United States Office of Water EPA-821-R-23-013
Environmental Protection Washington, DC 20460 December 13,2023
Agency
<&EPA Benefit Cost Analysis for
Revisions to the Effluent
Limitations Guidelines and
Standards for the Meat and
Poultry Products Point
Source Category
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v»EPA
United States
Environmental Protection
Agency
Benefit Cost Analysis for Revisions to the
Effluent Limitations Guidelines and
Standards for the Meat and Poultry Products
Point Source Category
EPA-821-R-23-013
December 13, 2023
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 Proposed Revisions to the MPP 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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BCAfor Proposed Revisions to the MPP ELGs
Table of Contents
Table of Contents
List of Figures iv
List of Tables v
Abbreviations vii
Executive Summary ES-1
1 Introduction 1-1
1.1 Meat and Poultry Products Facility Dischargers 1-2
1.2 Baseline and Regulatory Options Analyzed 1-2
1.3 Analytic Framework 1-5
1.3.1 Constant Prices 1-5
1.3.2 Technology Implementation Year 1-5
1.3.3 PeriodofAnalysis 1-6
1.3.4 Discount Rate and Year 1-6
1.3.5 Annualization of Future Costs and Benefits 1-6
1.3.6 Population and Income Growth 1-7
1.4 Report Organization 1-7
2 Benefits Overview 2-1
2.1 Human Health Impacts Associated with Changes in Surface Water Quality 2-4
2.1.1 Primary Contact Recreation 2-4
2.1.2 Drinking Water 2-5
2.1.3 Shellfish Consumption 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 2-9
2.2.2 Impacts on Threatened and Endangered Species 2-11
2.3 Economic Productivity 2-12
2.3.1 Drinking Water Treatment Costs 2-12
2.3.2 Wastewater Treatment Costs 2-14
2.3.3 Industrial and Agricultural Uses 2-16
2.3.4 Commercial Harvesting of Fish and Shellfish 2-17
2.3.5 Subsistence Harvesting of Fish and Shellfish 2-18
2.3.6 Tourism 2-18
2.3.7 Property Values 2-19
2.3.8 Capture of Methane 2-20
2.4 Changes in Air Pollution 2-20
2.5 Summary of Benefit Categories 2-21
3 Water Quality Effects of Regulatory Options 3-1
3.1 Changes in Pollutant Loadings 3-1
3.2 Waters Affected by Meat and Poultry Facility Discharges 3-2
3.2.1 Waters Affected by Direct Dischargers 3-3
3.2.2 Waters Affected by Indirect Dischargers 3-3
3.3 Water Quality Changes Downstream from Meat and Poultry Facilities 3-4
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BCAfor Proposed Revisions to the MPP ELGs
Table of Contents
3.3.1 WQI Data Sources 3-5
3.3.2 WQI Calculation 3-5
3.3.3 Baseline WQI 3-6
3.3.4 Estimated Changes in Water Quality from the Regulatory Options 3-6
3.4 Limitations and Uncertainty 3-7
4 Nonmarket Benefits from Water Quality Changes 4-1
4.1 Methods 4-2
4.2 Main Results 4-4
4.3 Alternative Model Results 4-5
4.4 Benefit Extrapolation 4-5
4.4.1 Benefits of Regulatory Option 2 4-6
4.4.2 Benefits Across Water Resources Regions 4-7
4.5 Limitations and Uncertainty 4-8
5 Climate Change and Air Quality-Related Disbenefits 5-1
5.1 Changes in Air Emissions 5-1
5.2 Climate Change Disbenefits 5-2
5.2.1 Data and Methodology 5-2
5.2.2 Results 5-11
5.3 Human Health Disbenefits 5-13
5.3.1 Data and Methodology 5-13
5.3.2 Results 5-14
5.4 Annualized Climate Change and Air Quality-Related Disbenefits of Regulatory Options 5-15
5.5 Limitations and Uncertainty 5-16
6 Summary of Estimated Total Monetized Benefits 6-1
7 Summary of Total Social Costs 7-1
7.1 Overview of Cost Analysis Framework 7-1
7.2 Key Findings for Regulatory Options 7-2
8 Benefits and Social Costs 8-1
8.1 Comparison of Benefits and Costs by Option 8-1
8.2 Analysis of Incremental Benefits and Costs 8-1
9 References 9-1
Appendix A: Water Quality Modeling A-l
SWAT Model Setup A-l
Representation of Point Source Discharges from Direct and Indirect Facilities A-2
Model Calibration A-3
Appendix B: WQI Calculation and Regional Subindices B-l
Appendix C: Methodology for Estimating WTP for Water Quality Changes C-l
Appendix D: Monetized Benefits and Social Costs using a 7 Percent Discount Rate D-l
Nonmarket Benefits from Water Quality Changes D-l
Climate Change and Air Quality Benefits D-l
Social Costs D-3
ii
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Table of Contents
Appendix E: Extrapolation ofNonmarket Benefits from Water Quality Changes E-l
Model Scope E-l
Extrapolation Approach E-4
Interpolation of Option 2 Benefits E-5
Limitations and Uncertainty E-5
Appendix F: Climate Change Disbenefits with Updated Social Cost of Greenhouse Gases F-l
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List of Figures
List of Figures
Figure 2-1: Summary of Benefits Resulting from the Regulatory Options 2-3
Figure 3-1: Map of the MPP direct discharge facility universe 3-3
Figure 3-2: Map of the MPP indirect discharge facility universe 3-4
Figure 5-1: Frequency Distribution of Interim SC-CO2 Estimates for 2020 (in 2020$ per Metric
Ton C02) 5-9
Figure 5-2: Frequency Distribution of Interim SC-CH4 Estimates for 2020 (in 2020$ per Metric
Ton CH4) 5-10
iv
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List of Tables
List of Tables
Table 1-1: Number of Facilities in MPP Industry by Process and Discharge Type 1-2
Table 1-2: Summary of Regulatory Options 1-4
Table 2-1: Summary of Changes to Annual Pollutant Loadings Compared to the Baseline 2-1
Table 2-2: Categories of Pollutants Present in MPP Discharges and Associated Health Effects 2-4
Table 2-3: Drinking Water Maximum Contaminant Levels and Goals for Selected Pollutants in MPP
Discharges 2-7
Table 2-4: Threatened and Endangered Species by Group and Vulnerability 2-11
Table 2-5: Estimated Welfare Effects of Changes in Pollutant Discharges from Meat and Poultry
Product Facilities 2-22
Table 3-1: Summary of Changes to Annual Loadings of Selected Pollutants Compared to the Baseline 3-1
Table 3-2: Summary of Changes to Annual Loadings of Pollutants Compared to the Baseline 3-2
Table 3-3: Estimated Percentage of Potentially Affected Reaches in Modeled Watersheds by WQI
Classification: Baseline Scenario 3-6
Table 3-4: Ranges of Estimated Water Quality Changes for Selected Water Resources Regions and
Regulatory Options, Compared to Baseline 3-7
Table 3-5: Limitations and Uncertainties in the Estimation of Water Quality Changes 3-7
Table 4-1: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality
Improvements in Selected Regions under Regulatory Options 1 and 3, using Model 1 and a 3
Percent Discount Rate (Main Estimates) 4-5
Table 4-2: Estimated Household and Total Annualized Willingness-to-Pay for Water Quality
Improvements in Selected Regions under Regulatory Options 1 and 3, using Model 2 and a 3
Percent Discount Rate (Alternative Model Analysis) 4-5
Table 4-3: Estimated Total Annualized Willingness-to-Pay for Water Quality Improvements in
Selected Regions under Regulatory Options, using Model 1 and a 3 Percent Discount Rate (Main
Estimates) 4-6
Table 4-4: Estimated Total Annualized Willingness-to-Pay for Water Quality Improvements in
Selected Regions under Regulatory Options, using Model 2 and a 3 Percent Discount Rate
(Alternative Estimates) 4-6
Table 4-5: Estimated Total Annualized Willingness-to-Pay for Water Quality Improvements under
Regulatory Options, using Model 1 and a 3 Percent Discount Rate (Main Estimates) 4-7
Table 4-6: Estimated Total Annualized Willingness-to-Pay for Water Quality Improvements under
Regulatory Options, using Model 2 and a 3 Percent Discount Rate (Main Estimates) 4-8
Table 4-7: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality Benefits 4-8
Table 5-1: Electricity eGRID U.S. Total Output Emission Rates 5-1
Table 5-2: Transportation Pollutant-Specific Emission Factors 5-2
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BCAfor Proposed Revisions to the MPP ELGs
List of Tables
Table 5-3: Estimated Incremental Changes in Air Pollutant Emissions (Tons/Year) 5-2
Table 5-4: Interim Estimates of the Social Cost of Methane and Social Cost of Carbon, 2025-2065 5-8
Table 5-5: Estimated Undiscounted and Total Present Value of Climate Disbenefits from Incremental
Changes in CH4 and CO2 Emissions under the Proposed Rule by Discount Rate (Millions of
2022$) 5-12
Table 5-6: Estimated Total Annualized Climate Disbenefits from Incremental Changes in CH4 and
CO2 Emissions under the Proposed Rule by Discount Rate (Millions of 2022$) 5-13
Table 5-7: Benefit per Ton Values by Emission Category, 3 Percent Discount Rate ($2022) 5-14
Table 5-8: Estimated Undiscounted and Total Present Value of Economic Value of Avoided Ozone
and PM2 5-Attributable Premature Mortality and Morbidity by Regulatory Option (Millions of
2022$, 3 Percent Discount Rate) 5-15
Table 5-9: Total Annualized Climate Change and Air Quality-Related Disbenefits by Regulatory
Option and Discount Rate (Millions of 2022$) 5-16
Table 5-10: Limitations and Uncertainties in the Analysis of Climate Change and Air Quality-Related
Disbenefits 5-16
Table 6-1: Summary of Total Annualized Benefits for Regulatory Options, Compared to Baseline, at
3 Percent (Millions of 2022$) 6-1
Table 7-1: Estimated Total Social Costs by Regulatory Option and Discharge Type Discounted at 3
Percent (Millions 2022$) 7-2
Table 7-2: Time Profile of Costs to Society (Millions 2022$) 7-3
Table 8-1: Total Estimated Annualized Benefits and Costs by Regulatory Option Compared to
Baseline, at 3 Percent Discount (Millions of 2022$) 8-1
Table 8-2: Analysis of Estimated Incremental Net Benefit of the Regulatory Options, Compared to
Baseline and to Other Regulatory Options, at 3 Percent Discount (Millions of 2022$) 8-2
VI
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BCAfor Proposed Revisions to the MPP ELGs
Abbreviations
Abbreviations
ACS
American Community Survey
BLS
Bureau of Labor Statistics
BNR
Biological nutrient removal
BOD
Biochemical oxygen demand
CBG
Census block group
CH4
Methane
CN
Curve number
C02
Carbon dioxide
COPD
Chronic obstructive pulmonary disease
CPI
Consumer Price Index
CWA
Clean Water Act
DAF
Dissolved air flotation
DBP
Disinfection byproduct
DMR
Discharge monitoring report
DO
Dissolved oxygen
EA
Environmental Assessment
ECHO
Enforcement and Compliance History Online
ECS
Equilibrium climate sensitivity
eGRID
Emissions & Generation Resource Integrated Database
ELGs
Effluent Limitations Guidelines and Standards
ESA
Endangered Species Act
FC
Fecal coliform
FUND
Climate Framework for Uncertainty, Negotiation, and Distribution
FWS
United States Fish and Wildlife Service
GDP
Gross domestic product
GHG
Greenhouse gas
HAB
Harmful algal bloom
HAWQS
Hydrologic and Water Quality System
HTF
Hypoxia task force
HUC
Hydrologic unit code
IAM
Integrated assessment model
IBI
Index of biotic integrity
IPCC
Intergovernmental Panel on Climate Change
IWG
Interagency Working Group on the Social Cost of Greenhouse Gases
MCL
Maximum contaminant level
MCLG
Maximum contaminant level goal
MOVES3
Motor Vehicle Emission Simulator (version 3)
MPP
Meat and Poultry Products
MRM
Meta-regression model
NSDWR
National Secondary Drinking Water Regulation
NHD
National hydrography dataset
NPDES
National Pollutant Discharge Elimination System
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BCAfor Proposed Revisions to the MPP ELGs
Abbreviations
03
Ozone
0MB
Office of management and budget
PM2.5
Particulate matter (fine inhalable particles with diameters 2.5 |_im and smaller)
POTW
Publicly owned treatment work
PWS
Public water system
SBREFA
Small Business Regulatory Enforcement Fairness Act
RIA
Regulatory Impact Analysis
SC-C02
Social cost of carbon
SC-CH4
Social cost of methane
SDWA
Safe Drinking Water Act
SDWIS
Safe Drinking Water Information System
SMCL
Secondary maximum contaminant level
SWAT
Soil and Water Assessment Tool
T&E
Threatened and endangered
TDD
Technical Development Document
TIP
Treatment in place
TN
Total nitrogen
TP
Total phosphorus
TPC
Typical pollutant concentration
TSD
Technical Support Document
TSS
Total suspended solids
TTHM
Total trihalomethanes
USD A
United States Department of Agriculture
WTP
Willingness to pay
WQI
Water quality index
viii
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BCAfor Proposed Revisions to the Meat and Poultry Products ELGs
Executive Summary
Executive Summary
Background
The U.S. Environmental Protection Agency (EPA) is proposing revisions to the technology-based effluent
limitations guidelines and standards (ELGs) for meat and poultry products (MPP) point source category,
40 Code of Federal Regulations (CFR) part 432, which EPA amended in 2004 (69 FR 54476). The
proposed rule establishes (1) more stringent nitrogen effluent limitations based on better performing
technologies, (2) new effluent limitations for phosphorus, and (3) pretreatment standards for MPP
facilities that discharge to a Publicly Owned Treatment Work (POTW).
Regulatory Options
EPA analyzed three regulatory options, labeled Option 1 through Option 3 and summarized in
Table ES-1. These options differ in the stringency of control technologies and resulting effluent limits,
and the applicability of these limits to MPP facilities. Specifically, the regulatory options include more
stringent effluent limitations on nitrogen, new effluent limitations on phosphorus, updated effluent
limitations for other pollutants including ammonia, new pretreatment standards for indirect dischargers,
and revised production thresholds for the subcategories in the existing rule. EPA is also requesting
comment on potentially establishing effluent limitations on chlorides for high chloride waste streams,
establishing effluent limitations for E. coli for direct dischargers, and including conditional limits for
indirect dischargers that discharge to POTWs that remove nutrients.
Under these options, EPA expects the revised ELGs to reduce the amount of nutrients and other pollutants
(e.g., biochemical oxygen demand, total suspended solids, oil and grease, fecal coliform, chlorides)
discharged to surface waters from the MPP industry, with consequent benefits including improvement in
water quality and aquatic habitats, reduced human and ecological health risk, enhanced natural resources,
and economic productivity benefits.
ES-1
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BCAfor Proposed Revisions to the Meat and Poultry Products ELGs
Executive Summary
Table ES-1: Summary of Regulatory Options
Option
Direct Dischargers
Indirect Dischargers
Technology Basis3
Applicable Facilities
Technology Basis3
Applicable Facilities
1
Adds to existing ELG:
full denitrification
chemical phosphorus removal
filter
meat further processors > 50
million Ibs/yr of finished product
meat slaughtering > 50 million
Ibs/yr live weight killed
poultry slaughtering >100 million
Ibs/yr of live weight killed
poultry further processors >7
million Ibs/yr of finished product
produced
Tenderers >10 million Ibs/yr of
raw material processed
Conventional pollution limits based
on:
screening/grit removal
dissolved air flotation (DAF),
and
dewatering/solids handling
meat further processors > 50
million Ibs/yr of finished product
meat slaughtering > 50 million
Ibs/yr live weight killed
poultry slaughtering >100 million
Ibs/yr of live weight killed
poultry further processors >7
million Ibs/yr of finished product
produced
Tenderers >10 million Ibs/yr of
raw material processed
2
Same technology as Option 1
Same facilities as Option 1
Option 1 technology plus:
anaerobic lagoon (BOD
pretreatment)
activated sludge (nitrification
and full denitrification)
chemical P removal
filter
Option 1 facilities plus:
slaughterhouses producing >200
million Ibs/yr
Tenderers processing >350
million Ibs/yr raw material
3
Same technology as Option 1
Phosphorus limits for:
all direct discharging facilities
producing > 10 million Ibs/yr
plus
Phosphorus and more stringent
nitrogen limits for:
all facilities producing >20 million
Ibs/yr.
Same technology as Option 2
Conventional limits for:
facilities producing >5 million
Ibs/yr
plus
Nitrogen and phosphorus limits for
all facilities >30 million Ibs/yr
a. See TDD for a description of these technologies (U.S. EPA, 2023m).
Source: U.S. EPA Analysis, 2023
ES-2
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BCAfor Proposed Revisions to the Meat and Poultry Products ELGs
Executive Summary
Table ES-2 summarizes the loading changes estimated to result from the regulatory options for selected
pollutants. The negative changes indicate reductions in pollutant loads to receiving waters.
Implementation of wastewater treatment technologies to meet effluent limits under the regulatory options
are also estimated to reduce loadings of other pollutants, including halogens (e.g., bromide, fluoride), total
organic carbon, sulfate, total dissolved solids, metals (e.g., aluminum, antimony, arsenic, barium,
beryllium, boron, cadmium, calcium, chromium, cobalt, copper, iron, lead, magnesium, manganese,
molybdenum, nickel, selenium, silver, sodium, thallium, tin, titanium, vanadium, and zinc), and
microbiological contaminants (e.g.,E. coli, enterococcus, and fecal coliform). See Section 3.1 for details.
Table
to the
ES-2: Summary of Changes to Annual Loadings of Selected Pollutants Compared
Baseline
Option
Discharge
Type
Changes in Annual Pollutant3 Loadings (millions lbs/year)
TN
TP
TSS
BOD
Oil and
Grease
Chlorides'3
1
Direct
-8.87
-7.68
-42.62
-1.55
-14.84
-190.46
Indirect
0
0
-11.78
-7.73
-1.59
-286.50
Total
-8.87
-7.68
-54.39
-9.28
-16.44
-476.96
2
Direct
-8.87
-7.68
-42.62
-1.55
-14.84
-190.46
Indirect
-35.95
-8.43
-39.19
-55.40
-13.88
-286.50
Total
-44.82
-16.11
-81.81
-56.95
-28.72
-476.96
3
Direct
-8.99
-7.83
-44.45
-1.57
-16.02
-190.46
Indirect
-67.18
-11.73
-48.86
-88.18
-27.36
-286.50
Total
-76.18
-19.56
-93.31
-89.75
-43.38
-476.96
a. Technologies implemented under the options are also estimated to reduce loadings of other pollutants. See Section 3.1 for
details.
b. Chlorides has the same removal under each option.
Source: U.S. EPA Analysis, 2023
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 5 for details of the methodology and
results). Table ES-3 summarizes the anticipated benefits of this rule, some of which EPA quantified but
did not monetize and others that were analyzed only qualitatively. Table ES-4 summarizes the national
benefits that EPA quantified and monetized using a 3 percent discount. The total use and nonuse values
for water quality changes shown in Table ES-4 are based on benefits explicitly modeled for a subset of
water resources regions, as well as results extrapolated to the remaining regions. The modeled and
extrapolated benefits are summarized in Table ES-5. Chapter 2 presents additional information on welfare
effects that EPA analyzed only qualitatively.
ES-3
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BCAfor Proposed Revisions to the Meat and Poultry Products ELGs
Executive Summary
Table ES-3: Estimated Wei
and Poultry Product Facili
fare Effects of Changes in Pollutant Discharges from Meat
ties
Benefit Category
Effect of Regulatory Options
Benefits Analysis
Quantified
Monetized
Human Health Benefits from Surface Water Quality Improvements
Human health effects from
exposure via recreational use
Reduced exposure to pathogens and HAB-related
illnesses from primary contact recreation and
recreationally caught and consumed fish and shellfish
Human health effects from
exposure via drinking water
Reduced exposure to high nitrate concentrations,
pathogens, and DBPs (which may be generated
indirectly due to nutrient enrichment and
eutrophication) in drinking water
Ecological Condition and Recreational Use Effects from Surface Water Quality Changes
Aquatic and wildlife habitat3
Improved ambient water quality in receiving and
downstream reaches
V
V
Water-based recreation3
Enhanced value of swimming, fishing, boating, and
near-water activities from water quality changes
Aesthetics3
Improved aesthetics from shifts in water clarity, color,
odor, including nearby site amenities for residing,
working, and traveling
Nonuse values3
Improved existence, option, and bequest values from
improved ecosystem health
Protection of T&E species
Improved T&E species habitat and potential effects on
T&E species populations
V
Market and Productivity Effects
Drinking water treatment costs
Improved quality of source water used for drinking
V
Wastewater treatment costs
Reduced wastewater treatment costs at POTWs
Agricultural water use
Improved quality of surface waters used for livestock
watering
Industrial water use
Reduced cost of industrial water treatment
Commercial fisheries
Improved fisheries yield and harvest quality due to
improved aquatic habitat
S
Subsistence Harvesting
Improved fisheries yield and harvest quality due to
improved aquatic habitat; Reduced risk of consuming
contaminated fish and shellfish
Tourism industries
Changes in participation in water-based recreation
Property values
Improved property values from changes in water
quality
Climate Change and Air Quality-Related Effects
Air emissions of PM2.5
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
Air emissions of NOx and S02
Changes in ecosystem effects; visibility impairment;
and human health effects from direct exposure to
NOx, S02, and hazardous air pollutants.
V
V
Air emissions of greenhouse
gases (GHG; CH4and C02)
Changes in climate change effects
V
V
a. These values are implicit in the total WTP for water quality improvements.
Source: U.S. EPA Analysis, 2023
ES-4
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BCAfor Proposed Revisions to the Meat and Poultry Products ELGs
Executive Summary
Table ES-4: Summary of Total Annualized Benefits for Regulatory Options, Compared to
Baseline, Discounted at 3 Percent (Millions of 2022$)
Benefit Category
Option 1
Option 2
Option 3
Use and nonuse values for water quality changes (total willingness to
pay for water quality improvements)3'b
$95.6
$166.1
$208.4
Climate change effects from changes in GHG emissions
-$1.9
-$7.0
-$10.1
Human health effects from changes in NOx, S02, and PM2.5 emissions
-$3.5
-$12.9
-$18.6
Total monetized benefits
$90.2
$146.2
$179.7
Additional benefits
+
+
+
a. Values reflect both modeled and extrapolated results using Model 1, as shown in Table ES-4. EPA modeled benefits for a
subset of water resources regions (i.e., regions 02, 03, 05, 07, and 08) and extrapolated the results to other regions,
accounting for the respective loading reductions and populations. See Section 4.4 for details.
b. EPA modeled benefits for options 1 and 3 and interpolated the results to estimate benefits for option 2.
+ Additional non-monetized health, ecological, market and economic productivity benefits (see Table ES-2 and Chapter 2)
Source: U.S. EPA Analysis, 2023
Table ES-5: Estimated Total Annualized Willingness-to-Pay for Water Quality
Improvements under Regulatory Options, using Model 1 and a 3 Percent Discount Rate
(Main Estimates)
Basis of Estimate
Total Annualized WTP (Millions 2022$)a b
Option 1
Option 2
Option 3
Regions explicitly modeled0
$42.3
$78.6
$101.9
Extrapolated regions
$53.3
$87.5
$106.5
U.S. totald
$95.6
$166.1
$208.4
a. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits.
b. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected water
resource regions (see Section 3 for details).
c. Sum of benefits estimated for explicitly modeled water resources regions (i.e., regions 02, 03, 05, 07, and 08) and used to
extrapolate to other regions. The modeled regions account for 22 percent to 52 percent of total loading reductions,
depending on the option and water quality parameter, and approximately half of the total population within the
conterminous United States.
d. Based on MPP facilities discharging (directly or indirectly) to waters within the conterminous United States.
Source: U.S. EPA Analysis, 2023
Social Costs of Regulatory Options
Table ES-6 presents the incremental social costs attributable to the regulatory options, calculated as the
difference between each option and the baseline. The regulatory options generally result in additional
costs across regulatory options and discount rates. Chapter 7 describes the social cost analysis. The
compliance costs of the regulatory options are detailed in the Regulatory Impact Analysis (RIA; U.S.
EPA, 2023j).
Table ES-6: Estimated Total Social Costs by Regulatory Option and Discharge Type
Discounted at 3 Percent (Millions 2022$)
Regulatory Option
Direct
Indirect
Total
Option 1
$216.5
$15.3
$231.9
Option 2
$216.5
$426.3
$642.8
ES-5
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BCAfor Proposed Revisions to the Meat and Poultry Products ELGs
Executive Summary
Table ES-6: Estimated Total Social Costs by Regulatory Option and Discharge Type
Discounted at 3 Percent (Millions 2022$)
Regulatory Option
Direct
Indirect
Total
Option 3
$223.7
$853.6
$1,077.3
Source: U.S. EPA Analysis, 2023.
Comparison of Benefits and Social Costs of Regulatory Options
In accordance with the requirements of Executive Order 12866: Regulatory Planning and Review and
Executive Order 13563: Improving Regulation and Regulatory Review, EPA compared the benefits and
costs of each regulatory option. Table ES-7 presents the monetized benefits and social costs attributable to
the regulatory options, at a 3 percent discount.
Table ES-7: Total Annuali
Discounted at 3 Percent (
zed Benefits and Social Costs by Regulatory Option,
Millions of 2022$)
Regulatory Option
Total Benefits
Total Social Costs
Monetized Benefits
Other Benefits
Option 1
$90.2
+
$231.9
Option 2
$146.2
+
$642.8
Option 3
$179.7
+
$1,077.3
+ There are also additional non-monetized health, ecological, market and economic productivity benefits (see Table ES-2 and
Chapter 2)
Source: U.S. EPA Analysis, 2023
ES-6
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BCAfor Proposed Revisions to the MPP ELGs
Introduction
1 Introduction
EPA is proposing to revise the technology-based effluent limitation guidelines (ELGs) that apply to
wastewater discharges from meat and poultry products (MPP) facilities. The MPP industry has
approximately 5,000 facilities across the country which engage in meat and/or poultry slaughter, further
processing, and/or rendering. Available data shows that MPP facilities discharge pollutants such as
nutrients (nitrogen and phosphorus), oil and grease, biochemical oxygen demand (BOD), chlorides,
pathogens, solids, and other substances (U.S. EPA, 2023e; 2023m). Discharges of these pollutants to
surface waters can affect aquatic ecosystems and human health. The current MPP ELGs, which were last
revised in 2004, include limitations only for nitrogen (total nitrogen and ammonia) and only for about 150
large MPP facilities that discharge directly to surface waters. The majority of MPP facilities discharge
their wastewater to a publicly owned treatment work (POTW),1 where wastewater from MPP facilities
can interfere with or pass through treatment.
In this proposed rule, EPA analyzed various regulatory options that would establish (1) more stringent
nitrogen effluent limitations based on better performing technologies, (2) new effluent limitations for
phosphorus, and (3) pretreatment standards for MPP facilities that discharge to a POTW. EPA is also
requesting comments on potentially establishing chlorides limits for high chloride waste streams, E. coli
limits for direct dischargers, and conditional limits for indirect dischargers that discharge to POTWs that
remove nutrients. Under these options, EPA expects the revised ELGs to reduce the amount of nutrients
and other pollutants (e.g., BOD, total suspended solids (TSS), oil and grease, fecal coliform, chlorides)
discharged to surface waters from the MPP industry, with consequent benefits including improvement in
water quality and aquatic habitats, reduced human and ecological health risk, enhanced natural resources,
and economic productivity benefits.
This document presents the Agency's analysis of the benefits and social costs of the regulatory options
and complements other analyses detailed in separate documents:
¦ Environmental Assessment for Proposed Revisions to the Meat and Poultry Products Effluent
Guidelines and Standards (EA; U.S. EPA, 2023e). The EA summarizes the potential environmental
and human health impacts that are estimated to result from the proposed regulatory options, if
implemented.
¦ Technical Development Document for Proposed Revisions to the Meat and Poultry Products Effluent
Guidelines and Standards (TDD; U.S. EPA, 2023m). The TDD summarizes the technical and
engineering analyses supporting the proposed rule, including technology assessment, treatment costs,
pollutant removal estimates, and explanations for the calculation of the effluent limitations and
standards.
¦ Regulatory Impact Analysis for Proposed Revisions to the Meat and Poultry Products Effluent
Guidelines and Standards (RIA; U.S. EPA, 2023j). 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 7 of this document. The RIA also provides information pertinent to
POTWs are treatment works (i.e., systems involved in the storage, treatment, and reclamation of liquid waste) that are
owned by a state or municipality.
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Introduction
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), various Executive Orders, and other requirements.
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 proposed rule and summarizes key analytic inputs and assumptions.
1.1 Meat and Poultry Products Facility Dischargers
The MPP point source category includes facilities "engaged in the slaughtering, dressing and packing of
meat and poultry products for human consumption and/or animal food and feeds. Meat and poultry
products for human consumption include meat and poultry from cattle, hogs, sheep, chickens, turkeys,
ducks and other fowl as well as sausages, luncheon meats and cured, smoked or canned or other prepared
meat and poultry products from purchased carcasses and other materials. Meat and poultry products for
animal food and feeds include animal oils, meat meal and facilities that render grease and tallow from
animal fat, bones and meat scraps." (See 40 CFR 432.1).
EPA estimates there are 5,055 facilities in total in the MPP industry, of which 1,176 facilities (23 percent)
do not discharge any wastewater to the environment (zero dischargers). The remaining 3,879 facilities
(77 percent) discharge wastewater directly to surface waters (direct dischargers) or send their wastewaters
to a POTW (indirect dischargers). The regulatory options EPA analyzed for this proposed rule applies to
these direct or indirect dischargers. Direct dischargers are mostly located in the eastern United States,
whereas indirect dischargers are distributed across the country. Table 1-1 summarizes the distribution of
MPP facilities by process and discharge type.
Table 1-1: Number of Facilities in MPP Industry by Process and Discharge Type
Process
Number of Facilities
Direct Dischargers
Indirect Dischargers
Zero Dischargers
Total
Meat First
47
509
270
826
Meat Further
29
2,741
690
3,460
Poultry First
70
168
52
290
Poultry Further
6
169
119
294
Render
19
121
45
185
Total
171
3,708
1,176
5,055
Source: U.S. EPA Analysis, 2023
1.2 Baseline and Regulatory Options Analyzed
The baseline for this analysis reflects existing conditions and applicable requirements in the absence of
the proposed rule, including applicable permit limits based on the 2004 ELGs and any treatment in place
at MPP facilities.
EPA is considering three regulatory options in this rulemaking. These options differ in the stringency of
control technologies and resulting effluent limits, and the applicability of these limits to MPP facilities.
Specifically, the regulatory options include more stringent effluent limitations on nitrogen, new effluent
limitations on phosphorus, updated effluent limitations for other pollutants including ammonia, new
pretreatment standards for indirect dischargers, and revised production thresholds for the subcategories in
the existing rule. EPA is also requesting comment on potentially establishing effluent limitations on
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Introduction
chlorides for high chloride waste streams, establishing effluent limitations for E. coli for direct
dischargers, and including conditional limits for indirect dischargers that discharge to POTWs that
remove nutrients. Table 1-2 summarizes the technology basis and applicability of the revised ELGs for
the three regulatory options EPA analyzed for this proposed rule. As described in the preamble for this
action, EPA is proposing Option 1 as the preferred option.
