential water supply of x (ig/L and 0(0) are the odds of lifetime bladder cancer in
03 The CWSSB dataset uses a 3-tiered approach to assign more specific boundaries to PWS service areas. Tier 1 includes all PWS
with explicit water service boundaries provided by states. Tier 2 assigns a boundary based on a match with a TIGER place name.
Any PWS not in tier 1 or 2 is assigned a circular boundary around provided water system centroids based on a statistical model
trained on explicit water service boundary data.
04 This is compared to the 2019 and 2023 analyses which used comities and ZIP codes, respectively, to determine the demographic
and socioeconomic characteristics of the population served.
05 See DBP Rule documentation at https://www.epa.gov/dwreginfo/stage-l-and-stage-2-disinfectants-and-disinfection-bvproducts-
rules
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the absence of exposure to TTHM in residential water supply. The log-linear relationship (Equation 4-1) has
the advantage of being independent from the baseline TTHM exposure level, which is highly uncertain for
most affected individuals due to lack of historical data.
4.3.3.3 Health Risk Model and Data Sources
EPA estimated changes in lifetime bladder cancer cases due to estimated changes in lifetime TTHM exposure
using a dynamic microsimulation model that estimates affected population life tables under different exposure
conditions. Life table approaches are standard among practitioners in demography and risk sciences and
provide a flexible method for estimating the probability and timing of health impacts during a defined period
(Miller & Hurley, 2003; Rockett, 2010).66 In this application, the life table approach estimates age-specific
changes in bladder cancer probability and models subsequent bladder cancer mortality, which is highly
dependent on the age at the time of diagnosis. This age-specific cancer probability addresses variability in
age-specific life expectancy across the population alive at the time the change occurs. This model allows for
quantification of relatively complex policy scenarios, including those that involve variable contaminant level
changes over time.
For this analysis, EPA assumed that the population affected by estimated changes in bromide discharges from
steam electric power plants is exposed to baseline TTHM levels prior to implementation of the regulatory
options - i.e., prior to 2025 - and to alternative TTHM levels from 2025 through 2049. As described in
Section 1.3.3, the period of analysis is based on the approximate life span of the longest-lived compliance
technology for any steam electric power plant (20 or more years) and the final year of implementation (2029).
The change in TTHM exposure affects the risk of developing bladder cancer beyond this period, however,
because the majority of cancer cases manifest during the latter half of the average individual life span (Hrudey
et al., 2015). To capture these effects while being consistent with the framework of evaluating costs and
benefits incurred from 2025-2049, EPA modeled changes in health outcomes resulting from changes in
exposure in 2025-2049. Since changes in cancer incidence occur long after exposure, EPA modeled
associated changes in cancer incidence through 2125, though only for the changes attributable to changed
exposures in the 2025-2049 timeframe.
Lifetime health risk model data sources, detailed in Table 4-6 (next page), include EPA SDWIS and UCMR
4, ACS 2021 (U.S. Census Bureau, 2019, 2021), the Surveillance, Epidemiology, and End Results (SEER)
program database (National Cancer Institute), and the Center for Disease Control (CDC) National Center for
Health Statistics.
66 EPA has used life table approaches to estimate health risks associated with radon in homes, formaldehyde exposure, and
Superfund and RCRA site chemicals exposure, among others (Pawel, D. J., & Puskin, J. S. (2004). The US Environmental
Protection Agency's assessment of risks from indoor radon. Health physics, 87( 1), 68-74. ; Munns, W. R., & Mitro, M. G.
(2006). Assessing risks to populations at Superfund and RCRA sites: Characterizing effects on populations. Ecological Risk
Assessment Support Center, Office of Research and .... ; National Research Council. (2011). Review of the Environmental
Protection Agency's Draft IRIS Assessment of Formaldehyde (978-0-309-21193-2). https://www.nap.edu/catalog/13142/review-
of-the-environmental-protection-agencys-draft-iris-assessment-of-formaldehyde).
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Table 4-6: Summary of Data Sources Used in Lifetime Health Risk Model
Data element
Modeled variability
Data source
Notes
Number of persons in the
affected population in 2025
Age: 1-year groups (ages 0 to
100)
Sex: males, females
Location: PWS service areas
identified based on available
Hydroshare CWSSB data, zip
codes for PWS from SDWISa and
the fourth Unregulated
Contaminant Monitoring Rule
(UCMR 4) database15, or the
county.
2021 American Community Survey
(ACS) (data on age- and sex-specific zip
code-level population [U.S. Census
Bureau, 2019, 2021] and age- and sex-
specific population projections from
Woods & Poole Economics Inc. (2021).
ACS data were in 5-year age groups. EPA assumed
uniform distribution within each age interval to
represent data as 1-year age groups. EPA then grew
the age- and sex-specific CBG population data to the
beginning of the analysis period (2025) using
corresponding county-specific growth rates calculated
using the Woods & Poole Economics Inc. (2021)
complete demographic database. EPA then computed
relevant age- and sex- population shares and used
them to distribute location-specific affected
population.
Bladder cancer incidence
rate (IR) per 100,000
persons
Age at diagnosis: 1-year groups
(ages 0 to 100)
Sex: males, females
SEER 21 (Surveillance Research
Program - National Cancer Institute,
2020b)c
Distinct SEER 21 IR data were available for ages 0,1-4,
5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-
84, 85+ years. EPA assumed that the same IR applies
to all ages within each age group.
General population
mortality rate
Age: 1-year groups (ages 0 to
100)
Sex: males, females
Center for Disease Control
(CDC)/National Center for Health
Statistics (NCHS) United States Life
Tables, 2017
EPA used age- and sex-specific probabilities of dying
within the integer age intervals.
Share of bladder cancer
incidence at specific cancer
stage
Age at diagnosis: 1-year groups
(ages 0 to 100)
Sex: males, females
Cancer stage: localized, regional,
distant, unstaged
SEER 21 distribution of bladder cancer
incidence over stages by age and sex at
diagnosis
Distinct SEER 21 data were available for ages 0-14,15-
39, 40-64, 65-74, 75+. EPA assumed that the same
cancer incidence shares by stage apply to all ages
within each age group.
Share of cancer deaths
among all-cause deaths
Age at diagnosis: 1-year groups
(ages 0 to 100)
Sex: males, females
Cancer type: Malignant neoplasm
of bladder
Underlying Cause of Death, 1999-2019
on CDC WONDER Online Database
(Centers for Disease Control and
Prevention, 2020)
EPA calculated share of cancer deaths among all-
cause deaths by age and sex by dividing the number
of cancer deaths during 1999-2019 with the number
of all-cause deaths during 1999-2019.
Relative bladder cancer
survival by cancer stage
Age at diagnosis: 1-year groups
(ages 0 to 100)
Sex: males, females
Duration: 1-year groups
(durations 0 to 100 years)
Cancer stage: localized, regional,
SEER 18 relative bladder cancer
survival by age at diagnosis, sex, cancer
stage and duration with diagnosis for
2000-2017 (Surveillance Research
Program - National Cancer Institute,
2020a)
Distinct SEER 18 data were available for ages at
diagnosis 0-14, 15-39, 40-64, 65-74, 75+. EPA
assumed that the same cancer relative survival
patterns apply to all ages within each age group.
SEER 18 contained data on relative survival among
persons that had bladder cancer for 0,1, 2, 3, 4, 5, 6,
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Table 4-6: Summary of Data Sources Used in Lifetime Health Risk Model
Data element
Modeled variability
Data source
Notes
distant, unstaged
Cancer type: Urinary Bladder
(Invasive & In Situ) Cancer
7, 8, 9, and 10 years. For disease durations longer
than 10 years EPA applied 10-year relative survival
rates.
a EPA's Safe Drinking Water Information System SDWIS: https://www3.epa.gov/enviro/facts/sdwis/search.html
b Where Hydroshare CWSSB data were not available, ICF matched zip-code level populations from the 2021 ACS data (U.S. Census Bureau, 2019, 2021) to zip codes associated with
PWS in the SDWIS 2022 Q4 dataset (U.S. EPA, 2022) or the UCMR 4 dataset (U.S. EPA, 2016a). The SDWIS dataset often contains a one-to-many relationship between PWS and zip
codes served, whereas the UCMR 4 dataset provides a one-to-one relationship between PWS and zip codes.
c SEER program, National Cancer Institute, National Institute of Health
Source: U.S. EPA Analysis, 2024.
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Table 4-7 summarizes sex- and age group-specific general population mortality rates and bladder cancer
incidence rates used in the model simulations, as well as the sex-specific share of the affected population for
each age group. Appendix D summarize sex- and age group-specific distribution of bladder cancer cases over
four analyzed stages as well as the age of onset-specific relative survival probability for each stage.
Using available data on cancer incidence and mortality, EPA calculated changes in bladder cancer cases
resulting from the regulatory options using the relationship between the change in TTHM concentrations and
the change in lifetime bladder cancer risk estimated by Regli et al. (2015) (see Section 4.3.3.2). The analysis
accounts for the gradual changes in lifetime exposures to TTHM following estimated changes in annual
average bromide discharges and associated TTHM exposure under the regulatory options compared to the
baseline.
Table 4-7: Summary of Sex- and Age-specific Mortality and Bladder Cancer Incidence Rates
Sex
Age group
Sex-specific share of the
affected population3
General population
mortality rate
(per 100,000)b
General population
bladder cancer incidence
rate (per 100,000)bc
Female
<1
0.006
579
0.000
Female
1-4
0.024
25
0.000
Female
5-9
0.029
12
0.000
Female
10-14
0.030
13
0.000
Female
15-19
0.031
33
0.000
Female
20-24
0.035
47
0.174
Female
25-29
0.040
60
0.264
Female
30-34
0.039
80
0.498
Female
35-39
0.035
113
0.891
Female
40-44
0.032
168
1.540
Female
45-49
0.030
254
2.856
Female
50-54
0.031
378
6.551
Female
55-59
0.032
558
11.381
Female
60-64
0.032
833
18.160
Female
65-69
0.027
1,256
29.084
Female
70-74
0.021
1,997
42.848
Female
75-79
0.015
3,271
57.612
Female
80-84
0.010
5,550
71.083
Female
85+
0.010
13,559
76.378
Male
<1
0.006
702
0.000
Male
1-4
0.025
31
0.000
Male
5-9
0.031
14
0.000
Male
10-14
0.030
19
0.000
Male
15-19
0.031
78
0.112
Male
20-24
0.032
136
0.298
Male
25-29
0.035
148
0.508
Male
30-34
0.040
165
1.103
Male
35-39
0.039
204
2.078
Male
40-44
0.035
281
4.153
Male
45-49
0.032
419
8.823
Male
50-54
0.030
631
18.898
Male
55-59
0.030
933
37.562
Male
60-64
0.030
1,361
67.458
Male
65-69
0.030
1,963
114.313
Male
70-74
0.023
2,977
175.990
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Table 4-7: Summary of Sex- and Age-specific Mortality and Bladder Cancer Incidence Rates
Sex
Age group
Sex-specific share of the
affected population3
General population
mortality rate
(per 100,000)b
General population
bladder cancer incidence
rate (per 100,000)bc
Male
75-79
0.018
4,704
244.517
Male
80-84
0.011
7,623
315.335
Male
85+
0.006
15,543
357.071
a Shares calculated for the total population served by potentially affected PWS, based on Hydroshare service areas data.
b Based on the general population of the United States.
cSingle age-specific rates were aggregated up to the age groups reported in the table using the individual age-specific number of
affected persons as weights.
Source: U.S. EPA analysis (2024) of 2021 ACS data (U.S. Census Bureau, 2019, 2021).
4.3.3.4 Model Implementation
EPA analyzed effects of the regulatory options using the dynamic microsimulation model and data sources
described in Section 4.3.3.3. As described above, EPA models TTHM changes (ATTHM) due to the
regulatory options as being in effect for the years 2025 through 2049. After 2049, EPA does not attribute
costs or changes in bromide loadings to the rule, and therefore does not model incremental changes in
exposures to TTHM.67
To estimate changes in bladder cancer incidence, EPA defined and quantified a set of 31,108 unique
combinations68 of the following parameters:
• Location and TTHM changes: 154 PWS groups;69
• Age: age of the population at the start of the evaluation period (2025), ranging from 0 to 100;
• Sex: population sex (male or female).
4.3.4 Step 4: Quantifying the Monetary Value of Benefits
EPA estimated total monetized benefits from avoided morbidity and mortality (also referred to as avoided
cancer cases and avoided cancer deaths, respectively, in this discussion) from estimated changes in bromide
discharges, and estimated changes in TTHM exposure and the resulting estimated bladder cancer incidence
rate using a 2 percent discount rate for each of the three regulatory options.7"
• Morbidity: To value changes in the economic burden associated with cancer morbidity EPA relied on
base willingness-to-pay (WTP) estimates from Bosworth, Cameron and DeShazo (2009) for
colon/bladder cancer in monetizing bladder cancer benefits. The base estimate of WTP per illness
avoided based on an affected population of 50,000 for a duration of ten years is $400,000 for
67 In other words, costs after 2049 = $0 and Abromide after 2049 is zero (hence ATTHM after 2049 is zero).
08 The set of 31,108 combinations was determined by multiplying the number of PWS groups by the number of ages and sexes
considered (154 x 101 x 2).
09 The PWS groups represent unique combinations of ATTHM values and typically consist of a directly affected PWS and other
PWSs serving populations located in the same county and purchasing water from the directly affected PWS. The number of PWS
in each PWS group ranges from 1 to 41.
70 In some cases, benefits are derived from a delay in cancer morbidity and mortality.
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colon/bladder cancer (2009 dollars). The value was adjusted for income growth using an assumed
elasticity of 0.45, the central elasticity estimate for severe and chronic health effects (U.S. EPA,
2023h); it ranged from $635,947 per case in 2025 to $786,916 per case in 2049. The product of this
value and the estimated aggregate reduction in risk of bladder cancer in a given year represents the
affected population's aggregate WTP to reduce its probability of bladder cancer in one year.
• Mortality: To value changes in excess mortality from bladder cancer EPA extrapolated the default
central tendency of the VSL distribution recommended for use in EPA's regulatory impact analyses,
$4.8 million (1990 dollars, 1990 income year), to future years, ranging from $13.54 million per death
in 2025 to $16.36 million per death in 2049 (U.S. EPA, 2010). The product of VSL and the estimated
aggregate reduction in risk of death in a given year represents the affected population's aggregate
WTP to reduce its probability of death in one year.
4.4 Results of Analysis of Human Health Benefits from Estimated Changes in Bromide
Discharges Analysis
Using the data EPA assembled on cancer incidence and mortality, the Agency estimated changes in bladder
cancer cases for the regulatory options using the relationship between TTHM concentrations and the lifetime
bladder cancer risk estimated by Regli et al. (2015). Figure 4-3 and Figure 4-4 show the estimated number of
bladder cancer cases and premature deaths avoided, respectively, under the three regulatory options by
decade. In each decade, the estimated number of bladder cancer cases is never in excess of 26 cases and the
estimated number of premature deaths avoided is never in excess of seven deaths avoided.
Options A and B provide the same reductions in bromide loadings and the same benefits, whereas Option C
provides additional loading reductions and consequently larger benefits. More than 50 percent of the modeled
avoided bladder cancer incidence associated with the regulatory options occurs between 2025 and 2059. This
pattern is consistent with existing cancer cessation lag models (e.g., Hrubec & McLaughlin, 1997, Hartge et
al., 1987, and Chen & Gibb, 2003) that show between 61 and 94 percent reduction in cancer risk in the first
25 years after exposure cessation (see Appendix D for detail). After 2059, the benefits attributable to
exposures incurred under the regulatory options in 2025-2049 decline due to comparably fewer people
surviving to mature ages.71 In the years after 2099, the avoided cases decline considerably and in the last two
decades considered in the analysis, the cancer incidences increase relative to baseline incidences.72
71 In the period between 2060 and 2099, the estimated avoided cases decline slowly as the living people exposed to the estimated
changes in TTHM levels reach 70 years (the age at which the highest annual incidence of bladder cancer is observed). According
to American Cancer Society, about 9 out of 10 people diagnosed with bladder cancer are over the age of 55. The average age at
the time of diagnosis is 73 (American Cancer Society. (2019). Key Statistics for Bladder Cancer. Retrieved 2019 from
https://www.cancer.org/cancer/bladder-cancer/about/key-statistics.html).
72 The increase in cancer cases in the last decade is due to the connection between survival and cancer incidence. Lower estimated
TTHM exposure due to reductions in bromide loadings under certain regulatory options reduces the estimated number of people
developing bladder cancer during the earlier years of the analysis and increases overall survival rates. Higher estimated rates of
survival lead to longer life spans and more people developing cancer later in life. This effect becomes more apparent closer to the
end of the evaluation period, at which point there are fewer people estimated to be alive in the baseline population compared to
the estimated number of people alive under certain regulatory option scenarios.
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Figure 4-3: Estimated Number of Bladder Cancer Cases Avoided under the Regulatory Options.
Source: U.S. EPA Analysis, 2024.
Figure 4-4: Estimated Number of Cancer Deaths Avoided under the Regulatory Options.
Source: U.S. EPA Analysis, 2024.
Table 4-8 summarizes the estimated changes in the incidence of bladder cancer from exposure to TTHM due
to the regulatory options and the value of benefits from avoided cancer cases, including avoided mortality and
morbidity. The table provides the present value of benefits from changes in TTHM exposure in 2025-2049 for
the period of analysis (2025-2049) and for the entire period with attributable benefits (through 2125).
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Table 4-8: Estimated Bromide-related Bladder Cancer Mortality and Morbidity Monetized Benefits
Regulatory
Option
Changes in cancer cases3 from
changes in TTHM exposure
2025-2049
Present value of discounted
benefits3 (million 2023$,
discounted to 2024 at 2 percent)
Annualized13 benefits (million
2023$, discounted to 2024 at 2
percent)
Total bladder
cancer cases
avoided
Total cancer
deaths avoided
Avoided
mortality
Avoided
morbidity
Total
Avoided
mortality
Avoided
morbidity
Total
Option A
98
28
$225.8
$40.4
$266.2
$11.3
$2.0
$13.4
Option B
(Final Rule)
98
28
$225.8
$40.4
$266.2
$11.3
$2.0
$13.4
Option C
104
29
$241.0
$43.1
$284.1
$12.1
$2.2
$14.3
aThe values account for the persisting health effects (up until 2125) from changes in TTHM exposure during the period of analysis
(2025-2049).
b Benefits are annualized over 25 years. The annualized benefits account for avoided mortality and morbidity during the period of
analysis (2025-2049) as well as persisting health effects (up until 2125) from reduced TTHM exposure through 2049.
Source: U.S. EPA Analysis, 2024
4.5 Additional Measures of Human Health Effects from Exposure to Steam Electric Pollutants
via Drinking Water Pathway
The regulatory options may result in relatively small changes to source water quality for additional parameters
that can adversely affect human health (see Section 2.1.1). Many pollutants in steam electric power plant
discharges have MCLs that set allowable levels in treated water. For some pollutants that have an MCL above
the MCLG, there may be incremental benefits from reducing concentrations below the MCL. In addition to
certain brominated DBPs discussed in the previous sections, there are no "safe levels" for lead and arsenic
and therefore any reduction in exposure to these pollutants is expected to yield benefits.73
Estimated concentrations of arsenic and lead in downstream reaches that serve as drinking water sources do
not exceed typical detection limits for these contaminants. The results show thallium concentrations in source
waters that exceed levels detectable by standard methods (0.005 j^ig/L) in one source water reach during
Period 1 but are below 0.005 (ig/L in all other modeled source waters. Relative to baseline concentrations, the
changes in arsenic, lead, and thallium concentrations are small (e.g., less than 0.005 (ig/L in Period 1 and less
than 0.007 (ig/L in Period 2 in source waters). Table 4-9 summarizes the direction of changes in arsenic, lead,
and thallium concentrations under the regulatory options for the two analysis periods. The magnitude of the
changes depends on the Period, regulatory option, source water reach, and PWS but is generally consistent
with the changes in halogen loadings associated with FGD wastewater and bottom ash transport water under
each analyzed regulatory option (see Table 3-1). During Period 1, all Options show either reductions or no
changes in arsenic, lead, and thallium concentrations for all source waters and PWS. During Period 2, the
three regulatory options also show estimated reductions in arsenic, lead, and thallium concentrations with
both the magnitude and scope (the number of reaches, PWS, and population served) of the reductions larger
than during Period 1.
73 Even in cases where the MCLG is equal to the MCL, there may be incremental health-related benefits associated with changes in
concentrations arising from the regulatory options since detection of the pollutants is subject to imperfect monitoring and
treatment may not remove all contaminants from the drinking water supplies, as evidenced by reported MCL violations for
inorganic and other contaminants at community water systems (U.S. Environmental Protection Agency. (2013b). Fiscal year
2011: Drinking water and ground water statistics. (EPA 816-R-l 3-003). Washington, DC: U.S. Environmental Protection
Agency, Office of Water).
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To assess potential additional drinking water-related health benefits, EPA estimated the changes in the
number of receiving reaches with drinking water intakes that have modeled pollutant concentrations
exceeding MCLs or MCLGs. EPA did this analysis for all of the pollutants listed in Table 2-2, except bromate
and TTHM.74 This analysis showed no changes in the number of MCL or MCLG exceedances under the
regulatory options during Period 1, when compared to the baseline. In addition, EPA found no reaches with
drinking water intakes that had modeled lead, arsenic, or thallium concentrations in excess of MCLs or
MCLGs under either the baseline or the regulatory options during Period 1, even where concentrations
increased as summarized in Table 4-9.75
During Period 2, EPA found 182 reaches with drinking water intakes that had modeled arsenic concentrations
in excess of the MCLG and 23 reaches with modeled lead concentrations in excess of the MCLG that showed
improvements under at least one of the regulatory options. The Agency concluded, based on these screening
analyses, that any additional benefits from changes in exposure to the pollutants examined in this analysis via
the drinking water pathway would be relatively small.
Table 4-9: Estimated Distribution of Changes in Source Water and PWS-Level Arsenic, Lead, and
Thallium Concentrations by Period and Regulatory Option, Compared to Baseline
Regulatory Option
Number of Source Water
Reaches
Number of PWSa
Population Served by PWS
(Millions)
Reduction
No Change
Reduction
No Change
Reduction
No Change
Period 1 (2025-2029)
Arsenic
Option A
215
13
849
67
28.0
1.1
Option B (Final Rule)
217
11
866
50
28.6
0.5
Option C
217
11
866
50
28.6
0.5
Lead
Option A
118
26
464
79
13.8
3.1
Option B (Final Rule)
118
26
464
79
13.8
3.1
Option C
118
26
464
79
13.8
3.1
Thallium
Option A
215
13
849
67
28.0
1.1
Option B (Final Rule)
217
11
866
50
28.6
0.5
Option C
217
11
866
50
28.6
0.5
Period 2 (2030-2049)
Arsenic
Option A
222
6
889
27
29.0
0.2
Option B (Final Rule)
223
5
894
22
29.1
0.1
Option C
223
5
894
22
29.1
0.1
Lead
Option A
130
14
493
50
15.5
1.4
Option B (Final Rule)
130
14
493
50
15.5
1.4
Option C
131
13
505
38
16.0
0.9
74 EPA did not consider MCL or MCLG exceedances for bromate and TTHM because the background data on these contaminants
in source waters is not readily available (e.g., these contaminants are not included in the TRI dataset). Additionally, modeled
discharges of bromate from steam electric plant effluent do not exceed EPA's MCL of 0.01 mg/L, but all exceed the MCLG of
zero.
75 EPA also found that there are no reaches with drinking water intakes that have pollutant concentrations exceeding human health
ambient water quality criteria for either the consumption of water and organism or the consumption of organism only.
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Table 4-9: Estimated Distribution of Changes in Source Water and PWS-Level Arsenic, Lead, and
Thallium Concentrations by Period and Regulatory Option, Compared to Baseline
Regulatory Option
Number of Source Water
Reaches
Number of PWSa
Population Served by PWS
(Millions)
Reduction
No Change
Reduction
No Change
Reduction
No Change
Thallium
Option A
222
6
889
27
29.0
0.2
Option B (Final Rule)
223
5
894
22
29.1
0.1
Option C
223
5
894
22
29.1
0.1
a. Includes systems potentially directly and/or indirectly affected by steam electric power plant discharges.
Source: U.S. EPA Analysis, 2024.
4.6 Limitations and Uncertainties
Table 4-10 summarizes principal limitations and sources of uncertainties associated with the estimated
changes in pollutant levels in source waters downstream from steam electric power plant discharges.
Additional limitations and uncertainties are associated with the estimation of pollutant loadings (see U.S.
EPA, U.S. EPA, 2020g). Note that the effect on benefits estimates indicated in the second column of the table
refers to the magnitude of the benefits rather than the direction (i.e., a source of uncertainty that tends to
underestimate benefits indicates expectation for either larger forgone benefits or larger realized benefits).
Table 4-10: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
Analysis does not account
for births within the
exposed population.
Underestimate
The analysis does not account for people born after
2025. This likely leads to an underestimate of benefits.
Analysis does not account
for migration within the
exposed population.
Uncertain
The analysis does not account for people leaving or
moving into the service area. The overall effect of this
factor on the estimated benefits is uncertain.
Bladder cancer risks are
estimated for populations
for which changes in
TTHM exposures relative
to baseline exposures
start at different ages,
including children.
Uncertain
The relative cancer potency of TTHM in children is
unknown, which may bias benefits estimates either
upward or downward. Past reviews found no clear
evidence that children are at greater risk of adverse
effects from bromoform or dibromochloromethane
exposure (U.S. EPA, 2005a) although certain modes of
action and health effects may be associated with
exposure to TTHM during childhood (U.S. EPA, 2016c).
Because bladder cancer incidence in children is very
small, EPA assesses any bias to be negligible.
For PWS with multiple
sources of water, the
analysis uses equal
contributions from each
source.
Uncertain
Data on the flow rates of individual source facilities are
not available and EPA therefore estimated that all
permanent active sources contribute equally to a PWS's
total supply. Effects of the regulatory option may be
greater or smaller than estimated, depending on actual
supply shares.
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Table 4-10: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
Changes in bromide
concentrations are
analyzed for active
permanent surface water
intakes and reservoirs
only.
Underestimate
The analysis includes only permanent active surface
water facilities associated with non-transient PWS
classified as "community water systems" that use
surface water as primary source. To the extent that
PWS using surface waters as secondary source or other
non-permanent surface water facilities are affected,
this approach understates the effects of the regulatory
options.
Changes in TTHM
formation depends only
on changes in bromide
levels.
Uncertain
The regulatory options are expected to affect bromide
levels in source water. Other factors such as
disinfection method, pH, temperature, and organic
content affect TTHM formation. EPA assumes that PWS
and source waters affected by steam electric power
plant discharges have similar characteristics as those
modeled in Regli et al. (2015).
Use of a national
relationship from Regli et
al. (2015) to relate
changes in bromide
concentration to changes
in TTHM concentration.
Uncertain
EPA did not collect site-specific information on factors
affecting TTHM formation at each potentially affected
drinking water treatment plant, but instead used the
median from a sample population of approximately 200
drinking water treatment systems. Use of the national
relationship from Regli et al. (2015) could either
understate or overstate actual changes in TTHM
concentrations for a given change in bromide
concentrations at any specific drinking water treatment
system.
Change in risk is based on
changes in exposure to
TTHMs rather than to
brominated
trihalomethanes
specifically.
Underestimate
Brominated species play a prominent role in the overall
toxicity of DBP exposure. Given that the regulatory
options predominantly affect the formation of
brominated DBPs, the estimated changes in cancer risk
resulting from regulatory options could be biased
downward. EPA report provides additional information
about health effects of DBPs (U.S. EPA, 2016c).
The analysis relies on
public-access SEER 18 5-
year relative bladder
cancer survival data to
model mortality patterns
in the bladder cancer
population.
Uncertain
Reliance on these data generates both a downward and
an upward bias. The downward bias is due to the short,
5-year excess mortality follow-up window. Survival
rates beyond 5 years following the initial diagnosis are
likely to be lower. The upward bias comes from the
inability to determine how many of the excess deaths
were deaths from bladder cancer.
The dose-response
function used to estimate
risk assumes causality of
bladder cancer from
exposure to disinfected
drinking water.
Overestimate
While the evidence supporting causality has increased
since EPA's Stage 2 DBP Rule, the weight of evidence is
still not definitive (see Regli et al. (2015)).
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4: Human Health Benefits via Drinking Water
Table 4-10: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
The relationship from
Regli et al. (2015) is a
linear approximation of
the odds ratios reported
in Villanueva et al. (2004).
Uncertain
Given the uncertainty about the historical, location-
specific TTHM baselines, Regli et al. (2015) provides a
reasonable approximation of the risk. However,
depending on the baseline TTHM exposure level, the
impact computed based on Regli et al. (2015) may be
larger or smaller than the impact computed using the
Villanueva et al. (2004)-reported odds ratios directly.
The analysis does not
account for the
relationship between
TTHM exposure and
bladder cancer within
certain subpopulations.
Overestimate
Epidemiological literature suggests that TTHM effects
could be greatest for the smoker population, whose
members are already at higher risk for bladder cancer.
Smoking prevalence has declined in the United States
and relationships estimated with data from the 1980s
and 1990s may overestimate future bladder cancer
impact. Robust synthesis estimates of the relationship
between TTHM and bladder cancer in the smoker
population are lacking, limiting EPA's ability to account
for smoking when modeling health effects.
The change in risk for a
given change in TTHM is
uncertain for changes in
TTHM concentrations that
are less than 1 ng/L.
Uncertain
EPA notes that the majority of the regulatory options
benefits are associated with PWS for which predicted
changes in TTHM concentration are greater than
1 ng/L. Although there is greater uncertainty in the
estimated changes in health risk associated with
changes in TTHM concentrations less than 1 ng/L, EPA
included these changes in the estimated benefits.
Benefits from the regulatory options may be greater or
smaller than estimated, depending on actual risk
changes.
Health effects associated
with DBP exposure other
than bladder cancer are
not quantified in this
analysis.
Uncertain
An EPA report discusses potential linkages between
DBP exposures and other health endpoints, e.g.,
developmental effects (with a short-term exposure)
and cancers other than bladder cancers (with a long-
term exposure), but there is insufficient data to fully
evaluate these endpoints (U.S. EPA, 2016c).
Discharge monitoring data
for bromide from steam
electric power plants are
limited and demonstrate
significant variability
based on site-specific
factors.
Uncertain
Limited bromide monitoring data are available to assess
bromide source water concentration estimates.
The analysis does not
consider pollutant sources
beyond those associated
with steam electric power
plants orTRI dischargers.
Underestimate
The analysis of other pollutants does not account for
natural background and anthropogenic sources that do
not report to TRI. This results in a potential
underestimate of the number of waters exceeding the
MCLor MCLG.
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Table 4-10: Limitations and Uncertainties in the Analysis of Human Health Benefits from Changes in
Discharges of Halogens and Other Pollutants Via the Drinking Water Pathway
Uncertainty/Limitation
Effect on Benefits Estimate
Notes
The analysis does not
account for populations
that consume bottled
water as their primary
drinking water source or
populations that practice
averting behaviors such as
purchasing bottled water
and filters in response to
drinking water violations.
Uncertain
Studies indicate that between 13 percent and
33 percent of the U.S. population consumes bottled
water as their primary drinking water source (Hu,
Morton & Mahler, 2011; Rosinger et al., 2018; Vieux et
al., 2020). Recent research also documents a
relationship between sales of bottled water and
violations of the SDWA (Allaire et al., 2019). The
benefits models do not consider populations who
consume bottled water as their primary drinking water
source or populations that practice averting behaviors
in response to poor drinking water quality. The overall
effect of not considering these populations on the
estimated benefits is uncertain.
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5 Human Health Effects from Changes in Pollutant Exposure via the Fish
Ingestion Pathway
EPA expects the regulatory options to affect human health risk by changing effluent discharges to surface
waters and, as a result, ambient pollutant concentrations in the receiving reaches. The EA provides details on
the health effects of steam electric pollutants (U.S. EPA, 2024b). Recreational and subsistence fishers (and
their household members) who consume fish caught76 in the reaches receiving steam electric power plant
discharges could benefit from reduced pollutant concentrations in fish tissue. This chapter presents EPA's
analysis of human health effects resulting from changes in exposure to pollutants in bottom ash transport
water, FGD wastewater and CRL via the fish consumption pathway. The analyzed health effects include:
• Changes in exposure to lead: This includes changes in neurological and cognitive damages in children
(ages 0-7) based on the impact of an additional IQ point on an individual's future earnings, and
changes in cardiovascular disease (CVD) premature mortality for adults.
• Changes in exposure to mercury: Changes in neurological and cognitive damages in infants from
exposure to mercury in-ntero based on the impact of an additional IQ point on an individual's future
earnings.
• Changes in exposure to arsenic: Changes in incidence of cancer cases and the COI associated with
treating skin cancer.
The total quantified human health effects included in this analysis represent only a subset of the potential
health effects estimated to result from the regulatory options. While additional adverse health effects are
associated with pollutants in bottom ash transport water and FGD wastewater (such as kidney damage from
cadmium or selenium exposure, gastrointestinal problems from zinc, thallium, or boron exposure, and others),
the lack of data on dose-response relationships77 between ingestion rates and these effects precluded EPA
from quantifying the associated health effects.
EPA's analysis of the monetary value of human health effects utilizes data and methodologies described in
Chapter 3 and in the EA (U.S. EPA, 2024b). The relevant data include the set of immediate and downstream
reaches that receive steam electric power plant discharges (/'. e., affected reaches), as defined by the NHD
COMID,78 the estimated ambient pollutant concentrations in receiving reaches, and estimated fish
consumption rates among different age and ethnic cohorts for affected recreational and subsistence fishers.
Section 5.1 describes how EPA identified the population potentially exposed to pollutants from steam electric
power plant discharges via fish consumption. Section 5.2 describes the methods for estimating fish tissue
pollutant concentrations and potential exposure via fish consumption in the affected population. Section 5.3 to
Section 5.6 describe EPA's analysis of various human health endpoints potentially affected by the regulatory
70 As detailed in Section 5.2 and Section 5.9, for the subset of recreational and subsistence fishers who consume catch from affected
reaches (i.e., do not practice catch-and-release), EPA assumed that all fish consumed consists of self-caught fish. EPA assumed
no exposure via fish consumption for all other households, including recreational and subsistence fishers who consume catch
from other reaches.
77 A dose response relationship is an increase in incidences of an adverse health outcome per unit increase in exposure to a toxin.
78 A COMID is a unique numeric identifier for a given waterbody (reach), assigned by a joint effort of the United States Geological
Survey and EPA.
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5: Human Health Benefits via Fish Ingestion
options, which are then summarized in Section 5.7. Section 5.8 provides additional measures of human health
benefits. Section 5.9 describes limitations and uncertainties.
5.1 Population in Scope of the Analysis
The population in scope of the analysis (/'. e., individuals potentially exposed to steam electric pollutants via
consumption of contaminated fish tissue) includes recreational and subsistence fishers who fish reaches
affected by steam electric power plant discharges (including receiving and downstream reaches), as well as
their household members.79 EPA estimated the number of people who are likely to fish affected reaches based
on typical travel distances to a fishing site and presence of substitute fishing locations. EPA notes that the
universe of sites potentially visited by recreational and subsistence fishers includes reaches subject to fish
consumption advisories (FCA).80 EPA expects that recreational fishers' responses to FCA presence are
reflected in their catch and release practices, as discussed below.
Since fish consumption rates vary across different age, racial and ethnic groups, and fishing mode
(recreational versus subsistence fishing), EPA estimated potential health effects separately for a number of
age-, ethnicity-, and mode-specific cohorts. For each Census Block Group (CBG) within 50 miles of an
affected reach, EPA assembled 2021 American Community Survey data on the number of people in 7 age
categories (0 to 1, 2, 3 to 5, 6 to 10, 11 to 15, 16 to 21, and 21 years or higher) for the analysis of benefits to
children from reductions in lead and mercury, and for cancer benefits from reductions in arsenic, and in 41
age categories for the analysis of adult lead benefits. EPA then subdivided each group according to 7
racial/ethnic categories:81 1) White non-Hispanic; 2) African-American non-Hispanic; 3) Tribal/Native
Alaskan non-Hispanic; 4) Asian/Pacific Islander non-Hispanic; 5) Other non-Hispanic (including multiple
races); 6) Mexican Hispanic; and 7) Other Hispanic.82 Within each racial/ethnic group, EPA further
subdivided the population according to recreational and subsistence fisher groups. The Agency assumed that
the 95th percentile of the general population fish consumption rate is representative of the subsistence fisher
consumption rate. Accordingly, the Agency assumed that 5 percent of the total fishers population practices
subsistence fishing.83 EPA also subdivided the affected population by income into poverty and non-poverty
79 The in-scope population excludes recreational and subsistence fishers who fish other reaches or certain affected waterbodies not
covered by the water quality models (i.e., Great Lakes and estuaries).
80 Based on EPA's review of studies documenting fishers' awareness of FCA and their behavioral responses to FCA, 57.0 percent
to 61.2 percent of fishers are aware of FCA, and 71.6 percent to 76.1 percent of those who are aware ignore FCA (Burger, J.
(2004). Fish consumption advisories: knowledge, compliance and why people fish in an urban estuary. Journal of Risk Research,
7(5), 463-479. , Jakus, P. M., Downing, M., Bevelhimer, M. S., & Fly, J. M. (1997). Do sportfish consumption advisories affect
reservoir anglers' site choice? Agricultural and Resource Economics Review, 26(2), 196-204. ; Jakus, P. M., McGuinness, M., &
Krupnick, A. J. (2002). The benefits and costs offish consumption advisories for mercury. ; Williams, R. L., O'Leary, J. T.,
Sheaffer, A. L., & Mason, D. (2000). An examination of fish consumption by Indiana recreational anglers: an on-site survey.
West Lafayette, IN: Purdue University. ). Therefore, only 17.4 percent of fishers may adjust their behavior in response to FCA
(U.S. Environmental Protection Agency. (2015a). Benefit and Cost Analysis for the Effluent Limitations Guidelines and
Standards for the Steam Electric Power Generating Point Source Category. (EPA-821-R-15-005). ). The analysis reflects EPA's
expectations that fishers responses to FCA are reflected in their catch and release practices.
81 The racial/ethnic categories are based on available fish consumption data as well as the breakout of ethnic/racial populations in
Census data, which distinguishes racial groups within Flispanic and non-Flispanic categories.
82 The Mexican Flispanic and Flispanic block group populations were calculated by applying the Census tract percent Mexican
Flispanic and Flispanic to the underlying block-group populations, since these data were not available at the block-group level.
83 Data are not available on the share of the fishing population that practices subsistence fishing. EPA assumed that 5 percent of
people who fish practice subsistence fishing, based on the assumed 95th percentile fish consumption rate for this population in
EPA's Exposure Factors Flandbook (see U.S. Environmental Protection Agency. (2011). Exposure Factors Handbook, 2011
Edition (Final). (EPA-600-R-09-025F). U.S. Environmental Protection Agency, Washington, DC).
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groups, based on the share of people below the federal poverty line.84 After subdividing population groups by
age, race, fishing mode, and poverty indicator, each CBG has 196 unique population cohorts (7 age groups x
7 ethnic/racial groups x 2 fishing modes [recreational versus subsistence fishing] x 2 poverty status
designations) for the analysis of benefits to children from reductions in lead and mercury and cancer benefits
from reductions in arsenic, and each CBG has 1,148 unique population cohorts (41 age groups * 7
ethnic/racial groups x 2 fishing modes [recreational versus subsistence fishing] x 2 poverty status
designations) for the analysis of adult lead benefits.
EPA distinguished the exposed population by racial/ethnic group and poverty status to support analysis of
potential environmental justice (EJ) considerations from baseline exposure to pollutants in steam electric
power plant discharges, and to allow evaluation of the effects of the regulatory options on mitigating any EJ
concerns. See EJA document for details of the EJ analysis. As noted below, distinguishing the exposed
population in this manner allows the Agency to account for differences in exposure among demographic
groups, where supported by available data.
Equation 5-1 shows how EPA estimated the population potentially exposed to steam electric pollutants,
ExPop(i)(s)(c), for CBG i in state 5 for cohort c.
Equation 5-1. ExPop(i)(s)(c) = Pop(i)(c)x %Fish(s) x CaR(c)
Where:
Pop(i)(c) = Total CBG population in cohort c. Age and racial/ethnicity-specific populations in each
CBG are based on data from the 2021 American Community Survey, which provides
population numbers for each CBG broken out by age and racial/ethnic group. To
estimate the population in each age- and ethnicity/race-specific group, EPA calculated
the share of the population in each racial/ethnic group and applied those percentages to
the population in each age group.
%Fish(s) = Fraction of people who live in households with fishers. To estimate what percentage of the
total population participates in fishing, EPA used region-specific U.S. Fish and Wildlife
Service (U.S. FWS, 2023) estimates of the population 16 and older who fish.85 EPA
assumed that the share of households that includes fishers is equal to the fraction of
people over 16 who participate in recreational fishing.
CaR(c) = Adjustment for catch-and-release practices. According to U.S. FWS (U.S. FWS, 2006) data,
approximately 23.3 percent of recreational fishers release all the fish they catch ("catch-
and-release" fishers). Fishers practicing "catch-and-release" would not be exposed to
steam electric pollutants via consumption of contaminated fish. For all recreational
fishers, EPA reduced the affected population by 23.3 percent. EPA assumed that
subsistence fishers do not practice "catch-and-release" fishing.
84 Poverty status is based on data from the Census Bureau's American Community Survey which determines poverty status by
comparing annual income to a set of dollar values called poverty thresholds that vary by family size, number of children, and the
age of the householder.
85 The share of the population who fishes ranges from 10 percent in the Pacific region to 22 percent in the West North Central
region. Other regions include the Middle Atlantic (12 percent), New England (12 percent), Mountain (15 percent), South Atlantic
(16 percent), East North Central (17 percent), West South Central (17 percent), and East South Central (20 percent).
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Table 5-1 summarizes the population living within 50 miles of reaches affected by steam electric power plant
discharges (see Section 5.2.1 for a discussion of this distance buffer) and EPA's estimate of the population
potentially exposed to the pollutants via consumption of subsistence- and recreationally-caught fish (based on
2021 population data and not adjusted for population growth during the analysis period). Of the total
population, 17 percent live within 50 miles of an affected reach and participate in recreational and/or
subsistence fishing, and 13 percent are potentially exposed to fish contaminated by steam electric pollutants in
bottom ash transport water, FGD wastewater, CRL, and legacy wastewater discharges.
Table 5-1: Summary of Population Potentially Exposed to Contaminated Fish Living within 50 Miles
of Affected Reaches (as of 2021)
Total population
126,726,686
Total fishers population3
21,532,470
Population potentially exposed to contaminated fishb c
16,766,257
a. Total population living within 50 miles of an affected reach multiplied by the state-specific share of the population who fishes
based on U.S. FWS (2023; 2018; between 10 percent and 22 percent, depending on the state).
b. Total fishers population adjusted to remove fishers practicing catch-and-release and who therefore do not consume self-caught
fish.
c. Analysis accounts for projected population growth so that the average population in scope of the analysis over the period of
2025 through 2049 is 10.8 percent higher than the population in 2021 presented in the table, or 18.6 million people. The analysis
estimates that the fraction of the U.S. population engaged in recreational and subsistence fishing remains constant from 2025
through 2049.
Source: U.S. EPA Analysis, 2024
5.2 Pollutant Exposure from Fish Consumption
EPA calculated an average fish tissue concentration for each pollutant for each CBG based on a length-
weighted average concentration for all reaches within 50 miles. Depending on the health endpoint used in the
analysis, EPA calculated either the average daily dose (ADD) or lifetime average daily dose (LADD) for each
combination of pollutant, cohort and CBG.
5.2.1 Fish Tissue Pollutant Concentrations
The set of reaches that may represent a source of contaminated fish for recreational and subsistence fishers in
each CBG depends on the typical distance fishers travel to fish. EPA assumed that fishers typically travel up
to 50 miles to fish,86 and used this distance to estimate the relevant fishing sites for the population of fishers in
each CBG.
Fishers may have several fishable sites to choose from within 50 miles of travel. To account for the effect of
substitute sites, EPA assumed that fishing efforts are uniformly distributed among all the available fishing
sites within 50 miles from the CBG (travel zone). For each CBG, EPA identified all fishable reaches within
50 miles (where distance was determined based on the Euclidean distance between the centroid of the CBG
and the midpoint of the reach) and the reach length in miles.
Studies of fishers behavior and practices have made similar observations (e.g., Sohngen, B., Zhang, W., Brnskotter, J., &
Sheldon, B. (2015). Results from a 2014 survey of Lake Erie anglers. Columbus, OH: The Ohio State University', Department of
Agricultural, Environmental and Development Economics and School of Environment & Natural Resources, and Sea Grant -
Illinois-Indiana. (2018). Lake Michigan anglers boost local Illinois and Indiana economies. Retrieved 2019, from
https://iiseagrant.org/lake-michigan-anglers-boost-illinois-and-indiana-local-economies/).
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EPA then calculated, for each CBG within the 50-mile buffer of a fishable reach, the fish tissue concentration
of As, Hg, and lead (Pb). Appendix E in U.S. EPA (2020b) describes the approach used to calculate fish
tissue concentrations of steam electric pollutants in the baseline and under each of the regulatory options.
For each CBG, EPA then calculated the reach length (Lengthweighted fish fillet concentration (C Fish Fuiet
(CBG)) based on all fishable reaches within the 50-mile radius according to Equation 5-2. See Appendix E for
additional details about the derivation of fish tissue concentration values.
Fm.atinn *9 r - ^=1 CFishFW,t(-i)"'Len9thi
Equation 5-2. CFishFillete(CBG) 2n=iLenflthi
5.2.2 Average Daily Dose
Exposure to steam electric pollutants via fish consumption depends on the cohort-specific fish consumption
rates. Table 5-2 summarizes the average fish consumption rates, expressed in daily grams per kilogram of
body weight (BW), according to the race/ethnicity and fishing mode. The rates reflect recommended values
for consumer-only intake of finfish in the general population from all sources, based on EPA's Exposure
Factors Handbook (U.S. EPA, 2011). For more details on these fish consumption rates, see the EA (U.S.
EPA, 2024b) and the uncertainty discussion in Section 5.9.
Table 5-2: Summary of Group-specific Consumption Rates for Fish Tissue Consumption Risk
Analysis
Race/ Ethnicity3
EA Cohort Nameb
Consumption Rate (g/kg BW/day)
Recreational
Subsistence
White (non-Hispanic)
Non-Hispanic White
0.67
1.9
African American (non-Hispanic)
Non-Hispanic Black
0.77
2.1
Asian/Pacific Islander (non-Hispanic)
Other, including Multiple Races
0.96
3.6
Tribal/Native Alaskan (non-Hispanic)
Other, including Multiple Races
0.96
3.6
Other non-Hispanic
Other, including Multiple Races
0.96
3.6
Mexican Hispanic
Mexican Hispanic
0.93
2.8
Other Hispanic
Other Hispanic
0.82
2.7
a. Each group is also subdivided into seven age groups (0-1, 2, 3-5, 6-10,11-15,16-20, Adult [21 or higher] and two income groups
[above and below the poverty threshold]).
b. See EA for details (U.S. EPA, 2024b).
Source: U.S. EPA Analysis, 2024
Equation 5-3 and Equation 5-4 show the cohort- and CBG-specific ADD and LADD calculations based on
fish tissue concentrations, consumption rates, and exposure duration and averaging periods detailed in the EA
(U.S. EPA, 2024b.
Equation 5-3. ADD(c)(i) =
Where:
ADD(c)(i) = average daily dose of pollutant from fish consumption for cohort c in CBG i
(milligrams [mg] per kilogram [kg] body weight [BW] per day)
Cflshjuet(j) = average fish fillet pollutant concentration consumed by humans for CBG /' (mg per kg)
CRflsh{c) = consumption rate of fish for cohort c (grams per kg BW per day); see Table 5-2
Ffish = fraction of fish from reaches within the analyzed distance from the CBG (percent; estimated value
of 100%)
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r- X- f A , WN ADD(c)(i)xED(c)xEF
Equation 5-4. LADD(c)(i) = ——
^ v J AT X365
Where:
LADD (c)(i) = lifetime average daily dose (mg per kg BW per day) for cohort c in CBG /'
ADD (c)(i) = average daily dose (mg per kg BW per day) for cohort c in CBG /'
ED(c) = exposure duration (years) for cohort c
lib' = exposure frequency (days; set to 350)
AT = averaging time (years; set to 70)
EPA used the doses of steam electric pollutants as calculated above from fish caught through recreational and
subsistence fishing in its analysis of benefits associated with the various human health endpoints described
below.
5.3 Health Effects in Children from Changes in Lead Exposure
Lead is a highly toxic pollutant that can cause a variety of adverse health effects in children of all ages. In
particular, elevated lead exposure may induce a number of adverse neurological effects in children, including
decline in cognitive function, conduct disorders, attentional difficulties, internalizing behavior,87 and motor
skill deficits (see NTP, 2012; ATSDR, 2020; U.S. EPA, 2024d, 2019e, 2020g, and 2024d). Elevated blood
lead level (BLL) in children may also slow postnatal growth in children ages one to 16, delay puberty in 8- to
17-year-olds, and decrease hearing and motor function (NTP, 2012; ATSDR, 2020; U.S. EPA, 2019e and
2024d). Lead exposure is also associated with adverse health outcomes related to the immune system,
including atopic and inflammatory responses (e.g., allergy and asthma) and reduced resistance to bacterial
infections. Studies have also found a relationship between lead exposure in expectant mothers and lower birth
weight in newborns (NTP, 2012; ATSDR, 2020; U.S. EPA, 2019e and 2024d; Zhu et al., 2010). For this final
rule, EPA estimated the effects of changes in neurological and cognitive damages to children ages 0 to 7 using
the dose-response relationship for IQ decrements (Crump et al., 2013).88
EPA estimated health effects from changes in exposure to lead to preschool children using BLL as a
biomarker of lead exposure. EPA modeled BLL under the baseline and regulatory option scenarios, and then
used a concentration-response relationship between BLL and IQ loss to estimate changes in IQ losses in the
affected population of children and changes in incidences of extremely low IQ scores (less than 70, or two
standard deviations below the mean). EPA calculated the monetary value of changes in children's health
effects based on the impact of an additional IQ point on an individual's future earnings.
EPA used the methodology described in Section 5.1 to estimate the population of children from birth to age
seven who live in recreational fisher and subsistence fisher households and are potentially exposed to lead via
87 Behavioral difficulties in children may include both externalizing behavior (e.g., inattention, impulsivity, conduct disorders), and
internalizing behaviors (e.g., withdrawn behaviors, symptoms of depression, fearfulness, and anxiety).
88 EPA also evaluated estimating the effects of changes in lead exposure on ADHD in children and low birthweight in infants, but
given the small magnitude of IQ point effects for the final rule and the fact that regulatory analyses for other rules have shown
avoided IQ losses to be larger than ADHD and birthweight effects, EPA did not quantify these additional benefits. For example,
the 2023 Lead and Copper Rule Improvements (LCRI) proposed rulemaking showed the cognitive development benefits from
avoided IQ losses to be 3 to 13 times ADHD benefits and 150 to 1,000 times low-birthweight benefits, depending on the scenario
and discount rate (U.S. Environmental Protection Agency. (2023f). Economic Analysis for the Proposed Lead and Copper Rule
Improvements. ).
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consumption of contaminated fish tissue. EPA notes that fish tissue is not the only route of exposure to lead
among children. Other routes of exposure may include drinking water, dust, and other food. EPA used
reference exposure values for these other routes of lead exposures and held these values constant for the
baseline and regulatory options scenarios. Since this health effect applies to children up to the seventh
birthday only, EPA restricted the analysis to the relevant age cohorts of fisher household members.
5.3.1 Data and Methodology
This analysis considers children who are born after implementation of the regulatory options and live in
recreational fisher and subsistence fisher households. It relies on EPA's Integrated Exposure, Uptake, and
Biokinetics (IEUBK) Model for Lead in Children (version 2; U.S. EPA, 2021a), 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 the
estimated total exposure, the model generates a predicted geometric mean BLL for a population of children
exposed to similar lead levels. See the 2013 BCA report (U.S. EPA, 2013a) for details.
For each CBG, EPA used the cohort-specific ADD based on Equation 5-3. EPA then multiplied the cohort-
specific ADD by the average body weight for each age group89 to calculate the "alternative source" dietary
input for the IEUBK model, which varied by option relative to the baseline. All other sources of lead were
held constant. Lead bioavailability and uptake after consumption vary 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 for the "alternative
source" provided in the IEUBK model, which is 50 percent for oral ingestion.
EPA used the IEUBK model to generate the geometric mean BLL for each cohort in each CBG under the
baseline and post-technology implementation scenarios. The IEUBK model processes daily intake to two
decimal places (fig/day). For this analysis, this means that some of the change between the baseline and
regulatory options is not accounted for by using the model (/'. e., IEUBK treats these very small changes as
zero). This aspect of the model contributes to potential underestimation of the lead-related health effects in
children arising from the regulatory options, since the estimated changes in health effects are driven by small
changes across large populations.
EPA used the Crump et al. (2013) dose-response function to estimate changes in IQ losses between the
baseline and regulatory options. Comparing the baseline and regulatory option results provides the changes in
IQ loss per child. Crump et al. (2013) concluded that there was statistical evidence that the exposure-response
is non-linear over the full range of BLL. Equation 5-5 shows an exposure-response function that represents
this non-linearity:
89 The average body weight values are 11.4 kg for ages 0 to 2,13.8 kg for ages 2 to less than 3, 18.6 kg for ages 3 to less than 6, and
31.8 kg for ages 6 to 7.
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Equation 5-5. AIQ = p1 x ln(BLL + 1)
Where:
/?! = -3.315 (log-linear regression coefficient on the lifetime blood lead level90)
Multiplying the result by the number of affected pre-school children yields the total change in the number of
IQ points for the affected population of children for the baseline and each regulatory option.
The IEUBK model estimates the mean of the BLL distribution in children, assuming a continuous exposure
pattern for children from birth through the seventh birthday. The 2021 American Community Survey
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 regulatory options, EPA divided the
estimated number of affected children by 7. This division adjusts the equation to apply only to children age 0
to 1. The estimated changes in IQ loss represent an annual value (i.e.. it would apply to the cohort of children
born each year after implementation).91 Equation 5-6 shows this calculation for the annual increase in total IQ
points.
Equation 5-6. AIQ(i)(c) = (ln(AGM(i)(c)) x CRF x (M7'i)(c)))
Where:
AIQ(i)(c) = the difference in total IQ points between the baseline and regulatory option scenarios for
cohort c in CBG i
Ln(AGM(i)(c¦)) = the log-linear change in the average BLL in affected population of children (|_ig/dL) for
cohort c in CBG i
CRF = -3.315, the log-linear regression coefficient from Crump et al. (2013)
ExCh(i)(c) = the number of affected children aged 0 to 7 for cohort c in CBG i
The available economic literature provides little empirical data on society's overall WTP to avoid a decrease
in children's IQ. To estimate 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 using the methodology presented in Salkever
(1995) but with more recent data from the 1997 National Longitudinal Survey of Youth (U.S. EPA, 2019d).
Updated results based on Salkever (1995) indicate that a one-point IQ reduction reduces expected lifetime
earnings by 2.63 percent. Table 5-3 summarizes the estimated values of an IQ point based on the updated
Salkever (1995) analysis using a 2 percent discount rate. For the lead analysis, the value is discounted to the
third year of life to represent the midpoint of the exposed children population. For the mercury analysis
(Section 5.5), the value of an IQ point is discounted to birth to better align the benefits of reducing exposure
90 The lifetime blood lead level in children ages 0 to 7 is defined as a mean from six months of age to present (Crump, K. S., Van
Landingham, C., Bowers, T. S., Cahoy, D., & Chandalia, J. K. (2013). A statistical reevaluation of the data used in the Lanphear
et al. pooled-analysis that related low levels of blood lead to intellectual deficits in children. Critical reviews in toxicology, 43(9),
785-799. ).
91 Dividing by seven undercounts overall benefits. Children from ages 1 to 7 (i.e., born prior to the base year of the analysis) are not
accounted for in the analysis, although they are also affected by changes in lead exposure.
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to mercury with in-utero exposure (U.S. EPA, 2019f). EPA also used an alternative value of an IQ point from
Lin, Lutter and Ruhm (2018) in a sensitivity analysis (see Appendix G).
Table 5-3: Value of an IQ Point (2023$) based on Expected
Reductions in Lifetime Earnings, 2 Percent Discount Rate
Discount Age
Value of an IQ Pointa,b (2023$)
Discounted to Age 3 (Lead)
$39,930
Discounted to Birth (Mercury)
$37,627
a. Values are adjusted for the cost of education.
b. EPA adjusted the value of an IQ point to 2023 dollars using the GDP
deflator.
Source: U.S. EPA (2019d) re-analysis of data from Salkever (1995); 2 percent
estimates calculated for U.S. EPA (2023f)
5.3.2 Results
Table 5-4 shows the benefits associated with changes in IQ losses from lead exposure via consumption of
self-caught fish. Avoided IQ point losses over the entire in-scope population of children with changes in lead
exposure is approximately 1 point. Estimated annualized benefits from avoided IQ losses are less than
$0.01 million.
Table 5-4: Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead under the
Regulatory Options, Compared to Baseline
Regulatory Option
Average Annual Number
of Children 0 to 7 in
Scope of the Analysis'3
Total Avoided IQ Point Losses,
2025 to 2049 in All Children 0
to 7 in Scope of the Analysisc
Annualized Value of Avoided IQ
Point Losses3 (Millions 2023$; 2%
Discount Rate)
Option A
1,555,558
1
<$0.01
Option B (Final Rule)
1,555,558
1
<$0.01
Option C
1,555,558
1
<$0.01
a. Based on estimate that the loss of one IQ point results in the loss of 2.63 percent of lifetime earnings, following updated
Salkever (1995) values from U.S. EPA (2019d).
b. The number of children in scope of the analysis is based on reaches analyzed across the regulatory options. Some of the
children included in this count see no changes in exposure under some options.
c. EPA notes that the IEUBK model does not analyze BLL changes beyond two decimal points and therefore the analysis omits
benefits from very small changes in lead exposure and resulting BLL changes.
Source: U.S. EPA Analysis, 2024
5.4 Health Effects in Adults from Changes in Lead Exposure
As described in Chapter 2 of this document and in the EA (U.S. EPA, 2024b), exposure to lead can result in
numerous adverse health effects in adults, including increasing the incidence of cardiovascular disease
premature mortality (e.g., Aoki et al., 2016; Lanphear et al., 2018; Navas-Acien, 2021; U.S. EPA, 2023f;
2024d).
To analyze the benefits of reducing exposure to lead from the consumption of self-caught fish, EPA adapted
the methodology used in the Agency's analysis of the 2023 Lead and Copper Rule Improvements (LCRI)
proposed rulemaking (U.S. EPA, 2023f) to reflect differences in exposure and affected populations. This
methodology relies on concentration-response functions relating adult BLL level to CVD mortality.
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5.4.1 Data and Methodology
The affected population is derived from that described in Section 5.1 and consists of adults aged 40 to 80 who
live in recreational and subsistence fisher households near reaches affected by steam electric power plant
discharges and who are potentially exposed to lead via consumption of contaminated fish tissue. To estimate
total exposure to lead for individuals from 40 to age 80, EPA relied on the All Ages Lead Model (AALM),92
which uses lead concentrations in a variety of media, including soil, dust, air, water, and food to predict lead
concentration in body tissues and organs of hypothetical individuals based on a simulated lifetime of lead
exposure (U.S. EPA, 2019a). EPA only varied lead intake via food to account for varying levels of lead
exposure caused by consuming exposed fish and left all other media at their recommended default value. To
estimate the "food" input for the AALM, EPA first estimated the cohort-specific ADD for each CBG based
on Equation 5-3. EPA then multiplied the cohort-specific ADD by the average body weight for each age
group in Table 5-5. Based on the estimated total exposure to lead, the model generates a predicted BLL
geometric mean for a population of adults.
Table 5-5: Estimated Average Body Weights (kg) by Age and Gender
Age
Males
Females
Age
Males
Females
Oto 1
9.30
9.30
43 to 44
89.48
71.59
1 to 2
11.30
11.50
44 to 45
87.00
74.86
2 to 3
13.70
13.30
45 to 46
84.61
81.15
3 to 4
16.40
15.20
46 to 47
93.27
74.94
4 to 5
18.80
18.10
47 to 48
80.87
68.24
5 to 6
20.20
20.70
48 to 49
85.58
82.10
6 to 7
22.90
22.00
49 to 50
88.84
75.55
7to 8
28.10
26.00
50 to 51
90.09
83.22
8 to 9
31.90
30.80
51 to 52
90.63
76.89
9 to 10
36.10
36.00
52 to 53
90.62
80.89
10 to 11
39.50
39.40
53 to 54
92.42
76.12
11 to 12
42.00
47.20
54 to 55
90.51
75.19
12 to 13
49.40
51.60
55 to 56
84.84
79.87
13 to 14
54.90
59.80
56 to 57
84.48
80.68
14 to 15
65.10
59.90
57 to 58
86.02
73.07
15 to 16
68.20
63.40
58 to 59
89.11
71.21
16 to 17
72.50
63.40
59 to 60
83.82
76.28
17 to 18
75.40
59.90
60 to 61
89.53
75.97
18 to 19
74.80
65.00
61 to 62
86.04
77.01
19 to 20
80.10
68.70
62 to 63
84.46
75.78
20 to 21
80.00
66.30
63 to 64
86.51
77.95
21 to 22
73.84
65.89
64 to 65
91.45
76.75
22 to 23
89.62
67.27
65 to 66
89.46
72.95
23 to 24
83.39
73.58
66 to 67
90.40
79.00
24 to 25
80.26
71.81
67 to 68
85.34
77.76
25 to 26
87.47
71.64
68 to 69
84.48
73.28
26 to 27
72.11
78.09
69 to 70
92.35
69.94
27 to 28
85.78
72.48
70 to 71
81.91
70.50
28 to 29
88.04
76.18
71 to 72
79.65
66.22
29 to 30
84.02
71.88
72 to 73
84.67
76.89
30 to 31
80.10
74.00
73 to 74
89.70
72.75
92 Hie AALM is an outgrowth of the IEUBK model used in the analysis described in Section 5.3.
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Table 5-5: Estimated Average Body Weights (kg) by Age and Gender
Age
Males
Females
Age
Males
Females
31 to 32
84.65
79.12
74 to 75
80.85
69.21
32 to 33
90.99
77.53
75 to 76
84.26
68.61
33 to 34
90.90
76.60
76 to 77
86.13
67.42
34 to 35
79.09
73.26
77 to 78
81.68
78.35
35 to 36
91.15
79.91
78 to 79
81.99
72.30
36 to 37
88.96
72.10
79 to 80
80.18
67.95
37 to 38
84.62
70.75
80 to 81
75.90
60.97
38 to 39
80.52
80.86
81 to 82
73.77
68.76
39 to 40
84.77
78.08
82 to 83
81.01
62.93
40 to 41
92.21
73.87
83 to 84
76.07
66.24
41 to 42
83.11
75.91
84 to 85
73.06
66.29
42 to 43
91.94
82.03
85+
74.10
59.68
Note: Data converted from ages in months to ages in years (e.g., age 1-2 year represents ages from 12 to 23 months).
Source: Adapted from Table 8-24 in U.S. EPA (2011)
Because the AALM assumes a linear relationship between lead intake from food ingestion and BLL, EPA
calculated age- and sex-specific slopes that approximate the linear relationship between lead intake from food
ingestion and BLL, instead of running the AALM for each CBG and cohort-specific lead intakes.93 EPA used
the age- and sex-specific slopes to scale a cohort's BLL given their lead intake from fish ingestion for the two
periods under the baseline and each regulatory option. EPA estimated small BLL changes during the period of
analysis, ranging between zero and 0.001 (ig/dL and with an average of 0.0007 (ig/dL across the exposed
population under Option C.
EPA relied on the relationship between BLL and CVD mortality from Aoki et al. (2016) and Lanphear et al.
(2018) to link the estimated BLL to changes in CVD mortality. Both studies use regression models to relate
log-transformed BLL to CVD mortality, as shown in Equation 5-7. To estimate the annual number of avoided
CVD mortality cases, EPA multiplied the estimated change in CVD mortality risk by the affected population
(Equation 5-8). Consistent with the methodology used in LCRI (U.S. EPA, 2023f), EPA assumed a 10-year
window of exposure. Therefore, the BLL (xj and X; in Equation 5-7 and Equation 5-8) represent an
individual's average BLL over the past ten years. EPA assumed that the change in lead intake, and resulting
change in BLL, occur instantaneously.94 Since the change in lead intake and BLL realistically occurs over
time, this assumption tends to overstate the benefits from the change in exposure to lead in fish tissue.
Equation 5-7.
A CVD Mortality = yt ^1 — e^l°az^x^
Equation 5-8.
Deaths Avoided = yt (l — * pop
93 This approach enables the analysis to remain sensitive to very small changes in BLL from changes in lead exposure.
94 In the LCRI analysis, EPA assumed that lead intake, and resulting BLL, changed gradually.
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Where:
yi = Hazard rate of CVD mortality in baseline scenario (i.e.. without the rule)
J3 = Beta coefficient, which represents the change in CVD mortality per unit change in BLL
Log: = Log transformation to the base z (i.e.. logio)
X2 = BLL associated with the regulatory option
xi = BLL associated with the baseline
pop = population for whom the change in BLL occurs
EPA obtained the baseline hazard rates of CVD mortality (y/) used in Equation 5-7 and Equation 5-8 from the
CDC's Wonder database (see Table 5-6).
Table 5-6: Baseline Hazard Rates of CVD Mortality by Age and Gender
Age
Male
Female
40-49
0.000786
0.000377
50-59
0.002186
0.000972
60-69
0.004598
0.002211
70-80
0.010802
0.006751
Source: U.S. EPA, 2023f, originally obtained from Centers for Disease Control and Prevention, 2014
EPA calculated low and high estimates of the effect of BLL on CVD mortality to reflect the uncertainty over
the best functional form that describes the relationship between BLL and CVD mortality. The low estimate (/?
= 0.36) is based on Aoki et al. (2016) and the high estimate (fi= 0.96) is based on Lanphear et al. (2018).
Using these beta coefficients in Equation 5-7 and Equation 5-8, EPA calculated high and low estimates of the
change in CVD mortality risk and the number annual deaths avoided under each regulatory option.
To value changes in CVD mortality, EPA used the VSL described in Section 4.3.4. The product of VSL and
the estimated population level reduction in risk of CVD mortality in a given year represents the affected
population's aggregate WTP to reduce the probability of CVD-related death in one year.
5.4.2 Results
Table 5-7 summarizes estimated benefits from avoided CVD mortality from reducing lead exposure via
consumption of self-caught fish under each regulatory option. The estimated benefits of the final rule range
from $0.16 million to $0.43 million.
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Table 5-7: Estimated Benefits from Avoided CVD Deaths for Adults Aged 40-80 For All Regulatory
Options, Compared to Baseline
Regulatory Option
Number of Adults in
Scope of the
Analysis3
Total CVD Deaths Avoided13
2025 to 2049 in All Adults in
Scope of Analysis
Annualized Value of Avoided
CVD Deaths (2% Discount Rate;
Millions 2023$)
Low
High
Low
High
Option A
21,684,921
0.42
1.13
$0.16
$0.43
Option B (Final Rule)
21,684,921
0.42
1.13
$0.16
$0.43
Option C
21,684,921
0.45
1.20
$0.17
$0.45
a. The number of adults in scope of the analysis is based on reaches analyzed across the regulatory options. Some of the adults
included in this count see no changes in exposure under some options. Benefits accrue to the subset of adults that experience
changes in exposure under one or more options (576,537 adults in 2025). Under the assumption that fishers would share their
catch with members of their household, EPA included household members in this subset.
b. Assumes that the distribution for the individuals experiencing lead-related CVD mortality is the same as the distribution of CVD
mortality irrespective of the cause.
Source: U.S. EPA Analysis, 2024
5.5 Heath Effects in Children from Changes in Mercury Exposure
Mercury can have a variety of adverse health effects on adults (e.g., vision defects, tremors, cerebellar
changes, and mortality) and children (e.g., neurological effects) (U.S. EPA, 2024b; Grandjean et al., 2014;
Hollingsworth & Rudik, 2021; Mergler et al., 2007; CDC, 2009). The regulatory options may change the
discharge of mercury to surface waters by steam electric power plants and therefore affect a range of human
health outcomes. Due to data limitations, however, EPA estimated only the monetary value of the changes in
IQ losses among children exposed to mercury in-ntero as a result of maternal consumption of contaminated
fish.
EPA identified the population of children exposed in-ntero starting from the CBG-specific population in
scope of the analysis described in Section 5.1. Therefore, this analysis only reflects health effects from
consumption of self-caught fish by households. Also, because this analysis focuses only on infants born after
implementation of the regulatory options, EPA further limited the analyzed population by estimating the
number of women between the ages of 15 and 44 potentially exposed to contaminated fish caught in the
affected waterbodies and multiplying the result by ethnicity-specific average fertility rates.95 This yields the
cohort-specific annual number of births for each CBG.
The U.S. Department of Health and Human Services provides fertility rates by race for 2021 in the National
Vital Statistics Report (Osterman et al., 2023). The fertility rate measures the number of births occurring per
1,000 women between the ages of 15 and 44 in a particular year. Fertility rates were highest for Hispanic
women at 63.4, followed by African Americans at 57.4, other race/ethnicities at 56.3, Caucasians at 54.4,
Native Americans at 50.8, and Asians at 49.6.
5.5.1 Data and Methodology
EPA used the ethnicity- and mode-specific consumption rates shown in Table 5-2 and calculated the CBG-
and cohort-specific mercury ADD based on Equation 5-3. As EPA is not aware of consumption rates specific
95 EPA acknowledges that fertility rates vary by age. However, the use of a single average fertility rate for all ages is not expected
to bias results because the average fertility rate reflects the underlying distribution of fertility rates by age.
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to pregnant women, the analysis uses the same consumption rates as in the general population within each
analyzed cohort.
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 Section 5.2.2), EPA used the
median conversion factor derived by Swartout and Rice (2000), who estimated that a 0.08 j^ig/kg body weight
increase in daily mercury dose is associated with a 1 ppm increase in hair concentration. Equation 5-9 shows
EPA's calculation of the total annual IQ changes for a given receiving reach.
IQL(i)(c) = IQ changes associated with in-utero exposure to mercury from maternal consumption of fish
contaminated with mercury for cohort c in CBG i
InExPop(i)(cj = population of infants in scope of the analysis for cohort c in CBG /' (the number of
births)
MADD(i)(c) = maternal ADD for cohort c in CBG i (j^ig/kg BW/day)
Conv = conversion factor for hair mercury concentration based on maternal mercury exposure
(0.08 (ig/kg BW/day per 1 ppm increase in hair mercury)
1)111'' = 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 changes across all analyzed CBGs yields the total changes in the number of IQ points
due to in-utero mercury exposure from maternal fish consumption under each analyzed regulatory option. The
benefits of the regulatory options are calculated as the change in IQ points between the baseline and modeled
post-technology implementation conditions under each of the regulatory options.
The available economic literature provides little empirical data on society's overall WTP to avoid a decrease
in children's IQ. To estimate 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, discounted to birth (Table 5-3). EPA also used
an alternative value of an IQ point from Lin, Lutter and Ruhm (2018) in a sensitivity analysis (see Appendix
5.5.2 Results
Table 5-8 shows the estimated changes in IQ point losses for infants exposed to mercury in-utero and the
corresponding monetary values. The final rule (Option B) results in 1,377 avoided IQ point losses over the
entire in-scope population of infants with changes in mercury exposure. The annualized benefits of avoided
IQ point losses are $1.98 million.
Equation 5-9.
Where:
IQL(i){c) = InExPop(i){c) * MADD(i){c) * * DRF
G).
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Table 5-8: Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure under the
Regulatory Options, Compared to Baseline
Regulatory Option
Number of Infants in Scope
of the Analysis per Yearb
Total Avoided IQ Point
Losses, 2025 to 2049 in All
Infants in Scope of the
Analysis
Annualized Value of Avoided
IQ Point Losses3
(Millions 2023$; 2% Discount
Rate)
Option A
201,850
1,190
$1.71
Option B (Final Rule)
201,850
1,377
$1.98
Option C
201,850
1,393
$2.00
a. Based on the estimate that the loss of one IQ point results in the loss of 2.63 percent of lifetime earnings discounted to birth,
following updated Salkever (1995) values from U.S. EPA (2019f).
b. The number of infants in scope of the analysis is based on reaches analyzed across the regulatory options. Some of the children
included in this count see no changes in exposure under some options.
Source: U.S. EPA Analysis, 2024
5.6 Estimated Changes in Cancer Cases from Arsenic Exposure
Among steam electric pollutants that can contaminate fish tissue and are analyzed in the EA, arsenic is the
only confirmed carcinogen with a published dose response function (see U.S. EPA, 2010).96 EPA used the
methodology presented in Section 3.6 of the 2015 BCA (U.S. EPA, 2015a) to estimate the number of annual
skin cancer cases associated with consumption of fish contaminated with arsenic from steam electric power
plant discharges under the baseline and the change corresponding to each regulatory option and the associated
monetary values. EPA's analysis shows negligible changes in skin cancer cases from exposure to arsenic via
consumption of self-caught fish under the regulatory options.97 Accordingly, the estimated benefits are also
negligible under all regulatory options and are not included in the total monetized benefits.
5.7 Monetary Values of Estimated Changes in Human Health Effects
Table 5-9 presents the estimated benefits under the regulatory options of changes in adverse human health
outcomes associated with the consumption of self-caught fish. The estimated benefits of the final rule
(Option B) range from $2.14 million to $2.41 million. Changes in mercury exposure for children account for
the majority of total monetary values from increases in adverse health outcomes.
Table 5-9: Estimated Benefits of Changes in Human Health Outcomes Associated with Fish
Consumption under the Regulatory Options, Compared to Baseline (Millions of 2023$; 2% Discount
Rate)
Regulatory Option
Changes in Lead
Exposure for
Children
Changes in Lead
Exposure for Adults
Changes in
Mercury Exposure
for Children
Total
Low
High
Low
High
Option A
<$0.01
$0.16
$0.43
$1.71
$1.87
$2.14
Option B (Final Rule)
<$0.01
$0.16
$0.43
$1.98
$2.14
$2.41
Option C
<$0.01
$0.17
$0.45
$2.00
$2.17
$2.45
Source: U.S. EPA Analysis, 2024
90 Although other pollutants, such as cadmium, are also likely to be carcinogenic (see U.S. Department of Health and Human
Services. (2012). Toxicological Profile for Cadmium. ), EPA did not identify dose-response functions to quantify the effects of
changes in these other pollutants.
97 The analysis estimated a reduction in the incidence of arsenic-related skin cancer cases of 0.01 cases between 2025 and 2049 for
all three regulatory options.
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5.8 Additional Measures of Potential Changes in Human Health Effects
As noted in the introduction to this chapter, untreated pollutants in steam electric power plant discharges have
been linked to additional adverse human health effects. EPA compared immediate receiving water
concentrations to human health-based NRWQC in U.S. EPA (2020g). To provide an additional measure of
the potential health effects of the regulatory options, EPA also estimated the changes in the number of
receiving and downstream reaches with pollutant concentrations in excess of human health-based NRWQC.
This analysis compares pollutant concentrations estimated for the baseline and each analyzed regulatory
option in receiving reaches and downstream reaches to criteria established by EPA for protection of human
health. EPA compared estimated in-water concentrations of antimony, arsenic, barium, cadmium, chromium,
cyanide, copper, lead, manganese, mercury, nitrate-nitrite as N, nickel, selenium, thallium, and zinc to EPA's
NRWQC protective of human health used by states and tribes (U.S. EPA, 2018c) and to MCLs.98 Estimated
pollutant concentrations in excess of these values indicate potential risks to human health. This analysis and
its findings are not additive to the preceding analyses in this chapter, but instead represent another way of
characterizing potential health effects resulting from changes in exposure to steam electric pollutants.
Table 5-10 shows the results of this analysis." During Period 1, EPA estimates that with baseline steam
electric pollutant discharges, concentrations of steam electric pollutants exceed human health criteria for at
least one pollutant in 375 reaches based on the "consumption of water and organism" criteria, and 112 reaches
based on the "consumption of organism only" criteria nationwide. During Period 2, concentrations of steam
electric pollutants exceed human health criteria for at least one pollutant in 326 reaches based on the
"consumption of water and organism" criteria, and 112 reaches based on the "consumption of organism only"
criteria nationwide under the baseline scenario. The estimated number of reaches with exceedances of
"consumption water and organism" criteria and with exceedances of "consumption of organism only" criteria
during both Period 1 and Period 2 decreases under all regulatory options.1"" For example, Option C eliminates
exceedances in 271 reaches (326-55) and reduces the number of exceedances in 237 reaches.
Table 5-10: Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric
Pollutants
Regulatory Option
Number of Reaches with Ambient
Concentrations Exceeding Human Health
Criteria for at Least One Pollutant3
Number of Reaches with Lower Number of
Exceedances, Relative to Baselineb
Consumption of Water
+ Organism
Consumption of
Organism Only
Consumption of Water
+ Organism
Consumption of
Organism Only
Period 1 (2025-2029)
Baseline
375
112
Not applicable
Not applicable
Option A
308
70
73
42
Option B (Final Rule)
298
68
90
52
Option C
274
68
117
52
98 For pollutants that do not have NRWQC protective of human health, EPA used MCLs. These pollutants include cadmium,
chromium, lead, and mercury.
99 Only reaches designated as fishable (i.e., Strahler Stream Order larger than 1) were included in the NRWQC exceedances
analysis.
100 EPA's analysis does not account for the fact that the NPDES permit for each steam electric power plant, like all NPDES permits,
is required to have limits more stringent than the technology-based limits established by an ELG, wherever necessary to protect
water quality standards. Because this analysis does not project where a permit will have more stringent limits than those required
by the ELG, it may overestimate any negative impacts to aquatic ecosystems and T&E species, including impacts that will not be
realized at all because the permits will be written to include limits as stringent as necessary to meet water quality standards as
required by the CWA.
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Table 5-10: Estimated Number of Reaches Exceeding Human Health Criteria for Steam Electric
Pollutants
Number of Reaches with Ambient
Concentrations Exceeding Human Health
Criteria for at Least One Pollutant3
Number of Reaches with Lower Number of
Regulatory Option
Exceedances, Relative to Baselineb
Consumption of Water
Consumption of
Consumption of Water
Consumption of
+ Organism
Organism Only
+ Organism
Organism Only
Period 2 (2030-2049)
Baseline
326
112
Not applicable
Not applicable
Option A
180
38
140
67
Option B (Final Rule)
78
8
222
79
Option C
55
0
237
84
a. Pollutants for which there was at least one exceedance in the baseline or regulatory options include antimony, arsenic,
chromium, cyanide, manganese, and thallium in Period 1 and arsenic, chromium, cyanide, manganese, and thallium in Period 2.
b. Pollutants for which there was at least one reach with lower number of exceedances relative to baseline include arsenic and
chromium in Period 1 and arsenic, chromium, cyanide, manganese, and thallium in Period 2.
Source: U.S. EPA Analysis, 2024
5.9 Limitations and Uncertainties
The analysis presented in this chapter does not include all possible human health effects associated with post-
technology implementation changes 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 effects included in this analysis represent only a subset of the potential health effects estimated to result
from the regulatory options. Section 2.1 provides a qualitative discussion of health effects omitted from the
quantitative analysis.
The methodologies and data used in the analysis of adverse health outcomes due to consumption of fish
contaminated with steam electric pollutants involve limitations and uncertainties. Table 5-11 summarizes the
limitations and uncertainties and indicates the direction of the potential bias. Additional limitations and
uncertainties associated with the environmental assessment analyses and data are discussed in the EA (see
U.S. EPA, 2024b).
Table 5-11: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish
Ingestion Pathway
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
Fishers are estimated to
evenly distribute their
activity over all available
fishing sites within the 50-
mile travel distance.
Uncertain
EPA estimated that all fishers travel up to 50 miles and
distribute their visits over all fishable sites within the area.
In fact, recreational and subsistence fishers may have
preferred sites (e.g., a site located closer to their home)
that they visit more frequently. The characteristics of these
sites, notably ambient water concentrations and fishing
advisories, affects exposure to pollutants, but EPA does not
have data to support a more detailed analysis of fishing
visits. The impact of this approach on monetary estimates is
uncertain since fewer/more fishers may be exposed to
higher/lower fish tissue concentrations than estimated by
EPA.
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Table 5-11: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish
Ingestion Pathway
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
The exposed population is
estimated based on
households in proximity to
affected reaches and the
fraction of the general
population who fish.
Uncertain
EPA estimated the share of households that includes fishers
to be equal to the fraction of people over 16 who are
fishers. This may double-count households with more than
one fisher over 16. However, the exposed population may
also include non-household members who also consume
the catch.
Fish intake rates used in
estimating exposure are
based on recommended
values for the general
consumer population.
Uncertain
The fish consumption rates used in the analysis are based
on the general consumer population, which may understate
or overstate the amount of fish consumed by fishers who
may consume fish at higher or lower rates than the general
population (e.g., Burger, 2013; U.S. EPA, 2011, 2013c)
Fish intake rates used in
estimating exposure do not
reflect potential lower fish
consumption by pregnant
women.
Overestimate
To the degree that pregnant women reduce their
consumption of self-caught fish when compared to women
in the general population, then exposure in the baseline
would be less and the final rule benefits from reduced
exposure to mercury correspondingly lower.
100 percent of fish
consumed by recreational
fishers is self-caught.
Overestimate
The fish consumption rates used in the analysis account for
all fish sources (i.e., store-bought or self-caught fish).
Assuming that recreational fishers consume only self-caught
fish may overestimate exposure to steam electric pollutants
from fish consumption. The degree of the overestimate is
unknown as the fraction offish consumed that is self-
caught varies significantly across different locations and
population subgroups (e.g., U.S. EPA, 2013c).
The number of subsistence
fishers was set to equal
5 percent of the total
number of fishers fishing the
affected reaches.
Uncertain
The magnitude of subsistence fishing in the United States or
individual states is not known. Using 5 percent may
understate or overstate the overall number of potentially
affected subsistence fishers (and their households) and
ignores potential variability in subsistence fishing rates
across racial/ethnic groups and different geographic
locations.
Value of an IQ point used to
quantify benefits health
effects from changes in lead
and mercury exposure
Uncertain
EPA used two alternative estimates of the value of an IQ
point in its analysis, following the methodology in U.S. EPA
(2019d; 2019e, 2020b). EPA acknowledges recent research
indicating higher IQ point values than those calculated
based on Salkever (1995) and Lin, Lutter and Ruhm (2018).
However, because the recent research was based on either
non-U.S. populations (e.g., Gronqvist, Nilsson & Robling,
2020 ) or unrepresentative subsets of the U.S. population
(Hollingsworth et al., 2020; Hollingsworth & Rudik,
2021),EPA continued to use IQ point values based on
Salkever (1995) and Lin, Lutter and Ruhm (2018).
There is a 0.18-point IQ loss
for each 1 ppm increase in
maternal hair mercury (i.e.,
the relationship is assumed
to be linear).
Uncertain
The exact form of the relationship between maternal body
mercury burden and IQ losses is uncertain. Using a linear
relationship may understate or overstate the IQ losses
resulting from a given change in mercury exposure.
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5: Human Health Benefits via Fish Ingestion
Table 5-11: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish
Ingestion Pathway
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
For the mercury- and lead-
related health impact
analyses, EPA assessed IQ
losses to be an appropriate
endpoint for quantifying
adverse cognitive and
neurological effects resulting
from childhood or in-utero
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 increased incidence of attention-
related and problem behaviors (NTP, 2012; U.S. EPA,
2005d). To the extent that these impacts create
disadvantages for children exposed to mercury and lead in
the absence of (or independent from) measurable IQ losses,
this analysis may underestimate the social welfare effects of
the regulatory options of changes in lead and mercury
exposure.
The IEUBK model processes
daily intake from "alternative
sources" to 2 decimal places
(Hg/day).
Underestimate
Since the fish-associated pollutant intakes are small, some
variation is missed by using this model (i.e., it does not
capture very small changes between the baseline and
regulatory options).
For the lead analysis in adults
EPA assumed that fishers
would share their catch with
household members.
Overestimate
EPA used CBG-specific estimates of persons per household
which range from 1.0 to 13.6 and average 2.6 members.
Not all individuals within a household may be adults.
The AALM only models BLL
from birth to age 60.
Uncertain
BLL for ages 61-80 were extrapolated, but because the
simulation of BLL levels off and becomes very predictable
after age 30 confidence in the extrapolation is high.
CVD mortality studies use a
single measurement of adult
BLL
Uncertain
The CVD studies used to derive the beta coefficients used in
Equation 5-7 and Equation 5-8 use a single measurement of
adult BLL.
EPA does not adjust BLLs for
hematocrit when using the
Aoki CVD mortality function.
Overestimate
Based on example calculations conducted in Abt Associates
(2023), which compared the two approaches using a
hypothetical scenario, the use of whole blood BLLs appears
to reasonable for scenarios such as the one in this analysis,
where BLLs changes are expected to be small.
EPA estimates avoided CVD
premature mortality impacts
for adults ages 40 through 80
only.
Underestimate
EPA did not estimate avoided premature CVD deaths for
populations younger than 40 or older than 80. This will
underestimate benefits because benefits are directly
proportional to the size of the affected population and
baseline mortality rates.
Uncertainty in the shape of
the dose-response function
for CVD premature mortality.
Uncertain
The mathematical form of the dose-response function for
lead CVD impacts is based on models that best fit the data
from the selected epidemiological studies. However,
uncertainty remains about the true shape of the function,
particularly at very low blood lead levels, for which there
are fewer historic data points. Estimating health impacts
using alternative mathematical functions that reflect these
alternative shapes is beyond the scope of this analysis.
Depending on the shape tested, benefit results could be
higher or lower.
Baseline CVD rates used in
the analysis of lead-related
CVD premature mortality in
adults did not consider
cause.
Uncertain
EPA assumed that the distribution for the age of the
individuals experiencing lead-related CVD premature
mortality is the same as the distribution of CVD mortality by
age and sex for CVD premature mortality irrespective of the
cause.
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5: Human Health Benefits via Fish Ingestion
Table 5-11: Limitations and Uncertainties in the Analysis of Human Health Effects via the Fish
Ingestion Pathway
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
EPA assumed that changes in
lead intake for adults and the
resulting change in BLL occur
instantaneously.
Overestimate
Because change in BLL in adults resulting from reduction in
lead intake realistically occurs over time, assuming an
instantaneous change in BLL is likely to overestimate
reduction in lead-related CVD premature mortality.
EPA did not monetize the
health effects associated
with changes in adult
exposure to mercury.
Underestimate
The scientific literature suggests that exposure to mercury
may have significant adverse health effects for adults (e.g.,
Hollingsworth & Rudik, 2021; Mergler et al., 2007; Center
for Disease Control and Prevention (CDC), 2009). If
measurable effects are occurring at current exposure levels,
excluding the effects of increased adult exposure results in
an underestimate of benefits.
EPA did not quantify other
health effects in children
from exposure to lead or
mercury.
Underestimate
As discussed in Section 2.1, exposure to lead could result in
additional adverse health effects in children (e.g., low birth
weight and neonatal mortality from in-utero exposure to
lead, or neurological effects in children exposed to lead
after age seven) (NTP, 2012; U.S. EPA, 2024d; U.S. EPA,
2019e; U.S. EPA, 2023f). Additional neurological effects
could also occur in children from exposure to mercury after
birth (Mergler et al., 2007; CDC, 2009). If measurable
effects are occurring at current exposure levels, excluding
additional health effects of increased children exposure
results in an underestimate of benefits.
EPA did not assess combined
health risk of multiple
pollutants.
Uncertain
The combined health risk of exposure to multiple pollutants
could be greater than that to a single pollutant (Evans,
Campbell & Naidenko, 2020). However, quantifying
cumulative risk is challenging because a mixture of
pollutants could affect a wide range of target organs and
endpoints (ATSDR, 2004, 2009). For example, different
carcinogens found in steam electric power plant discharges
may affect different organs (e.g., arsenic is linked to skin
cancer while cadmium is linked to kidney cancer). Other
synergistic effects may increase or lessen the risk. While
there are no existing methods to fully analyze and monetize
these effects, EPA quantified some of these effects in the
EA (U.S. EPA, 2024b).
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6: Nonmarket Benefits
6 Nonmarket Benefits from Water Quality Changes
As discussed in the EA (U.S. EPA, 2024b), heavy metals, nutrients, and other pollutants discharged by steam
electric power plants can have a wide range of effects on water resources downstream from the plants. These
environmental changes affect environmental goods and services valued by humans, including recreation;
commercial fishing; public and private property ownership; navigation; water supply and use; and existence
services such as aquatic life, wildlife, and habitat designated uses. Some environmental goods and services
(e.g., commercially caught fish) are traded in markets, and thus their value can be directly observed. Other
environmental goods and services (e.g., recreation and support of aquatic life) are not bought or sold directly
and thus do not have observable market values. This second type of environmental goods and services are
classified as "nonmarket." The estimated changes in the nonmarket values of the water resources affected by
the regulatory options (hereafter nonmarket benefits) are additive to market values (e.g., avoided costs of
producing various market goods and services).
The analysis of the nonmarket value of water quality changes resulting from the regulatory options follows
the same approach EPA used in the analysis of the 2015 and 2020 rules and 2023 proposal (U.S. EPA, 2015a,
2020b, 2023c). This approach, which is briefly summarized below, involves:
1. Characterizing the change in water quality under the regulatory options relative to the baseline
using a WQI and linking these changes to ecosystem services or potential uses that are valued by
society (see Section 3.4.2), and
2. Monetizing changes in the nonmarket value of affected water resources under the regulatory
options using a meta-analysis of surface water valuation studies that provide data on the public's
WTP for water quality changes (see Section 6.1).
The analysis accounts for improvements in water quality resulting from changes in nutrient, sediment, and
toxics concentrations in reaches potentially affected by bottom ash transport water and FGD wastewater
discharges. The assessment uses the CBG as the geographic unit of analysis, assigning a radial distance of
100 miles from the CBG centroid. EPA estimates that households residing in a given CBG value water quality
changes in all modeled reaches within this range, with all unaffected reaches being viable substitutes for
affected reaches within the area around the CBG. Appendix E in U.S. EPA (2020b) provides additional
details on EPA's approach.
6.1 Estimated Total WTP for Water Quality Changes
EPA estimated economic values of water quality changes at the CBG level using results of a meta-analysis of
189 estimates of total WTP (including both use and nonuse values) for water quality improvements, provided
by 59 original studies conducted between 1981 and 2017.101 The estimated econometric model allows
calculation of total WTP for changes in a variety of environmental services affected by water quality and
valued by humans, including changes in recreational fishing opportunities, other water-based recreation, and
101 Although the potential limitations and challenges of benefit transfer are well established (Desvousges, W. H, Smith, V. K., &
Fisher, A. (1987). Option price estimates for water quality improvements: a contingent valuation study for the Monongahela
River. Journal of Environmental Economics and Management, 14, 248-267. https://doi.Org/https://doi.org/10.1016/0095-
0696(87)90019-2 ), benefit transfers are a nearly universal component of benefit cost analyses conducted by and for government
agencies. As noted by Smith, V. K., Van Houtven, G., & Pattanayak, S. K. (2002). Benefit transfer via preference
calibration: "Prudential algebra" for policy. Land Economics, 78( 1), 132-152. , "nearly all benefit cost analyses rely on benefit
transfers, whether they acknowledge it or not."
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6: Nonmarket Benefits
existence services such as aquatic life, wildlife, and habitat designated uses. The model also allows EPA to
adjust WTP values based on the core geospatial factors predicted by theory to influence WTP, including:
scale (the size of affected resources or areas), market extent (the size of the market area over which WTP is
estimated), and the availability of substitutes. The meta-analysis regression is based on two models: Model 1
provides EPA's main estimate of non-market benefits, and Model 2 is used in a sensitivity analysis to develop
a range of estimates that account for uncertainty in the estimated WTP values (see Section 6.2 for Model 2
results). Appendix H provides details on how EPA used the meta-analysis to predict household WTP for each
CBG and year as well as the estimated regression equation, intercept and variable coefficients for the two
models used in this analysis. The appendix also provides names and definitions of the independent variable
and assigned values.
Based on the meta-analysis results, EPA multiplied the coefficient estimates for each variable (see Model 1
and Model 2 in Table H-3) by the variable levels calculated for each CBG or fixed at the levels indicated in
the "Assigned Value" column in Table H-3. The sum of these products represents the predicted natural log of
the WTP for a one-point improvement on the WQI (In OWTP) for a representative household in each CBG.
Equation 6-1 provides the equation used to calculate household benefits for each CBG.
Equation 6-1. HWTPYB = OWTPYiB x AWQ1B
where:
HWTPy,b = Annual household WTP in 2023$ in year Y for households located in
the CBG (5),
OWTPy.b = WTP for a one-point improvement on the WQI for a given year (7)
and the CBG (B), estimated by the meta-analysis function and
evaluated at the midpoint of the range over which water quality is
changed,
AWQIb = Estimated annual average water quality change for the CBG (B). See
Section 3.4 and Appendix C for details about the WQI calculation
methodology.
To estimate WTP for water quality improvements under the regulatory options, EPA first estimated water
quality improvements for each year within Period 1 and Period 2 (see Section 3.2.1 for details) and then
applied the meta-regression model (MRM) to estimate per household WTP for water quality improvements
for each year in the analysis period (2024-2049). As summarized in Table 6-1, average annual household
WTP estimates for the regulatory options, based on the main estimates from Model 1, range from $0.01 under
Option A to $0.03 under Option C.
To estimate total WTP (TWTP) for water quality changes for each CBG, EPA multiplied the per-household
WTP values for the estimated water quality change by the number of households within each CBG in a given
year and calculated the present value (PV) of the stream of WTP over the 25 years in EPA's period of
analysis. EPA then calculated annualized total WTP values for each CBG using a 2 percent discount rate as
shown in Equation 6-2.
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6: Nonmarket Benefits
Equation 6-2.
2049
TWTP,
HWTPy,b x HHy b
¦T = 2025
(l + 0
j\Y-2024
X
i x (1 + 0"
(1 + i)n+1 - 1
where:
TWTPb = Annualized total household WTP in 2023$ for households located in
the CBG (5),
HWTPy i; = Annual household WTP in 2023$ for households located in the CBG
(B) in year (Y),
HHy,b = the number of households residing in the CBG (B) in year (Y).
T = Year when benefits are realized
i = Discount rate (2 percent)
n = Duration of the analysis (25 years)1"2
EPA generated annual household counts for each CBG through the period of analysis based on projected
population growth following the method described in Section 1.3.6. Table 6-1 presents the main analysis
results, based on Model 1. For the final rule (Option B), the total annualized values of water quality changes
resulting from changes in toxics, nutrient and sediment discharges in these reaches are $1.24 million.
Table 6-1: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality
Improvements under the Regulatory Options, Compared to Baseline (Main Estimates)
Regulatory Option
Number of Affected
Households (Millions)3
Average Annual WTP Per
Household (2023$)b
Total Annualized WTP
(Millions 2023$; 2%
Discount Rate)b
Option A
58.7
$0.01
$0.79
Option B (Final Rule)
58.9
$0.02
$1.24
Option C
59.6
$0.03
$1.68
a. The number of affected households varies across options because of differences in the number of reaches that have non-zero
changes in water quality.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits.
Source: U.S. EPA Analysis, 2024
6.2 Sensitivity Analysis
Table 6-2 presents sensitivity analysis results produced from Model 2, including average annual household
WTP and total annualized values, for water quality improvements resulting from all regulatory options. For
the final rule (Option B), average annual household WTP estimates range from $0.02 to $0.05. Total
annualized values range from $1.31 million to $2.68 million.
102 See Section 1.3.3 for details on the period of analysis.
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Table 6-2: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality Changes
under the Regulatory Options, Compared to Baseline (Sensitivity Analysis)
Regulatory Option
Nu mber of Affected
Households (Millions)3
Average Annual WTP Per
Household (2023$)b
Total Annualized WTP (Millions
2023$; 2% Discount Rate)b
Low
High
Low
High
Option A
58.7
$0.01
$0.03
$0.86
$1.76
Option B (Final Rule)
58.9
$0.02
$0.05
$1.31
$2.68
Option C
59.6
$0.03
$0.07
$1.78
$3.65
a. The number of affected households varies across options because of differences in the number of reaches that have non-zero
changes in water quality.
b. Estimates based on Model 2, which provides a range of estimates that account for uncertainty in the WTP estimates as a
sensitivity analysis. For the AWQI variable setting in Model 2-based sensitivity analysis, EPA used values of 20 units to develop low
estimates and 7 units to develop high estimates (see Appendix H for details).
Source: U.S. EPA Analysis, 2024
6.3 Limitations and Uncertainties
Table 6-3 summarizes the limitations and uncertainties in the analysis of benefits associated with changes in
surface water quality and indicates the direction of any potential bias.
Table 6-3: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
Use of 100-mile buffer
for calculating water
quality benefits for each
CBG
Underestimate
The distance between surveyed households and the affected
waterbodies is not well measured by any of the explanatory variables
in the MRM. EPA would expect values for water quality changes to
diminish with distance (all else equal) between the household and
affected waterbody. The choice of 100 miles is based on typical driving
distance to recreational sites (i.e., 2 hours or 100 miles; Viscusi, Huber
& Bell, 2008), which captures approximately 80 percent of
recreational uses. However, it does not capture the full extent of
recreational use or recreational use for multiday trips. It also does not
capture the extent of market or population willingness to pay for
nonuse value. EPA used 100 miles to approximate the distance decay
effect on WTP values but acknowledges that distance decay effects
could occur at varying distances (i.e., closer or further than 100 miles)
and may exhibit more complex spatial patterns than a simple radius
approach. The analysis recognizes further uncertainty for people living
farther than 100 miles and does not assign any value for water quality
improvements in waters affected by this rulemaking despite literature
that shows that while WTP tends to decline with distance from the
waterbody, people value the quality of waters outside their region.
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Table 6-3: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
Selection of the
lnquality_ch variable
value in Model 2 for
estimating a range of
WTP values (sensitivity
analysis)
Uncertain
The value of an additional one-point improvement in WQI is expected
to decline as the magnitude of the water quality change increases. To
account for variability in WTP due to the magnitude of the valued
water quality changes, EPA estimated a range of WTP values for a one-
point improvement on the WQI using alternative settings for
lnquality_ch (AWQI= 20 and 7 units, respectively). These values were
based on the 25th and 75th percentile of water quality changes
included in the meta-data. To ensure that the benefit transfer function
satisfies the adding-up condition, this variable is treated as a
methodological (fixed) variable. The negative coefficient for
lnquality_ch implies that larger value settings produce smaller WTP
estimates for a one-point improvement, which is consistent with
economic theory; smaller value settings produce larger WTP estimates
for a one-point improvement. The selected values may bias the
estimated WTP values either upward or downward.
Potential hypothetical
bias in underlying stated
preference results
Uncertain
Following standard benefit transfer approaches, this analysis proceeds
under the assumption that each source study provides a valid,
unbiased estimate of the welfare measure under consideration (cf.
Moeltner, Boyle & Paterson, 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 practices for stated preference (e.g., the payment vehicle
variable is set to a non-voluntary value because use of voluntary
donations is prone to issues of free-riding).
Use of different water
quality measures in the
underlying meta-data
Uncertain
The estimation of WTP may be sensitive to differences in the
presentation of water quality changes 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. To account for potential
effects of the use of a different water quality metric (i.e., index of
biotic integrity (IBI)) on WTP values for a one-point improvement on
the WQI, EPA used a dummy variable in the MRM (see Appendix H for
details). In benefit transfer applications, the IBI variable is set to zero,
which is consistent with using the WQI.
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. Although meta-
analyses are often more flexible and accurate compared to other
types of transfer approaches (e.g., value transfers and benefit function
transfers) due to the data synthesis from multiple source studies
(Rosenberger and Phipps, 2007; Johnston et al., 2021), there is still a
potential for transfer errors (Shrestha, Rosenberger & Loomis, 2007)
and no transfer method is always superior relative to other benefit
transfer methods (Johnston et al., 2021).
Omission of Great Lakes
and estuaries from
analysis of benefits from
water quality changes
Underestimate
Five out of 92 (5 percent) steam electric power plants discharge to the
Great Lakes or estuaries. Due to limitations of the water quality
models used in the analysis of the regulatory options, these
waterbodies were excluded from the analysis. This omission likely
underestimates benefits of water quality changes from the regulatory
options.
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Table 6-3: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
The water quality model
accounts for only a
subset of sources of
toxic pollutants
contributing to baseline
concentrations
Uncertain
The overall impact of this limitation on the estimated WTP for water
quality changes is uncertain but is expected to be small. Toxic
pollutants are grouped into one parameter out of the seven
parameters included the WQI. Therefore, the effect of including
additional toxic pollutants on the estimated change in WQI is likely to
be small.
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7: Threatened & Endangered Species Benefits
7 Impacts and Benefits to Threatened and Endangered Species
7.1 Introduction
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 or other stressors. In many cases, T&E
species are given special protection due to inherent vulnerabilities to habitat modification, disturbance, or
other impacts of human activities. This chapter examines the projected change in environmental impacts of
steam electric power plant discharges on T&E species and the estimated benefits associated with the projected
changes resulting from the regulatory options.
As described in the EA (U.S. EPA, 2024b), the untreated chemical constituents of steam electric power plant
wastestreams can pose serious threats to ecological health due to the bioaccumulative nature of many
pollutants, high concentrations, and high loadings. Pollutants such as selenium, arsenic and mercury have
been associated with fish kills, disruption of growth and reproductive cycles and behavioral and physiological
alterations in aquatic organisms. 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. Eutrophication may also result in the loss of critical
submerged rooted aquatic plants (or macrophytes), and reduced DO levels, leading to anoxic or hypoxic
waters.
For species vulnerable to future extinction, even minor changes to growth and reproductive rates and small
levels of mortality may represent a substantial portion of annual population growth. To quantify the estimated
effects of the regulatory options compared to baseline, EPA conducted a screening analysis using as indicator
of benefits the changes in projected attainment of freshwater NRWQC. Specifically, EPA identified the
reaches that are projected to see changes in achievement of freshwater aquatic life NRWQC, assuming no
more stringent controls are established to meet applicable water quality standards (/'. e., water-quality-based
effluent limits issued under Section 301(b)(1)(C))). Using these projections, EPA then estimated the number
of T&E species whose recovery could be affected based on the species" habitat range. Because NRWQC are
recommended at levels to protect aquatic organisms, reducing the frequency at which aquatic life-based
NRWQC are exceeded could translate into reduced risk to T&E species and potential improvements in
species populations.1"3
In this chapter, EPA examines the current conservation status of species belonging to freshwater taxa and
identifies the extent to which the regulatory options, independent of consideration of additional water quality-
based controls, may benefit or adversely impact T&E species. The analysis generally follows the approach
EPA used forthe analyses of the 2015 and 2020 rules and 2023 proposal (U.S. EPA, 2015a, 2020b, 2023b),
including updates EPA made over time to the methodology, assumptions, and inputs to address comments or
103 Criteria are developed based on the 1985 Guidelines methods (U.S. Environmental Protection Agency. (1985). Guidelines for
Deriving Numerical National Water Quality Criteria for the Protection Of Aquatic Organisms and Their Uses. (PB85-227049).
Retrieved from https://www.epa.gov/sites/prodiiction/files/2016-02/documents/guidelines-water-qiMlity-criteria.pdf) and
generally reflect high quality toxicity data from at least eight different taxa groups that broadly represent aquatic organisms. To
the extent that more stringent levels are required to protect organisms in a particular location, that is addressed during the water
quality standard development process for that location.
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incorporate more recent data. As for the earlier analyses, this analysis provides a quantitative, but
unmonetized proxy for the benefits associated with the regulatory options.
7.2 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 may be disproportionately
imperiled relative to terrestrial species. For example, while 39 percent of freshwater and diadromous fish
species are imperiled (Jelks et al., 2008), a similar status review found that only 7 percent of North American
bird and mammal species are imperiled (Wilcove & Master, 2005). More recent studies of threats and
extinction trends in freshwater taxa also concluded that biodiversity is much more at risk in freshwater
compared to marine ecosystems (Winemiller, 2018).
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
has increased by 98 percent and 179 percent when compared to similar reviews conducted by the American
Fisheries Society in 1989 (Williams et al., 1989) and 1979 (Deacon et al., 1979), respectively. Despite
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 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 have imperilment levels ranging from a low of 22 percent for
Centrarchidae (sunfishes) to a high of 61 percent for Salmonidae (salmon) (Jelks et al., 2008).
7.3 T&E Species Potentially Affected by the Regulatory Options
To assess the potential effects of the regulatory options on T&E species, EPA used the U.S. FWS
Environmental Conservation Online System (ECOS) to construct a database of species that have habitats that
overlap with waters projected to improve due to reductions in pollutant discharge from steam electric power
plants under the regulatory options. The source data include all animal species currently listed or proposed for
listing under the ESA (U.S. FWS, 2020d).
7.3.1 Identifying T&E Species Potentially Affected by the Regulatory Options
To estimate the effects of the regulatory options on T&E species, EPA first compiled data on habitat ranges
for all species currently listed or under consideration for listing under the ESA. EPA obtained the
geographical distribution of T&E species in geographic information system (GIS) format from ECOS (U.S.
FWS, 2020b).
EPA constructed a screening database using the spatial data on species habitat ranges and all NHD reaches
downstream from steam electric power plants. This database included all T&E species whose habitat ranges
intersect reaches immediately receiving or downstream of steam electric power plant discharges. EPA used a
200-meter buffer on either side of each reach when estimating the intersection to account for waterbody
widths and any minor errors in habitat maps. EPA removed several species previously included in the analysis
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of the 2023 proposal because they were delisted from the ESA due to extinction, according to the USFWS
(U.S. Fish & Wildlife Service, 2023). The analysis retained a total of 184 T&E species.
EPA then classified these species on the basis of their vulnerability to changes in water quality for the purpose
of assessing potential impacts of the regulatory options. EPA obtained species life history data from a wide
variety of sources to assess T&E species" vulnerability to water pollution. For the purpose of this analysis,
species were classified as follows:
• Higher 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.
• Lower vulnerability - species whose habitats overlap bodies of water, but whose life history traits and
food sources are terrestrial.
Table 7-1 summarizes the results of this assessment. Appendix I lists all T&E species whose habitat ranges
intersect reaches immediately receiving or downstream of steam electric power plant discharges.
Table 7-1: Number of T&E Species with Habitat Range Intersecting Reaches Immediately Receiving
or Downstream of Steam Electric Power Plant Discharges, by Group
Species Group
Species Vulnerability
Lower
Moderate
Higher
Species Count
Amphibians
3
2
4
9
Arachnids
6
0
0
6
Birds
17
4
5
26
Clams
0
0
56
56
Crustaceans
0
0
5
5
Fishes
0
0
28
28
Insects
10
0
0
10
Mammals
13
1
1
15
Reptiles
13
0
6
19
Snails
1
0
9
10
Total
63
7
114
184
Source: U.S. EPA Analysis, 2024.
To estimate the potential impacts of the regulatory options, EPA focused the analysis on species with higher
vulnerability potentials based upon life history traits. EPA's further review of this subset of species resulted in
the removal from further analysis of those species endemic to isolated headwaters and natural springs, as
these waters are unlikely to receive steam electric power plant discharges in the scope of the final rule (see
Appendix I for details). A review of life history data for the remaining species shows pollution or water
quality issues as factors influencing species decline. This suggests that water quality issues may be important
to species recovery even if not emphasized explicitly in species recovery plans.
7.3.2 Estimating Effects of the Rule on T&E Species
EPA used the results of the water quality model described in Chapter 3 to flag those reaches where estimated
pollutant concentrations exceed the freshwater NRWQC under the baseline or the regulatory options (see
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Section 3.4.1.1). EPA estimated exceedances for two distinct periods (2025-2029 and 2030-2049) within the
overall analysis period (2025-2049). As described in Section 3.2.1, Period 1 corresponds to transition years
when the steam electric power plants would be installing treatment technologies to comply with the revised
limits, whereas Period 2 reflects post-technology implementation conditions when all plants meet applicable
revised limits.
EPA then linked the water quality model outputs with the species database described in the section above to
identify potentially "affected T&E species habitats" where the reaches intersecting the habitat range of a T&E
species do not meet the NRWQC under baseline conditions but do meet the NRWQC under one or more of
the regulatory options (i.e.. potential positive benefits). EPA compared dissolved concentration estimates for
eight pollutants to the freshwater acute and chronic NRWQC values104 to assess the exceedance status of the
reaches under the baseline and each regulatory option. Appendix I provides details on the number of
exceedances from steam electric power plants affecting T&E species of all vulnerability levels. Overall,
EPA's analysis indicates that 23 reaches intersecting the habitat ranges of 30 T&E species exceed NRWQC
under the baseline conditions in Period 1 and 19 reaches intersecting the habitat ranges of 27 T&E species
exceed NRWQC under the baseline conditions in Period 2. In Period 1 (2025-2029), exceedances
improvements occur in four reaches under option A, and in 16 reaches under options B and C. In Period 2
(2030-2049), NRWQC exceedances are eliminated or reduced in two reaches under option A, in 16 reaches
under option B, and in 19 reaches under option C.
Table 7-2, on the next page, provides additional details on the subset of species with higher vulnerability to
water pollution for which the regulatory options reduce the number of exceedances in at least one Period and
reach. EPA estimated that the improvements in water quality in Period 1 provide potential benefits to three
T&E species under option A and ten T&E species under options B and C, as indicated by changes in the
number of reaches with NRWQC exceedances. Improvements during Period 2 provide potential benefits to
one T&E species under option A, 12 T&E species under option B, and 14 T&E species under option C.
While NRWQC do not translate into a quantifiable level of harm or improvement to wildlife species exposed
to various contaminants, they may provide a useful proxy to indicate where significant improvements in water
quality may occur, recognizing that these improvements may not necessarily benefit species to the same
degree. Species have vastly different and unique life histories, and as a result, some may continue to face
detrimental impacts even where NRWQC exceedances are eliminated, while other species may either not face
detrimental impacts from water quality to begin with or may see benefits as the result of water quality
improvements even without changes in exceedances. Furthermore, conditions that do not exceed NRWQC
may still cause harm to species, especially those species with chronic exposure to contaminants such as heavy
metals. Roughly 30 percent (56 of 184) of species with designated habitats intersecting reaches affected by
steam electric power plant discharges are bivalves. Additionally, 15 percent (28 of 184) of species with
designated habitats receiving steam electric power plant discharges are fish. Such taxonomic groups face
consistent exposure to aquatic pollutants due to their entirely aquatic nature. Bivalves in particular fulfill vital
ecological roles as ecosystem engineers (Hancock & Ermgassen, 2019). Freshwater bivalves are crucial filter
feeders, removing metals, sediment, excess nutrients, and bacteria from surrounding water (Upper Midwest
Environmental Sciences Center, 2020). Healthy populations of freshwater bivalve species help improve water
quality and overall river/lake health by improving habitat for other aquatic invertebrates as well as finfish.
104 The eight pollutants are arsenic, cadmium, copper, lead, mercury, nickel, selenium, and zinc. For more information about the
aquatic life NRWQC, see the EA (U.S. Environmental Protection Agency. (2024b). Environmental Assessment for Supplemental
Effluent Guidelines and Standards for the Steam Electric Power Generating Point Source Category. (821-R-24-005). ).
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Species in which pollutants bioaccumulate may face detrimental or lethal effects at lower pollution levels over
time. For example, bivalves feed by filtering large amounts of water and face extended exposure to pollutants
over longer time spans compared to other species. As a result, populations of these species may suffer over
time as negative effects of chronic exposure add up. Table 7-2 shows the Snuffbox mussel (Epioblasma
triquetra), Sheepnose mussel (Plethobasus cyphyus), Spectaclecase mussel (Cumberlandia monodontct), and
Pink Mucket (Lampsilis abrupta) all seeing improvements across many reaches intersecting their habitat
ranges under the final rule (Option B). Publications from the USFWS warn that pollution and contamination
are key threats to survival for each of these four species due to both acute and chronic toxic effects (Butler,
2007; U.S. Fish & Wildlife Service, 1997, 2012a, 2012b). Such cumulative effects on these species could
further negatively impact local ecosystems by disrupting the filtering function provided by bivalves (Hancock
& Ermgassen, 2019). Non-bivalve species could see benefits from improvements as well. Water
contaminants, including metals, are a known threat to the survival of the Colorado Pikeminnow
(Ptychocheilus lucius), and although the impacts of many contaminants are not quantified for this species, it
demonstrates that this species could benefit from improvements to water quality (U.S. Fish & Wildlife
Service, 2022). While the number of reaches with improvements are indicative of the benefits to T&E species
provided by each option, it remains a rough indicator. However, for T&E species dependent on aquatic
systems for survival, such as bivalves and fishes, any level of improvement that increases the ability of the
species to survive and reproduce could enhance conservation and recovery efforts.
Table 7-2: Higher Vulnerability T&E Species Whose Habitat May be Affected by the Regulatory
Options Compared to Baseline (Shading Highlights Change from Baseline)
Species Name
State(s)
Number of Reaches with
NRWQC Exceedances for at
Least One Pollutant
Baseline
Option A
Option B
(Final Rule)
Option C
Period 1 (2025-2029)
Clubshell (Pleurobema clava)
Kentucky
1
1
1
1
Colorado pikeminnow (Ptychocheilus lucius)
New Mexico
6
3
3
3
Fanshell (Cyprogenia stegaria)
Kentucky/West Virginia
11
11
1
1
Frosted Flatwoods salamander (Ambystoma
cingulatum)
Florida
1
1
0
0
Humpback chub (Gila cypha)
Arizona
3
3
3
3
Orangefoot pimpleback (pearlymussel)
(Plethobasus cooperianus)
Kentucky
1
1
1
1
Pink mucket (pearlymussel) (Lampsilis abrupta)
Kentucky/Ohio/West Virginia
12
12
2
2
Razorback sucker (Xyrauchen texanus)
New Mexico
3
0
0
0
Ring pink mussel (Obovaria retusa)
Kentucky
1
1
1
1
Rough pigtoe (Pleurobema plenum)
Kentucky
1
1
1
1
Sheepnose mussel (Plethobasus cyphyus)
West Virginia/Ohio
11
11
1
1
Snuffbox mussel (Epioblasma triquetra)
West Virginia
10
10
0
0
Spectaclecase mussel (Cumberlandia
monodonta)
West Virginia
10
10
0
0
Topeka shiner (Notropis topeka)
Kansas
3
2
2
2
West Indian manatee (Trichechus manatus)
Florida
1
1
0
0
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Table 7-2: Higher Vulnerability T&E Species Whose Habitat May be Affected by the Regulatory
Options Compared to Baseline (Shading Highlights Change from Baseline)
Species Name
State(s)
Number of Reaches with
NRWQC Exceedances for at
Least One Pollutant
Baseline
Option A
Option B
(Final Rule)
Option C
Period 2 (2030-2049)
Clubshell (Pleurobema clava)
Kentucky
1
1
0
0
Colorado pikeminnow (Ptychocheilus lucius)
New Mexico
3
3
3
0
Fanshell (Cyprogenia stegaria)
Kentucky/West Virginia
11
11
0
0
Frosted Flatwoods salamander (Ambystoma
cingulatum)
Florida
1
1
0
0
Humpback chub (Gila cypha)
Arizona
3
3
3
0
Orangefoot pimpleback (pearlymussel)
(Plethobasus cooperianus)
Kentucky
1
1
0
0
Pink mucket (pearlymussel) (Lampsilis abrupta)
Kentucky/Ohio/West Virginia
12
12
0
0
Ring pink mussel (Obovaria retusa)
Kentucky
1
1
0
0
Rough pigtoe (Pleurobema plenum)
Kentucky
1
1
0
0
Sheepnose mussel (Plethobasus cyphyus)
West Virginia/Ohio
11
11
0
0
Snuffbox mussel (Epioblasma triquetra)
West Virginia
10
10
0
0
Spectaclecase mussel (Cumberlandia
monodonta)
West Virginia
10
10
0
0
Topeka shiner (Notropis topeka)
Kansas
2
0
0
0
West Indian manatee (Trichechus manatus)
Florida
1
1
0
0
Source: U.S. EPA Analysis, 2024
7.4 Limitations and Uncertainties
One limitation of EPA's analysis of the regulatory options" impacts on T&E species and their habitat is the
lack of data necessary to quantitively estimate population changes of T&E species and to monetize these
effects. The data required to estimate the response of T&E species populations to improved habitats are rarely
available. In addition, understanding the contribution of T&E species to ecosystem functions can be
challenging because: (1) it is often difficult to locate T&E species, (2) experimental studies including rare or
threatened species are limited; and (3) ecologists studying relationships between biodiversity and ecosystem
functions typically focus on overall species diversity or estimate species contribution to ecosystem functions
based on abundance (Dee et al., 2019). Finally, much of the wildlife economic literature focuses on
recreational benefits (i.e.. use values) that are not relevant for many protected species and the existing T&E
valuation studies tend to focus on species that many people consider to be "charismatic" (e.g., spotted owl,
salmon) (Richardson & Loomis, 2009). Although a relatively large number of economic studies have
estimated WTP for T&E protection, these studies focused on estimating WTP to avoid species loss/extinction,
reintroduction, increase in the probability of survival, or a substantial increase in species population (Subroy
et al., 2019; Richardson & Loomis, 2009). In addition, use of the MRMs developed by Subroy et al. (2019)
and Richardson and Loomis (2009) is not feasible for this analysis due to the challenges associated with
estimating T&E population changes from the final rule.