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Introduction
Table 1-2: Summary of Regulatory Options
Option
Direct Dischargers
Indirect Dischargers
Technology Basis3
Applicable Facilities
Technology Basis3
Applicable Facilities
1
Adds to existing ELG:
full denitrification
chemical phosphorus removal
filter
meat further processors > 50
million Ibs/yr of finished product
meat slaughtering > 50 million
Ibs/yr live weight killed
poultry slaughtering >100 million
Ibs/yr of live weight killed
poultry further processors >7
million Ibs/yr of finished product
produced
Tenderers >10 million Ibs/yr of
raw material processed
Conventional pollution limits based
on:
screening/grit removal
dissolved air flotation (DAF),
and dewatering/solids
handling
meat further processors > 50
million Ibs/yr of finished product
meat slaughtering > 50 million
Ibs/yr live weight killed
poultry slaughtering >100 million
Ibs/yr of live weight killed
poultry further processors >7
million Ibs/yr of finished product
produced
Tenderers >10 million Ibs/yr of
raw material processed
2
Same technology as Option 1
Same facilities as Option 1
Option 1 technology plus:
anaerobic lagoon (BOD
pretreatment)
activated sludge (nitrification
and full denitrification)
chemical P removal
filter
Option 1 facilities plus:
slaughterhouses producing >200
million Ibs/yr
Tenderers processing >350
million Ibs/yr raw material
3
Same technology as Option 1
Phosphorus limits for:
all direct discharging facilities
producing > 10 million Ibs/yr
plus
Phosphorus and more stringent
nitrogen limits for:
all facilities producing >20 million
Ibs/yr.
Same technology as Option 2
Conventional limits for:
facilities producing >5 million
Ibs/yr
plus
Nitrogen and phosphorus limits for
all facilities >30 million Ibs/yr
a. See TDD for a description of these technologies (U.S. EPA, 2023m).
Source: U.S. EPA Analysis, 2023
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Introduction
1.3 Analytic Framework
The analytic framework includes basic components used consistently throughout the analysis of benefits
and social costs of the regulatory options.
1. All values are presented in 2022 dollars;
2. Technology installation and the resulting pollutant loading changes occur at the end of the
estimated wastewater treatment technology implementation year;
3. Benefits and costs are analyzed over a 40-year period (2026 to 2065) which covers the years
when facilities are projected to implement wastewater treatment technologies to meet the revised
ELGs and the subsequent life of these technologies;
4. Future benefits and costs are discounted using rates of 3 percent and 7 percent back to 2025,
which is the expected year for the final rule publication;2
5. Benefits and costs are annualized; and
6. Future values account for annual U.S. population and income growth, unless noted otherwise.
These components are discussed in the sections below.
1.3.1 Constant Prices
This BCA applies a year 2022 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. Values are
adjusted to year 2022 dollars using appropriate indexes. For example, this BCA updates the WTP for
surface water quality improvements using the Consumer Price Index (CPI) and the values of the social
cost of carbon dioxide using the gross domestic product (GDP) deflator.
1.3.2 Technology Implementation Year
Benefits are projected to begin accruing when each plant implements the control technologies needed to
comply with any applicable best available technology economically achievable (BAT) effluent limitations
or pretreatment standards. For the economic impact and benefit analysis, EPA generally estimates that
MPP direct dischargers will implement control technologies to meet the applicable rule limitations and
standards as their permits are renewed, with technology implementation staggered over time, and no later
than 2030. EPA assumes that approximately 20 percent of MPP direct dischargers will comply each year,
between 2026 and 2030. In contrast, MPP indirect dischargers would have up to three years (i.e.. until the
end of 2028) to comply with the proposed regulations. For the benefits analysis, EPA assumes that
loading reductions and other benefits of technology implementation will start in 2028 to correspond to the
One exception is discounting of the benefits of avoided greenhouse gas emissions for which EPA uses discount rates of 2.5
percent, 3 percent, and 5 percent.
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Introduction
midpoint of the technology implementation period for direct dischargers and implementation deadline for
indirect dischargers.
1.3.3 Period of Analysis
As explained in the TDD, compliance technologies are assumed to have a useful life of either 20 years or
40 years. Hence, the period of analysis extends to 2065 to capture the estimated life of the longest lasting
compliance technology, starting from the first year of technology implementation in 2026. For those
compliance technologies with a useful life of 20 years, EPA assumes that facilities will incur replacement
costs in year 21 (to extend their useful life by another 20 years).
1.3.4 Discount Ra te and Year
This BCA estimates the annualized value of future benefits and social costs using a discount rate of
3 percent. This discount rate reflects society's valuation of differences in the timing of consumption (i.e..
the social rate of time preference), as recommended by the Office of Management and Budget (OMB) in
Circular A-4 (OMB, 2003b; 2023).3 For additional information, EPA also estimated annualized values of
future benefits and social costs using a discount rate of 7 percent to be consistent with the rate EPA has
historically presented based on OMB recommendations for evaluating regulation that would mainly
displace or alter the use of capital in the private sector (OMB, 2003a). Results using the 7 percent
discount rate are presented in Appendix D.
One exception to this practice is discounting of the benefits of avoided greenhouse gas emissions for
which EPA uses values of the social cost of methane (SC-CH4) and the social cost of carbon dioxide (SC-
CO2) developed by the Interagency Working Group on the Social Cost of Greenhouse Gases (IWG) using
discount rates of 2.5 percent, 3 percent, and 5 percent. Because greenhouse gases are long-lived and
subsequent damages of current emissions can occur over a long time, the approach to discounting greatly
influences the present value of future damages. The IWG published four sets of values for each of the SC-
CH4 and SC-CO2 for use in benefit-cost analyses (IWG, 2021): an average value resulting from integrated
assessment model runs for each of three discount rates (2.5 percent, 3 percent, and 5 percent), plus a
fourth value, selected as the 95th percentile of estimates based on a 3 percent discount rate. Chapter 5
provides additional details on climate change-related benefits estimated using these different discount
rates. When summarizing total annualized benefits, EPA includes climate-related benefit values estimated
using average SC-CO2 discounted at 3 percent.
All future cost and benefit values are discounted back to 2025, the anticipated rule promulgation year.
1.3.5 Annua Hzation of Future Costs and Benefits
Consistent with the timing of technology installation and loading reductions described above, EPA uses
the following equation to annualize the future stream of costs and benefits, assuming that costs and
benefits accrue at the end of each year in the analysis period:
3 On April 7,2023, OMB published a draft of proposed revisions to Circular A-4 for public comment (88 FR 20915). Among
the proposed revisions are changes to the recommended discount rates. Until the revisions are finalized, the 2003 version of
Circular A-4 remains in effect.
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Equation 1-1.
r(1 + r)r
AV = PV-
(1 + r)n - 1
Where AV is the annualized value, PV is the present value, r is the discount rate (e.g., 3 percent), and n is
the number of years (40 years).
1.3.6 Population and Income Growth
To account for future population growth or decline, EPA used 2021 National Aeronautics and Space
Administration (NASA) Socioeconomic Data and Applications Center (SEDAC) population forecasts for
the United States (Hauer & Center for International Earth Science Information Network - Columbia
University, 2021).4 EPA used the NASA SEDAC growth projections to adjust affected population
estimates for future years (i.e., from 2025 to 2063).
Because WTP is expected to increase as income increases, EPA accounted for income growth when
estimating WTP for water quality improvements. EPA projected future income over the applicable
analysis period year (i.e., from 2026 to 2065) based on income in 2021 (2021 American Community
Survey) and income growth rates obtained from historical and projected "real disposable personal
income" estimates (Energy Information Administration, 2023). Estimated growth rates, which vary by
year, are based on the ratio of the real disposable person income per person (i.e., real disposable personal
income / population) for a given year relative to the 2021 value. Since Energy Information Administration
projections are only through 2050, EPA used linear regression to estimate values for years 2051-2065.
1.4 Report Organization
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 regulatory options analyzed for this proposal, including benefits that EPA was only able to
analyze qualitatively.
¦ Chapter 3 summarizes the estimated changes in pollutant loadings and instream pollutant
concentrations anticipated under the regulatory options, including the description of the approach
EPA used to model changes in water quality across regions and regulatory options.
¦ Chapter 4 discusses EPA's analysis of nonmarket benefits of predicted changes in surface water
quality.
¦ Chapter 5 describes EPA's analysis of impacts associated with changes in emissions of air pollutants
associated with energy use, transportation, and other non-water quality effects of the regulatory
options.
These projections are based on Shared Socioeconomic Pathway 2 (SSP2) (Hauer & Center for International Earth Science
Information Network - Columbia University, 2021). SSP2 is a "middle-of-the-road" projection, where social, economic, and
technological trends do not shift markedly from historical patterns.
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Introduction
¦ Chapter 6 summarizes the monetized benefits across benefit categories.
¦ Chapter 7 summarizes the social costs of the regulatory options.
¦ Chapter 8 addresses the requirements of Executive Orders that EPA is required to satisfy for the final
rule, notably analysis of the benefits and costs of regulatory actions, as per Executive Order 14094 of
April 6, 2023 (Modernizing Regulatory Review), which supplemented and reaffirmed the principles
governing regulatory review in Executive Order 12866 of September 30, 1993 (Regulatory Planning
and Review), and Executive Order 13563 of January 18, 2011 (Improving Regulation and Regulatory
Review).
¦ Chapter 9 provides references cited in the text.
Several appendices provide additional details on selected aspects of analyses described in the main text of
the report.
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Benefits Overview
2 Benefits Overview
This chapter provides an overview of the welfare effects that would result from changes in pollutant
loadings due to implementation of the regulatory options analyzed for the proposed rule. EPA expects the
regulatory options would reduce discharge loads of various categories of pollutants when fully
implemented. The categories of pollutants affected by the proposed rule would include nutrients (total
nitrogen [TN] and total phosphorus [TP]), conventional pollutants (e.g., TSS, BOD, oil and grease) and
chlorides. The rule may also reduce loadings of bacteria and pathogens (e.g., fecal coliform bacteria,
Salmonella sp., Escherichia coli). Table 2-1 summarizes estimated changes in annual pollutant loads
under full implementation of the ELGs under each of the three regulatory options. The TDD provides
further detail on the loading changes.
Table 2-1: Summary of Changes to Annual Pollutant Loadings Compared to the Baseline
Regulatory
Option
Changes in Annual Pollutant Loadings (millions lbs/year)
TN
TP
TSS
BOD
Oil and Grease
Chlorides1
1
-8.87
-7.68
-54.39
-9.28
-16.44
-476.96
2
-44.82
-16.11
-81.81
-56.95
-28.72
-476.96
3
-76.18
-19.56
-93.31
-89.75
-43.38
-476.96
1 Chlorides has the same removal under all options.
Source: U.S. EPA Analysis, 2023
Reductions in the discharge of pollutants from MPP facilities may result in numerous environmental
changes, and, in turn, welfare effects to society. The schematic diagram in Figure 2-1 summarizes the
potential effects of the regulatory options, the expected environmental changes, and categories of benefits,
and EPA's approach to analyzing those welfare effects.
For example, the proposed rule is estimated to improve surface water quality by reducing excess
nutrients, pathogens such as E. coli, and sediment discharges. Improved surface water quality may in turn
provide (1) human health benefits via reduced exposure to contaminated waters used for primary contact
recreation, contaminated drinking water, fish, and shellfish, and toxic harmful algal blooms [HABs]
(either directly through skin contact, ingestion, or inhalation or indirectly through consumption of
contaminated fish and shellfish), (2) recreational and nonuse benefits, (3) reductions in drinking and
wastewater treatment costs, (4) reductions in fees paid by MPP indirect dischargers to POTWs,
(5) productivity benefits to agriculture, (6) benefits to the commercial fishing and shellfishing industries,
(7) benefits to subsistence fishers, (8) benefits to tourism, (9) improvements in property values.
In addition to water quality changes, the implementation of control technologies and other operational
changes in MPP facilities, and indirectly by POTWs, also affects air quality through changes in direct
emissions from wastewater treatment at MPP facilities and POTWs, or associated with changes in
electricity and fuel used to power treatment technologies or to transport solid wastes from MPP facilities
to landfills or land application areas. The negative impacts of these changes may be mitigated in part
through increased methane capture and sale.
For a more detailed description of MPP facility pollutants, their fate, transport, and impacts on human
health and the environment, see the EA.
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Benefits Overview
EPA was not able to monetize or quantify all categories of benefits from reducing MPP facility discharges
due to limitations of the available data and models to quantify the relationships between MPP facility
discharges, surface water quality, ecosystem response and other environmental effects (e.g., pollutant
exposure, individual and population-level health effects, species abundance), and how society may value
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 (see section 2.5 for a
summary of the benefits categories and how they are assessed). The remainder of this chapter provides a
qualitative discussion of the benefits applicable to the proposed rule, including human health effects,
ecological effects, economic productivity, and changes in air pollution. Some estimates of the monetary
value of benefits changes presented in this document rely on models with a variety of limitations and
uncertainties, as discussed in more detail in the respective chapters for the relevant benefit categories.
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Figure 2-1: Summary of Benefits Resulting from the Regulatory Options
Effect of Regulatory U Environmental
Options Hti Change
Reduced fish and
shellfish contamination
Reductions in nutrient
sediment, BOD, and
pathogen discharges to
surface waters
Improved surface
water quality
Increases in power
requirements and
trucking
Y
Change in emissions of
PM2.5, S02and NOx
Change in greenhouse
gases emissions
Changes in on-site
waste management
I
Habitat changes
Changes in pollutant
discharges to terrestrial
environments
Soil health
Benefits Category
Human Health Effects (Fish and Shellfish Consumption Exposure)
Reduced cases of ciguatera fish poisoning, and paralytic,
diarrhetic, amnesic, and neurotoxic shellfish poisoning
Human Health Effects (Drinking Water Exposure)
Reduced gastrointestinal illness from exposure to pathogens
Reduced developmental effects and risk of infant mortality
from exposure to nitrate/nitrite
Reduced cancer risk from exposure to DBPs in treated water
Ecological Conditions
Improved recreational and non-use values
Improved protection of T&E species
Reduced incidence offish kills
Improved aesthetics of surface waters (e.g., odor, clarity)
Economic Productivity
Reduced drinking water and wastewater treatment costs
Reduced fees paid by MPP indirect dischargers to POTWs
Reduced number and duration of shellfish bed closures
Potential increase in commercial aquaculture and fishery yields
Increase in subsistence harvests offish and shellfish
Increase in tourism
Enhanced property values
Increase in agricultural production from reduced livestock kills
Increase in revenue from methane sale
Human Health Effects
Changes in premature mortality, non-fatal heart attacks,
hospital admissions, emergency department visits, respiratory
symptoms, acute bronchitis, aggravated asthma, lost work
days and acute respiratory symptoms
Climate Change
Changes in physical, ecological, and economic impacts c
climate change
Ecological Conditions
Changes in wildlife habitat
Changes in the protection of T&E non-aquatic species
Diminished aesthetics
Economic Productivity
Changes to crop yields due to changes in loadings of pathogens
onto the landscape
Analyis Approach
~ Qualitative discussion
> Qualitative discussion
WTP for water quality improvements
Count of impacted T&E habitats (non-
monetized)
Qualitative discussion
Counts of impacted fisheries and
aquaculture sites (non-monetized)
Changes in downstream public water
systems source water quality (non-
monetized)
Qualitative discussion
Benefit pe r ton for changes in
directly-emitted PM25, S02and NOx
emissions
Qualitative discussion
> Social cost of greenhouse gases
~ Qualitative discussion
> Qualitative discussion
DBPs = disinfection byproducts; WTP = willingness to pay; T&E = threatened and endangered; POTWs = publicly owned treatment works
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Benefits Overview
2.1 Human Health Impacts Associated with Changes in Surface Water Quality
Pollutants present in MPP wastewater discharges (e.g., pathogenic E. coli, nitrogen, and phosphorus) can
cause a variety of adverse human health effects. Table 2-2 summarizes the human health effects of
selected pollutants present in MPP discharges. This summary is not exhaustive but instead highlights
some of the primary ways MPP discharges may affect human health. Other pollutants present in MPP
discharges, such as TSS, oil and grease, BOD, and halogens (e.g., bromide), may also have potential
human health effects indirectly by interfering with drinking water treatment or leading to the formation of
harmful disinfection byproducts. The EA provides more detailed discussions of MPP pollutants and their
health effects.
Table 2-2: Categories of Pollutants Present in MPP Discharges and Associated Health
Effects
Pollutant Category Human Health Effects
Pathogens Exposure to Streptococcus sp., E. coli, fecal coliform and other pathogenic
microorganisms through the ingestion of contaminated water during primary
contact recreation, drinking water, or consumption of contaminated shellfish can
lead to gastrointestinal illness (Oliveira et a!., 2011; U.S. EPA, 2009b; Wittman &
Flick, 1995)
Exposure to high levels of nitrogen in drinking water can lead to infant
methemoglobinemia, colorectal cancer, thyroid disease, and neural tube defects
(Ward etal., 2018; U.S. EPA, 2000)
Exposure to toxic HABs (whose development is influenced by excess nitrogen and
phosphorus) can lead to skin rashes, liver and kidney damage, neurological issues,
gastrointestinal symptoms or respiratory problems through ingestion or inhalation
(Backer, 2002; World Health Organization, 2021). Exposure to contaminated
shellfish from HAB toxins can lead to poisoning syndromes such as paralytic,
diarrhetic, amnesic, or neurotoxic shellfish poisoning (Hoagland et al., 2002; U.S.
EPA, 2015d)
Exposure to trihalomethane, which may be created as a disinfection by-product
(DBP) in treated drinking water (due to eutrophication through nutrient enrichment
and algae growth), can increase the risk of cancer (U.S. EPA, 2000)
Halogens (bromide) 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.
By reducing pollutant loads in MPP discharges, the regulatory options may reduce human exposure to
MPP pollutants in surface water via three exposure pathways discussed further below: (1) primary contact
recreation in waters affected by MPP discharges, (2) consumption of drinking water sourced from surface
waters affected by MPP discharges, and (3) consumption of shellfish taken from waters affected by MPP
discharges.
2.1.1 Primary Contact Recreation
Discharges from MPP facilities can affect the safety of recreational areas used for primary contact
recreation such as swimming (Section 4.2.5 of the EA provides a list of potentially affected sites). Meat
processing wastewater contains bacteria such as Streptococcus sp., E. coli, and fecal coliform (Mittal,
2004). Bacteria and pathogens enter the effluent stream from the blood, excrement, and offal of
Nutrients (nitrogen and
phosphorus)
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Benefits Overview
slaughtered livestock (The Environmental Integrity Project, 2018). Microorganisms may also be
introduced from rinsing the hide and carcass, which could have retained bacteria from livestock housing
areas, processing equipment and facility floor (Mittal, 2004). Additionally, the meat sludge byproduct in
effluent can provide the nutrients needed for the long-term survival and proliferation of some
microorganisms (Baskin-Graves et al., 2019). Untreated bacteria and pathogens from MPP direct
dischargers may affect the safety of surface water used for primary contact recreation. People exposed to
pathogens associated with poultry and livestock (/'. e., Salmonella, enterococci, E. coli, Campylobacter sp.,
and Cryptosporidium sp.) through ingestion during primary contact recreation may experience adverse
health effects (U.S. EPA, 2009b). These pathogens can cause gastrointestinal illness and lead to
symptoms such as diarrhea, abdominal pain, nausea, chills, and fever. The proposed rule would add E.
coli as a new regulated pollutant (to be used as an indicator for proper disinfection) for MPP direct
dischargers. This regulatory change may lead MPP direct dischargers to better disinfect their wastewater
and reduce the risk of human exposure to E. coli and other pathogenic microorganisms; this, in turn, may
lead to the avoidance of pathogen-related health effects.
Additionally, HABs, which can develop in response to excess nutrients, such as nitrogen and phosphorus
discharges from MPP dischargers, may also be of concern. Exposure to harmful HAB toxins through
primary and secondary contact recreation (/'. e., ingestion and inhalation) can cause skin rashes, liver and
kidney damage, neurological issues, gastrointestinal symptoms or respiratory problems (Backer, 2002;
World Health Organization, 2021).5 Hoagland et al. (2009) estimated that the annual costs of respiratory
emergency department visits between 2001 and 2006 associated with Karenia brevis algal blooms in
Sarasota County, Florida ranged from $0.03 to $0.17 million (in 2022$).6 The regulatory options would
lead to reductions in nutrients loadings from MPP facilities and, as a result, reduced occurrence of HABs
and incidence of HAB-related illnesses.
2.1.2 Drinking Water
Pollutants discharged by MPP dischargers to surface waters affect the quality of the source water used by
public water systems (PWS) that withdraw downstream from the facilities and may also affect the safety
of treated drinking water delivered by the PWS. This can be due to the pollutants not being removed
adequately during by the water treatment processes in place at drinking water treatment plants and/or the
formation of DBPs from the interaction between constituents found in MPP discharges and chemicals
used in drinking water treatment processes. For example, eutrophication (due to nutrient enrichment) and
algal organic matter can lead to the formation of the DBPs trihalomethanes which are carcinogenic
compounds that can pose a serious threat to human health if consumed (U.S. EPA, 2000). Bromide,
another pollutant present in MPP discharges, can contribute to the formation of brominated DBPs during
drinking water disinfection processes. 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
5 Schaefer et al. (2020) also provide evidence that individuals may be exposed to toxins from HABs via inhalation when in
close proximity to affected waters without any direct (primary or secondary) contact (e.g., worked on land near the water,
and visited a park or beach near affected waters).
6 Costs were converted from $0.02 to $0.13 million in 2008$ using the Bureau of Labor Statistic's Consumer Price Index for
All Urban Consumers (Bureau of Labor Statistics, 2023).
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Benefits Overview
DBPs, including three of the four regulated trihalomethanes7 (THM4, also referred to as total
trihalomethanes (TTHM) in this discussion) and two of the five regulated haloacetic acids8 (HAA5).
Additional unregulated brominated DBPs have been cited as an emerging class of water supply
contaminants that can potentially pose health risks to humans (S. D. Richardson et al., 2007; NTP, 2018;
U.S. EPA, 2016b).
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, 2016b). 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, 2016b). The relationships between
exposure to DBPs, specifically TTHMs and other halogenated compounds resulting from water
chlorination, and bladder cancer are further discussed in U.S. EPA (2019a). The relationship has been the
subject of multiple epidemiological studies (Cantor etal., 2010; U.S. EPA, 2005; NTP, 2018), ameta-
analysis (Villanueva etal., 2003; Costet etal., 2011), and pooled analysis (Villanueva et al., 2004). Regli
et al. (2015) conducted an analysis of potential bladder cancer risks associated with increased bromide
levels in surface source water and 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.
Additionally, high nitrate concentrations in drinking water can lead to infant methemoglobinemia,
colorectal cancer, thyroid disease, and neural tube defects (U.S. EPA, 2000; Ward etal., 2018). Lastly,
human exposure to E. coli through inadequate disinfection of drinking water can lead to adverse health
effects such as abdominal cramps, vomiting, diarrhea, and fever (U.S. EPA, 2009b).
Public drinking water supplies are subject to legally enforceable health-based maximum contaminant
levels (MCLs) established by EPA (U.S. EPA, 2023h). 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. There may be adverse health effects
from drinking water which contains contaminants exceeding the applicable MCL (i.e., violations) or
exceed a lower applicable MCLGs (even when no violation occurs). Table 2-3 shows the MCL and
MCLG for selected constituents or constituent derivatives of MPP effluent. The health benefits of the
proposed rule depend on whether reductions under the regulatory options will result in fewer PWS
violations of the applicable MCLs or reduce contaminants levels between the MCLs and MCLGs. For
example, reducing nitrate at public drinking water supplies that are in violation of the MCL to acceptable
limits (at or below 10 mg/L) would help prevent infant methemoglobinemia, colorectal cancer, thyroid
7 The four regulated trihalomethanes are bromodichloromethane, bromoform, chloroform, and dibromochloromethane.
8 The five regulated haloacetic acids are dibromoacetic acid, dichloroacetic acid, monobromoacetic acid, monochloroacetic
acid, and trichloroacetic acid.
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disease, and neural tube defects (U.S. EPA, 2000; Ward etal., 2018). However, since the MCL is set to
the MCLG there would be no additional health benefits from further reductions in nitrate at public
drinking water supplies that are meeting the MCL. In contrast, there may be incremental health benefits
for reductions in E. coli and certain trihalomethanes at public drinking water supplies that are meeting the
MCLs but not the MCLGs. The MCLGs for total coliform bacteria, bromodichloromethane, and
bromoform are set to zero and therefore any reduction is expected to provide benefits.
Table 2-3: Drinking Water Maximum Contaminant Levels and Goals for Selected
Pollutants in MPP Discharges
Pollutant
MCL
MCLG
Total coliforms (including fecal
5%a
0%
coliform and E. coli)
Nitrate-Nitrite as N
10 mg/L (Nitrate); 1 mg/L (Nitrite)
10 mg/L (Nitrate); 1 mg/L (Nitrite)
Total trihalomethanes (TTHM)
0.080 mg/Lb
Not applicable15
bromodichloromethane
Not applicable
0 mg/L
bromoform
Not applicable
0 mg/L
dibromochloromethane
Not applicable
0.06 mg/L
chloroform
Not applicable
0.07 mg/L
a. Fecal coliform and £ coli are bacteria whose presence indicates that the water may be contaminated with human or
animal waste. Disease-causing microbes (pathogens) in these wastes can cause diarrhea, cramps, nausea, headaches, or
other symptoms. These pathogens may pose a special health risk for infants, young children, and people with severely
compromised immune systems. No more than 5.0% of samples can test total coliform-positive (TC-positive) in a month. (For
water systems that collect fewer than 40 routine samples per month, no more than one sample can be total coliform-
positive per month.)
b. EPA has set the MCL for the total of four trihalomethanes. Although there is no collective MCLG for TTHM, there are
individual MCLGs for individual contaminants.
Source: 40 CFR 141.53 as summarized in U.S. EPA (2023h): National Primary Drinking Water Regulation, EPA 816-F-09-004
Data and modeling limitations do not allow EPA to quantify the changes in contaminant levels in treated
drinking water and the changes in the incidence of adverse health effects. However, as detailed in the EA,
EPA identified at least 92 PWS affected by MPP discharges, based on the PWS that withdraw from
source surface waters downstream from direct MPP dischargers.9 Of these PWS, seven reported at least
one violation of the applicable MCLs for total coliforms, nitrate-nitrite, or TTHM between 2004 and
2021. Eleven violations of the nitrates rule were first reported between 2004 and 2015. Four violations of
the TTHM rule were reported between 2004 and 2005. Three violations of the Revised Total Coliform
Rule, which went into effect in 2016, were reported between 2017 and 2021. The proposed MPP ELG
revisions may provide health benefits by reducing levels of the contaminants in source waters that may
contribute to PWS violations and the associated contaminant exposures through drinking water. This is in
addition to potential benefits from avoided treatment costs discussed in Section 2.3.1. EPA will continue
to assess potential methods for estimating human health benefits resulting from changes in source water
quality and welcomes comments and data to help in this assessment.
An unknown number of additional PWS withdraw from source surface waters downstream from POTWs receiving effluent
from indirect MPP dischargers or purchase treated waters from an affected PWS.
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2.1.3 Shellfish Consumption
EPA conducted an initial screening analysis which revealed that 9 recreational and 26 commercial
fishing/shellfishing areas are located downstream of MPP direct dischargers and may be affected by these
discharges.10 Section 4.2.2. of the EA provides detail on the potentially affected commercial fish species
and location of the affected commercial and federally owned recreational fishing areas. Pollutants
discharged by MPP facilities may affect human health through the consumption of contaminated shellfish
and, to a potentially lesser degree, contaminated fish.11 For example, phosphorus discharged by MPP
facilities can stimulate survival and reproduction of fecal bacteria in aquatic ecosystems, which can
pollute shellfish beds and lead to shellfish-borne diseases (Mallin & Cahoon, 2020; Oliveira el al., 2011;
Wittman & Flick, 1995). Additionally, some species ofHABs produce potent toxins that can accumulate
in fish and shellfish that feed on those algae, resulting in poisoning syndromes in human consumers (e.g.,
paralytic, diarrhetic, amnesic, or neurotoxic shellfish poisoning) (Hoagland el al.. 2002; U.S. EPA,
2015d). The annual average public health cost of shellfish poisoning (which includes lost productivity due
to sick days, costs of medical treatment and transportation, and costs associated with investigations into
the cause of illness) between 1987 and 1992 was estimated to be $0.7 million (2022$) (Hoagland etal.,
2002).1213 Given a significant increasing trend in all HAB events from 1990 to 2019 (D. M. Anderson el
al., 2021), the current public health cost of adverse HAB effects is likely to be much larger.
Monitoring of commercial harvest areas and bed closures may limit exposure to contaminated shellfish
and fish, with the exposure risk being relatively larger for recreational areas. Several studies have
reported incidents of shellfish poisoning among subsistence fishers (Adams et al., 2016; Kibler el al.,
2022; V. Trainer el al., 2014). Subsistence fishers may be more susceptible to shellfish poisoning due to
higher consumption rates of self-caught fish and shellfish. For example, subsistence harvesting of
shellfish is common in coastal Alaska (Ouzinkie, Kodiak, and Old Harbor) despite paralytic shellfish
poisoning risks due to recurrent toxic Alexandrium blooms (Kibler el al., 2022). Several MPP facilities
are located in coastal parts of Alaska and may contribute to HABs. Thus, EPA identified 7 MPP
dischargers in coastal Alaska or the Anchorage Borough, including one direct discharger on Kodiak
Island. Subsistence harvesters may also be less aware of shellfish bed closures and consumption
advisories. For example, paralytic shellfish poisoning incidents was found to be three times higher for
10 The screening analysis examined intersections between commercial aquaculture sites and recreational shellfishing sites
located in the immediate vicinities (within 200 meters) of MPP direct dischargers. EPA used data on aquaculture provided
by the National Oceanic and Atmospheric Administration (NOAA) and recreational shellfishing sites provided by the U.S.
Fish and Wildlife Service (National Oceanic and Atmospheric Administration [data set], 2022; U.S. Fish and Wildlife
Service, 2022).
11 Potential human exposure to contaminated fish through fish consumption, and associated illnesses such as ciguatera fish
poisoning (Hoagland et al., 2002), may be minimal since excess ammonia discharges and HABs can lead to fish kills
(Cloern, 2001; Jordan, 2007). Therefore, EPA considers the primary route of exposure to be through shellfish consumption.
12 Hoagland et al. (2002) obtained cost information from a literature review of the economic effects ofHABs for events in the
U.S. between 1987 and 1992. Cost estimates were based on the number of reported and unreported cases of shellfish
poisoning, a $1,400 cost for reported illnesses and a $1,100 cost for unreported illnesses, and a $1 million cost for
mortalities (Hoagland et al., 2002).
13 Costs were converted from $0.4 million in 2000$ using the Bureau of Labor Statistic's Consumer Price Index for All Urban
Consumers (Bureau of Labor Statistics, 2023).
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residents of Old Harbor compared to Kodiak due, in part, to differences in awareness to advisory
information (Kibler et al., 2022).
By reducing MPP nutrient discharges, the regulatory options may prevent human exposure to
contaminated fish and shellfish and reduce the incidence of shellfish-related poisoning. EPA is unable to
quantify these changes given the limitations of the available data and models necessary to link predicted
changes in nutrient loads, to HABs, shellfish toxin levels, exposure, and adverse health effects.
2.2 Ecological and Recreational Impacts Associated with Changes in Surface Water Quality
Wastewater from MPP facilities contains pollutants such as nutrients (nitrogen and phosphorus), BOD,
bacteria and pathogens, TSS, oil and grease, and chlorides. As detailed in the EA, discharges of these
pollutants to surface water can have a variety of environmental effects, including fish kills,14 reduction in
the survival and growth of aquatic organisms, and degradation of aquatic habitat. The adverse effects
associated with releases of MPP pollutants depend on many factors such as the chemical-specific
properties of the effluent, the mechanism, medium, and timing of releases, and site-specific
environmental conditions.
EPA expects pollution loading reductions under the regulatory options to improve 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 MPP facility pollutants. These changes have the potential to
increase ecosystem productivity and the propagation and health of resident species, including fish and
invertebrate populations, thus potentially enhancing commercial, recreational, and subsistence fisheries.
Water quality improvements may also enhance other recreational activities such as swimming and
boating, as well as nonuse values (e.g., option, existence, and bequest values) of the waters that receive
MPP facility discharges. The improvements could also contribute to the recovery of T&E species
sensitive to water pollution Finally, the proposed rule has the potential to impact nonuse values (e.g.,
option, existence, and bequest values) of the waters that receive MPP facility discharges.
2.2.1 Changes in Surface Water Quality
The regulatory options may affect the value of ecosystem services provided by surface waters impacted
by MPP dischargers. Increases in ammonia and the presence of HABs can lead to odor and water clarity
issues affecting the recreational and aesthetic value of the affected waters (Backer & McGillicuddy, 2006;
Baskin-Graves et al., 2019; U.S. EPA, 2000). Additionally, excessive amounts of phosphorus, ammonia,
and other forms of nitrogen can lead to low dissolved oxygen (DO) levels (Mallin & Cahoon, 2020; U.S.
EPA, 2001), which may, in turn, lead to the release of toxic metals from sediments and contamination of
surface waters and aquatic habitats (Li et al., 2013). The contamination of surface waters and aquatic
habitats may also adversely affect fish propagation and survival. By reducing discharges of nitrogen and
phosphorus pollutants to receiving reaches, the proposed regulatory options would reduce occurrence of
HABs and the probability of toxic metals being released from waterbody sediments, improve surface
water quality, and improve water clarity, odor, and DO levels.
14 For example, in 2019 discharges of partially treated wastewater from a MPP facility into the Mulberry Fork of the Black
Warrior River in Alabama resulted in a large fish kill (Alabama Department of Environmental Management, 2021).
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Society may value changes in ecosystem services resulting from the MPP regulatory options through a
number of mechanisms, including increased use and utility derived from subsistence fishing and
recreational activities (such as fishing, swimming, and boating). Individuals may also value the protection
of habitats and species that reside in waters affected by MPP dischargers, even when those individuals do
not use or anticipate future use of such waters for recreational or other purposes, resulting in nonuse
values.
As detailed in the EA and in Chapter 3 of this document, EPA quantified potential environmental impacts
from the regulatory options by estimating in-waterway concentrations of MPP facility 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 ecological changes for this regulatory
analysis. Section 3.3 of this report provides details on the parameters used in formulating the WQI and the
WQI methodology and calculations.
A variety of primary methods exist for estimating recreational use values, including both revealed and
stated preference methods (Freeman III, Herriges, & Kling, 2014). Where appropriate data are available
or can be collected, revealed preference methods can be employed for estimating use values. Some people
deem revealed preference methods more reliable because they rely on 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 (Freeman III, Herriges, & Kling, 2014).
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 (Johnston, Boyle, etal., 2017; OMB, 2003a). Stated preference methods rely on
carefully designed surveys, which either (1) ask people about their willingness to pay (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 etal., 2006; Johnston, Boyle, etal., 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 leave Agency analysts with benefit transfer as the only option for assessing certain types of
non-market values (R.S. Rosenberger & Johnston, 2008; Johnston etal., 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" (V.K. Smith, G. Van
Houtven, & S.K. Pattanayak, 2002, p. 134). It involves adapting research conducted for another purpose
to estimate values within a particular policy context (Johnston el 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 (R.S. Rosenberger & Johnston, 2008; Johnston et al., 2021).
However, EPA acknowledges that there is still a potential for transfer errors (see Kaul et al., 2013 for
additional discussion on benefit transfer error) and no transfer method is always superior (Johnston et al.,
2021).
To quantify and monetize the benefits of revisions to the MPP ELGs, EPA followed the same
methodology used in analyzing the proposed revisions to the technology-based ELGs for the steam
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electric generating point source category (U.S. EPA, 2023b). EPA relied on a benefit transfer approach
based on a meta-analysis of 59 surface water valuation studies to estimate the use and nonuse benefits of
improved surface water quality under the regulatory options. The valuation function includes explanatory
variables to enable more accurate value predictions for the surface waters affected by MPP dischargers,
linking these values to specific characteristics of affected water resources and households. This analysis is
presented in Chapter 4.
2.2.2 Impacts on Threatened and Endangered Species
For T&E species, even minor changes to reproductive rates and small improvements in mortality levels
may represent a substantial portion of annual population growth. By reducing discharges of MPP facility
pollutants to T&E habitats, the regulatory options have the potential to impact the survivability of some
T&E species living in these habitats. Section 9 of the Endangered Species Act (ESA) prohibits the take
(hunting/trapping/collecting) of endangered species. Section 4(d) of the ESA affords threatened species
similar protections with more flexibility on a case-by-case basis. As a result of not being legally hunted or
collected, T&E species primarily derive value primarily from nonuse values, such as existence, bequest,
and recreational values. In addition, pollutants from MPP dischargers may affect T&E species indirectly
by causing damage to food webs and ecosystem stability. Reducing discharges of MPP facility pollutants
to T&E habitats would benefit T&E species by improving species protection and survival.
EPA quantified but did not monetize the potential effects of the regulatory options on T&E species. As
detailed in Section 4.2.3 of the EA, EPA constructed databases to determine which species have habitat
ranges that intersect waters downstream from MPP direct dischargers and classified species according to
their vulnerability to water pollution. Species deemed to have 'higher" vulnerability to water pollution
from MPP discharges include species living in aquatic habitats for several life history stages and/or
species that obtain a majority of their food from aquatic sources. See the EA for additional
methodological details.
EPA identified 108 unique animal species with habitats that may be impacted by MPP direct dischargers.
Of these 108 species, the majority (75) are classified as having a higher vulnerability to water quality
impacts. Clams and fishes make up over half of the number of species potentially affected by the
proposed rule and both groups have a higher vulnerability to water quality impacts. Examples of other
affected species include the West Indian Manatee (mammal), Ozark Hellbender (amphibian), Slenderclaw
crayfish (crustacean), bog turtle (reptile), and Painted rocksnail (snail). Table 2-4 provides a breakdown
of the T&E species by group and vulnerability designation.
Table 2-4: Threatened and Endangered Species by Group and Vulnerability
Group
Lower
Moderate
Higher
Total Species Count
Amphibians
1
1
2
4
Birds
6
3
0
9
Clams
0
0
45
45
Crustaceans
0
0
3
3
Fishes
0
0
15
15
Insects
4
0
0
4
Mammals
7
1
1
9
Reptiles
10
0
6
16
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Table 2-4: Threatened and Endangered Species by Group and Vulnerability
Group
Lower
Moderate
Higher
Total Species Count
Snails
0
0
3
3
Total
28
5
75
108
Note: 'Higher' vulnerability includes 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 includes species living in aquatic habitats for one life
history stage and/or species that obtain some of their food from aquatic sources. 'Lower' vulnerability includes species
whose habitats overlap bodies of water, but whose life history traits and food sources are terrestrial.
EPA was unable to monetize the proposed rule's effects on T&E species due to a variety of challenges in
quantifying the response of T&E populations to changes in water quality, including availability of life
history and population dynamic data and the complex nature of aquatic ecosystems. 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, increase in the probability of survival, or an increase in
species population levels (Subroy etal., 2019; L. Richardson & Loomis, 2009). The studies summarized
in Subroy et al. (2019) suggest that people attach non-trivial economic value to protection of T&E
species. These values range from $16.74 per household (in 2022$) for Colorado pikeminnow to $165.03
(in 2022$) for lake sturgeon (both fish species).15 Together, the results of these studies indicate that
aggregate values for preservation of T&E species are likely to be significant. EPA is considering potential
monetization approaches for estimating the value of improved T&E habitat for the final rule analysis. The
agency solicits comments on the feasibility of quantifying the response of T&E populations to water
quality improvements and potential valuation approaches.
2.3 Economic Productivity
The regulatory options may have economic productivity effects stemming from changes in the quality of
waters used as sources of drinking water, for industrial processes, or for irrigation; changes in the quality
of wastewater received by POTWs; changes in commercial and subsistence shellfish and fish harvests,
tourism and property values; and changes in the generation, capture and sale of methane at MPP facilities
and POTWs. These benefits are discussed qualitatively in the following sections.
2.3.1 Drinking Wa ter Trea tment Costs
The proposed regulatory options have the potential to reduce drinking water treatment costs for PWS
affected by MPP dischargers by improving the quality of source waters. Treatment may be required to
meet the health based MCLs discussed in Section 2.1.2, or for aesthetic considerations such as taste, odor,
and color. EPA has established National Secondary Drinking Water Regulations (NSDWRs) that set non-
mandatory water quality standards, referred to as secondary maximum contaminant levels (SMCLs), for
15 contaminants. These contaminants are not considered to present a risk to human health and EPA does
not enforce the SMCLs.
Excess phosphorus in concentrations greater than 1.0 mg/L can interfere with the coagulation process in
drinking water treatment plants and reduce treatment efficiency (Mallin & Cahoon, 2020). Excess
chloride and TDS can corrode distribution system pipes and lead to the buildup of scale (a mineral
15 Costs were converted from 2016$ to 2022$ using the Bureau of Labor Statistics Consumer Price Index for All Urban
Consumers (Bureau of Labor Statistics, 2023).
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deposit), reducing water flow (U.S. EPA, 2023k). Additionally, high algal biomass, as a result of
eutrophication, can clog and corrode drinking water intake pipes, and increase the volume of chemicals
needed to purify the water (Nordin, 1985). The presence of algal biomass and other organic matter may
also contribute to higher treatment costs to avoid and control the formation of DBPs and their associated
adverse health effects discussed in section 2.1.2. Algal blooms, chlorides, and high concentrations of total
solids (TDS and TSS) may also impact the taste and smell of drinking water (Backer & McGillicuddy,
2006; U.S. EPA, 2012, 2023k), necessitating additional treatment.16 The increased cost of treating
drinking water due to excess nutrients and the presence of algal blooms can be substantial. For example,
the City of Waco, Texas incurred an estimated $89.5 million in costs from 2002 to 2012 (in 2022$)17 to
address poor drinking water quality due to excess nutrients (U.S. EPA, 2015c). In addition, the City of
Waco lost potentially up to $13.1 million (in 2022$)18 in revenue due to taste and odor problems resulting
in decreased water sales to neighboring communities prior to treatment plant upgrades (U.S. EPA, 2015c).
In another example, the City of Celina, Ohio incurred $16.7 million in 2010 (in 2022$)19 in increased
drinking water treatment costs associated with a blue-green algae outbreak (U.S. EPA, 2015c).
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). Price and Heberling (2018),
through a comprehensive review of the literature, developed average elasticities which relate percentage
changes in drinking water treatment costs to a 1 percent change in source water quality (measured either
in terms of pollutant concentrations or pollutant loadings). Using data from 15 U.S. studies, the authors
developed elasticities for various water quality parameters, including nitrogen concentrations, phosphorus
and sediment loadings, TOC, turbidity, and pH. The study found a 1 percent change in nitrogen (as
nitrate) concentration to lead to a 0.06 percent change in drinking water treatment costs. Similarly, the
study found a 1 percent change in phosphorus loads to lead to a 0.02 percent change in drinking water
treatment costs ranging from 0.02 to 0.19 percent and a 1 percent change in sediment loads leads to a
change in drinking water treatment costs ranging from 0.02 to 0.26 percent. Finally, a 1 percent reduction
in TOC leads to a 0.10 to 0.55 percent decrease in drinking water treatment costs. As part of the water
quality modeling described in Chapter 3, EPA identified estimated changes in pollutant concentration
under the regulatory options for reaches with public water system surface water intakes. However,
because of the limited data available on TIP and baseline operation and maintenance (O&M) costs for
systems potentially affected by the proposed rule, EPA was not able to monetize changes in treatment
16 EPA has established National Secondary Drinking Water Regulations (NSDWRs) that set non-mandatory water quality
standards or secondary maximum contaminant levels (SMCLs) for contaminants. The SMCLs serve as guidelines to assist
public water systems in managing their drinking water for aesthetic considerations, such as taste, color, and odor, and
technical considerations such as damage to water equipment or reduced effectiveness of treatment for other contaminants.
These contaminants are not considered to present a risk to human health at the SMCL. Chloride and TDS have SMCLs of
250 mg/L and 500 mg/L, respectively (U.S. EPA, 2023k).
17 Costs were converted from $70.2 million in 2012$ using the Bureau of Labor Statistic's Consumer Price Index for All
Urban Consumers (Bureau of Labor Statistics, 2023).
18 Costs were converted from $10.3 million in 2012$ using the Bureau of Labor Statistic's Consumer Price Index for All
Urban Consumers (Bureau of Labor Statistics, 2023).
19 Costs were converted from $13.1 million in 2012$ using the Bureau of Labor Statistic's Consumer Price Index for All
Urban Consumers (Bureau of Labor Statistics, 2023).
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costs as a result of these changes. The Agency will continue to assess approaches to monetizing these
benefits and welcomes comments on data to support these analyses.
Poor drinking water quality, actual or perceived, can also have economic impacts as consumers avert
consuming tap water and turn to more expensive bottled water. Research documents a relationship
between sales of bottled water and SDWA violations (Allaire et al, 2019).
Despite these findings, there are significant gaps in the literature that impede monetization of potential
drinking water treatment cost reductions from reductions in nutrients and eutrophication levels. Among
these gaps are limited information about how important water quality measures like nutrient
concentrations, algae presence and HABs (as measured, for example, by cyanobacteria cell density) affect
treatment costs,20 and an insufficient understanding of how relationships between treatment costs and
source water quality differ across treatment technologies (Heberling et al., 2022). These gaps are only
starting to be addressed. For example, a recent study by Heberling et al. (2022) assessed the avoided-
treatment costs from improving surface water quality for a drinking water treatment plant in Ohio. The
study found algal toxin to be a significant driver of treatment costs where the presence of a HAB toxin led
to a 2.56 percent increase in daily costs.
Results from EPA's review of literature on the relationship between treatment costs and source water
quality suggest that the regulatory options have the potential to reduce drinking water treatment cost at
affected PWS. These cost savings may be the result of avoiding expensive treatment upgrades that may be
necessary to meet applicable MCLs or may result from reduced costs to operate current treatment
processes, such as reduced chemical use (alum) to treat solids. As detailed in the EA and summarized in
Section 2.1.2, EPA identified 92 PWS that withdraw from surface waters downstream from MPP direct
dischargers. Limited information is available on the treatment in place (TIP) at these PWS. However, they
could potentially have to upgrade their existing treatment to meet applicable MCLs without the
improvements in source water quality achieved under the regulatory options. Such upgrades can be very
expensive. For example, Ribaudo et al. (2011) estimated the cost of nitrogen removal for individual
community water systems to range from $19,500 to $815,000 per year, depending on system size.
EPA is continuing to evaluate the application of engineering models or treatment cost elasticity approach
to quantify avoided treatment costs from improved source water quality and welcomes comments and
additional information to help quantification of avoided drinking water treatment costs under the
proposed rule. The agency also encourages comments on other measures of the benefits of improving
source waters quality such as households WTP to reduce contaminant levels below SMCLs.
2.3.2 Wastewa ter Trea tment Costs
The proposed regulatory options have the potential to transfer wastewater treatment costs at POTWs
receiving MPP discharges to the MPPs. Reduced treatment costs for POTWs may result from reduced
20 As evidence of this limited information, the average elasticities developed in Price and Heberling (2018) for nitrogen and
phosphorus loads were based on only one study each, when excluding studies that did not mitigate against potential omitted
variable bias by (1) incorporating control variables consistent with economic theory in their models (e.g., the volume of
treated water, surface water or ground-water sourced water, among others) or (2) using a panel fixed effects estimator when
panel data is employed. Only one additional study pertaining to treatment costs for phosphorus loads would be included
when removing this restriction.
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"pass through"21 and "interference"22 events, and improving the quality of biosolids. However, EPA also
notes that any cost savings to the POTWs may be offset by reduced treatment fees paid by the MPP
facilities to the POTWs. Because of a lack of data to estimate the changes, EPA is not quantifying these
cost savings in this analysis but notes that savings at POTWs would reduce the net social costs
attributable to this rule as discussed in Chapter 7. POTWs may conduct primary treatment, secondary
treatment, and advanced treatment.23 Conventional secondary biological treatment processes do not
remove phosphorus and nitrogen to a substantial extent and their removal often requires advanced
treatment such as biological nutrient removal (BNR) (U.S. EPA, 2004a).
Livestock slaughtering and cleaning can generate high TSS concentrations by introducing large amounts
of blood and offal into the waste stream (Amorim & Moura, 2021). TSS can contribute to complications
in wastewater treatment. Moreover, fats, oils, and grease are prone to float on top of effluent and can
reduce efficiency of the treatment process (Mittal, 2004). Lastly, nutrients such as organic nitrogen and
phosphorus have been found to be widespread in MPP wastewater, originating from bone, animal tissue,
blood, manure, and cleaning compounds (U.S. EPA, 2004c; Ziara etal., 2018). The cost of treating
nutrients in wastewater depends on their concentrations, as well as other factors such as the type of
technology utilized by the POTW (e.g., BNR technologies, activated sludge, lagoons and oxidation
ditches) and its size or treatment capacity (due to economies of scale) (U.S. EPA, 2015c). The regulatory
options will lead to changes in pre-treatment or best management practices (BMPs) at MPP facilities
which may result in reductions at POTWs of TSS, oil and grease, and nitrogen and phosphorus nutrient
loads, and, as a result, potential reductions in treatment costs.
However, the regulatory options would also reduce the BOD concentration discharged by MPP indirect
dischargers which may, in some cases, lead to increases in treatment costs at POTWs. This is because
nitrogen removal almost always relies on biological treatment which requires some carbon source such as
BOD (i.e., bacteria must have oxygen to break down the sewage) (U.S. EPA, 2004a). A lack of BOD in
the incoming wastewater may require a POTW to add a carbon source which can increase cost. This is
dependent on the design of the facility and how much of the needed carbon comes from the MPP indirect
discharger as opposed to other sources.
In addition, the regulatory options may also reduce the incidence of POTW pass through and interference
related to MPP wastewater strength (i.e., concentrations of BOD, TSS, oils and grease, and nitrogen) and,
in turn, reduce the occurrence of associated fines. "Interference is costly to POTWs in terms of worker
21 "Pass through" is defined in 40 CFR Part 403.3(p) as "A discharge that exits the POTW into waters of the United States in
quantities or concentrations that, alone or in conjunction with a discharge or discharges from other sources, is a cause of a
violation of any requirement of the POTW's NPDES [National Pollutant Discharge Elimination System] permit (including
an increase in the magnitude or duration of a violation)."
22 "Interference" is defined in the General Pretreatment Regulations (40 CFR Part 403) in terms of a discharge which, alone or
in combination with other discharges, inhibits or disrupts the POTW and causes it to violate its NPDES permit or applicable
sludge use or disposal regulations (U.S. EPA, 1987).
23 Primary treatment is the initial stage in the treatment of wastewater and involves the removal of coarse solids. Primary
treatment is followed by secondary treatment that can remove up to 90 percent of the organic matter in wastewater by using
biological treatment processes (the most common conventional methods are attached growth and suspended growth
processes). Secondary treatment may be followed by advanced treatment which can be an extension of conventional
secondary biological treatment (e.g., to remove nitrogen or phosphorus) and may also involve physical-chemical separation
techniques such as ion exchange and reverse osmosis (U.S. EPA, 2004a).
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safety, physical plant integrity, effectiveness of operation, and liability for NPDES permit violations"
(page 52, U.S. EPA (1987)). EPA studied a subset of POTWs that receive MPP wastewater discharges to
inform consideration of the need for national pretreatment standards for MPP facilities. Many of the
POTWs (approximately 73 percent) had violations for pollutants found in MPP wastewater, such as BOD,
TSS, chlorides, nutrients, and oil and grease. The collected data thus indicate that POTWs are not
adequately removing nutrients from MPP indirect dischargers and that MPP indirect dischargers are likely
contributing to interference and pass through incidents. Moreover, the regulatory options may also reduce
time and resource costs to POTWs related to the prevention measures (such as legal action) POTWs take
to avoid interference from MPP indirect dischargers.
Lastly, the regulatory options may affect the quantity and quality of biosolids generated in the wastewater
treatment process which may be sold and used in land applications (e.g., as fertilizer for farmers).
Biosolids are required to meet federal regulation (40 CFR Part 503) that set minimum requirements for
land applications, including limits to pathogens such as fecal coliform and Salmonella (U.S. EPA, 2004a).
40 CFR Part 503.14 requires that biosolids must be applied to land at the appropriate agronomic rate
which is the sludge application rate designed to provide the amount of nitrogen needed by the crop or
vegetation grown on land. The regulatory options would affect biosolids generated by POTWs receiving
MPP wastewater in two ways: (1) reduce the level of pathogens and thus potentially increase the quality
of biosolids (2) reduce the amount of nitrogen and phosphorus may and therefore decrease biosolids
effectiveness as a fertilizer (i.e.. increasing sludge application rates) and lower sales. Because POTWs are
likely to receive discharges from multiple sources, the overall effect of the regulatory options on the
quantity and quality of biosolids and revenue generated from their sale is likely to be small.
MPP facilities may also market recovered solids from their on-site wastewater treatment to offset some of
their costs. Benefits depend on the uses for these industrial sludges. The same is true for recovered oil and
grease, which can also be used as rendering feedstock. EPA is requesting input and available data to
better define the market for these products to quantify the potential benefits.
2.3.3 Industrial and Agricultural Uses
MPP dischargers can affect the quality of water used for industrial and agricultural uses. Some industrial
facilities treat water before use, and elevated sediment and turbidity levels resulting from MPP discharges
may require additional treatment (Osterkamp et al. 1998) or use of filters to improve water quality or
make a surface water source unusable. Even small amounts of suspended sediment can cause problems
for industrial operations such as vegetable processing or cloth manufacture. Suspended sediment also
increases the rate at which hydraulic equipment, pumps, and other equipment wear out, causing
accelerated depreciation of capital equipment. In addition, HABs can lead to the clogging of industrial
water intakes and cause problems for industrial facilities. As example of potential impacts on agricultural
uses, nutrients can increase eutrophication and promote cyanobacteria blooms in surface waters used for
livestock watering which can potentially kill livestock that drink from these waters (Backer, 2002; World
Health Organization, 2021). EPA did not quantify or monetize effects of quality changes in industrial or
agricultural water sources arising from the regulatory options due to the lack of data on direct MPP
dischargers that affect source water for industrial processes or livestock watering.
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2.3.4 Commercial Harvesting of Fish and Shellfish
Commercial harvest of fish and shellfish exists in salt waters and, to a certain extent, in the Great Lakes.
Commercial fishing potentially affected by MPP discharges includes aquaculture leases for fish
crustaceans, mollusks, and aquatic plants.24 Specifically, potential impacts to commercial fishing and
shellfishing exist along the Atlantic and Gulf Coasts with specific facilities discharging to the Albemarle
Sound, Chesapeake Bay, Delaware Bay, and the Gulf of Mexico. Section 4.2.2. of the EA provides detail
on the potentially affected commercial fish species and fishing areas located downstream from the MPP
dischargers. Eutrophication and the formation of HABs stemming from MPP facility discharges of
nutrients has the potential to negatively impact commercial harvest of fish and shellfish. HABs have
occurred in the Great Lakes and coastal areas across the country (Hoagland el al., 2002; Makarewicz el
al., 2006; Islam & Masaru, 2004; Jin, Thunberg, & Hoagland, 2008; V. L. Trainer et al., 2007; U.S. EPA,
2015c). HABs can affect commercial fisheries by directly causing fish kills, causing habitat loss leading
to lower ecosystem carrying capacity, forcing managers to establish closures, increasing the costs of
processing harvested shellfish, and causing consumer demand to shrink due to the perception of risk
(Hoagland et al., 2002; Suddleson & Hoagland, 2021; U.S. EPA, 2015c). In some cases, excessive
pollutant loadings due to toxic algal blooms can lead to the closure of shellfish beds, thereby reducing
shellfish harvests and causing economic losses from reduced harvests (Jin, Thunberg, & Hoagland, 2008;
V. L. Trainer et al., 2007; Islam & Masaru, 2004; Suddleson & Hoagland, 2021). These economic losses
may be significant. For example, Evans and Jones (2001) estimated the value of lost oyster harvests
between September and December 2000 in Galveston Bay, Texas due to the closure of shellfish beds
(which lasted 85 days) affected by a "red tide" event at $306,000 (in 2022$).25 In another example, Jin,
Thunberg, and Hoagland (2008) estimated the value of lost soft-shell crab and mussel harvests between
April and August 2005 in Maine due to the closure of shellfish beds affected by a "red tide" event at $3.2
million and $586,000 (in 2022$), respectively.26
Improved water quality due to reduced discharges of pollutants from MPP dischargers 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
surplus.27
24 Commercial fishing areas were identified using two datasets: National Oceanic and Atmospheric Association's (NOAA's)
aquaculture layer (National Oceanic and Atmospheric Administration [data set], 2022) and essential fish habitat (EFH)
mapper (National Oceanic and Atmospheric Administration, 2021). The former includes the location of aquaculture leases
within coastal and offshore waters. The areas covered include the Atlantic, Gulf, and Pacific coasts of the contiguous U.S.
The latter includes information on the geospatial distribution of commercially caught fish species. To assess potential
impacts from MPP direct dischargers, EPA identified commercial fishing areas that were within 200 meters of their 25-mile
downstream flow path.
25 Costs were converted from $240,000 in 2012$ using the Bureau of Labor Statistic's Consumer Price Index for All Urban
Consumers (Bureau of Labor Statistics, 2023).
26 Costs were converted from $2.5 million and $460,000 in 2012$ using the Bureau of Labor Statistic's Consumer Price Index
for All Urban Consumers (Bureau of Labor Statistics, 2023).
27 An increase in consumer surplus is unlikely since reduced discharges of pollutants from MPP dischargers would only affect
the local commercial harvest of fish and shellfish species. Thus, improvements in harvest are unlikely to occur at a large
enough scale to lead to subsequent price changes.
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EPA did not monetize impacts to commercial fisheries from reducing pollutants from MPP dischargers
under the regulatory options. Estimated increases in annual average pollutant loads under the regulatory
options may affect commercial harvest by enhancing local fish populations (e.g., reducing fish kills) and
reducing the number of days when shellfish beds are closed for harvest. The benefit to the economy from
the regulatory options effects on commercially harvested fish shellfish species is determined by the sum
of changes in both producer and consumer surplus. The change in producer surplus is a function of gross
revenue change from the change in the commercial harvest due to improved water quality.28 As shown by
existing economic studies (U.S. EPA, 2004b; U.S. EPA, 2014), economic impacts on local producers are
likely to be nontrivial. On the other hand, the overall effects to commercial fishery consumers arising
from the regulatory options are likely to be negligible. Most species of fish have numerous close
substitutes. The literature suggests that when there are plentiful substitute fish products, numerous fishers,
and a strong ex-vessel market, individual fishers are generally price takers. Therefore, the measure of
consumer welfare (consumer surplus) is unlikely to change because of small changes in fish and shellfish
landings, such as changes EPA expects under the regulatory options.
2.3.5 Subsistence Harvesting of Fish and Shellfish
Discharges of pollutants may, for reasons similar to those described in Section 2.3.4 (i.e., eutrophication
and the formation of HABs), potentially impact subsistence harvesting of fish and shellfish through fish
kills and fish and shellfish contamination. As shown in the EA, 50 unique MPP direct dischargers
discharge within 50 miles of 44 unique tribal areas potentially affecting subsistence fishing areas on tribal
lands (see Section 7 on the EA for detail on affected tribal lands and subsistence fishing areas).
Several studies have found losses of subsistence fishing due to HABs (U.S. EPA, V. L. Trainer et al.,
2007; 2015c). For example, subsistence fishers were heavily impacted after the closure of a recreational
razor clam fishery in 2003 due to domoic acid from HABs throughout the Washington and Oregon coast
(U.S. EPA, 2015c). Subsistence fishing may also be reduced due to bans on the harvesting of
contaminated shellfish or concerns related to the risk of shellfish poisoning caused by fecal bacteria and
HABs (see Section 2.1.3). The regulatory options would decrease discharges of nutrients from MPP
facilities leading to potential reductions in the frequency of toxic HAB formation and, as a result,
reductions in the risk of shellfish poisoning, thereby benefiting subsistence fishers.
2.3.6 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 (Mojica & Fletcher, 2020; Highfill & Franks, 2019).
Approximately 87 percent of MPP direct dischargers discharge to an area with potential for recreation.
Affected recreation area types include local parks, conservation easements, and state conservation areas
(see Section 4.2.5 of the EA for detail). Although the average minimum distance from a discharger to a
recreation area is 6.07 miles, quite a few recreational areas have MPP direct discharges less than a mile
upstream. Given proximity of the dischargers to recreational areas, there is a potential of negative effects
on water quality in recreational areas. For example, excess nutrients contained in MPP discharges may
28 Because normal profits are assumed to be a sufficient proxy for producer surplus, assessment of producer surplus is a
relatively straightforward calculation in which the change in producer surplus is calculated as a species- and region-specific
fraction of the change in gross revenue due to increased landings (U.S. EPA, 2004b; U.S. EPA, 2014).
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result in HABs, which have been shown to negatively affect tourism (Donald M Anderson el al., 2000;
Bechard, 2020b; Hoagland etal., 2002; Larkin & Adams, 2007; U.S. EPA, 2015c; Weir, Kourantidou, &
Jin, 2022). For example, both Larkin and Adams (2007) and Bechard (2020b), found that the presence of
HABs reduced monthly lodging sector sales and restaurant sector sales in the northwest and southwest
coasts of Florida. In another example, a full season closure for recreational shellfishing due to the
presence of HABs in Long Beach, Washington was estimated to cost $0.86 million (2022$). This estimate
includes lost revenue for gas stations, food stores, accommodations, and food service places (Weir,
Kourantidou, & Jin, 2022).2930
The effects of water quality on tourism are likely to be highly localized. Because few identified
recreational sites are in close proximity to MPP direct discharge points, negative impacts on tourism-
dependent local economies resulting from water quality effects on fishing and water-based recreation are
unlikely. However, MPP discharges may still affect fish, swimming safety, and aesthetic value of water
resources and thus recreational benefits, as described in Section 2.2.1EPA did not quantify or monetize
the effects of water quality on tourism and local economies due to the lack of data on recreational
behavior and visitation for the affected sites.
2.3.7 Property Values
Discharges of pollutants may affect the aesthetic quality of water resources by altering water clarity,
color, and odor in the receiving and downstream reaches. For example, water clarity, color, and odor may
be impacted by HABs and ammonia (Backer & McGillicuddy, 2006; Baskin-Graves el al., 2019; U.S.
EPA, 2000; U.S. EPA, 2015c). Studies suggest that properties are more desirable when located near
unpolluted water (e.g., Bin & Czajkowski, 2013; K.J. Boyle, Poor, & Taylor, 1999; Cassidy, Meeks, &
Moore, 2023; Gibbs etal., 2002; Kuwayama, Olmstead, & Zheng, 2022; Leggett & Bockstael, 2000; Liu,
Opaluch, & Uchida, 2017; M. R. Moore et al., 2020; Netusil, Kincaid, & Chang, 2014; Tang,
Heintzelman, & Holsen, 2018; Walsh et al., 2017; Wolf, Gopalakrishnan, & Klaiber, 2022). Moreover,
properties have been shown to lose value when located near HABs and persistent blooms of "red tide"
(Bechard, 2020a; Wolf, Gopalakrishnan, & Klaiber, 2022). Technologies implemented by MPP facilities
to comply with the regulatory options remove nutrients to varying degrees and have varying effects on
water eutrophication, algae production, water turbidity, and other surface water characteristics. Therefore,
the regulatory options may lead to property value benefits with reductions in nutrient and sediment
concentrations in adjacent surface waters.