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Table 7-3 summarizes limitations and uncertainties known to affect EPA's assessment of the impacts of the
final rule on T&E species. Note that the effect on benefits estimates indicated in the second column of the
table refers to the direction and magnitude of the benefits (i.e., a source of uncertainty that tends to
underestimate benefits indicates expectation for larger realized benefits).
Table 7-3: Limitations and Uncertainties in the Analysis of T&E Species Impacts and Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
The analysis does not
account for water quality
based effluent limits
Overestimate
This screening analysis is intended to isolate possible effects of
the regulatory options on T&E species, however, it does not
consider the fact that the NPDES permits for each steam
electric power plant, like all NPDES permits, are required to
have limits more stringent than the technology-based limits
established by an ELG wherever necessary to protect water
quality standards. Because this analysis does not project where
a permit will have more stringent limits than those required by
the ELG, it may overestimate any negative impacts to T&E
species in the baseline, and therefore overestimate benefits
under the regulatory options.
Intersection of T&E species
habitat with reaches affected
by steam electric plant
discharges is used as proxy
for exposure to steam
electric pollutants
Overestimate
EPA used the habitat range as the basis for assessing the
potential for impacts to the species from water quality
changes. This approach is reasonable given the lack of reach-
specific population data to support a national-level analysis,
but the Agency acknowledges that the habitat range of a
species does not necessarily indicate that the species is found
in individual reaches within the habitat range.
The change in T&E species
populations due to
improvement in water
quality under the regulatory
options is uncertain
Uncertain
Data necessary to quantitatively estimate population changes
are unavailable. Therefore, EPA used the methodology
described in Section 7.3.1 as a screening-level analysis to
estimate whether the regulatory options could contribute to a
change in the recovery of T&E species populations.
Only those T&E species listed
as threatened or endangered
under the ESA are included
in the analysis
Underestimate
The databases used to conduct this analysis include only
species protected under the ESA. Additional species may be
considered threatened or endangered by scientific
organizations but are not protected by the ESA (e.g., the
American Fisheries Society [Williams et al., 1993; Taylor et al.,
2007; Jelks et al., 2008]). The magnitude of the underestimate
is unknown. Although the proportion of imperiled freshwater
fish and mussel species is high (e.g., Jelks et al., 2008; Taylor et
al., 2007), the geographic distribution of these species may or
may not overlap with reaches affected by steam electric
discharges.
The potential for impact to
T&E species is also present
for changes in pollutant
concentrations that don't
result in changes in NRWQC
exceedances
Underestimate
EPA's analysis quantifies changes in whether a NRWQC is
exceeded in a given reach that intersects T&E species habitat
ranges. However, changes in pollutant concentrations have the
potential to result in impacts to T&E species even where they
do not result in changes in NRWQC exceedance status. There
are also potential impacts to T&E species from changes in
pollutants for which freshwater NRWQC are not available (e.g.,
salinity).
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Table 7-3: Limitations and Uncertainties in the Analysis of T&E Species Impacts and Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
EPA's water quality model
does not capture all sources
of pollutants with a potential
to impact aquatic T&E
species
Uncertain
EPA's water quality model focuses on toxic pollutant discharges
from steam electric power plants and certain other point
sources, but does not account for other pollution sources (e.g.,
historical contamination) or background levels. Adding these
other sources or background levels could result in additional
NRWQC exceedances under the baseline and/or regulatory
options, but it is uncertain how the regulatory options would
change the exceedance status of the intersected reaches.
Additionally, the water quality model does not capture
synergistic relationships between pollutants, which may
exacerbate adverse effects on T&E species.
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8 Air Quality-Related Benefits
The regulatory options evaluated may affect air quality through three main mechanisms: 1) changes in energy
used by steam electric power plants to operate wastewater treatment, ash handling, and other systems needed
to meet the limitations and standards under the regulatory options; 2) transportation-related emissions due to
the changes in trucking of CCR and other waste to on-site or off-site landfills; and 3) changes in the
electricity generation profile from increases in wastewater treatment costs compared to the baseline and the
resulting changes in EGU relative operating costs.
EPA estimated the climate-related benefits of changes in CO2 and methane (CH4) emissions, as well as the
human health benefits resulting from changes in particulate matter and ozone ambient exposure due to net
changes in emissions of NOx, SO2, and directly emitted fine particulate matter (PM2.5), also referred to as
primary PM2.5 emissions.
8.1 Changes in Air Emissions
With respect to the third mechanism mentioned in the introduction and as discussed in the RIA, EPA used the
Integrated Planning Model (IPM) to estimate the electricity market-level effects of the final rule (Option B).
IPM projects generation from coal to decrease in all model years as a result of the final rule. Over the period
of analysis, the reductions are largest in run years 2028 and 2035 (18.1 thousand GWh and 21.2 thousand
GWh, respectively), are somewhat smaller in 2030 and 2040 (10.6 thousand GWh and 6.7 thousand GWh),
and smallest in the last two run years of 2045 and 2050 (1.1 thousand GWh and 0.7 thousand GWh,
respectively). These changes are offset in part by an increase in generation from natural gas, nuclear, and
renewables. See details in Chapter 5 of the RIA (U.S. EPA, 2024e). The net effects of these changes in the
generation mix are reductions in air emissions that reflect differences in EGU emissions rates for these other
fuels or sources of energy, as compared to coal.
IPM outputs include estimated C02,NOx, and SO2 emissions to air from EGUs.1"5 EPA also used IPM
outputs to estimate EGU emissions of primary PM2.5 based on the methodology described in U.S. EPA
(2020c). Specifically, EPA estimated primary PM2 5 emissions by multiplying the generation predicted for
each IPM plant type (ultrasupercritical coal without carbon capture and storage, combined cycle, combustion
turbine, etc.) by a type-specific empirical emission factor derived from the 2019 National Emissions
Inventory (NEI) and other data sources. The emission factors reflect the fuel type (including coal rank), FGD
controls, and state emission limits for each plant type, where applicable.
Comparing emissions projected under Option B to those projected for the baseline provides an assessment of
the changes in air emissions resulting from changes in the profile of electricity generation under the final
rule.1"6 EPA used six of the seven IPM run years, shown in Table 8-1, to represent the period of analysis. IPM
provides outputs starting in 2028 and EPA therefore estimated no changes in air emissions from changes in
electricity generation in 2025 through 2027. The last run year (2055) falls outside of the analysis period of
2025-2049 and EPA does not include results for that year when estimating benefits.
105 EPA also estimated Hg, HC1 and PM10 emissions but does not use these estimates for the benefits analysis.
100 While EPA only ran IPM for the final rule (Option B), the Agency extrapolated the benefits estimated using these IPM outputs to
Option A and Option C to provide insight on the potential air quality-related effects of the other regulatory options. See Section
8.4 for details.
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Table 8-1: IPM Run Years
IPM Run Year
Years Represented
2028
2028-2029
2030
2030-2031
2035
2032-2037
2040
2038-2041
2045
2042-2047
2050
2048-2052
2055
2053-2059
Source: U.S. EPA, 2023e
As part of its analysis of non-water quality environmental impacts, EPA developed separate estimates of
changes in energy requirements for operating wastewater treatment and ash handling systems, and changes in
transportation needed to landfill solid waste and CCR (see TDD for details; U.S. EPA, 2024f). EPA estimated
CO2, NOx, and SO2 emissions associated with changes in energy requirements to power wastewater treatment
systems by multiplying plant-specific changes in electricity consumption by plant- or North American
Electric Reliability Corporation (NERC)-specific emission factors obtained from IPM for the baseline in run
year 20 3 5.107 EPA estimated the changes in air emissions associated with changes in transportation by
multiplying the increase in the number of miles traveled to dispose of CCR by average emission factors.
Table 8-2 and Table 8-3 respectively summarize the estimated changes in emissions associated with changes
in power requirements to operate treatment systems and with the incremental transportation of CCR and solid
waste under the regulatory options. For consistency, the tables present estimates for selected IPM model
years. EPA modeled emissions in each year based on when each plant is estimated to implement technologies
for each wastestream and any announced unit retirements. EPA estimates that changes in power requirements
and transportation will increase emissions slightly, relative to the baseline. The variations across regulatory
options reflect differences in treatment technologies and affected steam electric plants, whereas variations
across model years for a given regulatory option reflect the timing of technology implementation and
announced EGU retirements.1"8
Table 8-2: Estimated Changes in Air Pollutant Emissions Due to Increase in Power Requirements at
Steam Electric Power Plants 2025-2049, Compared to Baseline
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
CH4 (Million
Tons/Year)3
Option A
2028
0.034
0.015
0.020
Not estimated
Not estimated
2030
0.069
0.044
0.049
Not estimated
Not estimated
2035
0.069
0.044
0.049
Not estimated
Not estimated
2040
0.068
0.044
0.049
Not estimated
Not estimated
2045
0.068
0.044
0.049
Not estimated
Not estimated
2050
0.068
0.041
0.047
Not estimated
Not estimated
107 Applying grid emission factors developed for run year 2035 to the entire period of analysis may overstate emissions associated
with power requirements for operating treatment systems since emission factors decline during the period of analysis.
108 For the purpose of this analysis, EPA developed a time profile of air emissions changes based on plants' estimated technology
implementation years during the period of 2025 through 2029, as well as announced EGU retirements during the period of
analysis. For EGUs that retire during the analysis period, incremental power requirements and trucking associated with BA
transport water and FGD wastewater treatment cease, but those associated with CRL continue even after the unit retires.
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Table 8-2: Estimated Changes in Air Pollutant Emissions Due to Increase in Power Requirements at
Steam Electric Power Plants 2025-2049, Compared to Baseline
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
CH4 (Million
Tons/Year)3
Option B (Final Rule)
2028
0.073
0.043
0.066
Not estimated
Not estimated
2030
0.14
0.088
0.12
Not estimated
Not estimated
2035
0.14
0.088
0.12
Not estimated
Not estimated
2040
0.14
0.088
0.12
Not estimated
Not estimated
2045
0.14
0.087
0.12
Not estimated
Not estimated
2050
0.14
0.083
0.11
Not estimated
Not estimated
Option C
2028
0.085
0.052
0.070
Not estimated
Not estimated
2030
0.16
0.10
0.12
Not estimated
Not estimated
2035
0.16
0.10
0.12
Not estimated
Not estimated
2040
0.16
0.10
0.12
Not estimated
Not estimated
2045
0.16
0.098
0.12
Not estimated
Not estimated
2050
0.16
0.094
0.12
Not estimated
Not estimated
a. Values rounded to two significant figures. Positive values indicate an increase in emissions.
Source: U.S. EPA Analysis, 2024
Table 8-3: Estimated Changes in Air Pollutant Emissions Due to Increase in Trucking at Steam
Electric Power Plants 2025-2049, Compared to Baseline
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM25
(Thousand
Tons/Year)3
CH4 (Million
Tons/Year)3
Option A
2028
0.00041
0.00083
0.0000014
Not estimated
0.0000036
2030
0.00074
0.0016
0.0000025
Not estimated
0.0000070
2035
0.00074
0.0016
0.0000025
Not estimated
0.0000070
2040
0.00074
0.0016
0.0000025
Not estimated
0.0000070
2045
0.00074
0.0016
0.0000025
Not estimated
0.0000070
2050
0.00070
0.0015
0.0000024
Not estimated
0.0000066
Option B (Final Rule)
2028
0.00047
0.00097
0.0000016
Not estimated
0.0000042
2030
0.00087
0.0019
0.0000029
Not estimated
0.0000083
2035
0.00087
0.0019
0.0000029
Not estimated
0.0000083
2040
0.00087
0.0019
0.0000029
Not estimated
0.0000083
2045
0.00087
0.0019
0.0000029
Not estimated
0.0000083
2050
0.00083
0.0018
0.0000028
Not estimated
0.0000079
Option C
2028
0.00055
0.0012
0.0000019
Not estimated
0.0000050
2030
0.0012
0.0025
0.0000039
Not estimated
0.000011
2035
0.0012
0.0025
0.0000039
Not estimated
0.000011
2040
0.0012
0.0025
0.0000039
Not estimated
0.000011
2045
0.0011
0.0025
0.0000039
Not estimated
0.000011
2050
0.0011
0.0024
0.0000037
Not estimated
0.000010
a. Values rounded to two significant figures. Positive values indicate an increase in emissions.
Source: U.S. EPA Analysis, 2024
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Table 8-4 summarizes the estimated changes in pollutant emissions from electricity generation under the final
rule (i.e., Option B).1"9 Projected changes in the profile of electricity generation under Option B, compared to
the baseline, generally lead to national-level reductions in emissions for all air pollutants modeled. The
pattern of change follows the decline in coal generation described above. Thus, the largest declines in CO2,
NOx, SO2 and PM2.5 emissions occur in model years 2028 through 2035 before tapering off in the latter run
years of the analysis. Thus, at the national level, CO2 emissions are estimated to decrease by between
11 million and 16 million tons during run years 2028 through 2035 under the final rule when compared to the
baseline. Reductions in run years 2040 through 2050 are much smaller (0.7 million to 2.1 million tons per
year). In relative terms, the largest effect is SO2 emissions for the final rule is estimated to reduce baseline
emissions by approximately 5 percent in model year 2035.
The impact on emissions varies across regions and by pollutant with emissions increasing in some and
decreasing in other NERC regions, as detailed in the RIA (Table 5-4; U.S. EPA, 2024e).
Table 8-4: Estimated Changes in Pollutant Emissions Due to Changes in Electricity Generation
Profile, Compared to Baseline
Regulatory
Option
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
CH4 (Million
Tons/Year)3
2028
-16
-8.9
-11
-0.63
Not estimated
2030
-11
-7.4
-2.5
-0.38
Not estimated
Option B
2035
-13
-8.8
-13
-0.25
Not estimated
(Final Rule)
2040
-2.1
-3.2
-2.3
-0.16
Not estimated
2045
-1.4
-0.7
-1.0
-0.093
Not estimated
2050
-0.72
-0.45
-0.78
-0.12
Not estimated
a. Values rounded to two significant figures. Negative values indicate a reduction in emissions.
Source: U.S. EPA Analysis, 2024; See Chapter 5 in RIA for details on IPM (U.S. EPA, 2024e).
A comparison of estimated changes in emissions across the three mechanisms (Table 8-2, Table 8-3 and Table
8-4) for the final rule (Option B) shows that the largest effect on projected air emissions comes from the
change in the emissions profile of electricity generation at the market level. Table 8-5 presents the net
changes in emissions of the four pollutants compared to baseline. The next two sections quantify the climate
change and human health benefits associated with changes in emissions under the final rule (Option B).
Table 8-5: Estimated Net Changes in Air Pollutant Emissions Due to Changes in Power
Requirements, Trucking, and Electricity Generation Profile, Compared to Baseline
Regulatory
Option
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
CH4 (Million
Tons/Year)3
2028
-16
-8.9
-11
-0.63
0.0000042
2030
-11
-7.3
-2.4
-0.38
0.0000083
Option B
2035
-13
-8.7
-13
-0.25
0.0000083
(Final Rule)
2040
-1.9
-3.1
-2.2
-0.16
0.0000083
2045
-1.3
-0.63
-0.85
-0.093
0.0000083
2050
-0.58
-0.37
-0.67
-0.12
0.0000079
a. Values rounded to two significant figures. Negative values indicate a net reduction in emissions.
109 EPA did not run IPM for Option A and Option C.
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Table 8-5: Estimated Net Changes in Air Pollutant Emissions Due to Changes in Power
Requirements, Trucking, and Electricity Generation Profile, Compared to Baseline
Regulatory
Option
Year
C02 (Million
Tons/Year)3
NOx (Thousand
Tons/Year)3
S02 (Thousand
Tons/Year)3
Primary PM2.5
(Thousand
Tons/Year)3
CH4 (Million
Tons/Year)3
Source: U.S. EPA Analysis, 2024
8.2 Climate Change Benefits
8.2.1 Data and Methodology
EPA estimated the climate benefits of the net CO2 and CH4 emission changes expected from this final rule
using the estimates of the social cost of greenhouse gases (SC-GHG) - specifically, the social cost of carbon
(SC-CO2) and social cost of methane (SC-CH4)11" - that reflect recent advances in the scientific literature on
climate change and its economic impacts and incorporate recommendations made by the National Academies
of Science, Engineering, and Medicine (National Academies, 2017). EPA published and used these estimates
in the RIA for the December 2023 Final Oil and Gas New Source Performance Standards (NSPS)/Emissions
Guidelines (EG) Rulemaking, "Standards of Performance for New, Reconstructed, and Modified Sources and
Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review". EPA solicited
public comment on the methodology and use of these estimates in the RIA for the agency's December 2022
Oil and Gas NSPS/EG Supplemental Proposal (U.S. EPA, 20231) and has conducted an external peer review
of these estimates, as described further below.
The SC-GHG is the monetary value of the net harm to society associated with emitting a metric ton of the
GHG in question into the atmosphere in a given year, or the net benefit of avoiding that increase. In principle,
the SC-GHG includes the value of all climate change impacts (both negative and positive), including (but not
limited to) changes in net agricultural productivity, human health effects, property damage from increased
flood risk and natural disasters, disruption of energy systems, risk of conflict, environmental migration, and
the value of ecosystem services. The SC-GHG therefore reflects the societal value of reducing emissions of
the gas in question by one metric ton and is the theoretically appropriate value to use in conducting benefit-
cost analyses of policies that affect greenhouse gas emissions. In practice, data and modeling limitations
restrain the ability of SC-GHG estimates to include all physical, ecological, and economic impacts of climate
change, implicitly assigning a value of zero to the omitted climate damages. The estimates are, therefore, a
partial accounting of climate change impacts and likely underestimate of the marginal benefits of abatement.
EPA and other Federal agencies began regularly incorporating SC-GHG estimates in their benefit-cost
analyses conducted under E.O. 12866111 since 2008, following a Ninth Circuit Court of Appeals remand of a
rule for failing to monetize the benefits of reducing greenhouse gas emissions in that rulemaking process. The
values used by EPA from 2009 to 2016, and since 2021 - including in the proposal for this rulemaking - have
been consistent with those developed and recommended by the Interagency Working Group on the SC-GHG
110 Estimates of the social cost of greenhouse gases are gas specific (e.g., social cost of carbon (SC-CO2), social cost of methane
(SC-CHi), social cost of nitrous oxide (SC-N2O)), but collectively they are referenced as the social cost of greenhouse gases (SC-
GHG).
111 E.O. 12866, released in 1993 and still in effect today, requires that for all economically significant regulatory actions, an agency
provide an assessment of the potential costs and benefits of the regulatory action, and that this assessment include a quantification
of benefits and costs to the extent feasible. For purposes of this action, monetized climate benefits are presented for purposes of
providing a complete benefit-cost analysis under EO 12866 and other relevant executive orders. The estimates of change in GHG
emissions and the monetized benefits associated with those changes play no part in the record basis for this action.
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(IWG); and the values used from 2017 to 2020 were consistent with those required by E.O. 13783, which
disbanded the IWG. During 2015-2017, the National Academies conducted a comprehensive review of the
SC-CO2 and issued a final report in 2017 recommending specific criteria for future updates to the SC-CO2
estimates, a modeling framework to satisfy the specified criteria, and both near-term updates and longer-term
research needs pertaining to various components of the estimation process (National Academies, 2017). The
IWG was reconstituted in 2021 and E.O. 13990 directed it to develop a comprehensive update of its SC-GHG
estimates, recommendations regarding areas of decision-making to which SC-GHG should be applied, and a
standardized review and updating process to ensure that the recommended estimates continue to be based on
the best available economics and science going forward.
EPA is a member of the IWG and is participating in the IWG's work under E.O. 13990. As noted in previous
EPA RIAs, including in the proposal for this rulemaking, while that process continues, EPA is continuously
reviewing developments in the scientific literature on the SC-GHG, including more robust methodologies for
estimating damages from emissions, and looking for opportunities to further improve SC-GHG estimation 112
In the December 2022 Oil and Gas NSPS/EG Supplemental Proposal RIA, the Agency included a sensitivity
analysis of the climate benefits of the Supplemental Proposal using a new set of SC-GHG estimates that
incorporates recent research addressing recommendations of the National Academies (National Academies,
2017) in addition to using the interim SC-GHG estimates presented in the Technical Support Document:
Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under Executive Order 13990 (IWG,
2021) that the IWG recommended for use until updated estimates that address the National Academies"
recommendations are available.
EPA solicited public comment on the sensitivity analysis and the accompanying draft technical report,
External Review Draft of Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent
Scientific Advances, which explains the methodology underlying the new set of estimates, in the December
2022 Supplemental Oil and Gas Proposal. The response to comments document can be found in the docket for
that action.
To ensure that the methodological updates adopted in the technical report are consistent with economic theory
and reflect the latest science, EPA also initiated an external peer review panel to conduct a high-quality
review of the technical report, completed in May 2023. See 88 FR at 26075/2 noting this peer review process.
The peer reviewers commended the agency on its development of the draft update, calling it a much-needed
improvement in estimating the SC-GHG and a significant step toward addressing the National Academies"
recommendations with defensible modeling choices based on current science. The peer reviewers provided
numerous recommendations for refining the presentation and for future modeling improvements, especially
with respect to climate change impacts and associated damages that are not currently included in the analysis.
Additional discussion of omitted impacts and other updates have been incorporated in the technical report to
address peer reviewer recommendations. Complete information about the external peer review, including the
peer reviewer selection process, the final report with individual recommendations from peer reviewers, and
EPA"s response to each recommendation is available on EPA"s website.113
The remainder of this section provides an overview of the methodological updates incorporated into the SC-
GHG estimates used in this analysis. A more detailed explanation of each input and the modeling process is
112 EPA strives to base its analyses on the best available science and economics, consistent with its responsibilities, for example,
under the Information Quality Act.
113 See https://www.epa.gov/environmental-economics/scghg
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provided in the technical report, Supplementary Material for the RIA: EPA Report on the Social Cost of
Greenhouse Gases: Estimates Incorporating Recent Scientific Advances (U.S. EPA, 2023n). Appendix B
shows the climate benefits of the final rule using the interim SC-GHG (IWG, 2021) estimates presented in the
proposal BCA for comparison purposes.
The steps necessary to estimate the SC-GHG with a climate change integrated assessment model (IAM) can
generally be grouped into four modules: socioeconomics and emissions, climate, damages, and discounting.
The emissions trajectories from the socioeconomic module are used to project future temperatures in the
climate module. The damage module then translates the temperature and other climate endpoints (along with
the projections of socioeconomic variables) into physical impacts and associated monetized economic
damages, where the damages are calculated as the amount of money the individuals experiencing the climate
change impacts would be willing to pay to avoid them. To calculate the marginal effect of emissions, i.e.. the
SC-GHG in year t. the entire model is run twice—first as a baseline and second with an additional pulse of
emissions in year t. After recalculating the temperature effects and damages expected in all years beyond t
resulting from the adjusted path of emissions, the losses are discounted to a present value in the discounting
module. Many sources of uncertainty in the estimation process are incorporated using Monte Carlo techniques
by taking draws from probability distributions that reflect the uncertainty in parameters.
The SC-GHG estimates used by EPA and many other federal agencies since 2009 have relied on an ensemble
of three widely used IAMs: Dynamic Integrated Climate and Economy (DICE) (W. D. Nordhaus, 2010);
Climate Framework for Uncertainty, Negotiation, and Distribution (FUND) (Anthoff & Tol, 2013a, 2013b);
and Policy Analysis of the Greenhouse Gas Effect (PAGE) (Hope, 2013). In 2010, the IWG harmonized key
inputs across the IAMs, but all other model features were left unchanged, relying on the model developers'
best estimates and judgments. That is, the representation of climate dynamics and damage functions included
in the default version of each IAM as used in the published literature was retained.
The SC-GHG estimates in U.S. EPA (20231) no longer rely on the three IAMs (i.e., DICE, FUND, and
PAGE) used in previous SC-GHG estimates. Instead, EPA uses a modular approach to estimating the SC-
GHG, consistent with the National Academies' near-term recommendations (National Academies, 2017).
That is, the methodology underlying each component, or module, of the SC-GHG estimation process is
developed by drawing on the latest research and expertise from the scientific disciplines relevant to that
component. Under this approach, each step in the SC-GHG estimation improves consistency with the current
state of scientific knowledge, enhances transparency, and allows for more explicit representation of
uncertainty.
The socioeconomic and emissions module relies on anew set of probabilistic projections for population,
income, and GHG emissions developed under the Resources for the Future (RFF) Social Cost of Carbon
Initiative (Rennert et al., 2021). These socioeconomic projections (hereafter collectively referred to as the
RFF-SPs) are an internally consistent set of probabilistic projections of population, GDP, and GHG emissions
(CO2, CH4, and N2O) to 2300. Based on a review of available sources of long-run projections necessary for
damage calculations, the RFF-SPs stand out as being most consistent with the National Academies'
recommendations. Consistent with the National Academies' recommendation, the RFF-SPs were developed
using a mix of statistical and expert elicitation techniques to capture uncertainty in a single probabilistic
approach, taking into account the likelihood of future emissions mitigation policies and technological
developments, and provide the level of disaggregation necessary for damage calculations. Unlike other
sources of projections, they provide inputs for estimation out to 2300 without further extrapolation
assumptions. Conditional on the modeling conducted for the SC-GHG estimates, this time horizon is far
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enough in the future to capture the majority of discounted climate damages. Including damages beyond 2300
would increase the estimates of the SC-GHG. As discussed in U.S. EPA (2023n), the use of the RFF-SPs
allows for capturing economic growth uncertainty within the discounting module.
The climate module relies on the Finite Amplitude Impulse Response (FaIR) model (IPCC, 2021b; Millar et
al., 2017; Smith et al., 2018), a widely used Earth system model which captures the relationships between
GHG emissions, atmospheric GHG concentrations, and global mean surface temperature. The FaIR model
was originally developed by Richard Millar, Zeb Nicholls, and Myles Allen at Oxford University, as a
modification of the approach used in IPCC AR5 to assess the GWP and GTP (Global Temperature Potential)
of different gases. It is open source, widely used (e.g., IPCC (2018, 2021a)), and was highlighted by the
National Academies (2017) as a model that satisfies their recommendations for a near-term update of the
climate module in SC-GHG estimation. Specifically, it translates GHG emissions into mean surface
temperature response and represents the current understanding of the climate and GHG cycle systems and
associated uncertainties within a probabilistic framework. The SC-GHG estimates used in this RIA rely on
FaIR version 1.6.2 as used by the IPCC (2021a). It provides, with high confidence, an accurate representation
of the latest scientific consensus on the relationship between global emissions and global mean surface
temperature, offers a code base that is fully transparent and available online, and the uncertainty capabilities
in FaIR 1.6.2 have been calibrated to the most recent assessment of the IPCC (which importantly narrowed
the range of likely climate sensitivities relative to prior assessments). See U.S. EPA (2023n) for more details.
The socioeconomic projections and outputs of the climate module are inputs into the damage module to
estimate monetized future damages from climate change.114 The National Academies' recommendations for
the damage module, scientific literature on climate damages, updates to models that have been developed
since 2010, as well as the public comments received on individual EPA rulemakings and the IWG's February
2021 TSD, have all helped to identify available sources of improved damage functions. The IWG (e.g., IWG,
2010; 2016b, 2021), the National Academies (2017), comprehensive studies (e.g., Rose et al. (2014)), and
public comments have all recognized that the damages functions underlying the IWG SC-GHG estimates used
since 2013 (taken from DICE 2010 (W.D. Nordhaus, 2010); FUND 3.8 (Anthoff & Tol, 2013a, 2013b); and
PAGE 2009 (Hope, 2012)) do not include all of important physical, ecological, and economic impacts of
climate change. The climate change literature and the science underlying the economic damage functions
have evolved, and DICE 2010, FUND 3.8, and PAGE 2009 now lag behind the most recent research.
The challenges involved with updating damage functions have been widely recognized. Functional forms and
calibrations are constrained by the available literature and need to extrapolate beyond warming levels or
locations studied in that literature. Research focused on understanding how these physical changes translate
into economic impacts is still developing, and has received less public resources, relative to the research
focused on modeling and improving our understanding of climate system dynamics and the physical impacts
from climate change (Auffhammer, 2018). Even so, there has been a large increase in research on climate
114 In addition to temperature change, two of the three damage modules used in the SC-GHG estimation require global mean sea
level (GMSL) projections as an input to estimate coastal damages. Those two damage modules use different models for
generating estimates of GMSL. Both are based off reduced complexity models that can use the FaIR temperature outputs as
inputs to the model and generate projections of GMSL accounting for the contributions of thermal expansion and glacial and ice
sheet melting based on recent scientific research. Absent clear evidence on a preferred model, the SC-GHG estimates presented
in this chapter retain both methods used by the damage module developers. See U.S. Environmental Protection Agency. (2023n).
Supplementary Material for the Regulatory Impact Analysis for the Final Rulemaking, "Standards ofPerformance for New,
Reconstructed, and Modified Sources and Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector Climate
Review EPA Report on the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances. Retrieved
from https://www.epa.gov/system/files/documents/2023-12/epa_scghg_2023_report_fmal.pdf for more details.
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impacts and damages in the time since DICE 2010, FUND 3.8, and PAGE 2009 were published. Along with
this growth, there continues to be variation in methodologies and scope of studies, such that care is required
when synthesizing the current understanding of impacts or damages. Based on a review of available studies
and approaches to damage function estimation, EPA uses three separate damage functions to form the damage
module. They are:
1. a subnational-scale, sectoral damage function (based on the Data-driven Spatial Climate Impact
Model (DSCIM) developed by the Climate Impact Lab (Carleton et al., 2022; Climate Impact Lab
(CIL), 2023; Rode et al., 2021),
2. a country-scale, sectoral damage function (based on the Greenhouse Gas Impact Value Estimator
(GIVE) model developed under RFF's Social Cost of Carbon Initiative (Rennert et al., 2022), and
3. a meta-analysis-based damage function (based on Howard & Sterner, 2017).
The damage functions in DSCIM and GIVE represent substantial improvements relative to the damage
functions underlying the SC-GHG estimates used by EPA to date and reflect the forefront of scientific
understanding about how temperature change and sea level rise lead to monetized net (market and nonmarket)
damages for several categories of climate impacts. The models' spatially explicit and impact-specific
modeling of relevant processes allows for improved understanding and transparency about mechanisms
through which climate impacts are occurring and how each damage component contributes to the overall
results, consistent with the National Academies' recommendations. DSCIM addresses common criticisms
related to the damage functions underlying current SC-GHG estimates (e.g., Pindyck (2017)) by developing
multi-sector, empirically grounded damage functions. The damage functions in the GIVE model offer a direct
implementation of the National Academies' near-term recommendation to develop updated sectoral damage
functions that are based on recently published work and reflective of the current state of knowledge about
damages in each sector. Specifically, the National Academies noted that "[t]he literature on agriculture,
mortality, coastal damages, and energy demand provide immediate opportunities to update the [models]"
(National Academies, 2017, p. 199), which are the four damage categories currently in GIVE. A limitation of
both models is that the sectoral coverage is still limited, and even the categories that are represented are
incomplete. Neither DSCIM nor GIVE yet accommodate estimation of several categories of temperature
driven climate impacts (e.g., morbidity, conflict, migration, biodiversity loss) and only represent a limited
subset of damages from changes in precipitation. For example, while precipitation is considered in the
agriculture sectors in both DSCIM and GIVE, neither model takes into account impacts of flooding, changes
in rainfall from tropical storms, and other precipitation related impacts. As another example, the coastal
damage estimates in both models do not fully reflect the consequences of sea level rise-driven salt-water
intrusion and erosion, or sea level rise damages to coastal tourism and recreation. Other missing elements are
damages that result from other physical impacts (e.g., ocean acidification, non-temperature-related mortality
such as diarrheal disease and malaria) and the many feedbacks and interactions across sectors and regions that
can lead to additional damages.115 See U.S. EPA (2023n) for more discussion of omitted damage categories
and other modeling limitations. DSCIM and GIVE do account for the most commonly cited benefits
associated with CO2 emissions and climate change—CO2 crop fertilization and declines in cold related
mortality. As such, while the GIVE- and DSCIM-based results provide state-of-the-science assessments of
115 The one exception is that the agricultural damage function in DSCIM and GIVE reflects the ways that trade can help mitigate
damages arising from crop yield impacts.
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key climate change impacts, they remain partial estimates of future climate damages resulting from
incremental changes in CO2, CH4, and N2O.116
Finally, given the still relatively narrow sectoral scope of the recently developed DSCIM and GIVE models,
the damage module includes a third damage function that reflects a synthesis of the state of knowledge in
other published climate damages literature. Studies that employ meta-analytic techniques offer a tractable and
straightforward way to combine the results of multiple studies into a single damage function that represents
the body of evidence on climate damages that pre-date CIL and RFF's research initiatives.117 The first use of
meta-analysis to combine multiple climate damage studies was done by Tol (2009) and included 14 studies.
The studies in Tol (2009) served as the basis for the global damage function in DICE starting in version
2013R (Nordhaus, 2014). The damage function in the most recent published version of DICE, DICE 2016, is
from an updated meta-analysis based on a review of existing damage studies and included 26 studies
published over 1994-2013 (Nordhaus & Moffat, 2017). Howard and Sterner (2017) provide a more recent
published peer-reviewed meta-analysis of existing damage studies (published through 2016) and account for
additional features of the underlying studies. This study addresses differences in measurement across studies
by adjusting estimates such that the data are relative to the same base period. They also eliminate double
counting by removing duplicative estimates. Howard and Sterner's final sample is drawn from 20 studies that
were published through 2015. Howard and Sterner (2017) present results under several specifications, and
their analysis shows that the estimates are somewhat sensitive to defensible alternative modeling choices. As
discussed in detail in U.S. EPA (2023n), the damage module underlying the SC-GHG estimates in this
analysis includes the damage function specification (that excludes duplicate studies) from Howard and Sterner
(2017) that leads to the lowest SC-GHG estimates, all else equal.
The discounting module discounts the stream of future net climate damages to its present value in the year
when the additional unit of emissions was released. Given the long time horizon over which the damages are
expected to occur, the discount rate has a large influence on the present value of future damages. Consistent
with the findings of National Academies (2017), the economic literature, OMB Circular A-4's guidance for
regulatory analysis, and IWG recommendations to date (IWG, 2010, 2013; 2016a, 2016b, 2021), EPA
continues to conclude that the consumption rate of interest is the theoretically appropriate discount rate to
discount the future benefits of reducing GHG emissions and that discount rate uncertainty should be
accounted for in selecting future discount rates in this intergenerational context. OMB's Circular A-4 points
out that "the analytically preferred method of handling temporal differences between benefits and costs is to
adjust all the benefits and costs to reflect their value in equivalent units of consumption before discounting
them." (OMB, 2023)118 The damage module described above calculates future net damages in terms of
116 One advantage of the modular approach used by these models is that future research on new or alternative damage functions can
be incorporated in a relatively straightforward way. DSCIM and GIVE developers have work underway on other impact
categories that may be ready for consideration in future updates (e.g., morbidity and biodiversity loss).
117 Meta-analysis is a statistical method of pooling data and/or results from a set of comparable studies of a problem. Pooling in this
way provides a larger sample size for evaluation and allows for a stronger conclusion than can be provided by any single study.
Meta-analysis yields a quantitative summary of the combined results and current state of the literature.
118 The previous version of OMB's Circular A-4 similarly pointed out that "the analytically preferred method of handling temporal
differences between benefits and costs is to adjust all the benefits and costs to reflect their value in equivalent units of
consumption and to discount them at the rate consumers and savers would normally use in discounting future consumption
benefits" (U.S. Office of Management and Budget. (2023). Circular A-4: Regulatory Analysis. Retrieved from
https://www.whitehouse.gov/wp-content/uploads/2023/ll/CircularA-4.pdf, ibid.).
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reduced consumption (or monetary consumption equivalents), and so an application of this guidance is to use
the consumption discount rate to calculate the SC-GHG.119
For the SC-GHG estimates used in this analysis, EPA relies on a dynamic discounting approach that more
fully captures the role of uncertainty in the discount rate in a manner consistent with the other modules. Based
on a review of the literature and data on consumption discount rates, the public comments received on
individual EPA rulemakings, and the February 2021 TSD (IWG, 2021), and the National Academies (2017)
recommendations for updating the discounting module, the SC-GHG estimates rely on discount rates that
reflect more recent data on the consumption interest rate and uncertainty in future rates. Specifically, rather
than using a constant discount rate, the evolution of the discount rate over time is defined following the latest
empirical evidence on interest rate uncertainty and using a framework originally developed by Ramsey (1928)
that connects economic growth and interest rates. The Ramsey approach explicitly reflects (1) preferences for
utility in one period relative to utility in a later period and (2) the value of additional consumption as income
changes. The dynamic discount rates used to develop the SC-GHG estimates applied in this analysis have
been calibrated following the Newell, Pizer and Prest (2022) approach, as applied in Rennert et al. (2022).
This approach uses the Ramsey (1928) discounting formula in which the parameters are calibrated such that
(1) the decline in the certainty-equivalent discount rate matches the latest empirical evidence on interest rate
uncertainty estimated by Bauer and Rudebusch (2020, 2023) and (2) the average of the certainty-equivalent
discount rate over the first decade matches a near-term consumption rate of interest. Uncertainty in the
starting rate is addressed by using three near-term target rates (1.5, 2.0, and 2.5 percent) based on multiple
lines of evidence on observed market interest rates.
The resulting dynamic discount rate provides a notable improvement over the constant discount rate
framework used for SC-GHG estimation in EPA regulatory impact analyses to date. Specifically, it provides
internal consistency within the modeling and a more complete accounting of uncertainty consistent with
economic theory (Arrow et al., 2013; Cropper et al., 2014) and the National Academies' (2017)
recommendation to employ a more structural, Ramsey-like approach to discounting that explicitly recognizes
the relationship between economic growth and discounting uncertainty. This approach is also consistent with
the National Academies (2017) recommendation to use three sets of Ramsey parameters that reflect a range of
near-term certainty-equivalent discount rates and are consistent with theory and empirical evidence on
consumption rate uncertainty. Finally, the value of aversion to risk associated with net damages from GHG
emissions is explicitly incorporated into the modeling framework following the economic literature. See U.S.
EPA (2023n) for a more detailed discussion of the entire discounting module and methodology used to value
risk aversion in the SC-GHG estimates.
Taken together, the methodologies adopted in this SC-GHG estimation process allow for a more holistic
treatment of uncertainty than in past estimates by EPA. The updates incorporate a quantitative consideration
of uncertainty into all modules and use a Monte Carlo approach that captures the compounding uncertainties
across modules. The estimation process generates nine separate distributions of discounted marginal damages
per metric ton — the product of using three damage modules and three near-term target discount rates — for
each gas in each emissions year. These distributions have long right tails reflecting the extensive evidence in
119 See the discussion of the inappropriateness of discounting consumption-equivalent measures of benefits and costs using a rate of
return on capital in Circular A-4 (ibid., ibid.). Note that under the previous version of OMB's Circular A-4 EPA also concluded
that the use of the social rate of return on capital (7 percent under the 2003 OMB Circular A-4 guidance), which does not reflect
the consumption rate, to discount damages estimated in terms of reduced consumption would inappropriately underestimate the
impacts of climate change for the purposes of estimating the SC-GHG.
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the scientific and economic literature that shows the potential for lower-probability but higher-impact
outcomes from climate change, which would be particularly harmful to society. The uncertainty grows over
the modeled time horizon. Therefore, under cases with a lower near-term target discount rate - that give
relatively more weight to impacts in the future - the distribution of results is wider. To produce a range of
estimates that reflects the uncertainty in the estimation exercise while also providing a manageable number of
estimates for policy analysis, EPA combines the multiple lines of evidence on damage modules by averaging
the results across the three damage module specifications. The full results generated from the updated
methodology for methane and other greenhouse gases (SC-CO2, SC-CH4, and SC-N2O) for emissions years
2020 through 2080 are provided in U.S. EPA (2023n).
Table 8-6 presents the resulting averaged certainty-equivalent SC-CO2 and SC-CH4 estimates for emissions
occurring in 2025 to 2049 under each near-term discount rate that are used to estimate the climate benefits of
the CO2 and CH4 changes expected from the final rule. These estimates are reported in 2023 dollars but are
otherwise identical to those presented in U.S. EPA (20231). The SC-GHG increases overtime within the
models — i.e., the societal harm from one metric ton emitted in 2030 is higher than the harm caused by one
metric ton emitted in 2025 — because future emissions produce larger incremental damages as physical and
economic systems become more stressed in response to greater climatic change, and because GDP is growing
over time and many damage categories are modeled as proportional to GDP. EPA estimated the climate
benefits of the net CO2 and CH4 emission changes for each analysis year between 2025 and 2049 by applying
the annual SC-CO2 and SC-CH4 estimates, shown in Table 8-6, to the estimated changes in CO2 and CH4
emissions in the corresponding year under the regulatory options.
Table 8-6: Estimates of the Social Cost of Greenhouse Gas by Year and Near-Term Ramsey
Discount Rate, 2025-2049
Year
Social Cost of C02 (2023$/Metric Tonne C02)
Social Cost of CH4 (2023$/Metric Tonne CH4)
1.5%
2.0%
2.5%
1.5%
2.0%
2.5%
2025
$150
$250
$430
$1,800
$2,300
$3,200
2026
$150
$250
$420
$1,900
$2,400
$3,300
2027
$160
$250
$430
$2,000
$2,500
$3,400
2028
$160
$260
$440
$2,100
$2,600
$3,500
2029
$160
$260
$440
$2,200
$2,700
$3,600
2030
$170
$270
$450
$2,200
$2,800
$3,700
2031
$170
$270
$450
$2,300
$2,900
$3,800
2032
$170
$270
$460
$2,400
$3,000
$3,900
2033
$180
$280
$460
$2,500
$3,100
$4,000
2034
$180
$280
$470
$2,600
$3,200
$4,100
2035
$180
$290
$470
$2,700
$3,300
$4,300
2036
$190
$290
$480
$2,800
$3,400
$4,400
2037
$190
$300
$480
$2,900
$3,500
$4,500
2038
$190
$300
$490
$3,000
$3,600
$4,600
2039
$200
$310
$490
$3,000
$3,700
$4,700
2040
$200
$310
$500
$3,100
$3,800
$4,800
2041
$200
$310
$510
$3,200
$3,900
$5,000
2042
$210
$320
$510
$3,300
$4,000
$5,100
2043
$210
$320
$520
$3,400
$4,100
$5,200
2044
$220
$330
$520
$3,500
$4,200
$5,300
2045
$220
$330
$530
$3,600
$4,400
$5,500
2046
$220
$340
$540
$3,700
$4,500
$5,600
2047
$230
$340
$540
$3,800
$4,600
$5,700
2048
$230
$350
$550
$3,900
$4,700
$5,900
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Table 8-6: Estimates of the Social Cost of Greenhouse Gas by Year and Near-Term Ramsey
Discount Rate, 2025-2049
Year
Social Cost of C02 (2023$/Metric Tonne C02)
Social Cost of CH4 (2023$/Metric Tonne CH4)
1.5%
2.0%
2.5%
1.5%
2.0%
2.5%
2049
$230
$350
$550
$4,000
$4,800
$6,000
Note: Values shown are rounded to two significant figures, but the unrounded values were used in the calculations and are
available in the Appendix to U.S. EPA (2023n). These SC-GHG values are identical to those reported in U.S. EPA (2023n) adjusted
for inflation to 2023 dollars using the annual GDP Implicit Price Deflator values in the U.S. Bureau of Economic Analysis' (BEA)
National Income and Product Accounts Table 1.1.9 (U.S. BEA, 2023; U.S. BEA, 2024), which are 122.262 and 105.381, respectively
for 2023 and 2020. SC-C02 and SC-CH4 values are stated in $/metric tonne C02 and CH4, respectively (1 metric tonne equals 1.102
short tons) and vary depending on the year of emissions.
Source: U.S. EPA Analysis, 2024 based on U.S. EPA (20231; U.S. EPA (2023n).
The methodological updates incorporated in U.S. EPA (20231) and summarized above represent a major step
forward in bringing SC-GHG estimation closer to the frontier of climate science and economics and address
many of the near-term recommendations by the National Academies (2017). Nevertheless, the SC-GHG
estimates presented in Table 8-6 still have several limitations, as would be expected for any modeling exercise
that covers such a broad scope of scientific and economic issues across a complex global landscape. There are
still many categories of climate impacts and associated damages that are only partially or not reflected yet in
these estimates and sources of uncertainty that have not been fully characterized due to data and modeling
limitations. For example, the modeling omits most of the consequences of changes in precipitation, damages
from extreme weather events, the potential for nongradual damages from passing critical thresholds (e.g.,
tipping elements) in natural or socioeconomic systems, and non-climate mediated effects of GHG emissions.
The SC-CH4 estimates do not account for the direct health and welfare impacts associated with tropospheric
ozone produced by methane. Importantly, the updated SC-GHG methodology does not yet reflect interactions
and feedback effects within, and across, Earth and human systems. For example, it does not explicitly reflect
potential interactions among damage categories, such as those stemming from the interdependences of
energy, water, and land use. These, and other, interactions and feedbacks were highlighted by the National
Academies as an important area of future research for longer-term enhancements in the SC-GHG estimation
framework.
8.2.2 Results
Table 8-7 presents the undiscounted annual monetized climate benefits in selected years for Option B, the
final rule. Benefits are calculated using the three different estimates of the SC-GHG from Table 8-6 based on
the near-term Ramsey discount rates. EPA first mapped IPM emissions changes to corresponding years within
the period of analysis 2025-2049 based on Table 8-1 and assuming no changes in air emissions from
electricity generation between 2025 and 2027. For trucking and energy use, EPA estimated changes in air
emissions corresponding to the year each plant is estimated to implement changes in technology. Net CO2 and
CH4 changes each year are then multiplied by the SC-CO2 or SC-CH4 estimates for that year. EPA calculated
the present value of climate benefits as of the expected rule promulgation year of 2024 by discounting each
year-specific value to the year 2024 using the same near-term Ramsey discount rate used to calculate the
corresponding SC-GHG.120 That is, future climate benefits estimated with the SC-GHG at the 2.5 percent,
120 As discussed in U.S. Environmental Protection Agency. (2023n). Supplementary Material for the Regulatory Impact Analysis for
the Final Rulemaking, "Standards of Performance for New, Reconstructed, and Modified Sources and Emissions Guidelines for
Existing Sources: Oil and Natural Gas Sector Climate Review ": EPA Report on the Social Cost of Greenhouse Gases: Estimates
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2 percent, and 1.5 percent Ramsey rate are discounted to the base year of the analysis using a constant 2.5,2,
and 1.5 percent rate, respectively.
The profile of benefits is the result of both ELG effects and other factors. Thus, the larger benefits beginning
in 2028 coincide with the timing of compliance with the revised ELGs and impacts of the rule on the
generation mix, whereas the decline starting around 2038 coincide with emissions reductions already
projected in Base Case due to factors external to the revised ELGs. See Chapter 5 in the RIA for details on
IPM Base Case projections (U.S. EPA, 2024e).
Table 8-7: Estimated Undiscounted and Total Present Value of Climate Benefits from Changes in
CO2 and CH4 Emissions under the Final Rule, Compared to Baseline (Millions of 2023$)
Regulatory
Option
Year
Climate Benefits3'13
SC-GHG based on 1.5%
near term Ramsey
discount rate
SC-GHG based on 2%
near-term Ramsey
discount rate
SC-GHG based on 2.5%
near-term Ramsey
discount rate
Option B
(Final Rule)
2025
$0.0
$0.0
$0.0
2026
-$5.7
-$9.2
-$15.7
2027
-$8.4
-$13.5
-$22.9
2028
$2,393.4
$3,839.8
$6,457.1
2029
$2,424.2
$3,885.6
$6,533.2
2030
$1,642.7
$2,623.8
$4,380.6
2031
$1,677.0
$2,669.4
$4,437.7
2032
$1,993.3
$3,149.4
$5,235.7
2033
$2,033.2
$3,202.6
$5,288.9
2034
$2,059.8
$3,255.7
$5,355.4
2035
$2,099.6
$3,295.6
$5,421.8
2036
$2,139.5
$3,348.8
$5,475.0
2037
$2,179.4
$3,401.9
$5,541.4
2038
$340.5
$528.2
$860.5
2039
$346.7
$536.3
$868.7
2040
$352.8
$544.5
$878.9
2041
$358.9
$552.7
$889.2
2042
$242.4
$372.3
$597.1
2043
$246.4
$377.8
$603.9
2044
$251.9
$383.2
$610.7
2045
$255.9
$388.6
$617.5
2046
$260.0
$394.1
$625.7
2047
$264.2
$401.0
$632.7
2048
$121.7
$183.5
$288.7
2049
$123.6
$186.0
$291.9
Total present value
$18,774.7
$31,019.9
$53,649.9
Annualized value
$994.1
$1,557.7
$2,551.0
a. Values rounded to two significant figures.
Incorporating Recent Scientific Advances. Retrieved from https://www.epa.gov/system/files/documents/2023-
12/epa_scghg_2023_report_final.pdf, the error associated with using a constant discount rate rather than the certainty-equivalent
rate path to calculate the present value of a future stream of monetized climate benefits is small for analyses with moderate time
frames (e.g., 30 years or less). Ibid, also provides an illustration of the amount that climate benefits from reductions in future
emissions will be underestimated by using a constant discount rate relative to the more complicated certainty-equivalent rate
path.
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Table 8-7: Estimated Undiscounted and Total Present Value of Climate Benefits from Changes in
CO2 and CH4 Emissions under the Final Rule, Compared to Baseline (Millions of 2023$)
Regulatory
Option
Year
Climate Benefits3'b
SC-GHG based on 1.5%
near term Ramsey
discount rate
SC-GHG based on 2%
near-term Ramsey
discount rate
SC-GHG based on 2.5%
near-term Ramsey
discount rate
b. Climate benefits are based on changes in C02 and CH4 emissions and are calculated using three different estimates of the SC-
GHG (1.5 percent, 2 percent, and 2.5 percent near-term Ramsey discount rates).
Source: U.S. EPA Analysis, 2024
Table 8-8 shows the annualized climate benefits associated with changes in CO2 and CH4 emissions over the
2025-2049 period under each discount rate for the final rule by category of emissions. EPA annualized the
climate benefits to enable consistent reporting across benefit categories (e.g., benefits from improvement in
water quality). As noted above, the IPM model run provides outputs starting in 2028. For the years 2025
through 2027, EPA assumed no change in air emissions from changes in the profile of electricity generation.
For trucking and energy use, EPA estimated changes in air emissions corresponding to the year each plant is
estimated to implement changes in technology. For each SC-GHG estimate, EPA then calculated the
annualized benefits from the perspective of 2024 by discounting each year-specific value to the year 2024
using the same near-term discount rate used to calculate the SC-GHG. Using the SC-GHG values for the
2 percent near-term discount rate and using a 2 percent discount to annualize the benefits yields annualized
benefits of $1,558 million.
Table 8-8: Estimated Annualized Climate Benefits from Changes in CO2 and CH4 Emissions under
the Final Rule during the Period of 2025-2049 by Categories of Air Emissions and SC-GHG
Estimates, Compared to Baseline (Millions of 2023$)
Regulatory Option
Category of Air
Emissions
Annualized Climate Benefits313
1.5% Discount Rate
2.0% Discount Rate
2.5% Discount Rate
Option B (Final Rule)
Electricity generation
$1,014.0
$1,589.1
$2,602.8
Trucking
-$0.1
-$0.2
-$0.3
Energy use
-$19.7
-$31.2
-$51.4
Total
$994.2
$1,557.7
$2,551.1
a. Values rounded to two significant figures. Negative values indicate forgone benefits whereas positive values indicate positive
benefits.
b. Climate benefits are based on changes C02 and CH4 emissions and are calculated using three different estimates of the SC-C02
and SC-CH4 (1.5 percent, 2.0 percent, and 2.5 percent near-term Ramsey discount rates).
Source: U.S. EPA Analysis, 2024
Unlike many environmental problems where the causes and impacts are distributed more locally, GHG
emissions are a global externality making climate change a true global challenge. GHG emissions contribute
to damages around the world regardless of where they are emitted. Because of the distinctive global nature of
climate change, in the BCA for this final rule EPA centers attention on a global measure of climate benefits
from GHG reductions. Consistent with all IWG recommended SC-GHG estimates to date, the SC-GHG
values presented in Table 8-6 above provide a global measure of monetized damages from GHG emissions
and Tables 8-7 and 8-8 present the monetized global climate benefits of the GHG emission changes expected
from the final rule. This approach is the same as that taken in EPA regulatory analyses from 2009 through
2016 and since 2021. It is also consistent with guidance in Circular A-4 (OMB, 2003) that recommends
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reporting of important international effects.121 EPA also notes that EPA's cost estimates in RIAs, including
the cost estimates contained in this BCA, regularly do not differentiate between the share of compliance costs
expected to accrue to U.S. firms versus foreign interests, such as to foreign investors in regulated entities.122 A
global perspective on climate effects is therefore consistent with the approach EPA takes on costs. There are
many reasons, as summarized in this section - and as articulated by OMB and in IWG assessments (IWG,
2010, 2013; 2016a, 2016b, 2021), the 2015 Response to Comments (IWG, 2015) and in detail in U.S. EPA
(2023n) and in Appendix A of the Response to Comments document for the December 2023 Final Oil and
Gas NSPS/EG Rulemaking - why EPA focuses on the global value of climate change impacts when
analyzing policies that affect GHG emissions.
International cooperation and reciprocity are essential to successfully addressing climate change, as the global
nature of greenhouse gases means that a ton of GHGs emitted in any other country harms those in the United
States just as much as a ton emitted within the territorial United States. Assessing the benefits of U.S. GHG
mitigation activities requires consideration of how those actions may affect mitigation activities by other
countries, as those international mitigation actions will provide a benefit to U.S. citizens and residents by
mitigating climate impacts that affect U.S. citizens and residents. This is a classic public goods problem
because each country's reductions benefit everyone else, and no country can be excluded from enjoying the
benefits of other countries' reductions. The only way to achieve an efficient allocation of resources for
emissions reduction on a global basis — and so benefit the United States and its citizens and residents — is
for all countries to base their policies on global estimates of damages. A wide range of scientific and
121 The 2003 version of OMB Circular A-4 states when a regulation is likely to have international effects, "these effects should be
reported"; while OMB Circular A-4 recommends that international effects we reported separately, the guidance also explains that
"[different regulations may call for different emphases in the analysis, depending on the nature and complexity of the regulatory
issues." (U.S. Office of Management and Budget. (2003). Circular A-4: Regulatory Analysis. Retrieved from
https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/circulars/A4/a-4.pdf). The 2023 update to Circular A-4 states that
"In certain contexts, it may be particularly appropriate to include effects experienced by noncitizens residing abroad in your
primary analysis. Such contexts include, for example, when:
assessing effects on noncitizens residing abroad provides a useful proxy for effects on U.S. citizens and residents that are
difficult to otherwise estimate;
assessing effects on noncitizens residing abroad provides a useful proxy for effects on U.S. national interests that are not
otherwise fully captured by effects experienced by particular U.S. citizens and residents (e.g., national security interests,
diplomatic interests, etc.);
regulating an externality on the basis of its global effects supports a cooperative international approach to the regulation of
the externality by potentially inducing other countries to follow suit or maintain existing efforts; or
international or domestic legal obligations require or support a global calculation of regulatory effects" (U.S. Office of
Management and Budget. (2023). Circular A-4: Regulatory Analysis. Retrieved from https://www.whitehouse.gov/wp-
content/uploads/2023/ll/CircularA-4.pdf).
Due to the global nature of the climate change problem, the OMB recommendations of appropriate contexts for considering
international effects are relevant to the CO2 emission reductions expected from the final rule. For example, as discussed in
this RIA, a global focus in evaluating the climate impacts of changes in CO2 emissions supports a cooperative international
approach to GHG mitigation by potentially inducing other countries to follow suit or maintain existing efforts, and the
global SC-CO2 estimates better capture effects on U.S. citizens and residents and U.S. national interests that are difficult to
estimate and not otherwise fully captured.
122 For example, in the RIA for the 2018 Proposed Reconsideration of the Oil and Natural Gas Sector Emission Standards for New,
Reconstructed, and Modified Sources, EPA acknowledged that some portion of regulatory costs will likely "accrufe] to entities
outside U.S. borders" through foreign ownership, employment, or consumption (U.S. Environmental Protection Agency. (2018d).
Regulatory Impact Analysis for the Proposed Reconsideration of the Oil and Natural Gas Sector Emission Standards for New,
Reconstructed, and Modified Sources. (EPA-452/R-18-001). Retrieved from https://www.epa.gov/sites/default/files/2018-
09/documents/oil_and_natural_gas_nsps_reconsideration_proposal_ria.pdf, p. 3-13). In general, a significant share of U.S.
corporate debt and equities are foreign-owned, including in the oil and gas industry.
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economic experts have emphasized the issue of international cooperation and reciprocity as support for
assessing global damages of GHG emission in domestic policy analysis. Using a global estimate of damages
in U.S. analyses of regulatory actions allows the U.S. to continue to actively encourage other nations,
including emerging major economies, to also assess global climate damages of their policies and to take steps
to reduce emissions. For example, many countries and international institutions have already explicitly
adapted the global SC-GHG estimates used by EPA in their domestic analyses (e.g., Canada, Israel) or
developed their own estimates of global damages (e.g., Germany), and recently, there has been renewed
interest by other countries to update their estimates since the draft release of the updated SC-GHG estimates
presented in the December 2022 Oil and Gas NSPS/EG Supplemental Proposal RIA.123 Several recent studies
have empirically examined the evidence on international GHG mitigation reciprocity, through both policy
diffusion and technology diffusion effects. See U.S. EPA (2023n) for more discussion.
For all of these reasons, EPA believes that a global metric is appropriate for assessing the climate benefits of
avoided GHG emissions in this final RIA. In addition, as emphasized in the National Academies'
recommendations, "[i]t is important to consider what constitutes a domestic impact in the case of a global
pollutant that could have international implications that impact the United States." (National Academies,
2017) The global nature of GHG pollution and its impacts means that U.S. interests are affected by climate
change impacts through a multitude of pathways and these need to be considered when evaluating the benefits
of GHG mitigation to U.S. citizens and residents. The increasing interconnectedness of global economy and
populations means that impacts occurring outside of U.S. borders can have significant impacts on U.S.
interests. Examples of affected interests include direct effects on U.S. citizens and assets located abroad,
international trade, and tourism, and spillover pathways such as economic and political destabilization and
global migration that can lead to adverse impacts on U.S. national security, public health, and humanitarian
concerns. Those impacts point to the global nature of the climate change problem and are better captured
within global measures of the social cost of greenhouse gases.
In the case of these global pollutants, for the reasons articulated in this section, the assessment of global net
damages of GHG emissions allows EPA to fully disclose and contextualize the net climate benefits of GHG
emission changes expected from this final rule. EPA disagrees with public comments received on the
December 2022 Oil and Gas NSPS/EG Supplemental Proposal that suggested that EPA can or should use a
metric focused on benefits resulting solely from changes in climate impacts occurring within U.S. borders.
The global models used in the SC-GHG modeling described above do not lend themselves to be
disaggregated in a way that could provide sufficiently robust information about the distribution of the rule's
climate benefits to citizens and residents of particular countries, or population groups across the globe and
within the U.S. Two of the models used to inform the damage module, the GIVE and DSCIM models, have
spatial resolution that allows for some geographic disaggregation of future climate impacts across the world.
This permits the calculation of a partial GIVE and DSCIM-based SC-GHG measuring the damages from four
or five climate impact categories projected to physically occur within the U.S., respectively, subject to
caveats. As discussed at length in U.S. EPA (2023n), these damage modules are only a partial accounting and
do not capture all of the pathways through which climate change affects public health and welfare. Thus, they
only cover a subset of potential climate change impacts. Furthermore, the damage modules do not capture
123 In April 2023, the government of Canada announced the publication of an interim update to their SC-GHG guidance,
recommending SC-GHG estimates identical to EPA's updated estimates presented in the December 2022 Supplemental Proposal
RIA. The Canadian interim guidance will be used across all Canadian federal departments and agencies, with the values expected
to be finalized by the end of the year, https://www.canada.ca/en/environment-climate-change/services/climate-change/science-
research-data/social-cost-ghg.html.
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spillover or indirect effects whereby climate impacts in one country or region can affect the welfare of
residents in other countries or regions—such as how economic and health conditions across countries will
impact U.S. business, investments, and travel abroad.
Additional modeling efforts can and have shed further light on some omitted damage categories. For example,
the Framework for Evaluating Damages and Impacts (FrEDI) is an open-source modeling framework
developed by EPA124 to facilitate the characterization of net annual climate change impacts in numerous
impact categories within the contiguous U.S. and monetize the associated distribution of modeled damages
(Sarofim et al., 2021; U.S. EPA, 2021c). The additional impact categories included in FrEDI reflect the
availability of U.S.-specific data and research on climate change effects. As discussed in U.S. EPA (2023n)
results from FrEDI show that annual damages resulting from climate change impacts within the contiguous
U.S. (CONUS) (i.e., excluding Hawaii, Alaska, and U.S. territories) and for impact categories not represented
in GIVE and DSCIM are expected to be substantial. As discussed in U.S. EPA (2021c), results from FrEDI
show that annual damages resulting from climate change impacts within the contiguous U.S. (CONUS) (i.e.,
excluding Hawaii, Alaska, and U.S. territories) and for impact categories not represented in GIVE and
DSCIM are expected to be substantial. For example, FrEDI estimates a partial SC-CO2 of $47/mtC02 for
damages physically occurring within CONUS for 2030 emissions, under a 2 percent near-term Ramsey
discount rate)125 (Hartin et al., 2023), compared to a GIVE and DSCIM-based U.S.-specific SC-CO2 of
$19/mtC02 and $21/mtC02, respectively, for 2030 emissions.126
While the FrEDI results help to illustrate how monetized damages physically occurring within CONUS
increase as more impacts are reflected in the modeling framework, they are still subject to many of the same
limitations associated with the DSCIM and GIVE damage modules, including the omission or partial
124 The FrEDI framework and Technical Documentation have been subject to a public review comment period and an independent
external peer review, following guidance in EPA Peer-Review Handbook for Influential Scientific Information (ISI). Information
on the FrEDI peer-review is available at EPA Science Inventory
(https://cfpub.epa.gov/si/si public record report.cfm?dirEntrvID=360384&Lab=OAP).
125 As explained in U.S. Environmental Protection Agency. (2023n). Supplementary Material for the Regulatory Impact Analysis for
the Final Rulemaking, "Standards of Performance for New, Reconstructed, and Modified Sources and Emissions Guidelines for
Existing Sources: Oil and Natural Gas Sector Climate Review EPA Report on the Social Cost of Greenhouse Gases: Estimates
Incorporating Recent Scientific Advances. Retrieved from https://www.epa.gov/system/files/documents/2023-
12/epa_scghg_2023_report_final.pdf, Hartin, C., McDuffie, E. E., Noiva, K., Sarofim, M., Parthum, B., Martinich, J., Barr, S.,. .
. Fawcett, A. (2023). Advancing the estimation of future climate impacts within the United States. Earth Svst. Dvnam., 14(5),
1015-1037. https://doi.org/10.5194/esd-14-1015-2023 present partial SC-C02, SC-CH4, and SC-N20 estimates for a 2020
emissions pulse year. This same methodology was applied in U.S. Environmental Protection Agency. (2023n). Supplementary
Material for the Regulatory Impact Analysis for the Final Rulemaking, "Standards of Performance for New, Reconstructed, and
Modified Sources and Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review": EPA Report on
the Social Cost of Greenhouse Gases: Estimates Incorporating Recent Scientific Advances. Retrieved from
https://www.epa.gov/system/files/documents/2023-12/epa_scghg_2023_report_fmal.pdf to calculate the FrEDI-based partial SC-
GHG values for 2030 emissions. Updated the values from ibid, to 2023 dollars using the GDP deflator.
120 Updated the values from U.S. Environmental Protection Agency. (2023n). Supplementary Material for the Regulatory Impact
Analysis for the Final Rulemaking, "Standards ofPerformance for New, Reconstructed, and Modified Sources and Emissions
Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review ": EPA Report on the Social Cost of Greenhouse
Gases: Estimates Incorporating Recent Scientific Advances. Retrieved from https://www.epa.gov/system/files/documents/2023-
12/epa_scghg_2023_report_final.pdf to 2023 dollars using the GDP deflator. FrEDI estimates a partial SC-CHi of $684/mtCHi
for damages physically occurring within CONUS for 2030 emissions (under a 2 percent near-term Ramsey discount rate) (Hartin,
C., McDuffie, E. E., Noiva, K., Sarofim, M., Parthum, B., Martinich, J., Barr, S.,. . . Fawcett, A. (2023). Advancing the
estimation of future climate impacts within the United States. Earth Syst. Dynam., 14(5), 1015-1037. https://doi.org/10.5194/esd-
14-1015-2023 ) compared to a GIVE and DSCIM-based U.S.-specific SC-CH4 of $321/mtCHi and $87/mtCHi, respectively, for
2030 emissions.
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modeling of important damage categories.127 Finally, none of these modeling efforts - GIVE, DSCIM, and
FrEDI - reflect non-climate mediated effects of GHG emissions experienced by U.S. populations (other than
CO2 fertilization effects on agriculture). As one example of new research on non-climate mediated effects of
methane emissions, McDuffie et al. (2023) estimate the monetized increase in respiratory-related human
mortality risk from the ozone produced from a marginal pulse of methane emissions. Using the
socioeconomics from the RFF-SPs and the 2 percent near-term Ramsey discounting approach, this additional
health risk to U.S. populations is on the order of approximately $417/mtCH4 for 2030 emissions.128
Applying the U.S.-specific partial SC-GHG estimates derived from the multiple lines of evidence described
above to the GHG emissions changes expected under the final rule would yield substantial benefits. For
example, the present value of the climate benefits of the final rule over 2025-2049 as measured by FrEDI
from climate change impacts in CONUS are estimated to be $4.8 billion (under a 2 percent near-term Ramsey
discount rate). However, the numerous explicitly omitted damage categories and other modeling limitations
discussed above and throughout U.S. EPA (2023n) make it likely that these estimates underestimate the
benefits to U.S. citizens and residents of the GHG reductions from the final rule; the limitations in developing
a U.S.-specific estimate that accurately captures direct and spillover effects on U.S. citizens and residents
further demonstrates that it is more appropriate to use a global measure of climate benefits from GHG
reductions. EPA will continue to review developments in the literature, including more robust methodologies
for estimating the magnitude of the various damages to U.S. populations from climate impacts and reciprocal
international mitigation activities, and explore ways to better inform the public of the full range of GHG
impacts.
8.3 Human Health Benefits
8.3.1 Data and Methodology
As summarized in Table 8-5, the final rule is estimated to influence the level of pollutants emitted in the
atmosphere that adversely affect human health, including directly emitted PM2 5, as well as SO2 and NOx,
which are both precursors to ambient PM2 5. NOx emissions are also a precursor to ambient ground-level
ozone. The change in emissions alters the ambient concentrations, which in turn leads to changes in
127 Another method that has produced estimates of the effect of climate change on U.S.-specific outcomes uses a top-down approach
to estimate aggregate damage functions. Published research using this approach include total-economy empirical studies that
econometrically estimate the relationship between GDP and a climate variable, usually temperature. As discussed in U.S.
Environmental Protection Agency. (2023n). Supplementary Material for the Regulatory Impact Analysis for the Final
Rulemaking, "Standards of Performance for New, Reconstructed, and Modified Sources and Emissions Guidelines for Existing
Sources: Oil and Natural Gas Sector Climate Review": EPA Report on the Social Cost of Greenhouse Gases: Estimates
Incorporating Recent Scientific Advances. Retrieved from https://www.epa.gov/system/files/documents/2023-
12/epa_scghg_2023_report_final.pdf the modeling framework used in the existing published studies using this approach differ in
important ways from the inputs underlying the SC-GHG estimates described above (e.g., discounting, risk aversion, and scenario
uncertainty). Hence, we do not consider this line of evidence in the analysis for this RIA. Updating the framework of total-
economy empirical damage functions to be consistent with the methods described in this RIA and ibid, would require new
analysis. Finally, because total-economy empirical studies estimate market impacts, they do not include any non-market impacts
of climate change (e.g., heat related mortality) and therefore are also only a partial estimate. EPA will continue to review
developments in the literature and explore ways to better inform the public of the full range of GHG impacts.
128 See ibid, for more details. Updated to 2023 dollars using the GDP deflator.
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population exposure. EPA estimated the changes in the human health impacts associated with PM2 5 and
129
ozone.
This section summarizes EPA's approach to estimating the incidence and economic value of the PM2.5 and
ozone-related benefits estimated for the final rule (Option B). The approach entails two major steps: (1)
developing baseline and Option B spatial fields of air quality across the U.S. using nationwide photochemical
modeling and related analyses; and (2) using these spatial fields in BenMAP-CE130 to quantify the benefits
under Option B as compared to the baseline. In this approach, EPA used IPM projections of EGU air
emissions for the baseline and Option B (final rule).
8.3.1.1 Air Quality Modeling Methodology
As described in Appendix J, spatial fields of annual ozone and PM2.5 concentrations representing the baseline
and Option B were obtained from ozone source and PM source apportionment modeling. These PM2 5 and
ozone spatial fields were used as input to BenMAP-CE which, in turn, was used to quantify the benefits from
this rule.
EPA prepared spatial fields of air quality for the baseline and for Option B for two health-impact metrics:
annual mean PM2.5 and April through September seasonal average 8-hour daily maximum (MDA8) ozone
(AS-M03). The EGU emissions for the baseline and Option B, consisting of total NOx, SO2, and primary
PM2.5 emissions summarized by year and state, were obtained from the outputs of the IPM run, as described
above and in Chapter 5 of the RIA (U.S. EPA, 2024e). As such, the spatial fields do not account for changes
in emissions associated with power requirements to operate treatment systems or with transportation. See
Section 8.3.1 regarding limitations and uncertainty associated with the analysis of air quality related benefits.
The basic methodology for determining air quality changes is the same as that used in the RIAs from multiple
previous rules (U.S. EPA, 2019i; 2020b; 2020a, 2021b; 2022c). Appendix J provides an overview of the air
quality modeling and the methodologies EPA used to develop spatial fields of seasonal ozone and annual
PM2.5 concentrations. The appendix also provides selected figures showing the geographical and temporal
distribution of air quality changes.
EPA used air quality modeling to estimate health benefits associated with changes in ozone and PM2.5
concentrations that may occur because of Option B of the final rule relative to the baseline. Air quality
surfaces of the baseline reflect projected 2026 emission from all sources other than EGUs but reflect year-
specific projected emissions for EGUs for 2028, 2030, 2035, 3040, 2045 and 2050.131 While the CAMx air
quality modeling includes a range of pollution sources, contributions from non-EGU point sources, on-road
vehicles, non-road mobile equipment and marine vessels are held constant in this analysis, and the only
129 Ambient concentrations of both SO2 and NOx also pose health risks independent of PM2.5 and ozone, though EPA does not
quantify these impacts in this analysis (U.S. Environmental Protection Agency. (2016b). Integrated Science Assessment for
Oxides of Nitrogen: Health Criteria. (EPA/600/R-15/068). Retrieved from
http://ofmpub.epa.gov/eims/eimscomm.getfile?p_download_id=526855, U.S. Environmental Protection Agency. (2017b).
Integrated Science Assessment for Sulfur Oxides: Health Criteria. (EPA/600/R-17/451). Retrieved from
http://ofmpub.epa.gov/eims/eimscomm.getfile?p_download_id=533653)
130 The Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE) is described and found at:
https://www.epa.gov/benmap.
131 The air quality modeling techniques used for this analysis reflect non-EGU emissions as of 2026, so implementation or effects of
any changes in non-EGU emissions expected to occur after 2026 are not accounted for in this analysis. However, the effect of
non-EGU emissions on changes in pollution concentrations due to the final rule is likely to be small.
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changes are those associated with the projected impacts of the rule on the profile of electricity generation and
EGU emissions, as compared to the baseline. The modeled air quality changes do not include other potential
effects of the rule, such as changes in power requirements to run treatment systems or changes in CCR
transportation, which were estimated separately as described in Section 8.1 and were found to be negligible as
described in section 8.4.
8.3.1.2 PM2.5 and Ozone Related Health Impacts
EPA estimated the benefits of Option B using the open-source environmental Benefits Mapping and Analysis
Program—Community Edition (BenMAP-CE) (Sacks et al., 2018). The Estimating PM2.5- and Ozone-
Attributable Health Benefits Technical Support Document (TSD) fully describes the Agency's approach for
identifying those health endpoints to evaluate as well as quantifying their number and value (U.S. EPA,
2023p). In the TSD, the reader can find the rationale for selecting health endpoints to quantify; the
demographic, health and economic data used; modeling assumptions; and our techniques for quantifying
uncertainty.
Estimating the health benefits of reductions in PM2.5 and ozone exposure begins with estimating the change in
exposure for each individual and then estimating the change in each individual's risks for those health
outcomes affected by exposure. The dollar benefit of reducing the risk of each adverse effect is based on the
exposed individual's willingness to pay (WTP) for the risk change, assuming that each outcome is
independent of one another. The greater the magnitude of the risk reduction from a given change in
concentration, the greater the individual's WTP, all else equal. The social benefit of the change in health risks
equals the sum of the individual WTP estimates across all of the affected individuals residing in the United
States. We conduct this analysis by adapting primary research—specifically, air pollution epidemiology
studies and economic value studies—from similar contexts. This approach is sometimes referred to as
"benefits transfer." Below we describe the procedure we follow for: (1) selecting air pollution health
endpoints to quantify; (2) calculating counts of air pollution effects using a health impact function; (3)
specifying the health impact function with concentration-response parameters drawn from the epidemiological
literature.
The BenMAP-CE tool quantifies the number and value of air pollution-attributable premature deaths and
illnesses resulting from changes in PM2.5 and ozone concentrations. Table 8-9 reports the ozone and PM2.5-
related human health impacts effects EPA quantified and those the Agency did not quantify in this analysis of
the final rule. The list of benefit categories not quantified is not exhaustive. And, among the effects
quantified, it might not have been possible to quantify completely either the full range of human health
impacts or economic values.
Table 8-9: Human Health Effects of Ambient Ozone and PM2.5
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Premature
mortality from
exposure to
PM2.5
Adult premature mortality based on cohort study
estimates and expert elicitation estimates (age 65-99 or
age 30-99)
V
V
PM ISA
Infant mortality (age <1)
V
V
PM ISA
Morbidity from
exposure to
PM2.5
Heart attacks (age > 18)
V
V
PM ISA
Hospital admissions—cardiovascular (ages 65-99)
V
V
PM ISA
Emergency department visits— cardiovascular (age 0-99)
V
V
PM ISA
Hospital admissions—respiratory (ages 0-18 and 65-99)
V
V
PM ISA
Emergency room visits—respiratory (all ages)
V
V
PM ISA
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Table 8-9: Human Health Effects of Ambient Ozone and PM2.5
Category
Effect
Effect
Quantified
Effect
Monetized
More
Information
Cardiac arrest (ages 0-99; excludes initial hospital and/or
emergency department visits)
V
V
PM ISA
Stroke (ages 65-99)
V
V
PM ISA
Asthma onset (ages 0-17)
V
V
PM ISA
Asthma symptoms/exacerbation (6-17)
V
V
PM ISA
Lung cancer (ages 30-99)
V
V
PM ISA
Allergic rhinitis (hay fever) symptoms (ages 3-17)
V
V
PM ISA
Lost work days (age 18-65)
V
V
PM ISA
Minor restricted-activity days (age 18-65)
V
V
PM ISA
Hospital admissions—Alzheimer's disease (ages 65-99)
V
V
PM ISA
Hospital admissions—Parkinson's disease (ages 65-99)
V
V
PM ISA
Other cardiovascular effects (e.g., other ages)
—
—
PM ISAb
Other respiratory effects (e.g., pulmonary function, non-
asthma ER visits, non-bronchitis chronic diseases, other
ages and populations)
PM ISAb
Other nervous system effects (e.g., autism, cognitive
decline, dementia)
—
—
PM ISAb
Metabolic effects (e.g., diabetes)
—
—
PM ISAb
Reproductive and developmental effects (e.g., low birth
weight, pre-term births)
—
—
PM ISAb
Cancer, mutagenicity, and genotoxicity effects
—
—
PM ISAb
Mortality from
exposure to
ozone
Premature mortality based on short-term study
estimates (age 0-99)
V
V
Ozone ISA
Premature mortality based on long-term study estimates
(age 30-99)
V
V
Ozone ISA3
Morbidity from
exposure to
ozone
Hospital admissions—respiratory causes (ages 0-99)
V
V
Ozone ISA
Emergency department—respiratory (ages 0-99)
V
V
Ozone ISA
Asthma onset (0-17)
V
V
Ozone ISA
Asthma symptoms/exacerbation (asthmatics age 2-17)
V
V
Ozone ISA
Allergic rhinitis (hay fever) symptoms (ages 3-17)
V
V
Ozone ISA
Minor restricted-activity days (age 18-65)
V
V
Ozone ISA
School absence days (age 5-17)
V
V
Ozone ISA
Decreased outdoor worker productivity (age 18-65)
—
—
Ozone ISAb
Metabolic effects (e.g., diabetes)
—
—
Ozone ISAb
Other respiratory effects (e.g., premature aging of lungs)
—
—
Ozone ISAb
Cardiovascular and nervous system effects
—
—
Ozone ISAb
Reproductive and developmental effects
—
—
Ozone ISAbc
a. EPA assesses these benefits qualitatively due to data and resource limitations for this analysis. In other analyses EPA quantified
these effects as a sensitivity analysis.
b. EPA assesses these benefits qualitatively because of insufficient confidence in available data or methods.
c. EPA assesses these benefits qualitatively because current evidence is only suggestive of causality or there are other significant
concerns over the strength of the association.
Source: EPA Analysis, 2024
Counts of attributable effects are quantified using a health impact function, which combines information
regarding the: concentration-response relationship between air quality changes and the risk of a given adverse
outcome; population exposed to the air quality change; baseline rate of death or disease in that population;
and air pollution concentration to which the population is exposed. When used to quantify PM2.5- or ozone-
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related effects, the functions combine effect estimates (i.e.. the (3 coefficients) from epidemiological studies,
which portray the relationship between a change in air quality and a health effect, such as mortality,
associated with changes in estimated PM2 5 or ozone concentrations (supplied using the IPM market model
simulations described above), population data, and baseline death rates for each county in each year. After
having quantified PM2.5- and ozone-attributable cases of premature death and illness, EPA estimated the
economic value of these cases using willingness to pay (WTP) and cost of illness (COI) measures.
EPA estimated the number of PM2 5-attributable premature deaths using effect estimates from two
epidemiology studies examining two large population cohorts: an analysis of Medicare beneficiaries (Wu et
al., 2020) and the National Health Interview Survey (NHIS) (Pope et al., 2019). For ozone-related premature
deaths, EPA uses one epidemiological study that examines the relationship between long-term exposure to
ozone and mortality (Turner et al., 2016) and two studies that examine the relationship between short-term
exposure to ozone and mortality (Katsouyanni et al., 2009; Zanobetti & Schwartz, 2008).
EPA quantifies and monetizes effects the Integrated Science Assessment (ISA) identifies as having either a
causal or likely-to-be-causal relationship with the pollutant. Relative to the 2015 ISA, the 2020 ISA for Ozone
reclassified the casual relation between short-term ozone exposure and total mortality, changing it from
"likely to be causal" to "suggestive of, but not sufficient to infer, a causal relationship." The 2020 Ozone ISA
separately classified short-term 03 exposure and respiratory outcomes as being "causal" and long-term
exposure as being "likely to be causal." When determining whether there existed a causal relationship
between short- or long-term ozone exposure and respiratory effects, EPA evaluated the evidence for both
morbidity and mortality effects. The ISA identified evidence in the epidemiologic literature of an association
between ozone exposure and respiratory mortality, finding that the evidence was not entirely consistent and
there remained uncertainties in the evidence base.
EPA continues to quantify premature respiratory mortality attributable to both short- and long-term exposure
to ozone because doing so is consistent with: (1) the evaluation of causality noted above; and (2) EPA's
approach for selecting and quantifying endpoints described in the TSD "Estimating PM2.5- and Ozone-
Attributable Health Benefits," which was recently reviewed by the U.S. EPA Science Advisory Board (U.S.
EPA, 2023p; U.S. EPA Science Advisory Board, 2024).
Projected impacts of the final rule (Option B) show both decreased and increased levels of PM2 5 and ozone,
depending on the year and location, compared to the baseline (see maps in Appendix J for details). Some
portion of the air quality and health benefits from the final rule occur in areas not attaining the PM2 5 or Ozone
National Ambient Air Quality Standards (NAAQS). The analysis does not account for possible interactions
between NAAQS compliance and the final rule, which introduces uncertainty into the benefits (and forgone
benefits) estimates. If the final rule increases or decreases primary PM2 5, SO2 and NOx emissions and
consequentially PM2 5 and/or ozone concentrations, these changes may affect compliance with existing
NAAQS standards and subsequently affect the actual benefits (and forgone benefits) of the final rule.
8.3.2 Results
EPA reports below the estimated number of avoided PM2 5 and ozone-related premature deaths and illnesses
in each year for Option B, the final rule, relative to the baseline along with the 95 percent confidence interval
(see Table 8-10). The number of avoided premature deaths and illnesses under the final rule are calculated
from the sum of individual reduced mortality and illness risk across the population in a given year. Table 8-11
reports the estimated economic value of avoided premature deaths and illness for each analysis year relative
to the baseline.
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Table 8-10: Estimated Avoided PM2.5 and Ozone-Related Premature Deaths and Illnesses by Year for the Final Rule (Option B), Compared to
Baseline (95 Percent Confidence Interval)
Category and Basis
2028a
2030a
2035a
2040a
2045a
2050a
Avoided premature death among adultsb
PM2.5
Wu etal. (2020)
67
19
100
29
8.5
8.2
(59 to 75)
(16 to 21)
(91 to 120)
(25 to 32)
(7.5 to 9.5)
(7.2 to 9.1)
Pope III etal. (2019)
140
38
210
57
17
16
(100 to 180)
(27 to 48)
(150 to 270)
(41 to 73)
(12 to 22)
(12 to 21)
Avoided infant mortality
PM2.5
Woodruff, Darrow & Parker,
2008
0.16
0.034
0.2
0.052
0.016
0.015
(-0.10 to 0.42)
(-0.022 to 0.088)
(-0.12 to 0.51)
(-0.033 to 0.13)
(-0.010 to 0.041)
(-0.0092 to 0.037)
Ozone
(03)
Katsouyanni et al. (2009)c d and
Zanobetti et al. (2008)d pooled
2.1
2
2.9
1.3
0.38
0.18
(0.83 to 3.3)
(0.80 to 3.1)
(1.2 to 4.5)
(0.52 to 2.0)
(0.15 to 0.60)
(0.074 to 0.29)
Turner et al. (2016)°
46
44
63
29
8.4
4.1
(32 to 59)
(31 to 57)
(44 to 82)
(20 to 37)
(5.8 to 11)
(2.8 to 5.3)
All other morbidity effects
Acute Myocardial Infarcation
2.3
0.57
3.5
0.95
0.29
0.29
(1.3 to 3.2)
(0.33 to 0.79)
(2.0 to 4.9)
(0.55 to 1.3)
(0.17 to 0.40)
(0.17 to 0.40)
Hospital admissions—cardiovascular
(PM25)
9.9
2.7
15
4.2
1.3
1.2
(7.2 to 13)
(2.0 to 3.4)
(11 to 19)
(3.0 to 5.3)
(0.91 to 1.6)
(0.89 to 1.6)
Hospital admissions—respiratory
(PM25)
6.9
1.5
9.6
2.6
0.81
0.82
(2.4 to 11)
(0.50 to 2.5)
(3.2 to 16)
(0.87 to 4.3)
(0.28 to 1.3)
(0.28 to 1.3)
Hospital admissions—respiratoryd (03)
6
5.7
8.1
3.6
1.1
0.59
(-1.6 to 13)
(-1.5 to 13)
(-2.1 to 18)
(-0.95 to 8.1)
(-0.29 to 2.5)
(-0.15 to 1.3)
Hospital admissions—Alzheimer's
Disease (PM2.5)
37
8
57
16
5
5.2
(28 to 46)
(5.9 to 9.9)
(42 to 71)
(12 to 20)
(3.8 to 6.3)
(3.9 to 6.5)
Hospital admissions— Parkinson's
Disease (PM2.5)
4.6
1.3
6.6
1.8
0.51
0.51
(2.3 to 6.7)
(0.66 to 1.9)
(3.4 to 9.8)
(0.90 to 2.6)
(0.26 to 0.75)
(0.26 to 0.75)
ED visits cardiovascular (PM2.5)
21
5.3
30
8.3
2.6
2.5
(-8.0 to 48)
(-2.0 to 12)
(-12 to 70)
(-3.2 to 19)
(-0.99 to 6.0)
(-0.97 to 5.9)
ED visits respiratory (PM2.5)
41
11
56
15
4.8
4.6
(8.1 to 86)
(2.1 to 23)
(11 to 120)
(2.9 to 31)
(0.95 to 10)
(0.91 to 9.7)
ED visits—respiratory' (03)
110
96
140
62
20
9.7
(31 to 240)
(26 to 200)
(38 to 290)
(17 to 130)
(5.6 to 43)
(2.7 to 20)
Cardiac Arrest (PM2.5)
1
0.28
1.5
0.39
0.12
0.12
(-0.42 to 2.3)
(-0.11 to 0.63)
(-0.59 to 3.3)
(-0.16 to 0.89)
(-0.050 to 0.28)
(-0.048 to 0.27)
Stroke (PM2.5)
4.2
1.2
6
1.6
0.48
0.47
(1.1 to 7.1)
(0.30 to 2.0)
(1.5 to 10)
(0.41 to 2.7)
(0.13 to 0.83)
(0.12 to 0.81)
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8: Air Quality-Related Benefits
Table 8-10: Estimated Avoided PM2.5 and Ozone-Related Premature Deaths and Illnesses by Year for the Final Rule (Option B), Compared to
Baseline (95 Percent Confidence Interval)
Category and Basis
2028a
2030a
2035a
2040a
2045a
2050a
Lung Cancer (PM2.5)
4.7
1.3
7
2
0.61
0.59
(1.4 to 7.8)
(0.39 to 2.2)
(2.1 to 12)
(0.59 to 3.3)
(0.18 to 1.0)
(0.18 to 0.98)
Hay Fever/Rhinitis (PM2.5)
1,000
250
1,300
370
120
110
(240 to 1,700)
(60 to 430)
(320 to 2,300)
(89 to 640)
(28 to 200)
(27 to 190)
Hay Fever/Rhinitis8 (03)
2,000
1,700
2,300
1,000
320
150
(1,000 to 2,900)
(900 to 2,500)
(1,200 to 3,400)
(550 to 1,500)
(170 to 470)
(78 to 220)
Asthma Onset (PM2.5)
160
38
200
56
18
17
(150 to 160)
(36 to 39)
(200 to 210)
(54 to 58)
(17 to 19)
(16 to 18)
Asthma onset6 (O3)
340
290
400
180
55
25
(300 to 390)
(250 to 330)
(340 to 450)
(150 to 200)
(48 to 63)
(22 to 29)
Asthma symptoms- Albuterol use
29,000
7,200
40,000
11,000
3,400
3,300
(PM2.5)
(-14,000 to 71,000)
(-3,500 to 18,000)
(-19,000 to 96,000)
(-5,200 to 26,000)
(-1,700 to 8,300)
(-1,600 to 8,000)
Asthma symptoms (O3)
64,000
55,000
74,000
33,000
10,000
4,700
(-7,900 to 130,000)
(-6,800 to 110,000)
(-9,100 to 150,000)
(-4,100 to 69,000)
(-1,300 to 21,000)
(-580 to 9800)
Minor restricted-activity days (PM2.5)
45,000
11,000
61,000
17,000
5,400
5,200
(37,000 to 53,000)
(9,200 to 13,000)
(49,000 to 72,000)
(13,000 to 20,000)
(4,300 to 6,300)
(4,300 to 6,200)
Minor restricted-activity daysd f (O3)
30,000
26,000
35,000
16,000
5,000
2,400
(12,000 to 47,000)
(10,000 to 40,000)
(14,000 to 55,000)
(6,300 to 25,000)
(2,000 to 8,000)
(950 to 3,800)
Lost work days (PM2.5)
7,700
1,900
10,000
2,800
910
890
(6,500 to 8,800)
(1,600 to 2,200)
(8,700 to 12,000)
(2,400 to 3,200)
(760 to 1,000)
(750 to 1,000)
School absence days (03)
23,000
20,000
27,000
12,000
3,700
1,700
(-3,200 to 48,000)
(-2,800 to 41,000)
(-3,800 to 56,000)
(-1,700 to 25,000)
(-520 to 7,700)
(-240 to 3,600)
a. Values rounded to two significant figures. Negative values indicate forgone benefits (i.e., the number of avoided cases under the final rule is smaller than in the baseline). Lower
bound of confidence interval represents the 95 percent confidence estimate that is lower in value than the point estimate, while upper bound represents the estimate that is higher in
value than the point estimate.
b. EPA also quantified changes in premature infant mortality from exposure to PM2.5 but the estimated change was less than 1 for all years analyzed.
c. Applied risk estimate derived from April-September exposures to estimates of ozone across the May-September warm season.
d. Converted ozone risk estimate metric from MDA1 to MDA8.
e. Applied risk estimate derived from June-August exposures to estimates of ozone across the May-September warm season.
f. Applied risk estimate derived from full year exposures to estimates of ozone across the May-September warm season.
g. Converted ozone risk estimate metric from DA24 to MDA8
Source: U.S. EPA Analysis, 2024
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8: Air Quality-Related Benefits
Table 8-11: Estimated Discounted Economic Value of Avoided Ozone and PIVfe.s-Attributable
Premature Mortality and Illness for Option B (millions of 2023$)
Year
2% Discount Rate3
2028
$1,100
and
$2,600
2030
$390
and
$1,200
2035
$1,600
and
$3,900
2040
$500
and
$1,300
2045
$150
and
$380
2050
$140
and
$310
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify that they are
two separate estimates. The estimates do not represent lower- and upper-bound estimates and should not be summed.
Source: U.S. EPA Analysis, 2024
8.4 Annualized Air Quality-Related Benefits of Regulatory Options
EPA calculated the present value (discounted to 2024) of estimated air quality-related benefits over the
analysis period of 2025-2049 and annualized these values to provide a measure that is comparable to the way
other benefit categories and social costs are reported.
Section 8.2.1 provides benefit estimates for Option B, the final rule, based on the changes in the electricity
generation profile projected in IPM. EPA mapped changes in emissions due to changes in electricity
generation for each IPM run year to individual years within the analysis period of 2025-2049 (see Table 8-1).
Because IPM outputs are available only for 2028 onward, EPA conservatively assumed no benefits associated
with changes in the profile of electricity generation between 2025 and 2027. However, changes in the profile
of electricity generation and EGU emissions are likely to occur as steam electric power generating plants start
incurring costs to comply with the revised ELG between 2025 and 2029, and assuming no emission
reductions for the first three years of this period understates the air quality-related benefits of the final rule.
For energy use and trucking, EPA estimated changes in air emissions corresponding to the year each plant is
estimated to implement changes in technology. These emissions are included in the analysis of climate change
benefits. As discussed in Section 8.3.1.1, however, the analysis of human health benefits does not account for
other changes in pollutant emissions associated with power requirements to operate wastewater treatment
systems or transport CCR or other solid waste. EPA considered adjusting the estimated benefits in proportion
to the average ratio between total air emissions of NOx and SO2 (Table 8-5) and EGU emissions associated
with changes in the electricity generation profile (Table 8-4) but concluded that such an adjustment would
have a negligible effect on the estimated human health benefit estimates given the comparably small
emissions changes associated with power requirements and trucking. Therefore, EPA is presenting unadjusted
values for the final rule below.
For the climate change benefits, EPA used the same discount rate used to develop SC-GHG values. For the
human health benefits, EPA used the LT mortality benefit estimate at a 2 percent discount rate from Table
8-11.
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8: Air Quality-Related Benefits
Table 8-12: Total Annualized Air Quality-Related Benefits of Final Rule (Option B), Compared to the
Baseline, 2025-2049 (Millions of 2023$)
SC-GHG near-term discount
rate
Climate Change Benefits3
PM2.5and Ozone Related
Human Health Benefits at
2% Discount Rate3
Total3
1.5%
$990
$1,600
$2,600
2.0%
$1,600
$1,600
$3,200
2.5%
$2,600
$1,600
$4,200
a. Values rounded to two significant figures.
b. Values calculated based on the LT mortality benefits estimates at a 2 percent discount rate.
Source: U.S. EPA Analysis, 2024
Because EPA did not run IPM for Options A and C, EPA did not analyze climate and human health benefits
for these regulatory options. To provide insight into the potential air quality-related benefits across regulatory
options, EPA estimated benefits for Options A and C by scaling Option B benefits in proportion to the total
social costs of the respective options (see Chapter 11 in this document). Specifically, EPA calculated the ratio
of the benefits to total social costs for Option B, then multiplied total social costs for Options A and C by this
ratio. The scaling factor provides an order of magnitude approximation of the benefits by assuming
proportionality between air-related benefits and total social costs.132 While air-related benefits are expected to
be driven primarily by changes in the profile of electricity generation (see Table 8-4 and Table 8-5) and the
generation profile is affected most directly by the incremental technology implementation costs, the effects
may not be linear.
Table 8-13 summarizes the annualized air quality-related benefits of the regulatory options for the climate
change benefits estimated using the SC-GHG under the 2 percent near-term Ramsey discount rate and for
human health benefits discounted using a 2 percent discount rate.
Table 8-13: Total Annualized Air Quality-Related Benefits of Regulatory Options Based on
Extrapolation from Option B, Compared to the Baseline, 2025-2049 (Millions of 2023$)
Regulatory Option
Climate Change Benefits
(SC-GHG 2% near-term
discount rate)3
PM2.5and Ozone Related
Human Health Benefits at
2% Discount Rate3 b
Total3
Option Ac
$1,200
$1,200
$2,400
Option B (Final Rule)
$1,600
$1,600
$3,200
Option Cc
$1,900
$2,000
$3,900
a. Values rounded to two significant figures.
b. These values reflect the air-related human health benefits based on the LT mortality benefits estimates from changes in PM2.5 and
ozone levels.
c. EPA estimated air quality-related benefits for Options A and C by multiplying the total social costs for each option (see Section 11.2)
by the ratio of [air quality-related benefits / total social costs] for Option B. For the purpose of scaling benefits, EPA used the subset of
social costs associated with the wastestreams modeled in the benefits analyses.
Source: U.S. EPA Analysis, 2024
132 For the 2015 final rule, EPA analyzed two options using IPM and therefore had air-related benefits for both options. Using the
benefit/cost ratio of one option to estimate benefits of the other option resulted in benefits that were +7 percent than benefits
derived from the IPM outputs.
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8: Air Quality-Related Benefits
8.5 Limitations and Uncertainties
Table 8-14 summarizes the limitations and uncertainties associated with the analysis of the air quality-related
benefits. The second column of the table provides a conclusion of how the limitation affects the magnitude of
the benefits estimate relative to expected actual benefits (i.e.. a source of uncertainty that has the effect of
underestimating benefits indicates an expectation that expected actual benefits are larger than the estimate).
The analysis also incorporates uncertainties associated with IPM modeling, which are discussed in Chapter 5
in the RIA (U.S. EPA, 2024e). See Appendix J for additional discussions of the uncertainty associated with
the air quality modeling methodology.
Table 8-14: Limitations and Uncertainties in Analysis of Air Quality-Related Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
EPA extrapolated Option B
benefits to Options A and C.
Uncertain
EPA ran IPM only for the final rule (Option B) and used the
results to extrapolate benefits of Options A and C, based on
the ratios of annualized benefits and annualized social costs.
Air emissions and air quality changes are unlikely to follow
differences in social costs in a linear fashion, however, given
how marginal changes in operating costs for individual units
may affect dispatch of EGUs within the broader regional and
national electricity markets. Because benefits are dependent
on magnitude and, for human health benefits, the spatial
distribution of emissions changes, projected benefits for
Options A and C are uncertain.
EPA assumed no changes in
air emissions associated
with shifts in the mix of
electricity generation in
2025-2027 relative to
baseline
Underestimate
The first IPM year is 2028. Changes in the profile of electricity
generation and EGU emissions are likely to occur as steam
electric power generating plants start incurring costs to
comply with the revised ELG between 2025 and 2029, and
assuming no emission reductions for the first three years of
this technology implementation period understates the air
quality-related benefits of the final rule. This is even though
the changes in air emissions predicted in IPM are modest in
2028.
The modeled air quality
assumes a static
apportionment of EGU
sources and static emissions
from other sources.
Uncertain
As discussed in Appendix J, the source apportionment
contributions are informed by the spatial and temporal
distribution of the emissions from each source tag as they
occur in the future year modeled case. Thus, the contribution
modeling results do not consider the effects of any changes
to spatial distribution of EGU emissions within a state-fuel
tag between the future year modeled case and the baseline
and final rule scenarios analyzed in this RIA.
The modeled air quality
surfaces used in the analysis
of human health benefits
only reflect changes in
emissions associated with
changes in the electricity
generation profile.
Uncertain
EPA developed the spatial fields based on IPM projected
emissions changes for Option B. These projections do not
include additional changes in NOx and S02 emissions
associated with power requirements to operate wastewater
treatment systems or trucking to transport CCR and other
solid waste. While these emissions changes could affect
human health benefit estimates, such effects are expected to
be small overall given that these emissions generally
represent less than 2 percent of total NOx and S02emissions
changes.
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8: Air Quality-Related Benefits
Table 8-14: Limitations and Uncertainties in Analysis of Air Quality-Related Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
The methodology used to
create ozone and PM2.5 Air
Quality surfaces do not
account for nonlinear
impacts of precursor
emissions changes
Uncertain
Appendix J provides further details on this limitation.
All fine particles, regardless
of their chemical
composition, are equally
potent in causing premature
mortality.
Uncertain
The PM ISA concluded reaffirmed the conclusion reached in
the 2009 ISA that "many PM2.5 components and sources are
associated with many health effects and that the evidence
does not indicate that any one source or component is
consistently more strongly related with health effects than
PM2.5 mass." (U.S. EPA, 2009c, 2022d).
Assumed "Cessation" lag
between the change in
PM2.5 and ozone exposures
and the total realization of
changes in long-term
mortality effects.
Uncertain
The approach distributes the incidences of premature
mortality related to PM2.5 exposures over the 20 years
following exposure based on the advice of EPA's Science
Advisory Board Health Effect Subcommittee (SAB-HES) (U.S.
EPA, 2004a). This distribution is also assumed for long-term
mortality from ozone exposure. This distribution affects the
valuation of mortality benefits at different discount rates.
The actual distribution of effects over time is uncertain.
Climate changes may affect
ambient concentrations of
pollutants.
Uncertain
Estimated health benefits do not account for the influence of
future changes in the climate on ambient concentrations of
pollutants (U.S. Global Change Research Program, 2016). For
example, recent research suggests that future changes to
climate may create conditions more conducive to forming
ozone; the influence of changes in the climate on PM2.5
concentrations are less clear (Fann et al., 2015). The
estimated health benefits also do not consider the potential
for climate-induced changes in temperature to modify the
relationship between ozone and the risk of premature death
(Jhun et al., 2014; Ren, Williams, Mengersen, et al., 2008;
Ren, Williams, Morawska, et al., 2008). Modeling used to
estimate air quality changes from this final rule used
meteorological fields representing conditions that occurred
in 2016.
EPA did not analyze all
benefits of changes in
exposure to NOx, S02, and
other pollutants emitted by
EGUs.
Underestimate
The analysis focused on adverse health effects related to
PM2.5 and ozone levels. There are additional benefits from
changes in levels of NOx, S02 and other air pollutants emitted
by EGUs (e.g., mercury, HCI). These include health benefits
from changes in ambient N02 and S02 exposure, health
benefits from changes in mercury deposition, ecosystem
benefits associated with changes in emissions of NOx, S02,
PM, and mercury, and visibility impairment.
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9 Estimated Changes in Drinking Water Treatment and Dredging Costs
By reducing pollutant loads in receiving and downstream waters, the regulatory options have the potential to
reduce costs associated with uses of these waters. For example, numerous studies have shown an unequivocal
link between source water quality and the cost of drinking water treatment and changes in sediment
deposition has the potential to affect the cost of maintaining reservoirs and navigational waterways. This
chapter provides EPA's analysis of the changes in drinking water treatment and dredging costs associated
with the regulatory options.
9.1 Changes in Drinking Water Treatment Costs
As summarized in Chapter 2, the regulatory options have the potential to affect drinking water treatment costs
by reducing loadings of steam electric pollutants to surface waters used for drinking water supply. EPA
implemented a treatment cost elasticity approach to quantify avoided treatment costs from reductions in total
nitrogen (TN) and total suspended solids (TSS). The treatment cost elasticity approach has been used in recent
research estimating the social cost of nutrient pollution (Andarge, 2022), and it is supported by the economics
literature on drinking water treatment costs (see Price and Heberling (2018) for a review of 15 U.S. and 9
non-U.S. studies that estimate quantitative relationships between source water quality and drinking water
treatment costs).
The treatment cost elasticity approach differs from the work breakdown structure models that are more
frequently used to estimate changes in drinking water treatment costs as part of EPA regulatory analysis
(Khera, Ransom & Speth, 2013). In comparison to treatment cost elasticity approaches, work breakdown
structure models require more information on drinking water system treatment practices, source water
parameters, and how treatment process costs vary with changes in source water characteristics at different
production levels. In contrast, treatment cost elasticities are based on empirical studies of water system
behavior and observed costs, and thus they make fewer assumptions on how water systems respond to
changes in source water characteristics.
Given the relatively small drinking water treatment savings expected to accrue from this rule, EPA
implemented the more straightforward treatment cost elasticity approach to estimate the magnitude of impacts
to drinking water systems. The use of a treatment cost elasticity approach in regulatory analysis may provide
a rationale for academic researchers to develop additional treatment cost elasticities for application in future
regulatory impact assessments.
9.1.1 Data and Methodology
EPA applied the following steps to calculate avoided drinking water treatment costs associated with
reductions in TN and TSS:
1. Identify water systems with surface water intakes downstream of steam electric power plant
discharges.
2. Estimate TN and TSS baseline levels and reductions in source waters using SPARROW
modelling.
3. Convert TSS levels and reductions to turbidity levels and reductions following U.S. EPA
(2009b).
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4. Compute the percent change in TN and turbidity for each regulatory option and all regulatory
periods.
5. Estimate drinking water treatment costs at affected water systems using the median cost by
system size and source type according to responses to the 2006 Community Water System
Survey.
6. Estimate the percent change in drinking water treatment costs associated with reductions in TN
and turbidity levels using the elasticities in Price and Heberling (2018).
Further detail on the identification of water systems with affected intakes and SPARROW modelling is
provided in Chapter 3. For this analysis, EPA excludes water systems that purchase their water from affected
systems to avoid potentially double-counting benefits, although this assumption likely underestimates true
cost savings across all affected systems as discussed in the limitations section of this chapter. In addition,
EPA assumes that the blending ratio across intakes is uniform, such that a water system with multiple affected
intakes will see the average loadings change across all intakes. Intakes that are not affected by steam electric
power plant discharges in the baseline are assumed to have loadings changes of zero. Table 9-1 summarizes
the average annual changes in TSS, TN, and TP loadings at 233 directly affected water systems.
Table 9-1: Average Percent Change in Source Water Concentrations of TN, TP, and TSS Compared
to Baseline
Regulatory Option
Period 1 (2025-2029)
Period 2 (2030 -2049)
TSS
TN
TP
TSS
TN
TP
Option A
-0.0006
-0.008
-0.004
-0.0012
-0.009
-0.004
Option B (Final Rule)
-0.0006
-0.008
-0.004
-0.0013
-0.009
-0.004
Option C
-0.0009
-0.010
-0.005
-0.0015
-0.009
-0.005
Source: U.S. EPA Analysis, 2024.
Next, EPA incorporated expenditure data from the 2006 Community Water System Survey (CWWS, U.S.
EPA, 2009a) to assign drinking water systems baseline treatment expenditures. The CWSS was specifically
designed to support regulatory and policy analysis. It collected revenue and expenditure information from
1,314 community water systems using a stratified random sampling procedure to ensure representativeness
across water system types; the surveyors ensured data accuracy by sending experts to smaller systems to assist
completion of certain information fields (U.S. EPA, 2009a). The 2006 CWSS is the most recently available
survey of water systems that collected information needed to estimate drinking water treatment costs
separately from other types of expenditure category that are unlikely to vary with source water characteristics.
In addition, the survey data has been used in the academic literature to assess the importance of source-water
characteristics on drinking water treatment costs (Price & Heberling, 2020).
EPA uses only variable treatment cost expenditures in this analysis because the regulatory options are
anticipated to reduce loadings of pollutants that affect ongoing treatment costs rather than all system cost
categories. In particular, while systems may have already invested in costly capital equipment to address
baseline pollutant loadings from steam electric power plant effluents, EPA assumes that these capital
expenditures are largely irreversible. For example, some systems may have already invested in ion exchange
treatment processes to contend with nitrates (Khera et al., 2021). The assumption of irreversibility of certain
costs leads to an underestimate of true cost savings, as discussed in the limitations section of this chapter.
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After removing observations with missing values, treatment cost information was available in the CWSS for
418 drinking water systems. Treatment expenditure information was updated from 2006 to 2023 price levels
using the Consumer Price Index. Treatment costs are presented across system source type and population
served category in Table 9-2, which also lists the count of systems affected by the regulation.
Table 9-2: Median Drinking Water Treatment Expenditures by System Size and Source
Category
Groundwater
Surface Water
Affected
System Size
Median
CWSS System
Median
CWSS System
Systems
Treatment Cost
Count
Treatment Cost
Count
Count
Population <100
$27,740
14
$20,890
18
11
Population 101-500
$19,272
10
$279,412
21
8
Population 501-3,300
$49,137
19
$436,572
24
27
Population 3,301-10,000
$840,203
11
$1,679,000
27
47
Population 10,001-50,000
$660,920
25
$3,108,194
36
80
Population 50,001-100,000
$3,237,274
14
$2,263,000
38
23
Population 100,001-500,000
$9,927,596
16
$11,101,192
104
27
Population >500,00
$16,371,051
2
$90,992,030
39
10
Notes: Surface-water systems include systems sourcing from groundwater under the influence of surface water. Dollars estimated
to 2023$
Source: 2006 CWWS, U.S. EPA, 2009a.
The treatment cost information for 418 systems in Table 9-2 with available cost data in the CWSS
demonstrate that water systems sourcing from surface water tend to have higher treatment costs than water
systems that source from groundwater. In addition, for every system size category there are at least 18 water
systems that source from surface water with which to infer cost data for systems affected by this regulation. In
general, median treatment costs tend to increase with system size, with the exception of surface-water systems
serving a population of 50,001-100,000. The CWSS masks identifiers for specific water systems, and so it is
not possible to link any surveyed systems to the systems that are affected by this regulatory action. As such,
EPA assigns median cost values to water systems based on their size and source category. All directly
affected systems source primarily from surface water. Median treatment costs are used instead of average
treatment costs to reduce the influence of outlier observations.
Finally, EPA computes avoided drinking water treatment costs ACostitp for drinking water system period t,
and each water quality parameterp as:
A Concentrationitp
ACostitn = rin * * Costit
p p Concentrationitp
Where t]p represents the elasticity between source water concentrations of water quality parameter p and
drinking water treatment costs. EPA uses a range of total nitrogen elasticity values from 0.05 to 0.06 to
represent average elasticity values in Price and Heberling (2018). The elasticity of 0.05 is derived from a non-
U.S. study without key controls, but it is included as a possible low-range elasticity estimate to better
characterize uncertainty. For TSS, EPA uses the range of turbidity elasticity estimates of 0.10 to 0.12 from the
same study to represent low and high estimates, where these values are derived exclusively from studies with
controls for key confounders.
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9.1.2 Results
Annualized avoided costs across all drinking water systems affected by the regulatory options for TN, TSS,
and both parameters combined are summarized at the 2 percent discount rate in Table 9-3 (EPA provides
summaries at the 3 percent and 7 percent discount rates in Appendix B). Annualized cost savings related to
TN loadings reductions under the final rule range from $357,000 to $429,000. For TSS, annualized cost
savings range from $103,000 to $124,000 under the final rule (Option B). Under the final rule, total cost
savings to drinking water systems range from $460,000 to $552,000. Further details on methods specific to
TN and TSS are described in turn below.
Table 9-3: Annualized Estimated Drinking Water Treatment Cost Savings under the Regulatory
Options, Compared to Baseline (Million 2023$, 2 Percent Discount Rate)
Regulatory Option
TN
TSS
Combined
Low Estimate
High Estimate
Low Estimate
High Estimate
Low Estimate
High Estimate
Option A
$0,357
$0,429
$0,092
$0,111
$0,449
$0,539
Option B (Final Rule)
$0,357
$0,429
$0,103
$0,124
$0,460
$0,552
Option C
$0,460
$0,552
$0,133
$0,160
$0,592
$0,711
Source: U.S. EPA Analysis, 2024.
9.1.2.1 Nutrients
As described in Chapter 2, the incremental cost of treating drinking water to address excess nutrients can be
substantial. Price and Heberling (2018) combined prior studies of the effect of nutrients on drinking water
treatment costs, showing that a 1 percent change in nitrogen (as nitrate) concentration in source water leads to
a 0.05 - 0.06 percent change in drinking water treatment costs, depending on whether the studies control for
key confounders. Similarly, the authors show that a 1 percent increase in phosphorus loadings increases
drinking water treatment costs by 0 - 0.02 percent, where findings of zero represent a null statistical
relationship between phosphorus loadings and drinking water treatment costs. Given the uncertainty in the
treatment cost elasticities for phosphorus and the possibility of double-counting cost savings across nitrogen
and phosphorus, EPA does not calculate cost changes with respect to phosphorus loading reductions. To
characterize uncertainty in the relationship between source water TN and drinking water treatment costs, EPA
employed a low elasticity estimate of 0.05 and a high elasticity estimate of 0.06, representing the range of
values reported in Price and Heberling (2018).
Table 9-4 presents illustrative average cost savings from reductions in TN across all years in the regulatory
analysis and for all drinking water systems in each size category. These values are intended to illustrate the
magnitude of impacts across system size, and as such they are only averaged across all years in the regulatory
period and not annualized or discounted. For most system size categories, the average annual cost savings are
relatively small both in absolute terms and in relation to annual drinking water treatment costs, ranging from
roughly 0.01 percent to 0.03 percent of drinking water treatment costs. These small impacts are in part due to
the small impacts of the regulatory options on source water concentrations of TN as reported in Table 9-1.
Table 9-4: Estimated Average System-Level Annual Changes in
TN under the Regulatory Options, Compared to Baseline (2023$
Drinking Water Treatment Costs for
System Size
Low Estimate
High Estimate
Option A
Option B
(Final Rule)
Option C
Option A
Option B
(Final Rule)
Option C
Population <100
-5
-5
-8
-6
-6
-9
Population 101-500
-57
-57
-93
-69
-69
-111
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BCAfor Supplemental Steam Electric Power Generating ELGs 9: Drinking Water Treatment and Dredging Cost Savings
Table 9-4: Estimated Average System-Level Annual Changes in
TN under the Regulatory Options, Compared to Baseline (2023$
Drinking Water Treatment Costs for
System Size
Low Estimate
High Estimate
Option A
Option B
(Final Rule)
Option C
Option A
Option B
(Final Rule)
Option C
Population 501-3,300
-353
-353
-387
-423
-423
-464
Population 3,301-10,000
-481
-481
-482
-578
-578
-578
Population 10,001-50,000
-1,527
-1,527
-1,692
-1,833
-1,833
-2,030
Population 50,001-100,000
-230
-230
-430
-276
-276
-516
Population 100,001-500,000
-914
-914
-1,338
-1,097
-1,097
-1,606
Population >500,00
-17,526
-17,526
-23,804
-21,031
-21,031
-28,565
Notes: The presented annual cost changes by system size are not discounted or annualized and represent only changes to system
treatment costs averaged over each year of the regulatory analysis period. Treatment costs include only ongoing operation and
maintenance costs and exclude investments in irreversible capital equipment.
Source: U.S. EPA Analysis, 2024.
9.1.2.2 Total Suspended Solids
Reducing TSS from steam electric power plant effluent is expected to affect the turbidity of source waters
used by drinking water systems. Water systems address TSS using chemical treatment with coagulants such
as alum or ferrous sulfate. Coagulant application varies in dosage depending on the influent concentrations of
TSS, and thus water system variable costs for coagulant purchases vary with TSS in source water. Treatment
for TSS also produces coagulated sediment in proportion to the influent concentration of TSS and the quantity
of coagulant added, and disposal of this coagulated sediment results in additional variable costs for drinking
water systems.
The impacts of TSS on drinking water treatment costs have been quantified in prior EPA regulatory analyses
including the 2004 Meat and Poultry Products Effluent Limitation Guidelines as well as the 2009 Effluent
Limitation Guidelines and Standards for the Construction and Development Industry (see U.S. EPA, 2004b,
2009b). To calculate the changes in drinking water treatment costs associated with TSS, EPA first converts
TSS to turbidity and then applies the elasticity for turbidity from Price and Heberling (2018).
EPA uses the elasticity associated with turbidity in Price and Heberling (2018) instead of TSS because the
elasticity with respect to TSS is based on only one study with key controls and three studies overall. In
addition, two of the underlying studies informing the TSS elasticity date from 1987 and 1988, and this
relationship may have changed significantly since these studies were conducted. Further, the range of
elasticity values for TSS is more disperse and less certain, suggesting that a 1 percent change in sediment
loads could lead to a 0.05 to 0.24 percent change in treatment costs. In contrast, Price and Heberling (2018)
calculate an elasticity with respect to turbidity that is much more precisely estimated across twelve studies;
these studies suggest that a 1 percent increase in turbidity leads to an increase in drinking water costs of 0.10
to 0.14 percent. Aside from quality of underlying elasticity estimates, EPA follows the precedent set in in
U.S. EPA (2009b) by estimating TSS-related changes to drinking water costs via changes in turbidity.
EPA converted TSS concentrations into nephelometric turbidity units (NTUs) using the method employed in
U.S. EPA (2009b). In the prior analysis, TSS was converted to turbidity using Equation 9-1.
Equation 9-1.
TSS
Turbidity = -g-
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BCAfor Supplemental Steam Electric Power Generating ELGs 9: Drinking Water Treatment and Dredging Cost Savings
Where turbidity is measured in NTUs and TSS is measured in mg/L. In U.S. EPA (2009b), b was set to a
constant equal to 0.8, 1.5, or 2.2 to reflect low, medium, and high estimates of the relationship between TSS
and turbidity. For this analysis, EPA produces a range of plausible TSS-turbidity conversions using only the
low and high constants of 0.8 and 2.2. EPA also selected a range of elasticities of 0.10 and 0.12 based on
studies that include key controls for confounding variables as reported in Price and Heberling (2018).
Table 9-5 presents illustrative average cost savings from reductions in TSS and associated turbidity across all
years in the regulatory analysis and for all drinking water systems in each size category. These values are
intended to illustrate the magnitude of impacts across system size, and as such they are only averaged across
all years in the regulatory period and not annualized or discounted. The average annual system-level cost
changes are relatively small in comparison to typical system-level treatment costs across all size categories.
Table 9-5: Estimated Average System-Level Annual Changes in Drinking Water Treatment Costs for
TSS under the Regulatory Options, Compared to Baseline (2023$)
Low Estimate
High Estimate
System Size
Option A
Option B
(Final Rule)
Option C
Option A
Option B
(Final Rule)
Option C
Population<100
-1
-1
-1
-1
-2
-2
Population 101-500
-17
-21
-22
-20
-26
-27
Population 501-3,300
-67
-81
-82
-80
-97
-99
Population 3,301-10,000
-406
-415
-531
-487
-498
-638
Population 10,001-50,000
-258
-291
-308
-309
-349
-370
Population 50,001-100,000
-78
-90
-110
-94
-107
-133
Population 100,001-500,000
-628
-697
-932
-754
-838
-1,119
Population >500,00
-3,291
-3,821
-5,312
-3,970
-4,610
-6,401
Notes: The presented annual cost changes by system size are not discounted or annualized and represent only changes to system
treatment costs averaged over each year of the regulatory analysis period. Treatment costs include only ongoing operation and
maintenance costs and exclude investments in irreversible capital equipment.
Source: U.S. EPA Analysis, 2024.
9.2 Changes in Dredging Costs
As summarized in Chapter 2 and in Table 3-1, the regulatory options could result in relatively small changes
in suspended solid discharges by steam electric power plants, which could have an impact on the rate of
sediment deposition in affected reaches, including navigable waterways and reservoirs that require dredging
for maintenance.
Navigable waterways, including rivers, lakes, bays, shipping channels and harbors, are an integral part of the
United States" transportation network. They are prone to reduced functionality due to sediment build-up,
which can reduce the navigable depth and width of the waterway (Clark, Haverkamp & Chapman, 1985;
Ribaudo, 2011). In many cases, costly periodic dredging is necessary to keep them passable. The regulatory
options could increase or reduce costs for government and private entities responsible for maintenance of
navigable waterways by changing the need for dredging.
Reservoirs serve many functions, including water storage for drinking, irrigation, and hydropower uses, flood
control, and recreation. Streams and rivers carry sediment into reservoirs, where it can settle and build up at a
recorded average rate of 1.2 billion kilograms per reservoir every year (USGS, 2009). Sedimentation reduces
reservoir capacity (Graf et al., 2010) and the useful life of reservoirs unless measures such as dredging are
taken to reclaim capacity (Clark, Haverkamp & Chapman, 1985; Hargrove et al., 2010; Miranda, 2017).