EPA did not quantify or monetize the potential change in property values associated with the regulatory
options. The magnitude of the effect on property values depends on many factors, including the number
of housing units located in the vicinity of the affected waterbodies, community characteristics (e.g.,
residential density), housing stock (e.g., single family or multiple family), and the effects of MPP
pollutants on the aesthetic quality of surface water. There are no well-established models to predict
changes in the aesthetic quality of surface waters (e.g., clarity and odor) that may result from the changes
in pollutant concentrations under the regulatory options, and EPA therefore did not estimate impacts of
29 This estimate was based on an autoregressive distributed lag model of the change in foot traffic (using visitor foot traffic
data from 2018 to 2021) during recreational clamming closures (Weir, Kourantidou, & Jin, 2022).
30 Costs were converted from $0.8 million in 2021$ using the Bureau of Labor Statistic's Consumer Price Index for All Urban
Consumers (Bureau of Labor Statistics, 2023).
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the proposed 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, and the
Agency chose to avoid potential double-counting by not quantifying and monetizing this category.
2.3.8 Capture of Methane
As discussed later in Chapter 5, the regulatory options may lead to changes in methane (CH4) emitted
indirectly through changes in electricity consumption to power wastewater treatment processes.
Though there is no overall, net incremental change in CH4 emissions associated with wastewater
treatment technology, facilities may still have increased on-site emissions of CH4 that can be captured31
and used for on-site energy needs or marketed as renewable natural gas for electricity generation or
transportation (Bracmort etal., 2011). The regulatory options may provide additional incentives for MPP
facilities to capture the CH4 and use it beneficially (e.g., for energy generation or heating), which has
positive outcomes for MPP facilities and the environment. For example, the sale of captured CFUmay
provide MPP facilities additional revenue (see the RIA for additional details on potential revenue to MPP
facilities). Because CH4 is a potent greenhouse gas (GHG) its capture helps mitigate climate change
impacts (see Chapter 5 for details on changes in CH4 emissions). Generating energy and heat from
captured CFUalso potentially reduces use of non-renewable resources.
2.4 Changes in Air Pollution
The proposed rule has the potential to affect air pollution through two main mechanisms: (1) indirect
changes in CH4 CO2, NOx, and SO2 emissions associated with changes in electricity consumed to power
wastewater treatment processes at MPP facilities and POTWs, and (2) transportation-related air pollutant
emissions (CH4, CO2, NOx, and SO2) due to changes in the trucking of solid waste for land application,
landfilling, or composting.
CO2 and CH4 are greenhouse gases that EPA has determined endanger public health and welfare through
their contribution to climate change. EPA used estimates of the social cost of carbon and methane (SC-
CO2 and SC-CH4) to monetize the changes in emissions as a result of the proposed rule. SC-CO2 and SC-
CH4 (collectively referred to as the social cost of greenhouse gases or SC-GHGs) are metrics that estimate
the monetary value of projected impacts associated with marginal changes in emissions in a given year.
They include a wide range of anticipated climate impacts, such as net changes in agricultural productivity
and human health, property damage from increased flood risk, and changes in energy system costs, such
as reduced costs for heating and increased costs for air conditioning. For this analysis, EPA applied the
interim SC-GHG estimates recommended by the Interagency Working Group on the Social Cost of
Greenhouse Gases (IWG) (Interagency Working Group on the Social Cost of Greenhouse Gases, 2021).32
Chapter 5 details this analysis.
31 The capture of methane prevents its release as a greenhouse gas (GHG) into the atmosphere. Captured methane is generally
flared (releasing CO2, a less potent GHG into the atmosphere) or used for energy purposes (Bracmort etal., 2011).
32 EO 13990 directed the Interagency Working Group (IWG) 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 he recommended estimates continue to be based on the best available economics and science
going forward. The SC-GHG estimates used in this report are interim values until updated estimates of the impacts of
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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 used benefit-per-ton estimates for stationary and mobile sources (which represent the total
monetized human health benefits, including premature mortality and morbidity) to monetize human
health related impacts from changes in these emissions (Wolfe et al., 2019;U.S. EPA, 2023n). EPA
estimated the changes in energy use by MPP facilities and POTWs to power treatment processes. For
changes in electricity consumed, EPA used the Emissions & Generation Resource Integrated Database
(eGRID) to estimate changes in the tons of NOx and SO2 emissions (U.S. EPA, 2023d).33 Trucking
emissions were estimated based on the increased mileage traveled and emission factors from EPA's
MOVES3 Motor Vehicle Emission Simulator. The TSD provides additional details on the methodology.
EPA then multiplied estimates of the changes in tons of NOx and SO2 emissions by the estimated benefits
per ton of emissions reported in Wolfe et al., 2019. See Chapter 5 for details of this analysis.
In addition to health effects from air emissions, air pollution (e.g., PM2 5) 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., K. J. Boyle et
al., 2016; Poudyal, 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. EPA did not quantify or monetize the
effects of changes in air emissions on recreational opportunities and property values due to complexity of
the relationship between visibility and the levels of predominant pollutants in the atmosphere.
2.5 Summary of Benefit Categories
Table 2-5 summarizes the potential benefits of the regulatory options analyzed for the proposed 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 the use and nonuse values from
surface water quality improvements, and changes in air emissions. Other welfare effect categories,
including impacts on the habitats of T&E species, and commercial fisheries were quantified but not
monetized. Finally, EPA was not able to quantify or monetize other welfare effects, including drinking
and wastewater treatment cost reductions, impacts to subsistence harvesting, tourism, and property values,
climate change can be developed (Interagency Working Group on the Social Cost of Greenhouse Gases, 2021). In December
2023, EPA published new SC-GHG estimates as a supplement to a rulemaking finalizing "Standards of Performance for
New, Reconstructed, and Modified Sources and Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector
Climate Review." (U.S. Environmental Protection Agency, 20231) These new estimates reflect recent advances in the
scientific literature on climate change and its economic impacts and incorporate recommendations made by the National
Academies. As these values were not finalized at the time EPA conducted this analysis, EPA did not use them in the main
analysis. However, EPA is presenting disbenefits estimated using these values in Appendix F.
33 eGRID is a comprehensive source of data from EPA's Clean Air Markets Division on the environmental characteristics of
almost all electric power generated in the United States. The data includes emissions, emission rates, generation, heat input,
resource mix, and many other attributes.
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and some other human health risks. EPA evaluated these effects qualitatively as discussed above in
Sections 2.1 through 2.4.
Table 2-5: Estimated Welfare Effects of Changes in Pollutant Discharges from Meat and
Poultry Product Facilities
Category
Effect of Regulatory Options
Benefits Analysis
Quantified
Monetized
Methods (Report
Chapter where
Analysis is Detailed)
Human Health Benefits from Surface Water Quality Improvements
Reduced incidence of
adverse human health
effects (e.g., cases of
gastrointestinal illness)
from exposure to MPP
pollutants via
recreational use
Reduced exposure to pathogens and
HAB-related illnesses from primary
contact recreation and recreationally
caught and consumed fish and shellfish
Qualitative discussion
(Chapter 2)
Reduced incidence of
adverse human health
effects (e.g.,
developmental effects,
gastrointestinal illness,
cancer) from exposure
to MPP pollutants via
drinking water
Reduced exposure to high nitrate
concentrations, pathogens, and DBPs
(which may be generated indirectly
due to nutrient enrichment and
eutrophication) in drinking water
Qualitative discussion
(Chapter 2)
Ecological Condition and Recreational Use Effects from Surface Water Quality Changes
Aquatic and wildlife
Improved ambient water quality in
habitat3
receiving and downstream reaches
Water-based
Enhanced value of swimming, fishing,
recreation3
boating, and near-water activities from
water quality changes
Benefit transfer
Aesthetics3
Improved aesthetics from shifts in
V
V
(Chapter 4);
water clarity, color, odor, including
Qualitative discussion
nearby site amenities for residing,
(Chapter 2)
working, and traveling
Nonuse values3
Improved existence, option, and
bequest values from improved
ecosystem health
Protection of T&E
Improved T&E species habitat and
Qualitative discussion
species
potential effects on T&E species
V
(Chapter 2)
populations
Quantitative analysis
(EnvA)
Market and Productivity Effects
Drinking water
Improved quality of source water used
Qualitative discussion
treatment costs
for drinking
V
(Chapter 2)
Quantitative analysis
(EnvA)
Wastewater treatment
Reduced wastewater treatment costs
Qualitative discussion
costs
at POTWs
(Chapter 2)
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Table 2-5: Estimated Welfare Effects of Changes in Pollutant Discharges from Meat and
Poultry Product Facilities
Category
Effect of Regulatory Options
Benefits Analysis
Quantified
Monetized
Methods (Report
Chapter where
Analysis is Detailed)
Agricultural water use
Improved quality of surface waters
used for livestock watering
Qualitative discussion
(Chapter 2)
Industrial water use
Reduced cost of industrial water
treatment.
Qualitative discussion
(Chapter 2)
Commercial fisheries
Improved fisheries yield and harvest
quality due to improved aquatic
habitat
~
Qualitative discussion
(Chapter 2)
Subsistence Harvesting
Improved fisheries yield and harvest
quality due to improved aquatic
habitat; Reduced risk of consuming
contaminated fish and shellfish
Qualitative discussion
(Chapter 2)
Tourism industries
Changes in participation in water-
based recreation
Qualitative discussion
(Chapter 2)
Property values
Improved property values from
changes in water quality
Qualitative discussion
(Chapter 2)
Capture of CH4
Reduction in emissions of CH4
associated with wastewater treatment
Qualitative discussion
(Chapter 2)
Climate Change and Air Quality-Related Effects
Air emissions of PM2.5
Changes in mortality and morbidity
from exposure to particulate matter
(PM2.5) emitted directly or linked to
changes in NOxand S02 emissions
(precursors to PM2.5 and ozone)
V
V
Qualitative discussion
(Chapter 2); Health
benefits (Chapter 5)
Air emissions of NOx
and S02
Changes in ecosystem effects; visibility
impairment; and human health effects
from direct exposure to NOx, S02, and
hazardous air pollutants.
V
V
Qualitative discussion
(Chapter 2); Health
benefits (Chapter 5)
Air emissions of
greenhouse gases (CH4
and C02)
Changes in climate change effects
V
V
Qualitative discussion
(Chapter 2); Social
cost of GHG (Chapter
5)
a. These values are implicit in the total WTP for water quality improvements.
Source: U.S. EPA Analysis, 2023
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Water Quality Effects of Regulatory Options
3 Water Quality Effects of Regulatory Options
To evaluate the water quality effects of the regulatory options, EPA estimated the pollutant loading
reductions that would result from implementation of treatment under each regulatory option, accounting
for any existing treatment in place. EPA conducted this analysis for two MPP waste streams: 1) combined
MPP process wastewater and 2) high chlorides wastewater (as a segregated waste stream). This section
summarizes the changes in pollutant loads (refer to the TDD for details) and outlines the approach EPA
used to evaluate the effects of these changes on receiving and downstream waters, based on modeling
results. The resulting water quality changes inform the analysis of nonmarket benefits in Chapter 4.
3.1 Changes in Pollutant Loadings
EPA estimated pollutant loads for the three regulatory options EPA analyzed for this proposal, based on
four wastewater treatment technology systems for the combined MPP process waste stream (see Table
1-2). EPA estimated pollutant loads based on evaporation technology for both direct and indirect
dischargers with a high chlorides waste stream. EPA estimated baseline pollutant loadings using the
facility flows and the effluent pollutant concentrations associated with the treatment in place (TIP).
Wastewater treatment installed across the industry varies and some facilities already operate treatment
consistent with one of the technology systems included in the proposed rule regulatory options. Target
effluent concentrations were calculated for the pollutants of interest for each technology system, as well
as any treatment currently in place at a facility.
Table 3-1 summarizes the total, industry-level changes to annual pollutant loadings for the specific
pollutants of interest covered by the proposed rule under each regulatory option, compared to the
baseline. As shown, annual pollutant loading reductions increase from Option 1 to Option 3 for nutrients
and conventional pollutants (TSS, BOD, and oil and grease).
Table 3-1: Summary of Changes to Annual Loadings of Selected Pollutants Compared to
the Baseline
Discharge
Type
Changes in Annual Pollutant3 Loadings (millions lbs/year)
Option
TN
TP
TSS
BOD
Oil and
Grease
Chlorides'3
Direct
-8.87
-7.68
-42.62
-1.55
-14.84
-190.46
1
Indirect
0
0
-11.78
-7.73
-1.59
-286.50
Total
-8.87
-7.68
-54.39
-9.28
-16.44
-476.96
Direct
-8.87
-7.68
-42.62
-1.55
-14.84
-190.46
2
Indirect
-35.95
-8.43
-39.19
-55.40
-13.88
-286.50
Total
-44.82
-16.11
-81.81
-56.95
-28.72
-476.96
Direct
-8.99
-7.83
-44.45
-1.57
-16.02
-190.46
3
Indirect
-67.18
-11.73
-48.86
-88.18
-27.36
-286.50
Total
-76.18
-19.56
-93.31
-89.75
-43.38
-476.96
a. Technologies implemented under the options are also estimated to reduce loadings of other pollutants. See Table 3-2 for
details.
b. Chlorides has the same removal under each option.
Source: U.S. EPA Analysis, 2023
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Implementation of wastewater treatment technologies to meet effluent limits under the regulatory options
are also estimated to reduce loadings of other pollutants, including halogens (e.g., bromide, fluoride), total
organic carbon, sulfate, total dissolved solids, metals (e.g., aluminum, antimony, arsenic, barium,
beryllium, boron, cadmium, calcium, chromium, cobalt, copper, iron, lead, magnesium, manganese,
molybdenum, nickel, selenium, silver, sodium, thallium, tin, titanium, vanadium, and zinc), and
microbiological contaminants (e.g., E. coli, enterococcus, and fecal coliform). Table 3-2 summarizes total
loading reductions across the broader set of pollutants.
Table 3-2: Summary of Changes to Annual Loadings of Pollutants Compared to the
Baseline
Discharge
Type
Changes in Annual Pollutant
Loadings by Pollutant Group
Option
Classical/Biologicals3
(millions lbs/year)
Metalsb
(millions lbs/year)
Nutrientsc
(millions lbs/year)
Microbiological
(MPN/year)
Direct
-932
-4.15
-16.5
0
1
Indirect
-33
0.00
0.0
0
Total
-965
-4.15
-16.5
0
Direct
-932
-4.15
-16.5
0
2
Indirect
-1,310
-1.33
-44.4
0
Total
-2,242
-5.48
-60.9
0
Direct
-946
-4.20
-16.8
0
3
Indirect
-2,080
-3.27
-78.9
0
Total
-3,026
-7.47
-95.7
0
a. Classicals/biologicals include BOD, bromide, COD, chloride, fluoride, oil and grease, total organic carbon (TOC), sulfate, total
dissolved solids (TDS), and TSS.
b. Metals include aluminum, antimony, arsenic, barium, beryllium, boron, cadmium, calcium, chromium, cobalt, copper, iron,
lead, magnesium, manganese, molybdenum, nickel, selenium, silver, sodium, thallium, tin, titanium, vanadium, and zinc.
c. Nutrients include TN and TP.
d. Microbiological include E. coli, enterococcus, and fecal coliform.
Source: U.S. EPA Analysis, 2023
3.2 Waters Affected by Meat and Poultry Facility Discharges
EPA estimates the regulatory options potentially affect 3,879 MPP facilities. Some MPP discharge
locations could not be identified with available data sources (Detailed and Census Questionnaires, ECHO
database, and HAWQS point source database), which resulted in a smaller universe in this document than
what is represented elsewhere in associated rulemaking documents.34 EPA used the United States
Geological Survey (USGS) medium-resolution National Hydrography Dataset (NHD) (U.S. Geological
Survey, 2018) to represent and identify waters affected by MPP facility discharges, and used additional
attributes provided in version 2 of the NHDPlus dataset (U.S. EPA, 2019) to characterize these waters. In
the aggregate, the 3,879 MPP facilities discharge to 2,736 waterbodies (as categorized in NHDPlus),
including lakes, rivers, and estuaries. 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 MPP facility discharges (see Section 3.3). EPA did not assess pollutant
34 The Agency was unable to determine locational information for two direct discharge facilities (one percent of all direct
discharge facilities) and 378 indirect discharge facilities (a little over 10 percent of indirect discharge facilities).
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.
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loadings and water quality changes associated with these waterbodies because of the lack of a defined
flow path in NHDPlus, the complexity of flow patterns, and the relatively small changes in concentrations
expected. EPA did not quantify the water quality changes and resulting benefits to these systems.
3.2.1 Waters Affected by Direct Dischargers
EPA identified 169 unique MPP facilities affected by the regulatory options that discharge directly to a
total of 188 unique waterbodies (as categorized in NHDPlus). EPA identified the discharge type of the
direct dischargers based on the Detailed and Census Questionnaires. EPA determined the location of
direct dischargers based on data from the Detailed and Census Questionnaires, available data on permitted
point sources from the Hydrologic and Water Quality System (HAWQS), and EPA's Enforcement and
Compliance History Online (ECHO) database. EPA was able to locate all MPP direct discharge locations
with available data sources. The MPP direct discharge facilities are dispersed across the conterminous
United States, with the vast majority of facilities located east of the Rocky Mountains. Figure 3-1 depicts
the locations of the MPP direct discharge universe.
Figure 3-1: Map of the MPP direct discharge facility universe
3.2.2 Waters A ffected by Indirect Dischargers
EPA identified 3,330 unique facilities discharging indirectly to a total of 2,554 unique waterbodies (as
categorized in NHDPlus) via POTWs. EPA identified the discharge type of the indirect discharge
facilities based on the Detailed and Census Questionnaires. EPA determined the location of indirect
discharge facilities with data from the Detailed and Census Questionnaires, the HAWQS point source
dataset, and the ECHO database. Of the 3,708 indirect discharge facilities identified in the Detailed and
Census Questionnaires, 267 facilities did not have sufficient information in any dataset to determine a
location. A further 111 facilities have location information but are located outside the boundaries of the
conterminous United States. The final number of indirect discharge facilities included in the analyzed
MPP Direct Dischargers
I I State Boundaries
0 75 150 300 Miles
1 I I I I I I I I
K
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universe is 3,330. The MPP indirect discharge facilities are dispersed across the conterminous United
States with higher concentrations of facilities along the west coast, Midwest, and the east coast.
Figure 3-2: Map of the MPP indirect discharge facility universe
3.3 Water Quality Changes Downstream from Meat and Poultry Facilities
To evaluate the potential water quality impacts of the proposed rule, EPA developed models for selected
watersheds using HAWQS 2.0 and the Soil and Water Assessment Tool (SWAT; Neitsch et al., 2011).
The models delineate subbasins and reaches at the resolution of 12-digit Hydrologic Unit Codes (HUCs).
The models predict changes in concentrations of TN, TP, TSS, BOD, and DO as a result of the regulatory
options.36 For analytic efficiency, EPA modeled a subset of level 2 Hydrologic Unit Code (HUC) water
resource regions under selected regulatory scenarios to characterize the water quality changes due to the
proposed ELG revisions. The results help inform understanding of the rule benefits on receiving and
downstream waters.
EPA focused initial modeling efforts on five water resources regions and on the preferred regulatory
option (Option 1) and the most stringent regulatory option (Option 3). The five modeled regions are Mid-
Atlantic (region 02), South Atlantic-Gulf (03), Ohio (05), Upper Mississippi (07), and Lower Mississippi
(08). These five regions account for varying shares of the total loading reductions estimated for the three
regulatory options: approximately 51 percent of the total TN loading reductions, 44 to 47 percent of the
total TP loading reductions, and 22 to 31 percent of the total TSS loading reductions. EPA aims to expand
36 EPA did not include MPP facilities located outside the conterminous United States due to a lack of available data for
Alaska, Hawaii, and the U.S. Territories.
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the scope of explicitly modeled regions to cover all affected regions and regulatory options for the final
rule.37 Appendix A provides details on model setup, including calibration results.
Following the approach EPA used in previous regulatory analyses (e.g., see 2023 proposed Steam Electric
ELG; U.S. EPA, 2023i), EPA used a water quality index (WQI) to translate water quality measurements
for multiple parameters into a single numerical indicator (Corona et al, 2020; Johnston, Besedin, &
Holland, 2019; Walsh & Wheeler, 2013; Van Houtven et al, 2014) and to quantify overall improvements
under the regulatory options. Thus, the WQI link water quality changes from reduced nutrient, sediment,
and biochemical oxygen demand discharges to effects on human uses and support for aquatic and
terrestrial species habitat.
3.3.1 WQI Da ta Sources
The WQI includes six parameters: TN, TP, TSS, BOD, DO, and fecal coliform (FC). To calculate the
WQI, EPA used modeled concentrations for TN, TP, TSS, BOD, and DO from the HAWQS/SWAT
models. EPA obtained FC concentrations from the USGS National Water Information System (NWIS) for
2007-2022 and held these values constant between the baseline and regulatory options.38 EPA averaged
the FC data by adapting a common sequential averaging imputation technique which involves assigning
the average of ambient FC concentrations within a smaller hydrologic unit to hydrologic units within the
same larger hydrologic unit with missing data, and progressively expanding the geographical scope of the
hydrologic unit (Hydrologic unit code (HUC10, HUC8, HUC6, HUC4, and HUC2) to fill in all missing
data.39 This approach is based on the assumption that reaches located in the same watershed generally
share similar characteristics. This approach has not been peer reviewed, but it has been used by EPA for
similar rules (U.S. EPA, 2023i) and subject to public review during the associated comment periods.
3.3.2 WQI Calcula tion
The WQI provides a link between specific pollutant levels, as reflected in individual index parameters
(e.g., dissolved oxygen), and the presence of aquatic species and suitability of the water for particular
uses. The WQI used in this analysis uses the framework of the National Sanitation Foundation WQI
(McClelland, 1974) and the Oregon WQI (Dunnette, 1979), with adjustments made by Cude (2001) to
37 There are 18 water resource regions in the conterminous United States. However, EPA estimates that nine of the regions
would have small loading reductions (less than 2 percent) under the regulatory options because of the limited number of
MPP dischargers and/or technology in place at the discharging facilities. Adding two more regions to the set of modeled
regions (Missouri (10), and Arkansas-White-Red (11)) would increase the share of total loading reductions explicitly
modeled to between 79 and 91 percent for TN, between 85 and 94 percent for TP, and between 86 and 90 percent for TSS,
depending on the regulatory option.
38 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/.
39 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, 2022). 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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account for spatial and morphologic variability in the natural characteristics of streams. The WQI ranges
from 10 to 100 with low values indicating poor quality and high values indicating good water quality.
Implementing the WQI methodology involves three key steps: (1) obtaining water quality levels for each
of the six parameters included in the WQI - DO, TN, TP, BOD, FC, and TSS; (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 six parameters.
These steps are used to calculate the WQI value for the baseline and for each analyzed regulatory option.
The scope of the water quality modeling is the same as that for the analysis of nonmarket benefits of
water quality improvements discussed in Chapter 40. See details of the calculations in Appendix B: WQI
Calculation and Regional Subindices, including the subindex curves used to transform levels of individual
parameters.
3.3.3 Baseline WQI
Based on the estimated WQI value under the baseline scenario (WQI-BL), EPA categorized each of the
3,036 HUC12 modeled reaches using five WQI ranges (WQI < 25, 25
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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 3-4
presents water quality change ranges for the analyzed regulatory options under each analysis period.
Table 3-4: Ranges
Regions and Regu
of Estimated Water Quality Changes for Selected Water Resources
atory Options, Compared to Baseline
Region
Regulatory
Option
25th
Percentile3
AWQI
Median3
AWQI
75th
Percentile3
AWQI
AWQI
Interquartile3
Range
Maximum
AWQI
Number of
HUC12s with
Non-Zero
AWQI
02
Opt
on 1
2.49E-05
7.89E-05
4.08E-04
3.83E-04
1.44E-01
44
Opt
on 3
2.43E-04
1.95E-03
1.41E-02
1.38E-02
9.83E+00
240
03
Opt
on 1
3.91E-05
3.29E-04
3.58E-03
3.54E-03
2.67E+00
105
Opt
on 3
1.46E-04
1.93E-03
3.40E-02
3.39E-02
5.81E+00
542
05
Opt
on 1
2.55E-05
2.17E-04
3.79E-03
3.77E-03
6.09E-01
66
Opt
on 3
4.83E-05
4.69 E-04
7.81E-03
7.76E-03
1.58E+00
272
07
Opt
on 1
1.01E-06
1.37 E-04
2.42E-03
2.41E-03
1.38E+00
74
Opt
on 3
8.39E-05
1.73E-03
3.95E-02
3.94E-02
5.06E+00
387
08
Opt
on 1
8.63E-06
5.11E-05
5.75E-03
5.74E-03
3.53E-01
30
Opt
on 3
1.74E-05
1.62 E-04
1.87E-03
1.85E-03
4.22E-01
90
a. Quantiles are based on measurable changes in reaches downstream of MPP discharges.
Source: U.S. EPA Analysis, 2023
3.4 Limitations and Uncertainty
The methodologies and data used in the estimation of the water quality changes of the regulatory options
involve limitations and uncertainties. Table 3-5 summarizes the associated limitations and uncertainties
and indicates the direction of the potential bias. Regarding the uncertainties associated with estimated
loads, see the TDD (U.S. EPA, 2023m).
Table 3-5: Limitations and Uncertainties in the Estimation of Water Quality Changes
Uncertainty/Limitation
Effect on Estimates
Notes
Limited data are available
to validate water quality
concentrations estimated
by HAWQS/SWAT
Uncertain
While model estimates for flow have been calibrated against
observed streamflow data, there was limited observed water
quality data to calibrate model estimates for water quality.
Changes in WQI reflect
only reductions in
nutrient, suspended
sediment, BOD, and DO
concentrations
Underestimate
The estimated changes in WQI reflect only water quality changes
resulting directly from reductions in nutrient, suspended
sediment, BOD, and DO concentrations. They do not include
changes in other water quality parameters (e.g., fecal coliform)
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, the analysis
underestimates the water quality changes.
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Table 3-5: Limitations and Uncertainties in the Estimation of Water Quality Changes
Uncertainty/Limitation
Effect on Estimates
Notes
EPA used regional
averages of monitoring
data from 2007-2022 for
fecal coliform, 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 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 total suspended solids and
nutrient concentrations into subindex scores (see Section 3.3.2
and Appendix B: WQI Calculation and Regional Subindices)
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 4.
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Nonmarket Benefits from Water Quality Changes
4 Nonmarket Benefits from Water Quality Changes
As discussed in the EA, nutrients, bacteria and pathogens, conventional pollutants, and other pollutants
discharged by MPP facilities can have a wide range of effects on water resources downstream from MPP
facilities. These environmental changes affect environmental goods and services valued by humans,
including recreation; commercial fishing; public and private property ownership; water supply and use;
and existence services such as aquatic life, wildlife, and habitat designated uses. Some environmental
goods and services (e.g., commercially caught fish) are traded in markets, and thus their value can be
directly observed. Other environmental goods and services (e.g., recreation and support of aquatic life)
cannot be bought or sold directly and thus do not have observable market values. 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 Steam Electric ELGs (U.S. EPA, 2015a,
2020b; U.S. EPA, 2023a). As discussed in Section 3, initial water quality modeling is limited to five
water resource regions (HUC 02, 03, 05, 07, and 08) and regulatory options 1 and 3.40 Thus, the estimated
benefits are for selected regions rather than national-level benefits. The analytical approach, which is
briefly summarized below, involves:
¦ 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 3.3.4), and
¦ 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 4.1).
The analysis accounts for improvements in water quality resulting from concentration changes in
nutrients, bacteria and pathogens, conventional pollutants, and other pollutants in HUC 12s potentially
affected by MPP facility discharges. The assessment uses the U.S. Census Bureau's Census Block
Group41 (CBG) as the geographic unit of analysis, assigning a radial distance of 100 miles from the CBG
centroid. 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). EPA estimates that households residing in a given CBG value
water quality changes in all modeled HUC 12s within this range, with all unaffected HUC 12s being viable
substitutes for affected HUC 12s within the 100-mile buffer around the CBG. In this analysis, affected
HUC12s are restricted to (1) selected water resource regions for which water quality modeling was
40 EPA is continuing to model additional regions to inform understanding of benefits across the United States.
41 CBGs "are statistical divisions of census tracts, are generally defined to contain between 600 and 3,000 people, and are used
to present data and control block numbering." (U.S. Census Bureau, 2022).
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completed and (2) HUC12s that showed non-zero WQI changes under each option (see Section 3 for
more details).42
4.1 Methods
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 2017.43 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 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 (Section 4.2) and assumes that
households' WTP for a one-point improvement on the WQI (hereafter, one-point WTP) depends on
the average level of water quality between the baseline and the policy scenario.44 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
with respect to the scale of the water quality change, a theoretically desirable property.45
¦ Model 2 includes an additional variable (Inquality_ch) and 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 policy scenario and the total water quality change that
42 When calculating geospatial variables included in the meta-regression model (In ar agr, ln_ar ratio, and sub_proportion;
see Appendix D for details), EPA treated all HUC12s downstream from any MPP discharger as "affected HUC12s." This
universe of HUC12s captures all HUC12s that could experience water quality changes under various technology
control/regulatory options.
43 Although the potential limitations and challenges of benefit transfer are well established (Desvousges, Smith, & Fisher,
1987), benefit transfers are a nearly universal component of benefit cost analyses conducted by and for government
agencies. As noted by V. Kerry Smith, George Van Houtven, and Subhrendu K. Pattanayak (2002, p. 134), "nearly all
benefit cost analyses rely on benefit transfers, whether they acknowledge it or not."
44 In this model, the average WTP per unit of water quality approximates marginal WTP per an additional one point
improvement. This approximation is assumed to be valid at some point between WQI (baseline) and WQI (policy).
Therefore, WTP per unit of WQI changes is approximated at the midpoint of the water quality change valued for that meta-
data observation.
45 The adding-up 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 et al., 2018). However, with
the decision to limit "affected HUC12s" to HUC12s with non-zero changes under each regulatory option, rather than a
consistent set of waters across regulatory options, the adding-up condition does not hold across options. EPA use different
set of "affected HUC12s" across regulatory options to avoid including waterbodies affected only by under Option 3 in the
estimated average WQI change under Option land thus underestimating benefits for Option 1.
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respondents were asked to value46. Since environmental quality is considered by economists to be a
normal good,47 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
Inqualitych 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. To satisfy the adding-up
condition using this model, EPA treats the water quality change variable as a methodological variable,
using WQI change (AWQI) values of 20 and 7 to develop low and high estimates, respectively. These
values were based on the 75th and 25th percentile of water quality changes included in the meta-data
(see Section 4.3 for Model 2 results).
Appendix C provides more details about the differences between Models 1 and 2, details on how EPA
used the meta-analysis to predict household WTP for each CBG and year, and the estimated regression
equation, intercept, and variable coefficients for the two models. 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 C-3) by the variable levels calculated for each CBG or fixed at the levels
indicated in the "Assigned Value" column in Table C-3. The sum of these products represents the
predicted natural log of the one-point WTP (In OWTP) for a representative household in each CBG;
taking the exponential results in the estimate of OWTP. Equation 4-1 provides the equation used to
calculate household benefits for each CBG.
Equation 4-1. HWTPYB = OWTPYB x AWQIB
where:
HWTPy,b = Average annual household WTP in 2022$ in year Y for households
located in the CBG (B),
OWTPy.b = WTP for a one-point improvement on the WQI {i.e.. one-point
WTP) 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, and
AWQIb = Estimated annual average water quality change for the CBG (B).
To estimate WTP for water quality improvements under the regulatory options, EPA first estimated
annual average water quality improvements under the proposed rule and then applied the meta-regression
model (MRM) to estimate per household WTP for water quality improvements in a given CBG and year.
Monetary values of water quality improvements are estimated for all years from 2026 through 2065.
46 If the estimated WQI change is assigned to the Inquality ch variable, Model 2 would not satisfy adding up conditions
because WTP per one point improvement would be different for a one-step improvement (e.g., AWQI=10) versus a two-step
improvement (i.e., the sum of WTP =/(AWQI=5) and WTP =/(AWQI=5) does not equal WTP=/(AWQI=10)).