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BCAfor Supplemental Steam Electric Power Generating ELGs 9: Drinking Water Treatment and Dredging Cost Savings
9.2.1 Data and Methodology
In this analysis, EPA followed the same general methodology for estimating changes in costs associated with
changes in sediment depositions in navigational waterways and reservoirs that EPA used in the 2020 rule and
2023 proposal (U.S. EPA, 2020b, 2023b).133 The methodology utilizes information on historic dredging
locations, frequency of dredging, the amount of sediment removed, and dredging costs in conjunction with the
estimated changes in net sediment deposition (sedimentation minus erosion) in dredged waterways and
reservoirs under the regulatory options. Benefits are equal to avoided costs, calculated as the difference from
historical averages in total annualized dredging costs due to changes between the baseline and the regulatory
options.
9.2.2 Results
9.2.2.1 Estimated Changes in Navigational Dredging Costs
EPA identified 128 unique dredging jobs and 400 dredging occurrences134 within the affected reaches. This
corresponds to approximately 8 percent of the dredging occurrences with coordinates reported in the
Dredging Information System (U.S. Army Corps of Engineers, 2013). The recurrence interval for dredging
jobs ranged from one to 17 years across affected reaches and averaged 13 years. Dredging costs vary
considerably across geographic locations and dredging jobs from less than $1 per cubic yard at the Ohio River
(open channel)135 in Louisville, Kentucky to $534 per cubic yard at Herculaneum in St. Louis, Missouri.136
The median unit cost of dredging for the entire conterminous United States is $3.75 per cubic yard.
Table 9-6 presents low and high estimates of dredged sediment volume and dredging costs during the period
of 2025 through 2049 in navigational waterways that may be affected by steam electric plant discharges,
based on historical averages. EPA generated low and high estimates for navigational dredging by varying the
projected future dredging occurrence, including dredging frequency and job start as well as cost of dredging
for locations that did not report location specific costs (see U.S. EPA, 2015a, Appendix K for details).
Estimated total navigational dredging costs based on historical averages range from $57.3 million to
$130.8 million per year.
133 For the 2020 rule analysis, EPA made two improvements to the methodology used in 2015. First, dredging occurrences were
considered part of a single dredging job if the latitude and longitude coordinates were identical to within two decimal places.
Second, the 10th percentile and 90th percentile of costs and sediment dredged for dredging occurrences within USACE districts
were used to fill in missing values in the Low and Eligh scenarios. EPA also made one change to the methodology used to
estimate net sediment deposition at any given location in the reach network by using the TOTAL YIELD output variable from the
SPARROW models instead of INCTOTALYIELD. This change was implemented to be more inclusive of the upstream impacts
to affected COMIDs (INC TOTAL YIELD excluded upstream impacts).
134 Dredging jobs refer to unique sites/locations defined by the U.S. Army Corps of Engineers where dredging was conducted,
whereas dredging occurrences are unique instances when dredging was conducted and may include successive dredging at the
same location.
135 The cost per cubic yard at the Ohio River (open channel) is $0.37.
136 The second most expensive dredging job was $55.30 per cubic yard also in St. Louis.
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BCAfor Supplemental Steam Electric Power Generating ELGs 9: Drinking Water Treatment and Dredging Cost Savings
Table 9-6: Estimated Annual Average Navigational Dredging
Quantities and Costs at Affected Reaches Based on Historical
Averages
Total Sediment Dredged
(Millions Cubic Yards)
Annual Costs
(Millions of 2023$)
Low
High
Low
High
544.8
974.9
$57.3
$130.8
Source: U.S. EPA Analysis, 2024.
The difference between the estimated dredging costs using historical averages and costs resulting from the
reduction in sediment deposition under a regulatory option as compared to baseline represents the avoided
costs under the regulatory option. Table 9-7 presents estimated changes in navigational dredging costs for the
three regulatory options. Annualized benefits range from $3,800 to $4,700 under Option A and from $4,400
to $5,500 under Options B and C.
Table 9-7: Estimated Annualized Changes in Navigational Dredging Costs
under the Regulatory Options, Compared to Baseline
Total Reduction in Sediment
Annualized Avoided Costs
Regulatory Option
Dredged (Thousands Cubic
Yards)
(Millions of 2023$, 2% Discount
Rate)3
Low
High
Low
High
Option A
7.1
9.3
<$0.01
$0.01
Option B (Final Rule)
8.3
10.8
<$0.01
$0.01
Option C
8.5
11.0
<$0.01
$0.01
a. Positive values represent cost savings.
Source: U.S. EPA Analysis, 2024.
9.2.2.2 Estimated Changes in Reservoir Dredging Costs
EPA identified 2,009 reservoirs within the affected reaches with changes in sediment loads under at least one
of the regulatory options, corresponding to approximately one percent of the reservoirs represented in the
SPARROW models (Ator, 2019; Hoos & Roland Ii, 2019; Robertson & Saad, 2019; Wise, 2019; Wise,
Anning & Miller, 2019). EPA used USACE district regional estimates of average dredging costs to calculate
changes in reservoir dredging costs under the regulatory options. The median cost per cubic yard ranges from
$0.37 in the Louisville USACE District (Kentucky) to $52.42 in the Rock Island USACE District (Illinois),
with a median value of $8.99 for USACE districts which contain affected reservoirs. Table 9-8 presents low
and high estimates of the projected volume of sediment to be dredged during the period of 2025 through 2049
from these reservoirs as well as estimated annualized dredging costs, based on historical averages. The
estimated reservoir dredging costs based on historical averages range between $771.4 million and
$836.7 million.
Table 9-8: Estimated Annualized Reservoir Dredging Volume and
Costs based on Historical Averages
Total Sediment Dredged
(Millions Cubic Yards)
Annual Costs
(Millions of 2023$)
Low
High
Low
High
5,675.5
34,052.9
$771.4
$4,836.7
Source: U.S. EPA Analysis, 2024.
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BCAfor Supplemental Steam Electric Power Generating ELGs 9: Drinking Water Treatment and Dredging Cost Savings
The difference between the estimated dredging costs using historical averages and costs resulting from the
reduction in sediment deposition under a regulatory option as compared to baseline represents the avoided
costs for that regulatory option. Table 9-9 presents avoided costs for reservoir dredging under the regulatory
options, including low and high estimates. Annualized benefits are approximately $300 under Option A and
range from $300 to $400 under Options B and C.
Table 9-9: Estimated Total Annualized Changes in Reservoir Dredging Volume
and Costs under the Regulatory Options, Compared to Baseline
Total Reduction in Sediment
Annualized Avoided Costs3
Dredged
(Millions of 2023$ per Year, 2%
(Thousands Cubic Yards)
Discount Rate)
Regulatory Option
Low
High
Low
High
Option A
1.0
1.1
<$0.01
<$0.01
Option B (Final Rule)
1.2
1.3
<$0.01
<$0.01
Option C
1.2
1.4
<$0.01
<$0.01
a. Positive values represent cost savings.
Source: U.S. EPA Analysis, 2024.
9.3 Limitation and Uncertainty
Table 9-10 summarizes key uncertainties and limitations in the analysis of sediment dredging benefits. A
more detailed description is provided in Appendix K of the 2015 BCA (U.S. EPA, 2015a). Note that the effect
on benefits estimates indicated in the second column of the table refers to the magnitude of the benefits rather
than the direction (i.e.. a source of uncertainty that tends to underestimate benefits indicates expectation for
larger forgone benefits or for larger realized benefits). Uncertainties and limitations associated with
SPARROW model estimates of sediment deposition are discussed in the respective regional model reports
(Ator, 2019; Hoos & Roland Ii, 2019; Robertson & Saad, 2019; Wise, 2019; Wise, Anning & Miller, 2019).
Table 9-10: Limitations and Uncertainties in Analysis of Changes in Dredging Costs
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
EPA includes only TSS and TN in the
estimation of drinking water
treatment cost savings.
Underestimate
Drinking water systems may experience cost savings
due to TSS, nutrients, halogens, and metals, although
EPA lacks statistically reliable treatment cost
elasticities for parameters other than TSS and TN.
EPA assumes that only water
systems with surface water intakes
that are directly affected by steam
electric effluents have cost savings,
and so water purchasers indirectly
affected by the regulation do not
accrue cost savings.
Underestimate
Water systems that purchase water from directly-
affected systems may realize cost savings in the form
of lower water prices. These water systems are
excluded from the analysis due to uncertainties
surrounding price setting behavior among water
retailers.
EPA selects elasticity estimates in
Price and Heberling (2018) based on
models with complete controls.
Uncertain
Estimated relationships between source water
turbidity and TN levels are generally slightly higher
when including studies that did not incorporate key
controls for confounding variables.
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BCAfor Supplemental Steam Electric Power Generating ELGs 9: Drinking Water Treatment and Dredging Cost Savings
Table 9-10: Limitations and Uncertainties in Analysis of Changes in Dredging Costs
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
EPA imputes costs for all affected
systems based on a subset of public
systems available in the Community
Water System Survey (2006) and
uses median values rather than
average costs within size category.
Uncertain
The 2006 CWSS was designed to be a representative
sample of US drinking water systems, but it is possible
that drinking water systems sourcing from surface
waters affected by this regulation may have different
characteristics and higher or lower drinking water
treatment costs, on average. To the extent that
systems affected by the regulation differ in their
treatment costs from the 2006 CWSS systems, EPA
may over or under-estimate true cost savings.
EPA considers drinking water
treatment capital costs to be fully
realized and not recoverable, so
treatment cost savings only vary by
ongoing operations & maintenance
treatment costs.
Underestimate
Some capital expenditures can be reduced with
improvements in source water quality. For example,
water systems may be able to switch to less costly
treatment processes while still maintaining their water
quality objectives. These possible changes in capital
expenditures would result in an underestimate of true
cost savings.
Disposal costs for coagulated
sediment sludge may be significantly
higher if the sediment sludge also
contains other hazardous chemicals.
Underestimate
To the extent that sediment sludge from drinking
water systems affected by steam electric effluents
have more toxic chemicals than typical systems, EPA
expects that disposal costs for the sludge would be
higher.
The analysis of dredging cost savings
scales dredging volumes and costs in
proportion to the percent change in
sediment deposition in navigational
waterways and reservoirs.
Uncertain
EPA estimated a linear relationship between changes
in sediment deposition and dredging volumes and
costs which may not capture non-linear dynamics in
the relationships between sediment deposition and
dredging volumes and between dredging volumes and
costs.
The frequency of navigational
dredging is based on the proximity
of nearby dredging occurrences.
Uncertain
Because data in the U.S. Army Corps of Engineers
Database does not indicate whether different dredging
occurrences are part of a single dredging job, EPA
determined whether dredging occurrences are part of
a single dredging job by comparing their latitudinal and
longitudinal coordinates to two decimal places.
Changes in the precision of a job's coordinates would
affect the number of occurrences that are considered
part of the same dredging job. When precision is
changed to a single decimal place, the number of
occurrences that would be considered part of a single
dredging job increases (and vice-versa). A larger
(smaller) number of occurrences for a single dredging
job would increase (decrease) the frequency of
dredging and, as a result, total dredging costs over the
period of analysis.
The analysis of navigational
waterways includes only jobs
reported for 1998 through 2015.
Underestimate
Because some dredging jobs included in the U.S. Army
Corps of Engineers Database lack latitude and
longitude and the database does not use standardized
job names, EPA was only able to map approximately
64 percent of all recorded dredging occurrences. This
may lead to potential underestimation of historical
costs and changes in dredging costs under the
regulatory options.
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BCAfor Supplemental Steam Electric Power Generating ELGs 9: Drinking Water Treatment and Dredging Cost Savings
Table 9-10: Limitations and Uncertainties in Analysis of Changes in Dredging Costs
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
The analysis of reservoir dredging is
limited to reservoirs identified on
the NHD reach network.
Underestimate
The omission of other reservoirs could understate the
magnitude of estimated historical costs and changes in
reservoir dredging benefits if there are additional
reservoirs located downstream from steam electric
power plants.
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BCAfor Supplemental Steam Electric Power Generating ELGs
10: Total Monetized Benefits
10 Summary of Estimated Total Monetized Benefits
Table 10-1 summarizes the total annualized monetized benefits. Table 10-2 provides additional details on the
time profile of the monetized benefits.
The monetized benefits presented in these two tables do not account for all effects of the regulatory options,
including changes in certain cancer and non-cancer health risk (e.g., effects of halogenated disinfection
byproducts in drinking water, effects of cadmium on kidney functions and bone density), impacts of pollutant
load changes on T&E species habitat, etc. See Chapter 2 for a discussion of categories of benefits EPA did
not monetize. Chapter 4 through Chapter 9 provide more detail on the estimation methodologies for each
benefit category.
Table 10-1: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared to
Baseline (Millions of 2023$; 2 Percent Discount)
Benefit Category
Option A
Option B
(Final Rule)
Option C
Human Health
Changes in IQ losses in children from exposure to lead via fish
ingestion3
<$0.01
<$0.01
<$0.01
Changes in cardiovascular disease mortality from exposure to lead via
fish ingestion
$0.16-$0.43
$0.16-$0.43
$0.16-$0.45
Changes in IQ losses in children from exposure to mercury via fish
ingestion
$1.71
$1.98
$2.00
Changes in cancer risk from disinfection by-products in drinking water
$13.37
$13.37
$14.27
Ecological Conditions and Recreational Uses Changes
Use and nonuse values for water quality changes'5
$0.79
$1.24
$1.68
Market and Productivity Effects3
Changes in drinking water treatment costs
$0.45-$0.54
$0.46-$0.55
$0.59-$0.71
Changes in dredging costs3
<$0.01
<$0.01
<$0.01
Air Quality-Related Effects
Climate change effects from changes in greenhouse gas emissions0
$1,200
$1,600
$1,900
Human health effects from changes in NOx, S02, and PM2.5 emissions'^
$1,200
$1,600
$2,000
Total®
$2,417
$3,217
$3,919
Additional non-monetized benefits
Other avoided adverse health effects (cancer
and non-cancer) from reduced exposure to
pollutants discharged to receiving waters;
improvements in T&E species habitat and
potential effects on T&E species populations;
changes in property value from water quality
improvements; changes in ecosystem effects,
visibility impairment, and human health
effects from direct exposure to N02, S02, and
hazardous air pollutants.
a. "<$0.01" indicates that monetary values are greater than $0 but less than $0.01 million.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits. See Chapter 6 for details.
c. Values for air-quality related effects are rounded to two significant figures. EPA estimated the air quality-related benefits for
Option B. EPA extrapolated estimates of air quality-related benefits for Options A and C from the estimate for Option B that is
based on IPM outputs. For the purpose of scaling the air quality-related benefits, EPA used the subset of social costs associated
with the wastestreams modeled in the benefits analyses. See Chapter 8 for details.
d. The values reflect the LT estimates of human health effects from changes in PM2.5 and ozone levels. See Chapter 8 for details.
10-1
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10: Total Monetized Benefits
Table 10-1: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared to
Baseline (Millions of 2023$; 2 Percent Discount)
Benefit Category
Option A
Option B
(Final Rule)
Option C
e. Values for individual benefit categories may not sum to the total due to independent rounding.
Source: U.S. EPA Analysis, 2024
Table 10-2: Time Profile of Monetized Benefits (Millions of 2023$)
Year
Option A1'2
Option B (Final Rule)2
Option C1'2
2025
$3.2
$3.6
$4.5
2026
-$3.3
-$5.1
-$5.9
2027
-$5.9
-$9.5
-$11.4
2028
$4,904.4
$6,404.8
$7,906.1
2029
$4,904.7
$6,505.2
$7,906.5
2030
$2,908.1
$3,808.9
$4,709.9
2031
$3,008.8
$3,909.6
$4,710.6
2032
$5,409.5
$7,010.3
$8,611.3
2033
$5,410.1
$7,110.9
$8,711.9
2034
$5,510.7
$7,211.5
$8,812.6
2035
$5,511.3
$7,212.1
$8,813.1
2036
$5,511.7
$7,212.5
$8,913.6
2037
$5,612.2
$7,313.0
$8,914.1
2038
$1,412.6
$1,843.5
$2,214.5
2039
$1,413.1
$1,854.0
$2,315.0
2040
$1,413.6
$1,854.4
$2,315.5
2041
$1,414.0
$1,864.9
$2,316.0
2042
$584.5
$765.4
$936.5
2043
$594.9
$775.8
$947.0
2044
$595.4
$776.3
$957.5
2045
$605.9
$786.8
$958.0
2046
$606.4
$787.3
$968.6
2047
$616.8
$797.7
$979.0
2048
$397.2
$508.1
$619.5
2049
$397.6
$518.5
$629.9
Annualized Benefits Accounted in
$2,410.6
$3,211.3
$3,912.4
2025-2049, 2%
Annualized Value of Additional
$6.3
$6.3
$6.7
Benefits in 2050-2115, 2%3
Total Annualized Benefits, 2%
$2,417
$3,218
$3,919
1 EPA estimated the air quality-related benefits for Option B. EPA extrapolated estimates of air quality-related benefits for Options A
and C from the estimate for Option B that is based on IPM outputs. For the purpose of scaling the air quality-related benefits, EPA
used the subset of social costs associated with the wastestreams modeled in the benefits analyses.
2 Values for air-quality related effects included in the total for each year are rounded to two significant figures.
3 Accounts for avoided bladder cancer benefits in 2050-2115 from reductions in TTHM exposure in 2025-2049
Source: U.S. EPA Analysis, 2024.
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11: Total Social Costs
11 Summary of Total Social Costs
This chapter discusses EPA's estimates of the costs to society under the regulatory options. Social costs
include costs incurred by both private entities and the government (e.g., in implementing the regulation). As
described further in Chapter 10 of the RIA (U.S. EPA, 2024e), EPA did not evaluate incremental baseline
costs, and associated cost savings to state governments which would no longer have to evaluate and
incorporate best professional judgment into NPDES permits under the regulatory options. Consequently, the
only category of costs used to calculate social costs are estimated technology implementation costs for steam
electric power plants.
11.1 Overview of Costs Analysis Framework
The RIA (Chapter 3) presents EPA's development of costs for the estimated 858 steam electric power plants
within the scope of the final rule (U.S. EPA, 2024e). These costs (pre-tax) are used as the basis of the social
cost analysis.137 A subset of these plants (between 141 and 170, depending on the regulatory option) incur
non-zero incremental costs under the final rule (Option B), as compared to the baseline. The range
corresponds to the lower and upper bound cost scenarios that reflect the uncertainty associated with costs for
meeting limits for unmanaged CRL. As described in the RIA, the lower bound scenario reflects the sum of
point estimates of costs to meet FGD wastewater, BA transport water, legacy wastewater, and CRL limits,
plus the lower bound estimate of the cost to meet limits for unmanaged CRL, whereas the upper bound
scenario reflects the sum of the point estimates for the four wastestreams plus the upper bound estimate of the
cost to meet limits for unmanaged CRL.
As described earlier in Chapter 1, EPA estimated that steam electric power plants, in the aggregate, will
implement control technologies to meet revised limits for FGD wastewater, BA transport water, and CRL
between 2025 and 2029. EPA estimated that plants will implement control technologies to meet legacy
wastewater limits in 2044. For the analysis of social costs, EPA estimated a plant- and year-explicit schedule
of technology implementation cost outlays over the period of 2025 through 2049.138 This schedule accounts
for retirements and repowerings by zeroing-out O&M costs to operate BA and FGD treatment systems in
years following unit retirement or repowering, but continued O&M costs for CRL since treatment of the CRL
wastewater is expected to continue even after a unit ceases to generate electricity. After creating a cost-
incurrence 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.
Specifically, EPA assumed that capital costs for compliance technology equipment, installation, site
preparation, construction, and other upfront, non-annually recurring outlays associated with compliance with
the regulatory options are incurred in the modeled compliance year for each plant. Annual fixed O&M costs,
including regular annual monitoring, and annual variable O&M costs (e.g., operating labor, maintenance labor
and materials, electricity required to operate wastewater treatment systems, chemicals, combustion residual
137 As discussed in Section 3.1.1 of the RIA (U.S. Environmental Protection Agency. (2024e). Regulatory Impact Analysis for
Supplemental Effluent Limitations Guidelines and Standards for the Steam Electric Power Generating Point Source Category.
(821-R-24-007). ), EPA did not select the lowest-cost technology for five plants to meet zero-discharge limits for CRL. This
resulted in the estimated total compliance costs for Option B and Option C being overstated by approximately $6 million
(1.5 percent of total costs) on an after-tax basis.
138 The period of analysis extends through 2049 to capture a substantive portion of the life of the wastewater treatment technology at
any steam electric power plant (20 or more years), and the last year of technology implementation (2029).
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waste transport and disposal operation and maintenance) are incurred each year. Other non-annual recurring
costs are incurred at specified intervals of 5, 6, or 10 years. See Section 3.1.2 in the RIA for details.
Following the approach used for the analyses of the 2015 and 2020 rules, and 2023 proposal (U.S. EPA,
2015a, 2020b, 2023k), after technology implementation costs were assigned to the year of occurrence, the
Agency adjusted these costs for change between 2023 (the year when costs were estimated) 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.
The CCI and ECI adjustment factors were developed only through the year 2031; 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 approach, given that capital expenditures will occur by 2029 and
the uncertainty of long-term future price projections.
After developing the year-explicit schedule of total costs and adjusting them for predicted real change to the
year of their incurrence, EPA calculated the present value of these cost outlays as of the anticipated rule
promulgation year by discounting the cost in each year back to 2024 using a 2 percent discount rates,
following OMB regulatory analysis guidance in Circular A-4 (OMB, 2023). EPA calculated the constant
annual equivalent value (annualized value), again using the 2 percent discount rate, over a 25-year social cost
analysis period. EPA assumed no re-installation of wastewater treatment technology during the period
covered by the social cost analysis, i.e.. upfront capital costs are incurred only once.
To assess the economic costs of the regulatory options to society, EPA relied first on the estimated costs to
steam electric power plants for the labor, equipment, material, and other economic resources needed to
comply with the regulatory options (see U.S. EPA, 2024e for details). In this analysis, 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. EPA assumed in its social cost analysis that the regulatory
options do not affect the aggregate quantity of electricity that will be sold to consumers and, thus, that the
rule's social cost will include no changes in consumer and producer surplus from changes in electricity sales
by the electricity industry in aggregate. Given the small impact of the regulatory options on electricity
production cost for the total industry (see RIA Chapter 5, U.S. EPA, 2024e) and relatively inelastic electricity
demand with respect to price, at least in the short term (Burke & Abayasekara, 2018; Bernstein and Griffin
(2005)), this approach is reasonable for the social cost analysis (for more details on the impacts of the
regulatory options on electricity production cost, see RIA Chapter 5). 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 rule promulgation and technology implementation
139
years.
139 The specific assumptions of when each cost component is incurred can be found in Chapter 3 of the RIA (U.S. Environmental
Protection Agency. (2024a). Benefit and Cost Analysis for Supplemental Effluent Limitations Guidelines and Standards for the
Steam Electric Power Generating Point Source Category. (821-R-24-006). ).
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Finally, as discussed in Chapter 10 of the RIA (U.S. EPA, 2024e; see Section 10.7: Paperwork Reduction Act
of 1995), the regulatory options will not result in additional administrative costs for plants to implement, and
state and federal NPDES permitting authorities to administer, the rule. The social cost analysis therefore
focuses on the resource cost of compliance as the only direct cost incurred by society as a result of the final
rule.
11.2 Key Findings for Regulatory Options
Table 11-1 presents annualized incremental costs for the analyzed regulatory options, as compared to the
baseline.
Table 11-1: Summary of Estimated Incremental Annualized Costs for Regulatory Options (Millions of
2023$, 2 Percent Discount Rate)
Regulatory Option
Annualized Costs
Lower Bound
Upper Bound
Option A
$433.2
$960.9
Option B (Final Rule)
$536.2
$1,063.9
Option C
$622.4
$1,150.1
Source: U.S. EPA Analysis, 2024.
Table 11-2 and Table 11-3 provide additional detail on the social cost calculations for the lower bound and
upper bound cost scenarios, respectively. The tables compile, for each regulatory option, the assumed time
profiles of technology implementation costs incurred, relative to the baseline, as well as the annualized costs.
The maximum technology implementation outlays differ across the options but are incurred over the years
2025 through 2029, i.e., during the estimated window (defined as Period 1 in Section 3.2.1) when steam
electric power plants are expected to implement wastewater treatment technologies for FGD wastewater, BA
transport water, and CRL. Outlays increase in 2044 due to the implementation of treatment to meet legacy
wastewater limits as plants are assumed to start dewatering ponds in that year.
Table 11-2: Time Profile of Costs to Society (Millions of 2023$) - Lower Bound
Year
Option A
Option B (Final Rule)
Option C
2025
$1,096.8
$1,240.0
$1,349.2
2026
$613.0
$748.9
$1,009.8
2027
$1,010.1
$1,123.4
$1,328.2
2028
$1,152.8
$1,448.5
$1,679.5
2029
$718.9
$852.0
$1,027.6
2030
$285.3
$345.3
$399.1
2031
$293.2
$353.2
$406.4
2032
$293.2
$352.6
$405.8
2033
$292.2
$352.2
$405.9
2034
$294.4
$353.0
$405.9
2035
$293.0
$352.4
$405.9
2036
$286.3
$347.2
$401.9
2037
$290.4
$350.4
$403.5
2038
$289.8
$349.2
$402.4
2039
$288.7
$348.7
$402.3
2040
$290.9
$349.5
$402.4
2041
$289.4
$348.9
$402.4
2042
$286.2
$347.1
$401.8
2043
$289.7
$349.7
$402.8
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11: Total Social Costs
Table 11-2: Time Profile of Costs to Society (Millions of 2023$) - Lower Bound
Year
Option A
Option B (Final Rule)
Option C
2044
$289.8
$803.7
$856.9
2045
$288.7
$376.6
$430.3
2046
$290.9
$377.5
$430.3
2047
$290.1
$377.5
$431.0
2048
$286.3
$375.2
$429.8
2049
$289.7
$377.6
$430.8
Annualized Costs, 2%
$433.2
$536.2
$622.4
Source: U.S. EPA Analysis, 2024.
Table 11-3: Time Profile of Costs to Society (Millions of 2023$) - Upper Bound
Year
Option A
Option B (Final Rule)
Option C
2025
$1,853.6
$1,996.8
$2,106.0
2026
$1,011.7
$1,147.5
$1,408.5
2027
$1,772.3
$1,885.6
$2,090.4
2028
$2,967.8
$3,263.6
$3,494.5
2029
$1,649.2
$1,782.3
$1,957.9
2030
$692.3
$752.3
$806.1
2031
$709.9
$769.8
$823.0
2032
$708.5
$768.0
$821.2
2033
$707.6
$767.5
$821.2
2034
$710.1
$768.7
$821.5
2035
$709.0
$768.5
$822.0
2036
$699.7
$760.6
$815.3
2037
$707.0
$767.0
$820.1
2038
$705.1
$764.6
$817.7
2039
$704.1
$764.0
$817.7
2040
$706.6
$765.2
$818.0
2041
$705.5
$765.0
$818.5
2042
$699.6
$760.5
$815.2
2043
$706.4
$766.3
$819.5
2044
$705.1
$1,219.1
$1,272.2
2045
$704.1
$792.0
$845.6
2046
$706.6
$793.1
$846.0
2047
$706.2
$793.6
$847.1
2048
$699.7
$788.6
$843.2
2049
$706.4
$794.2
$847.4
Annualized Costs, 2%
$960.9
$1,063.9
$1,150.1
Source: U.S. EPA Analysis, 2024.
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12 Benefits and Social Costs
This chapter compares total monetized benefits and costs for the regulatory options. Benefits and costs are
compared on two bases: (1) incrementally for each of the options analyzed as compared to the baseline and
(2) incrementally across options. The comparison of benefits and costs also satisfies the requirements of E.O.
12866: Regulatory Planning and Review (58 FR 51735, October 4, 1993), as amended by E.O. 13563:
Improving Regulation and Regulatory Review (76 FR 3821, January 21, 2011) and E.O. 14094: Modernizing
Regulatory Review (88 FR 21879, April 11, 2023). See Chapter 9 in the RIA for details (U.S. EPA, 2024e).
12.1 Comparison of Benefits and Costs by Option
Chapters 10 and 11 present estimates of the benefits and costs, respectively, for the regulatory options as
compared to the baseline. Table 12-1 presents EPA's estimates of benefits and costs of the regulatory options,
annualized over 25 years. The table provides an approximate comparison of total monetized benefits and total
costs for the final rule due to differences in wastestreams included in the two analyses. Thus, the benefits
analysis omits loading reductions associated with meeting limits for unmanaged CRL and legacy wastewater,
even though the costs for meeting these limits are included in the total costs. EPA expects that including these
wastestreams in the analysis of benefits would increase the monetized benefits.
Table 12-1: Total Estimated Annualized Benefits and Costs by Regulatory Option and Discount
Rate, Compared to Baseline (Millions of 2023$, 2 Percent Discount Rate)
Regulatory Option
Total Monetized Benefitsab
Total Costs3
Lower Bound
Upper Bound
Option A
$2,417
$433.2
$960.9
Option B (Final Rule)
$3,217
$536.2
$1,063.9
Option C
$3,919
$622.4
$1,150.1
a. EPA's benefits analysis did not account for the effects of loading reductions associated with limits for unmanaged CRL and
legacy wastewater, whereas the total costs account for outlays for meeting these limits. See Chapter 11 for details on the lower
and upper bound cost scenarios.
b. EPA estimated the air quality-related benefits for the final rule (Option B) only. EPA extrapolated estimates of air quality-
related benefits for Options A and C from the estimate for Option B that is based on IPM outputs. See Chapter 8 for details.
Source: U.S. EPA Analysis, 2024.
12.2 Analysis of Incremental Benefits and Costs
In addition to comparing estimated benefits and costs for each regulatory option relative to the baseline, as
presented in the preceding section, EPA also estimated 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 and determines whether costs or benefits are greater for a given
option and by how much. In contrast, incremental analysis looks at the differential relationship of benefits and
costs across options and poses a different question: as increasingly more costly options are considered, by
what amount do benefits, costs, and net benefits (i.e.. benefits minus costs) change from option to option?
Incremental net benefit analysis provides insight into the net gain to society from imposing increasingly more
costly requirements.
EPA conducted the incremental net benefit analysis by calculating the change in net benefits, from option to
option, in moving from the least stringent option to successively more stringent options, where stringency is
determined based on total pollutant loads. As described in Chapter 1, the regulatory options differ in the
technology basis for different wastestreams. Thus, the difference in benefits and costs across the options
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derives from the characteristics of the wastestreams controlled by an option, the relative effectiveness of the
control technology in reducing pollutant loads, the timing of control technology implementation, and the
distribution and characteristics of steam electric power plants and of the receiving reaches. As was the case
for the comparison in Table 12-1, the calculation of net benefits is also an approximation due to the
differences in wastestreams included in the analysis of the benefits versus the costs.
As reported in Table 12-2, all options have positive net annual monetized benefits, meaning benefits exceed
costs. This is true despite the omission of additional loading reductions from unmanaged CRL and legacy
wastewater from the monetized benefits analysis. Net annual monetized benefit estimates range from $2,153
million under Option A to $2,681 million under Option C. Incremental net annual monetized benefit values
are also positive across all options, which means that the increase in benefits under the more stringent options
is larger than the increase in costs. The incremental net annual monetized benefits of moving from Option A
to the final rule (Option B) is $698 million, whereas the incremental net benefits of moving the final rule
(Option B) to Option C is $615 million.
Table 12-2: Analysis of Estimated Incremental Net Benefit of the Regulatory Options, Compared
to Baseline and to Other Regulatory Options (Millions of 2023$, 2 Percent Discount Rate)
Regulatory Option
Net Annualized Monetized Benefitsa b
Incremental Net Annualized Monetized
Benefit sc
Lower Bound
Upper Bound
Lower Bound
Upper Bound
Option A
$1,983
$1,456
NA
NA
Option B (Final Rule)
$2,681
$2,153
$698
$698
Option C
$3,296
$2,769
$615
$615
NA: Not applicable for Option A
a. Net benefits are calculated by subtracting total annualized costs from total annual monetized benefits, where both costs
and benefits are measured relative to the baseline.
b. EPA estimated the air quality-related benefits for the final rule (Option B) only. EPA extrapolated estimates of air quality-
related benefits for Options A and C from the estimate for Option B that is based on IPM outputs. See Chapter 8 for details.
c. Incremental net benefits are equal to the difference between net benefits of an option and net benefits of the previous,
less stringent option.
Source: U.S. EPA Analysis, 2024.
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13: Cited References
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Appendix A: Changes to Benefits Analysis
A Changes to Benefits Methodology since 2020 Final Rule Analysis
The table below summarizes the principal methodological changes EPA made to analyses of the benefits of
the final rule regulatory options, as compared to the analyses of the 2020 final rule (U.S. EPA, 2020b) and
2023 proposal (U.S. EPA, 2023c).
Table A-1: Changes to Benefits Analysis Since 2020 Final Rule
Benefits
Category and
Analysis
Component
Analysis Component
[2020 final rule analysis value]
Changes to Analysis for
Proposed Rule, relative to
2020 Final Rule
Changes to Analysis for 2024
Final Rule, relative to 2023
Proposed Rule
General inputs and pollutant loads
Universe of
plants, EGUs, and
receiving reaches
Analysis includes loadings for
all coal-fired units operating as
of 2020. The analysis also
reflects other updates to the
steam electric industry profile
through the end of 2019,
including the timing of
projected retirements and
refueling projects and existing
treatment technologies.
Analysis includes updates to
the steam electric industry
profile through the end of
2021, including the timing of
projected retirements and
refueling projects and existing
treatment technologies. See
TDD for details (U.S. EPA,
2023o).
Analysis includes further
updates to the steam electric
industry profile through August
25, 2023, including the timing
of projected retirements and
refueling projects and existing
treatment technologies. See
TDD for details (U.S. EPA,
2024f).
General pollutant
loadings and
concentrations
Affected reaches based on
immediate receiving reaches
and flow paths in medium-
resolution NHD.
Updated immediate receiving
reaches (and associated
downstream reaches) for
selected plants. Discharges
include CRL discharge outfalls.
Updated immediate receiving
reaches (and associated
downstream reaches) for
selected plants. Discharges
include legacy wastewater
discharge outfalls.
SPARROW modeling of nutrient
and sediment concentrations in
receiving and downstream
reaches based on the most
recent five regional SPARROW
models that use the medium-
resolution NHD stream
network.
No change.
No change.
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Appendix A: Changes to Benefits Analysis
Table A-1: Changes to Benefits Analysis Since 2020 Final Rule
Benefits
Category and
Analysis
Component
Analysis Component
[2020 final rule analysis value]
Changes to Analysis for
Proposed Rule, relative to
2020 Final Rule
Changes to Analysis for 2024
Final Rule, relative to 2023
Proposed Rule
Uses the annual average
loadings for two distinct
periods during the analysis:
2021-2028 and 2029-2047, with
pre-technology implementation
loads set equal to current loads
and post-retirement or
repowering loads set to zero.
The two analysis periods are
2025-2029 and 2030-2049.
No change.
Water quality
index
Expresses overall water quality
changes using a seven-
parameter index that includes
subindex curve parameters for
nutrients and sediment based
on the regional SPARROW
models.
No change.
EPA used updated subindex
curves for TN, TP, and TSS
derived using NARS water
quality assessment data and
defined at the level of the
associated NARS ecoregions.
Population and
socioeconomic
characteristics
Based on 2017 ACS data.
Based on 2019 ACS data.
Based on 2021 ACS data.
Human health benefits from changes in exposure to halogenated disinfection byproducts in drinking water
Public water
systems affected
by bromide
discharges
Modeled changes in bromide
concentrations in source water
of public water systems.
Modeled changes in bromide
concentrations in source water
of public water systems and
total trihalomethane
concentrations in drinking
water.
No change from 2023 proposal.
SDWIS database
with PWS
network and
population
served
information
SDWIS 2020Q1 data
SDWIS 2021Q1 data
SDWIS 2022Q4 data
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Appendix A: Changes to Benefits Analysis
Table A-1: Changes to Benefits Analysis Since 2020 Final Rule
Benefits
Category and
Analysis
Component
Analysis Component
[2020 final rule analysis value]
Changes to Analysis for
Proposed Rule, relative to
2020 Final Rule
Changes to Analysis for 2024
Final Rule, relative to 2023
Proposed Rule
Lifetime changes
in incidence of
bladder cancer
Qualitative discussion. EPA
received public comments that
further evaluation of certain
DBPs should be completed and
that the analysis at proposal
should be subjected to peer
review. EPA acknowledges that
further study in this area
should be conducted, including
peer review of the model used
at proposal. EPA will continue
to evaluate the scientific data
on the health impacts of DBPs.
Applied lifetime risk model to
estimate changes in bladder
cancer incidence in population
served by public water systems.
The modeling approach is
generally the same EPA used
for the 2019 proposed rule
analysis. It is also consistent
with that in a study by
Weisman et al. (2022) which
also applied the dose-response
information from Regli et al.
(2015) with more recent DBP
data to estimate the potential
number of bladder cancer cases
associated with chlorination
DBPs in drinking water.
Weisman et al. (2022) found
that the weight of evidence
supporting causality further
increased since Regli et al.,
2015.
No change.
Monetization of
changes in
incidence of
bladder cancer
Because EPA did not calculate
changes in incidence of bladder
cancer, the Agency was unable
to monetize this effect.
Mortality valued using VSL (U.S.
EPA, 2010, updated 2014).
Morbidity valued based on COI
(Greco et al., 2019).
Mortality valued using VSL (U.S.
EPA, 2010, updated 2014).
Morbidity valued based on
WTP from Bosworth, Cameron
and DeShazo (2009).
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Appendix A: Changes to Benefits Analysis
Table A-1: Changes to Benefits Analysis Since 2020 Final Rule
Benefits
Category and
Analysis
Component
Analysis Component
[2020 final rule analysis value]
Changes to Analysis for
Proposed Rule, relative to
2020 Final Rule
Changes to Analysis for 2024
Final Rule, relative to 2023
Proposed Rule
Non-market benefits from water quality improvements
WTP for water
quality
improvements
Benefits valued using a MRM
EPA added 10 new studies to
the 2015 meta-data, revised
existing observations as needed
to improve consistency within
the dataset, and re-estimated
the MRM (see ICF, 2022b for
details). Similar to the 2015
MRM, the model includes
spatial characteristics of the
affected water resources: size
of the market, waterbody
characteristics (length and
flow), availability of substitute
sites, and land use type in the
adjacent counties.
Variables characterizing the
availability of substitute sites,
size of the market, and land-
use were revised based on
changes in the universe of
receiving reaches and CBGs
included in the analysis.
No change, except from
updates to the model scope
and variables to reflect changes
in the universe of receiving
reaches and CBGs.
Effects on T&E
species
Categorical analysis based on
designated critical habitat
overlap/proximity to reaches
with estimated changes in
NRWQC exceedances.
EPA updated the list of species
included in the analysis based
on the 2020 ECOS online
database (U.S. FWS, 2020d).
EPA also relied on the habitat
range of T&E species in
determining whether reaches
downstream from steam
electric power plant outfalls
intersect species habitat (U.S.
FWS, 2020b), rather than
"critical habitat" as the term is
defined in the ESA. EPA
included all species categorized
as having higher vulnerability to
water pollution in its analysis
(see Chapter 7 and Appendix 1
for details). The only exception
is species endemic to springs
and headwaters.
EPA updated the list of species
based on critical habitats as of
January 4, 2024, as well as the
scope of the analysis to reflect
additional receiving waters. At
this time, EPA also adjusted
analysis to remove species
delisted by the USFWS in 2023
due to extinction (U.S. Fish &
Wildlife Service, 2023).
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Appendix A: Changes to Benefits Analysis
Table A-1: Changes to Benefits Analysis Since 2020 Final Rule
Benefits
Analysis Component
Changes to Analysis for
Changes to Analysis for 2024
Category and
[2020 final rule analysis value]
Proposed Rule, relative to
Final Rule, relative to 2023
Analysis
2020 Final Rule
Proposed Rule
Component
Air quality-related effects
Emissions
Emissions from changes in
Emissions from changes in
Emissions from changes in
changes
electricity generation profile
electricity generation profile
electricity generation profile
from 2020 IPM runs.
from 2022 IPM runs.
from 2024 IPM runs.
Energy use-associated
Energy use-associated
Energy use-associated
emissions were updated to
emissions were updated to
emissions were updated to
reflect emission factors
reflect emission factors
reflect emission factors
estimated using the 2020 IPM
estimated using the 2022 IPM
estimated using the 2024 IPM
runs.
runs.
runs.
Air quality
Used the ACE modeling
Updated methodology to
Updated methodology to
changes
methodology to estimate
reflect the most recent air
reflect the most recent air
changes in air pollutant
quality surfaces.
quality surfaces. See Appendix J
concentrations.
for details.
Monetization of
Used BenMAP-CE model to
No change.
No change.
health effects
estimate associated human
health benefits.
Monetization of
Used E.O. 13783 domestic-only
Used IWG (2021)
Used EPA (20231) updated
changes in GHG
SC-GHG values at 3 and 7
recommended interim global
global SC-GHG values at 1.5
emissions
percent discounts in main
SC-GHG values at 2.5, 3
percent, 2.0 percent, and
analysis. Presented results
(average and 95%), and 5
2.5 percent near-term Ramsey
based on global SC-GHG values
percent discount rates.
discount rates. Presented
under 2.5, 3, and 7 percent
results based on IWG (2021)
discount rates in sensitivity
interim SC-GHG values in
analysis.
Appendix.
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Appendix B: Results at 3 percent and 7 percent
B Estimated Costs and Benefits Using Discount Rates from the Proposal
This appendix provides costs and benefits of the final rule using the discount rates used in the proposal BCA
to facilitate comparison with the benefits analysis presented at proposal (see 2023 BCA; U.S. EPA, 2023c).
As is the case throughout the document, monetary values in this appendix are presented in 2023 dollars (as
compared to 2021 dollars for values in the 2023 BCA (U.S. EPA, 2023c)).
B.1 Benefits
Table B-1: Estimated Bromide-related Bladder Cancer Mortality and Morbidity Monetized Benefits
Changes in cancer cases from
changes in TTHM exposure
2025-20493
Benefits (million 2023$, discounted to 2024)
Regulatory Option
Annualized13
Total bladder
Total cancer
Annualized13
benefits from
cancer cases
deaths
benefits from
avoided
Total annualized13
avoided
avoided
avoided mortality
morbidity
benefits
3%
7%
3%
7%
3%
7%
Option A
98
28
$9.5
$5.8
$1.7
$1.1
$11.3
$7.0
Option B (Final Rule)
98
28
$9.5
$5.8
$1.7
$1.1
$11.3
$7.0
Option C
104
29
$10.2
$6.3
$1.9
$1.2
$12.1
$7.5
aThe analysis accounts for the persisting health effects (up until 2125) from changes in TTHM exposure during the period of
analysis (2025-2049).
b Benefits are annualized over 25 years.
Source: U.S. EPA Analysis, 2024
Table B-2: Estimated Benefits from Avoided IQ Losses for Children Exposed to Lead under the
Regulatory Options, Compared to Baseline
Regulatory Option
Average Annual
Number of Children 0
to 7 in Scope of the
Analysis'3
Total Avoided IQ Point
Losses, 2025 to 2049 in
All Children 0 to 7 in
Scope of the Analysisc
Annualized Value of Avoided IQ Point
Losses3 (Millions 2023$)
3% Discount Rate
7% Discount Rate
Option A
1,555,558
0.93
<$0.01
<$0.01
Option B (Final Rule)
1,555,558
0.93
<$0.01
<$0.01
Option C
1,555,558
0.93
<$0.01
<$0.01
a. Based on estimate that the loss of one IQ point results in the loss of 2.63 percent of lifetime earnings, following updated Salkever
(1995) values from U.S. EPA (2019d).
b. The number of children in scope of the analysis is based on reaches analyzed across the regulatory options. Some of the children
included in this count see no changes in exposure under some options.
c. EPA notes that the IQ point losses are very small. EPA further notes that the IEUBK model does not analyze blood lead level
changes beyond two decimal points.
Source: U.S. EPA Analysis, 2024
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Appendix B: Results at 3 percent and 7 percent
Table B-3: Estimated Benefits from Avoided IQ Losses for Infants from Mercury Exposure under the
Regulatory Options, Compared to Baseline
Regulatory Option
Number of Infants in
Scope of the Analysis per
Yearb
Total Avoided IQ Point
Losses, 2025 to 2049 in
All Infants in Scope of the
Analysis
Annualized Value of Avoided IQ Point
Losses3 (Millions 2023$)
3% Discount Rate
7% Discount Rate
Option A
201,850
1,190
$1.02
$0.18
Option B (Final Rule)
201,850
1,377
$1.18
$0.21
Option C
201,850
1,393
$1.19
$0.21
a. Based on the estimate that the loss of one IQ point results in the loss of 2.63 percent of lifetime earnings discounted to birth,
following updated Salkever (1995) values from U.S. EPA (2019f).
b. The number of infants in scope of the analysis is based on reaches analyzed across the regulatory options. Some of the children
included in this count see no changes in exposure under some options.
Source: U.S. EPA Analysis, 2024
Table B-4: Estimated Benefits from Avoided CVD Deaths for Adults (aged 40-80) under the
Regulatory Options, Compared to Baseline
Regulatory Option
Number of
Adults in Scope
of the Analysis
per Year3
Total CVD Deaths
Avoided, 2025 to 2049 in
All Adults in Scope of
the Analysis'3
Annualized Value of Avoided CVD Deathsc
(Millions 2023$)
3% Discount Rate
7% Discount Rate
Low
High
Low
High
Low
High
Option A
19,571,228
0.42
1.13
$0.16
$0.42
$0.14
$0.37
Option B (Final Rule)
19,571,228
0.42
1.13
$0.16
$0.42
$0.14
$0.37
Option C
19,571,228
0.45
1.20
$0.16
$0.43
$0.14
$0.38
a. The number of adults in scope of the analysis is based on reaches analyzed across the regulatory options. Some of the adults
included in this count see no changes in exposure under some options. Benefits accrue to the subset of adults that experience
changes in exposure under one or more options (576,537 adults in 2025). Under the assumption that fishers would share their
catch with members of their household, EPA included household members in this subset.
b. Assumes that the distribution for the individuals experiencing CVD premature mortality that is caused by lead is the same as
the distribution of CVD premature mortality irrespective of the cause.
Source: U.S. EPA Analysis, 2024
Table B-5: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality
Improvements under the Regulatory Options, Compared to Baseline (Main Estimates)
Regulatory Option
Number of Affected
Households
(Millions)3
Average Annual WTP
Per Household
(2023$)b
Total Annualized WTP (Millions 2023$)b
3% Discount Rate
7% Discount Rate
Option A
58.7
$0.01
$0.77
$0.70
Option B (Final Rule)
58.9
$0.02
$1.21
$1.10
Option C
59.6
$0.03
$1.64
$1.50
a. The number of affected households varies across options because of differences in the number of reaches that have non-zero
changes in water quality.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits.
Source: U.S. EPA Analysis, 2024
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Appendix B: Results at 3 percent and 7 percent
Table B-6: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality Changes
under the Regulatory Options, Compared to Baseline (Sensitivity Analysis)
Regulatory Option
Number of
Affected
Households
(Millions)3
Average Annual WTP
Per Household
(2023$)b
Total Annualized WTP (Millions 2023$)b
3% Discount Rate3,b
7% Discount Rate3
Low
High
Low
High
Low
High
Option A
58.7
$0.01
$0.03
$0.84
$1.71
$0.74
$1.52
Option B (Final Rule)
58.9
$0.02
$0.05
$1.27
$2.60
$1.12
$2.30
Option C
59.6
$0.03
$0.07
$1.73
$3.55
$1.55
$3.17
a. The number of affected households varies across options because of differences in the number of reaches that have non-zero
changes in water quality.
b. Estimates based on Model 2, which provides a range of estimates that account for uncertainty in the WTP estimates as a
sensitivity analysis. For the AWQI variable setting in Model 2-based sensitivity analysis, EPA used values of 20 units to develop low
estimates and 7 units to develop high estimates (see Appendix H for details).
Source: U.S. EPA Analysis, 2024
Table B-7: Estimated Annualized Climate Benefits from Changes in CO2 and CH4 Emissions under
the Final Rule during the Period of 2025-2049 by Categories of Air Emissions and Interim SC-GHG
Estimates, Compared to Baseline (Millions of 2023$)
Regulatory Option
Category of Air
Emissions
Annualized Climate Benefits3'13
5.0% Average
3.0% Average
2.5% Average
3.0% 95th
Percentile
Option B
(Final Rule)
Electricity generation
$142.8
$435.9
$620.8
$1,323.6
Trucking
-$0.0
-$0.1
-$0.1
-$0.2
Energy use
-$2.6
-$8.2
-$11.8
-$25.1
Total
$140.2
$427.6
$608.9
$1,298.4
a. Values rounded to two significant figures. Negative values indicate forgone benefits whereas positive values indicate positive
benefits.
b. Climate benefits estimated using interim SC-GHG (IWG, 2021).
Source: U.S. EPA Analysis, 2024
Table B-8: Estimated Discounted Economic Value of Avoided Ozone and PM2.5-Attributable
Premature Mortality and Illness for Option B (95 Percent Confidence Interval; millions of 2023$)
Year
3% Discount Rate3
7% Discount Rate3
2028
$1,000
and
$2,500
$890
and
$2,200
($170 to $2500)
($300 to $6,500)
($120 to $2,200)
($240 to $5,800)
2030
$380
and
$1,200
$320
and
$1,000
($77 to $890)
($150 to $3,000)
($51 to $770)
($110 to $2,700)
2035
$1,600
and
$3,700
$1,400
and
$3,300
($240 to $4,000)
($430 to $9,800)
($180 to $3,500)
($350 to $8,800)
2040
$480
and
$1,200
$410
and
$1,100
($78 to $1,200)
($140 to $3,200)
($57 to $1,000)
($120 to $2,900)
2045
$150
and
$370
$130
and
$330
($24 to $360)
($44 to $970)
($17 to $320)
($36 to $870)
2050
$130
and
$300
$120
and
$260
($19 to $330)
($34 to $790)
($15 to $290)
($28 to $700)
a Values rounded to two significant figures. The two benefits estimates are separated by the word "and" to signify that they are
two separate estimates. The estimates do not represent lower- and upper-bound estimates and should not be summed.
Source: U.S. EPA Analysis, 2024
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Appendix B: Results at 3 percent and 7 percent
Table B-9: Estimated Annualized Changes in Navigational Dredging Costs under the Regulatory
Options, Compared to Baseline
Regulatory Option
Total Reduction in Sediment
Dredged (Thousands Cubic
Yards)
3% Discount Rate
(Millions of 2023$ per Year)3
7% Discount Rate
(Millions of 2023$ per Year)3
Low
High
Low
High
Low
High
Option A
7.1
9.3
<$0.01
$0.01
<$0.01
<$0.01
Option B (Final Rule)
8.3
10.8
<$0.01
$0.01
<$0.01
$0.01
Option C
8.5
11.0
<$0.01
$0.01
<$0.01
$0.01
a. Positive values represent cost savings.
Source: U.S. EPA Analysis, 2024.
Table B-10: Estimated Total Annualized Changes in Reservoir Dredging Volume and Costs under the
Regulatory Options, Compared to Baseline
Total Reduction in Sediment
Dredged
(Thousands Cubic Yards)
Costs at 3% Discount Rate3
(Millions of 2023$ per Year)
Costs at 7% Discount Rate3
(Millions of 2023$ per Year)
Regulatory Option
Low
High
Low
High
Low
High
Option A
1.0
1.1
<$0.01
<$0.01
<$0.01
<$0.01
Option B (Final Rule)
1.2
1.3
<$0.01
<$0.01
<$0.01
<$0.01
Option C
1.2
1.4
<$0.01
<$0.01
<$0.01
<$0.01
a. Positive values represent cost savings.
Source: U.S. EPA Analysis, 2024.
Table B-10: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared
to Baseline, at 3 Percent (Millions of 2023$)
Benefit Category
Option A
Option B
(Final Rule)
Option C
Human Health
Changes in IQ losses in children from exposure to lead3
<$0.01
<$0.01
<$0.01
Changes in cardiovascular disease premature mortality from
exposure to lead
$0.16-$0.42
$0.16-$0.42
$0.16- $0.43
Changes in IQ losses in children from exposure to mercury
$1.05
$1.21
$1.23
Changes in cancer risk from disinfection by-products in drinking
water
$11.28
$11.28
$12.06
Ecological Conditions and Recreational Uses Changes
Use and nonuse values for water quality changes'5
$0.77
$1.21
$1.64
Market and Productivity Effects3
Changes in drinking water treatment costs
Changes in dredging costs3
<$0.01
<$0.01
<$0.01
Air Quality-Related Effectsc
Climate change effects from changes in greenhouse gas
emissions'^
$330
$430
$520
Human health effects from changes in NOx, S02, and PM2.5
emissions0
$1,200
$1,600
$2,000
Total®
$1,544
$2,044
$2,536
a. "<$0.01" indicates that monetary values are greater than $0 but less than $0.01 million.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits. See Chapter 6 for details.
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BCAfor Supplemental Steam Electric Power Generating ELGs Appendix B: Results at 3 percent and 7 percent
Table B-10: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared
to Baseline, at 3 Percent (Millions of 2023$)
Benefit Category
Option A
Option B
(Final Rule)
Option C
c. Values for air-quality related effects are rounded to two significant figures. EPA estimated the air quality-related benefits for the
final rule (Option B). EPA extrapolated estimates of air quality-related benefits for Options A and C from the estimate for Option B
that is based on IPM outputs. See Chapter 8 for details.
d. Climate change benefits are based on interim SC-GHG values for the 3 percent discount rate (IWG, 2021), discounted and
annualized using a 3 percent discount.
e. Values for individual benefit categories may not sum to the total due to independent rounding.
Source: U.S. EPA Analysis, 2024
Table B-11: Summary of Estimated Total Annualized Benefits of the Regulatory Options, Compared
to Baseline, at 7 Percent (Millions of 2023$)
Benefit Category
Option A
Option B
(Final Rule)
Option C
Human Health
Changes in IQ losses in children from exposure to lead3
<$0.01
<$0.01
<$0.01
Changes in cardiovascular disease premature mortality from
exposure to lead
$0.14-$0.37
$0.14-$0.37
$0.14- $0.38
Changes in IQ losses in children from exposure to mercury
$0.19
$0.22
$0.22
Changes in cancer risk from disinfection by-products in drinking
water
$6.99
$6.99
$7.53
Ecological Conditions and Recreational Uses Changes
Use and nonuse values for water quality changes'5
$0.70
$1.10
$1.50
Market and Productivity Effects3
Changes in drinking water treatment costs
Changes in dredging costs3
<$0.01
<$0.01
<$0.01
Air Quality-Related Effectsc
Climate change effects from changes in greenhouse gas
emissions'^
$330
$430
$520
Human health effects from changes in NOx, S02, and PM2.5
emissions06
$1,100
$1,400
$1,700
Total'
$1,438
$1,839
$2,230
a. "<$0.01" indicates that monetary values are greater than $0 but less than $0.01 million.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits. See Chapter 6 for details.
c. Values for air-quality related effects are rounded to two significant figures. EPA estimated the air quality-related benefits for the
final rule (Option B). EPA extrapolated estimates of air quality-related benefits for Options A and C from the estimate for Option B
that is based on IPM outputs. See Chapter 8 for details.
d. Climate change benefits are based on interim SC-GHG values for the 3 percent discount rate (IWG, 2021), discounted and
annualized using a 3 percent discount.
e. The values reflect the LT estimates of human health effects from changes in PM2.5 and ozone levels. See Chapter 8 for details.
f. Values for individual benefit categories may not sum to the total due to independent rounding.
Source: U.S. EPA Analysis, 2024
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Appendix B: Results at 3 percent and 7 percent
B.2 Social Costs
Table B-12: Summary of Estimated Incremental Annualized Costs for Regulatory Options (Millions of
2023$)
Regulatory Option
Annualized Costs
3% Discount Rate
7% Discount Rate
Lower Bound
Upper Bound
Lower Bound
Upper Bound
Option A
$444.2
$974.7
$478.7
$1,028.7
Option B (Final Rule)
$544.8
$1,077.2
$580.1
$1,130.1
Option C
$633.0
$1,165.4
$676.5
$1,226.5
Source: U.S. EPA Analysis, 2024.
B.3 Social Benefits and Costs
Table B-14: Total Estimated Annualized Benefits and Costs by Regulatory Option and Discount
Rate, Compared to Baseline (Millions of 2023$)
Regulatory Option
3% Discount
7% Discount
Total
Monetized
Benefits313
Total Costs
Total Monetized
Benefits313
Total Costs
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Option A
$1,544
$444.2
$974.7
$1,244
$478.7
$1,028.7
Option B (Final Rule)
$2,044
$544.8
$1,077.2
$1,653
$580.1
$1,130.1
Option C
$2,536
$633.0
$1,165.4
$2,056
$676.5
$1,226.5
a. EPA estimated the air quality-related benefits for the final rule (Option B) only. EPA extrapolated estimates of air quality-
related benefits for Options A and C from the estimate for Option B that is based on IPM outputs. See Chapter 8 for details.
b. Climate change benefits are based on interim SC-GHG values for the 3 percent discount rate (IWG, 2021), discounted and
annualized using a 3 percent discount.
Source: U.S. EPA Analysis, 2024.
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Appendix C: WQI Calculation & Subindices
C WQI Calculation and Regional Subindices
C.1 WQI Calculation
The first step in the implementation of the WQI involves obtaining water quality levels for each parameter,
and for each waterbody, under both the baseline conditions and each regulatory option. Some parameter levels
are modeled values (TN, TP, TSS, arsenic, cadmium, chromium, copper, lead, mercury, nickel, selenium, and
zinc) and vary from the baseline depending on the regulatory option, while others are field measurements
(FC, BOD, and DO) and are left unchanged between the baseline and regulatory options.
The second step involves transforming the parameter measurements into subindex values that express water
quality conditions on a common scale of 10 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 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 of the nine ecoregions used for the 2013-2014 and 2018-2019 National Rivers and Stream
Assessment (NRSA) (U.S. EPA, 2020e, 2023j). For each of the nine ecoregions, EPA derived the
transformation curves by assigning a score of 100 to the 10th percentile of the observations within each
ecoregion (i.e.. using the 10th 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 toxics-specific subindex curve based on the number of NRWQC
exceedances for toxics in each waterbody. National freshwater chronic NRWQC values are available for
arsenic, cadmium, chromium, copper, lead, mercury, nickel, selenium, and zinc. See the EA for details on the
NRWQC (U.S. EPA, 2020g; U.S. EPA, 2024b). To develop this subindex curve, EPA used an approach
developed by the Canadian Council of Ministers of the Environment (CCME, 2001). The CCME water
quality index is based on three attributes of water quality that relate to water quality objectives: scope
(number of monitored parameters that exceed water quality standard or toxicological benchmark); frequency
(number of individual measurements that do not meet objectives, relative to the total number of measurements
for the time period of interest) and amplitude (i.e.. amount by which measured values exceed the standards or
benchmarks). Following the CCME approach, EPA's toxics subindex considers the number of parameters
with exceedances of the relevant water quality criterion. With regards to frequency, EPA modeled long-term
annual average concentrations in ambient water, and therefore any exceedance of an NRWQC may indicate
that ambient concentrations exceed NRWQC 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 NRWQC then
the water is impaired for that constituent and the level of exceedance is of secondary concern. Using this
approach, the subindex curve for toxics assigns the lowest subindex score of 0 to waters where exceedances
are observed for all nine of the toxics analyzed, and a maximum score of 100 to waters where there are no
exceedances. Intermediate values are distributed evenly between 0 and 100.