47 Environmental quality, including water quality, is a "normal" good because people want more of it as their real incomes
increase.
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Implementation of technology required to meet rule requirements will be based on a phased approach
during the first five years of the analysis period (2026-2030). To account for phased technology
implementation in the benefits analysis, EPA assumed that full benefits start in Year 3 of the analysis
period, or 2028. This assumption underestimates benefits in 2026 and 2027 but overestimates benefits in
2028 and 2029 when technology upgrades are still ongoing. As summarized in Table 4-1, the estimated
average annual household WTP, based on Model 1, is $0.67 for Option 1 and $1.27 for Option 3.
To estimate total WTP (TWTP) for water quality changes for each CBG, EPA multiplied the per-
household average annual WTP values for the estimated annual average water quality change by the
number of households within each CBG in a given year and calculated the present value (PV) of the
stream of WTP over the 40 years in EPA's period of analysis. EPA then calculated annualized total WTP
values for each CBG using 3 percent and 7 percent discount rates as shown in Equation 4-2.
Equation 4-2.
2065 . ,
HWTPy B x HHy B \ / J x (1 + 0
TWTPb x '
(1 + i)Y-25 I » (1 + Qn+1
T=2026 ' V
where:
TWTPb = Annualized total household WTP in 2022$ for households located
in the CBG (B),
HWTPy,b = Average annual household WTP in 2022$ for households located
in the CBG (B) in year (Y),
HHy.b = the number of households residing in the CBG (B) in year (Y).
T = Year when benefits are realized
i = Discount rate (3 or 7 percent)
n = Duration of the analysis (40 years)48
EPA generated annual household counts for each CBG through the period of analysis based on projected
population growth following the method described in Section 1.3.6.
4.2 Main Results
Table 4-1 presents the main analysis results, based on Model 1, and water quality modeling results for
five water resource regions (02, 03, 05, 07, and 08), and a 3 percent discount rate; results based on a 7
percent discount rate are presented in Appendix D. The total annualized value of water quality
improvements from reducing nutrients, bacteria and pathogens, conventional pollutants, and other
pollutants discharges from MPP facilities to affected HUC12s, for the preferred option (Option 1), is
$42.3 million.
48 See Section 1.3.3 for details on the period of analysis.
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Table 4-1: Estimated Household an
Quality Improvements in Selected F
Model 1 and a 3 Percent Discount F
d Total Annualized Willingness-to-Pay for Water
legions under Regulatory Options 1 and 3, using
late (Main Estimates)
Regulatory
Option
Nu mber of Affected
Households (Millions)3
Average Annual WTP Per
Household (2022$)b
Total Annualized WTP
(Millions 2022$)bc
Option 1
67.2
$0.67
$42.3
Option 3
85.5
$1.27
$101.9
a. The number of affected households varies across options because of differences in the number of HUC12s that have non-
zero changes in water quality.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits.
c. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected level
2 HUC water resource regions (see Section 3 for details).
Source: U.S. EPA Analysis, 2023
4.3 Alternative Model Results
Table 4-2 presents alternative benefit estimates based on Model 2 using a 3 percent discount rate and
water quality modeling results for five water resource regions (02, 03, 05, 07, and 08). EPA used two
settings of the AWQI variable (Inqnality ch) to generate low and high estimates using Model 2. As
discussed in Section 4.1, one-point WTP is expected to decrease when the total WQI change increases.
Thus, EPA used values of 20 units to develop low estimates and 7 units to develop high estimates.
Average annual household WTP estimates for the preferred option (Option 1) range from $0.24 (low
estimate) to $0.50 (high estimate). Total annualized values range from $16.1 million (low estimate) to
$33.0 million (high estimate).
Table 4-2: Estimated Household and Total Annualized Willingness-to-Pay for Water
Quality Improvements in Selected Regions under Regulatory Options 1 and 3, using
Model 2 and a 3 Percent Discount Rate (Alternative Model Analysis)
Regulatory
Option
Number of Affected
Households (Millions)3
Average Annual WTP Per
Household (2022$)b
Total Annualized WTP
(Millions 2022$)bc
Low
High
Low
High
Option 1
67.2
$0.24
$0.50
$16.1
$33.0
Option 3
85.5
$0.46
$0.94
$38.1
$78.0
a. The number of affected households varies across options because of differences in the number of HUC12s that have non-
zero changes in water quality.
b. Estimates based on Model 2, an alternative model that includes the AWQI variable (lnquality_ch). For the AWQI variable
setting in the Model 2-based analysis, EPA used values of 20 units to develop low estimates and 7 units to develop high
estimates (see Appendix C for details).
c. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected level 2
HUC resource regions (see Section 3 for details).
Source: U.S. EPA Analysis, 2023
4.4 Benefit Extrapolation
As described in Section 3.3 and above, for analytic efficiency, EPA modeled a subset of five water
resource regions under selected regulatory scenarios to characterize the water quality changes due to the
proposed ELG revisions. EPA focused initial modeling efforts on Mid-Atlantic (region 02), South
Atlantic-Gulf (03), Ohio (05), Upper Mississippi (07), and Lower Mississippi (08) and on Option 1 and
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Option 3. This section describes extrapolation of these results to Option 2 and to other water resources
regions to provide insight into the potential magnitude of national benefits of the proposed rule. Appendix
E provides additional details on the approach.
4.4.1 Benefits of Reg ula tory Option 2
Option 2 falls between regulatory Option 1 and Option 3 in terms of the stringency of the limits and the
resulting loading reductions. Accordingly, EPA interpolated between the benefits obtained for the two
options modeled explicitly to approximate probable benefits of Option 2. The interpolation accounts for
the estimated reductions in TN, TP, and TSS loadings achieved under the three options, adjusted to
account for the relative scale of the three parameters49 and their relative influence on the overall WQI
score. Appendix E provides additional details on the approach.
Specifically, EPA first calculated an aggregate loading reduction measure for each option as the weighted
sum of TN, TP, and TSS loading reductions. EPA then interpolated the total WTP linearly between
Options 1 and 3 using these aggregate loading reduction measures. Table 4-3 and Table 4-4 present the
estimated total WTP for Option 2 based on Model 1 (main results) and Model 2 (alternative model
results).
Table 4-3: Estimated Total Annualized Willingness-to-Pay for Water Quality
Improvements in Selected Regions under Regulatory Options, using Model 1 and a 3
Percent Discount Rate (Main Estimates)
Regulatory Option
Total Annualized WTP (Millions 2022$)a b
Option 1
$42.3
Option 2
$78.6
Option 3
$101.9
a. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits.
b. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected water
resource regions (see Section 3 for details).
Source: U.S. EPA Analysis, 2023
Table 4-4: Estimated Total Annualized Willingness-to-Pay for Water Quality
Improvements in Selected Regions under Regulatory Options, using Model 2 and a 3
Percent Discount Rate (Alternative Estimates)
Regulatory Option
Total Annualized WTP (Millions 2022$)a b
Low
High
Option 1
$16.1
$33.0
Option 2
$29.5
$60.4
Option 3
$38.1
$78.0
49 Expressed in mg/L, concentrations of TSS tend to be approximately one order of magnitude (10 times) larger than TN
concentrations (e.g., 40 mg/L vs. 4 mg/L). TN concentrations in turn tend to be approximately one order of magnitude (10
times) larger than TP (e.g., 4 mg/L vs. 0.4 mg/L).
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Table 4-4: Estimated Total Annualized Willingness-to-Pay for Water Quality
Improvements in Selected Regions under Regulatory Options, using Model 2 and a 3
Percent Discount Rate (Alternative Estimates)
Regulatory Option
Total Annualized WTP (Millions 2022$)a,b
Low
High
a. Estimates based on Model 2, an alternative model that includes the AWQI variable (lnquality_ch). For the AWQI variable
setting in the Model 2-based analysis, EPA used values of 20 units to develop low estimates and 7 units to develop high
estimates (see Appendix C for details).
b. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected water
resource regions (see Section 3 for details).
Source: U.S. EPA Analysis, 2023
4.4.2 Benefits Across Water Resources Regions
Loading reductions achieved under the regulatory options vary across regions based on the number and
characteristics of the MPP facilities. Building on the approach described above to interpolate between
Option 1 and Option 3, EPA extrapolated the results obtained for explicitly modeled regions to the other
water resources regions based on the respective aggregate loading reductions for the two sets of regions
and relative shares of the total population. Appendix E provides additional details on the approach.
EPA notes that this approach provides an approximate estimate of the potential national benefits of the
proposed rule. This estimate is subject to uncertainty given the assumptions implicit in the extrapolation
method, including assumptions regarding the characteristics of receiving waters in the different regions
(e.g., stream order, flow, baseline water quality) and populations (e.g., income) among other factors. EPA
expects the five explicitly modeled regions to capture a significant share of the total benefits of the
proposed rule. Thus, the five explicitly modeled regions together account for 45 percent to 49 percent of
the aggregate loading reductions across the conterminous United States, with the shares varying across
regulatory options and parameters. For example, under Option 1 the five explicitly modeled regions
account for 51 percent of total TN reductions, 44 percent of total TP reductions, and 22 percent of total
TSS reductions. Additionally, approximately half of the total population of the conterminous United
States in 2010 lived in the five explicitly modeled regions (U.S. EPA, 2017a).
Table 4-5: Estimated Total Annualized Willingness-to-Pay for Water Quality
Improvements under Regulatory Options, using Model 1 and a 3 Percent Discount Rate
(Main Estimates)
Basis of Estimate
Total Annualized WTP (Millions 2022$)a,b
Option 1
Option 2
Option 3
Regions explicitly modeled0
$42.3
$78.6
$101.9
Extrapolated regions
$53.3
$87.5
$106.5
U.S. totald
$95.6
$166.1
$208.4
a. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits.
b. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected water
resource regions (see Section 3 for details).
c. Sum of benefits estimated for explicitly modeled water resources regions (i.e., regions 02, 03, 05, 07, and 08) and used to
extrapolate to other regions.
d. Based on MPP facilities discharging (directly or indirectly) to waters within the conterminous United States.
Source: U.S. EPA Analysis, 2023
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Table 4-6: Estimated Total Annualized Willingness-to-Pay for Water Quality
Improvements under Regulatory Options, using Model 2 and a 3 Percent Discount Rate
(Main Estimates)
Basis of Estimate
Total Annualized WTP (Millions 2022$)a,b
Option 1
Option 2
Option 3
Low
High
Low
High
Low
High
Regions explicitly modeled0
$16.1
$33.0
$29.5
$60.4
$38.1
$78.0
Extrapolated regions
$20.3
$41.6
$32.9
$67.3
$39.8
$81.5
U.S. totald
$36.4
$74.6
$62.3
$127.7
$77.9
$159.5
a. Estimates based on Model 2, an alternative model that includes the AWQI variable (lnquality_ch). For the AWQI variable
setting in the Model 2-based analysis, EPA used values of 20 units to develop low estimates and 7 units to develop high
estimates (see Appendix C for details).
b. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected water
resource regions (see Section 3 for details).
c. Sum of benefits estimated for explicitly modeled water resources regions (i.e., regions 02, 03, 05, 07, and 08) and used to
extrapolate to other regions.
d. Based on MPP facilities discharging (directly or indirectly) to waters within the conterminous United States.
Source: U.S. EPA Analysis, 2023
4.5 Limitations and Uncertainty
Table 4-7 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 4-7: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality
Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
Water quality modeling
limited to selected
watersheds
Underestimate
EPA assessed water quality improvements resulting from the
proposed rule in selected water resource regions for analytic
efficiency (see Section 3 for details). Thus, the modeled
nonmarket benefits from water quality changes are regional-
level rather than national-level and are, thus, underestimated.
Interpolated Option 2
benefits
Uncertain
EPA interpolated benefits for Option 2 from the model results for
Options 1 and 3, based on the aggregate load reductions. The
interpolation assumes a linear relationship between loading
reductions and total WTP. While the interpolation is applied to
results for the same water resource region (i.e., same affected
waters and population), there is still uncertainty in the assumed
relationship between loading reductions and total WTP owing to
variations across the three options in the distribution of loading
reductions spatially and across pollutants.
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Table 4-7: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality
Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
Extrapolated national
benefits
Uncertain
EPA extrapolated water quality improvement benefits modeled
for selected water resources regions to other regions based on
the estimated loading reductions and population. The approach
assumes regions are similar in terms of the characteristics of
affected waters (e.g., flow, stream order, pollutant source
contributions), populations (e.g., income), and other factors.
Additionally, the extrapolation is based on results representing a
relatively small share of the overall loading reductions estimated
nationwide. EPA plans to model additional water resources
regions to increase the share of explicitly modeled versus
extrapolated estimates to reduce the uncertainty.
Use of 100-mile buffer for
calculating water quality
benefits for each CBG
Underestimate
The distance between the surveyed households and the affected
waterbodies is not well measured by any of the explanatory
variables in the MRM. EPA would expect values for water quality
changes to diminish with distance (all else equal) between the
home and affected waterbody. The choice of 100 miles is based
on typical driving distance to recreational sites (i.e., 2 hours or
100 miles; Viscusi, Huber, & Bell, 2008). Therefore, EPA used 100
miles to approximate the distance decay effect on WTP values.
However, there are limitations associated with the 100-mile
assumption since 1) approximately 80 percent of day trips occur
within this distance (i.e., not 100 percent), 2) multi-day trips tend
to involve greater distances than 100 miles, and 3) nonuse values
likely extend beyond 100 miles, particularly for well-known
waterbodies with which many U.S. households are familiar. The
analysis underestimates WTP to the degree that people living
farther than 100 miles place value on water quality
improvements for these waterbodies. The literature shows that
while WTP tends to decline with distance from the waterbody,
people place value on the quality of waters outside their region.
Selection of the
lnquality_ch variable value
in Model 2 for estimating
a range of WTP values
(alternative model
analysis)
Uncertain
One-point WTP 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 values for one-point WTP 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 WQI
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 (i.e., higher values would
result in lower one-point WTP estimates, lower values would
result in higher one-point WTP estimates).
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Table 4-7: Limitations and Uncertainties in the Analysis of Nonmarket Water Quality
Benefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
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
benefit transfer practices.
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 C 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. R.S.
Rosenberger and Stanley (2006) define transfer error as the
difference between the transferred and actual, generally
unknown, value. Although 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), there is still a potential
for transfer errors (Shrestha, Rosenberger, & Loomis, 2007) and
no transfer method is always superior (Johnston et al., 2021).
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Climate Change and Air Quality-Related Disbenefits
5 Climate Change and Air Quality-Related Disbenefits
The regulatory options evaluated may affect air quality through three main mechanisms: (1) CH4, CO2,
NOx, and SO2 emissions from changes in electricity consumption at MPP facilities and POTWs given
changes in treatment processes; and (2) transportation-related CH4, CO2, NOx, and SO2 emissions due to
changes in trucking of wastes from MPP facilities to landfills.
Because the changes in pollutant emissions are net increases, the effects on society are negative, i.e.,
disbenefits. EPA thus estimated the climate-related disbenefits of changes in CO2 and CH4 emissions, as
well as the human health disbenefits resulting from changes in fine particulate matter (PM2.5) and ozone
ambient exposure due to net changes in emissions of NOx and SO2.5" PM2.5 is 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).
5.1 Changes in Air Emissions
EPA estimated changes in energy use, most notably electricity consumption (MWh), at MPP facilities and
POTWs associated with changes in treatment processes. The approach is detailed in the TDD (U.S. EPA,
2023m) and briefly summarized below.
EPA used emission rates from its 2021 Emissions and Generation Resource Integrated Database (eGRID)
to estimate emissions of CH4, CO2, NOx, and SO2. Table 5-1 presents pollutant emission rates from
eGRID.
Table 5-1: Electricity eGRID U.S. Total Output
Emission Rates
Pollutant
Emission Rate (Ib/MWh)
ch4
0.071
C02
852
NOx
0.5
S02
0.5
Source: U.S. EPA, 2023c
EPA also estimated air emissions associated with the operation of transportation vehicles by multiplying
the estimated distance traveled between MPP facilities and the off-site location for disposal of solid waste
by pollutant-specific emission factors obtained from EPA's Motor Vehicle Emission Simulator
(MOVES3; see Table 5-2).
50 Emissions of nitrogen oxides (NOx) lead to formation of both ozone and PM2 .5 while SO2 emissions lead to formation of
PM2.5 only.
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Table 5-2: Transportation Pollutant-Specific
Emission Factors
Pollutant
Emission Factor (ton/mile)
ch4
6.18x10 s
C02
0.0020
NOx
4.47xl0"6
S02
6.84xl0"9
Source: U.S. EPA, 2021, 2023g
Table 5-3 presents the estimated changes in air pollutant emissions for each regulatory option by
category. The TDD details the methodology.
Table 5-3: Estimated Incremental Changes in Air Pollutant Emissions (Tons/Year)
Category
CH4
C02
NOx
S02
Option 1
Energy use
2.2
26,600
15.7
16.6
Transportation
0.03
960
2.2
0.003
Option 2
Energy use
8.2
98,400
57.7
61.2
Transportation
0.1
2,490
5.6
0.01
Option 3
Energy use
11.8
142,000
83.4
88.2
Transportation
0.1
3,030
6.8
0.01
a. Positive values indicate a net increase in emissions.
Source: EPA Analysis, 2023
5.2 Climate Change Disbenefits
5.2.1 Data and Methodology
EPA estimated the climate disbenefits of the net CH4 and CO2 emission changes expected from the
regulatory options using the estimates of the social cost of greenhouse gases (SC-GHG)51, specifically
using the social cost of methane (SC-CH4) and social cost of carbon (SC-CO2). SC-GHG estimates
represent the monetary value of the net harm to society associated with a marginal increase in GHG
emissions in a given year. SC-GHG estimates include 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-GHGs
therefore reflect 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 CH4
and CO2 emissions. In practice, data and modeling limitations naturally restrain the ability of SC-GHG
estimates to include all the important physical, ecological, and economic impacts of climate change, such
that the estimates are a partial accounting of climate change impacts and will, therefore, tend to be
51 Estimates of the social cost of greenhouse gases are gas specific (e.g., social cost of carbon (SC-CO2), social cost of methane
(SC-CH4), social cost of nitrous oxide (SC-N2O)), but collectively they are referred to as the social cost of greenhouse gases
(SC-GHG).
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underestimates of the marginal benefits of abatement. EPA and other Federal agencies began regularly
incorporating SC-GHG estimates in their benefit-cost analyses conducted under EO 1286652 since 2008,
following a Ninth Circuit Court of Appeals remand of a rule for failing to monetize the benefits of
reducing CO2 emissions in that rulemaking process.
In 2017, the National Academies of Sciences, Engineering, and Medicine published a report that provides
a roadmap for how to update SC-GHG estimates used in Federal analyses going forward to ensure that
they reflect advances in the scientific literature (National Academies of Sciences, 2017b). The National
Academies' report recommended specific criteria for future SC-GHG updates, 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. The research community has made considerable progress
in developing new data and methods that help to advance various components of the SC-GHG estimation
process in response to the National Academies' recommendations.
In a first-day executive order (EO 13990), Protecting Public Health and the Environment and Restoring
Science to Tackle the Climate Crisis, President Biden called for a renewed focus on updating the SC-
GHG estimates to reflect the latest science, noting that "it is essential that agencies capture the full
benefits of reducing greenhouse gas emissions as accurately as possible." Important steps have been taken
to begin to fulfill this directive of EO 13990. In February 2021, the IWG released a technical support
document (hereinafter the "February 2021 TSD") that provided a set of IWG recommended SC-GHG
estimates while work on a more comprehensive update is underway to reflect recent scientific advances
relevant to SC-GHG estimation (IWG, 2021). In addition, as discussed further below, EPA has developed
an updated SC-GHG methodology in the regulatory impact analysis of EPA's December 2023 final oil
and gas standards, following an external peer review and a public comment process.53 As these values
were not finalized at the time EPA conducted this analysis, EPA did not use them in the main analysis to
monetize the estimated climate disbenefits of this proposed rule. However, EPA is presenting disbenefits
estimated using these values in Appendix F and requests comments on whether the Agency should
proceed with using these values in the main analysis.
EPA has applied the IWG's recommended interim SC-GHG estimates in the Agency's regulatory benefit-
cost analyses published since the release of the February 2021 TSD and is likewise using them in this
BCA. EPA evaluated the SC-GHG estimates in the February 2021 TSD and determined that these
estimates are appropriate for use in estimating the social disbenefits of GHG emissions expected to occur
as a result of the proposed rule.
The SC-GHG estimates presented in the February 2021 SC-GHG TSD were developed over many years,
using transparent process, peer-reviewed methodologies, the best science available at the time of that
52 Presidents since the 1970s have issued executive orders requiring agencies to conduct analysis of the economic
consequences of regulations as part of the rulemaking development process. EO 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 disbenefits are presented for purposes of providing a
complete benefit-cost analysis under EO 12866 and other relevant EOs. The estimates of change in GHG emissions and
monetized disbenefits associated with those changes play no part in the record basis for this action.
53 See https://www.epa.gov/environmental-economics/scghg
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process, and with input from the public. Specifically, in 2009, an IWG that included EPA and other
executive branch agencies and offices was established to ensure that agencies had access to the best
available information when quantifying the benefits of reducing GHG emissions in benefit-cost analyses.
The IWG published SC-CO2 estimates in 2010 that were developed from an ensemble of three widely
cited integrated assessment models (IAMs) that estimate climate damages using highly aggregated
representations of climate processes and the global economy combined into a single modeling framework.
The three IAMs were run using a common set of input assumptions in each model for future population,
economic, and CO2 emissions growth, as well as equilibrium climate sensitivity (ECS) a measure of
the globally averaged temperature response to increased atmospheric CO2 concentrations. These estimates
were updated in 2013 based on new versions of each IAM.54 In August 2016 the IWG published estimates
of SC-CH4 and SC-N2O using methodologies that are consistent with the methodology underlying the SC-
CO2 estimates. In 2015, as part of the response to public comments received to a 2013 solicitation for
comments on the SC-CO2 estimates, the IWG announced a National Academies of Sciences, Engineering,
and Medicine review of the SC-CO2 estimates to offer advice on how to approach future updates to
ensure that the estimates continue to reflect the best available science and methodologies. In January
2017, the National Academies released their final report, Valuing Climate Damages: Updating Estimation
of the Social Cost of Carbon Dioxide, and recommended 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 of Sciences, 2017b). Shortly thereafter, in March 2017, President Trump issued EO 13783,
which disbanded the IWG, withdrew the previous technical support documents, and directed agencies to
"ensure" SC-GHG estimates used in regulatory analyses "are consistent with the guidance contained in
OMB Circular A-4", "including with respect to the consideration of domestic versus international impacts
and the consideration of appropriate discount rates" (EO 13783, Section 5(c)). Benefit-cost analyses
following EO 13783 used SC-GHG estimates that attempted to focus on the specific share of climate
change damages in the United States as captured by the models (which did not reflect many pathways by
which climate impacts affect the welfare of U.S. citizens and residents) and were calculated using two
default discount rates recommended by Circular A-4 (OMB, 2003a), 3 percent and 7 percent.55 All other
methodological decisions and model versions used in the SC-GHG calculations remained the same as
those used by the IWG in 2010 and 2013, respectively.
On January 20, 2021, President Biden issued EO 13990, which re-established an IWG and directed the
group to develop an update of the SC-GHG estimates that reflect the best available science and the
recommendations ofNational Academies (86 FR 7037, January 25, 2021). In February 2021, the IWG
recommended the interim use of the most recent SC-GHG estimates developed by the IWG prior to the
54 Dynamic Integrated Climate and Economy (DICE) 2010 (Nordhaus, 2010), Climate Framework for Uncertainty,
Negotiation, and Distribution (FUND) 3.8 (Anthoff & Tol, 2013a, 2013b), and Policy Analysis of the Greenhouse Gas
Effect (PAGE) 2009 (Hope, 2012).
55 EPA regulatory analyses under EO 13783 included sensitivity analyses based on global SC-GHG values and using a lower
discount rate of 2.5 percent. OMB Circular A-4 (OMB, 2003a) recognizes that special considerations arise when applying
discount rates if intergenerational effects are important. In the IWG's 2015 Response to Comments, OMBas a co-chair of
the IWGmade clear that "Circular A-4 is a living document," that "the use of 7 percent is not considered appropriate for
intergenerational discounting," and that "[t]here is wide support for this view in the academic literature, and it is recognized
in Circular A-4 itself." OMB, as part of the IWG, similarly repeatedly confirmed that "a focus on global SCC estimates in
[regulatory impact analyses] is appropriate" (IWG, 2015).
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group being disbanded in 2017, adjusted for inflation (IWG, 2021). As discussed in the February 2021
SC-GHG TSD, the IWG's selection of these interim estimates reflected the immediate need to have SC-
GHG estimates available for agencies to use in regulatory benefit-cost analyses and other applications that
were developed using a transparent process, peer reviewed methodologies, and the science available at the
time of that process. The February 2021 update also recognized the limitations of the interim estimates
and encouraged agencies to use their best judgment in, for example, considering sensitivity analyses using
lower discount rates. The IWG published a Federal Register notice on May 7, 2021, soliciting comment
on the February 2021 SC-GHG TSD and on how best to incorporate the latest peer-reviewed scientific
literature in order to develop an updated set of SC-GHG estimates. The EPA has applied the IWG's
interim SC-GHG estimates in regulatory analyses published since the release of the February 2021 SC-
GHG TSD, and is likewise using them in the benefit-cost analysis calculations in this BCA.
As noted above, EPA participated in the IWG but has also independently evaluated the interim SC-CO2
estimates published in the February 2021 TSD and determined they are appropriate to use to estimate
climate benefits for this action. EPA has also evaluated the supporting rationale of the February 2021
TSD, including the studies and methodological issues discussed therein, and concludes that it agrees with
the rationale for these estimates presented in the TSD and summarized below. The February 2021 SC-
GHG TSD provides a complete discussion of the IWG's initial review conducted under EO 13990. In
particular, the IWG found that the SC-GHG estimates used under EO 13783 fail to reflect the full impact
of GHG emissions in multiple ways. First, the IWG concluded that those estimates fail to capture many
climate impacts that can affect the welfare of U.S. citizens and residents. 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 are
better captured within global measures of the social cost of greenhouse gases.
In addition, 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. A wide range of scientific and economic experts have emphasized the issue of reciprocity as
support for considering global damages of GHG emissions. 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 take significant steps to reduce emissions. 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 is for all countries to base their policies on global estimates of damages.
As a member of the IWG involved in the development of the February 2021 SC-GHG TSD, EPA agrees
with this assessment and, therefore, in this BCA EPA centers attention on a global measure of SC-CO2.
This approach is the same as that taken in EPA regulatory analyses over 2009 through 2016. A robust
estimate of climate damages only to U.S. citizens and residents that accounts for the myriad of ways that
global climate change reduces the net welfare of U.S. populations does not currently exist in the literature.
As explained in the February 2021 TSD, existing estimates are both incomplete and an underestimate of
total damages that accrue to the citizens and residents of the United States because they do not fully
capture the regional interactions and spillovers discussed above, nor do they include all of the important
physical, ecological, and economic impacts of climate change recognized in the climate change literature,
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as discussed further below. EPA, as a member of the IWG, 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 carbon impacts.
Second, the IWG concluded that the use of the social rate of return on capital (7 percent under OMB
Circular A-4 guidance, as published in 2003; Circular A-4 was subsequently revised in 2023) to discount
the future benefits of reducing GHG emissions inappropriately underestimates the impacts of climate
change for the purposes of estimating the SC-GHG. Consistent with the findings of National Academies
(2017a) and the economic literature, the IWG continued to conclude that the consumption rate of interest
is the theoretically appropriate discount rate in an intergenerational context (Interagency Working Group
on Social Cost of Carbon, 2013; Interagency Working Group on Social Cost of Carbon United States
Government, 2010; IWG, 2016), and recommended that discount rate uncertainty and relevant aspects of
intergenerational ethical considerations be accounted for in selecting future discount rates.56 Furthermore,
the damage estimates developed for use in the SC-GHG are estimated in consumption-equivalent terms,
and so an application of OMB Circular A-4 (2003)'s guidance for regulatory analysis would then use the
consumption discount rate to calculate the SC-GHG. As a member of the IWG involved in the
development of the February 2021 SC-GHG TSD, EPA agrees with this assessment and will continue to
follow developments in the literature pertaining to this issue. EPA also notes that while OMB Circular A-
4, as published in 2003, recommends using 3 percent and 7 percent discount rates as "default" values,
Circular A-4 also reminds agencies that "different regulations may call for different emphases in the
analysis, depending on the nature and complexity of the regulatory issues and the sensitivity of the benefit
and cost estimates to the key assumptions." On discounting, Circular A-4 (2003) recognizes that "special
ethical considerations arise when comparing benefits and costs across generations," and Circular A-4
(2003) acknowledges that analyses may appropriately "discount future costs and consumption
benefits... at a lower rate than for intragenerational analysis." In the 2015 Response to Comments on the
Social Cost of Carbon for Regulatory Impact Analysis, OMB, EPA, and the other IWG members
recognized that "Circular A-4 is a living document" and "the use of 7 percent is not considered
appropriate for intergenerational discounting. There is wide support for this view in the academic
literature, and it is recognized in Circular A-4 itself." Thus, EPA concludes that a 7 percent discount rate
is not appropriate to apply to value the social cost of greenhouse gases in the analysis presented in this
analysis. Furthermore, in the 2023 revisions to Circular A-4, OMB no longer recommends the use of a 7
percent discount rate (OMB, 2023). In this analysis, to calculate the present and annualized values of
climate benefits, EPA uses the same discount rate as the rate used to discount the value of damages from
future GHG emissions, for internal consistency. That approach to discounting follows the same approach
that the February 2021 SC-GHG TSD recommends "to ensure internal consistencyi.e., future damages
from climate change using the SC-GHG at 2.5 percent should be discounted to the base year of the
analysis using the same 2.5 percent rate." EPA has also consulted the National Academies' 2017
56 GHG emissions are stock pollutants, with damages associated with what has accumulated in the atmosphere over time, and
they are long lived such that subsequent damages resulting from emissions today occur over many decades or centuries
depending on the specific greenhouse gas under consideration. In calculating the SC-GHG, the stream of future damages to
agriculture, human health, and other market and non-market sectors from an additional unit of emissions are estimated in
terms of reduced consumption (or consumption equivalents). Then that stream of future damages is discounted 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.
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recommendations on how SC-GHG estimates can "be combined in RIAs with other cost and benefits
estimates that may use different discount rates." The National Academies reviewed "several options,"
including "presenting all discount rate combinations of other costs and benefits with [SC-GHG]
estimates."
While the IWG works to assess how best to incorporate the latest, peer reviewed science to develop an
updated set of SC-GHG estimates, it recommended the interim estimates to be the most recent estimates
developed by the IWG prior to the group being disbanded in 2017. The estimates rely on the same models
and harmonized inputs and are calculated using a range of discount rates. As explained in the February
2021 SC-GHG TSD, the IWG has concluded that it is appropriate for agencies to revert to the same set of
four values drawn from the SC-GHG distributions based on three discount rates as were used in
regulatory analyses between 2010 and 2016 and subject to public comment. For each discount rate, the
IWG combined the distributions across models and socioeconomic emissions scenarios (applying equal
weight to each) and then selected a set of four values for use in benefit-cost analyses: an average value
resulting from the model runs for each of three discount rates (2.5 percent, 3 percent, and 5 percent), plus
a fourth value, selected as the 95th percentile of estimates based on a 3 percent discount rate. The fourth
value was included to provide information on potentially higher-than-expected economic impacts from
climate change, conditional on the 3 percent estimate of the discount rate. As explained in the February
2021 SC-GHG TSD, and EPA agrees, this update reflects the immediate need to have an operational SC-
GHG for use in regulatory benefit-cost analyses and other applications that was developed using a
transparent process, peer-reviewed methodologies, and the science available at the time of that process.