Table C-l presents parameter-specific functions used for transforming water quality data into water quality
subindices for freshwater waterbodies for the six pollutants with individual subindices. Table C-2 presents the
subindex values for toxics. The equation parameters for each of the nine ecoregion-specific TSS, TN, and TP
subindex curves are provided in the next section. The curves include threshold values below or above which
the subindex score does not change in response to changes in parameter levels. For example, improving DO
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix C: WQI Calculation & Subindices
levels from 10.5 mg/L to 12 mg/L or from 2 mg/L to 3.3 mg/L would result in no change in the DO subindex
score.
Table C-1: Freshwater Water Quality Subindices
Parameter
Concentrations
Concentration
Subindex
Unit
Dissolved Oxygen (DO)
DO saturation <100%
DO
DO <3.3
mg/L
10
DO
3.3 < DO < 10.5
mg/L
-80.29+31.88xDO-1.401xDO2
DO
DO > 10.5
mg/L
100
100% < DO saturation < 275%
DO
NA
mg/L
100 x exp((DOsat - 100) x -1.197xl0"2)
275% < DO saturation
DO
NA
mg/L
10
Fecal Coliform (FC
FC
FC > 1,600
cfu/100 mL
10
FC
50 < FC < 1,600
cfu/100 mL
98 x exp((FC - 50) x -9.9178xl0"4)
FC
FC < 50
cfu/100 mL
98
Total Nitrogen (TN
a
TN
TN >TNio
mg/L
10
TN
TNioo < TN < TNio
mg/L
a x exp(TNxb); where a and b are ecoregion-
specific values
TN
TN < TNioo
mg/L
100
Total Phosphorus (TP)b
TP
TP > TPio
mg/L
10
TP
TPioo < TP < TPio
mg/L
a x exp(TPxb); where a and b are ecoregion-
specific values
TP
TP < TPioo
mg/L
100
Suspended Solids
TSS
TSS > TSS 10
mg/L
10
TSS
TSS 100 < TSS < TSS 10
mg/L
a x exp(TSSxb); where a and b are ecoregion-
specific values
TSS
TSS < TSS 00
mg/L
100
Biochemical Oxygen Demand, 5-day (BOD)
BOD
BOD >8
mg/L
10
BOD
BOD <8
mg/L
100 xexp(BODx-0.1993)
a. TN10 and TN100 are ecoregion-specific TN concentration values that correspond to subindex scores of 10 and 100,
respectively. Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)
b. TP10 and TP100 are ecoregion-specific TP concentration values that correspond to subindex scores of 10 and 100, respectively.
Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)
c. TSS10 and TSS100 are ecoregion-specific SSC concentration values that correspond to subindex scores of 10 and 100,
respectively. Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001)
Source: EPA Analysis, 2024, based on methodology in Cude (2001).
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Appendix C: WQI Calculation & Subindices
Table C-2: Freshwater Water Quality Subindex for Toxics
Number of Toxics with NRWQC
Subindex
Exceedances
0
100.0
1
88.9
2
77.8
3
66.7
4
55.6
5
44.4
6
33.3
7
22.2
8
11.1
9
0.0
The final step in implementing the WQI involves combining the individual parameter subindices into a single
WQI value that reflects the overall water quality across the parameters. EPA calculated the overall WQI for a
given reach using a geometric mean function and assigned all WQ parameters an equal weight of 0.143 (l/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 C-l presents EPA's calculation of the overall WQI score.
Equation C-1.
wQir = n?=iQiwi
WQIr = the multiplicative water quality index (from 0 to 100) for reach r
Qi = the water quality subindex measure for parameter z
Wi = the weight of the z-th parameter (0.143)
n = the number of parameters (i.e.. seven)
C.2 Regional Subindices
The following tables provide 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 k>, WQ
Parameter i0o, a, and b are specified in Table C-3 for TSS, Table C-4 for TN, and Table C-5 for TP.
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Appendix C: WQI Calculation & Subindices
Table C-3: TSS Subindex Curve Parameters, by Ecoregion
Ecoregion
a
b
TSSioo
TSSio
Coastal Plains
109.34
-0.015
5.86
156.84
Northern Appalachians
108.11
-0.061
1.29
39.27
Northern Plains
102.07
-0.001
18.10
2,049.20
Southern Appalachians
114.22
-0.012
10.88
199.43
Southern Plains
102.19
-0.001
15.53
1,667.06
Temperate Plains
114.02
-0.003
46.30
858.85
Upper Midwest
101.24
-0.021
0.59
111.70
Western Mountains
108.48
-0.018
4.51
131.95
Xeric
101.72
-0.003
6.53
887.38
Source: U.S. EPA Analysis, 2024
Table C-4: TN Subindex Curve Parameters, by Ecoregion
Ecoregion
a
b
TNioo
TN10
Coastal Plains
148.67
-0.85
0.47
3.17
Northern Appalachians
128.25
-1.08
0.23
2.36
Northern Plains
124.98
-0.40
0.56
6.37
Southern Appalachians
178.79
-0.95
0.61
3.04
Southern Plains
113.00
-0.22
0.55
10.95
Temperate Plains
123.62
-0.13
1.57
18.65
Upper Midwest
119.92
-0.40
0.45
6.20
Western Mountains
121.28
-1.99
0.10
1.25
Xeric
130.03
-1.06
0.25
2.43
Source: U.S. EPA Analysis, 2024
Table C-5: TP Subindex Curve Parameters, by Ecoregion
Ecoregion
a
b
TPioo
TPio
Coastal Plains
116.13
-5.33
0.03
0.46
Northern Appalachians
104.31
-5.75
0.01
0.41
Northern Plains
117.76
-13.58
0.01
0.18
Southern Appalachians
115.90
-1.02
0.15
2.41
Southern Plains
114.66
-4.37
0.03
0.56
Temperate Plains
103.46
-0.66
0.05
3.56
Upper Midwest
140.90
-1.58
0.22
1.67
Western Mountains
107.15
-3.89
0.02
0.61
Xeric
108.89
-9.72
0.01
0.25
Source: U.S. EPA Analysis, 2024
C-4
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
D Additional Details on Modeling Change in Bladder Cancer Incidence from
Change in TTHM Exposure
D.1 Details on Life Table Approach
D. 1.1 Health Impact Function
Figure D-l shows the dependence between lifetime odds of bladder cancer and drinking water TTHM
concentration as reported by Villanueva et al. (2004). These data were used by Regli et al. (2015) to estimate
the log-linear relationship in Equation 4-1, which is also displayed in Figure D-l. As described in Chapter 4,
Regli et al. (2015) showed that, while the original analysis deviated from linearity, particularly at low doses,
the overall pooled exposure-response relationship for TTHM could be well-approximated by a linear slope
factor that predicted an incremental lifetime cancer risk of 1 in ten thousand exposed individuals (10 ~4) per
1 |_ig/L increase in TTHM.14"
Figure D-1: Estimated Relationships between Lifetime Bladder Cancer Risk and TTHM Concentrations
in Drinking Water
THM4, ug/L
Source: Regli et al. (2015)
140 Regli, S., Chen, J., Messner, M., Elovitz, M. S., Letkiewicz, F. J., Pegram, R. A., Pepping, T. J.,. . . Wright, J. M. (2015).
Estimating potential increased bladder cancer risk due to increased bromide concentrations in sources of disinfected drinking
waters. Environmental Science & Technology', 49(22), 13094-13102. addressed some of the limitations noted in the Hrudey, S.
E., Backer, L. C., Humpage, A. R., Krasner, S. W., Michaud, D. S., Moore, L. E., Singer, P. C.,. . . Stanford, B. D. (2015).
Evaluating evidence for association of human bladder cancer with drinking-water chlorination disinfection by-products. Journal
of Toxicology and Environmental Health, PartB, 18(5), 213-241. analysis. They suggested that the seeming discrepancy
between the slope factor derived from the pooled epidemiological data and that from animal studies was due primarily to (1)
potentially high human exposures to DBPs by the inhalation route, and (2) that trihalomethanes were acting as proxies for other
carcinogenic DBPs.
D-1
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
EPA used the Regli et al. (2015) relationship between the lifetime odds of bladder cancer and lifetime TTHM
exposure from drinking water to derive a set of age-specific health impact functions. A person's lifetime
TTHM exposure from drinking water by age a—denoted by xa—is defined as:
Equation D-l. xa = TTH M t, x0 = 0.
See Table D-1 at the end of this section for definitions of all variables used in the equations in this appendix.
Assuming a baseline exposure of za and a regulatory option exposure of xa (i.e., exposure following
implementation of a regulatory option), the relative risk (RR) of bladder cancer by age a under the option
exposure relative to the baseline exposure can be expressed as:
Equation D-2
RR(xa,za) = max
1 - PAF,
/ Q(Xg) \ 1 /
' l 0(za) ) ' V
LRn
O(Xg)
0(za)
LR„ + 1
where LRa is the lifetime risk of bladder cancer within age interval [0, a] (Fay et al. 2003) under baseline
conditions and PAF is the environmental exposure-related population attributable fraction of bladder cancer
incidence set at 0.0394. As such, this equation implies that EPA caps the magnitude of TTHM-related
cumulative bladder cancer risk reduction at the PAF of 3.94 percent to ensure plausibility of the estimated
bladder cancer benefits size. EPA developed this PAF estimate based on a review of literature on
environmental contaminant-attributable risk estimates for cancers (ICF, 2022a).
Combining Equation D-l and Equation D-2 shows that the relative risk of bladder cancer by age a based on
Regli et al. (2015) depends only on the lifetime risk and on the magnitude of change in TTHM concentration
from baseline concentration, Axa = xa — za, but not on the baseline TTHM level:
(0( oVe0 00427';ta\ —^ / o(0)-e000427Xa \
Equation D-3. RRRegiietad^,za) = max 1 - PAF, ¦ [LRa ¦ o\le0,0i27.Za ~ LRa + l)
= max[l - PAF,e-° 00427
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
al. (2004) requires detailed information on the baseline TTHM exposure for the population of interest which
is not available.
D.1.2 Health Risk Model
To estimate the health effects of changes in TTHM exposure, the health risk model tracks evolution of two
populations over time —the bladder cancer-free population and the bladder cancer population. These two
populations are modeled for both the baseline annual TTHM exposure scenario and for the regulatory options
TTHM exposure scenarios. Populations in the scenarios are demographically identical but they differ in the
TTHM levels to which they are exposed. The population affected by change in bromide discharges associated
with a regulatory option is assumed to be exposed to baseline TTHM levels prior to the regulatory option
implementation year (in this case 2024) and to alternative TTHM levels that reflect the impact of technology
implementation under each regulatory option starting in 2025.
To capture these effects while being consistent with the remainder of the cost-benefit framework, EPA
modeled changes in health outcomes resulting from changes in exposure between 2025 and 2049. For these
exposures, EPA modeled effects out to 2124 to capture the resultant lagged changes in lifetime bladder cancer
risk, but did not attribute changes in bromide loadings and TTHM exposures to the regulatory options beyond
2049.142
EPA tracks mortality and bladder cancer experience for a set of model populations defined by sex, location,
and age attained by 2025, which is denoted by A = 0,1,2,3,... 100. Each model population is followed from
birth (corresponding to calendar year 2025 — ^4) to age 100, using a one-year time step. Below, we first
describe the process for quantifying the evolution of model population A under the baseline TTHM exposure
assumptions. We then describe the process for quantifying the evolution of the population under the
regulatory option TTHM exposures. Finally, we describe the process for estimating the total calendar year y-
specific health benefits which aggregate estimates over all model populations (A = 0,1,2,3,... 100).
Evolution of Model Population A under Baseline TTHM Exposure
Given a model population A, for each current age a and calendar year y, the following baseline exposure
zay = ~T,i=o Baseline TTHMiy_a+( dependent quantities are computed:
• 'c=o,a,y (za,y): The number of bladder cancer-free living individuals at the beginning of age a, in year
y;
• dc=Q,a,y(za,y): The number of deaths among bladder cancer-free individuals aged a during the year
y;
• 'c=i,a,y (za,y): The number of new bladder cancer cases among individuals aged a during the year y.
To compute each quantity above, EPA makes an assumption about the priority of events that terminate a
person's existence in the pool of bladder cancer-free living individuals. These events are general population
142 This approach is equivalent to assuming that TTHM levels revert back to baseline conditions at the end of the regulatory option
costing period.
D-3
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
deaths that occur with probability143 qc=o,a and new bladder cancer diagnoses that occur with probability ya.
which is approximated by age-specific annual bladder cancer incidence rate IRa ¦ 10"5. In the model, EPA
assumes that the new cancer diagnoses occur after general population deaths and uses the following recurrent
equations for ages a > 0:144
Equation D-4.
lc=0,a,y(za,y) = ^C=0,a-l,y-l{za-l,y-l) ~ ^C=0,a-l,y-l(za-l,y-l) — ^C=l,a-l,y-l(za-l,y-l)
Equation D-5. dc=0ay{zay^ —
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
Equation D-8. qs=s^k = 1 - r^'a+1 (l - qc=0,a+k)-
rS=s,a,k
In estimating additional deaths in the cancer population in the year of diagnosis (i.e., when k = 0), EPA
accounts only for cancer population deaths that are in excess of the general population deaths. As such, the
estimate of additional cancer population deaths is computed as follows:
Equation D-9. ^S=s,a,yfi(za,y) ~ (
In years that follow the initial diagnosis year (i.e.. k > 0), EPA uses the following recurrent equations to
estimate the number of people living with bladder cancer and the annual number of deaths in the bladder
cancer population:
Equation D-10. ls=Sja,y,k(za,y-k) = ^S=s,a,y,k- \ (za,y-k) ~ <^S=s,a,y,fc-l(za,y-fc)i
Equation D-11. ds=s,a,y,k{za,y-k) ~ tfs=s,a,k ' ^S=s,a,y,k(za,y-k)-
Because EPA is interested in bladder cancer-related deaths rather than all deaths in the bladder cancer
population, EPA also tracks the number of excess bladder cancer population deaths (i.e.. the number of deaths
in the bladder cancer population over and above the number of deaths expected in the general population of
the same age). The excess deaths are computed as:
Equation D-12. &S=s,a,y,k{za,y-k) ~ tfs=s,a,k ' ls=s,a,y,k(.za,y—k) ~ tfc=0,a+k ' ^¦S=s,a,y,k{za,y-k)
Evolution of Model Population A under the Regulatory Option TTHM Exposure
Under the baseline conditions when the change in TTHM is zero (i.e.. before 2025), EPA approximates the
annual bladder cancer probability ya by age-specific annual bladder cancer incidence rate IRa ¦ 10~5. As
described in Section 4, current empirical evidence links TTHM exposure to the lifetime bladder cancer risk,
rather than annual bladder cancer probability. EPA computes the TTHM-dependent annual new bladder
cancer cases under the regulatory option conditions, lc=i,a,y{xa,y)> in three steps. First, EPA recursively
estimates LRay{zay), the lifetime risk of bladder cancer within age interval [0, a] under the baseline
conditions:
Equation D-13. LRay(zay) — ~ ~ ~~~ ~ ^j=o ^c :(^/,v /t ¦ ,<)> c? > 0 and LR0y_^z0— 0
Second, the result of Equation D-13 is combined with the relative risk estimate RR(xay, zay), based on Regli
etal. (2015):
Equation D-14. LRay (xa y) = RR (xa y, za y)L/?a y (za y)
D-5
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
This results in a series of lifetime bladder cancer risk estimates under the option conditions. Third, EPA
computes a series of new annual bladder cancer case estimates under the option conditions as follows:
Equation D-15. lc=l,a,y(xa,y^) — ( ¦ : v ¦ : (^l.1 ¦ : v ¦ : ) — LRa,y{%a,y)) ' ^C=0,0,y—A (^0,y—x)
Health Effects and Benefits Attributable to Regulatory Options
To characterize the overall impact of the regulatory option in a given year y, for each model population
defined by age a in 2025, sex, and location, EPA calculates three quantities: the incremental number of new
stage s bladder cancer cases (NCA y s ). the incremental number of individuals living with stage s bladder
cancer (LCAys), and the incremental number of excess deaths in the bladder cancer population (EDA y). The
formal definitions of each of these quantities are given below:
Equation D-16.
N CA,y,s = [0 < y — 2025 + A < 100] ¦ (js=s,y-2025+A,y,o(zy-2025+A,y) — h=s,y-2024+A,0 (xy-2025+Ay))
Equation D-17.
Z100
[0 < y — 2025 + A + k ^ 100]
k=l
¦ (js=s,y-2025+A-k,y,k{zy-2025+A-k,y-k) ~ h=s,y-202S+A-k,y,k (xy-2025+^l-fc,y-fc))
Equation D-18.
Z100
[0 < y — 2025 + A + k
k=O
< 100] I (es=Sjy-202s+A-k,y,k(zy-2025+A-k,y-k) es=Sjy_2025+A_kjyjk(xy_2025+A_kjy_k)^
SES
E^A.y
' Jk = 0
These calculations are carried out to 2125, when those aged 0 years in 2025 attain the age of 100.
Table D-1: Health Risk Model Variable Definitions
Variable
Definition
O(x)
The odds of lifetime bladder cancer incident for an individual exposed to a lifetime average TTHM
concentration in residential water supply of x (ug/L)
a
Current age or age at cancer diagnosis
A person's lifetime option TTHM exposure by age a
za
A person's lifetime baseline TTHM exposure by age a
LRa
Lifetime risk of bladder cancer within age interval [0, a) under the baseline conditions
IRa
Age-specific baseline annual bladder cancer incidence rate
RR(%ai Za)
Relative risk of bladder cancer by age a given baseline exposure za and option exposure xa
PAF
Population attributable fraction of bladder cancer incidence
A
Age in 2025 (years)
y
Calendar year
%a,y
A person's lifetime option TTHM exposure by age a given that this age occurs in year y
Za,v
A person's lifetime baseline TTHM exposure by age a given that this age occurs in yeary
lc=0,a,y (^a,y )
The baseline number of bladder cancer-free living individuals at the beginning of age a given that
this age occurs in year y
D-6
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BCA for Supplemental Steam Electric Power Generating ELGs Appendix D: Bladder Cancer Model Details
Table D-1: Health Risk Model Variable Definitions
Variable
Definition
dc=0,a,y C^a,y )
The baseline number of deaths among bladder cancer-free individuals at age a given that this age
occurs in yeary
lc=l,a,y
The baseline number of new bladder cancer cases at age a given that this age occurs in year y
-------
BCA for Supplemental Steam Electric Power Generating ELGs Appendix D: Bladder Cancer Model Details
and age group-specific distribution of bladder cancer cases over four analyzed stages as well as onset-specific
relative survival probability for each stage.
D-8
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
Table D-2: Summary of Baseline Bladder Cancer Incidence Data Used in the Model
Females
Males
Age
Incidence
Percent of Incidence in Stage
Incidence
Percent of Incidence in Stage
per100K
Localized
Regional
Distant
Unstaged
per100K
Localized
Regional
Distant
Unstaged
<1
-
77
4.5
14
4.5
-
66
23
11
0
1-4
-
77
4.5
14
4.5
-
66
23
11
0
5-9
-
77
4.5
14
4.5
-
66
23
11
0
10-14
-
77
4.5
14
4.5
-
66
23
11
0
15-19
-
82
8.2
5.1
4.9
0.11
90
4.8
3.1
2.5
20-24
0.17
82
8.2
5.1
4.9
0.3
90
4.8
3.1
2.5
25-29
0.26
82
8.2
5.1
4.9
0.51
90
4.8
3.1
2.5
30-34
0.5
82
8.2
5.1
4.9
1.1
90
4.8
3.1
2.5
35-39
0.89
82
8.2
5.1
4.9
2.1
90
4.8
3.1
2.5
40-44
1.5
83
8.6
6.1
2.7
4.2
85
7.4
4.9
2.5
45-49
2.9
83
8.6
6.1
2.7
8.8
85
7.4
4.9
2.5
50-54
6.6
83
8.6
6.1
2.7
19
85
7.4
4.9
2.5
55-59
11
83
8.6
6.1
2.7
38
85
7.4
4.9
2.5
60-64
18
83
8.6
6.1
2.7
67
85
7.4
4.9
2.5
65-69
29
84
7.9
5.6
2.8
114
86
6.7
4.3
2.9
70-74
43
84
7.9
5.6
2.8
176
86
6.7
4.3
2.9
75-79
58
80
7.1
5.8
6.8
245
85
6.2
4.1
5.2
80-84
71
80
7.1
5.8
6.8
315
85
6.2
4.1
5.2
85+
76
80
7.1
5.8
6.8
357
85
6.2
4.1
5.2
D-9
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
Table D-3: Summary of Relative and Absolute Bladder Cancer Survival Used in the Model
Age at
Diagnosis
Follow-Up
Time
Females
Males
Relative Survival by Stage
(Percent)
Absolute Surv
by Stage
val (Average)
Percent)
Relative Survival by Stage
(Percent)
Absolute Survival (Average) by
Stage (Percent)
Localized
Regional
Distant
Un staged
Localized
Regional
Distant
Un staged
Localized
Regional
Distant
Un staged
Localized
Regional
Distant
Un staged
Ages 15-39
1 year
98
79
20
90
97
79
20
90
99
85
46
100
97
83
45
98
Ages 15-39
2 years
97
58
4
83
96
57
4
83
99
67
23
97
96
65
22
95
Ages 15-39
3 years
96
47
0
80
95
46
0
79
98
60
14
95
96
58
13
92
Ages 15-39
4 years
95
39
0
80
94
39
0
79
97
58
11
91
95
56
11
89
Ages 15-39
5 years
95
32
0
80
93
32
0
79
96
56
11
91
94
54
11
89
Ages 15-39
6 years
94
28
0
80
93
27
0
79
96
56
9
91
93
54
9
89
Ages 15-39
7 years
94
28
0
80
92
27
0
79
96
56
7
91
93
54
7
88
Ages 15-39
8 years
93
28
0
80
92
27
0
78
95
56
7
91
92
54
7
88
Ages 15-39
9 years
93
28
0
80
91
27
0
78
94
52
5
91
91
51
4
88
Ages 15-39
10 years
93
28
0
80
91
27
0
78
93
52
5
85
90
50
4
82
Ages 40-64
1 year
97
73
34
84
92
69
32
80
98
78
36
85
90
72
33
78
Ages 40-64
2 years
95
53
15
81
90
50
14
76
96
57
16
79
87
52
15
72
Ages 40-64
3 years
94
45
9
77
88
42
9
72
94
48
11
75
85
43
10
67
Ages 40-64
4 years
93
40
7
76
87
37
7
70
93
43
9
73
83
38
8
65
Ages 40-64
5 years
92
37
5
74
85
34
5
69
91
40
8
71
81
35
7
63
Ages 40-64
6 years
91
36
5
74
84
33
5
68
90
38
7
68
79
33
7
60
Ages 40-64
7 years
90
34
4
73
82
31
4
66
89
37
7
66
77
32
6
57
Ages 40-64
8 years
89
32
4
71
80
29
4
64
88
36
7
64
75
30
6
54
Ages 40-64
9 years
88
31
4
70
79
28
3
63
87
35
7
61
73
29
6
51
Ages 40-64
10 years
87
31
4
70
77
27
3
62
86
34
7
61
71
28
6
51
Ages 65-74
1 year
95
67
25
72
88
62
24
66
97
74
32
81
86
66
29
72
Ages 65-74
2 years
92
48
11
67
83
44
10
61
94
55
16
75
82
48
13
65
Ages 65-74
3 years
90
38
8
63
80
34
7
57
92
47
11
72
77
39
9
60
Ages 65-74
4 years
88
34
6
60
77
30
5
52
89
42
8
69
73
34
6
56
Ages 65-74
5 years
86
31
5
58
73
26
5
50
88
39
6
66
70
31
5
52
Ages 65-74
6 years
85
28
5
56
71
23
4
47
86
36
6
64
66
27
4
49
D-10
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
Table D-3: Summary of Relative and Absolute Bladder Cancer Survival Used in the Model
Females
Males
Relative Survival by Stage
Absolute Survival (Average)
Relative Survival by Stage
Absolute Survival (Average) by
(Percent)
by Stage
Percent)
(Percent)
Stage (Percent]
Age at
Diagnosis
Follow-Up
Time
Localized
Regional
Distant
Unstaged
Localized
Regional
Distant
Unstaged
Localized
Regional
Distant
Unstaged
Localized
Regional
Distant
Unstaged
Ages 65-74
7 years
84
27
4
54
68
22
3
44
84
34
5
61
62
25
4
45
Ages 65-74
8 years
82
25
4
52
64
20
3
41
82
32
5
57
58
23
4
40
Ages 65-74
9 years
81
25
3
51
61
19
2
39
80
30
4
56
54
20
3
38
Ages 65-74
10 years
79
25
3
51
58
18
2
37
79
29
4
56
50
19
3
36
Ages 75+
1 year
86
48
17
39
44
25
9
20
92
60
22
59
45
30
11
29
Ages 75+
2 years
81
36
8
32
40
18
4
16
87
44
10
51
42
21
5
24
Ages 75+
3 years
77
30
6
27
38
15
3
13
84
38
7
45
38
17
3
21
Ages 75+
4 years
76
28
5
24
36
13
2
11
81
35
5
40
35
15
2
17
Ages 75+
5 years
73
26
4
22
33
12
2
10
79
33
5
37
33
14
2
15
Ages 75+
6 years
71
24
4
22
31
11
2
9
76
32
4
34
30
13
2
13
Ages 75+
7 years
69
22
3
20
29
9
1
8
74
29
3
31
27
11
1
11
Ages 75+
8 years
68
21
3
18
27
8
1
7
72
28
3
29
25
10
1
10
Ages 75+
9 years
66
21
2
18
25
8
1
7
70
28
3
26
22
9
1
8
Ages 75+
10 years
65
18
2
18
23
6
1
6
68
28
3
23
20
8
1
7
D-11
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix D: Bladder Cancer Model Details
Table D-4: Summary of All-Cause and Bladder Cancer Mortality Data Used in the Model
Females
Males
Age
Rate per100K
Percent
Bladder Cancer
Rate per100K
Percent Bladder
Cancer
All-Cause
Bladder
Cancer
All-Cause
Bladder
Cancer
<1
579
-
0
702
-
0
1-4
25
-
0
31
-
0
5-9
12
-
0
14
-
0
10-14
13
-
0
19
-
0
15-19
33
-
0
78
-
0
20-24
47
-
0
136
0.009
0.01
25-29
60
0.019
0.03
148
0.016
0.01
30-34
80
0.037
0.05
165
0.055
0.03
35-39
113
0.111
0.10
204
0.142
0.07
40-44
168
0.230
0.14
281
0.380
0.14
45-49
254
0.471
0.19
419
1.05
0.25
50-54
378
0.893
0.24
631
2.39
0.38
55-59
558
1.64
0.29
933
5.13
0.55
60-64
833
2.88
0.35
1,361
9.72
0.71
65-69
1,256
4.88
0.39
1,963
16.9
0.86
70-74
1,997
8.62
0.43
2,977
28.8
0.97
75-79
3,271
14.1
0.43
4,704
48.8
1.04
80-84
5,550
22.8
0.41
7,623
81.8
1.07
85+
13,559
40.6
0.30
15,543
151
0.97
D.2 Detailed Results from Analysis
The health impact model assumes that the regulatory changes begin in 2025 and end by 2049 and thus TTHM
changes are in effect during this period. After 2049, TTHM levels return to baseline levels, i.e., ATTHM is
zero. Due to the lasting effects of changes in TTHM exposure, the benefits of the policies after 2049 were
included in the final calculations for each option. Table D-5 summarizes the health impact and valuation
results in millions of 2023 dollars for each regulatory option, as shown graphically and discussed in Section
4.4.
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Appendix D: Bladder Cancer Model Details
Table D-5: Number of Adverse Health Effects Avoided Over Time Starting from 2025
Option
Evaluation period
Total"
2025-2029
2030-2039
2040-2049
2050-2059
2060-2069
2070-2079
2080-2089
2090-2099
2100-2109
2110-2119
2120-2125
Cancer morbidity cases avoidedac
Options A & B
3
17
25
12
12
12
10
6
2
0
0
98
Option C
4
18
26
13
13
12
10
7
2
0
0
104
Excess cancer deaths avoidedbc
Options A & B
1
4
6
4
3
3
3
2
1
0
0
28
Option C
1
4
6
4
4
4
3
2
1
0
0
29
Value of morbidity avoided (million 2023 dollars, 2% discount rate)c
Options A & B
$1.94
$9.48
$12.32
$5.15
$4.29
$3.38
$2.32
$1.24
$0.35
-$0.05
-$0.02
$40.39
Option C
$2.44
$9.95
$12.89
$5.51
$4.58
$3.61
$2.47
$1.33
$0.38
-$0.05
-$0.03
$43.07
Value of mortality avoided (million 2023 dollars, 2% discount rate)c
Options A & B
$7.52
$45.60
$64.14
$34.90
$25.20
$20.52
$14.97
$9.26
$3.59
$0.19
-$0.05
$225.84
Option C
$9.44
$48.58
$67.19
$37.12
$26.96
$21.92
$15.97
$9.88
$3.83
$0.21
-$0.05
$241.02
Notes:
a. Number of TTHM-attributable bladder cancer cases that are expected to be avoided under the policy in the calendar time period.
b. Number of excess deaths among the TTHM-attributable bladder cancer cases that are expected to be avoided under the policy in the calendar time period.
c. Number of attributable cases and deaths are rounded to the nearest digit. Values of avoided morbidity and mortality are rounded to the nearest cent. Negative values represent
increases in the number of cases/deaths and morbidity/mortality costs.
d. Total TTHM-attributable adverse health effects that are expected to be avoided between 2025 and 2125 as a result of the regulatory option changes in 2025-2049.
Source: U.S. EPA Analysis, 2024
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Appendix D: Bladder Cancer Model Details
D.3 Temporal Distribution of Benefits
Figure D-2 and Figure D-3 illustrate patterns of changes in benefits for the three regulatory options for the
100-year simulation period of 2025 through 2125 based on the cumulative annual value of morbidity avoided
and the cumulative annual value of mortality, respectively (values are undiscounted). These figures show the
gradual increase in benefits for Options A, B, and C between 2025 and 2049, which continues but at a
reduced rate after 2049 until levelling off around 2111. As discussed in Section 4.4, benefits decrease during
the final decades for Options A, B, and C. The benefits associated with Options A and B are smaller than
those of Option C.
Figure D-2: Cumulative Annual Value of Cancer Morbidity Avoided, 2025-2125 (Million 2023$
-•-Options A&B -^-Option C
Source: U.S. EPA Analysis, 2024.
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Appendix D: Bladder Cancer Model Details
Figure D-3: Cumulative Annual Value of Mortality Avoided, 2025-2125 (Million 2023$ undiscounted).
-•-Options A&B -»-Option C
Source: U.S. EPA Analysis, 2024.
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Appendix E: Water & Fish Tissue Concentrations
E Derivation of Ambient Water and Fish Tissue Concentrations in Downstream
Reaches
This appendix describes the methodology EPA used to estimate water and fish tissue concentrations under the
baseline and each of the regulatory options. The concentrations are used as inputs to estimate the water
quality changes and human health benefits of the regulatory options. Specifically, EPA used ambient water
toxics concentrations to derive fish tissue concentrations used to analyze human health effects from
consuming self-caught fish (see Chapter 5) and to analyze non-use benefits of water quality changes (see
Chapter 6). Nutrient and suspended solids concentrations are used to support analysis of non-use benefits
from water quality changes (see Chapter 6).
The overall modeling methodology builds on data and methods described in the EA and TDD for the
regulatory options (U.S. EPA, 2024b; 2024f). The following sections discuss calculations of the toxics
concentrations in ambient water and fish tissue and nutrient and sediment concentrations in ambient water.
E.1 Toxics
E.1.1 Estimating Water Concentrations in each Reach
EPA first estimated the baseline and regulatory option toxics concentrations in reaches receiving steam
electric power plant discharges and downstream reaches.
The D-FATE model (see Chapter 3) was used to estimate water concentrations. The model tracks the fate and
transport of discharged pollutants through a reach network defined based on the medium resolution NHD.147
The hydrography network represented in the D-FATE model consists of 11,607 reaches within 300 km of a
steam electric power plant, 11,080 of which are estimated to be potentially fishable.148
The analysis involved the following key steps for the baseline and each of the regulatory options:
• Summing plant-level loadings to the receiving reach. EPA summed the estimated plant-level
annual average loads for each unique reach receiving plant discharges from steam electric power
plants in the baseline and under the regulatory options. For a description of the approach EPA used to
identify the receiving waterbodies, see U.S. EPA, 2023g.
• Performing dilution and transport calculations. The D-FATE model calculates the concentration
of the pollutant in a given reach based on the total mass transported to the reach from upstream
sources and the EROM flows for each reach from NHDPlus v2. In the model, a plant is assumed to
147 The USGS's National Hydrology Dataset (NHD) 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 several contiguous features. Each reach code occurs only once throughout
the nation and once assigned a reach code is permanently associated with its reach. If the reach is deleted, its reach code is
retired.
148 Reaches represented in the D-FATE model are those estimated to be potentially fishable based on type and physical
characteristics. Because the D-FATE model calculates the movement of a chemical release downstream using flow data, reaches
must have at least one downstream or upstream connecting reach and have a non-negative flow and velocity. The D-FATE model
does not calculate concentrations for certain types of reaches, such as coastlines, treatment reservoirs, and bays; the downstream
path of any chemical is assumed to stop if one of these types of reach is encountered.
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Appendix E: Water & Fish Tissue Concentrations
release its annual load at a constant rate throughout the year. Each source-pollutant release is tracked
throughout the NHD reach network until the terminal reach.149
• Specifying concentrations in the water quality model. The D-FATE model includes background
data on estimated annual average pollutant concentrations to surface waters from facilities that
reported to the TRI in 2019. EPA added background concentrations where available to concentration
estimates from steam electric power plant dischargers.
EPA used the approach above to estimate annual average concentrations of ten toxics: arsenic, cadmium,
hexavalent chromium, copper, lead, mercury, nickel, selenium, thallium, and zinc.
E. 1.2 Estimating Fish Tissue Concentrations in each Reach
To support analysis of the human health benefits associated with water quality improvements (see Chapter 5),
EPA estimated concentrations of arsenic, lead, and mercury in fish tissue based on the D-FATE model
outputs discussed above.
The methodology follows the same general approach described in the EA for estimating fish tissue
concentrations for receiving reaches (U.S. EPA, 2024b), but applies the calculations to the larger set of
reaches modeled using D-FATE, which include not only the receiving reaches analyzed in the EA, but also
downstream reaches. Further, the calculations use D-FATE-estimated concentrations as inputs, which account
not only for the steam electric power plant discharges, but also other major dischargers that report to TRI.
The analysis involved the following key steps for the baseline and each of the regulatory options:
7. Obtaining the relationship between water concentrations and fish tissue concentrations.
EPA used the results of the Immediate Receiving Water (IRW) model (see EA, U.S. EPA, 2023g)
to parameterize the linear relationship between water concentrations in receiving reaches and
composite fish tissue concentrations (representative of trophic levels 3 and 4 fish consumed) in
these same reaches for each of the three toxics.
8. Calculating fish tissue data for affected reaches. For reaches for which the D-FATE model
provides non-zero water concentrations (i.e.. reaches affected by steam electric power plants or
other TRI dischargers), EPA used the relationship obtained in Step 1 to calculate a preliminary
fish tissue concentration for each pollutant.
The analysis provides background toxic-specific composite fish fillet concentrations for each reach modeled
in the D-FATE model (Table E-l).
Table E-1: Background Fish Tissue Concentrations,
based on 10th Percentile
Parameter
Pollutant Concentration (mg/kg)
As
0.039
Hg
0.058
Pb
0.039
Source: U.S. EPA Analysis, 2024
149 For some analyses, EPA limits the scope of reaches to 300 km (186 miles) downstream from steam electric power plant outfalls.
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Appendix E: Water & Fish Tissue Concentrations
E.2 Nutrients and Suspended Sediment
EPA used the USGS's regional SPARROW models to estimate nutrient and sediment concentrations in
receiving and downstream reaches. The regional models used for this analysis are the five regional models
developed for the Pacific, Southwest, Midwest, Southeast, and Northeast regions for flow, total nitrogen
(TN), total phosphorus (TP), and suspended sediment (Ator, 2019; Hoos & Roland Ii, 2019; Robertson &
Saad, 2019; Wise, 2019; Wise, Anning & Miller, 2019). EPA adjusted the models to include a variable for
steam electric discharges using the following steps:
• Specifying a source load parameter for steam electric discharges. The regional SPARROW
models do not include an explicit explanatory variable for point sources related to industrial
dischargers (non publicly owned treatment works). EPA recalibrated the regional models by adding a
variable for steam electric loadings, initially setting all loadings for this parameter equal to zero,
assigning this new variable a calibration coefficient value of 1, and specifying zero land-to-water
delivery effects associated with this new variable.
• Appending steam electric TN, TP, and TSS loadings to regional input data. Once the regional
SPARROW models were recalibrated to include the steam electric loadings variable, EPA added the
steam electric TN, TP, and TSS15" loadings to the model input data and ran each regional model for
each pollutant to obtain catchment-level TN, TP, and SSC predictions.
For Periods 1 and 2, the SPARROW models output predicted annual average baseline and regulatory option
concentrations in each reach. EPA compared the baseline predictions to the predictions obtained for each of
the regulatory options to estimate changes in concentrations.
150 TSS loadings are converted to SSC values at this step by using location-specific relationships built into the SPARROW regional
models.
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Appendix F: Georeferencing of Water Intakes
F Georeferencing Surface Water Intakes to the Medium-resolution Reach
Network
For the 2024 final rule analysis, EPA used the following steps to assign PWS surface water intakes to waters
represented in the medium-resolution NHD Plus version 2 dataset and identify those intakes potentially
affected by steam electric power plant discharges.
1. Identify the downstream flowpath via NHD Plus Version 2 Flowlines for all steam electric
dischargers.
2. Identify intakes within a 5-kilometer buffer of the downstream flowpath. This distance is used to
limit the set of points to be visually reviewed in the next step and provides an upper bound of the
distance between an intake and its potential associated receiving water.
3. Visually review the location of each intake within the five-kilometer buffer to determine whether
the intake is on a waterbody downstream of steam electric power plant discharges. The visual
assessment accounts for hydrographic connectivity and flow direction.
EPA then paired the intakes that were confirmed to be impacted to the closest NHD COMID based on a
simple cartesian distance.
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Appendix G: IQ Sensitivity Analysis
G Sensitivity Analysis for IQ Point-based Human Health Effects
EPA monetized the value of an IQ point based on the methodology from Salkever (1995) but with more
recent data from the 1997 National Longitudinal Survey of Youth (U.S. EPA, 2019d). As a sensitivity
analysis of the benefits of changes in lead and mercury exposure, EPA used alternative, more conservative
estimates provided in Lin, Lutter and Ruhm (2018), which indicate that a one-point IQ reduction reduces
expected lifetime earnings by 1.39 percent, as compared to 2.63 percent based on Salkever (1995). As noted
in Sections 5.3 and 5.4, values of an IQ point used in the analysis of health effects in children from lead
exposure are discounted to the third year of life to represent the midpoint of the exposed children population,
and values of an IQ point used in the analysis of health effects associated with in-utero exposure to mercury
are discounted to birth. Table G-l summarizes the estimated values of an IQ point based on Lin, Lutter and
Ruhm (2018), using 2 percent, 3 percent, and 7 percent discount rates.
Table G-1: Value of an IQ Point (2023$) based on
Expected Reductions in Lifetime Earnings
Discount Rate
Value of an IQ Point3 (2023$)
Value of an IQ point Discounted to Age 3 (Lead)
2 Percent
$21,653
3 percent
$13,718
7 percent
$2,885
Value of an IQ point Discounted to Birth (Mercury)
2 Percent
$20,404
3 percent
$12,554
7 percent
$2,355
a. Values are adjusted for the cost of education.
Source: U.S. EPA, 2019d and 2019e analysis of data from Lin, Lutter and
Ruhm (2018); 2 percent estimates calculated for U.S. EPA (2023f)
G.1 Health Effects in Children from Changes in Lead Exposure
Table G-2 shows the benefits associated with avoided IQ losses from lead exposure via fish consumption. The
total net change in avoided IQ point losses over the entire population of children with reductions in lead
exposure is approximately one point. Annualized benefits of avoided IQ losses from reductions in lead
exposure, based on the Lin, Lutter and Ruhm (2018) IQ point value, range from approximately $100
(7 percent discount rate) to $800 (2 percent discount rate).
Table G-2: Estimated Benefits of Avoided IQ Losses for Children Exposed to Lead under the
Regulatory Options, Compared to Baseline
Regulatory Option
Average Annual
Number of Children 0
to 7 in Scope of the
Analysis'3
Total Avoided IQ Point
Losses, 2025 to 2049,
in All Children 0 to 7 in
Scope of the Analysis
Annualized Value of Changes in IQ Point
Losses3
(Thousands of 2023$)
2% Discount
Rate
3% Discount
Rate
7% Discount
Rate
Option A
1,555,558
0.93
00
o
¦uy
LO
O
¦uy
1
O
¦uy
Option B (Final Rule)
1,555,558
0.93
00
o
¦uy
LO
o
¦uy
i
o
¦uy
Option C
1,555,558
0.93
00
o
¦uy
LO
o
¦uy
i
o
¦uy
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BCA for Supplemental Steam Electric Power Generating ELGs Appendix G: IQ Sensitivity Analysis
Table G-2: Estimated Benefits of Avoided IQ Losses for Children Exposed to Lead under the
Regulatory Options, Compared to Baseline
Regulatory Option
Average Annual
Number of Children 0
to 7 in Scope of the
Analysis'3
Total Avoided IQ Point
Losses, 2025 to 2049,
in All Children 0 to 7 in
Scope of the Analysis
Annualized Value of Changes in IQ Point
Losses3
(Thousands of 2023$)
2% Discount
Rate
3% Discount
Rate
7% Discount
Rate
a. Based on estimates that the loss of one IQ point results in the loss of 1.39 percent of lifetime earnings (following Lin, Lutter and
Ruhm (2018) values from U.S. EPA, 2019d).
b. The number of affected children is based on reaches analyzed across the regulatory options. Some of the children included in
this count see no changes in exposure under some options.
Source: U.S. EPA Analysis, 2024
G.2 Heath Effects in Children from Changes in Mercury Exposure
Table G-3 shows the estimated changes in avoided IQ point losses for infants exposed to mercury in-utero
and the corresponding monetary benefits, using 2 percent, 3 percent, and 7 percent discount rates. The final
rule (Option B) results in 1,377 avoided IQ point losses over the entire in-scope population of infants with
changes in mercury exposure. Annualized benefits of avoided IQ losses from reductions in mercury exposure,
based on the Lin, Lutter and Ruhm (2018) IQ point value, range from $0.1 million (7 percent discount rate) to
$1.1 million (2 percent discount rate) under the final rule (Option B).
Table G-3: Estimated Benefits of Avoided IQ Losses for Infants from Mercury Exposure under the
Regulatory Options, Compared to Baseline
Regulatory Option
Average Annual
Number of Infants in
Scope of the Analysis'3
Total Avoided IQ Point
Losses, 2025 to 2049,
in All Infants in Scope
of the Analysis
Annualized Value of Changes in IQ Point
Losses3 (Millions 2023$)
2% Discount
Rate
3% Discount
Rate
7% Discount
Rate
Option A
201,850
1,190
$0.9
$0.6
1
O
¦uy
Option B (Final Rule)
201,850
1,377
$1.1
$0.6
i
o
¦uy
Option C
201,850
1,393
$1.1
$0.7
i
o
¦uy
a. Based on estimates that the loss of one IQ point results in the loss of 1.39 percent of lifetime earnings (following Lin, Lutter and
Ruhm (2018) values from U.S. EPA, 2019d and 2019e).
b. The number of affected children is based on reaches analyzed across the regulatory options. Some of the children included in
this count see no changes in exposure under some options.
Source: U.S. EPA Analysis, 2024
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Appendix H: WTP Estimation Methodology
H Methodology for Estimating WTP for Water Quality Changes
To estimate the nonmarket benefits of the water quality changes resulting from the regulatory options, EPA
used updated results from a meta-analysis of stated preference studies described in detail in Appendix H in the
2015 BCA (U.S. EPA, 2015a). To update results of the 2015 meta-analysis, EPA first conducted a literature
review and identified 10 new studies to augment the existing meta-data. EPA also performed quality
assurance on the meta-data, identifying revisions that improved accuracy and consistency within the meta-
data, and added or removed observations from existing studies, as appropriate. EPA then re-estimated the
MRM and made additional improvements to the model by introducing explanatory variables to account for
different survey methodologies, WTP estimation methodologies, payment mechanisms, and water quality
metrics used in some of the added studies. A memorandum titled "Revisions to the Water Quality Meta-Data
and Meta-Regression Models after the 2020 Steam Electric Analysis through December 2021" (ICF, 2022b)
details changes to the meta-data and MRMs following the 2020 Steam Electric ELG analysis (U.S. EPA,
2020f), summarizes how the studies and observations included in the meta-data have changed from 2015 to
2020 to present, and compares the latest MRM results with those from 2015 (U.S. EPA, 2015a) and 2020
(U.S. EPA, 2020f).
Table H-l summarizes studies in the revised meta-data, including number of observations from each study,
state-level study location, waterbody type, geographic scope, and household WTP summary statistics. In total,
the revised meta-data includes 189 observations from 59 stated preference studies that estimated per
household WTP (use plus nonuse) for water quality changes in U.S. waterbodies. The studies address various
waterbody types including, rivers, lakes, salt ponds/marshes, and estuaries. The ten studies added to the meta-
data since 2015 are shaded in Table H-l.
Table H-1: Primary Studies Included in the Meta-data
Obs. In
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Study
Meta-
data
State(s)
Mean
Min
Max
Aiken (1985)
1
CO
river/
stream and
lake
Entire state
$238.19
$238.19
$238.19
Anderson and
Edwards (1986)
1
Rl
salt pond
/marsh
Coastal salt ponds
(South Kingstown,
Charlestown, and
Narragansett)
$222.82
$222.82
$222.82
Banzhaf et al.
(2006)
2
NY
lake
Adirondack Park, New
York State
$70.86
$66.69
$75.03
Banzhaf et al.
(2016)
1
VA, WV,
TN, NC,
GA
river/
stream
Southern Appalachian
Mountains region
$18.67
$18.67
$18.67
Bockstael,
McConnell and
Strand (1989)
2
MD, DC,
VA
estuary
Chesapeake Bay
(Baltimore-Washington
Metropolitan Area)
$137.31
$93.30
$181.32
Borisova et al.
(2008)
2
VA/WV
river/
stream
Opequon Creek
watershed
$42.54
$22.25
$62.83
Cameron and
Huppert (1989)
1
CA
estuary
San Francisco Bay
$61.07
$61.07
$61.07
Carson et al.
(1994)
2
CA
estuary
Southern California
Bight
$73.24
$50.81
$95.67
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Appendix H: WTP Estimation Methodology
Table H-1: Primary Studies Included in the Meta-data
Study
Obs. In
Meta-
data
State(s)
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Mean
Min
Max
Choi and Ready
(2019)
6
PA
river/
stream
Three creek
watersheds: Spring,
Mahantango, and
Conewago
$4.56
$1.73
$10.40
Clonts and
Malone (1990)
2
AL
river/
stream
15 free-flowing rivers,
AL
$112.28
$96.56
$128.00
Collins and
Rosenberger
(2007)
1
WV
river/
stream
Cheat River Watershed
$22.43
$22.43
$22.43
Collins,
Rosenberger
and Fletcher
(2009)
1
WV
river/
stream
Deckers Creek
Watershed
$229.82
$229.82
$229.82
Corrigan (2008)
1
IA
lake
Clear Lake
$152.03
$152.03
$152.03
Croke, Fabian
and Brenniman
(1986-1987)
6
IL
river/
stream
Chicago metropolitan
area river system
$90.25
$75.60
$107.18
De Zoysa (1995)
1
OH
river/
stream
Maumee River Basin
$86.53
$86.53
$86.53
Desvousges,
Smith and
Fisher (1987)
12
PA
river/
stream
Monongahela River
basin (PA portion)
$72.98
$24.46
$169.24
Downstream
Strategies LLC
(2008)
2
PA
river/
stream
West Branch
Susquehanna River
watershed
$15.70
$13.19
$18.21
Farber and
Griner (2000)
6
PA
river/
stream
Loyalhanna Creek and
Conemaugh River
basins (western PA)
$93.91
$20.45
$183.21
Hayes, Tyrell
and Anderson
(1992)
2
Rl
estuary
Upper Narragansett
Bay
$490.05
$481.71
$498.38
Herriges and
Shogren (1996)
1
IA
lake
Storm Lake watershed
$76.09
$76.09
$76.09
Hite (2002)
2
MS
river/
stream
Entire state
$74.09
$71.81
$76.36
Holland and
Johnston (2017)
6
ME
river/
stream
Merriland, Branch
Brook and Little River
Watershed
$13.90
$8.16
$21.27
Huang, Haab.
T.C. and
Whitehead
(1997)
2
NC
estuary
Albemarle and Pamlico
Sounds
$318.92
$314.43
$323.40
Interis and
Petrolia (2016)
10
AL/LA
estuary
Mobile Bay, AL;
Barataria-Terrebonne
estuary, LA
$87.91
$45.00
$140.47
Irvin, Haab and
Hitzhusen
(2007)
4
OH
river/
stream and
lake
Entire state
$26.72
$24.22
$28.64
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Appendix H: WTP Estimation Methodology
Table H-1: Primary Studies Included in the Meta-data
Study
Obs. In
Meta-
data
State(s)
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Mean
Min
Max
Johnston and
Ramachandran
(2014)
3
Rl
river/
stream
Pawtuxet watershed
$14.11
$7.05
$21.16
Johnston,
Swallow and
Bauer (2002)
1
Rl
river/
stream
Wood-Pawcatuck
watershed
$48.08
$48.08
$48.08
R. J. Johnston
et al. (2017)
3
Rl
river/
stream
Pawtuxet watershed
$4.79
$2.40
$7.19
Kaoru (1993)
1
MA
salt pond
/marsh
Martha's Vineyard
$269.56
$269.56
$269.56
Lant and
Roberts (1990)
3
1 A/I L
river/
stream
Des Moines, Skunk,
English, Cedar,
Wapsipinicon, Turkey;
Illinois: Rock, Edwards,
La Moine, Sangamon,
Iroquois, and
Vermillion River basins
$177.47
$152.94
$190.26
Lant and Tobin
(1989)
9
1 A/I L
river/
stream
Edwards River,
Wapsipinicon River,
and South Skunk
drainage basins
$68.59
$50.04
$83.40
Lichtkoppler
and Blaine
(1999)
1
OH
river/
stream and
lake
Ashtabula River and
Ashtabula Harbor
$51.69
$51.69
$51.69
Lindsey (1994)
8
MD
estuary
Chesapeake Bay
$82.37
$41.18
$126.02
Lipton (2004)
1
MD
estuary
Chesapeake Bay
Watershed
$78.88
$78.88
$78.88
Londono
Cadavld and
Ando (2013)
2
IL
river/
stream
Cities of Champaign
and Urbana
$47.70
$44.30
$51.10
Loomis (1996)
1
WA
river/
stream
Elwha River
$114.75
$114.75
$114.75
Lyke (1993)
2
Wl
river/
stream and
lake
Wisconsin Great Lakes
$97.10
$73.68
$120.52
Mathews,
Homans and
Easter (1999)
1
MN
river/
stream
Minnesota River
$22.36
$22.36
$22.36
Moore et al.
(2018)
2
MD, VA,
DC, DE,
NY, PA,
WV, CT,
FL, GA,
ME, MA,
NH, NJ,
NC, Rl,
SC, VT
lake
Chesapeake Bay
Watershed
$131.21
$77.75
$184.67
Nelson et al.
(2015)
2
UT
river/
stream and
lake
Entire state
$259.70
$167.07
$352.33
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Table H-1: Primary Studies Included in the Meta-data
Obs. In
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Study
Meta-
data
State(s)
Mean
Min
Max
Opaluch et al.
1
NY
estuary
Peconic Estuary System
$170.73
$170.73
$170.73
(1998)
Roberts and
1
MN/SD
lake
Mud Lake
$10.30
$10.30
$10.30
Leitch (1997)
Rowe et al.
1
CO
river/
Eagle River
$165.95
$165.95
$165.95
(1985)
stream
Sanders, Walsh
4
CO
river/
Cache la Poudre,
$198.13
$99.89
$258.99
and Loomis
stream
Colorado, Conejos,
(1990)
Dollores, Elk,
Encampment, Green,
Gunnison, Los Pinos,
Piedra, and Yampa
rivers
Schulze et al.
4
MT
river/
Clark Fork River Basin
$75.19
$56.62
$95.54
(1995)
stream
Shrestha and
2
FL
river/
Lake Okeechobee
$192.92
$170.12
$215.72
Alavalapatl
stream and
watershed
(2004)
lake
Stumborg,
2
Wl
lake
Lake Mendota
$103.94
$82.28
$125.59
Baerenklau and
Watershed
Bishop (2001)
Sutherland and
1
MT
river/
Flathead River drainage
$180.05
$180.05
$180.05
Walsh (1985)
stream and
lake
system
Takatsuka
4
TN
river/
Clinch River watershed
$353.72
$224.28
$483.16
(2004)
stream
Van Houtven et
32
VA, NC,
lake
Entire state (separate
$316.16
$260.91
$374.11
al. (2014)
SC, AL,
GA, KY,
MS, TN
observations for each
state)
Wattage (1993)
2
IA
river/
stream
Bear Creek watershed
$53.68
$49.61
$57.76
Welle (1986)
4
MN
lake
Entire state
$175.44
$135.13
$227.59
Welle and
3
MN
lake
Lake Margaret and
$178.91
$13.06
$351.48
Hodgson (2011)
Sauk River Chain of
Lakes watersheds
Wey (1990)
1
Rl
salt pond
/marsh
Great Salt Pond (Block
Island)
$78.85
$78.85
$78.85
Whitehead
3
NC
river/
Neuse River watershed
$230.79
$33.93
$450.72
(2006)
stream
Whitehead and
2
NC
river/
Tar-Pamlico River
$43.08
$39.33
$46.82
Groothuis
stream
(1992)
Whitehead et
1
NC
estuary
Albermarle-Pamlico
$115.56
$115.56
$115.56
al. (1995)
estuary system
Whittington
1
TX
estuary
Galveston Bay estuary
$240.09
$240.09
$240.09
(1994)
Zhao, Johnston
3
Rl
river/
Pawtuxet watershed
$7.19
$3.59
$10.78
and Schultz
stream and
(2013)
lake
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Appendix H: WTP Estimation Methodology
Table H-1: Primary Studies Included in the Meta-data
Obs. In
Waterbody
Type(s)
Geographic Scope
WTP Per Householc
(2019$)
Study
Meta-
data
State(s)
Mean
Min
Max
Source: U.S. EPA Analysis, 2024
Similar to the 2015 MRM, the updated MRM satisfies the adding-up condition, a theoretically desirable
property.151 This condition ensures that if the model were used to estimate WTP for the cumulative water
quality change resulting from several CWA regulations, the benefits estimates would be equal to the sum of
benefits from using the model to estimate WTP for water quality changes separately for each rule (Moeltner,
2019; Newbold etal., 2018).
The meta-analysis is based on 189 observations from 59 stated preference studies, published between 1985
and 2021. The variables in the meta-data fall into four general categories:
• Study methodology and year variables characterize such features as the year in which a study was
conducted, payment vehicle and elicitation formats, and publication type. These variables are
included to explain differences in WTP across studies but are not expected to vary across benefit
transfer for different policy applications.
• Region and surveyed populations variables characterize such features as the geographical region
within the United States in which the study was conducted, the average income of respondent
households, and the representation of users and nonusers within the survey sample.
• Sampled market and affected resource variables characterize features such as the geospatial scale (or
size) of affected waterbodies, the size of the market area over which populations were sampled, as
well as land cover and the quantity of substitute waterbodies.
• Water quality (baseline and change) variables characterize baseline conditions and the extent of the
water quality change. To standardize the results across these studies, EPA expressed water quality
(baseline and change) in each study using the 100-point WQI, if they did not already employ the WQI
orWQL.
In the latest version of the MRM, EPA built upon published versions of the MRM (R. J. Johnston et al., 2017;
Johnston, Besedin & Holland, 2019; U.S. EPA, 2020b; U.S. EPA, 2015a), with revisions to better account for
methodological differences in the underlying studies (see ICF (2022b) for detail on changes in the meta-data
and the explanatory variables used in the regression equation).
EPA also revised regional indicators to match the U.S. Census regions (U.S. Census Bureau, n.d.). To correct
for heteroskedasticity, the model is estimated using weighted least squares with observations weighted by
sample size and robust standard errors (Nelson & Kennedy, 2009). Detailed discussion of this approach can
be found in Vedogbeton and Johnston (2020). A comprehensive review of these methods is provided by
Stanley (2005).
151 For a WTP function WTP (WQIo, WQI2, Yo) to satisfy the adding-up property, it must meet the simple condition that
WTP(WQIo, WQIi , Yo) + WTP(WQIi, WQI2, Yo - WTP(WQIo, WQIi, Yo)) = WTP(WQIo, WQI2, Yo) for all possible values
of baseline water quality (WQIo), potential future water quality levels (WQIi and WQI2), and baseline income (Yo).
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Appendix H: WTP Estimation Methodology
Table H-2 provides definitions and presents descriptive statistics for variables included in the MRM, based on
the meta-data studies.
Table H-2: Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
Dependent Variable
ln_OWTP
Natural log of WTP per unit (one point) of
water quality improvement, per household.
Natural log of
2019$
1.873
1.391
OWTPa
WTP per unit of water quality improvement,
per household.
2019$
15.931
23.595
Study Methodology and Year
OneShotVal
Binary variable indicating that the study's
survey only included one valuation question.
Binary
(Value: 0 or 1)
0.534
0.500
tax_onlyb
Binary variable indicating that the payment
mechanism used to elicit WTP is increased
taxes.
Binary
(Value: 0 or 1)
0.397
0.491
user_costb
Binary variable indicating that the payment
mechanism used to elicit WTP is increased
user costs.