Those estimates were subject to public comment in the context of dozens of proposed rulemakings as well
as in a dedicated public comment period in 2013.
Table 5-4 presents the interim SC-CH4 and SC-CO2 estimates across all the model runs for each discount
rate for emissions occurring in 2024 to 2063. Values for 2024 through 2050 are reported in 2022 dollars
but are otherwise identical to those presented in the IWG's 2016 TSD (IWG, 2016). Values for 2051 to
2063 were linearly extrapolated from values published through 2050. For purposes of capturing
uncertainty around the SC-GHG estimates in analyses, the IWG's February 2021 TSD emphasizes the
importance of considering all four of the SC-GHG values. The SC-GHG values increase over time 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 disbenefits of the estimated CH4 and CO2 emissions for each analysis year between
2024 and 2063 by applying the annual SC-CH4 and SC-CO2 estimates, shown in Table 5-4, to the
estimated changes in CH4 and CO2 emissions in the corresponding year under the regulatory options. EPA
then calculated the present value and annualized value of climate disbenefits as of the expected rule
promulgation year of 2024 by discounting each year-specific value to the year 2024 using the same rate
used to calculate the corresponding SC-GHG.
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Table 5-4: Interim Estimates of the Social Cost of Methane and Social Cost of Carbon, 2025-2065
Social Cost of Methane
Social Cost of Carbon
2022$/Metric Tonne CH4
2022$/Metric Tonne C02
5%
3%
2.5%
3% 95th
5%
3%
2.5%
3% 95th
Year
Average
Average
Average
Percentile
Average
Average
Average
Percentile
2025
$894
$1,901
$2,460
$5,032
$19
$63
$93
$189
2026
$928
$2,013
$2,572
$5,255
$19
$64
$94
$193
2027
$962
$2,013
$2,572
$5,367
$20
$65
$96
$197
2028
$984
$2,124
$2,683
$5,479
$21
$67
$97
$201
2029
$1,017
$2,124
$2,795
$5,702
$21
$68
$99
$205
2030
$1,051
$2,236
$2,795
$5,814
$22
$69
$100
$209
2031
$1,085
$2,236
$2,907
$5,926
$22
$70
$102
$213
2032
$1,118
$2,348
$2,907
$6,150
$23
$72
$103
$217
2033
$1,118
$2,348
$3,019
$6,373
$24
$73
$105
$222
2034
$1,230
$2,460
$3,131
$6,485
$24
$74
$106
$226
2035
$1,230
$2,460
$3,131
$6,709
$25
$75
$108
$230
2036
$1,230
$2,572
$3,243
$6,821
$26
$77
$109
$234
2037
$1,342
$2,572
$3,354
$7,044
$26
$78
$111
$239
2038
$1,342
$2,683
$3,354
$7,156
$27
$79
$112
$243
2039
$1,342
$2,795
$3,466
$7,380
$28
$81
$114
$247
2040
$1,454
$2,795
$3,466
$7,491
$28
$82
$115
$251
2041
$1,454
$2,907
$3,578
$7,715
$29
$83
$117
$255
2042
$1,565
$2,907
$3,690
$7,827
o
m
¦uy
00
¦uy
$118
$259
2043
$1,565
$3,019
$3,690
$8,050
o
m
¦uy
ID
00
¦uy
$120
$263
2044
$1,565
$3,019
$3,802
$8,162
r1
m
¦uy
00
¦uy
$121
$267
2045
$1,677
$3,131
$3,913
$8,386
$32
00
00
¦uy
$123
$271
2046
$1,677
$3,131
$3,913
$8,498
m
m
¦uy
$90
$124
$275
2047
$1,677
$3,243
$4,025
$8,610
m
m
¦uy
$91
$126
$279
2048
$1,789
$3,354
$4,137
$8,833
$34
$92
$127
$283
2049
$1,789
$3,354
$4,137
$8,945
LO
m
¦uy
$93
$129
$287
2050
$1,901
$3,466
$4,249
$9,169
LO
m
¦uy
$95
$130
$291
2051
$1,911
$3,533
$4,314
$9,300
$36
$96
$132
$295
2052
$1,960
$3,609
$4,389
$9,468
$37
$97
$133
$299
2053
$2,011
$3,687
$4,466
$9,638
$38
$99
$135
$303
2054
$2,062
$3,767
$4,544
$9,812
$39
$100
$137
$307
2055
$2,115
$3,849
$4,623
$9,989
$39
$102
$138
$312
2056
$2,169
$3,932
$4,704
$10,169
$40
$103
$140
$316
2057
$2,224
$4,017
$4,786
$10,353
$41
$104
$141
$320
2058
$2,281
$4,104
$4,869
$10,540
$42
$106
$143
$325
2059
$2,339
$4,193
$4,954
$10,730
$43
$107
$145
$329
2060
$2,399
$4,284
$5,041
$10,923
$44
$109
$147
$334
2061
$2,461
$4,377
$5,128
$11,120
$45
$110
$148
$339
2062
$2,524
$4,471
$5,218
$11,321
$46
$112
$150
$344
2063
$2,588
$4,568
$5,309
$11,525
$47
$114
$152
$348
2064
$2,654
$4,667
$5,402
$11,733
00
¦uy
$115
$154
$353
2065
$2,722
$4,768
$5,496
$11,945
$49
$117
$155
$358
Note: These values are identical to those reported in the February 2021 TSD (IWG, 2021), adjusted for inflation to 2022 dollars
using the annual GDP Implicit Price Deflator values in the U.S. Bureau of Economic Analysis' (BEA) NIPA Table 1.1.9 (U.S.
Bureau of Economic Analysis, 2023). Values are rounded to the nearest dollar and vary depending on the year of emissions.
EPA extrapolated past 2050 assuming exponential growth based on the period 2045-2050.
Source: U.S. EPA Analysis, 2023, based on IWG (2021)
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There are several limitations and uncertainties associated with the SC-GHG estimates presented in Table
5-4. Some uncertainties are captured within the analysis, while other areas of uncertainty have not yet
been quantified in a way that can be modeled. Figure 5-1 and Figure 5-2 present the quantified sources of
uncertainty in the form of frequency distributions for the SC-CFU and SC-CO2 estimates for emissions in
2020 (in 2020 dollars). The distribution of SC-GHG estimates reflect uncertainty in key model parameters
such as the equilibrium climate sensitivity, as well as uncertainty in other parameters set by the original
model developers. To highlight the difference between the impact of the discount rate and other
quantified sources of uncertainty, the bars below the frequency distributions provide a symmetric
representation of quantified variability in the SC-GHG estimates for each discount rate. As illustrated by
the figure, the assumed discount rate plays a critical role in the ultimate estimates of the SC-GHG. This is
because GHG emissions today continue to impact society far out into the future, so with a higher discount
rate, costs that accrue to future generations are weighted less, resulting in a lower estimate. As discussed
in the February 2021 SC-GHG TSD, there are other sources of uncertainty that have not yet been
quantified and are thus not reflected in these estimates.
Figure 5-1: Frequency Distribution of Interim SC-CO2 Estimates for 2020 (in 2020$ per Metric Ton
CO2)
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
Social Cost of Carbon in 2020 [2020$ / metric ton C02]
Source: 2021 TSD
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Figure 5-2: Frequency Distribution of Interim SC-CI-U Estimates for 2020 (in 2020$ per Metric Ton
CH4)
E
to
o
o
o
> Average = $670
3% Average = $1500
2.5% Average = $2000
3%
95th Pet. = $3900
EEEBelbsbb
HE tS C3DC
Discount Rate
~ 5.0%
~ 3.0%
~ 2.5%
5 - 95 Percentile
of Simulations
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500
Social Cost of Methane in 2020 [2020$ / metric ton CH4]
Source: 2021 TSD
The interim SC-GHG estimates presented in Table 5-4 have several limitations. First, the current
scientific and economic understanding of discounting approaches suggests discount rates appropriate for
intergenerational analysis in the context of climate change are likely to be less than 3 percent, near
2 percent or lower (IWG, 2021). Second, the IAMs used to produce these interim estimates do not include
all of the important physical, ecological, and economic impacts of climate change recognized in the
climate change literature and the science underlying their "damage functions" i. e., the core parts of the
IAMs that map global mean temperature changes and other physical impacts of climate change into
economic (both market and nonmarket) damages lags behind the most recent research. For example,
limitations include the incomplete treatment of catastrophic and non-catastrophic impacts in the integrated
assessment models, their incomplete treatment of adaptation and technological change, the incomplete
way in which inter-regional and intersectoral linkages are modeled, uncertainty in the extrapolation of
damages to high temperatures, and inadequate representation of the relationship between the discount rate
and uncertainty in economic growth over long time horizons. Likewise, the socioeconomic and emissions
scenarios used as inputs to the models do not reflect new information from the last decade of scenario
generation or the full range of projections.
The modeling limitations do not all work in the same direction in terms of their influence on the SC-GHG
estimates. However, the IWG has recommended that, taken together, the limitations suggest that the
interim SC-GHG estimates used in this proposed rule likely underestimate the damages from net CH4 and
CO2 emissions. In particular, the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment
Report (IPCC, 2007), which was the most current IPCC assessment available at the time when the IWG
decision overthe ECS input was made, concluded that SC-CO2 estimates "very likely...underestimate the
damage costs" due to omitted impacts. Since then, the peer-reviewed literature has continued to support
this conclusion, as noted in the IPCC's Fifth Assessment report (Intergovernmental Panel on Climate
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Change, 2014) and other recent scientific assessments (e.g., IPCC, 2018, 2019a, 2019b); U.S. Global
Change Research Program (U.S. Global Change Research Program, 2016, 2018); and the National
Academies of Sciences, Engineering, and Medicine (National Academies of Sciences, 2017a, 2019).
These assessments confirm and strengthen the science, updating projections of future climate change and
documenting and attributing ongoing changes. For example, sea level rise projections from the IPCC's
Fourth Assessment report ranged from 18 to 59 centimeters by the 2090s relative to 1980-1999, while
excluding any dynamic changes in ice sheets due to the limited understanding of those processes at the
time (IPCC, 2007). A decade later, the Fourth National Climate Assessment projected a substantially
larger sea level rise of 30 to 130 centimeters by the end of the century relative to 2000, while not ruling
out even more extreme outcomes (U.S. Global Change Research Program, 2018). EPA has reviewed and
considered the limitations of the models used to estimate the interim SC-GHG estimates, and concurs
with the February 2021 SC-GHG TSD's assessment that, taken together, the limitations suggest that the
interim SC-GHG estimates likely underestimate the damages from GHG emissions. The February 2021
SC-GHG TSD briefly previews some of the recent advances in the scientific and economic literature that
the IWG is actively following and that could provide guidance on, or methodologies for, addressing some
of the limitations with the interim SC-GHG estimates. The IWG is currently working on a comprehensive
update of the SC-GHG estimates taking into consideration recommendations from the National
Academies of Sciences, Engineering and Medicine, recent scientific literature, public comments received
on the February 2021 TSD and other input from experts and diverse stakeholder groups (National
Academies of Sciences, 2017a). 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
going forward. Most recently, EPA presented a set of updated SC-GHG estimates in the regulatory impact
analysis of EPA's December 2023 final oil and gas standards that incorporates recent advances in the
climate science and economics literature. Specifically, the updated methodology incorporates new
literature and research consistent with the National Academies' near-term recommendations on
socioeconomic and emissions inputs, climate modeling components, discounting approaches, and
treatment of uncertainty, and an enhanced representation of how physical impacts of climate change
translate to economic damages in the modeling framework based on the best and readily adaptable
damage functions available in the peer reviewed literature. EPA solicited public comment on the draft
technical report, which explains the methodology underlying the new set of estimates, in the docket for
the proposed Oil and Gas rule. EPA also put the draft technical report through an external peer review.
More information about this process and public comment opportunities is available on EPA's website.57
EPA's technical report will be among the many technical inputs available to the IWG as it continues its
work.
5.2.2 Results
Table 5-5 presents the undiscounted annual monetized climate disbenefits in selected years for each
regulatory option. The disbenefits are calculated using the four sets of estimates of the SC-GHG from
Table 5-4 (model average at 2.5 percent, 3 percent, and 5 percent discount rates; and 95th percentile at
3 percent discount rate). EPA multiplied estimated CH4 and CO2 emissions for each year within the
period of analysis by the SC-CH4 and SC-CO2 estimates, respectively, for that year. The negative values
57 See https://www.epa.gov/environmental-economics/scghg
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indicate that these are disbenefits due to the net increase in CH4 and CO2 emissions under the proposed
rule.
Table 5-5: Estimated Undiscounted and Total Present Value of Climate Disbenefits from
Incremental Changes in CH4 and CO2 Emissions under the Proposed Rule by Discount
Methane Benefits3
Carbon Dioxide Benefits3
Regulatory
5%
3%
2.5%
3% 95th
5%
3%
2.5%
3% 95th
Option
Year
Average
Average
Average
percentile
Average
Average
Average
percentile
2028
-$0,002
-$0,004
-$0.01
-$0.01
-$0.5
-$1.7
-$2.4
-$5.0
2033
-$0,002
-$0,005
-$0.01
-$0.01
-$0.6
-$1.8
-$2.6
-$5.5
1
2043
-$0,003
-$0.01
-$0.01
-$0.02
-$0.8
-$2.1
-$3.0
-$6.6
2053
-$0,004
-$0.01
-$0.01
-$0.02
-$0.9
-$2.5
-$3.4
-$7.6
2063
-$0.01
-$0.01
-$0.01
-$0.02
-$1.2
-$2.8
-$3.8
-$8.7
TPVb
-$0.04
-$0.1
-$0.2
-$0.3
-$10.7
-$44.3
-$68.3
-$135.4
2028
-$0.01
-$0.02
-$0.02
-$0.04
-$1.9
-$6.1
-$8.9
-$18.4
2033
-$0.01
-$0.02
-$0.02
-$0.05
-$2.2
-$6.7
-$9.6
-$20.3
2
2043
-$0.01
-$0.02
-$0.03
-$0.1
-$2.8
-$7.8
-$11.0
-$24.1
2053
-$0.02
-$0.03
-$0.03
-$0.1
-$3.5
-$9.0
-$12.3
-$27.7
2063
-$0.02
-$0.03
-$0.04
-$0.1
-$4.3
-$10.4
-$13.9
-$31.9
TPVb
-$0.2
-$0.5
-$0.6
-$1.2
-$39.1
-$162.0
-$250.0
-$495.7
2028
-$0.01
-$0.02
--$0.03
-$0.1
-$2.7
-$8.8
-$12.8
-$26.4
2033
-$0.01
-$0.03
-$0.03
-$0.1
-$3.1
-$9.6
-$13.8
-$29.1
3
2043
-$0.02
-$0.03
-$0.04
-$0.1
-$4.0
-$11.3
-$15.8
-$34.6
2053
-$0.02
-$0.04
-$0.05
-$0.1
-$5.0
-$13.0
-$17.8
-$39.9
2063
-$0.03
-$0.05
-$0.1
-$0.1
-$6.2
-$14.9
-$20.0
-$45.8
TPVb
-$0.2
-$0.7
-$0.9
-$1.8
-$56.2
-$232.9
-$359.4
-$712.5
a. Values rounded to two significant figures. Negative values indicate disbenefits. Climate impacts are based on changes in CH4
and C02 emissions and are calculated using four different estimates of the SC-CH4 and SC-C02 (model average at 2.5 percent, 3
percent, and 5 percent discount rates; and 95th percentile at 3 percent discount rate). The IWG emphasized the importance and
value of considering the benefits calculated using all four estimates. As discussed in the Technical Support Document: Social
Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under EO 13990 (IWG, 2021), a consideration of climate
benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also warranted when discounting
intergenerational impacts.
b. TPV represents the total present value from 2025-2065.
Source: U.S. EPA Analysis, 2023
Table 5-6 presents the annualized climate disbenefits associated with changes in GHG emissions over the
2025-2065 period under each discount rate by regulatory option and category of emissions. EPA
annualized the climate disbenefits to enable consistent reporting across benefit categories (e.g., benefits
from improvement in water quality). All values are negative since net pollutant emissions increase under
the proposed rule. Using the average SC-GHG values for the 3 percent discount rate and using a 3 percent
discount rate to annualize the benefits yields annualized climate disbenefits for the preferred option of
$10.0 million.
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Table 5-6: Estimated Total Annualized Climate Disbenefits from Incremental Changes in
Cm and CO2 Emissions under the Proposed Rule by Discount Rate (Millions of 2022$)
Pollutant
Discount Rate
Regulatory Option
Option 1
Option 2
Option 3
Methane3
5% Average
-$0,003
-$0.01
-$0.01
3% Average
-$0.01
-$0.02
-$0.03
2.5% Average
-$0.01
-$0.03
-$0.04
3% 95th percentile
-$0.01
-$0.05
-$0.08
Carbon
dioxide3
5% Average
-$0.62
-$2.28
-$3.27
3% Average
-$1.91
-$7.01
-$10.1
2.5% Average
-$2.72
-$9.96
-$14.3
3% 95th percentile
-$5.86
-$21.4
-$30.8
Total
5% Average
-$0.62
-$2.29
-$3.29
3% Average
-$1.92
-$7.03
-$10.1
2.5% Average
-$2.73
-$9.99
-$14.4
3% 95th percentile
-$5.87
-$21.5
-$30.9
a. Values rounded to two significant figures. Negative values indicate disbenefits. Climate impacts are based on changes in
CH4 and C02 emissions and are calculated using four different estimates of the SC-CH4 and SC-C02 (model average at 2.5
percent, 3 percent, and 5 percent discount rates; and 95th percentile at 3 percent discount rate). The IWG emphasized the
importance and value of considering the benefits calculated using all four estimates. As discussed in the Technical Support
Document: Social Cost of Carbon, Methane, and Nitrous Oxide Interim Estimates under EO 13990 (IWG, 2021), a
consideration of climate benefits calculated using discount rates below 3 percent, including 2 percent and lower, are also
warranted when discounting intergenerational impacts.
Source: U.S. EPA Analysis, 2023
As discussed above, the IWG is currently working on a comprehensive update of the SC-GHG estimates
under EO 13990, taking into consideration recommendations from the National Academies of Sciences,
Engineering, and Medicine, recent scientific literature, and public comments received on the February
2021 SC-GHG TSD. EPA is a member of the IWG and is participating in the IWG's review and updating
process under EO 13990. In December 2023, EPA published new SC-GHG estimates as a supplement to
a rulemaking finalizing "Standards of Performance for New, Reconstructed, and Modified Sources and
Emissions Guidelines for Existing Sources: Oil and Natural Gas Sector Climate Review." (U.S.
Environmental Protection Agency, 20231) These new estimates reflect recent advances in the scientific
literature on climate change and its economic impacts and incorporate recommendations made by the
National Academies (National Academies of Sciences, 2017b). As these values were not finalized at the
time EPA conducted this analysis, EPA did not use them in the main analysis presented in this section to
monetize the estimated climate disbenefits of this proposed rule. However, EPA is presenting disbenefits
estimated using these values in Appendix F and requests comments on whether the Agency should
proceed with using these values in the main analysis.
5.3 Human Health Disbenefits
5.3.1 Data and Methodology
As summarized in Table 5-3, the regulatory options are estimated to result in small increases in the
emissions of pollutants that adversely affect human health, including SO2 and NOx, which are both
precursors to ambient PM2.5. NOx emissions are also a precursor to ambient ground-level ozone. The
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Climate Change and Air Quality-Related Disbenefits
change in emissions alters the ambient concentrations, which in turn leads to changes in population
exposure. EPA estimates the changes in the human health impacts associated with PM2.5 and ozone.58
To estimate human health impacts and monetize the disbenefits of these changes in air emissions, EPA
applied published benefit per ton estimates of changes in PM2.5 and ozone precursors, to the estimates of
SO2 and NOx emissions reported in Table 5-3. Table 5-7 presents benefit per ton estimates for 2025
through 2040 by emissions category using 3 percent discount rate (U.S. EPA, 2023n; Wolfe etal., 2019).
For transportation emissions, EPA applied the 2025 benefit per ton estimates to estimated emissions
changes for each year in the period of analysis. For electricity usage, EPA applied the estimates available
for 2025 to changes estimated in each year within 2025-2029, estimates available for 2030 to changes
estimated in each year within 2030-2034, estimates available for 2035 to changes estimated in each year
within 2035-2039, and estimates available for 2040 to the remainder of the analysis period 2040-2063.
Table 5-7: Benefit per Ton Values by Emission Category, 3 Percent Discount Rate
($2022)
Benefit per ton,
S02 ($/ton)
Benefit per ton, NOx ($/ton)
Category
Year and Basis
PM2.5-related
benefits
Ozone-related
benefits
Electricity
2025
$62,526
$8,461
$108,174
usage3
2030
$70,568
$9,481
$142,722
2035
$79,177
$10,614
$157,447
2040
$86,539
$11,554
$169,907
Transportation15
2025; Krewski et al., 2009
$315,961
$7,413
2025; Lepeule et al., 2012
$716,988
$17,013
a. Estimate of total dollar value of benefits (mortality and morbidity) for changes in emissions from electricity generating units.
Updated from 2019 dollars to 2022 dollars using the GDP deflator (GDP deflator 2022 / GDP deflator 2019 = 1.333). [U.S. EPA,
2023n]
b. National average estimate of total dollar value of benefits (mortality and morbidity) for changes in emissions from on-road,
heavy duty diesel vehicles in 2025. Updated from 2015 dollars using the GDP deflator (GDP deflator 2022 / GDP deflator 2015
= 1.215). [Wolfe etal., 2019]
5.3.2 Results
Table 5-8 presents the undiscounted annual monetized human health disbenefits in selected years for each
regulatory option, using a 3 percent discount rate. EPA multiplied estimated changes in SO2 and NOx
emissions each year by the corresponding benefit per ton estimates for that year. Benefits are negative
{i.e., disbenefits) due to the net increase in SO2 and NOx emissions under the proposed rule.
58 Ambient concentrations of both SO2 and NOx also pose health risks independent of PM2.5 and ozone, though EPA does not
quantify these impacts in this analysis (U.S. EPA, 2016a;U.S. EPA, 2017b).
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Climate Change and Air Quality-Related Disbenefits
Table 5-8: Estimated Undiscounted and Total Present Value of Economic Value of
Avoided Ozone and PIVh.s-Attributable Premature Mortality and Morbidity by Regulatory
Option (Millions of 2022$, 3 Percent Discount Rate)
Regulatory Option
Year
so2
NOx
Krewski et al.
(2009)
2025; Lepeule
et al., 2012
Krewski et al.
(2009)
2025; Lepeule
et al., 2012
1
2028
-$1.0
-$1.0
-$1.8
-$1.9
2033
-$1.2
-$1.2
-$2.4
-$2.4
2043
-$1.4
-$1.4
-$2.9
-$2.9
2053
-$1.4
-$1.4
-$2.9
-$2.9
2063
-$1.4
-$1.4
-$2.9
-$2.9
TPVa
-$27.1
-$27.2
-$53.9
-$54.3
2
2028
-$3.8
-$3.8
-$6.8
-$6.8
2033
-$4.3
-$4.3
-$8.8
-$8.9
2043
-$5.3
-$5.3
-$10.5
-$10.6
2053
-$5.3
-$5.3
-$10.5
-$10.6
2063
-$5.3
-$5.3
-$10.5
-$10.6
TPVa
-$100.0
-$100.1
-$197.7
-$198.7
3
2028
-$5.5
-$5.5
-$9.8
-$9.8
2033
-$6.2
-$6.2
-$12.7
-$12.8
2043
-$7.6
-$7.6
-$15.2
-$15.2
2053
-$7.6
-$7.6
-$15.2
-$15.2
2063
-$7.6
-$7.6
-$15.2
-$15.2
TPVa
-$144.1
-$144.2
-$285.5
-$286.8
a. TPV represents the total present value from 2025-2065.
Source: U.S. EPA Analysis, 2023
5.4 Annualized Climate Change and Air Quality-Related Disbenefits of Regulatory Options
Table 5-9 presents the total annualized air quality-related disbenefits by regulatory option. For the climate
change disbenefits, EPA used the same discount rate used to develop SC-GHG values. For the human
health disbenefits, EPA used a 3 percent discount rate. Changes in air pollutant emissions under the
preferred option (Option 1) result in annualized disbenefits of $5.4 million.
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Climate Change and Air Quality-Related Disbenefits
Table 5-9: Total Annualized Climate Change and Air Quality-Related Disbenefits by
Regulatory Option and Discount Rate (Millions of 2022$)
Human Health (at 3
Percent Discount Rate)
Total
2025;
2025;
Regulatory
Krewski et
Lepeule et
Krewski et
Lepeule et
Option
SC-GHG Discount Rate
Climate Change
al. (2009)
al., 2012
al. (2009)
al., 2012
3% (Average)
-$1.9
-$3.5
-$3.5
-$5.4
-$5.4
1
5% (Average)
-$0.6
-$3.5
-$3.5
-$4.1
-$4.1
2.5% (Average)
-$2.7
-$3.5
-$3.5
-$6.2
-$6.3
3% (95th Percentile)
-$5.9
-$3.5
-$3.5
-$9.4
-$9.4
3% (Average)
-$7.0
-$12.9
-$12.9
-$19.9
-$20.0
2
5% (Average)
-$2.3
-$12.9
-$12.9
-$15.2
-$15.2
2.5% (Average)
-$10.0
-$12.9
-$12.9
-$22.9
-$22.9
3% (95th Percentile)
-$21.5
-$12.9
-$12.9
-$34.4
-$34.4
3% (Average)
-$10.1
-$18.6
-$18.6
-$28.7
-$28.8
3
5% (Average)
-$3.3
-$18.6
-$18.6
-$21.9
-$21.9
2.5% (Average)
-$14.4
-$18.6
-$18.6
-$32.9
-$33.0
3% (95th Percentile)
-$30.9
-$18.6
-$18.6
-$49.5
-$49.6
Source: U.S. EPA Analysis, 2023
5.5 Limitations and Uncertainty
Table 5-10 summarizes the limitations and uncertainties associated with the analysis of the climate
change and air quality-related impacts.
Table 5-10: Limitations and Uncertainties in the Analysis of Climate Change and Air
Quality-Related Disbenefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
EPA used emission factors
from eGRID to estimate
indirect emissions impacts
from increased electricity
consumption
Overestimate
The eGRID database provides emission factors based on historical
electricity generation (observed or estimated using 2021 data). It
is designed to be used to estimate the emissions footprint of
marginal changes in electricity consumption, assuming a constant
generation mix. eGRID provides static emission factors based on
historical data and likely overstates emissions associated with the
increased power consumption to operate MPP wastewater
treatment systems since emission factors are expected to decline
in the coming decades as the United States increasingly
transitions to clean energy or expand carbon capture, utilization
and storage using incentives in the 2022 Inflation Reduction Act
Energy Infrastructure Reinvestment Program.
EPA used the industrial
boilers sector as a proxy
for the wastewater
treatment sector.
Unknown
Benefit per ton values are not available for the wastewater
treatment sector. EPA used the industrial sources it deemed most
similar in terms of spatial distribution as wastewater treatment.
However, differences in the distribution of wastewater emissions
sources relative to the exposed populations, when compared to
industrial boilers, may result in smaller or larger health impacts.
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Climate Change and Air Quality-Related Disbenefits
Table 5-10: Limitations and Uncertainties in the Analysis of Climate Change and Air
Quality-Related Disbenefits
Uncertainty/Limitation
Effect on Benefits
Estimate
Notes
EPA did not analyze all
benefits of changes in
exposure to NOx, S02and
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.
EPA did not analyze
potential changes in
greenhouse gases
associated with changes in
meat sales
Overestimate
Increases in meat prices may result in reduced sales in meat and
poultry products. EPA did not estimate net changes in
greenhouse gas emissions associated with the production of
these products as compared to their substitutes.
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Summary of Estimated Total Monetized Benefits
6 Summary of Estimated Total Monetized Benefits
Table 6-1 summarizes the total annualized monetized benefits using a 3 percent discount rate. The
monetized benefits do not account for all anticipated effects of the regulatory options, including human
health (e.g., avoided illnesses from exposure through recreational uses), ecological (e.g., impacts of
pollutant load changes on T&E species habitat), market and productivity benefits (e.g., drinking water
treatment cost savings). See Chapter 2 for a discussion of categories of benefits EPA did not monetize.
Table 6-1: Summary of Total Annualized Benefits for Regulatory Options, Compared to
Baseline, at 3 Percent (Millions of 2022$)
Benefit Category
Option 1
Option 2
Option 3
Use and nonuse values for water quality changes
$95.6
$166.1
$208.4
Climate change effects from changes in GHG emissions
-$1.9
-$7.0
-$10.1
Human health effects from changes in NOx, S02, and PM2.5 emissions
-$3.5
-$12.9
-$18.6
Total monetized benefits
$90.2
$146.2
$179.7
Additional benefits
+
+
+
+ Additional non-monetized health, ecological, market and economic productivity benefits (see Table ES-2 and Chapter 2)
Source: U.S. EPA Analysis, 2023
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Summary of Total Social Costs
7 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 9 of the RIA, EPA did not evaluate incremental baseline costs, and
associated cost savings to state governments. To calculate social costs, EPA estimated technology
implementation costs for MPP facilities and administrative costs to MPP facilities, states and POTWs,
and the Agency.
7.1 Overview of Cost Analysis Framework
Chapter 3 of the RIA presents EPA's development of cost estimates for MPP facilities within the scope of
the proposed rule. These costs, calculated on a pre-tax basis, are used in the social cost analysis.
For the analysis of social costs, EPA estimated a year-explicit schedule of technology implementation
cost outlays over the period 2026-2065. EPA estimated that MPP dischargers will install treatment
technologies based on a compliance schedule specific to discharge type. All direct dischargers will install
treatment technologies over five years, with an estimated 20 percent doing so each year. Direct
dischargers will also incur annual O&M costs on the same schedule. All indirect dischargers will
implement technologies in year three and will begin incurring annual O&M costs beginning in that year.
In addition, since EPA estimated that 70 percent of capital has a useful life of 20 years, direct dischargers
will incur 100 percent of capital compliance costs when they first install the technology within years one
through five and 70 percent of the capital compliance costs again within years 21 through 25 as some of
the previously installed technology reaches its end of life. Indirect dischargers will incur 100 percent of
capital compliance costs in year three and 70 percent in year 24.
EPA summed annual facility-level costs to develop estimates for the total costs of compliance in each
year of the analysis period and calculated the present and annualized values of these costs using a 3
percent discount rate over the 40-year analysis period. EPA assumed that capital costs are incurred in the
relevant compliance year for each facility, and annual O&M costs (operating labor, waste transport and
disposal operation, etc.) are incurred each year after technology implementation. See Chapter 3 in the RIA
for more details.
EPA used estimated costs to dischargers for labor, capital, and other resources necessary to ensure
compliance with the regulatory options to assess costs to society. In this analysis, market prices for these
resources are the opportunity cost to society. EPA assumed an inelastic supply of MPP products, meaning
that the regulatory options do not affect the quantity of goods sold by the industry. This assumption is
consistent with EPA's market impact analysis (Chapter 6 of the RIA) which shows that the regulatory
options have a small impact on the production of MPP products and that demand is relatively inelastic
with respect to price. As discussed in Section 2.3.2, POTWs receiving MPP discharges may incur lower
wastewater treatment costs due to reductions in influent pollutant loads and improvements in the quality
of biosolids. EPA did not estimate the cost savings at POTWs but to the extent that they offset some of
the compliance costs incurred by indirect dischargers, the cost savings will reduce the total social costs
attributable to this rule.