Binary
(Value: 0 or 1)
0.021
0.144
voluntb
Binary variable indicating that WTP was
estimated using a payment vehicle described
as voluntary as opposed to, for example,
property taxes.
Binary
(Value: 0 or 1)
0.058
0.235
RUM
Binary variable indicating that the study used a
Random Utility Model to estimate WTP.
Binary
(Value: 0 or 1)
0.566
0.497
IBI
Binary variable indicating that the study used
the index of biotic integrity as the water
quality metric.
Binary
(Value: 0 or 1)
0.079
0.271
Inyear
Natural log of the year in which the study was
conducted (i.e., data was collected), converted
to an index by subtracting 1980.
Natural log of
years (year
ranges from
1981 to 2017).
2.629
0.979
non_reviewed
Binary variable indicating that the study was
not published in a peer-reviewed journal.
Binary
(Value: 0 or 1)
0.159
0.366
thesis
Binary variable indicating that the study is a
thesis.
Binary
(Value: 0 or 1)
0.079
0.271
lump_sum
Binary variable indicating that the study
provided WTP as a one-time, lump sum or
provided annual WTP values for a payment
period of five years or less. This variable
enables the policy analyst to estimate annual
WTP values by setting lump_sum=0.
Binary
(Value: 0 or 1)
0.180
0.385
Region and Surveyed Populations
census_southc
Binary variable indicating that the affected
waters are located entirely within the South
Census region, which includes the following
states: DE, MD, DC, WV, VA, NC, SC, GA, FL, KY,
TN, MS, AL, AR, LA, OK, and TX.
Binary
(Value: 0 or 1)
0.349
0.478
census_midwestc
Binary variable indicating that the affected
waters are located entirely within the Midwest
Census region, which includes the following
states: OH, Ml, IN, IL, Wl, MN, IA, MO, ND, SD,
NE, and KS.
Binary
(Value: 0 or 1)
0.228
0.420
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Appendix H: WTP Estimation Methodology
Table H-2: Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
census_westc
Binary variable indicating that the affected
waters are located entirely within the West
Census region, which includes the following
states: MT, WY, CO, NM, ID, UT, AZ, NV, WA,
OR, and CA.
Binary
(Value: 0 or 1)
0.090
0.287
non users
Binary variable indicating that the survey was
implemented over a population of nonusers
(default category for this variable is a survey of
any population that includes both users and
nonusers).
Binary
(Value: 0 or 1)
0.058
0.235
In income
Natural log of the median income (in 2019$)
for the sample area of each study based on
historical U.S. Census data. It was designed to
provide a consistent income variable given
differences in reporting of respondent income
across studies in the meta-data (i.e., mean vs.
median). Also, some studies do not report
respondent income. This variable was
estimated for all studies in the meta-data
regardless of whether the study reported
summary statistics for respondent income.
Natural log of
income (2019$)
10.946
0.160
Sampled Market and Affected Resource
swim_use
Binary variable indicating that the affected
use(s) stated in the survey instrument include
swimming.
Binary
(Value: 0 or 1)
0.222
0.417
gamefish
Binary variable indicating that the affected use
stated in the survey instrument is game
fishing.
Binary
(Value: 0 or 1)
0.190
0.394
ln_ar_agrd
Natural log of the proportion of the affected
resource area that is agricultural based on
National Land Cover Database, reflecting the
nature of development in the area
surrounding the resource. The affected
resource area is defined as all counties that
intersect the affected resource(s).
Natural log of
proportion
(Proportion
Range: 0 to 1;
km2/km2)
-1.648
0.912
ln_ar_ratio
The ratio of the sampled area, in km2, relative
to the affected resource area. When not
explicitly reported in the study, the affected
resource area is measured as the total area of
counties that intersect the affected
resource(s), to create the variable
ar_total_area. From here, ln_ar_ratio =
log(sa_area / ar_total_area), where sa_area is
the size of the sampled area in km2.
Natural log of
ratio (km2/km2)
-0.594
2.408
sub_proportione
The water bodies affected by the water quality
change, as a proportion of all water bodies of
the same hydrological type in the sampled
area. The affected resource appears in both
the numerator and denominator when
calculating sub_proportion. The value can
range from 0 to 1.
Proportion
(Range: 0 to 1;
km/km)
0.351
0.401
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Table H-2: Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
Water Quality Baseline and Change
ln_Q
Natural log of the mid-point of the baseline
and policy water quality: Q = (l/2)( WQI-BL +
WQI-PC).
Natural log of
WQI units
3.944
0.295
lnquality_ch
Natural log of the change in mean water
quality (quality_ch), specified on the WQI.
Natural log of
WQI units
2.552
0.801
a. Provided for informational purposes. Model uses the natural log version of the OWTP variable as the dependent variable.
b. The payment types omitted from the payment type binary variables are: (1) increased prices, (2) increased prices and/or taxes,
(3) multiple methods, (4) earmarked fund, and (5) not specified/unknown.
c. The regions omitted from the regional binary variables are the Northeast Census region (ME, NH, VT, MA, Rl, CT, NY, PA, and
NJ) and the Chesapeake Bay (studies focused on the Chesapeake Bay or Chesapeake Bay Watershed since the Chesapeake Bay
Watershed spans two Census regions).
d. In addition to the ln_ar_agr variable, EPA tested a variable forthe proportion of the affected resource area that is developed,
but it did not improve model fit.
e. The sub_proportion estimation method differs by waterbody type. For rivers, the calculation is the length of the affected river
reaches as a proportion of all reaches of the same order. For lakes and ponds, the calculation is the area of the affected
waterbody as a proportion of all water bodies of the same National Hydrography Dataset classification. For bays and estuaries,
the calculation is the shoreline length of the waterbody as a proportion of all analogous (e.g., coastal) shoreline lengths. To
account for observations where multiple waterbody types are affected, the variable sub_proportion is defined as the maximum of
separate substitute proportions for rivers, lakes, and estuaries/bays.
Source: U.S. EPA Analysis, 2024.
Using the updated meta-data, EPA developed MRMs that predict how WTP for a one-point improvement on
the WQI (hereafter, one-point WTP) depends on a variety of methodological, population, resource, and water
quality change characteristics. The estimated MRMs predict the one-point WTP values that would be
generated by a stated preference survey with a particular set of characteristics chosen to represent the water
quality changes and other specifics of the regulatory options where possible, and best practices in economic
literature (e.g., excluding outlier responses from estimating WTP). As with the 2015 meta-analysis, EPA
developed two MRMs (U.S. EPA, 2015a). Model 1 is used to provide EPA's main estimate of non-market
benefits. Model 2 provides alternative estimates by including an additional variable (Inqnality_ch), which
accounts for the magnitude of WQI changes (e.g., low or high) and the associated effect on estimated WTP
values. The two models differ only in how they account forthe magnitude of the water quality changes
presented to respondents in the original stated preference studies:
• Model 1 assumes that individuals" one-point WTP depends on the average level of water quality
between the baseline and regulatory options. It does not depend on the magnitude of the water quality
change specified in the surveys of studies included in the underlying meta-data. This restriction
means that the meta-model satisfies the adding-up condition, a theoretically desirable property.
• Model 2 allows one-point WTP to depend not only on the average level of water quality but also on
the magnitude of the water quality change specified in the surveys of studies included in the
underlying meta-data. The model allows for the possibility that the WTP for a one-point improvement
on the WQI depends on both the average level of water quality between the baseline and the
regulatory options and the total water quality change that respondents were asked to value. Since
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Appendix H: WTP Estimation Methodology
environmental quality is considered by economists to be a normal good,152 one-point WTP is
expected to decrease when the total WQI change increases according to the law of diminishing
marginal utility. As indicated by a negative sign on the Inqnalitych coefficient, the estimated WTP
for a one-point improvement on the WQI scale is larger when respondents were asked to value a 10-
point improvement compared to a 20-point improvement. EPA used Model 2 to generate alternative
estimates of non-market benefits. This model provides a better statistical fit to the meta-data, but it
satisfies the adding-up condition only if the same magnitude of the water quality change is considered
(e.g., 10 points). To uniquely define the demand curve and satisfy the adding-up condition using this
model, EPA treats the water quality change variable as a methodological variable and therefore must
make an assumption about the size of the water quality change that would be appropriate to use in a
stated preference survey designed to value water quality changes resulting from the regulatory
options.
EPA used the two MRMs in a benefit transfer approach that follows standard methods described by Johnston
et al. (2005), Shrestha, Rosenberger and Loomis (2007), and Rosenberger and Phipps (2007). Based on
benefit transfer literature (e.g., Stapler & Johnston, 2009; Boyle & Wooldridge, 2018), methodological
variables are assigned values that either reflect "best practices" associated with reducing measurement errors
in primary studies or set to their mean values over the meta-data. The literature also recommends setting
variables representing policy outcomes and policy context (/'. e., resource and population characteristics) at the
levels that might be expected from a regulation. The benefit transfer approach uses CBGs as the geographic
unit of analysis.153 This approach involves estimating benefits in each CBG and year, based on the following
general benefit function:
Equation H-1.
ln((WrPyB) = Intercept + ^ (coefficient^) x (independent variable valuet)
Where
Iji(OWTPt.b)
coefficient
independent
variable values
= The predicted natural log of one-point household WTP for a given year (7)
and CBG (B).
= A vector of variable coefficients from the meta-regression.
= A vector of independent variable values. Variables include baseline water
quality level ( WQI-BLt.b) and expected water quality under the regulatory
option (WQI-PCy,b) for a given year and CBG.
152 Environmental quality, including water quality, is a "normal" good because people want more of it as their real incomes increase.
153 A Census Block Group is a group of Census Blocks (the smallest geographic unit for the Census) in a contiguous area that never
crosses a State or county boundary. A block group typically contains a population between 600 and 3,000 individuals. There are
239,780 block groups in the United States based on the 2020 Census. See https://www.census.gov/geographies/reference-
files/time-series/geo/tallies.html. http://www.census.gov/geo/maps-data/data/tallies/tractblock.html.
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Appendix H: WTP Estimation Methodology
Here, ln(OWTPYiB) is the dependent variable in the meta-analysis—the natural log of an average WTP per one
point improvement per household, in a given CBG B for water quality in a given year 7.154 The baseline water
quality level ( WQI-BLy,b) and expected water quality under the regulatory option ( WQI-PCy,b) were based on
water quality in waterbodies within a 100-mile buffer of the centroid of each CBG. A buffer of 100 miles is
consistent with Viscusi, Huber and Bell (2008) and with the assumption that the majority of recreational trips
would occur within a 2-hour drive from home. Because one-point WTP is assumed to depend, according to
Equation H-l, on both baseline water quality level ( WQI-BLy,b) and expected water quality under the
regulatory option (WQI-PCy.b), EPA estimated the one-point WTP for water quality changes resulting from
the regulatory options at the mid-point of the range over which water quality was changed, WQIy.b =
(1/2) (WQI-BLt,b + WQI-PCt.b).
In this analysis, EPA estimated WTP for the households in each CBG for waters within a 100-mile radius of
that CBG's centroid. EPA chose the 100 mile-radius because households are likely to be most familiar with
waterbodies and their qualities within the 100-mile distance. However, this assumption may be an
underestimate of the distance beyond which households have familiarity with and WTP for waterbodies
affected by steam electric power plant discharges and their quality. By focusing on a buffer around the CBG
as a unit of analysis, rather than buffers around affected waterbodies, each household is included in the
assessment exactly once, eliminating the potential for double-counting of households.155 Total national WTP
is calculated as the sum of estimated CBG-level WTP across all CBGs that have at least one affected
waterbody within 100 miles. Using this approach, EPA is unable to analyze the WTP for CBGs with no
affected waters within 100 miles. Appendix E in U.S. EPA (2020b) describes the methodology used to
identify the relevant populations.
In each CBG and year, predicted WTP per household is tailored by choosing appropriate input values for the
meta-analysis parameters describing the resource(s) valued, the extent of resource changes (i.e.. WQI- PCy,b),
the scale of resource changes relative to the size of the buffer and relative to available substitutes, the
characteristics of surveyed populations (e.g., users, nonusers), and other methodological variables. For
example, EPA projected that household income (an independent variable) changes over time, resulting in
household WTP values that vary by year.
Table H-3 provides details on how EPA used the meta-analysis to predict household WTP for each CBG and
year. The table presents the estimated regression equation intercepts and variable coefficients (coefficient,) for
the two models, and the corresponding independent variables names and assigned values. The MRM allows
the Agency to forecast WTP based on assigned values for model variables that are chosen to represent a
resource change in the context of the regulatory options.
In this instance, EPA assigned six study and methodology variables, (thesis, volunt, nonreviewed, lump sum,
user cost, IBP) a value of zero. Three methodological variables (OneShotVal, taxonly, RUM) were included
with an assigned value of 1. For the study year variable (Inyear), EPA gave the variable a value of 3.6109 (or
the ln(2017-1980)), which is the maximum value in the meta-data. This value assignment reflects atime trend
interpretation of the variable. Model 2 includes an additional variable, water quality change (In quality_ch),
which allows the benefit transfer function to reflect differences in one-point WTP based on the magnitude of
154 To satisfy the adding-up condition, as noted above, EPA normalized WTP values reported in the studies included in the meta-
data so that the dependent variable is WTP for a one-point improvement on the WQI.
155 Population double-counting issues can arise when using "distance to waterbody" to assess simultaneous improvements to many
waterbodies.
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changes presented to survey respondents when eliciting WTP values. To ensure that the benefit transfer
function satisfies the adding-up condition, the Inqnalitych variable was treated as a demand curve shifter,
similar to the methodological control variables, and held fixed for the benefit calculations. To estimate low
and high sensitivity analysis values of WTP for water quality changes resulting from the regulatory options,
EPA estimated one-point WTP using two alternative settings of the Inqnalitych variable: AWQI = 7 units
and AWQI = 20 units. These two values represent the 25th percentile and 75th percentile values of the meta-
data.
All but one of the region and surveyed population variables vary based on the characteristics of each CBG.
EPA set the variable nonusersonly to zero for all CBGs because water quality changes are expected to
enhance both use and non-use values of the affected resources and thus benefit both users and nonusers (a
nonuser value of 1 implies WTP values that are representative of nonusers only, whereas the default value of
0 indicates that both users and nonusers are included in the surveyed population). For median household
income, EPA used CBG-level median household income data from the 2021 American Community Survey
(5-year data) and accounted for projected income growth over the analysis period using the methodology
described in Section 1.3.6.
The geospatial variables corresponding to the sampled market and scale of the affected resources (Inarctgr,
ln_arratio, sub proportion) vary based on attributes of the CBG and attributes of the nearby affected
resources. For all options, the affected resource is based on the 9,358 NHD reaches potentially affected by
steam electric power generating plant discharges under baseline conditions. The affected resource for each
CBG is the portion of the 9,358 reaches that falls within the 100-mile buffer of the CBG. Spatial scale is held
fixed across regulatory options. The variable corresponding to the sampled market (ln_arratio) is set to the
mean value across all COMIDs within the scope of the analysis and thus does not vary across affected CBGs.
Because data on specific recreational uses of the water resources affected by the regulatory options are not
available, the recreational use variables (swimiise, game fish) are set to zero, which corresponds to
"unspecified" or "all" recreational uses in the meta-data.156 Water quality variables (O and Inqnalitych) vary
across CBGs and regulatory options based on the magnitude of the reach-length weighted average water
quality changes in resources within scope of the analysis within the 100-mile buffer of each CBG.
Table H-3: Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
Study Methodology and Year
intercept
-2.823
-10.020
OneShotVal
0.247
0.552
1
Binary variable indicating that the study's survey only
included one valuation question. Set to one because one
valuation scenario follows best practices for generating
incentive-compatible WTP estimates (Carson, Groves & List,
2014; Johnston, Boyle, et al., 2017).
156 If a particular recreational use was not specified in the survey instrument, EPA assessed that survey
respondents were thinking of all relevant uses.
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Appendix H: WTP Estimation Methodology
Table H-3: Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
tax_only
-0.177
-0.478
1
Binary variable indicating that the payment mechanism used
to elicit WTP is increased taxes. Set to one because using
taxes as the payment mechanism generates incentive-
compatible WTP estimates and is inclusive of both users and
nonusers.
user_cost
-0.873
-1.199
0
Binary variable indicating that the payment mechanism used
to elicit WTP is increased user cost. Set to zero because user
cost payment mechanisms are less inclusive of nonusers
than tax-based payment mechanisms.
volunt
-1.656
-1.870
0
Binary variable indicating that WTP was estimated using a
payment vehicle described as voluntary as opposed to, for
example, property taxes. Set to zero because hypothetical
voluntary payment mechanisms are not incentive
compatible (Johnston, Boyle, et al., 2017).
RUM
0.901
0.680
1
Binary variable indicating that the study used a Random
Utility Model to estimate WTP. Set to one because use of a
Random Utility Model to estimate WTP is a standard best
practice in modern stated preference studies.
IBI
-2.355
-2.185
0
Binary variable indicating that the study used the Index of
Biotic Integrity as the water quality metric. Set to zero
because the meta-regression uses the WQI as the water
quality metric, not the Index of Biotic Integrity.
Inyear
-0.135
-0.362
ln(2017-1980)
Natural log of the year in which the study was conducted
(i.e., data were collected), converted to an index by
subtracting 1980. Set to the natural log of the maximum
value from the meta-data (ln(2017-1980)) to reflect a time
trend interpretation of the variable.
non_reviewed
-0.233
-0.247
0
Binary variable indicating that the study was not published
in a peer-reviewed journal. Set to zero because studies
published in peer-reviewed journals are preferred.
thesis
0.431
0.580
0
Binary variable indicating that the study is a thesis or
dissertation. Set to zero because studies published in peer-
reviewed journals are preferred.
lump_sum
0.534
0.518
0
Binary variable indicating that the study provided WTP as a
one-time, lump sum or provided annual WTP values for a
payment period of five years or less. Set to zero to reflect
that the majority of studies from the meta-data estimated
an annual WTP, and to produce an annual WTP prediction.
Region and Surveyed Population
census_south
0.693
0.990
Varies
Binary variable indicating that the affected waters are
located entirely within the South Census region, which
includes the following states: DE, MD, DC, WV, VA, NC, SC,
GA, FL, KY, TN, MS, AL, AR, LA, OK, and TX. Set based on the
state in which the CBG is located.
census_midwest
0.667
0.945
Varies
Binary variable indicating that the affected waters are
located entirely within the Midwest Census region, which
includes the following states: OH, Ml, IN, IL, Wl, MN, IA, MO,
ND, SD, NE, and KS. Set based on the state in which the CBG
is located.
H-12
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix H: WTP Estimation Methodology
Table H-3: Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
census_west
0.393
0.400
Varies
Binary variable indicating that the affected waters are
located entirely within the West Census region, which
includes the following states: MT, WY, CO, NM, ID, UT, AZ,
NV, WA, OR, and CA. Set based on the state in which the
CBG is located.
nonusers
-0.283
-0.380
0
Binary variable indicating that the sampled population
included nonusers only; the alternative case includes all
households. Set to zero to estimate the total value for water
quality changes for all households, including users and
nonusers.
Inincome
0.478
1.199
Varies
Natural log of median household income values assigned
separately for each CBG. Varies by year based on the
estimated income growth in future years.
Sampled Market and Affected Resource
swim use
0.300
0.361
0
Binary variables indicating that the affected use(s) stated in
the survey instrument include swimming and gamefishing.
Set to zero, which corresponds to all recreational uses, since
data on specific recreational uses of the reaches affected by
steam electric power plant discharges are not available.
gamefish
0.871
0.531
0
ln_ar_agr
-0.572
-0.654
Varies
Natural log of the proportion of the affected resource area
which is agricultural based on the National Land Cover
Database, reflecting the nature of development in the area
surrounding the resource. Used Census county boundary
layers to identify counties that intersect affected resources
within the 100-mile buffer of each CBG. For intersecting
counties, calculated the fraction of total land area that is
agricultural using the National Land Cover Dataset. The
ln_ar_agr variable was coded in the metadata to reflect the
area surrounding the affected resources.
ln_ar_ratio
-0.157
-0.153
3.648
The natural log of the ratio of the sampled area (sa_area)
relative to the affected resource area (defined as the total
area of counties that intersect the affected resource[s])
(ar_total_area). In the context of the steam electric
scenario, sa_area is set based on the total area within the
100-mile buffer from the COMIDs in scope of the analysis,
while ar_total_area is set based on the area of counties
intersecting each affected reach (COMID). ln_ar_ratio is set
to the mean value from all COMIDs within the scope of the
analysis.
sub_proportion
0.993
0.650
Varies
The size of the resources within the scope of the analysis
relative to available substitutes. Calculated as the ratio of
affected reaches miles to the total number of reach miles
within the buffer that are the same or greater than the
order(s) of the affected reaches within the buffer. Its value
can range from 0 to 1.
H-13
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix H: WTP Estimation Methodology
Table H-3: Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
Water Quality
ln_Q
-0.666
-0.259
Varies
Because WTP for a one-point improvement on the WQI is
assumed to depend on both baseline water quality and
expected water quality under the regulatory option, this
variable is set to the natural log of the mid-point of the
range of water quality changes due to the regulatory
options, WQI y,b = (1/2)(WQI-BLy,b + WQI-PCy,b)- Calculated
as the length-weighted average WQI score for all potentially
affected reaches within the 100-mile buffer of each CBG.
lnquality_ch
NA
-0.683
ln(7)
ln(20)
In_quality_ch was set to the natural log of AWQI=7 or
AWQI=20 for high and low estimates of the one-point WTP,
respectively. These two values represent the 25th percentile
and 75th percentile values of the meta-data.
H-14
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix I: T&E Species
I Identification of Threatened and Endangered Species Potentially Affected by
the Final Rule Regulatory Options
As discussed in Chapter 7, EPA identified a total of 184 T&E species whose habitat range intersects reaches
affected by steam electric power plant discharges. These species include amphibians, arachnids, birds, clams,
crustaceans, fishes, insects, mammals, reptiles, and snails. Table 1-1 summarizes the number of species within
each group that have habitat ranges intersecting reaches with NRWQC exceedances for at least one pollutant
under the baseline or regulatory options in Period 1 (2025-2029) or Period 2 (2030-2049). As shown in the
table, several species of amphibians, birds, clams, fishes, mammals, and reptiles have habitat ranges
overlapping reaches with baseline exceedances in Period 1. There are no additional exceedances under any of
the regulatory options, but water quality improvements under each regulatory option reduce the number of
exceedances from the baseline conditions.
Table 1-1: Number of T&E Species with Habitat Range Intersecting Reaches Downstream from Steam
Electric Power Plant Outfalls, by Species Group
Species Group
Number of Individual Species with NRWQC Exceedances for at Least One Pollutant in Reaches
Intersecting their Habitat Range
Period 1
Period 2
Baseline
Option A
Option B
(Final
Rule)
Option C
Baseline
Option A
Option B
(Final
Rule)
Option C
Amphibians
1
1
0
0
1
1
0
0
Arachnids
0
0
0
0
0
0
0
0
Birds
6
6
6
6
5
5
5
0
Clams
9
9
9
9
9
9
0
0
Crustaceans
0
0
0
0
0
0
0
0
Fishes
4
4
4
4
3
3
3
0
Insects
0
0
0
0
0
0
0
0
Mammals
5
5
5
5
4
4
0
0
Reptiles
5
5
0
0
5
5
0
0
Snails
0
0
0
0
0
0
0
0
Total
30
30
24
24
27
27
8
0
Source: U.S. EPA Analysis, 2024
Table 1-2 provides further details on the 184 T&E species whose habitat range intersects reaches affected by
steam electric power plant discharges. The table denotes, for each species, the number of reaches with at least
one reported exceedance of a NRWQC in the baseline or regulatory options in Period 1 and Period 2. The
table also includes the results of EPA's assessment of species vulnerability to water pollution. As noted in
Chapter 7, EPA classified species as follows:
• Higher 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.
• Lower vulnerability - species whose habitats overlap bodies of water, but whose life history traits and
food sources are terrestrial.
M
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix I: T&E Species
EPA obtained species life history data from a wide variety of sources to assess T&E species vulnerability to
water pollution. These sources included: U.S. DOI, 2019; Froese and Pauly, 2019; NatureServe, 2020;
NOAA Fisheries, 2020; Southwest Fisheries Science Center (SWFSC), 2019; U.S. FWS, 2019a, 2019b,
2019c, 2019d, 2019e, 2019f, 2019g, 2020a, 2020b, 2020c, 2020e, 2020f, 2020g, 2020h, 2020i, 2020j, 2020k;
Upper Colorado River Endangered Fish Recovery Program, 2020.
Section 7.3.2 discusses impacts on selected higher vulnerability species whose habitat ranges intersect reaches
with estimated changes in NRWQC exceedance status under the regulatory options.
Table 1-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls (Shading Highlights Change from Baseline)
Species
Group
Species
Count
Species Name
Vulnerability
Number of Intersected Reaches
Exceeding NRWQC for at Least One
Pollutant
Period 1
Period 2
Baseline
Option A
Option B (Final Rule)
Option C
Baseline
Option A
Option B (Final Rule)
Option C
Amphibians
9
Ambystoma bishopi
Moderate
0
0
0
0
0
0
0
0
Ambystoma cingulatum
Higher
1
1
0
0
1
1
0
0
Bufo houstonensis
Moderate
0
0
0
0
0
0
0
0
Cryptobranchus alleganiensis bishopi
Higher
0
0
0
0
0
0
0
0
Necturus alabamensis
Higher
0
0
0
0
0
0
0
0
Phaeognathus hubrichti
Lower
0
0
0
0
0
0
0
0
Plethodon nettingi
Lower
0
0
0
0
0
0
0
0
Rana pretiosa
Higher
0
0
0
0
0
0
0
0
Rana sevosa
Lower
0
0
0
0
0
0
0
0
Arachnids
6
Cicurina baronia
Lower
0
0
0
0
0
0
0
0
Cicurina madia
Lower
0
0
0
0
0
0
0
0
Cicurina venii
Lower
0
0
0
0
0
0
0
0
Cicurina vesper a
Lower
0
0
0
0
0
0
0
0
Tayshaneta microps
Lower
0
0
0
0
0
0
0
0
Texella cokendolpheri
Lower
0
0
0
0
0
0
0
0
Birds
26
Ammodramus savannarum floridanus
Lower
0
0
0
0
0
0
0
0
Aphelocoma coerulescens
Lower
0
0
0
0
0
0
0
0
Brachyramphus marmoratus
Moderate
0
0
0
0
0
0
0
0
Calidris canutus rufa
Lower
11
11
0
0
11
11
0
0
Campephilus principalis
Lower
0
0
0
0
0
0
0
0
Charadrius melodus
Moderate
3
2
2
2
2
0
0
0
Coccyzus americanus
Lower
6
3
3
3
3
3
3
0
Empidonax traillii extimus
Lower
3
0
0
0
0
0
0
0
Eremophila alpestris strigata
Lower
0
0
0
0
0
0
0
0
Falcofemoralis septentrionalis
Lower
0
0
0
0
0
0
0
0
Grus americana
Moderate
0
0
0
0
0
0
0
0
Grus canadensis pulla
Higher
0
0
0
0
0
0
0
0
Gymnogyps californianus
Lower
0
0
0
0
0
0
0
0
Laterallus jamaicensis ssp. jamaicensis
Lower
0
0
0
0
0
0
0
0
1-2
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix I: T&E Species
Table 1-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls (Shading Highlights Change from Baseline)
Species
Count
Species Name
Vulnerability
Period 1
Period 2
Baseline
Option A
Option B (Final Rule)
Option C
Baseline
Option A
Option B (Final Rule)
Option C
Mycteria americana
Moderate
2
2
1
1
2
2
0
0
Numenius borealis
Lower
0
0
0
0
0
0
0
0
Picoides borealis
Lower
1
1
0
0
1
1
0
0
Polyborus plancus audubonii
Lower
0
0
0
0
0
0
0
0
Rallus obsoletus yumanensis
Higher
0
0
0
0
0
0
0
0
Rostrhamus sociabilis plumbeus
Higher
0
0
0
0
0
0
0
0
Setophaga chrysoparia
Lower
0
0
0
0
0
0
0
0
Sterna antillarum browni
Higher
0
0
0
0
0
0
0
0
Sterna dougallii dougallii
Higher
0
0
0
0
0
0
0
0
Strix occidentalis caurina
Lower
0
0
0
0
0
0
0
0
Strix occidentalis lucida
Lower
0
0
0
0
0
0
0
0
Tympanuchus cupido attwateri
Lower
0
0
0
0
0
0
0
0
Amblema neislerii
Higher
0
0
0
0
0
0
0
0
Arcidens wheeleri
Higher
0
0
0
0
0
0
0
0
Cumberlandia monodonta
Higher
10
10
0
0
10
10
0
0
Cyprogenia stegaria
Higher
11
11
1
1
11
11
0
0
Dromus dromas
Higher
0
0
0
0
0
0
0
0
Elliptio chipolaensis
Higher
0
0
0
0
0
0
0
0
Elliptio lanceolata
Higher
0
0
0
0
0
0
0
0
Elliptio spinosa
Higher
0
0
0
0
0
0
0
0
Elliptoideus sloatianus
Higher
0
0
0
0
0
0
0
0
Epioblasma brevidens
Higher
0
0
0
0
0
0
0
0
Epioblasma capsaeformis
Higher
0
0
0
0
0
0
0
0
Epioblasma obliquata obliquata
Higher
0
0
0
0
0
0
0
0
Epioblasma rangiana
Higher
0
0
0
0
0
0
0
0
Epioblasma triquetra
Higher
10
10
0
0
10
10
0
0
Fusconaia cor
Higher
0
0
0
0
0
0
0
0
Fusconaia cuneolus
Higher
0
0
0
0
0
0
0
0
Fusconaia masoni
Higher
0
0
0
0
0
0
0
0
Hamiota altilis
Higher
0
0
0
0
0
0
0
0
Hamiota perovalis
Higher
0
0
0
0
0
0
0
0
Hamiota subangulata
Higher
0
0
0
0
0
0
0
0
Hemistena lata
Higher
0
0
0
0
0
0
0
0
Lampsilis abrupta
Higher
12
12
2
2
12
12
0
0
Lampsilis higginsii
Higher
0
0
0
0
0
0
0
0
Lampsilis rafinesqueana
Higher
0
0
0
0
0
0
0
0
Lampsilis virescens
Higher
0
0
0
0
0
0
0
0
Lasmigona decorata
Higher
0
0
0
0
0
0
0
0
Leptodea leptodon
Higher
0
0
0
0
0
0
0
0
Number of Intersected Reaches
Exceeding NRWQC for at Least One
Pollutant
56
1-3
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix I: T&E Species
Table 1-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls (Shading Highlights Change from Baseline)
Species
Group
Species
Count
Species Name
Vulnerability
Number of Intersected Reaches
Exceeding NRWQC for at Least One
Pollutant
Period 1
Period 2
Baseline
Option A
Option B (Final Rule)
Option C
Baseline
Option A
Option B (Final Rule)
Option C
Margaritifera hembeli
Higher
0
0
0
0
0
0
0
0
Margaritifera marrianae
Higher
0
0
0
0
0
0
0
0
Medionidus acutissimus
Higher
0
0
0
0
0
0
0
0
Medionidus parvulus
Higher
0
0
0
0
0
0
0
0
Medionidus penicillatus
Higher
0
0
0
0
0
0
0
0
Obovaria retusa
Higher
1
1
1
1
1
1
0
0
Parvaspina collina
Higher
0
0
0
0
0
0
0
0
Plethobasus cicatricosus
Higher
0
0
0
0
0
0
0
0
Plethobasus cooperianus
Higher
1
1
1
1
1
1
0
0
Plethobasus cyphyus
Higher
11
11
1
1
11
11
0
0
Pleurobema clava
Higher
1
1
1
1
1
1
0
0
Pleurobema decisum
Higher
0
0
0
0
0
0
0
0
Pleurobema furvum
Higher
0
0
0
0
0
0
0
0
Pleurobema georgianum
Higher
0
0
0
0
0
0
0
0
Pleurobema hanleyianum
Higher
0
0
0
0
0
0
0
0
Pleurobema perovatum
Higher
0
0
0
0
0
0
0
0
Pleurobema plenum
Higher
1
1
1
1
1
1
0
0
Pleurobema pyriforme
Higher
0
0
0
0
0
0
0
0
Pleurobema taitianum
Higher
0
0
0
0
0
0
0
0
Pleuronaia dolabelloides
Higher
0
0
0
0
0
0
0
0
Potamilus capax
Higher
0
0
0
0
0
0
0
0
Potamilus inflatus
Higher
0
0
0
0
0
0
0
0
Ptychobranchus greenii
Higher
0
0
0
0
0
0
0
0
Ptychobranchus subtentus
Higher
0
0
0
0
0
0
0
0
Quadrula cylindrica cylindrica
Higher
0
0
0
0
0
0
0
0
Quadrula fragosa
Higher
0
0
0
0
0
0
0
0
Theliderma intermedia
Higher
0
0
0
0
0
0
0
0
Theliderma sparsa
Higher
0
0
0
0
0
0
0
0
Villosa fabalis
Higher3
0
0
0
0
0
0
0
0
Crustaceans
5
Antrolana lira
Higher
0
0
0
0
0
0
0
0
Cambarus aculabrum
Higher
0
0
0
0
0
0
0
0
Cambarus zophonastes
Higher
0
0
0
0
0
0
0
0
Orconectes shoupib
Higher
0
0
0
0
0
0
0
0
Palaemonias alabamae
Higher
0
0
0
0
0
0
0
0
Fishes
28
Acipenser oxyrinchus (=oxyrhynchus)
desotoi
Higher
0
0
0
0
0
0
0
0
Amblyopsis rosae
Higher
0
0
0
0
0
0
0
0
Chrosomus saylori
Higher3
0
0
0
0
0
0
0
0
Cyprinella caerulea
Higher
0
0
0
0
0
0
0
0
1-4
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BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix I: T&E Species
Table 1-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls (Shading Highlights Change from Baseline)
Species
Count
10
15
Species Name
Vulnerability
Period 1
Period 2
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Elassoma alabamae
Higher3
0
0
0
0
0
0
0
0
Etheostoma boschungi
Higher
0
0
0
0
0
0
0
0
Etheostoma chienense
Higher
0
0
0
0
0
0
0
0
Etheostoma etowahae
Higher
0
0
0
0
0
0
0
0
Etheostoma nianguae
Higher
0
0
0
0
0
0
0
0
Etheostoma phytophilum
Higher
0
0
0
0
0
0
0
0
Etheostoma rubrum
Higher
0
0
0
0
0
0
0
0
Etheostoma scotti
Higher
0
0
0
0
0
0
0
0
Etheostoma trisella
Higher
0
0
0
0
0
0
0
0
Gila cypha
Higher
3
3
3
3
3
3
3
0
Gila elegans
Higher
0
0
0
0
0
0
0
0
Macrhybopsis tetranema
Higher
0
0
0
0
0
0
0
0
Notropis cahabae
Higher
0
0
0
0
0
0
0
0
Notropis topeka (=tristis)
Higher
3
2
2
2
2
0
0
0
Oncorhynchus apache
Higher
0
0
0
0
0
0
0
0
Percina aurora
Higher
0
0
0
0
0
0
0
0
Percina rex
Higher
0
0
0
0
0
0
0
0
Percina tanasi
Higher
0
0
0
0
0
0
0
0
Ptychocheilus lucius
Higher
6
3
3
3
3
3
3
0
Salvelinus confluentus
Higher
0
0
0
0
0
0
0
0
Scaphirhynchus albus
Higher
0
0
0
0
0
0
0
0
Scaphirhynchus suttkusi
Higher
0
0
0
0
0
0
0
0
Speoplatyrhinus poulsoni
Higher3
0
0
0
0
0
0
0
0
Xyrauchen texanus
Higher
3
0
0
0
0
0
0
0
Batrisodes venyivi
Lower
0
0
0
0
0
0
0
0
Bombus affinis
Lower
0
0
0
0
0
0
0
0
Euphydryas editha taylori
Lower
0
0
0
0
0
0
0
0
Hesperia dacotae
Lower
0
0
0
0
0
0
0
0
Lycaeides melissa samuelis
Lower
0
0
0
0
0
0
0
0
Neonympha mitchellii mitchellii
Lower
0
0
0
0
0
0
0
0
Nicrophorus americanus
Lower
0
0
0
0
0
0
0
0
Rhadine exilis
Lower
0
0
0
0
0
0
0
0
Rhadine infernalis
Lower
0
0
0
0
0
0
0
0
Somatochlora hineana
Lower
0
0
0
0
0
0
0
0
Antilocapra americana sonoriensis
Lower
0
0
0
0
0
0
0
0
Canis lupus
Lower
0
0
0
0
0
0
0
0
Corynorhinus (=Plecotus) townsendii
ingens
Lower
0
0
0
0
0
0
0
0
Number of Intersected Reaches
Exceeding NRWQC for at Least One
Pollutant
1-5
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Appendix I: T&E Species
Table 1-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls (Shading Highlights Change from Baseline)
Species
Group
Species
Count
Species Name
Vulnerability
Number of Intersected Reaches
Exceeding NRWQC for at Least One
Pollutant
Period 1
Period 2
Baseline
Option A
Option B (Final Rule)
Option C
Baseline
Option A
Option B (Final Rule)
Option C
Corynorhinus (=Plecotus) townsendii
virginianus
Lower
0
0
0
0
0
0
0
0
Eum ops floridan us
Lower
0
0
0
0
0
0
0
0
Lynx canadensis
Lower
3
0
0
0
0
0
0
0
Mustela nigripes
Lower
0
0
0
0
0
0
0
0
Myotis grisescens
Moderate
1
1
1
1
1
1
0
0
Myotis septentrionalis
Lower
16
15
5
5
15
13
0
0
Myotis sodalis
Lower
12
12
2
2
12
12
0
0
Puma (=Felis) concolor (all subsp. except
coryi)
Lower
0
0
0
0
0
0
0
0
Puma (=Felis) concolor coryi
Lower
0
0
0
0
0
0
0
0
Thomomys mazama pugetensis
Lower
0
0
0
0
0
0
0
0
Thomomys mazama yelmensis
Lower
0
0
0
0
0
0
0
0
Trichechus manatus
Higher
1
1
0
0
1
1
0
0
Reptiles
19
Alligator mississippiensis
Higher
0
0
0
0
0
0
0
0
Caretta caretta
Lower
1
1
0
0
1
1
0
0
Chelonia mydas
Lower
1
1
0
0
1
1
0
0
Crocodylus acutus
Higher
0
0
0
0
0
0
0
0
Dermochelys coriacea
Lower
1
1
0
0
1
1
0
0
Drymarchon couperi
Lower
1
1
0
0
1
1
0
0
Eretmochelys imbricata
Lower
1
1
0
0
1
1
0
0
Eumeces egregius lividus
Lower
0
0
0
0
0
0
0
0
Glyptemys muhlenbergii
Higher
0
0
0
0
0
0
0
0
Gopherus agassizii
Lower
0
0
0
0
0
0
0
0
Gopherus polyphemus
Lower
0
0
0
0
0
0
0
0
Graptemys flavimaculata
Higher
0
0
0
0
0
0
0
0
Lepidochelys kempii
Lower
0
0
0
0
0
0
0
0
Neoseps reynoldsi
Lower
0
0
0
0
0
0
0
0
Pituophis melanoleucus lodingi
Lower
0
0
0
0
0
0
0
0
Pituophis ruthveni
Lower
0
0
0
0
0
0
0
0
Pseudemys alabamensis
Higher
0
0
0
0
0
0
0
0
Sistrurus catenatus
Lower
0
0
0
0
0
0
0
0
Sternotherus depressus
Higher
0
0
0
0
0
0
0
0
Snails
10
Athearnia anthonyi
Higher
0
0
0
0
0
0
0
0
Campeloma decampi
Higher
0
0
0
0
0
0
0
0
Elimia crenatella
Higher
0
0
0
0
0
0
0
0
L eptoxis for em an i
Higher
0
0
0
0
0
0
0
0
Leptoxis taeniata
Higher
0
0
0
0
0
0
0
0
Lioplax cyclostomaformis
Higher
0
0
0
0
0
0
0
0
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Appendix I: T&E Species
Table 1-2: T&E Species with Habitat Range Intersecting Reaches Downstream from Steam Electric
Power Plant Outfalls (Shading Highlights Change from Baseline)
Species
Group
Species
Count
Species Name
Vulnerability
Number of Intersected Reaches
Exceeding NRWQC for at Least One
Pollutant
Period 1
Period 2
Baseline
Option A
Option B (Final Rule)
Option C
Baseline
Option A
Option B (Final Rule)
Option C
Marstonia ogmorhaphe
Higher
0
0
0
0
0
0
0
0
Pleurocera foreman i
Higher
0
0
0
0
0
0
0
0
Triodopsis platysayoides
Lower
0
0
0
0
0
0
0
0
Tulotoma magnifica
Higher
0
0
0
0
0
0
0
0
a Species that could be categorized as highly vulnerable to water quality changes are endemic only to waters (headwater streams
and springs) that are not likely to receive discharges from steam electric plants or be affected by upstream discharges. This may be
reflected in a lower vulnerability rating for certain species.
b U.S. Fish and Wildlife Service proposed delisting this species on September 23, 2020. See notice of proposed rulemaking
"Endangered and Threatened Wildlife and Plants: Removal of the Nashville Crayfish from the Federal List of Endangered and
Threatened Wildlife." (85 FR 59732)
Source: U.S. EPA Analysis, 2024
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Appendix J: Air Quality Modeling Methodology
J Methodology for Modeling Air Quality Changes for the Final Rule
As noted in Chapter 8, EPA used photochemical modeling to create air quality surfaces157 that were then used
in air pollution benefits calculations of the final rule. The modeling-based surfaces captured air pollution
impacts resulting from changes in electricity generation profiles due to the incremental costs to generate
electricity at plants incurring water treatment costs and did not simulate the impact of emissions changes
resulting from changes in energy use by steam electric power plants or resulting from changes in trucking of
CCR and other waste. This appendix describes the source apportionment modeling and associated methods
used to create air quality surfaces for the baseline scenario and a scenario representing water treatment
technology implementation-driven EGU profile changes for 7 analytic years: 2028, 2030, 2035, 2040, 2045,
and 2050. EPA created air quality surfaces for the following pollutants and metrics: annual average PM2.5;
April-September average of 8-hr daily maximum (MDA8) ozone (AS-M03).
New ozone and PM source apportionment modeling outputs were created to support analyses in the RIAs for
multiple final EGU rulemaking efforts. The basic methodology for determining air quality changes is the
same as that used in the RIAs from multiple previous rules (U.S. EPA, 2019i, 2020a, 2020b, 2021b, 2022c).
EPA calculated baseline and Final Rule EGU emissions estimates of NOx and SO2 for all seven IPM model
years from the Integrated Planning Model (IPM) (Chapter 5 of the RIA; U.S. EPA, 2024e). EPA also used
IPM outputs to estimate EGU emissions of PM2.5 based on the methodology described in U.S. EPA (2020c).
This appendix provides additional details on the source apportionment modeling simulations and on the
methods used to translate these emissions scenarios into air quality surfaces.
J.1 Air Quality Modeling Simulations
The air quality modeling utilized a 2016-based modeling platform which included meteorology and base year
emissions from 2016 and projected future-year emissions for 2026 for all sectors other than EGUs and 2030
for EGUs. The air quality modeling included photochemical model simulations for a 2016 base year and a
future year representing the combined 2026/2030 emissions described above to provide hourly concentrations
of ozone and PM2.5 component species nationwide. In addition, source apportionment modeling was
performed for the future year to quantify the contributions to ozone from NOx emissions and to PM2 5 from
NOx, SO2 and directly emitted PM2.5 emissions from EGUs on a state-by-state and fuel-type basis. As
described below, the modeling results for 2016 and the future year, in conjunction with EGU emissions data
for the baseline and three illustrative scenarios in 2028, 2030, 2035, 2040, 2045, and 2050 were used to
construct the air quality surfaces that reflect the influence of emissions changes between the baseline and the
three illustrative scenarios in each year.
The air quality model simulations (/. e., model runs) were performed using the Comprehensive Air Quality
Model with Extensions (CAMx) version 7.10158 (Ramboll Environ, 2020). The nationwide modeling domain
(i.e., the geographic area included in the modeling) covers all lower 48 states plus adjacent portions of Canada
and Mexico using a horizontal grid resolution of 12 x 12 km shown in Figure J-l. CAMx requires a variety of
input files that contain information pertaining to the modeling domain and simulation period. These include
gridded, hourly emissions estimates and meteorological data, and initial and boundary concentrations. The
meteorological data and the initial and boundary concentrations were identical to those described in U.S. EPA
157 "air quality surfaces" refers to continuous gridded spatial fields using a 12-km grid-cell resolution
158 This CAMx simulation set the Rscale NH3 dry deposition parameter to 0 which resulted in more realistic model predictions of
PM2.5 nitrate concentrations than using a default Rscale parameter of 1.
J-1
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Appendix J: Air Quality Modeling Methodology
(2023a). Separate emissions inventories were prepared for the 2016 base year and the projected future year.
All other inputs (/. e., meteorological fields, initial concentrations, ozone column, photolysis rates, and
boundary concentrations) were specified for the 2016 base year model application and remained unchanged
for the projection-year model simulation.
2016 base year emissions are described in detail in U.S. EPA (2023q). The types of sources included in the
emission inventory include stationary point sources such as EGUs and non-EGUs; non-point emissions
sources including those from oil and gas production and distribution, agriculture, residential wood
combustion, fugitive dust, and residential and commercial heating and cooking; mobile source emissions from
onroad and nonroad vehicles, aircraft, commercial marine vessels, and locomotives; wild, prescribed, and
agricultural fires; and biogenic emissions from vegetation and soils. Future year emissions from all sources
other than EGUs were based on the 2026 emissions projections described in U.S. EPA (2023q). The Post-
IRA 2022 Reference Case of EPA's Power Sector Platform v6 using Integrated Planning Model (IPM), which
includes the Final GNP. was also reflected159. The EGU projected inventory represents demand growth, fuel
resource availability, generating technology cost and performance, and other economic factors affecting
power sector behavior. It also reflects environmental rules and regulations, consent decrees and settlements,
plant closures, and newly built units for the calendar year 2030. In this analysis, the projected EGU emissions
include provisions of tax incentives impacting electricity supply in the Inflation Reduction Act of 2022 (IRA),
Final GNP, 2021 Revised Cross-State Air Pollution Rule Update (RCU), the 2016 Standards of Performance
for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources, the Mercury and
Air Toxics Rule (MATS) finalized in 2011, and other finalized rules. Documentation and results of the Post-
IRA 2022 Reference Case, where the Final GNP was also included for EGUs, are available at
(https://www.epa.gov/power-sector-modeling/final-pm-naaqs).
Model predictions of ozone and PM2j concentrations were compared against ambient measurements (U.S.
EPA, 2022a; 2022b). Ozone and Pljfe model evaluations showed model performance that was adequate for
applying these model simulations for the purpose of creating air quality surfaces to estimate ozone and PM2.5
benefits.
Figure J-1: Air Quality Modeling Domain
/
15S https://www.epa.gov/power-sector-modeliiig/post-ira-2022-reference-case
J-2
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Appendix J: Air Quality Modeling Methodology
The contributions to ozone and PM2.5 component species (e.g., sulfate, nitrate, ammonium, elemental carbon
(EC), organic aerosol (OA), and crustal material16") from EGU emissions in individual states and from each
EGU-fuel type were modeled using the "source apportionment" tool. In general, source apportionment
modeling quantifies the air quality concentrations formed from individual, user-defined groups of emissions
sources or "tags". These source tags are tracked through the transport, dispersion, chemical transformation,
and deposition processes within the model to obtain hourly gridded161 contributions from the emissions in
each individual tag to hourly modeled concentrations. For this RIA we used the source apportionment
contribution data to provide a means to estimate the effect of changes in emissions from each group of
emissions sources (i.e.. each tag) to changes in ozone and PM2.5 concentrations. Specifically, we applied
outputs from source apportionment modeling for ozone and PM2.5 component species using the future year
modeled case to obtain the contributions from EGUs emissions in each state and fuel-type to ozone and PM2.5
component species concentrations in each 12 x 12 km model grid cell nationwide. Ozone contributions were
modeled using the Anthropogenic Precursor Culpability Assessment (APCA) tool and PM2.5 contributions
were modeled suing the Particulate Matter Source Apportionment Technology (PSAT) tool.
(Ramboll Environ, 2020). The ozone source apportionment modeling was performed for the period April
through September to provide data for developing spatial fields for the April through September maximum
daily eight hour (MDA8) (i.e., AS-M03) average ozone concentration exposure metric. The PM2.5 source
apportionment modeling was performed for a full-year to provide data for developing annual average PM2.5
spatial fields. Table J-l, Table J-2, and Table J-3 provide emissions that were tracked for each source
apportionment tag.
Table J-1: Future-year Emissions Allocated to Each Modeled Coal State Source Apportionment Tag
State
Ozone Season NOx
Annual NOx emissions
Annual S02 emissions
Annual PM2.5 emissions
Tag
Emissions
AL5
NA
5,046
1,929
700
AL+ MS5
2,541
AR4
NA
304
331
51
AZ
1,005
2,536
4,515
609
CA
222
511
99
27
CO
19
269
287
21
CT
0
0
0
0
DC
0
0
0
0
DE
0
0
0
0
FL
1,110
1,401
7,163
277
GA
1,654
2,534
3,247
159
IA
8,354
18,776
9,656
1,203
ID
0
0
0
0
IL
1,639
3,742
6,773
270
IN
4,886
18,146
26,584
2,252
KS1
NA
214
121
NA
KY
3,551
7,333
7,127
560
LA2-4
NA
47
NA
NA
MA
0
0
0
0
100 Crustal material refers to elements that are commonly found in the earth's crust such as Aluminum, Calcium, Iron, Magnesium,
Manganese, Potassium, Silicon, Titanium and the associated oxygen atoms.
101 Hourly contribution information is provided for each grid cell to provide spatial patterns of the contributions from each tag.
J-3
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Appendix J: Air Quality Modeling Methodology
Table J-1: Future-year Emissions Allocated to Each Modeled Coal State Source Apportionment Tag
State
Ozone Season NOx
Annual NOx emissions
Annual S02 emissions
Annual PM2.5 emissions
Tag
Emissions
MD3
NA
139
272
31
MD + PA3
708
NA
NA
NA
ME
0
0
0
0
Ml
1,532
4,071
12,478
380
MN
724
1,549
3,289
94
MO
2,947
23,480
38,989
853
MS5
NA
252
507
23
MT
3,771
8,842
4,056
1,252
NC
266
482
634
35
ND
8,583
19,562
25,398
1,923
NE1
NA
17,507
43,858
NA
NE + KS1
7,817
NA
NA
374
NH
0
0
0
0
NJ
0
0
0
0
NM
1,442
2,757
6,800
1,739
NV
0
1
1
0
NY
0
0
0
0
OH
3,152
10,485
21,721
901
OK4
NA
212
152
21
OR
0
0
0
0
PA3
NA
1,530
4,932
167
Rl
0
0
0
0
SC
807
1,939
3,429
364
SD
418
1,100
1,022
27
TN
259
259
269
32
—1
X
NA
7,031
NA
NA
TX + LA2
NA
NA
11,607
1,578
TX-reg4
2,698
NA
NA
NA
UT
2,702
4,236
7,625
232
VA
466
1,124
259
445
VT
0
0
0
0
WA
0
0
0
0
Wl
866
2,137
838
90
WV
6,824
16,358
17,631
1,753
WY
6,066
13,222
11,754
1,024
1KS and NE emissions grouped into multi-state tag for direct PM2.5 and ozone season NOx
2LA and TX emissions grouped into multi-state tag for S02 and direct PM2.5
3MD and PA emissions grouped into multi-state tag for ozone season NOx
4AR, LA,OK and TX emissions grouped into multi-state tag ("TX-reg") for ozone season NOx
5AL and MS emissions group into multi-state tag for ozone season NOx
Table J-2: Future-year Emissions Allocated to Each Modeled Natural Gas EGU State Source
Apportionment Tag
State
Ozone Season NOx
Annual NOx emissions
Annual S02 emissions
Annual PM2.5 emissions
Tag
Emissions
AL
2,833
5,132
0
1,979
AR
1,651
2,957
0
632
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Appendix J: Air Quality Modeling Methodology
Table J-2: Future-year Emissions Allocated to Each Modeled Natural Gas EGU State Source
Apportionment Tag
State
Ozone Season NOx
Annual NOx emissions
Annual S02 emissions
Annual PM2.5 emissions
Tag
Emissions
AZ
1,759
3,146
0
686
CA
1,960
5,773
0
1,964
CO
957
1,825
0
461
CT
461
778
0
160
DC
6
11
0
7
DE
383
502
0
134
FL
7,550
14,372
0
4,996
GA
2,279
4,182
0
1,740
IA
875
1,106
0
327
ID
336
513
0
185
IL
1,624
2,705
0
825
IN
1,180
2,166
0
955
KS
329
621
0
54
KY
980
2,806
0
699
LA
3,771
8,706
0
2,158
MA
482
725
0
244
MD
402
710
0
435
ME
232
273
0
21
Ml
6,523
11,372
0
1,508
MN
661
928
0
87
MO
587
875
0
342
MS
1,926
3,860
0
1,140
MT
11
19
0
7
NC
1,803
3,426
0
1,213
ND
25
41
0
3
NE
13
47
0
4
NH
120
136
0
34
NJ
1,024
1,910
0
608
NM
733
1,128
0
131
NV
1,693
2,471
0
648
NY
2,793
5,125
0
1,270
OH
1,838
3,824
0
1,617
OK
1,558
2,448
0
546
OR
5
188
0
87
PA
6,811
12,386
0
3,280
Rl
115
153
0
73
SC
1,092
2,090
0
917
SD
93
105
0
11
TN
464
1,107
0
388
TX
7,652
14,715
0
3,567
UT
1,189
1,779
0
514
VA
1,836
3,409
0
1,087
VT
4
8
0
6
WA
485
1,311
0
464
Wl
847
1,447
0
369
WV
109
180
0
50
WY
203
206
0
28
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Appendix J: Air Quality Modeling Methodology
Table J-3: Future-year Emissions Allocated to Each Other EGU Source Apportionment Tag
State
Ozone Season NOx
Annual NOx emissions
Annual S02 emissions
Annual PM2.5 emissions
Tag
Emissions
usa
20,611
48,619
9,631
7,915
a Only includes US emissions from the contiguous 48 states.
Examples of the magnitude and spatial extent of ozone and PM2.5 contributions are provided in Figure J-2
through Figure J-5 for EGUs in California, Georgia, Iowa, and Ohio. These figures show how the magnitude
and the spatial patterns of contributions of EGU emissions to ozone and PM2.5 component species depend on
multiple factors including the magnitude and location of emissions as well as the atmospheric conditions that
influence the formation and transport of these pollutants. For instance, NOx emissions are a precursor to both
ozone and PM2.5 nitrate. However, ozone and nitrate form under very different types of atmospheric
conditions, with ozone formation occurring in locations with ample sunlight and ambient VOC concentrations
while nitrate formation requires colder and drier conditions and the presence of gas-phase ammonia.
California's complex terrain that tends to trap air and allow pollutant build-up combined with warm sunny
summer and cooler dry winters and sources of both ammonia and VOCs make its atmosphere conducive to
formation of both ozone and nitrate. While the magnitude of EGU NOx emissions from gas plus coal EGUs is
substantially larger in Iowa than in California (Table J-l and Table J-2), the emissions from California lead to
larger maximum contributions to the formation of those pollutants due to the conducive conditions in that
state. Georgia and Ohio both had substantial NOx emissions. While maximum ozone impacts shown for
Georgia and Ohio EGUs are similar order of magnitude to maximum ozone impacts from California EGUs,
nitrate impacts are negligible in both Georgia and Ohio due to less conducive atmospheric conditions for
nitrate formation in those locations. California EGU SO2 emissions in the future year source apportionment
modeling are several orders of magnitude smaller than SO2 emissions in Ohio and Georgia (Table J-l) leading
to much smaller sulfate contributions from California EGUs than from Ohio and Georgia EGUs. PM2.5
organic aerosol EGU contributions in this modeling come from primary PM2.5 emissions rather than
secondary atmospheric formation. Consequently, the impacts of EGU emissions on this pollutant tend to
occur closer to the EGU sources than impacts of secondary pollutants (ozone, nitrate, and sulfate) which have
spatial patterns showing a broader regional impact. These patterns demonstrate how the model captures
important atmospheric processes which impact pollutant formation and transport from emissions sources.
Finally, Figure J-6 and Figure J-7 show EGU ozone and PM2.5 contributions from all contiguous U.S. EGUs
split out by fuel type. The spatial differences between coal EGU, natural gas EGU, and other EGU
contributions reflect the varying location and magnitude of emissions from each type of EGU.
J-6
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Appendix J: Air Quality Modeling Methodology
Figure J-2: Maps of California EGU Tag contributions to a) April-September Seasonal Average MDA8
Ozone (ppb); b) Annual Average PM2.5 Nitrate (pg/m3); c) Annual Average PM2.5 sulfate (pg/m3); d)
Annual Average PM2.5 Organic Aerosol (pg/m3)
a) Apr-Sep MDA8 03
>00 m
b) Annual PM2.5 nitrate
010
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-------
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Appendix J: Air Quality Modeling Methodology
Figure J-3: Maps of Georgia EGU Tag contributions to a) April-September Seasonal Average MDA8
Ozone (ppb); b) Annual Average PM2.5 Nitrate (pg/m3); c) Annual Average PM2.5 sulfate (pg/m3); d)
Annual Average PM2.5 Organic Aerosol (pg/m3)
a) Apr-Sep MDA8 03
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c) Annual PM2.5 sulfate
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J-8
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Appendix J: Air Quality Modeling Methodology
Figure J-4: Maps of Iowa EGU Tag contributions to a) April-September Seasonal Average MDA8
Ozone (ppb); b) Annual Average PM2.5 Nitrate (pg/m3); c) Annual Average PM2.5 sulfate (pg/m3); d)
Annual Average PM2.5 Organic Aerosol (pg/m3)
a) Apr-Sep MDA8 03
b) Annual PM2 5 nitrate
•M
- / ?\ l •/
p~"—J t f JfrL J VAk
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c) Annual PM2.5 sulfate
d) Annual PM2.5 OA
019
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1 WWW WW
j-9
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Appendix J: Air Quality Modeling Methodology
Figure J-5: Maps of Ohio EGU Tag contributions to a) April-September Seasonal Average MDA8
Ozone (ppb); b) Annual Average PM2.5 Nitrate (pg/m3); c) Annual Average PM2.5 sulfate (pg/m3); d)
Annual Average PM2.5 Organic Aerosol
(pg/m3)
a) Apr-Sep MDA8 03
-
b) Annual PM2.5 nitrate
01a
u*
t'
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f
tm IM
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tm
(•OXOIL Mtt • tm *IUJ.KJ1
M
Figure J-6: Maps of National EGU Tag contributions to April-September Seasonal Average MDA8
ozone (ppb) by fuel for a) Coal EGUs; b) Natural Gas EGUs; c) All Other EGUs
a) Apr-Sep MDA8 Ozone contributions
from US Coal EGUs
from US Natural Gas EGUs
n:<$-
Vs '
I *
4A -
J\
/
/
\\
y
.ygr'tf
c) Apr-Sep MDA8 Ozone contributions
from US Other EGUs
1 Kfrrr
yv / r~~~] V-
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r&um
1 , y
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y 1
j-10
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Appendix J: Air Quality Modeling Methodology
Figure J-7: Maps of National EGU Tag contributions to Annual Average PM2.5 (|jg/m3) by fuel for a)
Coal EGUs; b) Natural Gas EGUs; c) All Other EGUs
0 50
a) Annual PM2.5 contributions from US
b) Annual PM2.5 contributions from US
c) Annual PM2.5 contributions from US
0.45
Coal EGUs
Natural Gas EGUs
Other EGUs
040
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J.2 Applying Modeling Outputs to Create Spatial Fields
In this section we describe the method for creating spatial fields of AS-M03 and annual average PM2 5 based
on the 2016 and future year modeling. The foundational data include (1) ozone and speciated PM.s
concentrations in each model grid cell from the 2016 and the future year modeling, (2) ozone and speciated
PM2.5 contributions in the future year of EGUs emissions from each state in each model grid cell162, (3)
future year emissions from EGUs that were input to the contribution modeling (Table J-l, Table J-2, and
Table J-3), and (4) the EGU emissions from IPM for baseline and policy scenarios in year of analysis (2028,
2030, 2035, 2040, 2045, and 2050). The method to create spatial fields applies scaling factors to gridded
source apportionment contributions based on emissions changes between future year projections and the
baseline and the control cases to the modeled contributions. This method is described in detail below.