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Summary of Total Social Costs
EPA also calculated the one-time and annual administrative costs of compliance. One-time administrative
costs include the cost for facilities to read and comprehend the rule and the cost to Control Authorities
and the Agency to review the ELGs and establish monitoring requirements. Control Authorities will incur
annual costs to review direct dischargers" monitoring reports and take enforcement action as needed. The
Agency will incur annual costs to review pollutant data from MPP dischargers for compliance. Annual
costs are incurred in accordance with dischargers" compliance schedule.59
Control Authorities" annual costs are proportional to the percentage of direct dischargers in compliance
each year. Therefore, their annual costs increase by 20 percent each year from years one through five, as
direct dischargers come into compliance. After year five, Control Authorities incur 100 percent of total
annual costs each year. The Agency's annual costs to review direct dischargers" data are incurred on the
same schedule. The Agency also incurs annual costs to review indirect dischargers" data proportional to
the percentage in compliance. Therefore, the Agency will not incur annual costs to review indirect
dischargers" data until year 3, at which point the Agency will incur 100 percent of costs through year 40.
7.2 Key Findings for Regulatory Options
Table 7-1 presents annualized incremental costs for the regulatory options, as compared to the baseline,
discounted at 3 percent. Appendix D presents annualized incremental costs discounted at 7 percent.
Table 7-1: Estimated Total Social Costs by Regulatory Option and Discharge Type
Discounted at 3 Percent (Millions 2022$)
Regulatory Option
Direct
Indirect
Total
Option 1
$216.5
$15.3
$231.9
Option 2
$216.5
$426.3
$642.8
Option 3
$223.7
$853.6
$1,077.3
Option lwith chlorides
$279.6
$109.9
$389.6
Option 2 with chlorides
$279.6
$520.9
$800.5
Option 3 with chlorides
$286.8
$948.2
$1,235.0
Source: U.S. EPA Analysis, 2023.
Table 7-2 provides additional details on the social cost calculations. For each regulatory option, the table
presents the time profiles of incremental costs incurred compared to the baseline. The annualized costs,
discounted at 3 percent, are presented as well. Estimated costs are highest in year three (2028), when
20 percent of direct and 100 percent of indirect dischargers incur capital costs, and year 24 (2049), when
20 percent of direct dischargers and 100 percent of indirect dischargers incur 70 percent of capital costs.
Year three is also when Control Authorities and the Agency incur 60 percent of annual costs for direct
dischargers and 100 percent of annual costs for indirect dischargers.
59 EPA estimated one-time administrative costs to read the rule for Options 1, 2, and 3, with and without chlorides, and annual
administrative costs for Options 1 and 2, with and without chlorides.
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Summary of Total Social Costs
Table 7-2: Time Profile of Costs to Society (Millions 2022$)
Year
Option 1
Option 2
Option 3
Option 1 with
chlorides
Option 2 with
chlorides
Option 3 with
chlorides
2025
$0.0
$0.0
$0.0
$0.0
$0.0
$0.0
2026
$191.9
$191.9
$198.1
$251.1
$251.1
$257.3
2027
$229.7
$229.7
$237.2
$300.2
$300.2
$307.7
2028
$353.1
$2,403.8
$4,942.3
$880.4
$2,931.1
$5,469.5
2029
$321.7
$682.8
$1,043.3
$499.6
$860.7
$1,221.2
2030
$361.3
$722.4
$1,084.2
$550.4
$911.5
$1,273.4
2031
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2032
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2033
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2034
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2035
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2036
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2037
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2038
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2039
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2040
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2041
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2042
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2043
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2044
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2045
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2046
$316.1
$677.2
$1,037.6
$490.9
$852.0
$1,212.3
2047
$316.1
$677.2
$1,037.6
$490.9
$852.0
$1,212.3
2048
$316.1
$677.2
$1,037.6
$490.9
$852.0
$1,212.3
2049
$365.6
$1,909.4
$3,795.0
$792.8
$2,336.6
$4,222.2
2050
$316.1
$677.2
$1,037.6
$490.9
$852.0
$1,212.3
2051
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2052
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2053
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2054
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2055
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2056
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2057
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2058
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2059
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2060
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2061
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2062
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2063
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2064
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
2065
$210.7
$571.8
$928.8
$351.9
$713.0
$1,070.0
PV, 3%
$5,359.4
$14,858.2
$24,900.8
$9,004.5
$18,503.3
$28,545.9
Annualized
costs, 3%
$231.9
$642.8
$1,077.3
$389.6
$800.5
$1,235.0
Source: U.S. EPA Analysis, 2023.
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Benefits and Social Costs
8 Benefits and Social Costs
This chapter compares total monetized benefits and costs for the regulatory options. Benefits and costs are
compared on two bases: (1) incrementally for each of the options analyzed as compared to the baseline
and (2) incrementally across options. The comparison of benefits and costs also satisfies the requirements
of Executive Order 12866: Regulatory Planning and Review and Executive Order 13563: Improving
Regulation and Regulatory Review (see Chapter 9 in the RIA).
8.1 Comparison of Benefits and Costs by Option
Chapters 6 and 7 present estimates of the benefits and costs, respectively, for the regulatory options.
Table 8-1 presents EPA's estimates of the annualized benefits and costs of the regulatory options.
Table 8-1: Total Estimated Annualized Benefits and Costs by Regulatory Option
Compared to Baseline, at 3 Percent Discount (Millions of 2022$)
Regulatory Option
Total Benefits
Total Costs
Monetized Benefits
Other Benefits
Option 1
$90.2
+
$231.9
Option 2
$146.2
+
$642.8
Option 3
$179.7
+
$1,077.3
+ Additional non-monetized health, ecological, market and economic productivity benefits (see Table ES-2 and Chapter 2)
Source: U.S. EPA Analysis, 2023.
8.2 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 some insight into the net gain to society from
imposing increasingly more costly requirements, but does not provide a full account for those gains given
the share of the benefits that cannot be monetized. For example, the analysis omits important categories of
benefits discussed further in Section 2 that include, but are not limited to, reduced incidence of adverse
human health effects from exposure to MPP pollutants via recreational use or drinking water, water
quality improvements in receiving and downstream reaches and the associated enhancement of
swimming, fishing, boating, and near-water activities, aesthetic values from shifts in water clarity, color,
or odor, improved ecosystem health, including benefits to T&E species habitat and populations, as well as
various market benefits such as reduced drinking water and wastewater treatment costs, improved
fisheries yield and harvest quality, and improved property values.
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BCAfor Proposed Revisions to the MPP ELGs
Benefits and Social Costs
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.
Table 8-2: Analysis of Estimated Incremental Net Benefit of the Regulatory Options,
Compared to Baseline and to Other Regulatory Options, at 3 Percent Discount (Millions
of 2022$)
Regulatory Option
Net Annual Benefits3
Incremental Net Annual
Monetized Benefits
Other Benefits
Monetized Benefits'3
Option 1
-$141.7
+
N/A
Option 2
-$496.6
+
-$354.9
Option 3
-$897.6
+
-$401.0
+ Additional non-monetized health, ecological, market and economic productivity benefits (see Table ES-2 and Chapter 2)
a. Net annual other benefits were not quantified and therefore the net values shown are based on monetized benefits only.
However, given generally increasing pollutant loading reductions as one moves from Option 1 to Option 2, and from Option
2 to Option 3, EPA anticipates the other benefits to also increase as one moves from Option 1 to Option 2, and from Option 2
to Option 3.
b. Net annual other benefits were not quantified and therefore the incremental net values shown are based on monetized
benefits only.
Source: U.S. EPA Analysis, 2023.
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Environmental Research and Public Health, 15(1), 1557. doi: 10.3390/ijerphl5071557
Wattage, P. M. (1993). Measuring the benefits of water resource protection from agricultural
contamination: results form a contingent valuation study. (Ph.D. dissertation). Forestry, Iowa
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Weir, M. J., Kourantidou, M., & Jin, D. (2022). Economic impacts of harmful algal blooms on fishery-
dependent communities. Harmful Algae, 118, 102321.
Welle, P. G. (1986). Potential economic impacts of acid deposition: a contingent valuation study of
Minnesota. (Ph.D Dissertation submitted to the University of Wisconsin-Madison).
Welle, P. G., & Hodgson, J. B. (2011). Property owner's willingness to pay for water quality
improvements: contingent valuation estimates in two central Minnesota Watersheds. Journal of
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BCAfor Proposed Revisions to the MPP ELGs
Appendix A: Water Quality Modeling
Appendix A: Water Quality Modeling
This section describes the methodology used to analyze the potential hydrologic and water quality effects in
response to the proposed rule for the MPP industry.
SWAT Model Setup
EPA used HAWQS 2.0 to develop the initial SWAT models and extract data necessary to characterize the
watersheds. HAWQS is a web-based interface that streamlines the development of SWAT watershed models
by providing pre-loaded input data and modeling support capabilities for setting up models, running
simulations, and processing outputs (2023). SWAT is a commonly used public domain semi-distributed
mechanistic watershed model that is used to evaluate the effects of land management and agricultural
practices on water, sediment, and chemical fluxes across a wide range of watershed sizes, land uses, and
physiographic provinces (Neitsch etal., 2011). HAWQS provides pre-loaded national input data necessary to
develop SWAT watershed models at subbasin resolutions that range from the 14-digit HUC (HUC14) to the
8-digit HUC (HUC8).
For the water quality models described in Section 3.3, EPA developed watershed models with HUC 12
subbasins using the HAWQS 2.0 interface. Table A-l summarizes the pre-processed input datasets available
within the HAWQS framework that were used in developing these models.
Table A-1: Case Si
tudy Models Input Dataset Summary
Input Dataset
Source
Specifications
Weather
Parameter-elevation Regressions on Independent Slopes Model (PRISM)
1981 - 2020
(gridded)
Soil
U.S. Department of Agriculture (USDA) National Resources Conservation
2018
Service (NRCS) Soil Survev Geographic (SSURGO) Database
USDA NRCS State Soil Geographic (STATSGO) Database
2018
Land Use
National Land Cover Database (NLCD)
2016
USDA National Agricultural Statistics Service (NASS) Cropland Data Layer
2014-2017
(CDL)
USDA NASS Fields
2006-2010
U.S. Fish and Wildlife Service (FWI) National Wetlands Inventory (NWI)
2018
Aerial Deposition
National Atmospheric Deposition Program (NADP)
1980 - 2020
(monthly)
Watershed
Boundaries
EPA NHDPIus v2
2019
Stream Networks
EPA NHDPIus v2
2019
Elevation
USGS National Elevation Dataset (NED)
2018 (IO-
meter DEM)
Point Sources
EPA Hypoxia Task Force (HTF)
2019
EPA Integrated Compliance Information System National Pollutant
2019
Discharge Elimination System (ICIS-NPDES)
Management Data
USDA NRCS crop management zone data
2010
Ponds, Potholes, and
Reservoirs
U.S. Army Corps of Engineers (USACE) National Inventory of Dams (NID)
2018
EPA NHDPIus v2
2019
Crop Data
USDA NASS CDL
2014 - 2017
Wetlands
U.S. Fish and Wildlife Service National Wetland Inventory (NWI)
2018
Water Use
USGS Water Use in the United States
2015
Source: U.S. EPA Analysis, 2023
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BCAfor Proposed Revisions to the MPP ELGs
Appendix A: Water Quality Modeling
SWAT also allows the user to choose among hydrology and water quality settings that determine how various
SWAT processes are modeled. Table A-2 summarizes the relevant setting specifications used in
HAWQS/SWAT models.
Table A-2: Summary of Relevant SWAT Hydrology and Water Quality Settings
SWAT Process
Associated
SWAT File
Specifications
Potential evaporation
basins, bsn
Penman/Monteith method
Water routing
basins, bsn
Variable travel time
Curve number (CN) calculation
basins.bsn
Calculates daily CN value as a
function of soil moisture
Instream sediment model
basins.bsn
Bagnold model
Source: U.S. EPA Analysis, 2023
Representation of Point Source Discharges from Direct and Indirect Facilities
HAWQS 2.0 includes default point source data to represent loadings not associated with land areas, such as
permitted discharges from POTWs or industrial facilities, including MPP dischargers. The point source
dataset used for the case study models includes data for flows, nitrogen, phosphorus, fecal coliform, E. coli,
CBOD, and TSS by subbasin (HUC12). The parameters follow the standard SWAT model input data format
for annual average discharges (reccnst.dat):
¦ Flow: (FLO) in cubic meters per day
¦ Nitrogen: nitrate (N03), nitrite (N02), ammonia (NH3), and organic nitrogen (ORGN), all in kilograms
per day
¦ Phosphorus: mineral phosphorus (MINP) and organic phosphorus (ORGP) in kilograms per day
¦ Pathogens: E. coli (BACTP), and fecal coliform (BACTLP) in colony forming units (CFU) per 100 mL60
¦ Organic enrichment: CBOD (CBOD) in kilograms per day
¦ Sediment: TSS (SED) in metric tons (Mton) per day
Default point source data included in HAWQS 2.0 reflect 2019 annual average loadings from permitted point
source dischargers. The scope includes discharges covered by NPDES individual permits from POTW and
non-POTW facilities, whether they are classified as minor or major. Point source data for MPP direct
dischargers were updated to reflect 2021 loadings from MPP permitted dischargers, whereas all other point
source dischargers were left unchanged to their 2019 default values. All point source estimates were derived
from the sources described below.
¦ EPA ICIS-NPDES Discharge Monitoring Reports (DMRs): ICIS-NPDES is an information management
system that tracks permit compliance and enforcement status of facilities regulated by the NPDES permit
program. DMRs are part of facilities" compliance verification process. These datasets include reported
outfall flows and loadings or concentrations from NPDES-permitted facilities. In particular, the datasets
include NPDES and outfall identifiers, geographic coordinates, parameters monitored, monitoring
frequencies, statistical bases applied to report the values, and measured values in standardized units. The
o0 E. coli was mapped to persistent bacteria and fecal coliform was mapped to less persistent bacteria based on review of the
documentation of the pathogen modeling routines and past model applications.
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BCAfor Proposed Revisions to the MPP ELGs
Appendix A: Water Quality Modeling
DMR data are formatted as monthly measurements adjusted to DMR value standard units at each NPDES
facility outfall.
¦ EPA ECHO Water Pollutant Loading Tool, Hypoxia Task Force (HTF) Nutrient Modeling Dataset: Total
nutrient loads for all relevant NPDES-permitted point source facilities are summarized in a national
dataset from EPA's ECHO Water Pollutant Loading Tool, Nutrient Modeling (HTF Search). This dataset
reports annual total nitrogen (TN) and total phosphorus (TP) loads. The annual nutrient loading values
include both 1) aggregated TN and TP loads from facilities reporting nutrient concentrations in DMRs
and 2) modeled data where EPA imputed loads for facilities without DMR-reported nutrient data using
Typical Pollutant Concentrations (TPCs) applied to facilities based on Standard Industrial Classification
(SIC) code, flow class, and season. DMR data for 2019 and 2021 were extracted for nutrients, pathogens,
BOD, TSS, and flows, where available.
¦ For select direct dischargers that were not reflected in the default point source dataset in HAWQS 2.0,
EPA used the baseline loadings developed for this rulemaking analysis (described in Section 3.1).
The primary data source (HTF or DMR) determined the process by which the point source data were
summarized. The HTF dataset served as the primary basis for annual nutrient loadings; for nutrients, DMR
data were used secondarily to distribute total nutrient loadings across discharge outfalls and nutrient species.
For pathogens (E. coli and fecal coliform), BOD, and TSS, the primary data source was DMR. The DMR data
were used in combination with permit and facility characteristics to estimate total loadings and concentrations
across discharge outfalls. External outfalls associated with NPDES-permitted dischargers were georeferenced
to the HUC14s based on the outfall coordinates. The HAWQS 2.0 technical documentation has additional
details on the development of the point source data (U.S. EPA, 2023f).
Model Calibration
SWAT parameters in initial models reflect default values from SWAT, as modified where applicable during
HAWQS calibration (U.S. EPA, 2023f).
The SWAT calibration procedure involved four main steps:
1. Collect observed data within the case study modeling locations;
2. Run the model in "calibration mode" and iteratively adjust model parameters so that the predicted
monthly streamflow and loadings time series approximate observed streamflow and loadings
within the bounds of uncertainties of model inputs and estimates developed directly from
observed data (using the USGS' Load Estimator [LOADEST]);
3. Run the statistical tests in SWAT's Calibration and Uncertainty Program (SWAT-CUP) to
produce the calibration statistical metrics; and
4. Finalize the calibration parameters and update the project database and input files for further
scenario analysis.
For the regions described in Section 3.3, flow calibration was completed for each HUC12 with sufficient
observed data. HUC12s without sufficient observed flow data or HUC12s that did not result in a successful
calibration were assigned calibration parameters from similar watersheds within the same HUC2 region,
identified by a cluster analysis of watershed characteristics. There were insufficient observed water quality
data to conduct calibration at the same scale, both spatially and temporally. Spatially, observed water quality
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BCAfor Proposed Revisions to the MPP ELGs
Appendix A: Water Quality Modeling
data were available at far fewer locations than observed flow data. Temporally, continuous flow time series
were often available for gage stations, but water quality data is more often collected as discrete grab samples.
The frequency and duration of sampling affected which observed water quality data sites were appropriate for
calibration, even with the use of USGS' LOADEST to estimate water quality time series from the discrete
grab samples. The HAWQS 2.0 technical documentation has additional details on calibration procedures.
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BCAfor Proposed Revisions to the MPP ELGs
Appendix B: WQI Calculation and Subindices
Appendix B: WQI Calculation and Regional Subindices
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 field measurements while others are modeled values.
The second step involves transforming the parameter measurements into subindex values that express water
quality conditions on a common scale of 10 to 100. EPA used the subindex transformation curves developed
by Dunnette (Dunnette, 1979) and Cude (Cude, 2001a) for the Oregon WQI for BOD, DO, and FC. For TSS,
TN, and TP concentrations, EPA adapted the approach developed by Cude (Cude, 2001a) 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 National River5 and Streams
Assessment (NRSA) using data from the 2013-2014 and 2018-2019 NRSAs.61 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 conditions"), and
a score of 70 to the median concentration. An exponential equation was then fitted to the two concentration-
score pairs for each ecoregion following the approach used in Cude (Cude, 2001b).
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. Following McClelland's approach,
EPA calculated the overall WQI using a weighted geometric mean function.
Equation B-l presents EPA's calculation of the overall WQI score.
Equation B-1.
wQir = n?=iQim
WQIr = the multiplicative water quality index (from 10 to 100) for subbasin r
Qi = the water quality subindex measure for parameter z
Wi = the weight of the z-th parameter
n = the number of parameters (z. e., six)
The WQI parameter weights (Table B-l) are based on the parameter weights used in the WQI developed by
Cude (Cude, 2001a) and updated for EPA's C&D analysis (U.S. EPA, 2009a).62
01 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. The 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.
02 EPA (Schaafsma, 2015) revised the weights originally developed by McClelland (McClelland, 1974) by redistributing the
weights to the six parameters retained in the EPA WQI (excluding temperature and pH) so that the ratio among the parameters is
maintained and the weights sum to one.
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BCAfor Proposed Revisions to the MPP ELGs Appendix B: WQI Calculation and Subindices
Table B-1: WQI Parameter Weights
Parameter
Weight
Dissolved Oxygen
0.24
Fecal Coliform
0.22
Biochemical Oxygen Demand
0.15
Total Nitrogen
0.14
Total Phosphorus
0.14
Total Suspended Solids
0.11
Source: U.S. EPA Analysis, 2023, based on methodology in Schaafsma, 2015
Table B-2 presents parameter-specific functions used for transforming water quality data into water quality
subindices for freshwater waterbodies for the six pollutants with individual subindices. 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 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 B-2: Freshwater Water Quality Subindices
Parameter
Concentrations
Concentration
Unit
Subindex
Dissolved Oxygen (DO)
DO saturation <100%
DO
DO < 3.3
mg/L
10
DO
3.3 < DO < 10.5
mg/L
-80.29+31.88xDO-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
TNa
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
TPb
TP
TP > TP10
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
TSSC
TSS
TSS > TSS io
mg/L
10
TSS
TSS ioo < TSS < TSS io
mg/L
a x exp(TSSxb); where a and b are ecoregion-specific values
TSS
TSS < TSS oo
mg/L
100
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BCAfor Proposed Revisions to the MPP ELGs
Appendix B: WQI Calculation and Subindices
Table B-2: Freshwater Water Quality Subindices
Parameter
Concentrations
Concentration
Unit
Subindex
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 (2001a)
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 (2001a)
c. TSS10 and TSS100 are ecoregion-specific TSS concentration values that correspond to subindex scores of 10 and 100,
respectively. Use of 10 and 100 for the lower and upper bounds of the WQI subindex score follow the approach in Cude (2001a)
Source: U.S. EPA Analysis, 2023, based on methodology in Cude (2001a).
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 i0, WQ
Parameter k>o, a, and b are specified in Table B-3 for TSS, Table B-4 for TN, and Table B-5 for TP.
Table B-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, 2023
Table B-4: TN Subindex Curve Parameters, by Ecoregion
Ecoregion
a
b
TNioo
TNio
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, 2023
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BCAfor Proposed Revisions to the MPP ELGs
Appendix B: WQI Calculation and Subindices
Table B-5: TP Subindex Curve Parameters, by Ecoregion
Eco region
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, 2023
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BCAfor Proposed Revisions to the MPP ELGs
Appendix C: Methodology for Estimating WTP
Appendix C: 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 the 2015
Steam Electric rule BCA (U.S. EPA, 2015a; see Appendix H). To update results of the 2015 meta-
analysis, EPA first conducted a literature review and identified ten 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, 2022) details changes to the meta-data and
MRMs following the 2020 Steam Electric ELG analysis (U.S. EPA, 2020c), 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, 2020c).
Table C-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 C-l.
Table C-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
Aiken (1985)
1
CO
river/
stream and
lake
Entire state
$238.19
$238.19
$238.19
G. D. 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
$62.83
C-l
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BCAfor Proposed Revisions to the MPP ELGs
Appendix C: Methodology for Estimating WTP
Table C-1. Primary Studies Included in the
M eta-data
Study
Obs. In
Meta-
data
State(s)
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Mean
Min
Max
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
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
$323.40
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Appendix C: Methodology for Estimating WTP
Table C-1. Primary Studies Included in the
M eta-data
Study
Obs. In
Meta-
data
State(s)
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Mean
Min
Max
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
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
IA/IL
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
IA/IL
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
$22.36
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Table C-1. Primary Studies Included in the
M eta-data
Obs. In
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Study
Meta-
data
State(s)
Mean
Min
Max
C. Moore et al.
2
MD, VA,
lake
Chesapeake Bay
$131.21
$77.75
$184.67
(2018)
DC, DE,
NY, PA,
WV, CT,
FL, GA,
ME,
MA, NH,
NJ, NC,
Rl, SC,
VT
Watershed
N. M. Nelson
2
UT
river/
Entire state
$259.70
$167.07
$352.33
et al. (2015)
stream and
lake
Opaluch et al.
1
NY
estuary
Peconic Estuary
$170.73
$170.73
$170.73
(1998)
System
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,
4
CO
river/
Cache la Poudre,
$198.13
$99.89
$258.99
Walsh, and
stream
Colorado, Conejos,
Loomis (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,
Watershed
and Bishop
(2001)
Sutherland and
1
MT
river/
Flathead River
$180.05
$180.05
$180.05
Walsh (1985)
stream and
lake
drainage system
Takatsuka
4
TN
river/
Clinch River
$353.72
$224.28
$483.16
(2004)
stream
watershed
Van Houtven
32
VA, NC,
lake
Entire state (separate
$316.16
$260.91
$374.11
et al. (2014)
SC, AL,
GA, KY,
MS, TN
observations for each
state)
Wattage
2
IA
river/
Bear Creek watershed
$53.68
$49.61
$57.76
(1993)
stream
Welle (1986)
4
MN
lake
Entire state
$175.44
$135.13
$227.59
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Appendix C: Methodology for Estimating WTP
Table C-1. Primary Studies Included in the
Meta-data
Study
Obs. In
Meta-
data
State(s)
Waterbody
Type(s)
Geographic Scope
WTP Per Household (2019$)
Mean
Min
Max
Welle and
Hodgson
(2011)
3
MN
lake
Lake Margaret and
Sauk River Chain of
Lakes watersheds
$178.91
$13.06
$351.48
Wey (1990)
1
Rl
salt pond
/marsh
Great Salt Pond (Block
Island)
$78.85
$78.85
$78.85
Whitehead
(2006)
3
NC
river/
stream
Neuse River
watershed
$230.79
$33.93
$450.72
Whitehead
and Groothuis
(1992)
2
NC
river/
stream
Tar-Pamlico River
$43.08
$39.33
$46.82
Whitehead et
al. (1995)
1
NC
estuary
Albermarle-Pamlico
estuary system
$115.56
$115.56
$115.56
Whittington
(1994)
1
TX
estuary
Galveston Bay estuary
$240.09
$240.09
$240.09
Zhao,
Johnston, and
Schultz (2013)
3
Rl
river/
stream and
lake
Pawtuxet watershed
$7.19
$3.59
$10.78
Similar to the 2015 MRM, the updated MRM satisfies the adding-up condition, a theoretically desirable
property.63 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 mechanism 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.
03 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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¦ 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, 2015b, 2020a), with revisions to better account for
methodological differences in the underlying studies (see ICF (2022) 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 (J. P. 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).
Table C-2 provides definitions and presents descriptive statistics for variables included in the MRM,
based on the meta-data studies.
Table C-2. Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
Dependent Variable
ln_OWTP
Natural log of WTP per unit 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 mechanism
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 (RUM) 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 (IBI) as
the water quality metric (rather than a
WQI).
Binary
(Value: 0 or 1)
0.079
0.271
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Table C-2. Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
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
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
nonusers_only
Binary variable indicating that the survey
was implemented over a population of
nonusers only (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
Inincome
Natural log of the median income (in
2019$) for the sample area of each study
based on historical U.S. Census data. It
was designed to provide a consistent
income variable given differences in
reporting of respondent income across
studies in the meta-data (i.e., mean vs.
median). Also, some studies do not
report respondent income. This variable
was estimated for all studies in the
Natural log of
income (2019$)
10.946
0.160
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Table C-2. Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
meta-data regardless of whether the
study reported summary statistics for
respondent income.
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(s) stated in the survey
instrument include 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 (NLCD), reflecting the
nature of land use 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 or km2/
km2)
0.351
0.401
Water Quality Baseline and Change
ln_Q
Natural log of the mid-point of the
baseline and policy water quality: Q =
(1/2)(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 collectively 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.
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Table C-2. Definition and Summary Statistics for Model Variables
Variable
Definition
Units
Mean
St. Dev.
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 for the 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.
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 using 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 (Inqnalitych), 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 for the
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 the policy. 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 policy
scenario and the total water quality change that respondents were asked to value. Since environmental
quality is considered by economists to be a normal good,64 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 Inqnality ch 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
04 Environmental quality, including water quality, is a "normal" good because people want more of it as their real incomes
increase.
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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 cil. (2005), Shrestha, Rosenberger, and Loomis (2007), and R.S. Rosenberger & Phipps, 2007.
Based on the benefit transfer literature (e.g., Stapler & Johnston, 2009; K.J. 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.65 The transfer approach involved projecting
benefits in each CBG and year, based on the following general benefit function:
Equation B-1.
In^OWTPy,b) = Intercept + (coefficientj) x (independent variable value{)
Where
InfOWTI'yjj) = The predicted natural log of household WTP for a one-point
improvement in WQI score in a given year (7) and CBG (B).
coefficient = A vector of variable coefficients from the meta-regression.
independent = A vector of independent variable values. Variables include baseline
variable values water quality level ( WQI-BLt.b) and expected water quality under the
regulatory option ( WQI-PCy,b) for a given year and CBG.
Here, ln(OWTPr,B) is the dependent variable in the meta-analysisthe natural log of an average WTP per
one-point WQI score improvement per household, in a given CBG B for water quality in a given year 7.66
The baseline water quality level (WQI-BLt.b) and expected water quality under the regulatory option
(WQI-1'( V,/;) were based on water quality in waterbodies both within the selected water resource regions
and 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 the finding that approximately 80 percent of recreational trips occur
05 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 comity 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
00 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.
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within a 2-hour drive from home.67 Because one-point WTP is assumed to depend, according to Equation
B-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 the selected water
resource regions and 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 within which households
have familiarity with and WTP for waterbodies affected by MPP facility 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.68 Total WTP is calculated as the sum of estimated CBG-level WTP
across all CBGs that have at least one affected waterbody whose water quality is improved within the
selected water resource regions and within 100 miles. Using this approach, EPA is unable to analyze the
WTP for CBGs with no affected waters within 100 miles of the selected water resource regions even
though households in those CBGs may value waters for use purposes farther than 100 miles from their
home or are familiar with and have nonuse values for such waters.
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 C-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, tax only, 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 a time 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 changes presented to survey respondents when eliciting WTP
67 According to Viscusi, Huber, and Bell (2008), the EPA National Center for Environmental Economics used data from the
1996 National Survey on Recreation and the Environment to calculate that 77.9 percent of boating visits, 78.1 percent of
fishing visits, and 76.9 percent of swimming recreational visits are within a 100 mile radius of users' homes.
68 Population double-counting issues can arise when using "distance to waterbody" to assess simultaneous improvements to
many waterbodies.
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Appendix C: Methodology for Estimating WTP
values. To ensure that the benefit transfer function satisfies the adding-up condition, the lnqnalitych
variable was treated as a demand curve shifter, similar to the methodological control variables, and held
fixed for the benefit calculations across all CBGs. To estimate low and high alternative analysis values of
WTP for water quality changes resulting from the regulatory options, EPA estimated one-point WTP
using two alternative settings of the lnqnalitych 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 nonuse 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.
Table C-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 a!., 2017).
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 mechanism 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 (RUM) to estimate WTP. Set to one because
use of a RUM 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 IBI as
the water quality metric. Set to zero because the meta-
regression uses the WQI as the water quality metric, not
the IBI.
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Appendix C: Methodology for Estimating WTP
Table C-3. Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
Inyear
-0.135
-0.362
In (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.
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.
n on users
-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.
In income
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.
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Table C-3. Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
Sampled Market and Affected Resource
swim_use
0.300
0.361
0
Binary variables that identify studies in which swimming
and gamefish uses are specifically identified. Set to zero,
which corresponds to all recreational uses, since data on
specific recreational uses of the reaches in HUC12s
affected by MPP facility discharges are not available.
game fish
0.871
0.531
0
ln_ar
-0.572
-0.654
Varies
Natural log of the proportion of the affected resource
area which is agricultural based on National Land Cover
Database, reflecting the nature of development in the
area surrounding the resource. Used Census county
boundary layers to identify counties that intersect
affected HUC12s (i.e., HUC12s downstream from any MPP
discharger) within the 100-mile buffer of each CBG. For
intersecting counties, calculated the fraction of total land
area that is agricultural using the National Land Cover
Dataset (NLCD). The ln_ar_agr variable was coded in the
meta-data to reflect the area surrounding the affected
resources.
ln_ar_ratio
-0.157
-0.153
0.00846
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). For the MPP scenario, ln_ar_ratio is
calculated for each HUC12 within the scope of the
analysis (i.e., HUC12s downstream from any MPP
discharger), and the final value is set to the mean value
across all affected HUC12s. For each affected HUC12,
sa_area is set based on the total area within the 100-mile
buffer that intersects CBGs (i.e., excludes portions of the
buffer that intersect coastal areas), while ar_total_area is
set based on the area of counties intersecting each
affected HUC12.
sub_proportion
0.993
0.650
Varies
The size of the resources within the scope of the analysis
relative to available substitutes. Calculated for each CBG
as the ratio of reach miles within the 100-mile buffer and
within affected HUC12s (i.e., HUC12s downstream from
any MPP discharger) to the total reach miles within the
100-mile buffer. Its value can range from 0 to 1.
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 for the baseline and policy
scenarios, WQIy,b = (1/2)(WQI-BLy,b + WQI-PCy,b).
Calculated as the length-weighted average WQI score for
all potentially affected HUC12s (i.e., HUC12s with non-
zero changes under each regulatory option) within the
100-mile buffer of each CBG.