Spatial fields of ozone and PM; 5 in the future year were created based on "fusing" modeled data with
measured concentrations at air quality monitoring locations. To create the spatial fields for each future
emissions scenario these fused future year model fields are used in combination with future year source
apportionment modeling and the EGU emissions for each scenario and analytic year1®. Contributions from
each state and fuel EGU contribution "tag" were scaled based on the ratio of emissions in the year/scenario
being evaluated to the emissions in the modeled future year scenario. Contributions from tags representing
sources other than EGUs are held constant at 2026 levels for each of the scenarios and year. For each scenario
and year analyzed, the scaled contributions from all sources were summed together to create a gridded surface
of total modeled ozone and PM.s. The process is described in a step-by-step manner below starting with the
methodology for creating AS-M03 spatial fields followed by a description of the steps for creating annual
PM25 spatial fields.
102 Contributions from EGUs were modeled using projected emissions for the future year modeled scenario. The resulting
contributions were used to construct spatial fields in 2028, 2030, 2035, 2040, 2045, and 2050.
163 i.e., 2028,2030, 2035, 2040,2045, and 2050
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Appendix J: Air Quality Modeling Methodology
J. 2.1 Ozone
1. Create fused spatial fields of future year AS-M03 incorporating information from the air quality
modeling and from ambient measured monitoring data. The enhanced Voronoi Neighbor Average
(eVNA) technique (Gold et al., 1997; US EPA, 2007; Ding et al., 2015) was applied to ozone model
predictions in conjunction with measured data to create modeled/measured fused surfaces that leverage
measured concentrations at air quality monitor locations and model predictions at locations with no
monitoring data.
1.1. The AS-M03 eVNA spatial fields are created for the 2016 base year with EPA's software package,
Software for the Modeled Attainment Test - Community Edition (SMAT-CE) using 3 years of
monitoring data (2015-2017) and the 2016 modeled data.
1.2. The model-predicted spatial fields (i.e.. not the eVNA fields) of AS-M03 in 2016 were paired with
the corresponding model-predicted spatial fields in the future year to calculate the ratio of AS-M03
between 2016 and the future year in each model grid cell.
1.3. To create a gridded future year eVNA surfaces, the spatial fields of 2016/future year ratios created in
step (1.2) were multiplied by the corresponding eVNA spatial fields for 2016 created in step (1.1) to
produce an eVNA AS-M03 spatial field for the future year using equation 1.
Modelgjuture Eq-1
• eVNAg juture is the eVNA concentration of AS-M03 or PM2 5 component species in grid-
cell, g, in the future year
• eVNAg 2016 is the eVNA concentration of AS-M03 or PM2 5 component species in grid-
cell, g, in 2016
• Modelgjuture is the CAMx modeled concentration of AS-M03 or PM2 5 component
species in grid-cell, g, in the future year
• Modelg 2016 is the CAMx modeled concentration of AS-M03 or PM2 5 component in
grid-cell, g, in 2016
2. Create spatial fields of total EGU AS-M03 contributions for each combination of scenario and analytic
year evaluated.
2.1. Use the EGU ozone season NOX emissions for the 2028 baseline and the corresponding future
year modeled EGU ozone season emissions (Table J-l, Table J-2, and Table J-3) to calculate
the ratio of 2028 baseline emissions to future year modeled emissions for each EGU tag (i.e.. an
ozone scaling factor calculated for each state-fuel combination)164. These scaling factors are
provided in Table J-4, Table J-5, and Table J-l 1.
2.2. Calculate adjusted gridded AS-M03 EGU contributions that reflect differences in state-fuel
EGU NOX emissions between the modeled future year and the 2028 baseline by multiplying
the ozone season NOX scaling factors by the corresponding gridded AS-M03 ozone
contributions from each state-fuel EGU tag.
2.3. Add together the adjusted AS-M03 contributions for each EGU-state tag to produce spatial
164 Preliminary testing of this methodology showed unstable results when very small magnitudes of emissions were tagged
especially when being scaled by large factors. To mitigate this issue, in cases where state-fuel EGU tags were associated with no
or very small emissions, tags were combined into multi-state regions.
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Appendix J: Air Quality Modeling Methodology
fields of adjusted EGU totals for the 2028 baseline.165
2.4. Repeat steps 2.1 through 2.3 for the 2028 final rule scenario and for the baseline and final rule
scenarios for each additional analytic year. The scaling factors for the baseline scenarios and
the final rule scenarios are provided in Table J-4, Table J-5, and Table J-l 1.
3. Create a gridded spatial field of AS-M03 associated with IPM emissions for the 2028 baseline by
combining the EGU AS-M03 contributions from steps (2.3) with the corresponding contributions to AS-
M03 from all other sources. Repeat for each of the EGU contributions created in step (2.4) to create
separate gridded spatial fields for the rest of the baseline and final rule scenarios for each analytic year.
Steps 2 and 3 in combination can be represented by equation 2:
AS-M03g i y = eVNAg y
AS-M03g i y = eVNAg y
T
Cg,fires Cg usanthro \ ' ^EGUVOC,g,t
Cg,Tot Cg,Tot Cgjot ^g,Tot Eq-2
Cg,BC Cg,int ^g,bio
• AS-M03g i y is the estimated fused model-obs AS-M03 for grid-cell, "g", scenario, "i"166, and year,
«y,167.
• eVNAg juture is the future year eVNA future year AS-M03 concentration for grid-cell "g" calculated
using Eq-1.
• Cg,Tot is the total modeled AS-M03 for grid-cell "g" from all sources in the future year source
apportionment modeling
• Cg,BC is the future year AS-M03 modeled contribution from the modeled boundary inflow;
• Cg.int is the future year AS-M03 modeled contribution from international emissions within the
modeling domain;
• Cg,bio is the future year AS-M03 modeled contribution from biogenic emissions;
• Cg,fireS is the future year AS-M03 modeled contribution from fires;
• Cg,usanthro's the total future year AS-M03 modeled contribution from U.S. anthropogenic sources
other than EGUs;
• CEGUvoc,g,t is the future year AS-M03 modeled contribution from EGU emissions of VOCs from
state, "t";
165 The contributions from the unaltered 03V tags are added to the summed adjusted 03N EGU tags.
166 Scenario "i" can represent either the baseline or the final rule scenario
167 Analytic year "y" can represent 2028, 2030, 2035, 2040,2045 or 2050
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Appendix J: Air Quality Modeling Methodology
• CecuNOx,g,t is the future year AS-M03 modeled contribution from EGU emissions of NOx from tag,
"t"; and ' '
• SNOx,t,i,y is the EGU NOx scaling factor for tag, "t", scenario "i", and year, "y".
J.2.2 PM2. s
4. Create fused spatial fields of future year annual PM2 5 component species incorporating information from
the air quality modeling and from ambient measured monitoring data. The eVNA technique was applied
to PM2 5 component species model predictions in conjunction with measured data to create
modeled/measured fused surfaces that leverage measured concentrations at air quality monitor locations
and model predictions at locations with no monitoring data.
4.1. The quarterly average PM2 5 component species eVNA spatial fields are created for the 2016 base
year with EPA's SMAT-CE software package using 3 years of monitoring data (2015-2017) and the
2016 modeled data.
4.2. The model-predicted spatial fields (i.e.. not the eVNA fields) of quarterly average PM2 5 component
species in 2016 were paired with the corresponding model-predicted spatial fields in the future year
to calculate the ratio of PM2 5 component species between 2016 and the future year in each model
grid cell.
4.3. To create a gridded future year eVNA surfaces, the spatial fields of 2016/future year ratios created in
step (4.2) were multiplied by the corresponding eVNA spatial fields for 2016 created in step (4.1) to
produce an eVNA annual average PM2 5 component species spatial field for the future year using
(Eq-1).
5. Create spatial fields of total EGU speciated PM2 5 contributions for each year/scenario evaluated.
5.1. Use the annual total NOX, S02 and PM2.5 emissions for the 2028 baseline scenario and the
corresponding future year modeled EGU NOX, SO_,2 and PM2.5 emissions to calculate the
ratio of 2028 baseline emissions to future year modeled emissions for each EGU state-fuel
contribution tag (i.e.. annual NOX, S02 and PM2.5 scaling factors calculated for each state and
fuel combination). These scaling factors are provided in Table J-6 through Table J-l 1.
5.2. Calculate adjusted gridded annual PM2 5 component species EGU contributions that reflect
differences in state-EGU NOx, SO2 and primary PM2 5 emissions between the modeled future
year and the 2028 baseline by multiplying the annual NOx, SO2 and PM2 5 scaling factors by the
corresponding annual gridded PM2 5 component species contributions from each state-fuel EGU
tag168.
5.3. Add together the adjusted PM2 5 contributions of for each EGU state-fuel tag to produce spatial
fields of adjusted EGU totals for each PM2 5 component species.
5.4.Repeat steps 5.1 through 5.3 for the 2028 final rule scenario and forthe baseline and final rule
scenarios for each additional analytic year. The scaling factors for all PM2 5 component species
forthe baseline and the final rule scenarios are provided in Table J-6 through Table J-l 1.
6. Create gridded spatial fields of each PM2 5 component species for the 2028 baseline by combining the
EGU annual PM25 component species contributions from step (5.3) with the corresponding contributions
168 Scaling factors for components that are formed through chemical reactions in the atmosphere were created as follows: scaling
factors for sulfate were based on relative changes in annual SO2 emissions; scaling factors for nitrate were based on relative
changes in annual NOx emissions. Scaling factors for PM2.5 components that are emitted directly from the source (OA, EC,
crustal) were based on the relative changes in annual primary PM2.5 emissions between the future year modeled emissions and the
baseline and the final rule scenarios in each year.
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Appendix J: Air Quality Modeling Methodology
to annual PM2 5 component species from all other sources. Repeat for each of the EGU contributions
created in step (5.4) to create separate gridded spatial fields for the rest of the baseline and policy
scenarios and analytic years.
7. Create gridded spatial fields of total PM2 5 mass by combining the component species surfaces for sulfate,
nitrate, organic aerosol, elemental carbon and crustal material with ammonium, and particle-bound.
Ammonium and particle-bound water concentrations are calculated for each scenario based on nitrate and
sulfate concentrations along with the ammonium degree of neutralization in the base year modeling in
accordance with equations from the SMAT-CE modeling software.
Steps 5 and 6 result in equation 3 for PM2 5 component species: sulfate, nitrate, organic aerosol, elemental
carbon and crustal material.
• PMs g i y is the estimated fused model-obs PM component species "s" for grid-cell, "g", scenario,
• eVNAs gjuture is the future year eVNA PM concentration for component species "s" in grid-cell "g"
calculated using Eq-1.
• Cs,g,Tot is the total modeled PM component species "s" for grid-cell "g" from all sources in the future
year source apportionment modeling
• Cs,g,BC is the future year PM component species "s" modeled contribution from the modeled
boundary inflow;
• Cs,g,int is the future year PM component species "s" modeled contribution from international
emissions within the modeling domain;
• Cs,g,bio is the future year PM component species "s" modeled contribution from biogenic emissions;
• Cs,g,fires is the future year PM component species "s" modeled contribution from fires;
• Cs,g,usanthro is the total future year PM component species "s" modeled contribution from U.S.
anthropogenic sources other than EGUs;
169 Scenario "i" can represent either baseline or the final rule scenario.
170 Analytic year "y" can represent 2028, 2030, 2035, 2040,2045, or 2050
PMs,g,i,y = eVNAs,g,y
Eq-3
"i"169, and year,' y
" ''170.
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Appendix J: Air Quality Modeling Methodology
• CEGUs,g,t is the future year PM component species ""s" modeled contribution from EGU emissions of
NOx, SO2, or primary PM2 5 from tag, ""t": and
• Ss,t,i,y is the EGU scaling factor for component species ""s". tag, ""t". scenario ""i". and year, ""y".
Scaling factors for nitrate are based on annual NOx emissions, scaling factors for sulfate are based on
annual SO2 emissions, scaling factors for primary PM2.5 components are based on primary PM2.5
emissions.
Selected maps showing changes in air quality concentrations between the final rule and the baseline are
provided later in this appendix.
J.3 Scaling Factors Applied to Source Apportionment Tags
Table J-4: Baseline and Final Rule Scenario Ozone Scaling Factors for Coal EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
ALMS3
1.40
1.65
1.47
1.47
0.38
0.38
1.19
1.65
1.47
1.47
0.38
0.38
AZ
0.01
1.43
1.13
0.00
0.00
0.98
0.01
1.40
1.15
0.00
0.00
0.98
CA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
CO
139.01
1.28
1.98
1.98
1.98
1.98
139.01
1.28
1.98
1.98
1.98
1.98
CT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
DC
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
DE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
FL
0.47
1.24
0.10
0.10
0.03
0.03
0.44
0.93
0.10
0.10
0.03
0.03
GA
0.00
0.18
0.00
0.00
0.00
0.00
0.00
0.43
0.00
0.00
0.00
0.00
IA
1.17
1.18
0.77
0.46
0.42
0.81
1.17
1.18
0.72
0.46
0.42
0.81
ID
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
IL
0.97
0.96
0.81
0.14
0.00
0.00
0.97
0.96
0.77
0.10
0.00
0.00
IN
1.35
0.76
0.19
0.19
0.00
0.00
1.35
0.77
0.19
0.19
0.00
0.00
KY
0.79
0.95
0.97
0.83
0.06
0.15
0.65
0.84
0.60
0.57
0.00
0.15
MA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
MDPAb
3.14
3.17
2.58
1.06
1.30
1.31
3.07
3.07
2.53
1.06
1.30
1.37
ME
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Ml
0.75
0.00
0.00
0.00
0.00
0.00
0.73
0.00
0.00
0.00
0.00
0.00
MN
2.41
2.25
0.00
0.00
0.00
0.00
2.41
2.25
0.00
0.00
0.00
0.00
MO
2.72
1.57
0.67
0.31
0.27
0.56
2.68
1.59
0.66
0.28
0.26
0.52
MT
1.07
1.12
1.11
0.99
0.00
0.78
1.07
1.12
1.10
0.99
0.00
0.77
NC
9.89
6.41
2.86
1.50
2.86
3.98
12.69
9.43
2.86
0.00
2.57
3.98
ND
1.09
1.08
0.25
0.24
0.01
0.02
1.08
1.07
0.25
0.24
0.01
0.02
NEKSC
1.79
1.87
0.76
0.59
0.41
0.68
1.55
1.61
0.76
0.59
0.39
0.68
NH
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NJ
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NM
0.98
0.98
0.01
0.01
0.01
0.01
0.98
0.98
0.01
0.01
0.01
0.01
NV
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NY
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
OH
0.58
1.07
0.00
0.00
0.00
0.70
0.57
0.77
0.00
0.00
0.00
0.68
OR
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Rl
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
SC
0.81
2.22
3.18
3.18
0.00
0.00
0.48
2.21
3.18
3.18
0.00
0.00
SD
0.87
1.33
0.00
0.00
0.00
0.00
0.87
1.33
0.00
0.00
0.00
0.00
TN
3.89
0.01
0.00
0.00
0.00
0.00
3.79
0.01
0.00
0.00
0.00
0.00
TX-regd
2.69
2.03
1.54
0.95
0.44
1.40
2.64
2.15
1.56
0.95
0.44
1.39
J-16
-------
BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix J: Air Quality Modeling Methodology
Table J-4: Baseline and Final Rule Scenario Ozone Scaling Factors for Coal EGU Tags
State
Baseline
Final Rule
Tag
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
UT
1.00
0.06
0.06
0.06
0.04
0.00
1.00
0.06
0.06
0.06
0.04
0.00
VA
0.65
0.45
0.00
0.00
0.00
0.00
0.65
0.41
0.00
0.00
0.00
0.00
VT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
WA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Wl
1.66
2.16
0.36
0.00
0.00
0.66
1.66
2.16
0.36
0.00
0.00
0.66
WV
0.92
1.16
0.92
0.27
0.10
0.10
0.76
1.00
0.58
0.27
0.10
0.10
WY
1.26
1.12
1.12
0.61
0.53
0.52
1.26
1.12
1.12
0.61
0.53
0.52
ALMS
1.40
1.65
1.47
1.47
0.38
0.38
1.19
1.65
1.47
1.47
0.38
0.38
aALMS: AL, MS
bMDPA: MD, PA
CNEKS: NE, KS
dTX-reg: AR, LA, OK, TX
Table J-5: Baseline and Final Rule Scenario Ozone Scaling Factors for Natural Gas EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
AL
0.53
0.61
0.49
0.39
0.27
0.37
0.52
0.50
0.49
0.38
0.27
0.36
AR
0.65
0.68
0.43
0.20
0.10
0.18
0.65
0.68
0.43
0.19
0.10
0.18
AZ
0.69
0.68
0.67
0.68
0.45
0.69
0.69
0.68
0.67
0.68
0.45
0.69
CA
0.92
0.94
0.85
0.52
0.02
0.04
0.92
0.94
0.85
0.51
0.02
0.04
CO
3.26
0.63
0.50
0.48
0.12
0.17
3.27
0.63
0.50
0.47
0.12
0.17
CT
1.04
0.98
0.89
0.00
0.01
0.01
1.05
0.98
0.89
0.00
0.01
0.01
DC
0.86
0.59
0.33
0.21
0.16
0.16
0.86
0.59
0.33
0.21
0.16
0.16
DE
0.79
0.80
0.38
0.37
0.38
0.43
0.77
0.78
0.38
0.37
0.32
0.42
FL
1.08
1.03
1.04
0.89
0.66
0.65
1.07
1.04
1.03
0.89
0.65
0.64
GA
0.58
0.54
0.52
0.42
0.38
0.41
0.58
0.53
0.52
0.41
0.38
0.41
IA
0.53
0.42
0.16
0.04
0.01
0.04
0.53
0.43
0.15
0.04
0.01
0.05
ID
0.60
0.90
0.90
0.90
0.04
0.09
0.59
0.90
0.88
0.88
0.03
0.09
IL
0.69
0.61
0.42
0.21
0.00
0.00
0.67
0.62
0.41
0.20
0.00
0.00
IN
0.75
0.63
0.38
0.20
0.15
0.21
0.74
0.64
0.38
0.20
0.16
0.21
KS
1.38
1.32
0.25
0.14
0.10
0.03
1.39
1.33
0.33
0.15
0.11
0.03
KY
0.87
0.81
0.69
0.57
0.38
0.49
0.96
0.90
0.83
0.66
0.45
0.59
LA
1.04
1.00
0.72
0.45
0.41
0.56
1.03
1.00
0.71
0.45
0.40
0.56
MA
0.60
0.67
0.66
0.84
0.47
0.64
0.59
0.68
0.66
0.80
0.45
0.64
MD
1.51
1.33
1.12
0.84
0.79
1.04
1.34
1.24
1.10
0.83
0.72
1.04
ME
1.16
1.15
0.59
0.63
0.36
0.56
1.16
1.15
0.59
0.64
0.36
0.56
Ml
0.68
0.70
0.55
0.41
0.23
0.40
0.67
0.63
0.54
0.40
0.23
0.40
MN
0.92
0.84
0.34
0.17
0.13
0.21
0.85
0.78
0.34
0.17
0.13
0.21
MO
0.59
0.59
0.20
0.08
0.04
0.06
0.57
0.57
0.20
0.08
0.04
0.06
MS
0.64
0.62
0.50
0.45
0.29
0.34
0.63
0.59
0.50
0.44
0.26
0.33
MT
0.95
1.10
0.08
0.14
0.02
0.24
0.95
0.79
0.08
0.14
0.02
0.34
NC
0.77
0.59
0.68
0.63
0.51
0.59
0.73
0.55
0.69
0.62
0.48
0.59
ND
0.85
1.85
0.34
0.96
0.14
0.66
0.85
1.84
0.34
0.96
0.14
0.16
NE
5.91
5.92
0.28
0.87
0.02
1.02
5.80
5.98
0.33
0.87
0.05
1.02
NH
0.67
0.51
0.41
0.41
0.41
0.40
0.68
0.51
0.41
0.41
0.41
0.40
NJ
0.81
0.85
0.61
0.49
0.46
0.75
0.77
0.85
0.59
0.48
0.45
0.74
NM
1.00
0.84
0.77
0.35
0.47
0.40
1.00
0.84
0.77
0.33
0.48
0.40
J-17
-------
BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix J: Air Quality Modeling Methodology
Table J-5: Baseline and Final Rule Scenario Ozone Scaling Factors for Natural Gas EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
NV
0.33
0.25
0.19
0.21
0.12
0.09
0.33
0.25
0.19
0.21
0.11
0.09
NY
1.03
0.99
0.65
0.28
0.28
0.28
0.97
0.97
0.62
0.28
0.28
0.28
OH
1.02
0.97
0.84
0.71
0.62
0.80
1.11
1.07
0.94
0.80
0.71
0.81
OK
1.69
1.57
0.48
0.33
0.32
0.32
1.65
1.56
0.48
0.33
0.32
0.33
OR
63.29
0.00
0.00
0.00
0.00
0.00
63.38
0.00
0.00
0.00
0.00
0.00
PA
0.79
0.69
0.34
0.24
0.23
0.35
0.74
0.64
0.35
0.23
0.23
0.35
Rl
0.69
0.75
0.71
0.88
0.89
0.46
0.69
0.75
0.72
0.88
0.89
0.46
SC
0.93
0.96
0.59
0.59
0.56
0.83
0.91
0.94
0.58
0.59
0.60
0.83
SD
0.59
0.59
0.17
0.06
0.03
0.07
0.54
0.59
0.16
0.06
0.02
0.07
TN
1.12
1.09
1.07
0.90
0.51
0.72
1.13
1.10
1.05
0.87
0.49
0.64
TX
0.99
0.89
0.47
0.28
0.15
0.32
0.98
0.88
0.47
0.28
0.15
0.32
UT
0.50
0.43
0.34
0.37
0.31
0.41
0.50
0.43
0.34
0.37
0.30
0.41
VA
0.89
0.85
0.54
0.32
0.26
0.12
0.84
0.83
0.54
0.31
0.17
0.12
VT
0.00
0.37
3.53
3.99
0.00
1.58
0.00
0.37
3.53
3.99
0.00
1.58
WA
0.08
0.23
0.79
0.74
0.02
0.02
0.08
0.23
0.85
0.74
0.02
0.02
Wl
0.74
0.70
0.58
0.30
0.14
0.24
0.73
0.66
0.41
0.30
0.14
0.23
WV
1.19
1.12
0.33
0.13
0.07
2.97
1.25
1.18
0.39
0.16
0.11
2.99
WY
0.01
0.04
0.06
0.06
0.00
0.05
0.01
0.05
0.06
0.05
0.00
0.05
Table J-6: Baseline and Final Rule Scenario Nitrate Scaling Factors for Coal EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
AL
1.33
1.45
1.65
1.54
0.14
0.23
1.36
1.50
1.65
1.54
0.14
0.23
AR
39.93
8.30
3.83
0.71
0.28
2.49
39.48
8.53
3.83
0.71
0.28
2.51
AZ
0.47
0.97
0.59
0.20
0.15
0.69
0.47
0.97
0.60
0.19
0.15
0.69
CA
0.24
0.36
0.16
0.13
0.00
0.00
0.24
0.36
0.16
0.13
0.00
0.00
CO
25.56
0.97
0.37
0.41
0.37
0.40
25.64
0.97
0.37
0.41
0.37
0.40
CT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
DC
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
DE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
FL
0.89
1.20
0.26
0.26
0.14
0.18
0.76
1.01
0.26
0.26
0.14
0.18
GA
0.23
0.12
0.00
0.00
0.00
0.00
0.53
0.35
0.00
0.00
0.00
0.00
IA
1.20
1.16
0.68
0.28
0.19
0.57
1.20
1.19
0.65
0.27
0.19
0.57
ID
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
IL
0.98
0.92
0.62
0.14
0.00
0.00
0.98
0.93
0.58
0.10
0.00
0.00
IN
1.29
0.64
0.11
0.11
0.00
0.00
1.36
0.68
0.11
0.11
0.00
0.00
KS
45.15
46.03
3.08
3.08
0.00
0.00
36.98
39.58
3.08
3.08
0.00
0.00
KY
1.38
1.12
1.15
1.00
0.07
0.16
1.19
1.01
0.77
0.70
0.05
0.16
LA
24.63
16.33
25.37
13.43
2.22
16.83
24.63
16.56
26.42
13.43
2.22
16.83
MA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
MD
3.54
3.54
3.54
3.54
2.97
3.42
3.54
3.54
3.54
3.54
2.97
3.42
ME
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Ml
0.74
0.00
0.00
0.00
0.00
0.00
0.73
0.00
0.00
0.00
0.00
0.00
MN
2.97
2.31
0.00
0.00
0.00
0.00
2.97
2.25
0.00
0.00
0.00
0.00
MO
1.41
1.06
0.43
0.04
0.03
0.09
1.39
1.06
0.43
0.04
0.03
0.08
MS
4.02
3.60
1.06
1.00
1.00
1.00
1.94
3.60
1.06
1.00
1.00
1.00
MT
1.07
1.09
1.08
1.02
0.38
0.79
1.07
1.10
1.08
1.02
0.38
0.79
NC
19.19
11.95
3.66
3.51
3.84
4.16
21.30
11.96
3.68
2.58
3.69
4.16
J-18
-------
BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix J: Air Quality Modeling Methodology
Table J-6: Baseline and Final Rule Scenario Nitrate Scaling Factors for Coal EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
ND
1.03
1.03
0.25
0.25
0.01
0.02
1.03
1.03
0.26
0.25
0.01
0.02
NE
1.14
1.13
0.61
0.37
0.18
0.46
1.03
1.02
0.61
0.37
0.17
0.46
NH
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NJ
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NM
0.99
0.99
0.01
0.01
0.01
0.01
0.99
0.99
0.01
0.01
0.01
0.01
NV
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NY
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
OH
0.90
0.94
0.19
0.00
0.00
0.40
0.81
0.84
0.25
0.00
0.00
0.40
OK
12.10
5.08
3.11
3.11
1.03
1.03
11.50
5.19
3.11
3.11
1.03
1.03
OR
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
PA
3.05
2.94
2.61
1.19
1.16
1.23
2.98
2.88
2.56
1.20
1.15
1.22
Rl
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
SC
1.15
1.92
2.98
2.98
0.00
0.00
0.98
1.91
2.98
2.98
0.00
0.00
SD
0.93
1.11
0.00
0.00
0.00
0.00
0.93
1.11
0.00
0.00
0.00
0.00
TN
7.49
1.00
0.00
0.00
0.00
0.00
7.39
1.00
0.00
0.00
0.00
0.00
TX
1.02
1.13
0.87
0.47
0.12
0.42
1.03
1.20
0.88
0.47
0.12
0.41
UT
3.50
0.09
0.09
0.09
0.06
0.04
3.50
0.09
0.09
0.09
0.06
0.04
VA
0.67
0.41
0.12
0.00
0.00
0.00
0.67
0.31
0.12
0.00
0.00
0.00
VT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
WA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Wl
1.84
2.07
0.38
0.00
0.00
0.27
1.81
2.10
0.37
0.00
0.00
0.27
WV
1.25
1.30
0.97
0.27
0.09
0.10
1.06
1.16
0.61
0.27
0.09
0.10
WY
1.32
1.15
1.14
0.61
0.48
0.51
1.32
1.15
1.14
0.61
0.48
0.51
Table J-7: Baseline and Final Rule Scenario Nitrate Scaling Factors for Natural Gas EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
AL
0.59
0.60
0.45
0.27
0.16
0.23
0.58
0.53
0.46
0.27
0.16
0.23
AR
0.56
0.68
0.38
0.13
0.06
0.12
0.56
0.68
0.38
0.13
0.06
0.12
AZ
0.73
0.85
0.83
0.75
0.37
0.62
0.73
0.85
0.82
0.74
0.38
0.62
CA
0.76
0.88
0.97
0.67
0.16
0.19
0.76
0.89
0.97
0.67
0.15
0.19
CO
2.02
0.71
0.72
0.76
0.30
0.51
2.02
0.71
0.72
0.74
0.30
0.50
CT
0.92
0.81
0.66
0.00
0.01
0.01
0.92
0.81
0.66
0.00
0.01
0.01
DC
0.63
0.47
0.26
0.18
0.13
0.11
0.63
0.47
0.26
0.17
0.13
0.12
DE
0.79
0.76
0.33
0.29
0.30
0.42
0.77
0.70
0.33
0.29
0.26
0.41
FL
1.11
1.06
1.01
0.73
0.49
0.51
1.10
1.05
1.00
0.72
0.49
0.50
GA
0.68
0.63
0.54
0.29
0.22
0.26
0.67
0.62
0.54
0.29
0.22
0.26
IA
0.49
0.42
0.13
0.03
0.01
0.04
0.49
0.42
0.13
0.03
0.01
0.04
ID
1.02
1.36
1.39
1.24
0.60
0.84
1.00
1.35
1.36
1.21
0.59
0.84
IL
0.54
0.54
0.29
0.12
0.00
0.00
0.53
0.54
0.29
0.12
0.00
0.00
IN
0.67
0.59
0.34
0.12
0.08
0.12
0.65
0.59
0.34
0.12
0.09
0.12
KS
0.96
0.87
0.20
0.07
0.05
0.02
0.96
0.92
0.25
0.08
0.06
0.02
KY
0.81
0.76
0.46
0.25
0.15
0.22
0.88
0.80
0.55
0.30
0.17
0.27
LA
0.96
0.94
0.61
0.27
0.24
0.34
0.95
0.93
0.60
0.27
0.24
0.34
MA
0.64
0.66
0.54
0.61
0.33
0.52
0.63
0.67
0.54
0.58
0.32
0.52
MD
1.47
1.35
1.05
0.72
0.66
0.82
1.36
1.27
1.03
0.72
0.61
0.85
ME
1.64
1.34
0.63
0.58
0.34
0.55
1.64
1.37
0.63
0.60
0.34
0.55
Ml
0.65
0.71
0.43
0.30
0.15
0.28
0.65
0.64
0.43
0.28
0.15
0.27
J-19
-------
BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix J: Air Quality Modeling Methodology
Table J-7: Baseline and Final Rule Scenario Nitrate Scaling Factors for Natural Gas EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
MN
1.02
0.95
0.36
0.15
0.09
0.18
0.97
0.90
0.36
0.15
0.09
0.18
MO
0.52
0.52
0.19
0.06
0.03
0.05
0.50
0.51
0.19
0.06
0.03
0.05
MS
0.61
0.56
0.36
0.24
0.15
0.19
0.58
0.53
0.35
0.23
0.13
0.18
MT
0.66
0.80
0.05
0.08
0.01
0.14
0.66
0.61
0.05
0.08
0.01
0.20
NC
0.89
0.67
0.72
0.55
0.47
0.62
0.85
0.64
0.72
0.54
0.45
0.62
ND
0.66
1.32
0.26
0.60
0.09
0.41
0.66
1.33
0.26
0.60
0.09
0.10
NE
2.05
1.80
0.13
0.31
0.01
0.28
2.04
1.84
0.14
0.30
0.01
0.28
NH
0.78
0.59
0.44
0.38
0.36
0.41
0.79
0.58
0.43
0.38
0.36
0.41
NJ
0.82
0.83
0.51
0.34
0.39
0.67
0.78
0.81
0.48
0.34
0.38
0.66
NM
0.74
0.66
0.64
0.33
0.39
0.36
0.74
0.66
0.64
0.32
0.39
0.37
NV
0.50
0.39
0.44
0.40
0.23
0.18
0.50
0.39
0.44
0.40
0.23
0.18
NY
0.91
0.89
0.55
0.16
0.16
0.17
0.88
0.89
0.54
0.16
0.16
0.17
OH
1.00
0.98
0.87
0.59
0.42
0.61
1.10
1.08
0.96
0.66
0.48
0.60
OK
1.43
1.20
0.34
0.21
0.20
0.21
1.38
1.18
0.34
0.21
0.20
0.21
OR
5.58
0.96
0.50
0.00
0.00
0.00
5.58
0.96
0.49
0.00
0.00
0.00
PA
0.69
0.61
0.35
0.21
0.18
0.31
0.66
0.57
0.35
0.20
0.18
0.31
Rl
0.76
0.76
0.64
0.71
0.68
0.45
0.77
0.77
0.65
0.71
0.68
0.45
SC
0.94
0.96
0.67
0.56
0.55
0.83
0.88
0.94
0.67
0.56
0.55
0.83
SD
0.55
0.55
0.16
0.06
0.04
0.08
0.51
0.57
0.15
0.06
0.04
0.08
TN
1.02
0.97
0.79
0.41
0.23
0.34
1.02
0.96
0.77
0.40
0.22
0.30
TX
0.97
0.88
0.42
0.17
0.08
0.20
0.97
0.88
0.42
0.17
0.08
0.20
UT
0.52
0.62
0.56
0.58
0.46
0.61
0.52
0.62
0.55
0.57
0.46
0.61
VA
0.84
0.80
0.43
0.20
0.15
0.09
0.80
0.75
0.42
0.20
0.10
0.09
VT
0.10
0.16
1.53
1.73
0.00
0.68
0.10
0.16
1.53
1.73
0.00
0.68
WA
0.43
0.36
0.72
0.97
0.44
0.27
0.43
0.36
0.74
0.97
0.43
0.26
Wl
0.66
0.67
0.45
0.18
0.08
0.14
0.65
0.63
0.34
0.18
0.08
0.14
WV
1.02
0.89
0.22
0.08
0.04
3.06
1.10
0.95
0.30
0.14
0.09
3.06
WY
0.01
0.04
0.06
0.06
0.00
0.05
0.01
0.05
0.06
0.05
0.00
0.05
Table J-8: Baseline and Final Rule Scenario Sulfate Scaling Factors for Coal EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
AL
4.96
5.39
7.07
5.96
0.34
0.55
5.29
5.56
6.77
6.49
0.34
0.55
AR
118.10
7.02
4.45
1.09
0.42
2.83
116.64
7.40
4.45
1.09
0.42
2.85
AZ
0.48
1.42
1.16
0.32
0.31
1.47
0.48
1.42
1.16
0.32
0.31
1.47
CA
0.33
0.50
0.26
0.19
0.00
0.00
0.33
0.50
0.26
0.19
0.00
0.00
CO
14.31
0.98
0.20
0.22
0.21
0.23
14.40
0.98
0.20
0.22
0.21
0.23
CT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
DC
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
DE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
FL
0.98
1.16
0.50
0.50
0.38
0.50
0.89
1.03
0.50
0.50
0.38
0.50
GA
0.04
0.09
0.00
0.00
0.00
0.00
0.10
0.23
0.00
0.00
0.00
0.00
IA
1.31
1.25
0.78
0.32
0.21
0.66
1.31
1.27
0.75
0.31
0.21
0.66
ID
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
IL
1.01
0.73
0.48
0.10
0.00
0.00
1.01
0.74
0.46
0.08
0.00
0.00
IN
0.89
0.56
0.12
0.13
0.00
0.00
0.91
0.60
0.12
0.13
0.00
0.00
KS
52.35
51.92
11.39
11.39
0.00
0.00
43.14
45.52
11.39
11.39
0.00
0.00
KY
2.68
2.12
1.88
1.71
0.09
0.21
2.41
2.01
1.47
1.39
0.06
0.21
J-20
-------
BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix J: Air Quality Modeling Methodology
Table J-8: Baseline and Final Rule Scenario Sulfate Scaling Factors for Coal EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
MA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
MD
3.54
3.54
3.54
3.54
2.97
3.42
3.54
3.54
3.54
3.54
2.97
3.42
ME
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Ml
0.85
0.00
0.00
0.00
0.00
0.00
0.85
0.00
0.00
0.00
0.00
0.00
MN
1.68
1.47
0.00
0.00
0.00
0.00
1.68
1.43
0.00
0.00
0.00
0.00
MO
2.20
1.08
0.71
0.10
0.12
0.36
2.17
1.09
0.70
0.09
0.11
0.35
MS
4.02
3.60
1.06
1.00
1.00
1.00
1.94
3.60
1.06
1.00
1.00
1.00
MT
1.85
2.06
1.92
1.30
0.39
0.86
1.85
2.07
1.89
1.30
0.39
0.86
NC
7.31
5.14
1.88
1.67
2.03
1.38
8.56
4.95
1.89
1.36
1.90
1.38
ND
0.94
1.00
0.94
0.93
0.03
0.03
0.94
1.01
0.94
0.93
0.03
0.03
NE
0.96
0.95
0.58
0.35
0.18
0.57
0.92
0.91
0.57
0.35
0.17
0.57
NH
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NJ
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NM
1.00
1.00
0.01
0.01
0.01
0.01
1.00
1.00
0.01
0.01
0.01
0.01
NV
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NY
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
OH
0.78
0.61
0.29
0.00
0.00
0.36
0.63
0.65
0.16
0.00
0.00
0.35
OK
37.84
4.77
2.54
2.54
1.68
1.68
37.24
4.85
2.54
2.54
1.68
1.68
OR
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
PA
4.25
4.06
3.94
1.63
1.83
1.72
4.26
4.15
4.02
1.67
1.85
1.73
Rl
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
SC
0.73
1.22
1.76
1.76
0.00
0.00
0.65
1.22
1.76
1.76
0.00
0.00
SD
1.05
1.27
0.00
0.00
0.00
0.00
1.06
1.27
0.00
0.00
0.00
0.00
TN
20.55
1.57
0.00
0.00
0.00
0.00
20.19
1.57
0.00
0.00
0.00
0.00
TXLAa
1.86
2.39
2.25
1.61
0.42
1.29
1.86
2.45
2.28
1.60
0.42
1.28
UT
0.93
0.06
0.06
0.05
0.04
0.02
0.94
0.06
0.06
0.05
0.04
0.02
VA
0.11
0.07
0.02
0.00
0.00
0.00
0.11
0.05
0.02
0.00
0.00
0.00
VT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
WA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Wl
3.50
3.83
1.15
0.00
0.00
0.69
3.93
3.88
1.11
0.00
0.00
0.69
WV
1.40
1.39
1.08
0.36
0.12
0.13
1.31
1.21
0.75
0.35
0.12
0.13
WY
1.26
0.98
0.97
0.49
0.37
0.37
1.26
0.98
0.97
0.49
0.37
0.37
Note: Emissions of Louisiana are less 10 tpy in the original source apportionment modeling. Air quality impacts and emissions from
Texas and Louisiana were combined.
aTXLA: Louisiana and Texas
Table J-9: Baseline and Final Rule Primary PM2.5 Scaling Factors for Coal EGU Tags
State
Baseline
Final Rule
Tag
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
AL
1.20
1.31
1.43
1.33
0.14
0.22
1.21
1.36
1.43
1.33
0.14
0.22
AR
20.02
7.10
3.14
0.08
0.03
2.20
19.77
7.32
3.14
0.08
0.03
2.22
AZ
0.38
1.17
0.61
0.18
0.16
0.76
0.38
1.18
0.61
0.17
0.16
0.76
CA
0.24
0.36
0.16
0.13
0.00
0.00
0.24
0.36
0.16
0.13
0.00
0.00
CO
13.37
1.19
0.51
0.54
0.51
0.53
13.47
1.19
0.51
0.54
0.51
0.53
CT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
DC
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
DE
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
FL
1.40
1.84
0.25
0.25
0.13
0.17
1.32
1.82
0.25
0.25
0.13
0.17
j-21
-------
BCAfor Supplemental Steam Electric Power Generating ELGs
Appendix J: Air Quality Modeling Methodology
Table J-9: Baseline and Final Rule Primary PM2.5 Scaling Factors for Coal EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
GA
0.03
0.06
0.00
0.00
0.00
0.00
0.06
0.14
0.00
0.00
0.00
0.00
IA
1.17
1.14
0.67
0.28
0.19
0.57
1.17
1.16
0.64
0.27
0.19
0.57
ID
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
IL
1.17
0.95
0.57
0.03
0.00
0.00
1.17
0.95
0.56
0.02
0.00
0.00
IN
1.28
0.60
0.20
0.20
0.00
0.00
1.32
0.63
0.20
0.20
0.00
0.00
KY
1.30
1.19
0.77
0.36
0.16
0.36
1.03
1.07
0.42
0.34
0.10
0.36
MA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
MD
3.54
3.54
3.54
3.54
2.97
3.42
3.54
3.54
3.54
3.54
2.97
3.42
ME
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Ml
0.83
0.00
0.00
0.00
0.00
0.00
0.82
0.00
0.00
0.00
0.00
0.00
MN
3.50
2.70
0.00
0.00
0.00
0.00
3.51
2.62
0.00
0.00
0.00
0.00
MO
3.04
1.33
0.54
0.11
0.10
0.26
2.96
1.34
0.54
0.10
0.10
0.25
MS
4.02
3.60
1.06
1.00
1.00
1.00
1.94
3.60
1.06
1.00
1.00
1.00
MT
0.98
0.98
0.98
0.98
0.38
0.79
0.98
0.98
0.98
0.98
0.38
0.78
NC
21.57
17.32
6.08
6.14
6.26
8.67
19.27
14.75
6.12
4.19
6.10
8.67
ND
0.94
0.98
0.78
0.72
0.04
0.08
0.94
0.98
0.78
0.72
0.04
0.08
NEKSa
3.70
3.68
0.80
0.50
0.15
0.43
2.81
2.91
0.80
0.50
0.14
0.43
NH
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NJ
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NM
0.98
0.99
0.01
0.01
0.01
0.01
0.98
0.99
0.01
0.01
0.01
0.01
NV
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
NY
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
OH
0.83
1.08
0.19
0.00
0.00
0.46
0.93
1.04
0.24
0.00
0.00
0.46
OK
14.75
8.14
8.94
8.94
1.00
1.00
14.17
8.57
8.94
8.94
1.00
1.00
OR
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
PA
3.12
3.04
2.28
1.14
1.14
1.10
2.74
2.71
1.91
1.05
1.03
1.01
Rl
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
SC
1.03
2.17
3.78
3.78
0.00
0.00
0.91
2.16
3.78
3.78
0.00
0.00
SD
0.93
1.11
0.00
0.00
0.00
0.00
0.93
1.11
0.00
0.00
0.00
0.00
TN
16.88
1.00
0.00
0.00
0.00
0.00
16.63
1.00
0.00
0.00
0.00
0.00
TXLAb
1.10
1.30
1.15
0.65
0.14
0.55
1.11
1.37
1.16
0.65
0.14
0.54
UT
2.92
0.06
0.06
0.06
0.04
0.02
2.89
0.06
0.06
0.06
0.04
0.02
VA
0.46
0.29
0.08
0.00
0.00
0.00
0.46
0.21
0.08
0.00
0.00
0.00
VT
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
WA
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Wl
2.11
2.36
0.46
0.00
0.00
0.33
2.10
2.39
0.45
0.00
0.00
0.33
WV
1.29
1.45
1.23
0.56
0.06
0.06
1.29
1.47
1.13
0.55
0.06
0.06
WY
1.03
1.10
1.08
0.54
0.44
0.43
1.03
1.10
1.08
0.54
0.44
0.43
Note: Emissions of Louisiana and Kansas are less 10 tpy in the original source apportionment modeling. Air quality impacts and
emissions from those states were combined with nearby states.
a NEKS: Nebraska and Kansas
bTXLA: Louisiana and Texas
Table J-10: Baseline and Final Rule Primary PM2.5 Scaling Factors for Natural Gas EGU Tags
State
Baseline
Final Rule
Tag
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
AL
0.85
0.84
0.71
0.46
0.31
0.39
0.84
0.82
0.71
0.45
0.31
0.39
AR
0.63
0.82
0.43
0.10
0.07
0.10
0.63
0.81
0.43
0.10
0.06
0.10
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Table J-10: Baseline and Final Rule Primary PM2.5 Scaling Factors for Natural Gas EGU Tags
State
Tag
Baseline
Final Rule
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
AZ
0.70
0.85
0.86
0.74
0.39
0.79
0.70
0.85
0.86
0.73
0.38
0.79
CA
0.96
1.06
0.98
0.77
0.20
0.24
0.96
1.07
0.97
0.77
0.20
0.24
CO
1.23
0.74
0.77
0.75
0.32
0.51
1.22
0.74
0.77
0.73
0.32
0.50
CT
0.78
0.67
0.60
0.00
0.00
0.03
0.78
0.67
0.60
0.00
0.00
0.03
DC
0.15
0.13
0.11
0.10
0.08
0.07
0.15
0.13
0.11
0.09
0.08
0.08
DE
0.62
0.64
0.31
0.27
0.30
0.48
0.59
0.53
0.30
0.26
0.26
0.47
FL
0.97
0.98
0.95
0.77
0.55
0.57
0.97
0.98
0.94
0.77
0.55
0.57
GA
0.84
0.81
0.72
0.41
0.30
0.37
0.84
0.80
0.72
0.41
0.30
0.37
IA
0.50
0.48
0.20
0.06
0.01
0.08
0.50
0.47
0.20
0.07
0.01
0.08
ID
1.22
1.65
1.68
1.49
0.76
1.04
1.21
1.63
1.65
1.47
0.74
1.03
IL
0.49
0.55
0.28
0.13
0.00
0.00
0.49
0.55
0.28
0.13
0.00
0.00
IN
0.67
0.67
0.44
0.15
0.10
0.15
0.66
0.67
0.43
0.15
0.11
0.15
KS
1.11
1.01
0.19
0.08
0.04
0.03
1.12
1.05
0.21
0.09
0.04
0.03
KY
0.75
0.72
0.49
0.34
0.18
0.30
0.90
0.86
0.66
0.45
0.24
0.37
LA
0.79
0.80
0.64
0.29
0.19
0.31
0.79
0.79
0.63
0.28
0.19
0.31
MA
0.48
0.46
0.34
0.28
0.19
0.26
0.48
0.46
0.34
0.28
0.18
0.26
MD
1.05
1.08
0.85
0.63
0.61
0.75
1.01
0.99
0.83
0.63
0.58
0.77
ME
1.75
1.44
0.51
0.50
0.29
0.45
1.74
1.49
0.52
0.52
0.29
0.44
Ml
0.75
0.87
0.63
0.48
0.28
0.43
0.75
0.81
0.63
0.46
0.27
0.43
MN
0.57
0.52
0.21
0.08
0.05
0.09
0.53
0.49
0.21
0.08
0.05
0.09
MO
0.30
0.33
0.10
0.03
0.01
0.02
0.28
0.33
0.10
0.02
0.01
0.02
MS
0.88
0.84
0.51
0.32
0.18
0.24
0.86
0.79
0.50
0.31
0.16
0.23
MT
0.17
0.21
0.03
0.03
0.00
0.05
0.17
0.17
0.03
0.03
0.00
0.07
NC
0.87
0.70
0.76
0.60
0.55
0.73
0.86
0.68
0.76
0.59
0.54
0.74
ND
0.47
0.92
0.19
0.43
0.06
0.22
0.47
0.86
0.17
0.43
0.06
0.07
NE
2.35
2.21
0.30
0.78
0.01
0.74
2.32
2.24
0.36
0.78
0.05
0.74
NH
0.59
0.43
0.31
0.27
0.25
0.29
0.59
0.42
0.31
0.27
0.25
0.29
NJ
0.82
0.84
0.52
0.40
0.42
0.77
0.78
0.81
0.47
0.40
0.42
0.76
NM
0.52
0.52
0.89
0.99
0.86
1.34
0.52
0.53
0.89
1.00
0.89
1.36
NV
0.72
0.84
0.83
0.85
0.36
0.28
0.72
0.83
0.83
0.85
0.35
0.28
NY
0.86
0.85
0.59
0.26
0.27
0.28
0.85
0.86
0.58
0.26
0.27
0.28
OH
0.95
0.95
0.89
0.63
0.42
0.63
1.05
1.04
0.97
0.68
0.48
0.63
OK
1.00
0.79
0.22
0.07
0.06
0.06
0.97
0.78
0.22
0.07
0.06
0.06
OR
3.29
0.74
0.39
0.00
0.00
0.00
3.29
0.74
0.39
0.00
0.00
0.00
PA
0.83
0.80
0.60
0.37
0.33
0.51
0.83
0.80
0.61
0.36
0.33
0.51
Rl
0.83
0.78
0.65
0.38
0.35
0.34
0.84
0.80
0.66
0.38
0.35
0.34
SC
0.80
0.86
0.64
0.51
0.53
0.77
0.77
0.85
0.63
0.52
0.54
0.77
SD
0.73
0.73
0.25
0.13
0.11
0.21
0.72
0.73
0.19
0.18
0.10
0.22
TN
1.08
1.05
0.88
0.46
0.26
0.39
1.08
1.04
0.86
0.45
0.26
0.35
TX
0.90
0.83
0.45
0.19
0.09
0.24
0.89
0.82
0.45
0.19
0.09
0.24
UT
0.66
0.87
0.84
0.88
0.69
0.92
0.66
0.87
0.84
0.88
0.68
0.92
VA
0.81
0.73
0.47
0.26
0.17
0.12
0.79
0.71
0.47
0.23
0.14
0.12
VT
0.00
0.00
0.03
0.03
0.00
0.01
0.00
0.00
0.03
0.03
0.00
0.01
WA
0.44
0.48
0.58
0.59
0.39
0.36
0.44
0.48
0.58
0.59
0.39
0.36
Wl
0.56
0.66
0.43
0.18
0.08
0.15
0.55
0.64
0.36
0.18
0.08
0.15
WV
0.51
0.38
0.10
0.12
0.09
4.54
0.63
0.50
0.23
0.21
0.15
4.54
WY
0.01
0.04
0.03
0.03
0.00
0.01
0.01
0.05
0.03
0.01
0.00
0.01
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Appendix J: Air Quality Modeling Methodology
Table J-11: Baseline and Final Rule Scaling Factors for Other EGU Tags
Baseline
Final Rule
State Tag
2028
2030
2035
2040
2045
2050
2028
2030
2035
2040
2045
2050
Seasonal NOx
1.16
1.16
1.10
1.04
1.03
1.08
1.16
1.16
1.10
1.04
1.03
1.16
Annual NOx
1.17
1.17
1.11
1.03
1.00
1.06
1.17
1.17
1.11
1.03
1.00
1.17
Annual S02
1.00
1.01
1.00
0.90
0.87
0.87
1.00
1.01
1.00
0.90
0.87
1.00
Annual PM2.5
1.37
1.37
1.32
1.27
1.20
1.49
1.37
1.37
1.32
1.27
1.20
1.37
J.4 Air Quality Surface Results
The spatial fields of baseline AS-M03 and Annual Average PM2.5 in 2028 are presented in Figure J-8 and J-9,
respectively. It is important to recognize that ozone is a secondary pollutant, meaning that it is formed
through chemical reactions of precursor emissions in the atmosphere. As a result of the time necessary for
precursors to mix in the atmosphere and for these reactions to occur, ozone can either be highest at the
location of the precursor emissions or peak at some distance downwind of those emissions sources. The
spatial gradients of ozone depend on a multitude of factors including the spatial patterns of NOx and VOC
emissions and the meteorological conditions on a particular day. Thus, on any individual day, high ozone
concentrations may be found in narrow plumes downwind of specific point sources, may appear as urban
outflow with large concentrations downwind of urban source locations or may have a more regional signal.
However, in general, because the AS-M03 metric is based on the average of concentrations over more than
180 days in the spring and summer, the resulting spatial fields are rather smooth without sharp gradients,
compared to what might be expected when looking at the spatial patterns of MDA8 ozone concentrations on
specific high ozone episode days. PM2 5 is made up of both primary and secondary components. Secondary
PM2.5 species sulfate and nitrate often demonstrate regional signals without large local gradients while
primary PM2.5 components often have heterogenous spatial patterns with larger gradients near emissions
sources. Both secondary and primary PM2.5 contribute to the spatial patterns shown in Figure J-9 as
demonstrated by the extensive areas of elevated concentrations over much of the Eastern US which have large
secondary components and hotspots in urban areas which are impacted by primary PM emissions.
Figure J-10 through Figure J-15 present the model-predicted changes in the AS-M03 between the baseline
and the final rule for 2028, 2030, 2035, 2040, 2045, and 2050 calculated as final rule minus the baseline.
Figures J-16 to J-21 present the model-predicted changes in annual average PM2.5 between the baseline and
final rule for 2028, 2030, 2035, 2040, 2045, and 2050 calculated as the final rule minus the baseline. The
spatial patterns shown in the figures are a result of (1) of the spatial distribution of EGU sources that are
predicted to have changes in emissions and (2) of the physical or chemical processing that the model
simulates in the atmosphere. While SO2, NOx and primary PM2 5 emissions changes all contributed to the
PM2.5 changes depicted in Figures J-16 through J-21, the PM2.5 component species with the larger changes
was sulfate and consequently the SO2 emissions changes have the largest impact on predicted changes in
PM2.5 concentrations through sulfate, ammonium and particle-bound water impacts. The spatial fields used to
create these maps serve as an input to the benefits analysis.
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Appendix J: Air Quality Modeling Methodology
Figure J-8: Map of AS-M03 in the 2028 Baseline
159 239
Min = 24.504 at (396,11), Max = 71.596 at (48,99)
Figure J-9: Map of Annual Average PM2.5 in the 2028 Baseline
159 239 318
Min = 1.458 at (127,138), Max = 86.142 at (164,15)
PM2.5
Basel ine_2028
70.0
65.0
60.0
55.0
50.0
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Appendix J: Air Quality Modeling Methodology
Figure J-10: Map of Change in Apr-September MDA8 Ozone (ppb): 2028 Final Rule - Baseline
Min = -0.465 at (278,81), Max = 1.738 at (316,101)
Figure J-11: Map of Change in Apr-September MDA8 Ozone (ppb): 2030 Final Rule - Baseline
1 80 159 239 318 397
Min = -0.680 at (308,134), Max = 1.894 at (316,101)
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Figure J-12: Map of Change in Apr-September MDA8 Ozone (ppb): 2035 Final Rule - Baseline
1 80 159 239 318 397
Min = -0.553 at (310,131), Max = 0.030 at (206,55)
Figure J-13: Map of Change in Apr-September MDA8 Ozone (ppb): 2040 Final Rule - Baseline
1 80 159 239 318 397
Mln = -0.959 at (316,101), Max = 0.022 at (312,162)
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Figure J-14: Map of Change in Apr-September MDA8 Ozone (ppb): 2045 Final Rule - Baseline
1 80 159 239 318 397
Min = -0.186 at (316,101), Max = 0.036 at (290,147)
Figure J-15: Map of Change in Apr-September MDA8 Ozone (ppb): 2050 Final Rule - Baseline
1 80 159 239 318 397
Min = -0.043 at (257,106), Max = 0.042 at (275,114)
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Appendix J: Air Quality Modeling Methodology
Figure J-16: Map of Change in Annual Mean PM2.5 (ng/m3): 2028 Final Rule - Baseline
159 239
Min = -0.030 at (308,134), Max = 9.88E-3 at (283,113)
Figure J-17: Map of Change in Annual Mean PM2.5 (ug/m3): 2030 Final Rule - Baseline
159 239
Min = -0.018 at (192,141), Max = 0.018 at (283,113)
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Figure J-18: Map of Change in Annual Mean PM2.5 (ng/m3): 2035 Final Rule - Baseline
Min = -0.055 at (320,141), Max = 6.38E-3 at (283,113)
Figure J-19: Map of Change in Annual Mean PM2.5 (ng/m3): 2040 Final Rule - Baseline
1 80 159 239 318 397
Min = -0.012 at (296,130), Max = 0.012 at (278,81)
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Figure J-20: Map of Change in Annual Mean PM2.5 (ng/m3): 2045 Final Rule - Baseline
159 239
Min = -4.57E-3 at (342,121), Max = 7.50E-3 at (312,162)
Figure J-21: Map of Change in Annual Mean PM2.5 (ug/m3): 2050 Final Rule - Baseline
159 239
Min = -3.38E-3 at (298,107), Max = 7.94E-3 at (283,113)
J.5 Uncertainties and Limitations of the Air Quality Methodology
One limitation of the scaling methodology for creating ozone and PMg.® surfaces associated with the baseline
or final rule scenarios described above is that the methodology treats air quality changes from the tagged
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sources as linear and additive. It therefore does not account for nonlinear atmospheric chemistry and does not
account for interactions between emissions of different pollutants and between emissions from different
tagged sources. The method applied in this analysis is consistent with how air quality estimations have been
made in several prior regulatory analyses (U.S. EPA, 2012, 2019h, 2020d). We note that air quality is
calculated in the same manner for the baseline and for the final rule, so any uncertainties associated with these
assumptions is propagated through results for both the baseline and final rule scenarios in the same manner. In
addition, emissions changes between baseline and the final rule are relatively small compared to modeled
future year emissions that form the basis of the source apportionment approach described in this appendix.
Previous studies have shown that air pollutant concentrations generally respond linearly to small emissions
changes of up to 30 percent (Cohan & Napelenok, 2011; Cohan et al., 2005; Dunker et al., 2002; Koo, Dunker
& Yarwood, 2007; Napelenok et al., 2006; Zavala et al., 2009). A second limitation is that the source
apportionment contributions are informed by the spatial and temporal distribution of the emissions from each
source tag as they occur in the future year modeled case. Thus, the contribution modeling results do not allow
us to consider the effects of any changes to spatial distribution of EGU emissions within a state-fuel tag
between the future year modeled case and the baseline and final rule scenarios analyzed in this RIA. Finally,
the future year CAMx-modeled concentrations themselves have some uncertainty. While all models have
some level of inherent uncertainty in their formulation and inputs, the base-year 2016 model outputs have
been evaluated against ambient measurements and have been shown to adequately reproduce spatially and
temporally varying concentrations.
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