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Appendix C: Methodology for Estimating WTP
Table C-3. Independent Variable Assignments for Surface Water Quality Meta-Analysis
Variable
Coefficient
Assigned
Value
Explanation
Model 1
Model 2
lnquality_ch
NA
-0.683
ln(7)
ln(20)
ln_quality_ch was set to the natural log of AWQI=7 or
AWQI=20 for high and low estimates of one-point WTP,
respectively.
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Appendix D: Benefits and Costs using 7% Discount Rate
Appendix D: Monetized Benefits and Social Costs using a 7 Percent
Discount Rate
Nonmarket Benefits from Water Quality Changes
Sections 4.2 and 4.3 present main model and alternative model results, respectively, using a 3 percent
discount rate and water quality modeling for five water resources regions (HUC regions 02, 03, 05, 07,
and 08). This appendix presents nonmarket benefits from water quality changes using a 7 percent discount
rate and water quality modeling for five water resources regions (HUC regions 02, 03, 05, 07, and 08).
Table D-l presents results based on Model 1, whereas Table D-2 presents alternative benefit estimates
based on Model 2, using the same low and high Inqnality ch settings as described in Section 4.3.
Table D-1: Estimated Household and Total Annualized Willingness-to-Pay for Water
Quality Improvements in Selected Regions under Regulatory Options, using Model 1
and 7 Percent Discount Rate (Main Estimates)
Regulatory Option
Number of Affected
Households (Millions)3
Average Annual WTP Per
Household (2022$)b
Total Annualized WTP
(Millions 2022$)bc
Option 1
67.2
$0.67
$39.4
Option 3
85.5
$1.27
$94.7
a. The number of affected households varies across options because of differences in the number of HUC12s that have non-
zero changes in water quality.
b. Estimates based on Model 1, which provides EPA's main estimate of non-market benefits.
c. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected level
2 HUC water resource regions (see Section 3 for details).
Source: U.S. EPA Analysis, 2023
Table D-2: Estimated Household and
Quality Improvements in Selected Rei
and 7 Percent Discount Rate (Alterna
Total Annualized Willingness-to-Pay for Water
gions under Regulatory Options, using Model 2
ive Model Analysis)
Regulatory Option
Number of Affected
Households (Millions)3
Average Annual WTP Per
Household (2022$)b
Total Annualized WTP
(Millions 2022$)bc
Low
High
Low
High
Option 1
67.2
$0.24
$0.50
$15.0
$30.7
Option 3
85.5
$0.46
$0.94
$35.3
$72.3
a. The number of affected households varies across options because of differences in the number of HUC12s that have non-
zero changes in water quality.
b. Estimates based on Model 2, an alternative model that includes the DWQI variable (lnquality_ch). For the AWQI variable
setting in the Model 2-based analysis, EPA used values of 20 units to develop low estimates and 7 units to develop high
estimates (see Appendix C for details).
c. Estimated benefits are regional-level rather than national-level since water quality modeling was limited to selected level 2
HUC water resource regions (see Section 3 for details).
Source: U.S. EPA Analysis, 2023
Climate Change and Air Quality Benefits
Section 5.3 presents the monetized health effects from the changes in air emissions attributable to the
proposed rule using a 3 percent discount rate. This section presents the estimated health effects using a
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Appendix D: Benefits and Costs using 7% Discount Rate
7 percent discount rate, based on the benefit per ton values in Table D-3. The results in Table D-4 also
includes the estimated climate change benefits presented in Section 5.2.
Table D-3: Benefit per Ton Values by Emission Category, at 7 Percent Discount Rate
($2022)
Benefit per ton,
S02 ($/ton)
Benefit per ton, NOx ($/ton)
Category
Year and Basis
PM2.5-related
benefits
Ozone-related
benefits
Electricity
2025
$56,296
$7,601
$96,734
usage3
2030
$63,432
$8,529
$127,997
2035
$71,248
$9,537
$141,589
2040
$77,817
$10,342
$152,916
Transportation15
2025; Krewski et al., 2009
$291,656
$6,805
2025; Lepeule et al., 2012
$656,226
$15,798
a. Estimate of total dollar value of benefits (mortality and morbidity) for changes in emissions from electricity generating units.
Updated from 2019 dollars to 2022 dollars using the GDP deflator (GDP deflator 2022 / GDP deflator 2019 = 1.333). [U.S. EPA,
2023n]
b. National average estimate of total dollar value of benefits (mortality and morbidity) for changes in emissions from on-road,
heavy duty diesel vehicles in 2025. Updated from 2015 dollars using the GDP deflator (GDP deflator 2022 / GDP deflator 2015 =
1.215). [Wolfe etai, 2019]
Table D-4: Total Annualized Climate Change and Air Quality-Related Benefits by
Regulatory Option (Millions of 2022$)
Human Health Benefits at
7 Percent Discount Rate
Total
2025;
2025;
Regulatory
Climate Change
Krewski et
Lepeule et
Krewski et
Lepeule et
Option
SC-GHG Discount Rate
Benefits
al. (2009)
al., 2012
al. (2009)
al., 2012
3% (Average)
-$1.9
-$2.7
-$2.8
-$4.7
-$4.7
1
5% (Average)
-$0.6
-$2.7
-$2.8
-$3.4
-$3.4
2.5% (Average)
-$2.7
-$2.7
-$2.8
-$5.5
-$5.5
3% (95th Percentile)
-$5.9
-$2.7
-$2.8
-$8.6
-$8.6
3% (Average)
-$7.0
-$10.1
-$10.1
-$17.1
-$17.1
2
5% (Average)
-$2.3
-$10.1
-$10.1
-$12.4
-$12.4
2.5% (Average)
-$10.0
-$10.1
-$10.1
-$20.1
-$20.1
3% (95th Percentile)
-$21.5
-$10.1
-$10.1
-$31.6
-$31.6
3% (Average)
-$10.1
-$14.5
-$14.6
-$24.6
-$24.7
3
5% (Average)
-$3.3
-$14.5
-$14.6
-$17.8
-$17.9
2.5% (Average)
-$14.4
-$14.5
-$14.6
-$28.9
-$28.9
3% (95th Percentile)
-$30.9
-$14.5
-$14.6
-$45.4
-$45.5
Source: U.S. EPA Analysis, 2023
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Appendix D: Benefits and Costs using 7% Discount Rate
Social Costs
Section 7.2 presented the total social costs discounted and annualized using a 3 percent discount rate.
Table D-5 provides social costs discounted at 7 percent.
Table D-5: Estimated Total Social Costs by Regulatory Option and Discharge Type,
7 Percent Discount Rate (Million of 2022$)
Regulatory Option
Direct
Indirect
Total
Option 1
$211.7
$15.3
$227.0
Option 2
$211.7
$420.0
$631.7
Option 3
$218.7
$848.9
$1,067.5
Option 1 with chlorides
$273.7
$107.9
$381.7
Option 2 with chlorides
$273.7
$512.7
$786.4
Option 3 with chlorides
$280.7
$941.5
$1,222.2
Source: U.S. EPA Analysis, 2023.
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Appendix E: Benefit Extrapolation
Appendix E: Extrapolation of Nonmarket Benefits from Water Quality
Changes
EPA is modeling water quality improvements using SWAT and estimating the total public WTP for these
water quality improvements using a model that relates WQI values (see section 3.3 and Appendix B) to
the characteristics of the affected resources, population, and other factors (see section 4.1 and
Appendix C). As described in Section 3 and 4, due to data and modeling constraints,69 EPA performed the
detailed analysis for selected water resources regions and regulator}' options.
To provide insight into the potential magnitude of total monetized benefits of the three regulatory options
analyzed for the proposed rule, EPA extrapolated water quality benefits for the subset of explicitly
modeled water resources regions and regulatory options to obtain national estimates across regulatory
options. The extrapolation approach described in this Appendix was designed to be readily implemented
using available information and to provide transparency, but relies on simplifying assumptions regarding
the characteristics of affected resources and benefiting populations across the regions.
Model Scope
Figure E-l shows the map of the level 2 Hydro logic Unit Code (HUC) water resource regions.
Figure E-l: Map of HUC2 water resources regions (source: USGS)
Souris R<
Rainy
- Pacific
Northwest
Missouri
» Mid
Atlantic
Upper 1
Mississippi
Upper
Colorado
California
Arka rasas-White- Red
Lower
Colorado
Lower
Mississippi
Hawaii
Alaska
Caribbean
Water Resource Regions
Table E-l provides the number of MPP facilities and share of total industry load reductions across the
regions, by pollutant and regulatory option. As shown in the table, a subset of the 18 water resources
regions accounts for a disproportionate share of total pollutant load reductions across the regulatory
69 At the time of this report, flow calibration was completed for eight of the 18 regions in the conterminous United States and
additional time would be needed to calibrate and complete model set up for additional regions, before calculating the
required MRM geospatial variables, completing model runs, and analyzing the results.
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Appendix E: Benefit Extrapolation
options. Thus, for the preferred Option (Option 1), regions 02 (Mid Atlantic), 03 (South Atlantic-Gulf),
05 (Ohio), 07 (Upper Mississippi), and 08 (Lower Mississippi) together capture 51.0 percent of the total
TN load reductions, 43.6 percent of total TP load reductions, and 21.5 percent of total TSS load
reductions. Together, regions 10 (Missouri) and 11 (Arkansas-White-Red) account for an additional
40.4 percent of TN, 50.4 percent of TP, and 68.8 percent of TSS total load reductions, but calibration had
not yet been completed for these two regions at the time of this report, therefore limiting EPA's ability to
account for these additional reductions through explicit modeling.
In general, the greater the share of total load reductions explicitly modeled, the less consequential is
extrapolation uncertainty when scaling the results to the rest of the conterminous United States. Thus, if
EPA modeled the seven regions noted above (i.e., 02, 03, 05, 07, 08, 10, and 11), then the benefits
associated with over 90 percent of the total loading reductions estimated under Option 1 would have been
modeled explicitly, leaving less than 10 percent of the total loading reductions as needing to have their
associated benefits estimated by extrapolation.
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Appendix E: Benefit Extrapolation
Table E-1: Number of MPP Facilities and Share of Loading Reductions by Water Resource Region, Pollutant, and
Regulatory Option
Water resource region
Number of MPP Facilities
TN
TP
TSS
Direct
Indirect
Option 1
Option 2
Option 3
Option 1
Option 2
Option 3
Option 1
Option 2
Option 3
01: New England
0
137
0.0%
0.0%
0.3%
0.0%
0.0%
0.2%
0.0%
0.0%
0.1%
02: Mid Atlantic
26
529
0.9%
8.0%
9.4%
0.7%
4.6%
6.1%
5.6%
6.9%
7.9%
03: South Atlantic-Gulf
31
459
32.3%
23.3%
18.7%
24.2%
21.4%
20.4%
9.6%
9.6%
9.9%
04: Great Lakes
9
224
1.5%
1.9%
4.4%
0.7%
2.0%
2.6%
1.7%
2.4%
2.8%
05: Ohio
12
254
3.1%
5.9%
6.8%
3.1%
5.5%
5.3%
3.6%
4.3%
4.4%
06: Tennessee
3
67
2.4%
2.6%
2.0%
2.1%
2.0%
1.9%
3.7%
2.7%
2.5%
07: Upper Mississippi
18
405
10.5%
11.5%
15.3%
12.8%
12.7%
13.5%
1.4%
6.3%
1.1%
08: Lower Mississippi
11
49
4.2%
2.0%
1.8%
2.8%
1.9%
1.9%
1.2%
1.1%
1.2%
09: Souris-Red-Rainy
0
10
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
10: Missouri
14
219
22.8%
13.0%
11.5%
31.4%
22.1%
19.9%
62.3%
47.1%
43.3%
11: Arkansas-White-Red
22
145
17.6%
18.7%
15.7%
19.0%
19.5%
18.1%
6.6%
11.6%
11.2%
12: Texas-Gulf
8
179
4.7%
6.6%
6.0%
3.1%
4.8%
4.9%
0.7%
3.0%
3.2%
13: Rio Grande
0
31
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
14: Upper Colorado
0
12
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
15: Lower Colorado
0
39
0.0%
0.4%
0.2%
0.0%
0.2%
0.2%
0.0%
0.2%
0.2%
16: Great Basin
1
48
0.0%
1.7%
1.4%
0.0%
1.0%
1.1%
0.0%
1.0%
1.0%
17: Pacific Northwest
2
135
0.0%
0.4%
1.0%
0.0%
0.2%
0.5%
0.0%
0.1%
0.3%
18: California
0
415
0.0%
4.0%
5.4%
0.0%
2.1%
3.5%
3.6%
3.7%
4.3%
Total3
157
3,357
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
Regions 02+03+05+07+08
98
1,696
51.0%
50.7%
51.9%
43.6%
46.1%
47.2%
21.5%
28.2%
31.1%
Regions 10+11
36
364
40.4%
31.7%
27.1%
50.4%
41.5%
38.0%
68.8%
58.7%
54.5%
Other Regions
23
1,297
8.6%
17.6%
20.9%
6.0%
12.4%
14.9%
9.7%
13.0%
14.4%
a. An additional 14 direct dischargers and 351 indirect dischargers could not be assigned to a region due to missing location information.
Source: U.S. EPA Analysis, 2023.
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Appendix E: Benefit Extrapolation
Extrapolation Approach
EPA extrapolates modeled total WTP for the explicitly modeled regions to the rest of the conterminous
United States based on the relative loading reductions under each option. The approach rests on the
relationships between reductions in the point source loadings of individual pollutants, changes in-stream
concentrations in receiving and downstream waters, WQI changes, the estimated WTP for water quality
improvements, and populations who value these improvements. Implicit in this approach is the
assumption that the affected waters in non-modeled regions are similar to those in the explicitly modeled
regions with respect to hydrography (e.g., flow, stream order), contributions of pollutant sources within
the watersheds, and substitute reaches. The approach also implicitly assumes that populations valuing
these improvements are similar with respect to socioeconomic characteristics (e.g., income), proximity to
improving waters, and other factors.
The extrapolation first divides the annualized WTP for modeled regions by an aggregate measure of
pollutant load reductions to obtain a unit benefit value for each option (in dollars per pounds). This unit
benefit value is then applied to the remaining regions to estimate benefits of loading reductions in these
regions, accounting for differences in the size of the respective populations. Specifically, to estimate total
WTP for the unmodeled regions under each option, EPA multiplies the unit benefit value by the aggregate
load reductions in unmodeled regions and the ratio of the respective populations in the two sets of
regions:
^ _ §TWTPmodeied
, option
PopiildtiOTlun-modeied
q> 1 W1 runmodeled,Option ALOCLUunmodeiediOption ^ A j j ^ n
ALoadrnodeied0pf-ion P opulationmodeled
The aggregate load measure reflects the estimated reductions in MPP loads of TN, TP, and TSS70 in each
region, adjusted to account for the relative influence of the three pollutants on the changes in WQI scores.
The load adjustment is done because instream TP concentrations are generally an order of magnitude
smaller than TN concentrations, which are in turn much smaller than TSS concentrations. Furthermore, in
the WQI, the TN and TP subindex scores each have a higher influence on the overall WQI than the TSS
subindex score. Thus, EPA calculated the weighted sum of loading reductions for TN, TP and TSS, where
the weights reflect the relative magnitude of instream pollutant concentrations, as well as the pollutants'
relative weights in the WQI score. See Table E-2 for details.
The relative magnitude of instream pollutant concentrations is based on the midpoint of concentrations
corresponding to scores of 10 and 100 for the WQI subindex curves presented in Appendix B,
benchmarked to the midpoint of TP concentrations. On average, the midpoint TN concentrations across
the nine ecoregions is 0.2 times the corresponding midpoint TP concentrations. On average, the midpoint
TSS concentrations across the nine ecoregions is 0.006 times the corresponding midpoint TP
concentrations.
70ICF is also modeling changes in biochemical oxygen demand and using dissolved oxygen modeled in SWAT.
E-4
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BCAfor Proposed Revisions to the MPP ELGs Appendix E: Benefit Extrapolation
Table E-2: Adjustment factors used to calculate the aggregate load reductions
WQI weight
WQI relative
Relative
Overall weight
Parameter
(see Table B-2)
weight for TN, TP,
magnitude of
applied to load
and TSS
concentrations
reductions
[a]
[b]
[a]x[b]
Dissolved Oxygen
0.24
Fecal Coliform
0.22
Biochemical Oxygen Demand
0.15
Total Nitrogen
0.14
0.36
0.2
0.084
Total Phosphorus
0.14
0.36
1.0
0.359
Total Suspended Solids
0.11
0.28
0.006
0.002
Source: U.S. EPA Analysis, 2023.
The aggregate load changes are thus calculated using the following equation where ALoad, ATN, ATP and
ATSS are load changes in kg.
ALoad = 0.084 X ATN + 0.359 X ATP + 0.002 X ATSS
Table E-3 summarizes pollutant loading reductions across water resources regions and regulatory options,
including the aggregate pollutant load reductions used as basis for calculating the extrapolation scaling
factor.
EPA calculated the population adjustment based on the 2010 population in each of the water resources
regions (U.S. EPA, 2017a). The population in the explicitly modeled regions is 152.2 million people,
compared to 154.3 million people in the remaining regions, resulting in an adjustment factor of 1.01.
Interpolation of Option 2 Benefits
EPA interpolates Option 2 results based on modeled total WTP for Option 1 and Option 3, assuming that
the total WTP is proportional to the loading reductions for the three options. Thus, the aggregate loading
reductions for Option 2 (4.4 million kg for the explicitly modeled water resources regions) fall between
those of Option 1 (1.6 million kg) and Option 3 (6.0 million kg), so EPA interpolated the total WTP
estimates linearly:
$TWTPmodeied
,Option 2
$TWTPmodeiedoptionl
+ (ALocidmodeiedioption2 ALocidmodeiedioption i)
($TWTPmodeiedoption3 §TWT Pmodeied0ption i)
x
(ALoctdmodeiedQpfion3 ALoadmodeiedioption-^
Limitations and Uncertainty
The extrapolation approach rests on assumptions that factors determining the WTP are similar across
regions, such as the characteristics of receiving waters (e.g., stream order, flow, baseline water quality)
and populations (e.g., income), and differences in WTP across regions is then mostly determined by the
magnitude of loading reductions and populations.
E-5
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BCAfor Proposed Revisions to the MPP ELGs
Appendix E: Benefit Extrapolation
The uncertainty in the total national benefits is driven primarily by the share of the benefits that was
estimated based on extrapolation, as opposed to modeled explicitly. The five explicitly modeled regions
together account for 45 percent to 49 percent of the aggregate loading reductions across the conterminous
United States, with the shares varying across regulatory options and parameters. For example, under
Option 1 the five explicitly modeled regions account for 51 percent of total TN reductions, 44 percent of
total TP reductions, and 22 percent of total TSS reductions. Additionally, approximately half of the total
population of the conterminous United States in 2010 lived in the five explicitly modeled regions (U.S.
EPA, 2017a). Accordingly, almost half of the total benefits extrapolated based on these two primary
factors were explicitly modeled (44 percent of total Option 1 benefits, 49 percent of the total Option 3
benefits), with the explicitly modeled benefits providing lower bounds of the total benefit estimates for
these two options.
E-6
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BCAfor Proposed Revisions to the MPP ELGs
Appendix E: Benefit Extrapolation
Table E-3: Loading Reductions by Water Resource Region, Pollutant, and Regulatory Option
Water resource region
TN L
oad Reduc
million kg
tion
TP L
oad Reduc
million kg
tion
TSS
Load Reduc
million kg
:tion
Aggregs
te Load Re
million kg
duction
Option 1
Option 2
Option 3
Option 1
Option 2
Option 3
Option 1
Option 2
Option 3
Option 1
Option 2
Option 3
01: New England
-
-
0.110
-
-
0.015
-
-
0.053
-
-
0.015
02: Mid Atlantic
0.035
1.620
3.193
0.024
0.334
0.523
1.378
2.574
3.237
0.014
0.260
0.461
03: South Atlantic-Gulf
1.276
4.728
6.321
0.831
1.552
1.761
2.365
3.544
4.078
0.409
0.959
1.169
04: Great Lakes
0.058
0.384
1.503
0.025
0.147
0.223
0.415
0.874
1.136
0.014
0.087
0.208
05: Ohio
0.122
1.188
2.293
0.105
0.398
0.458
0.897
1.574
1.808
0.049
0.245
0.360
06: Tennessee
0.096
0.528
0.692
0.074
0.147
0.163
0.912
0.987
1.013
0.036
0.099
0.118
07: Upper Mississippi
0.416
2.335
5.165
0.438
0.918
1.167
0.346
2.345
3.182
0.193
0.529
0.857
08: Lower Mississippi
0.167
0.404
0.606
0.097
0.141
0.164
0.304
0.425
0.493
0.049
0.085
0.110
09: Souris-Red-Rainy
-
-
0.022
-
-
0.004
-
-
0.004
-
-
0.003
10: Missouri
0.901
2.624
3.885
1.079
1.602
1.719
15.320
17.452
17.809
0.488
0.823
0.972
11: Arkansas-White-Red
0.695
3.791
5.297
0.652
1.411
1.560
1.615
4.305
4.626
0.295
0.832
1.012
12: Texas-Gulf
0.188
1.343
2.024
0.106
0.346
0.422
0.166
1.115
1.328
0.054
0.239
0.323
13: Rio Grande
-
-
-
-
-
-
-
-
0.001
-
-
0.000
14: Upper Colorado
-
-
0.006
-
-
0.000
0.000
0.000
0.001
0.000
0.000
0.001
15: Lower Colorado
-
0.080
0.084
-
0.017
0.019
-
0.086
0.090
-
0.013
0.014
16: Great Basin
-
0.343
0.485
-
0.073
0.097
-
0.368
0.416
-
0.056
0.076
17: Pacific Northwest
0.000
0.087
0.331
0.000
0.015
0.041
0.000
0.019
0.131
0.000
0.013
0.043
18: California
-
0.807
1.823
-
0.153
0.300
0.888
1.372
1.756
0.001
0.125
0.263
Total3
3.954
20.260
33.838
3.431
7.255
8.636
24.605
37.040
41.162
1.603
4.364
6.005
Explicitly modeled regions
(02+03+05+07+08)
-2.017
-10.275
-17.577
-1.496
-3.344
-4.072
-5.289
-10.462
-12.797
-0.715
-2.079
-2.957
Other regions
-1.937
-9.986
-16.262
-1.935
-3.912
-4.563
-19.316
-26.578
-28.364
-0.888
-2.285
-3.048
- No loading reduction.
Source: U.S. EPA Analysis, 2023.
E-7
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BCAfor Proposed Revisions to the MPP ELGs
Appendix F: Alternative Social Cost of Greenhouse Gases
Appendix F: Climate Change Disbenefits with Updated Social Cost of
Greenhouse Gases
As discussed in Section 5.2, in December 2023, EPA published new estimates of the social cost of
greenhouse gases (U.S. Environmental Protection Agency, 20231). These estimates 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 of Sciences, 2017b). As the values were still draft at the time this analysis was conducted,
EPA did not use them in the main analysis but is presenting results based on these estimates in this
Appendix for additional information.
For a complete discussion of the methodology underlying these updated SC-GHG estimates, see EPA
(20231) and the final RIA for the Oil and Gas final rule. Public comments and responses to public
comments received on these estimates, and complete information about the external peer review of these
estimates, can be found in the docket for the Oil and Gas rule. All replication instructions and computer
code for the estimates, a link to the public comments, and all files related to the peer review process,
including EPA's response to the peer reviewer recommendations are also available on EPA's website:
https://www.epa.gov/environmental-economics/scghg.
Table F-1: Estimates of the Social Cost of Methane and Social Cost of Carbon by Near-
term Ramsey Discount Rate, 2025-2065
Year
Social Cost of Methane
(2022$/Metric Tonne CH4)
Social Cost of Carbon
(2022$/Metric Tonne C02)
2.5%
2.0%
1.5%
2.5%
2.0%
1.5%
2025
$1,800
$2,300
$3,100
$150
$240
$400
2026
$1,900
$2,300
$3,200
$150
$240
$410
2027
$1,900
$2,400
$3,300
$150
$250
$410
2028
$2,000
$2,500
$3,400
$160
$250
$420
2029
$2,100
$2,600
$3,400
$160
$250
$430
2030
$2,200
$2,700
$3,500
$160
$260
$430
2031
$2,200
$2,800
$3,700
$160
$260
$440
2032
$2,300
$2,900
$3,800
$170
$270
$440
2033
$2,400
$3,000
$3,900
$170
$270
$450
2034
$2,500
$3,100
$4,000
$170
$270
$450
2035
$2,600
$3,200
$4,100
$180
$280
$460
2036
$2,700
$3,300
$4,200
$180
$280
$460
2037
$2,800
$3,400
$4,300
$180
$290
$470
2038
$2,800
$3,500
$4,400
$190
$290
$470
2039
$2,900
$3,600
$4,600
$190
$290
$480
2040
$3,000
$3,700
$4,700
$190
$300
$480
2041
$3,100
$3,800
$4,800
$200
$300
$490
2042
$3,200
$3,900
$4,900
$200
$310
$490
2043
$3,300
$4,000
$5,000
$200
$310
$500
2044
$3,400
$4,100
$5,200
$210
$320
$500
F-l
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BCAfor Proposed Revisions to the MPP ELGs
Appendix F: Alternative Social Cost of Greenhouse Gases
Table F-1: Estimates of the Social Cost of Methane and Social Cost of Carbon by Near-
term Ramsey Discount Rate, 2025-2065
Social Cost of Methane
Social Cost of Carbon
(2022$/Metric Tonne CH4)
(2022$/Metric Tonne C02)
Year
2.5%
2.0%
1.5%
2.5%
2.0%
1.5%
2045
$3,500
$4,200
$5,300
$210
$320
$510
2046
$3,600
$4,300
$5,400
$220
$330
$520
2047
$3,700
$4,400
$5,500
$220
$330
$520
2048
$3,800
$4,500
$5,600
$220
$340
$530
2049
$3,900
$4,600
$5,800
$230
$340
$530
2050
$4,000
$4,700
$5,900
$230
$340
$540
2051
$4,100
$4,800
$6,000
$230
$350
$550
2052
$4,100
$4,900
$6,100
$240
$350
$550
2053
$4,200
$5,000
$6,200
$240
$360
$560
2054
$4,300
$5,100
$6,300
$240
$360
$560
2055
$4,400
$5,200
$6,500
$250
$370
$570
2056
$4,500
$5,300
$6,600
$250
$370
$570
2057
$4,600
$5,400
$6,700
$250
$370
$580
2058
$4,700
$5,500
$6,800
$260
$380
$580
2059
$4,700
$5,600
$6,900
$260
$380
$590
2060
$4,800
$5,700
$7,000
$260
$390
$590
2061
$4,900
$5,800
$7,100
$260
$390
$600
2062
$5,000
$5,900
$7,200
$270
$390
$600
2063
$5,100
$6,000
$7,400
$270
$400
$600
2064
$5,100
$6,100
$7,500
$270
$400
$610
2065
$5,200
$6,200
$7,600
$280
$400
$610
Note: These values are identical to those reported in U.S. EPA (2022, Table A.5.1), adjusted for inflation to 2022 dollars using
the annual GDP Implicit Price Deflator values (127.224 / 113.784 = 1.118) in the U.S. Bureau of Economic Analysis' (BEA) NIPA
Table 1.1.9 (U.S. Bureau of Economic Analysis, 2023). This table displays the values rounded to two significant figures. The
annual unrounded values used in the calculations in this RIA are available in Appendix A.5 of U.S. EPA (20231) and at:
www.epa.gov/environmental-economics/scghg.
Source: U.S. EPA Analysis, 2023, based on Table A.5.1 in U.S. Environmental Protection Agency, 20231.
Table F-2 presents the undiscounted annual monetized climate disbenefits in selected years for each
regulatory option. The disbenefits are calculated using the three sets of SC-GHG estimates of the draft
SC-GHG from Table F-1 (based on near-term Ramsey discount rate of 2.5 percent, 2 percent, and 1.5
percent). EPA multiplied estimated CH4 and CO2 emissions for each year within the period of analysis by
the SC-CH4 and SC-CO2 estimates, respectively, for that year. The negative values indicate that these are
disbenefits due to the net increase in CH4 and CO2 emissions under the proposed rule.
F-2
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BCAfor Proposed Revisions to the MPP ELGs
Appendix F: Alternative Social Cost of Greenhouse Gases
Table F-2: Estimated Undiscounted and Total Present Value of Climate Disbenefits from
Incremental Changes in CH4 and CO2 Emissions under the Proposed Rule by Discount
Rate (Mil
ions of 2022$)
Regulatory
Option
Year
Methane Benefits3
Carbon Dioxide Benefits3
2.5%
2.0%
1.5%
2.5%
2.0%
1.5%
1
2028
-$0,004
-$0,005
-$0.01
-$3.9
-$6.2
-$10.5
2033
-$0,005
-$0,006
-$0.01
-$4.3
-$6.7
-$11.1
2043
-$0,007
-$0,008
-$0.01
-$5.1
-$7.8
-$12.5
2053
-$0,009
-$0,010
-$0.01
-$6.0
-$8.9
-$13.9
2063
-$0,010
-$0,012
-$0.02
-$6.7
-$9.9
-$15.1
TPVb
-$0.16
-$0.2
-$0.3
-$116.6
-$197.7
-$350.1
2
2028
-$0.02
-$0.02
-$0.03
-$14.2
-$22.8
-$38.4
2033
-$0.02
-$0.02
-$0.03
-$15.7
-$24.7
-$40.7
2043
-$0.02
-$0.03
-$0.04
-$18.6
-$28.6
-$45.6
2053
-$0.03
-$0.04
-$0.05
-$21.9
-$32.6
-$50.8
2063
-$0.04
-$0.05
-$0.06
-$24.7
-$36.2
-$55.2
TPVb
-$0.6
-$0.8
-$1.1
-$426.9
-$723.7
-$1,281.6
3
2028
-$0.02
-$0.03
-$0.04
-$20.4
-$32.8
-$55.2
2033
-$0.03
-$0.03
-$0.04
-$22.5
-$35.5
-$58.5
2043
-$0.04
-$0.04
-$0.05
-$26.8
-$41.0
-$65.6
2053
-$0.05
-$0.05
-$0.07
-$31.5
-$46.9
-$73.0
2063
-$0.05
-$0.06
-$0.08
-$35.5
-$52.1
-$79.3
TPVb
-$0.8
-$1.1
-$1.6
-$613.6
-$1,040.3
-$1,842.3
a. Values rounded to two significant figures. Negative values indicate disbenefits. Climate impacts are based on changes in CH4
and C02 emissions and are calculated using three different estimates of the SC-CH4 and SC-C02.
b. TPV represents the total present value from 2025-2065.
Source: U.S. EPA Analysis, 2023
Table F-3 presents the annualized climate disbenefits associated with changes in GHG emissions over the
2025-2065 period under each discount rate by regulatory option and category of emissions.
Table F-3: Estimated Total Annualized Climate Disbenefits from Incremental Changes in
Cm and CO2 Emissions under the Proposed Rule by Discount Rate (Millions of 2022$)
Pollutant
Discount Rate
Regulatory Option
Option 1
Option 2
Option 3
Methane3
2.5%
-$0,006
-$0.02
-$0.03
2.0%
-$0.01
-$0.03
-$0.04
1.5%
-$0.01
-$0.04
-$0.05
Carbon
2.5%
-$4.65
-$17.0
-$24.4
dioxide3
2.0%
-$7.23
-$26.5
-$38.0
1.5%
-$11.7
-$42.8
-$61.6
Total
2.5%
-$4.65
-$17.0
-$24.5
2.0%
-$7.23
-$26.5
-$38.1
1.5%
-$11.7
-$42.9
-$61.6
a. Values rounded to two significant figures. Negative values indicate disbenefits. Climate impacts are based on changes in
CH4 and CO2 emissions and are calculated using three different estimates of the SC-CH4 and SC-C02.
Source: U.S. EPA Analysis, 2023
F-3
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